WorldWideScience

Sample records for satellite mtsat-1r estimation

  1. Assessment of the calibration performance of satellite visible channels using cloud targets: application to Meteosat-8/9 and MTSAT-1R

    Science.gov (United States)

    Ham, S.-H.; Sohn, B. J.

    2010-11-01

    To examine the calibration performance of the Meteosat-8/9 Spinning Enhanced Visible Infra-Red Imager (SEVIRI) 0.640-μm and the Multi-functional Transport Satellite (MTSAT)-1R 0.724-μm channels, three calibration methods are employed. Total eight months during the 2004-2007 period are used for SEVIRI, and total seven months during the 2007-2008 period are used for MTSAT-1R. First, a ray-matching technique is used to compare Meteosat-8/9 and MTSAT-1R visible channel reflectances with the well-calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) 0.646-μm channel reflectances. Spectral differences of the response function between the two channels of interest are taken into account for the comparison. Second, collocated MODIS cloud products are used as inputs to a radiative transfer model (RTM) to calculate Meteosat-8/9 and MTSAT-1R visible channel reflectances. In the simulation, cloud three-dimensional (3-D) radiative effect associated with subgrid variations is taken into account using the lognormal-independent column approximation (LN-ICA) to minimize the simulation bias caused by the plane-parallel homogeneous assumption. Third, an independent method uses the typical optical properties of deep convective clouds (DCCs) to simulate reflectances of selected DCC targets. Although all three methods are not in perfect agreement, the results suggest that calibration coefficients of Meteosat-8/9 0.640-μm channels are underestimated by 6-7%. On the other hand, the calibration accuracy of MTSAT-1R visible channel appears to be variable with the target reflectance itself because of an underestimate of calibration coefficient (up to 20%) and a non-zero space offset. The results further suggest that the solar channel calibration scheme combining the three methods in this paper can be used as a tool to monitor the calibration performance of visible sensors that are particularly not equipped with an onboard calibration system.

  2. An extended lookup table of cloud detection for MTSAT-1R

    Science.gov (United States)

    Chen, Wuhan; Zhong, Bo; Li, Weisheng; Wu, Shanlong; Yu, Shanshan

    2014-11-01

    Cloud detection is a key work for the estimation of solar radiation from remote sensing. Particularly, the detection of thin cirrus cloud and the edges of thicker cloud is critical and difficult. To obtain accurate estimates of cloud cover of MTSAT-1R image, we propose an effective cloud detection algorithm for improving the detection of thin cirrus cloud and the edges of thicker cloud. Using the brightness temperature difference (BTD) and lookup table to identify cloud-free and cloud-filled pixels is not sufficient for MTSAT-1R data on the region of China. Therefore, a new lookup table (LUT) is made by extending the original one. On the basis of the exiting method, in order to apply to the MTSAT-1R satellite data in China region, we expand the scope of the latitude and extend the applicable scope of satellite zenith angle. We change the interpolation method from linear mode to nonlinear mode. The evaluation results indicate that our proposed method is effective for the cirrus and the edges of thicker cloud detection of MTSAT-1R in China region.

  3. GHRSST Level 2P Western Pacific Regional Skin Sea Surface Temperature from the Multifunctional Transport Satellite 1R (MTSAT-1R) (GDS version 1)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Multi-functional Transport Satellites (MTSAT) are a series of geostationary weather satellites operated by the Japan Meteorological Agency (JMA). MTSAT carries an...

  4. Coastal Geostationary Sea Surface Temperature (SST) Products from NOAA GOES and Japanese MTSAT-1R satellites, coastal United States, 2000 - present (NCEI Accession 0108128)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The NOAA's Office of Satellite and Data Distribution (OSDPD) generates geostationary sea surface temperature (SST) products. These products are derived from NOAA's...

  5. Japanese Advanced Meteorological Imager: a next generation GEO imager for MTSAT-1R

    Science.gov (United States)

    Puschell, Jeffery J.; Lowe, Howard A.; Jeter, James W.; Kus, Steven M.; Hurt, W. Todd; Gilman, David; Rogers, David L.; Hoelter, Roger L.; Ravella, Russ

    2002-09-01

    The Japanese Advanced Meteorological Imager (JAMI) introduces next generation technology geosynchronous earth orbit (GEO) imagers for operational meteorological remote sensing. Raytheon Santa Barbara Remote Sensing is building JAMI for Space Systems/Loral as the imager subsystem for Japan's MTSAT-1R system. JAMI represents the best balance between heritage and newer space-qualified technology and meets all Japan Ministry of Transport MTSAT requirements from beginning to end of life with considerable margin, using a simple, inherently low risk design. The advanced technology built into this imager benefits operational meteorological imaging for Japan, East Asia and Australia by enabling significantly better radiometric sensitivity and absolute accuracy, higher spatial resolution and faster full disk coverage times than available from current GEO imagers. JAMI is on schedule for an on time or early delivery to Space Systems/Loral.

  6. Detection of nighttime sea fog/stratus over the Huanghai Sea using MTSAT-1R IR data

    Institute of Scientific and Technical Information of China (English)

    GAO Shanhong; WU Wei; ZHU Leilei; FU Gang; HUANG Bin

    2009-01-01

    A dual channel difference (DCD) method is applied to detect nighttime sea fog/stratus over the Huanghai Sea using the infrared (IR) data of shortwave (3.5-4.0 μm) and longwave (10.3-11.3 μm) channels from the Multi-functional Transport Satellite (MTSAT)-IR, i.e., shortwave minus longwave brightness temperature difference (SLTD). Twenty-four sea fog events over the Huanghai Sea during March to July of 2006 and 2007 are chosen to determine a suitable value of SLTD for nighttime sea fog/stratus detection, and it is found that the value of-5.5-2.5℃ can be taken as a criterion. Two case examples of sea fog events are especially demonstrated in detail utilizing the criterion, and the results show that the derived sea fog/stratus coverage is quite reasonable.This coverage information is very helpful to analyze the formation and evolution of sea fog/stratus during night and can provide sea fog researchers with observational evidences for model results verification. However, more efforts are needed to further obtain vertical extent information of sea. fog/stratus and attempt to discriminate between sea fog and stratus.

  7. Comparison of satellite reflectance algorithms for estimating ...

    Science.gov (United States)

    We analyzed 10 established and 4 new satellite reflectance algorithms for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense water truth collected within one hour of image acquisition to develop simple proxies for algal blooms and to facilitate portability between multispectral satellite imagers for regional algal bloom monitoring. Narrow band hyperspectral aircraft images were upscaled spectrally and spatially to simulate 5 current and near future satellite imaging systems. Established and new Chl-a algorithms were then applied to the synthetic satellite images and then compared to calibrated Chl-a water truth measurements collected from 44 sites within one hour of aircraft acquisition of the imagery. Masks based on the spatial resolution of the synthetic satellite imagery were then applied to eliminate mixed pixels including vegetated shorelines. Medium-resolution Landsat and finer resolution data were evaluated against 29 coincident water truth sites. Coarse-resolution MODIS and MERIS-like data were evaluated against 9 coincident water truth sites. Each synthetic satellite data set was then evaluated for the performance of a variety of spectrally appropriate algorithms with regard to the estimation of Chl-a concentrations against the water truth data set. The goal is to inform water resource decisions on the appropriate satellite data acquisition and processing for the es

  8. Regional Bias of Satellite Precipitation Estimates

    Science.gov (United States)

    Modrick, T. M.; Georgakakos, K. P.; Spencer, C. R.

    2012-12-01

    Satellite-based estimates of precipitation have improved the spatial availability of precipitation data particularly for regions with limited gauge networks due to limited accessibility or infrastructure. Understanding the quality and reliability of satellite precipitation estimates is important, especially when the estimates are utilitized for real-time hydrologic forecasting and for fast-responding phenomena. In partnership with the World Meteorological Organization (WMO), the U.S. Agency of International Development (USAID) and the National Ocean and Atmospheric Administration (NOAA), the Hydrologic Research Center has begun implementation of real-time flash flood warning systems for diverse regions around the world. As part of this effort, bias characteristics of satellite precipitation have been examined in these various regions, such includes portions of Southeastern Asia, Southeastern Europe, the Middle East, Central America, and the southern half of the African continent. The work has focused on the Global Hydro-Estimator (GHE) precipitation product from NOAA/NESDIS. These real-time systems utilize the GHE given low latency times of this product. This presentation focuses on the characterization of precipitation bias as compared to in-situ gauge records, and the regional variations or similarities. Additional analysis is currently underway considering regional bias for other satellite precipitation products (e.g., CMORPH) for comparison with the GHE results.

  9. Estimating Soil Moisture from Satellite Microwave Observations

    Science.gov (United States)

    Owe, M.; VandeGriend, A. A.; deJeu, R.; deVries, J.; Seyhan, E.

    1998-01-01

    Cooperative research in microwave remote sensing between the Hydrological Sciences Branch of the NASA Goddard Space Flight Center and the Earth Sciences Faculty of the Vrije Universiteit Amsterdam began with the Botswana Water and Energy Balance Experiment and has continued through a series of highly successful International Research Programs. The collaboration between these two research institutions has resulted in significant scientific achievements, most notably in the area of satellite-based microwave remote sensing of soil moisture. The Botswana Program was the first joint research initiative between these two institutions, and provided a unique data base which included historical data sets of Scanning Multifrequency Microwave Radiometer (SN4NM) data, climate information, and extensive soil moisture measurements over several large experimental sites in southeast Botswana. These data were the basis for the development of new approaches in physically-based inverse modelling of soil moisture from satellite microwave observations. Among the results from this study were quantitative estimates of vegetation transmission properties at microwave frequencies. A single polarization modelling approach which used horizontally polarized microwave observations combined with monthly composites of Normalized Difference Vegetation Index was developed, and yielded good results. After more precise field experimentation with a ground-based radiometer system, a dual-polarization approach was subsequently developed. This new approach realized significant improvements in soil moisture estimation by satellite. Results from the Botswana study were subsequently applied to a desertification monitoring study for the country of Spain within the framework of the European Community science research programs EFEDA and RESMEDES. A dual frequency approach with only microwave data was used for this application. The Microwave Polarization Difference Index (MPDI) was calculated from 37 GHz data

  10. Estimation of surface insolation using sun-synchronous satellite data

    Science.gov (United States)

    Darnell, Wayne L.; Staylor, W. Frank; Gupta, Shashi K.; Denn, Fred M.

    1988-01-01

    A technique is presented for estimating insolation at the earth's surface using only sun-synchronous satellite data. The technique was tested by comparing the insolation results from year-long satellite data sets with simultaneous ground-measured insolation taken at five continental United States sites. Monthly average insolation values derived from the satellite data showed a standard error of 4.2 W/sq m, or 2.7 percent of the average ground insolation value.

  11. An enhanced algorithm to estimate BDS satellite's differential code biases

    Science.gov (United States)

    Shi, Chuang; Fan, Lei; Li, Min; Liu, Zhizhao; Gu, Shengfeng; Zhong, Shiming; Song, Weiwei

    2016-02-01

    This paper proposes an enhanced algorithm to estimate the differential code biases (DCB) on three frequencies of the BeiDou Navigation Satellite System (BDS) satellites. By forming ionospheric observables derived from uncombined precise point positioning and geometry-free linear combination of phase-smoothed range, satellite DCBs are determined together with ionospheric delay that is modeled at each individual station. Specifically, the DCB and ionospheric delay are estimated in a weighted least-squares estimator by considering the precision of ionospheric observables, and a misclosure constraint for different types of satellite DCBs is introduced. This algorithm was tested by GNSS data collected in November and December 2013 from 29 stations of Multi-GNSS Experiment (MGEX) and BeiDou Experimental Tracking Stations. Results show that the proposed algorithm is able to precisely estimate BDS satellite DCBs, where the mean value of day-to-day scattering is about 0.19 ns and the RMS of the difference with respect to MGEX DCB products is about 0.24 ns. In order to make comparison, an existing algorithm based on IGG: Institute of Geodesy and Geophysics, China (IGGDCB), is also used to process the same dataset. Results show that, the DCB difference between results from the enhanced algorithm and the DCB products from Center for Orbit Determination in Europe (CODE) and MGEX is reduced in average by 46 % for GPS satellites and 14 % for BDS satellites, when compared with DCB difference between the results of IGGDCB algorithm and the DCB products from CODE and MGEX. In addition, we find the day-to-day scattering of BDS IGSO satellites is obviously lower than that of GEO and MEO satellites, and a significant bias exists in daily DCB values of GEO satellites comparing with MGEX DCB product. This proposed algorithm also provides a new approach to estimate the satellite DCBs of multiple GNSS systems.

  12. Models for estimation of land remote sensing satellites operational efficiency

    Science.gov (United States)

    Kurenkov, Vladimir I.; Kucherov, Alexander S.

    2017-01-01

    The paper deals with the problem of estimation of land remote sensing satellites operational efficiency. Appropriate mathematical models have been developed. Some results obtained with the help of the software worked out in Delphi programming support environment are presented.

  13. Channel Estimation And Multiuser Detection In Asynchronous Satellite Communications

    CERN Document Server

    Chaouech, Helmi; 10.5121/ijwmn.2010.2411

    2010-01-01

    In this paper, we propose a new method of channel estimation for asynchronous additive white Gaussian noise channels in satellite communications. This method is based on signals correlation and multiuser interference cancellation which adopts a successive structure. Propagation delays and signals amplitudes are jointly estimated in order to be used for data detection at the receiver. As, a multiuser detector, a single stage successive interference cancellation (SIC) architecture is analyzed and integrated to the channel estimation technique and the whole system is evaluated. The satellite access method adopted is the direct sequence code division multiple access (DS CDMA) one. To evaluate the channel estimation and the detection technique, we have simulated a satellite uplink with an asynchronous multiuser access.

  14. THE STOCHASTIC ESTIMATION OF SATELLITE CLOCK CORRECTION INFORMATION IN WADGPS

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    Using autocorrelation information of the pseudorange errors generated by se lective availability (SA) frequency dithering, we have constructed a simple first order stochas tic model for SA effects. This model has been used in a Kalman filter to account for the stochastic behavior of SA dithering in estimating satellite clock information in wide area dif ferential GPS. We have obtained fifteen percent improvement in the user positioning using the correlation information on the satellite clock information in a Kalman filter, when comparing the results obtained using a regular least square estimation.

  15. Sampling errors in rainfall estimates by multiple satellites

    Science.gov (United States)

    North, Gerald R.; Shen, Samuel S. P.; Upson, Robert

    1993-01-01

    This paper examines the sampling characteristics of combining data collected by several low-orbiting satellites attempting to estimate the space-time average of rain rates. The several satellites can have different orbital and swath-width parameters. The satellite overpasses are allowed to make partial coverage snapshots of the grid box with each overpass. Such partial visits are considered in an approximate way, letting each intersection area fraction of the grid box by a particular satellite swath be a random variable with mean and variance parameters computed from exact orbit calculations. The derivation procedure is based upon the spectral minimum mean-square error formalism introduced by North and Nakamoto. By using a simple parametric form for the spacetime spectral density, simple formulas are derived for a large number of examples, including the combination of the Tropical Rainfall Measuring Mission with an operational sun-synchronous orbiter. The approximations and results are discussed and directions for future research are summarized.

  16. DOA estimation for attitude determination on communication satellites

    Directory of Open Access Journals (Sweden)

    Yang Bin

    2014-06-01

    Full Text Available In order to determine an appropriate attitude of three-axis stabilized communication satellites, this paper describes a novel attitude determination method using direction of arrival (DOA estimation of a ground signal source. It differs from optical measurement, magnetic field measurement, inertial measurement, and global positioning system (GPS attitude determination. The proposed method is characterized by taking the ground signal source as the attitude reference and acquiring attitude information from DOA estimation. Firstly, an attitude measurement equation with DOA estimation is derived in detail. Then, the error of the measurement equation is analyzed. Finally, an attitude determination algorithm is presented using a dynamic model, the attitude measurement equation, and measurement errors. A developing low Earth orbit (LEO satellite which tests mobile communication technology with smart antennas can be stabilized in three axes by corporately using a magnetometer, reaction wheels, and three-axis magnetorquer rods. Based on the communication satellite, simulation results demonstrate the effectiveness of the method. The method could be a backup of attitude determination to prevent a system failure on the satellite. Its precision depends on the number of snapshots and the input signal-to-noise ratio (SNR with DOA estimation.

  17. DOA estimation for attitude determination on communication satellites

    Institute of Scientific and Technical Information of China (English)

    Yang Bin; He Feng; Jin Jin; Xiong Huagang; Xu Guanghan

    2014-01-01

    In order to determine an appropriate attitude of three-axis stabilized communication satellites, this paper describes a novel attitude determination method using direction of arrival (DOA) estimation of a ground signal source. It differs from optical measurement, magnetic field measurement, inertial measurement, and global positioning system (GPS) attitude determination. The proposed method is characterized by taking the ground signal source as the attitude reference and acquiring attitude information from DOA estimation. Firstly, an attitude measurement equation with DOA estimation is derived in detail. Then, the error of the measurement equation is analyzed. Finally, an attitude determination algorithm is presented using a dynamic model, the attitude measurement equation, and measurement errors. A developing low Earth orbit (LEO) satellite which tests mobile communication technology with smart antennas can be stabilized in three axes by corporately using a magnetometer, reaction wheels, and three-axis magnetorquer rods. Based on the communication satellite, simulation results demonstrate the effectiveness of the method. The method could be a backup of attitude determination to prevent a system failure on the satellite. Its precision depends on the number of snapshots and the input signal-to-noise ratio (SNR) with DOA estimation.

  18. Estimated Satellite Cluster Elements in Near Circular Orbit

    Science.gov (United States)

    1988-12-01

    values of the covariance matriz P to see if the filter performs as well as it believes it is performing [4:page 3391. 1.1.. Thuth Model The truth...between satellites will bc affected. Since the measurements contain no informa- L tion on absolute downrange position, it is impossible to estimate

  19. Estimation of satellite antenna phase center offsets for Galileo

    Science.gov (United States)

    Steigenberger, P.; Fritsche, M.; Dach, R.; Schmid, R.; Montenbruck, O.; Uhlemann, M.; Prange, L.

    2016-08-01

    Satellite antenna phase center offsets for the Galileo In-Orbit Validation (IOV) and Full Operational Capability (FOC) satellites are estimated by two different analysis centers based on tracking data of a global GNSS network. The mean x- and y-offsets could be determined with a precision of a few centimeters. However, daily estimates of the x-offsets of the IOV satellites show pronounced systematic effects with a peak-to-peak amplitude of up to 70 cm that depend on the orbit model and the elevation of the Sun above the orbital plane. For the IOV y-offsets, no dependence on the orbit model exists but the scatter strongly depends on the elevation of the Sun above the orbital plane. In general, these systematic effects are significantly smaller for the FOC satellites. The z-offsets of the two analysis centers agree within the 10-15 cm level, and the time series do not show systematic effects. The application of an averaged Galileo satellite antenna model obtained from the two solutions results in a reduction of orbit day boundary discontinuities by up to one third—even if an independent software package is used.

  20. Local gravity disturbance estimation from multiple-high-single-low satellite-to-satellite tracking

    Science.gov (United States)

    Jekeli, Christopher

    1989-01-01

    The idea of satellite-to-satellite tracking in the high-low mode has received renewed attention in light of the uncertain future of NASA's proposed low-low mission, Geopotential Research Mission (GRM). The principal disadvantage with a high-low system is the increased time interval required to obtain global coverage since the intersatellite visibility is often obscured by Earth. The U.S. Air Force has begun to investigate high-low satellite-to-satellite tracking between the Global Positioning System (GPS) of satellites (high component) and NASA's Space Transportation System (STS), the shuttle (low component). Because the GPS satellites form, or will form, a constellation enabling continuous three-dimensional tracking of a low-altitude orbiter, there will be no data gaps due to lack of intervisibility. Furthermore, all three components of the gravitation vector are estimable at altitude, a given grid of which gives a stronger estimate of gravity on Earth's surface than a similar grid of line-of-sight gravitation components. The proposed Air Force mission is STAGE (Shuttle-GPS Tracking for Anomalous Gravitation Estimation) and is designed for local gravity field determinations since the shuttle will likely not achieve polar orbits. The motivation for STAGE was the feasibility to obtain reasonable accuracies with absolutely minimal cost. Instead of simulating drag-free orbits, STAGE uses direct measurements of the nongravitational forces obtained by an inertial package onboard the shuttle. The sort of accuracies that would be achievable from STAGE vis-a-vis other satellite tracking missions such as GRM and European Space Agency's POPSAT-GRM are analyzed.

  1. Potential for Using Satellite Lidar for Seasonal Snow Depth Estimation

    Science.gov (United States)

    Jasinski, M. F.; Stoll, J.; Harding, D. J.; Fassnacht, S. R.; Carabajal, C. C.; Markus, T.

    2013-12-01

    This study evaluates the potential for estimating snow depth in complex mountainous terrain using high resolution satellite lidar. For over three decades, satellite remote sensing of snow depth and water equivalent has relied primarily on passive microwave sensors with an approximately 25 km footprint. While successfully employed in many global water balance analyses, their large footprints, necessary to capture the natural emission of the surface, are too coarse to define the spatial heterogeneity of mountain watershed-scale snow due to variable topography and vegetation. In this study, the capability of satellite lidar altimetry for estimating snow depth was evaluated primarily using surface elevations observed by the Geoscience Laser Altimeter Sensor (GLAS) flown on board the Ice, Cloud, and land Elevation Satellite from 2003-2009, with a footprint size of ~70m. The evaluation includes the analysis of GLAS waveforms at near-repeat locations during snow-off and snow on conditions, using several snow depth estimation approaches, focusing on the Uinta Mountains of NE Utah. Also presented is the concept for the ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS), currently set to launch in July 2016, and its potential capability for characterizing snow depth. The opportunity for partnering through NASA's Early Adopter Program using prototype aircraft observations also is presented.

  2. Satellite images analysis for shadow detection and building height estimation

    Science.gov (United States)

    Liasis, Gregoris; Stavrou, Stavros

    2016-09-01

    Satellite images can provide valuable information about the presented urban landscape scenes to remote sensing and telecommunication applications. Obtaining information from satellite images is difficult since all the objects and their surroundings are presented with feature complexity. The shadows cast by buildings in urban scenes can be processed and used for estimating building heights. Thus, a robust and accurate building shadow detection process is important. Region-based active contour models can be used for satellite image segmentation. However, spectral heterogeneity that usually exists in satellite images and the feature similarity representing the shadow and several non-shadow regions makes building shadow detection challenging. In this work, a new automated method for delineating building shadows is proposed. Initially, spectral and spatial features of the satellite image are utilized for designing a custom filter to enhance shadows and reduce intensity heterogeneity. An effective iterative procedure using intensity differences is developed for tuning and subsequently selecting the most appropriate filter settings, able to highlight the building shadows. The response of the filter is then used for automatically estimating the radiometric property of the shadows. The customized filter and the radiometric feature are utilized to form an optimized active contour model where the contours are biased to delineate shadow regions. Post-processing morphological operations are also developed and applied for removing misleading artefacts. Finally, building heights are approximated using shadow length and the predefined or estimated solar elevation angle. Qualitative and quantitative measures are used for evaluating the performance of the proposed method for both shadow detection and building height estimation.

  3. Velocity estimation of an airplane through a single satellite image

    Institute of Scientific and Technical Information of China (English)

    Zhuxin Zhao; Gongjian Wen; Bingwei Hui; Deren Li

    2012-01-01

    The motion information of a moving target can be recorded in a single image by a push-broom satellite. A push-broom satellite image is composed of many image lines sensed at different time instants. A method to estimate the velocity of a flying airplane from a single image based on the imagery model of the linear push-broom sensor is proposed. Some key points on the high-resolution image of the plane are chosen to determine the velocity (speed and direction). The performance of the method is tested and verified by experiments using a WorldView-1 image.%The motion information of a moving target can be recorded in a single image by a push-broom satellite.A push-broom satellite image is composed of many image lines sensed at different time instants.A method to estimate the velocity of a flying airplane from a single image based on the imagery model of the linear push-broom sensor is proposed.Some key points on the high-resolution image of the plane are chosen to determine the velocity (speed and direction).The performance of the method is tested and verified by experiments using a WorldView-1 image.

  4. Estimating Monthly Rainfall from Geostationary Satellite Imagery Over Amazonia, Brazil.

    Science.gov (United States)

    Cutrim, Elen Maria Camara

    The infrared regression and the grid-history satellite rainfall estimating techniques were utilized to estimate monthly rainfall in Amazonia during one month of the rainy season (March, 1980) and one month of the dry season (September, 1980). The estimates were based on 3-hourly SMS-II infrared and visible images. Three sets of coefficients for the grid history method (Marajo, Arabian Sea, and GATE) were used to estimate rainfall. The estimated rain was compared with gauge measurements over the region. The infrared regression technique overestimated by a factor of 1.5. The Marajo coefficients yielded the best estimate, especially for eastern Amazonia. In the wet month Marajo coefficients overestimated rain by 10% and in the dry month by 70%. The Arabian Sea coefficients overestimated rain and the GATE coefficients slightly underestimated rain for Amazonia. Two maps of monthly rainfall over Amazonia were constructed for March and September, 1980, combining the ground station and satellite inferred rainfall of the grid history method using the Marajo coefficients. The satellite observations and ground data were mutually compatible and were contourable on these final, composite maps. Monthly rainfall was found to be much more inhomogeneous than previously reported. In March there was a belt of high precipitation trending southwest, with higher values and sharpest gradients in the coastal area. The upper Amazon was also an area of high precipitation, both north and south of the equator. In Roraima rainfall decreased drastically to the north. In September, the area of highest precipitation was the northwestern part of Amazonas State (northern hemisphere). Rainfall elsewhere was very localized and in northeastern Amazonia varied from 0 to 150 mm. Even though the grid history method presented better results for estimating rainfall over Amazonia, the IR model could be utilized more efficiently and economically on an operational basis if the calibration were properly made

  5. Daily Emission Estimates in China Constrained by Satellite Observations

    Science.gov (United States)

    Mijling, B.; van der A, R.

    2013-01-01

    Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. We present a new algorithm specifically designed to use daily satellite observations of column concentrations for fast updates of emission estimates of short-lived atmospheric constituents on a mesoscopic scale (~25Å~25 km2). The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates of East China, using the CHIMERE model on a 0.25 degree resolution together with tropospheric NO2 column retrievals of the OMI and GOME-2 satellite instruments.

  6. Sampling errors in satellite estimates of tropical rain

    Science.gov (United States)

    Mcconnell, Alan; North, Gerald R.

    1987-01-01

    The GATE rainfall data set is used in a statistical study to estimate the sampling errors that might be expected for the type of snapshot sampling that a low earth-orbiting satellite makes. For averages over the entire 400-km square and for the duration of several weeks, strong evidence is found that sampling errors less than 10 percent can be expected in contributions from each of four rain rate categories which individually account for about one quarter of the total rain.

  7. Calibration of Ocean Forcing with satellite Flux Estimates (COFFEE)

    Science.gov (United States)

    Barron, Charlie; Jan, Dastugue; Jackie, May; Rowley, Clark; Smith, Scott; Spence, Peter; Gremes-Cordero, Silvia

    2016-04-01

    Predicting the evolution of ocean temperature in regional ocean models depends on estimates of surface heat fluxes and upper-ocean processes over the forecast period. Within the COFFEE project (Calibration of Ocean Forcing with satellite Flux Estimates, real-time satellite observations are used to estimate shortwave, longwave, sensible, and latent air-sea heat flux corrections to a background estimate from the prior day's regional or global model forecast. These satellite-corrected fluxes are used to prepare a corrected ocean hindcast and to estimate flux error covariances to project the heat flux corrections for a 3-5 day forecast. In this way, satellite remote sensing is applied to not only inform the initial ocean state but also to mitigate errors in surface heat flux and model representations affecting the distribution of heat in the upper ocean. While traditional assimilation of sea surface temperature (SST) observations re-centers ocean models at the start of each forecast cycle, COFFEE endeavors to appropriately partition and reduce among various surface heat flux and ocean dynamics sources. A suite of experiments in the southern California Current demonstrates a range of COFFEE capabilities, showing the impact on forecast error relative to a baseline three-dimensional variational (3DVAR) assimilation using operational global or regional atmospheric forcing. Experiment cases combine different levels of flux calibration with assimilation alternatives. The cases use the original fluxes, apply full satellite corrections during the forecast period, or extend hindcast corrections into the forecast period. Assimilation is either baseline 3DVAR or standard strong-constraint 4DVAR, with work proceeding to add a 4DVAR expanded to include a weak constraint treatment of the surface flux errors. Covariance of flux errors is estimated from the recent time series of forecast and calibrated flux terms. While the California Current examples are shown, the approach is

  8. Improvement in airsea flux estimates derived from satellite observations

    OpenAIRE

    Bentamy, Abderrahim; Grodsky, Semyon A.; Katsaros, Kristina; Mestas-nunez, Alberto M.; Blanke, Bruno; Desbiolles, Fabien

    2013-01-01

    A new method is developed to estimate daily turbulent airsea fluxes over the global ocean on a 0.25 degrees grid. The required surface wind speed (w(10)) and specific air humidity (q(10)) at 10m height are both estimated from remotely sensed measurements. w(10) is obtained from the SeaWinds scatterometer on board the QuikSCAT satellite. A new empirical model relating brightness temperatures (T-b) from the Special Sensor Microwave Imager (SSM/I) and q(10) is developed. It is an extension of th...

  9. GPS SATELLITE SIMULATOR SIGNAL ESTIMATION BASED ON ANN

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Multi-channel Global Positioning System (GPS) satellite signal simulator is used to provide realistic test signals for GPS receivers and navigation systems. In this paper, signals arriving the antenna of GPS receiver are analyzed from the viewpoint of simulator design. The estimation methods are focused of which several signal parameters are difficult to determine directly according to existing experiential models due to various error factors. Based on the theory of Artificial Neural Network (ANN), an approach is proposed to simulate signal propagation delay,carrier phase, power, and other parameters using ANN. The architecture of the hardware-in-the-loop test system is given. The ANN training and validation process is described. Experimental results demonstrate that the ANN designed can statistically simulate sample data in high fidelity.Therefore the computation of signal state based on this ANN can meet the design requirement,and can be directly applied to the development of multi-channel GPS satellite signal simulator.

  10. Fast Emission Estimates in China Constrained by Satellite Observations (Invited)

    Science.gov (United States)

    Mijling, B.; van der A, R.

    2013-12-01

    Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Unfortunately, bottom-up emission inventories, compiled from large quantities of statistical data, are easily outdated for an emerging economy such as China, where rapid economic growth changes emissions accordingly. Alternatively, top-down emission estimates from satellite observations of air constituents have important advantages of being spatial consistent, having high temporal resolution, and enabling emission updates shortly after the satellite data become available. Constraining emissions from concentration measurements is, however, computationally challenging. Within the GlobEmission project of the European Space Agency (ESA) a new algorithm has been developed, specifically designed for fast daily emission estimates of short-lived atmospheric species on a mesoscopic scale (0.25 × 0.25 degree) from satellite observations of column concentrations. The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates in East China, using the CHIMERE model together with tropospheric NO2 column retrievals of the OMI and GOME-2 satellite instruments. The observations are used to construct a monthly emission time series, which reveal important emission trends such as the emission reduction measures during the Beijing Olympic Games, and the impact and recovery from the global economic crisis. The algorithm is also able to detect emerging sources (e.g. new power plants) and improve emission information for areas where proxy data are not or badly known (e.g. shipping emissions). The new emission estimates result in a better

  11. Bayesian estimation of animal movement from archival and satellite tags.

    Directory of Open Access Journals (Sweden)

    Michael D Sumner

    Full Text Available The reliable estimation of animal location, and its associated error is fundamental to animal ecology. There are many existing techniques for handling location error, but these are often ad hoc or are used in isolation from each other. In this study we present a Bayesian framework for determining location that uses all the data available, is flexible to all tagging techniques, and provides location estimates with built-in measures of uncertainty. Bayesian methods allow the contributions of multiple data sources to be decomposed into manageable components. We illustrate with two examples for two different location methods: satellite tracking and light level geo-location. We show that many of the problems with uncertainty involved are reduced and quantified by our approach. This approach can use any available information, such as existing knowledge of the animal's potential range, light levels or direct location estimates, auxiliary data, and movement models. The approach provides a substantial contribution to the handling uncertainty in archival tag and satellite tracking data using readily available tools.

  12. Groundwater Modelling For Recharge Estimation Using Satellite Based Evapotranspiration

    Science.gov (United States)

    Soheili, Mahmoud; (Tom) Rientjes, T. H. M.; (Christiaan) van der Tol, C.

    2017-04-01

    Groundwater movement is influenced by several factors and processes in the hydrological cycle, from which, recharge is of high relevance. Since the amount of aquifer extractable water directly relates to the recharge amount, estimation of recharge is a perquisite of groundwater resources management. Recharge is highly affected by water loss mechanisms the major of which is actual evapotranspiration (ETa). It is, therefore, essential to have detailed assessment of ETa impact on groundwater recharge. The objective of this study was to evaluate how recharge was affected when satellite-based evapotranspiration was used instead of in-situ based ETa in the Salland area, the Netherlands. The Methodology for Interactive Planning for Water Management (MIPWA) model setup which includes a groundwater model for the northern part of the Netherlands was used for recharge estimation. The Surface Energy Balance Algorithm for Land (SEBAL) based actual evapotranspiration maps from Waterschap Groot Salland were also used. Comparison of SEBAL based ETa estimates with in-situ abased estimates in the Netherlands showed that these SEBAL estimates were not reliable. As such results could not serve for calibrating root zone parameters in the CAPSIM model. The annual cumulative ETa map produced by the model showed that the maximum amount of evapotranspiration occurs in mixed forest areas in the northeast and a portion of central parts. Estimates ranged from 579 mm to a minimum of 0 mm in the highest elevated areas with woody vegetation in the southeast of the region. Variations in mean seasonal hydraulic head and groundwater level for each layer showed that the hydraulic gradient follows elevation in the Salland area from southeast (maximum) to northwest (minimum) of the region which depicts the groundwater flow direction. The mean seasonal water balance in CAPSIM part was evaluated to represent recharge estimation in the first layer. The highest recharge estimated flux was for autumn

  13. Estimates of lightning NOx production from GOME satellite observations

    Directory of Open Access Journals (Sweden)

    K. F. Boersma

    2005-01-01

    Full Text Available Tropospheric NO2 column retrievals from the Global Ozone Monitoring Experiment (GOME satellite spectrometer are used to quantify the source strength and 3-D distribution of lightning produced nitrogen oxides (NOx=NO+NO2. A sharp increase of NO2 is observed at convective cloud tops with increasing cloud top height, consistent with a power-law behaviour with power 5±2. Convective production of clouds with the same cloud height are found to produce NO2 with a ratio 1.6/1 for continents compared to oceans. This relation between cloud properties and NO2 is used to construct a 10:30 local time global lightning NO2 production map for 1997. An extensive statistical comparison is conducted to investigate the capability of the TM3 chemistry transport model to reproduce observed patterns of lightning NO2 in time and space. This comparison uses the averaging kernel to relate modelled profiles of NO2 to observed NO2 columns. It exploits a masking scheme to minimise the interference of other NOx sources on the observed total columns. Simulations are performed with two lightning parameterizations, one relating convective preciptation (CP scheme to lightning flash distributions, and the other relating the fifth power of the cloud top height (H5 scheme to lightning distributions. The satellite-retrieved NO2 fields show significant correlations with the simulated lightning contribution to the NO2 concentrations for both parameterizations. Over tropical continents modelled lightning NO2 shows remarkable quantitative agreement with observations. Over the oceans however, the two model lightning parameterizations overestimate the retrieved NO2 attributed to lightning. Possible explanations for these overestimations are discussed. The ratio between satellite-retrieved NO2 and modelled lightning NO2 is used to rescale the original modelled lightning NOx production. Eight estimates of the lightning NOx production in 1997 are obtained from spatial and temporal

  14. Estimates of lightning NOx production from GOME satellite observations

    Directory of Open Access Journals (Sweden)

    H. M. Kelder

    2005-05-01

    Full Text Available Tropospheric NO2 column retrievals from the Global Ozone Monitoring Experiment (GOME satellite spectrometer are used to quantify the source strength and 3D distribution of lightning produced nitrogen oxides (NOx=NO2+NO2. A sharp increase of NO2 is observed at convective cloud tops with increasing cloud top height, consistent with a power-law behaviour with power 5±2. Convective production of clouds with the same cloud height are found to produce NO2 with a ratio 1.6/1 for continents compared to oceans. This relation between cloud properties and NO2 is used to construct a 10:30 local time global lightning NO2 production map for 1997. An extensive statistical comparison is conducted to investigate the capability of the TM3 chemistry transport model to reproduce observed patterns of lightning NO2 in time and space. This comparison uses the averaging kernel to relate modelled profiles of NO2 to observed NO2 columns. It exploits a masking scheme to minimise the interference of other NOx sources on the observed total columns. Simulations are performed with two lightning parametrisations, one relating convective preciptation (CP scheme to lightning flash distributions, and the other relating the fifth power of the cloud top height (H5 scheme to lightning distributions. The satellite-retrieved NO2 fields show significant correlations with the simulated lightning contribution to the NO2 concentrations for both parametrisations. Over tropical continents modelled lightning NO2 shows remarkable quantitative agreement with observations. Over the oceans however, the two model lightning parametrisations overestimate the retrieved NO2 attributed to lightning. Possible explanations for these overestimations are discussed. The ratio between satellite-retrieved NO2 and modelled lightning NO2 is used to rescale the original modelled lightning NOx production. Eight estimates of the lightning NOx production in 1997 are obtained from spatial and temporal correlation

  15. Satellite Driven Estimation of Primary Productivity of Agroecosystems in India

    Science.gov (United States)

    Patel, N. R.; Dadhwal, V. K.; Agrawal, S.; Saha, S. K.

    2011-08-01

    Earth observation driven ecosystem modeling have played a major role in estimation of carbon budget components such as gross primary productivity (GPP) and net primary production (NPP) over terrestrial ecosystems, including agriculture. The present study therefore evaluate satellite-driven vegetation photosynthesis (VPM) model for GPP estimation over agro-ecosystems in India by using time series of the Normalized Difference Vegetation Index (NDVI) from SPOT-VEGETATION, cloud cover observation from MODIS, coarse-grid C3/C4 crop fraction and decadal grided databases of maximum and minimum temperatures. Parameterization of VPM parameters e.g. maximum light use efficiency (ɛ*) and Tscalar was done based on eddy-covariance measurements and literature survey. Incorporation of C3/C4 crop fraction is a modification to commonly used constant maximum LUE. Modeling results from VPM captured very well the geographical pattern of GPP and NPP over cropland in India. Well managed agro-ecosystems in Trans-Gangetic and upper Indo-Gangetic plains had the highest magnitude of GPP with peak GPP during kharif occurs in sugarcane-wheat system (western UP) and it occurs in rice-wheat system (Punjab) during Rabi season. Overall, croplands in these plains had more annual GPP (> 1000 g C m-2) and NPP (> 600 g C m-2) due to input-intensive cultivation. Desertic tracts of western Rajasthan showed the least GPP and NPP values. Country-level contribution of croplands to national GPP and NPP amounts to1.34 Pg C year-1 and 0.859 Pg C year-1, respectively. Modeled estimates of cropland NPP agrees well with ground-based estimates for north-western India (R2 = 0.63 and RMSE = 108 g C m-2). Future research will focus on evaluating the VPM model with medium resolution sensors such as AWiFS and MODIS for rice-wheat system and validating with eddy-covariance measurements.

  16. SATELLITE DRIVEN ESTIMATION OF PRIMARY PRODUCTIVITY OF AGROECOSYSTEMS IN INDIA

    Directory of Open Access Journals (Sweden)

    N. R. Patel

    2012-08-01

    Full Text Available Earth observation driven ecosystem modeling have played a major role in estimation of carbon budget components such as gross primary productivity (GPP and net primary production (NPP over terrestrial ecosystems, including agriculture. The present study therefore evaluate satellite-driven vegetation photosynthesis (VPM model for GPP estimation over agro-ecosystems in India by using time series of the Normalized Difference Vegetation Index (NDVI from SPOT-VEGETATION, cloud cover observation from MODIS, coarse-grid C3/C4 crop fraction and decadal grided databases of maximum and minimum temperatures. Parameterization of VPM parameters e.g. maximum light use efficiency (ε* and Tscalar was done based on eddy-covariance measurements and literature survey. Incorporation of C3/C4 crop fraction is a modification to commonly used constant maximum LUE. Modeling results from VPM captured very well the geographical pattern of GPP and NPP over cropland in India. Well managed agro-ecosystems in Trans-Gangetic and upper Indo-Gangetic plains had the highest magnitude of GPP with peak GPP during kharif occurs in sugarcane-wheat system (western UP and it occurs in rice-wheat system (Punjab during Rabi season. Overall, croplands in these plains had more annual GPP (> 1000 g C m-2 and NPP (> 600 g C m-2 due to input-intensive cultivation. Desertic tracts of western Rajasthan showed the least GPP and NPP values. Country-level contribution of croplands to national GPP and NPP amounts to1.34 Pg C year-1 and 0.859 Pg C year-1, respectively. Modeled estimates of cropland NPP agrees well with ground-based estimates for north-western India (R2 = 0.63 and RMSE = 108 g C m-2. Future research will focus on evaluating the VPM model with medium resolution sensors such as AWiFS and MODIS for rice-wheat system and validating with eddy-covariance measurements.

  17. Effects of systematic sampling on satellite estimates of deforestation rates

    Energy Technology Data Exchange (ETDEWEB)

    Steininger, M K; Godoy, F; Harper, G, E-mail: msteininger@conservation.or [Center for Applied Biodiversity Science-Conservation International, 2011 Crystal Drive Suite 500, Arlington, VA 22202 (United States)

    2009-09-15

    Options for satellite monitoring of deforestation rates over large areas include the use of sampling. Sampling may reduce the cost of monitoring but is also a source of error in estimates of areas and rates. A common sampling approach is systematic sampling, in which sample units of a constant size are distributed in some regular manner, such as a grid. The proposed approach for the 2010 Forest Resources Assessment (FRA) of the UN Food and Agriculture Organization (FAO) is a systematic sample of 10 km wide squares at every 1 deg. intersection of latitude and longitude. We assessed the outcome of this and other systematic samples for estimating deforestation at national, sub-national and continental levels. The study is based on digital data on deforestation patterns for the five Amazonian countries outside Brazil plus the Brazilian Amazon. We tested these schemes by varying sample-unit size and frequency. We calculated two estimates of sampling error. First we calculated the standard errors, based on the size, variance and covariance of the samples, and from this calculated the 95% confidence intervals (CI). Second, we calculated the actual errors, based on the difference between the sample-based estimates and the estimates from the full-coverage maps. At the continental level, the 1 deg., 10 km scheme had a CI of 21% and an actual error of 8%. At the national level, this scheme had CIs of 126% for Ecuador and up to 67% for other countries. At this level, increasing sampling density to every 0.25 deg. produced a CI of 32% for Ecuador and CIs of up to 25% for other countries, with only Brazil having a CI of less than 10%. Actual errors were within the limits of the CIs in all but two of the 56 cases. Actual errors were half or less of the CIs in all but eight of these cases. These results indicate that the FRA 2010 should have CIs of smaller than or close to 10% at the continental level. However, systematic sampling at the national level yields large CIs unless the

  18. Estimation of Satellite PCO Offsets for BeiDou based on MGEX Net Solution

    Science.gov (United States)

    Yize, Zhang; Junping, Chen; Bin, Wu; Jiexian, Wang

    2015-04-01

    BeiDou Satellite Navigation System currently has a total 14 satellites including GEO/IGSO/MEO satellites and providing a regional PNT service. Due to a lack of publicly available antenna phase center offsets (PCO) for the BeiDou satellites, conventional values of (+0.6 m, 0.0 m, +1.1 m) are recommended for orbit and clock determination of the GEO/IGSO/MEO satellites, which needs to be further estimation and refinement. In this paper, we propose a multi-GNSS network solution for the estimation of BeiDou satellite PCO. More than 35 ground stations of International GNSS MGEX tracking network are used to determine the BeiDou satellite PCO. In this strategy, the GPS and BeiDou satellite orbits and clocks are derived from IGS final products, and GPS satellite PCO and PCV are fixed according to igs08.atx. The BeiDou satellites PCO are estimated together with the station clock, troposphere delay and LC combination ambiguity parameter. Result shows that the RMS of phase residuals for all stations is 1.8cm and is 1.6m for code residual, respectively. The estimated PCO is different for each satellite. Appling the new PCO for precise point positioning, we found that the positioning error improves from 6cm to 2cm in height.

  19. Estimation of Satellite Orientation from Space Surveillance Imagery Measured with an Adaptive Optics Telescope

    Science.gov (United States)

    1996-12-01

    SATELLITE ORIENTATION FROM SPACE SURVEILLANCE IMAGERY MEASURED WITH AN ADAPTIVE OPTICS TELESCOPE THESIS Gregory E. Wood Lieutenant, USAF AFIT/GSO/ENP...the official policy or position of the Department of Defense or the U. S. Government. AFIT/GSO/ENP/96D-02 ESTIMATION OF SATELLITE ORIENTATION FROM...surveillance operations. xii ESTIMATION OF SATELLITE ORIENTATION FROM SPACE SURVEILLANCE IMAGERY MEASURED WITH AN ADAPTIVE OPTICS TELESCOPE

  20. Sea ice-atmospheric interaction: Application of multispectral satellite data in polar surface energy flux estimates

    Science.gov (United States)

    Steffen, Konrad; Key, J.; Maslanik, J.; Schweiger, A.

    1993-01-01

    This is the third annual report on: Sea Ice-Atmosphere Interaction - Application of Multispectral Satellite Data in Polar Surface Energy Flux Estimates. The main emphasis during the past year was on: radiative flux estimates from satellite data; intercomparison of satellite and ground-based cloud amounts; radiative cloud forcing; calibration of the Advanced Very High Resolution Radiometer (AVHRR) visible channels and comparison of two satellite derived albedo data sets; and on flux modeling for leads. Major topics covered are arctic clouds and radiation; snow and ice albedo, and leads and modeling.

  1. Femto-satellite Swarm State and Density Estimation Project

    Data.gov (United States)

    National Aeronautics and Space Administration — NASA is planning future missions involving fleets of small satellites in LEO and GEO that can exhibit autonomous collective behavior. Such a "swarm of...

  2. Gravity Anomalies and Estimated Topography Derived from Satellite Altimetry

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — In many areas of the global ocean, the depth of the seafloor is not well known because survey lines by ships are hundreds of kilometers apart. Satellites carrying...

  3. How Well Can We Estimate Error Variance of Satellite Precipitation Data Around the World?

    Science.gov (United States)

    Gebregiorgis, A. S.; Hossain, F.

    2014-12-01

    The traditional approach to measuring precipitation by placing a probe on the ground will likely never be adequate or affordable in most parts of the world. Fortunately, satellites today provide a continuous global bird's-eye view (above ground) at any given location. However, the usefulness of such precipitation products for hydrological applications depends on their error characteristics. Thus, providing error information associated with existing satellite precipitation estimates is crucial to advancing applications in hydrologic modeling. In this study, we present a method of estimating satellite precipitation error variance using regression model for three satellite precipitation products (3B42RT, CMORPH, and PERSIANN-CCS) using easily available geophysical features and satellite precipitation rate. The goal of this work is to explore how well the method works around the world in diverse geophysical settings. Topography, climate, and seasons are considered as the governing factors to segregate the satellite precipitation uncertainty and fit a nonlinear regression equation as function of satellite precipitation rate. The error variance models were tested on USA, Asia, Middle East, and Mediterranean region. Rain-gauge based precipitation product was used to validate the errors variance of satellite precipitation products. Our study attests that transferability of model estimators (which help to estimate the error variance) from one region to another is practically possible by leveraging the similarity in geophysical features. Therefore, the quantitative picture of satellite precipitation error over ungauged regions can be discerned even in the absence of ground truth data.

  4. Satellite estimates of urban development for hydrological modelling

    DEFF Research Database (Denmark)

    Kaspersen, Per Skougaard; Drews, Martin

    We investigate the applicability of medium resolution Landsat satellite imagery for mapping temporal changes in urban land cover in European cities for direct use in urban flood models. The overarching aim is to provide accurate and costand resource-efficient quantification of temporal changes...

  5. Satellite air temperature estimation for monitoring the canopy layer heat island of Milan

    DEFF Research Database (Denmark)

    Pichierri, Manuele; Bonafoni, Stefania; Biondi, Riccardo

    2012-01-01

    2007 and 2010 were processed. Analysis of the canopy layer heat island (CLHI) maps during summer months reveals an average heat island effect of 3–4K during nighttime (with some peaks around 5K) and a weak CLHI intensity during daytime. In addition, the satellite maps reveal a well defined island shape......In this work, satellite maps of the urban heat island of Milan are produced using satellite-based infrared sensor data. For this aim, we developed suitable algorithms employing satellite brightness temperatures for the direct air temperature estimation 2 m above the surface (canopy layer), showing...

  6. Estimation of evaporation rates over the Arabian Sea from Satellite data

    Digital Repository Service at National Institute of Oceanography (India)

    Rao, M.V.; RameshBabu, V.; Rao, L.V.G.; Sastry, J.S.

    Utilizing both the SAMIR brightness temperatures of Bhaskara 2 and GOSSTCOMP charts of NOAA satellite series, the evaporation rates over the Arabian Sea for June 1982 are estimated through the bulk aerodynamic method. The spatial distribution...

  7. Estimated Depth Maps of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery (Draft)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Estimated shallow-water, depth maps were produced using rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations in the...

  8. Estimated Depth Maps of the Northwestern Hawaiian Islands Derived from High Resolution IKONOS Satellite Imagery

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Estimated shallow-water, depth maps were produced using rule-based, semi-automated image analysis of high-resolution satellite imagery for nine locations in the...

  9. A Method for Estimating BeiDou Inter-frequency Satellite Clock Bias

    Directory of Open Access Journals (Sweden)

    LI Haojun

    2016-02-01

    Full Text Available A new method for estimating the BeiDou inter-frequency satellite clock bias is proposed, considering the shortage of the current methods. The constant and variable parts of the inter-frequency satellite clock bias are considered in the new method. The data from 10 observation stations are processed to validate the new method. The characterizations of the BeiDou inter-frequency satellite clock bias are also analyzed using the computed results. The results of the BeiDou inter-frequency satellite clock bias indicate that it is stable in the short term. The estimated BeiDou inter-frequency satellite clock bias results are molded. The model results show that the 10 parameters of model for each satellite can express the BeiDou inter-frequency satellite clock bias well and the accuracy reaches cm level. When the model parameters of the first day are used to compute the BeiDou inter-frequency satellite clock bias of the second day, the accuracy also reaches cm level. Based on the stability and modeling, a strategy for the BeiDou satellite clock service is presented to provide the reference of our BeiDou.

  10. Evapotranspiration estimation using a normalized difference vegetation index transformation of satellite data

    Science.gov (United States)

    Seevers, P.M.; Ottmann, R. W.

    1994-01-01

    Evapotranspiration of irrigated crops on two irrigation service areas along the lower Colorado River was estimated using a normalized difference vegetation index of satellite data. A procedure was developed which equated the index to crop coefficients. Evapotranspiration estimates for fields for three dates of thematic mapper data were highly correlated with ground estimates. Service area estimates using thematic mapper and Advanced Very High Resolution Radiometer data agreed well with estimates based on US Geological Survey gauging station data.

  11. Estimating the yaw-attitude of BDS IGSO and MEO satellites

    Science.gov (United States)

    Dai, Xiaolei; Ge, Maorong; Lou, Yidong; Shi, Chuang; Wickert, Jens; Schuh, Harald

    2015-10-01

    Precise knowledge and consistent modeling of the yaw-attitude of GNSS satellites are essential for high-precision data processing and applications. As the exact attitude control mechanism for the satellites of the BeiDou Satellite Navigation System (BDS) is not yet released, the reverse kinematic precise point positioning (PPP) method was applied in our study. However, we confirm that the recent precise orbit determination (POD) processing for GPS satellites could not provide suitable products for estimating BDS attitude using the reverse PPP because of the special attitude control switching between the nominal and the orbit-normal mode. In our study, we propose a modified processing schema for studying the attitude behavior of the BDS satellites. In this approach, the observations of the satellites during and after attitude switch are excluded in the POD processing, so that the estimates, which are needed in the reverse PPP, are not contaminated by the inaccurate initial attitude mode. The modified process is validated by experimental data sets and the attitude yaw-angles of the BDS IGSO and MEO satellites are estimated with an accuracy of better than . Furthermore, the results confirm that the switch is executed when the Sun elevation is about and the actual orientation is very close to its target one. Based on the estimated yaw-angles, a preliminary attitude switch model was established and reintroduced into the POD, yielding to a substantial improvement in the orbit overlap RMS.

  12. Development and validation of satellite based estimates of surface visibility

    Science.gov (United States)

    Brunner, J.; Pierce, R. B.; Lenzen, A.

    2015-10-01

    A satellite based surface visibility retrieval has been developed using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements as a proxy for Advanced Baseline Imager (ABI) data from the next generation of Geostationary Operational Environmental Satellites (GOES-R). The retrieval uses a multiple linear regression approach to relate satellite aerosol optical depth, fog/low cloud probability and thickness retrievals, and meteorological variables from numerical weather prediction forecasts to National Weather Service Automated Surface Observing System (ASOS) surface visibility measurements. Validation using independent ASOS measurements shows that the GOES-R ABI surface visibility retrieval (V) has an overall success rate of 64.5% for classifying Clear (V ≥ 30 km), Moderate (10 km ≤ V skill during June through September, when Heidke skill scores are between 0.2 and 0.4. We demonstrate that the aerosol (clear sky) component of the GOES-R ABI visibility retrieval can be used to augment measurements from the United States Environmental Protection Agency (EPA) and National Park Service (NPS) Interagency Monitoring of Protected Visual Environments (IMPROVE) network, and provide useful information to the regional planning offices responsible for developing mitigation strategies required under the EPA's Regional Haze Rule, particularly during regional haze events associated with smoke from wildfires.

  13. Analysis on BDS Satellite Internal Multipath and Its Impact on Wide-lane FCB Estimation

    Directory of Open Access Journals (Sweden)

    RUAN Rengui

    2017-08-01

    Full Text Available To the issue of the satellite internal multipath (SIMP of BeiDou satellites, it proposed and emphasized that the SIMP model should be established as a function of the nadir angle with respect to the observed satellite rather than the elevation of the measurement, so that it can be used for receivers at various altitude. BDS data from global distributed stations operated by the International Monitoring and Assessment System (iGMAS and the Multi-GNSS Experiment (MGEX of the International GNSS Service (IGS are collected and a new SIMP model as a piece-wise linear function of the nadir angle is released for the IGSO-and MEO-satellite groups and for B1, B2 and B3 frequency band individually. The SIMP of GEO,IGSO and MEO satellites is further analyzed with B1/B2 dual-frequency data onboard the FengYun-3 C(FY3C satellite at an altitude of~830 km, and it showed that, for nadir angles smaller than 7°, the SIMP values for GEO is quite close to the IGSO's, especially for B2, which may suggest that the SIMP model for IGSO satellites possibly also works for GEO satellites. It also demonstrated that, when the nadir angle is smaller than 12°for the MEO and 7°for the IGSO, the estimated SIMP model with data from FY3C is considerable consistent with that estimated with data collected at ground stations. Experiments are carried out to investigate the impacts of the SIMP on wide-lane fractional cycle bias (FCB estimation for BDS satellites. The result indicates that, with the correction of the estimated SIMP, the repeatability of the FCB series is significantly improved by more than 60% for all satellites. Specifically, for the MEO and IGSO satellites, the repeatability is smaller than 0.05 cycle; the repeatability of 0.023 and 0.068 cycles achieved for GEO satellites C01 and C02 respectively with the estimated SIMP model for IGSO satellites.

  14. Spaceborne GPS receiver antenna phase center offset and variation estimation for the Shiyan 3 satellite

    Directory of Open Access Journals (Sweden)

    Gu Defeng

    2016-10-01

    Full Text Available In determining the orbits of low Earth orbit (LEO satellites using spaceborne GPS, the errors caused by receiver antenna phase center offset (PCO and phase center variations (PCVs are gradually becoming a major limiting factor for continued improvements to accuracy. Shiyan 3, a small satellite mission for space technology experimentation and climate exploration, was developed by China and launched on November 5, 2008. The dual-frequency GPS receiver payload delivers 1 Hz data and provides the basis for precise orbit determination within the range of a few centimeters. The antenna PCO and PCV error characteristics and the principles influencing orbit determination are analyzed. The feasibility of PCO and PCV estimation and compensation in different directions is demonstrated through simulation and in-flight tests. The values of receiver antenna PCO and PCVs for Gravity Recovery and Climate Experiment (GRACE and Shiyan 3 satellites are estimated from one month of data. A large and stable antenna PCO error, reaching up to 10.34 cm in the z-direction, is found with the Shiyan 3 satellite. The PCVs on the Shiyan 3 satellite are estimated and reach up to 3.0 cm, which is slightly larger than that of GRACE satellites. Orbit validation clearly improved with independent k-band ranging (KBR and satellite laser ranging (SLR measurements. For GRACE satellites, the average root mean square (RMS of KBR residuals improved from 1.01 cm to 0.88 cm. For the Shiyan 3 satellite, the average RMS of SLR residuals improved from 4.95 cm to 4.06 cm.

  15. Spaceborne GPS receiver antenna phase center offset and variation estimation for the Shiyan 3 satellite

    Institute of Scientific and Technical Information of China (English)

    Gu Defeng; Lai Yuwang; Liu Junhong; Ju Bing; Tu Jia

    2016-01-01

    In determining the orbits of low Earth orbit (LEO) satellites using spaceborne GPS, the errors caused by receiver antenna phase center offset (PCO) and phase center variations (PCVs) are gradually becoming a major limiting factor for continued improvements to accuracy. Shiyan 3, a small satellite mission for space technology experimentation and climate exploration, was developed by China and launched on November 5, 2008. The dual-frequency GPS receiver payload delivers 1 Hz data and provides the basis for precise orbit determination within the range of a few centime-ters. The antenna PCO and PCV error characteristics and the principles influencing orbit determi-nation are analyzed. The feasibility of PCO and PCV estimation and compensation in different directions is demonstrated through simulation and in-flight tests. The values of receiver antenna PCO and PCVs for Gravity Recovery and Climate Experiment (GRACE) and Shiyan 3 satellites are estimated from one month of data. A large and stable antenna PCO error, reaching up to 10.34 cm in the z-direction, is found with the Shiyan 3 satellite. The PCVs on the Shiyan 3 satellite are estimated and reach up to 3.0 cm, which is slightly larger than that of GRACE satellites. Orbit validation clearly improved with independent k-band ranging (KBR) and satellite laser ranging (SLR) measurements. For GRACE satellites, the average root mean square (RMS) of KBR resid-uals improved from 1.01 cm to 0.88 cm. For the Shiyan 3 satellite, the average RMS of SLR resid-uals improved from 4.95 cm to 4.06 cm.

  16. Controlling the Chaos Using Fuzzy Estimation in a Gyrostat Satellite

    Science.gov (United States)

    Guran, Ardeshir

    In this paper, we present a study of the dynamical behavior in a Kelvin type gyrostat satellite. We firstly obtain the Hamiltonian equations of our model by using Cardan angles as generalized coordinates. Then, we make this Hamiltonian dimensionless and calculate motion equations for this dimensionless system. The study of the Poincare's sections of this system shows us that chaotic motion regimes are present for specific parameter values. The main goal of this work is the finding of stabilizing orbits by using a control technique, the fuzzy control of Poincare map method, so that it can be applied to stabilize special periodic orbits in this system. Finally, we expect that the technique can be useful for a better understanding of control theory and their applications in gyrostat problems.

  17. Satellite-scale Estimates of the "b Parameter" Relating Vegetation Water Content and SMOS Optical Thickness

    Science.gov (United States)

    Patton, J. C.; Hornbuckle, B. K.

    2013-12-01

    Microwave radiation emitted by Earth's land surface is primarily determined by soil moisture and vegetation. One of the effects of vegetation on surface microwave emissions is often termed the "vegetation optical thickness" or "vegetation opacity" and is often abbreviated as tau. Retrievals of soil moisture from microwave radiometer measurements requires knowledge of tau. The Soil Moisture and Ocean Salinity (SMOS) satellite measures microwave radiation at multiple incidence angles, enabling the simultaneous retrieval of soil moisture and tau. Other soil moisture satellites, such as the upcoming Soil Moisture Active Passive (SMAP) satellite, only measure at single incidence angles and may need auxiliary sources of tau data in order to retrieve soil moisture. One proposed method for estimating tau for these satellites is by relating reflectance data, e.g. the normalized difference vegetation index, to vegetation water content (VWC), then relating VWC to tau. VWC and tau can be related through the b parameter, i.e. tau = b x VWC. Values of b for different land cover types have been estimated from tower (~1 m) and airplane (~10-100 m) data, but have not been measured at the satellite scale (~10 km). Estimating b at the satellite scale from measurements at smaller scales is difficult because the effective value of b in a satellite pixel may not be well represented by linear weighted average based on the fraction of each land cover type in the pixel. However, by relating county crop yields, estimated by the USDA National Agricultural Statistics Service, to measurements of SMOS tau, and by using certain allometric relationships, such as the ratio of water to dry matter and the harvest index of crops, we can estimate b at the satellite scale. We have used this method to estimate b for each Iowa county for the years 2010-2012. Initial results suggest that b may change year to year; our current estimates for b in Iowa range from 0.065 in 2010 to 0.100 in 2012. These

  18. An adaptive Multiplicative Extened Kalman Filter for Attitude Estimation of Marine Satellite Tracking Antenna

    DEFF Research Database (Denmark)

    Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar

    2016-01-01

    Satellite tracking is a challenging task for marine applications due to the disturbance from ocean waves. An Attitude Heading and Reference System (AHRS) for measuring ship attitude, based on Microelectromechanical Systems (MEMS) sensors, is a key part for satellite tracking. In this paper......, an adaptive Multiplicative Extended Kalman Filter (MEKF) for attitude estimation of Marine Satellite Tracking Antenna (MSTA) is presented with the measurement noise covariance matrix adjusted according to the norm of accelerometer measurements, which can significantly reduce the slamming influence from waves...

  19. Estimation of Vegetation Aerodynamic Roughness of Natural Regions Using Frontal Area Density Determined from Satellite Imagery

    Science.gov (United States)

    Jasinski, Michael F.; Crago, Richard

    1994-01-01

    Parameterizations of the frontal area index and canopy area index of natural or randomly distributed plants are developed, and applied to the estimation of local aerodynamic roughness using satellite imagery. The formulas are expressed in terms of the subpixel fractional vegetation cover and one non-dimensional geometric parameter that characterizes the plant's shape. Geometrically similar plants and Poisson distributed plant centers are assumed. An appropriate averaging technique to extend satellite pixel-scale estimates to larger scales is provided. ne parameterization is applied to the estimation of aerodynamic roughness using satellite imagery for a 2.3 sq km coniferous portion of the Landes Forest near Lubbon, France, during the 1986 HAPEX-Mobilhy Experiment. The canopy area index is estimated first for each pixel in the scene based on previous estimates of fractional cover obtained using Landsat Thematic Mapper imagery. Next, the results are incorporated into Raupach's (1992, 1994) analytical formulas for momentum roughness and zero-plane displacement height. The estimates compare reasonably well to reference values determined from measurements taken during the experiment and to published literature values. The approach offers the potential for estimating regionally variable, vegetation aerodynamic roughness lengths over natural regions using satellite imagery when there exists only limited knowledge of the vegetated surface.

  20. Multi-satellite rainfall sampling error estimates – a comparative study

    Directory of Open Access Journals (Sweden)

    A. Loew

    2012-10-01

    Full Text Available This study focus is set on quantifying sampling related uncertainty in the satellite rainfall estimates. We conduct observing system simulation experiment to estimate sampling error for various constellations of Low-Earth orbiting and geostationary satellites. There are two types of microwave instruments currently available: cross track sounders and conical scanners. We evaluate the differences in sampling uncertainty for various satellite constellations that carry instruments of the common type as well as in combination with geostationary observations. A precise orbital model is used to simulate realistic satellite overpasses with orbital shifts taken into account. With this model we resampled rain gauge timeseries to simulate satellites rainfall estimates free of retrieval and calibration errors. We concentrate on two regions, Germany and Benin, areas with different precipitation regimes. Our results show that sampling uncertainty for all satellite constellations does not differ greatly depending on the area despite the differences in local precipitation patterns. Addition of 3 hourly geostationary observations provides equal performance improvement in Germany and Benin, reducing rainfall undersampling by 20–25% of the total rainfall amount. Authors do not find a significant difference in rainfall sampling between conical imager and cross-track sounders.

  1. Sampling error study for rainfall estimate by satellite using a stochastic model

    Science.gov (United States)

    Shin, Kyung-Sup; North, Gerald R.

    1988-01-01

    In a parameter study of satellite orbits, sampling errors of area-time averaged rain rate due to temporal sampling by satellites were estimated. The sampling characteristics were studied by accounting for the varying visiting intervals and varying fractions of averaging area on each visit as a function of the latitude of the grid box for a range of satellite orbital parameters. The sampling errors were estimated by a simple model based on the first-order Markov process of the time series of area averaged rain rates. For a satellite of nominal Tropical Rainfall Measuring Mission (Thiele, 1987) carrying an ideal scanning microwave radiometer for precipitation measurements, it is found that sampling error would be about 8 to 12 pct of estimated monthly mean rates over a grid box of 5 X 5 degrees. It is suggested that an observation system based on a low inclination satellite combined with a sunsynchronous satellite simultaneously might be the best candidate for making precipitation measurements from space.

  2. Efficiency assessment of using satellite data for crop area estimation in Ukraine

    Science.gov (United States)

    Gallego, Francisco Javier; Kussul, Nataliia; Skakun, Sergii; Kravchenko, Oleksii; Shelestov, Andrii; Kussul, Olga

    2014-06-01

    The knowledge of the crop area is a key element for the estimation of the total crop production of a country and, therefore, the management of agricultural commodities markets. Satellite data and derived products can be effectively used for stratification purposes and a-posteriori correction of area estimates from ground observations. This paper presents the main results and conclusions of the study conducted in 2010 to explore feasibility and efficiency of crop area estimation in Ukraine assisted by optical satellite remote sensing images. The study was carried out on three oblasts in Ukraine with a total area of 78,500 km2. The efficiency of using images acquired by several satellite sensors (MODIS, Landsat-5/TM, AWiFS, LISS-III, and RapidEye) combined with a field survey on a stratified sample of square segments for crop area estimation in Ukraine is assessed. The main criteria used for efficiency analysis are as follows: (i) relative efficiency that shows how much time the error of area estimates can be reduced with satellite images, and (ii) cost-efficiency that shows how much time the costs of ground surveys for crop area estimation can be reduced with satellite images. These criteria are applied to each satellite image type separately, i.e., no integration of images acquired by different sensors is made, to select the optimal dataset. The study found that only MODIS and Landsat-5/TM reached cost-efficiency thresholds while AWiFS, LISS-III, and RapidEye images, due to its high price, were not cost-efficient for crop area estimation in Ukraine at oblast level.

  3. Temporal scaling analysis of irradiance estimated from daily satellite data and numerical modelling

    Science.gov (United States)

    Vindel, Jose M.; Navarro, Ana A.; Valenzuela, Rita X.; Ramírez, Lourdes

    2016-11-01

    The temporal variability of global irradiance estimated from daily satellite data and numerical models has been compared for different spans of time. According to the time scale considered, a different behaviour can be expected for each climate. Indeed, for all climates and at small scale, the persistence decreases as this scale increases, but the mediterranean climate, and its continental variety, shows higher persistence than oceanic climate. The probabilities of maintaining the values of irradiance after a certain period of time have been used as a first approximation to analyse the quality of each source, according to the climate. In addition, probability distributions corresponding to variations of clearness indices measured at several stations located in different climate zones have been compared with those obtained from satellite and modelling estimations. For this work, daily radiation data from the reanalysis carried out by the European Centre for Medium-Range Weather Forecasts and from the Satellite Application Facilities on climate monitoring have been used for mainland Spain. According to the results, the temporal series estimation of irradiance is more accurate when using satellite data, independent of the climate considered. In fact, the coefficients of determination corresponding to the locations studied are always above 0.92 in the case of satellite data, while this coefficient decreases to 0.69 for some cases of the numerical model. This conclusion is more evident in oceanic climates, where the most important errors can be observed. Indeed, in this case, the RRMSE derived from the CM-SAF estimations is 20.93%, while in the numerical model, it is 48.33%. Analysis of the probabilities corresponding to variations in the clearness indices also shows a better behaviour of the satellite-derived estimates for oceanic climate. For the standard mediterranean climate, the satellite also provides better results, though the numerical model improves

  4. Estimating the Retrievability of Temperature Profiles from Satellite Infrared Measurements

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A method is developed to assess retrievability, namely the retrieval potential for atmospheric temperature profiles, from satellite infrared measurements in clear-sky conditions. This technique is based upon generalized linear inverse theory and empirical orthogonal function analysis. Utilizing the NCEP global temperature reanalysis data in January and July from 1999 to 2003, the retrievabilities obtained with the Atmospheric Infrared Sounder (AIRS) and the High Resolution Infrared Radiation Sounder/3 (HIRS/3)sounding channel data are derived respectively for each standard pressure level on a global scale. As an incidental result of this study, the optimum truncation number in the method of generalized linear inverse is deduced too. The results show that the retrievabilities of temperature obtained with the two datasets are similar in spatial distribution and seasonal change characteristics. As for the vertical distribution, the retrievabilities are low in the upper and lower atmosphere, and high between 400 hPa and 850 hPa. For the geographical distribution, the retrievabilities are low in the low-latitude oceanic regions and in some regions in Antarctica, and relatively high in mid-high latitudes and continental regions. Compared with the HIRS/3 data, the retrievability obtained with the AIRS data can be improved by an amount between 0.15 and 0.40.

  5. Multi-day convective-environmental evolution prior to tropical cyclone formation from geostationary satellite measurements

    Science.gov (United States)

    Chang, Minhee; Ho, Chang-Hoi; Park, Myung-Sook

    2016-04-01

    Tropical cyclones (TCs) are developed through persistent latent heating taken from deep convective process. By analyzing aircraft and polar-orbit satellite observations, distinct upper-level warm-core induced by strong updraft was found in pre-TCs while vertically uniform temperature profile is found in non-developers. Precipitation is also broader and more frequent in developing disturbances than in nondeveloping ones. However, large uncertainties remain in determining which disturbance will develop into TC by using observation snap-shots. Here, five-day systematic evolution of deep convection and environments in developing (80) and non-developing (491) disturbances are examined over the western North Pacific for 20072009 by using geostationary satellite observation. Daily, positive tendencies in the hourly time series of the area of the MTSAT-1R infrared (IR) and water vapor (WV) brightness temperature difference intensification was driven only after from Day 3 with rapid increase in relative vorticity and abrupt convective burst. There also exist many non-developing cases with mCB (54 %), which appear to candidates of TC formation as gradually increasing their convective area from Day 1 to Day 4. Due to the initially weak large-scale vorticity, they eventually decay on Day 5. For nondeveloping disturbances without mCB (46%), initially weak large-scale vorticity as well as dry atmosphere resulted in one-time deep convection and decay. Thus, this study suggests that the multiple days of convective burst, which initially accompanies strong low- to mid-troposphere large-scale vorticity, is important in TC formation.

  6. Combining Satellite Microwave Radiometer and Radar Observations to Estimate Atmospheric Latent Heating Profiles

    Science.gov (United States)

    Grecu, Mircea; Olson, William S.; Shie, Chung-Lin; L'Ecuyer, Tristan S.; Tao, Wei-Kuo

    2009-01-01

    In this study, satellite passive microwave sensor observations from the TRMM Microwave Imager (TMI) are utilized to make estimates of latent + eddy sensible heating rates (Q1-QR) in regions of precipitation. The TMI heating algorithm (TRAIN) is calibrated, or "trained" using relatively accurate estimates of heating based upon spaceborne Precipitation Radar (PR) observations collocated with the TMI observations over a one-month period. The heating estimation technique is based upon a previously described Bayesian methodology, but with improvements in supporting cloud-resolving model simulations, an adjustment of precipitation echo tops to compensate for model biases, and a separate scaling of convective and stratiform heating components that leads to an approximate balance between estimated vertically-integrated condensation and surface precipitation. Estimates of Q1-QR from TMI compare favorably with the PR training estimates and show only modest sensitivity to the cloud-resolving model simulations of heating used to construct the training data. Moreover, the net condensation in the corresponding annual mean satellite latent heating profile is within a few percent of the annual mean surface precipitation rate over the tropical and subtropical oceans where the algorithm is applied. Comparisons of Q1 produced by combining TMI Q1-QR with independently derived estimates of QR show reasonable agreement with rawinsonde-based analyses of Q1 from two field campaigns, although the satellite estimates exhibit heating profile structure with sharper and more intense heating peaks than the rawinsonde estimates. 2

  7. The STRatospheric Estimation Algorithm from Mainz (STREAM): estimating stratospheric NO2 from nadir-viewing satellites by weighted convolution

    Science.gov (United States)

    Beirle, Steffen; Hörmann, Christoph; Jöckel, Patrick; Liu, Song; Penning de Vries, Marloes; Pozzer, Andrea; Sihler, Holger; Valks, Pieter; Wagner, Thomas

    2016-07-01

    The STRatospheric Estimation Algorithm from Mainz (STREAM) determines stratospheric columns of NO2 which are needed for the retrieval of tropospheric columns from satellite observations. It is based on the total column measurements over clean, remote regions as well as over clouded scenes where the tropospheric column is effectively shielded. The contribution of individual satellite measurements to the stratospheric estimate is controlled by various weighting factors. STREAM is a flexible and robust algorithm and does not require input from chemical transport models. It was developed as a verification algorithm for the upcoming satellite instrument TROPOMI, as a complement to the operational stratospheric correction based on data assimilation. STREAM was successfully applied to the UV/vis satellite instruments GOME 1/2, SCIAMACHY, and OMI. It overcomes some of the artifacts of previous algorithms, as it is capable of reproducing gradients of stratospheric NO2, e.g., related to the polar vortex, and reduces interpolation errors over continents. Based on synthetic input data, the uncertainty of STREAM was quantified as about 0.1-0.2 × 1015 molecules cm-2, in accordance with the typical deviations between stratospheric estimates from different algorithms compared in this study.

  8. Wave Period and Coastal Bathymetry Estimations from Satellite Images

    Science.gov (United States)

    Danilo, Celine; Melgani, Farid

    2016-08-01

    We present an approach for wave period and coastal water depth estimation. The approach based on wave observations, is entirely independent of ancillary data and can theoretically be applied to SAR or optical images. In order to demonstrate its feasibility we apply our method to more than 50 Sentinel-1A images of the Hawaiian Islands, well-known for its long waves. Six wave buoys are available to compare our results with in-situ measurements. The results on Sentinel-1A images show that half of the images were unsuitable for applying the method (no swell or wavelength too small to be captured by the SAR). On the other half, 78% of the estimated wave periods are in accordance with buoy measurements. In addition, we present preliminary results of the estimation of the coastal water depth on a Landsat-8 image (with characteristics close to Sentinel-2A). With a squared correlation coefficient of 0.7 for ground truth measurement, this approach reveals promising results for monitoring coastal bathymetry.

  9. Geothermal Heat Flux Underneath Ice Sheets Estimated From Magnetic Satellite Data

    DEFF Research Database (Denmark)

    Fox Maule, Cathrine; Purucker, M.E.; Olsen, Nils

    The geothermal heat flux is an important factor in the dynamics of ice sheets, and it is one of the important parameters in the thermal budgets of subglacial lakes. We have used satellite magnetic data to estimate the geothermal heat flux underneath the ice sheets in Antarctica and Greenland....... By using satellite data, we are able to make heat flux maps covering the entire Antarctic continent and all of Greenland. We find that the heat flux varies from less than 50 to more than 150~mW/m2 underneath the ice sheets. To validate our results, we have compared our heat flux estimate with geologic...

  10. Pico satellite attitude estimation via Robust Unscented Kalman Filter in the presence of measurement faults.

    Science.gov (United States)

    Soken, Halil Ersin; Hajiyev, Chingiz

    2010-07-01

    In the normal operation conditions of a pico satellite, a conventional Unscented Kalman Filter (UKF) gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, UKF gives inaccurate results and diverges by time. This study introduces Robust Unscented Kalman Filter (RUKF) algorithms with the filter gain correction for the case of measurement malfunctions. By the use of defined variables named as measurement noise scale factor, the faulty measurements are taken into consideration with a small weight, and the estimations are corrected without affecting the characteristics of the accurate ones. Two different RUKF algorithms, one with single scale factor and one with multiple scale factors, are proposed and applied for the attitude estimation process of a pico satellite. The results of these algorithms are compared for different types of measurement faults in different estimation scenarios and recommendations about their applications are given.

  11. Estimation of vegetation cover resilience from satellite time series

    Directory of Open Access Journals (Sweden)

    T. Simoniello

    2008-07-01

    Full Text Available Resilience is a fundamental concept for understanding vegetation as a dynamic component of the climate system. It expresses the ability of ecosystems to tolerate disturbances and to recover their initial state. Recovery times are basic parameters of the vegetation's response to forcing and, therefore, are essential for describing realistic vegetation within dynamical models. Healthy vegetation tends to rapidly recover from shock and to persist in growth and expansion. On the contrary, climatic and anthropic stress can reduce resilience thus favouring persistent decrease in vegetation activity.

    In order to characterize resilience, we analyzed the time series 1982–2003 of 8 km GIMMS AVHRR-NDVI maps of the Italian territory. Persistence probability of negative and positive trends was estimated according to the vegetation cover class, altitude, and climate. Generally, mean recovery times from negative trends were shorter than those estimated for positive trends, as expected for vegetation of healthy status. Some signatures of inefficient resilience were found in high-level mountainous areas and in the Mediterranean sub-tropical ones. This analysis was refined by aggregating pixels according to phenology. This multitemporal clustering synthesized information on vegetation cover, climate, and orography rather well. The consequent persistence estimations confirmed and detailed hints obtained from the previous analyses. Under the same climatic regime, different vegetation resilience levels were found. In particular, within the Mediterranean sub-tropical climate, clustering was able to identify features with different persistence levels in areas that are liable to different levels of anthropic pressure. Moreover, it was capable of enhancing reduced vegetation resilience also in the southern areas under Warm Temperate sub-continental climate. The general consistency of the obtained results showed that, with the help of suited analysis

  12. A Novel Sampling Method for Satellite-Based Offshore Wind Resource Estimation

    DEFF Research Database (Denmark)

    Badger, Merete; Badger, Jake; Hasager, Charlotte Bay

    Synthetic aperture radar (SAR) measurements from satellites can be used to estimate the spatial wind speed variation offshore in great detail. The radar senses cm-scale roughness at the sea surface which can be translated to wind speed at the height 10 m using an empirical geophysical model......-based wind climatology have improved gradually as more data were collected. The satellite scenes have been treated as random samples and weighted equally in our previous analyses. Here we introduce a novel sampling strategy based on the wind class methodology that is normally applied in numerical modeling...... climatologically representative large-scale meteorological conditions for the region of interest. The wind classes are used to make the most representative selection of satellite images from the ENVISAT image catalogue. A minimum of one satellite image is chosen per wind class. The frequency of occurrence of each...

  13. Estimation of Leaf Area Index Using IRS Satellite Images

    Directory of Open Access Journals (Sweden)

    A Faridhosseini

    2012-12-01

    Full Text Available Estimation of vegetation cover attributes, such as the Leaf Area Index (LAI, is an important step in identifying the amount of water use for some plants. The goal of this study is to investigate the feasibility of using IRS LISS-III data to retrieve LAI. To get a LAI retrieval model based on reflectance and vegetation index, detailed field data were collected in the study area of eastern Iran. In this study, atmospheric corrected IRS LISS-III imagery was used to calculate Normalized Difference Vegetation Index (NDVI. Data of 50 samples of LAI were measured by Sun Scan System – SS1 in the study area. In situ measurements of LAI were related to widely use spectral vegetation indices (NDVI. The best model through analyzing the results was LAI = 19.305×NDVI+5.514 using the method of linear-regression analysis. The results showed that the correlation coefficient R2 was 0.534 and RMSE was 0.67. Thereby, suggesting that, when using remote sensing NDVI for LAI estimation, not only is the choice of NDVI of importance but also prior knowledge of plant architecture and soil background. Hence, some kind of landscape stratification is required before using multi- spectral imagery for large-scale mapping of vegetation biophysical variables.

  14. Building Damage Estimation by Integration of Seismic Intensity Information and Satellite L-band SAR Imagery

    Directory of Open Access Journals (Sweden)

    Nobuoto Nojima

    2010-09-01

    Full Text Available For a quick and stable estimation of earthquake damaged buildings worldwide, using Phased Array type L-band Synthetic Aperture Radar (PALSAR loaded on the Advanced Land Observing Satellite (ALOS satellite, a model combining the usage of satellite synthetic aperture radar (SAR imagery and Japan Meteorological Agency (JMA-scale seismic intensity is proposed. In order to expand the existing C-band SAR based damage estimation model into L-band SAR, this paper rebuilds a likelihood function for severe damage ratio, on the basis of dataset from Japanese Earth Resource Satellite-1 (JERS-1/SAR (L-band SAR images observed during the 1995 Kobe earthquake and its detailed ground truth data. The model which integrates the fragility functions of building damage in terms of seismic intensity and the proposed likelihood function is then applied to PALSAR images taken over the areas affected by the 2007 earthquake in Pisco, Peru. The accuracy of the proposed damage estimation model is examined by comparing the results of the analyses with field investigations and/or interpretation of high-resolution satellite images.

  15. Vegetation Height Estimation Near Power transmission poles Via satellite Stereo Images using 3D Depth Estimation Algorithms

    Science.gov (United States)

    Qayyum, A.; Malik, A. S.; Saad, M. N. M.; Iqbal, M.; Abdullah, F.; Rahseed, W.; Abdullah, T. A. R. B. T.; Ramli, A. Q.

    2015-04-01

    Monitoring vegetation encroachment under overhead high voltage power line is a challenging problem for electricity distribution companies. Absence of proper monitoring could result in damage to the power lines and consequently cause blackout. This will affect electric power supply to industries, businesses, and daily life. Therefore, to avoid the blackouts, it is mandatory to monitor the vegetation/trees near power transmission lines. Unfortunately, the existing approaches are more time consuming and expensive. In this paper, we have proposed a novel approach to monitor the vegetation/trees near or under the power transmission poles using satellite stereo images, which were acquired using Pleiades satellites. The 3D depth of vegetation has been measured near power transmission lines using stereo algorithms. The area of interest scanned by Pleiades satellite sensors is 100 square kilometer. Our dataset covers power transmission poles in a state called Sabah in East Malaysia, encompassing a total of 52 poles in the area of 100 km. We have compared the results of Pleiades satellite stereo images using dynamic programming and Graph-Cut algorithms, consequently comparing satellites' imaging sensors and Depth-estimation Algorithms. Our results show that Graph-Cut Algorithm performs better than dynamic programming (DP) in terms of accuracy and speed.

  16. Characterization of satellite based proxies for estimating nucleation mode particles over South Africa

    Directory of Open Access Journals (Sweden)

    A.-M. Sundström

    2014-10-01

    Full Text Available In this work satellite observations from the NASA's A-Train constellation were used to derive the values of primary emission and regional nucleation proxies over South Africa to estimate the potential for new particle formation. As derived in Kulmala et al. (2011, the satellite based proxies consist of source terms (NO2, SO2 and UV-B radiation, and a sink term describing the pre-existing aerosols. The first goal of this work was to study in detail the use of satellite aerosol optical depth (AOD as a substitute to the in situ based condensation sink (CS. One of the major factors affecting the agreement of CS and AOD was the elevated aerosol layers that increased the value of column integrated AOD but not affected the in situ CS. However, when the AOD in the proxy sink was replaced by an estimate from linear bivariate fit between AOD and CS, the agreement with the actual nucleation mode number concentration improved somewhat. The second goal of the work was to estimate how well the satellite based proxies can predict the potential for new particle formation. For each proxy the highest potential for new particle formation were observed over the Highveld industrial area, where the emissions were high but the sink due to pre-existing aerosols was relatively low. Best agreement between the satellite and in situ based proxies were obtained for NO2/AOD and UV-B/AOD2, whereas proxies including SO2 in the source term had lower correlation. Even though the OMI SO2 boundary layer product showed reasonable spatial pattern and detected the major sources over the study area, some of the known minor point sources were not detected. When defining the satellite proxies only for days when new particle formation event was observed, it was seen that for all the satellite based proxies the event day medians were higher than the entire measurement period median.

  17. Potential-field estimation from satellite data using scalar and vector Slepian functions

    CERN Document Server

    Plattner, Alain

    2013-01-01

    In the last few decades a series of increasingly sophisticated satellite missions has brought us gravity and magnetometry data of ever improving quality. To make optimal use of this rich source of information on the structure of Earth and other celestial bodies, our computational algorithms should be well matched to the specific properties of the data. In particular, inversion methods require specialized adaptation if the data are only locally available, their quality varies spatially, or if we are interested in model recovery only for a specific spatial region. Here, we present two approaches to estimate potential fields on a spherical Earth, from gradient data collected at satellite altitude. Our context is that of the estimation of the gravitational or magnetic potential from vector-valued measurements. Both of our approaches utilize spherical Slepian functions to produce an approximation of local data at satellite altitude, which is subsequently transformed to the Earth's spherical reference surface. The ...

  18. Assessing satellite AOD based and WRF/CMAQ output PM2.5 estimators

    Science.gov (United States)

    Cordero, Lina; Wu, Yonghua; Gross, Barry M.; Moshary, Fred

    2013-05-01

    Fine particulate matter measurements (PM2.5) are essential for air quality monitoring and related public health; however, the shortage of reliable measurmennts constrains researchers to use other means for obtaining reliable estimates over large scales. In particular, model forecasters and satellite community use their respective products to develop ground particulate matter estimations but few experiments have explored how the remote sensing approaches compare to the high resolution models. . In this paper we focus on studying the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Geostationary Operational Environmental Satellites (GOES) regression based estimates in comparison to more direct bias corrected outputs from the Community Multiscale Air Quality (CMAQ) model, We use a two-year dataset (2005-2006) and apply urban, season and hour filters to illustrate the agreement between estimated and in-situ measured fine particulate matter from the New York State Department of Environmental Conservation (NYSDEC). We first begin by analyzing the correspondence between ground aerosol optical depth (AOD) measurements from an AERONET (AErosol RObotic NETwork) Cimel sun/sky radiometer with both satellite and model products in one urban location; we show that satellite readings perform better than model outputs, especially during the summer (RMODIS>=0.65, RCMAQ>=0.37). This is a clear symptom of the difficulty in the models to properly model realistic optical properties. We then turn to a direct assessment of PM2.5 presenting individual comparisons between ground PM2.5 measurements with satellite/model predictions and demonstrate the higher accuracy from model estimations (RurbanMODIS >= 0.74, RurbanCMAQ >= 0.77; Rnon-urbanMODIS >= 0.48, Rnon-urbanCMAQ >= 0.78). In general, we find that the bias corrected CMAQ estimates are superior to satellite based estimators except at very high resolution. Finally, we show that when using both model and

  19. Satellite clock corrections estimation to accomplish real time ppp: experiments for brazilian real time network

    Science.gov (United States)

    Marques, Haroldo; Monico, João; Aquino, Marcio; Melo, Weyller

    2014-05-01

    The real time PPP method requires the availability of real time precise orbits and satellites clocks corrections. Currently, it is possible to apply the solutions of clocks and orbits available by BKG within the context of IGS Pilot project or by using the operational predicted IGU ephemeris. The accuracy of the satellite position available in the IGU is enough for several applications requiring good quality. However, the satellites clocks corrections do not provide enough accuracy (3 ns ~ 0.9 m) to accomplish real time PPP with the same level of accuracy. Therefore, for real time PPP application it is necessary to further research and develop appropriated methodologies for estimating the satellite clock corrections in real time with better accuracy. Currently, it is possible to apply the real time solutions of clocks and orbits available by Federal Agency for Cartography and Geodesy (BKG) within the context of IGS Pilot project. The BKG corrections are disseminated by a new proposed format of the RTCM 3.x and can be applied in the broadcasted orbits and clocks. Some investigations have been proposed for the estimation of the satellite clock corrections using GNSS code and phase observable at the double difference level between satellites and epochs (MERVAT, DOUSA, 2007). Another possibility consists of applying a Kalman Filter in the PPP network mode (HAUSCHILD, 2010) and it is also possible the integration of both methods, using network PPP and observables at double difference level in specific time intervals (ZHANG; LI; GUO, 2010). For this work the methodology adopted consists in the estimation of the satellite clock corrections based on the data adjustment in the PPP mode, but for a network of GNSS stations. The clock solution can be solved by using two types of observables: code smoothed by carrier phase or undifferenced code together with carrier phase. In the former, we estimate receiver clock error; satellite clock correction and troposphere, considering

  20. Estimation of clear-sky insolation using satellite and ground meteorological data

    Science.gov (United States)

    Staylor, W. F.; Darnell, W. L.; Gupta, S. K.

    1983-01-01

    Ground based pyranometer measurements were combined with meteorological data from the Tiros N satellite in order to estimate clear-sky insolations at five U.S. sites for five weeks during the spring of 1979. The estimates were used to develop a semi-empirical model of clear-sky insolation for the interpretation of input data from the Tiros Operational Vertical Sounder (TOVS). Using only satellite data, the estimated standard errors in the model were about 2 percent. The introduction of ground based data reduced errors to around 1 percent. It is shown that although the errors in the model were reduced by only 1 percent, TOVS data products are still adequate for estimating clear-sky insolation.

  1. Estimation of land remote sensing satellites productivity based on the simulation technique

    Science.gov (United States)

    Kurenkov, Vladimir I.; Kucherov, Alexander S.; Yakischik, Artem A.

    2017-01-01

    The problem of estimating land remote sensing satellites productivity is considered. Here, productivity is treated as a number of separate survey objects taken in a definite time. Appropriate mathematical models have been developed. Some results obtained with the help of the software worked out in Delphi programming support environment are presented.

  2. Satellite estimates of wide-range suspended sediment concentrations in Changjiang (Yangtze) estuary using MERIS data

    NARCIS (Netherlands)

    Shen, F.; Verhoef, W.; Zhou, Y.; Salama, M.S.; Liu, X.

    2010-01-01

    The Changjiang (Yangtze) estuarine and coastal waters are characterized by suspended sediments over a wide range of concentrations from 20 to 2,500 mg l-1. Suspended sediment plays important roles in the estuarine and coastal system and environment. Previous algorithms for satellite estimates of sus

  3. Satellite remote sensing for estimating leaf area index, FPAR and primary production. A literature review

    Energy Technology Data Exchange (ETDEWEB)

    Boresjoe Bronge, Laine [SwedPower AB, Stockholm (Sweden)

    2004-03-01

    Land vegetation is a critical component of several biogeochemical cycles that have become the focus of concerted international research effort. Most ecosystem productivity models, carbon budget models, and global models of climate, hydrology and biogeochemistry require vegetation parameters to calculate land surface photosynthesis, evapotranspiration and net primary production. Therefore, accurate estimates of vegetation parameters are increasingly important in the carbon cycle, the energy balance and in environmental impact assessment studies. The possibility of quantitatively estimating vegetation parameters of importance in this context using satellite data has been explored by numerous papers dealing with the subject. This report gives a summary of the present status and applicability of satellite remote sensing for estimating vegetation productivity by using vegetation index for calculating leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR). Some possible approaches for use of satellite data for estimating LAI, FPAR and net primary production (NPP) on a local scale are suggested. Recommendations for continued work in the Forsmark and Oskarshamn investigation areas, where vegetation data and NDVI-images based on satellite data have been produced, are also given.

  4. An adaptive Multiplicative Extened Kalman Filter for Attitude Estimation of Marine Satellite Tracking Antenna

    DEFF Research Database (Denmark)

    Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar

    2016-01-01

    , an adaptive Multiplicative Extended Kalman Filter (MEKF) for attitude estimation of Marine Satellite Tracking Antenna (MSTA) is presented with the measurement noise covariance matrix adjusted according to the norm of accelerometer measurements, which can significantly reduce the slamming influence from waves...

  5. Stochastic estimation of dynamically changing object orientation parameters using satellite measurements

    OpenAIRE

    Lukasevich, V. I.; Kramarov, S. O.; Sokolov, Sergey V.

    2015-01-01

    It is solved a problem of a posteriori estimation of dynamically modified parameters of angular movement of the object by satellite measurements. There are shown advantages of application of the methods of stochastic non-linear dynamic filtration before single-stage measurements. It is represented an example, showing efficiency of proposed approach.

  6. Combining satellite altimetry and gravimetry data to improve Antarctic mass balance and gia estimates

    NARCIS (Netherlands)

    Gunter, B.C.; Didova, O.; Riva, R.E.M.; van den Broeke, M.R.; Ligtenberg, S.R.M.; Lenaerts, J.T.M.; King, M.; Urban, T.

    2012-01-01

    This study explores an approach that simultaneously estimates Antarctic mass balance and glacial isostatic adjustment (GIA) through the combination of satellite gravity and altimetry data sets. The results improve upon previous efforts by incorporating reprocessed data sets over a longer period of t

  7. Satellite estimates of wide-range suspended sediment concentrations in Changjiang (Yangtze) estuary using MERIS data

    NARCIS (Netherlands)

    Shen, F.; Verhoef, W.; Zhou, Y.; Salama, M.S.; Liu, X.

    2010-01-01

    The Changjiang (Yangtze) estuarine and coastal waters are characterized by suspended sediments over a wide range of concentrations from 20 to 2,500 mg l-1. Suspended sediment plays important roles in the estuarine and coastal system and environment. Previous algorithms for satellite estimates of

  8. Combining satellite altimetry and gravimetry data to improve Antarctic mass balance and gia estimates

    NARCIS (Netherlands)

    Gunter, B.C.; Didova, O.; Riva, R.E.M.; van den Broeke, M.R.; Ligtenberg, S.R.M.; Lenaerts, J.T.M.; King, M.; Urban, T.

    2012-01-01

    This study explores an approach that simultaneously estimates Antarctic mass balance and glacial isostatic adjustment (GIA) through the combination of satellite gravity and altimetry data sets. The results improve upon previous efforts by incorporating reprocessed data sets over a longer period of

  9. Robust double gain unscented Kalman filter for small satellite attitude estimation

    Science.gov (United States)

    Cao, Lu; Yang, Weiwei; Li, Hengnian; Zhang, Zhidong; Shi, Jianjun

    2017-08-01

    Limited by the low precision of small satellite sensors, the estimation theories with high performance remains the most popular research topic for the attitude estimation. The Kalman filter (KF) and its extensions have been widely applied in the satellite attitude estimation and achieved plenty of achievements. However, most of the existing methods just take use of the current time-step's priori measurement residuals to complete the measurement update and state estimation, which always ignores the extraction and utilization of the previous time-step's posteriori measurement residuals. In addition, the uncertainty model errors always exist in the attitude dynamic system, which also put forward the higher performance requirements for the classical KF in attitude estimation problem. Therefore, the novel robust double gain unscented Kalman filter (RDG-UKF) is presented in this paper to satisfy the above requirements for the small satellite attitude estimation with the low precision sensors. It is assumed that the system state estimation errors can be exhibited in the measurement residual; therefore, the new method is to derive the second Kalman gain Kk2 for making full use of the previous time-step's measurement residual to improve the utilization efficiency of the measurement data. Moreover, the sequence orthogonal principle and unscented transform (UT) strategy are introduced to robust and enhance the performance of the novel Kalman Filter in order to reduce the influence of existing uncertainty model errors. Numerical simulations show that the proposed RDG-UKF is more effective and robustness in dealing with the model errors and low precision sensors for the attitude estimation of small satellite by comparing with the classical unscented Kalman Filter (UKF).

  10. Effect of satellite formations and imaging modes on global albedo estimation

    Science.gov (United States)

    Nag, Sreeja; Gatebe, Charles K.; Miller, David W.; de Weck, Olivier L.

    2016-05-01

    We confirm the applicability of using small satellite formation flight for multi-angular earth observation to retrieve global, narrow band, narrow field-of-view albedo. The value of formation flight is assessed using a coupled systems engineering and science evaluation model, driven by Model Based Systems Engineering and Observing System Simulation Experiments. Albedo errors are calculated against bi-directional reflectance data obtained from NASA airborne campaigns made by the Cloud Absorption Radiometer for the seven major surface types, binned using MODIS' land cover map - water, forest, cropland, grassland, snow, desert and cities. A full tradespace of architectures with three to eight satellites, maintainable orbits and imaging modes (collective payload pointing strategies) are assessed. For an arbitrary 4-sat formation, changing the reference, nadir-pointing satellite dynamically reduces the average albedo error to 0.003, from 0.006 found in the static referencecase. Tracking pre-selected waypoints with all the satellites reduces the average error further to 0.001, allows better polar imaging and continued operations even with a broken formation. An albedo error of 0.001 translates to 1.36 W/m2 or 0.4% in Earth's outgoing radiation error. Estimation errors are found to be independent of the satellites' altitude and inclination, if the nadir-looking is changed dynamically. The formation satellites are restricted to differ in only right ascension of planes and mean anomalies within slotted bounds. Three satellites in some specific formations show average albedo errors of less than 2% with respect to airborne, ground data and seven satellites in any slotted formation outperform the monolithic error of 3.6%. In fact, the maximum possible albedo error, purely based on angular sampling, of 12% for monoliths is outperformed by a five-satellite formation in any slotted arrangement and an eight satellite formation can bring that error down four fold to 3%. More than

  11. Evaluation of short-period rainfall estimates from Kalpana-1 satellite using MET software

    Indian Academy of Sciences (India)

    Soma Sen Roy; Subhendu Brata Saha; Hashmi Fatima; S K Roy Bhowmik; P K Kundu

    2012-10-01

    The INSAT Multispectral Rainfall Algorithm (IMSRA) technique for rainfall estimation, has recently been developed to meet the shortcomings of the Global Precipitation Index (GPI) technique of rainfall estimation from the data of geostationary satellites; especially for accurate short period rainfall estimates. This study evaluates the 3-hourly precipitation estimates by this technique as well as the rainfall estimates by the GPI technique using data of the Kalpana-1 satellite, over the Indian region for the south-west monsoon season of 2010 to understand their relative strengths and weaknesses in estimating short period rainfall. The gridded 3 hourly accumulated TRMM satellite (3B42 V6 product or TMPA product) and surface raingauge data for stations over the Indian region for the same period is used as the standard measure of rainfall estimates. The Method for Object-based Diagnostic Evaluation (MODE) utility of the METv3.0 software, has been used for the evaluation purpose. The results show that the new IMSRA technique is closer to the TMPA rainfall estimate, in terms of areal spread, geometric shape and location of rainfall areas, as compared to the GPI technique. The overlap of matching rainfall areas with respect to TMPA rainfall patches is also higher for the IMSRA estimates as compared to the GPI values. However, both satellite rainfall estimates are observed to be generally higher compared to the TMPA measurements. However, the values for the highest 10% of the rainfall rates in any rainfall patch, is generally higher for rainfall measured by the IMSRA technique, as compared to the estimates by the GPI technique. This may partly be due to the capping maximum limit of 3 mm/hr for rainfall measured by the GPI technique limits the total 3-hour accumulation to 9 mm even during heavy rainfall episodes. This is not so with IMSRA technique, which has no such limiting value. However, this general overestimation of the rainfall amount, measured by both techniques

  12. Bias adjustment of satellite-based precipitation estimation using gauge observations: A case study in Chile

    Science.gov (United States)

    Yang, Zhongwen; Hsu, Kuolin; Sorooshian, Soroosh; Xu, Xinyi; Braithwaite, Dan; Verbist, Koen M. J.

    2016-04-01

    Satellite-based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground-based observations are limited. However, existing satellite-based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM-GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN-CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009-2014. The historical data (satellite and gauge) for 2009-2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN-CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root-mean-square errors and mean biases. The systematic biases of the PERSIANN-CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground-based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide.

  13. Evaluation of Clear Sky Models for Satellite-Based Irradiance Estimates

    Energy Technology Data Exchange (ETDEWEB)

    Sengupta, M.; Gotseff, P.

    2013-12-01

    This report describes an intercomparison of three popular broadband clear sky solar irradiance model results with measured data, as well as satellite-based model clear sky results compared to measured clear sky data. The authors conclude that one of the popular clear sky models (the Bird clear sky model developed by Richard Bird and Roland Hulstrom) could serve as a more accurate replacement for current satellite-model clear sky estimations. Additionally, the analysis of the model results with respect to model input parameters indicates that rather than climatological, annual, or monthly mean input data, higher-time-resolution input parameters improve the general clear sky model performance.

  14. Modifications of the heliostat procedures for irradiance estimates from satellite images

    Energy Technology Data Exchange (ETDEWEB)

    Beyer, H.G.; Costanzo, Claudio; Heinemann, Detlev [Oldenburg Univ. (Germany). Fachbereich 8 - Physik

    1996-03-01

    Images taken by geostationary satellites may be used to estimate solar irradiance fluxes at the earth`s surface. The Heliostat method is a widely applied procedure for this task. It is based on the empirical correlation between a satellite derived cloud index and the irradiance at the ground. Modifications to this procedure that may reduce the temporal variability of the correlation are presented. The modified method may open the way to the use of a generic relation of cloud index and global irradiance. (author)

  15. Reusable Reentry Satellite (RRS) system design study: System cost estimates document

    Science.gov (United States)

    1991-01-01

    The Reusable Reentry Satellite (RRS) program was initiated to provide life science investigators relatively inexpensive, frequent access to space for extended periods of time with eventual satellite recovery on earth. The RRS will provide an on-orbit laboratory for research on biological and material processes, be launched from a number of expendable launch vehicles, and operate in Low-Altitude Earth Orbit (LEO) as a free-flying unmanned laboratory. SAIC's design will provide independent atmospheric reentry and soft landing in the continental U.S., orbit for a maximum of 60 days, and will sustain three flights per year for 10 years. The Reusable Reentry Vehicle (RRV) will be 3-axis stabilized with artificial gravity up to 1.5g's, be rugged and easily maintainable, and have a modular design to accommodate a satellite bus and separate modular payloads (e.g., rodent module, general biological module, ESA microgravity botany facility, general botany module). The purpose of this System Cost Estimate Document is to provide a Life Cycle Cost Estimate (LCCE) for a NASA RRS Program using SAIC's RRS design. The estimate includes development, procurement, and 10 years of operations and support (O&S) costs for NASA's RRS program. The estimate does not include costs for other agencies which may track or interface with the RRS program (e.g., Air Force tracking agencies or individual RRS experimenters involved with special payload modules (PM's)). The life cycle cost estimate extends over the 10 year operation and support period FY99-2008.

  16. Estimation of volcanic ash refractive index from satellite infrared sounder data

    Science.gov (United States)

    Ishimoto, H.; Masuda, K.

    2014-12-01

    The properties of volcanic ash clouds (cloud height, optical depth, and effective radius of the particles) are planned to estimate from the data of the next Japanese geostationary meteorological satellite, Himawari 8/9. The volcanic ash algorithms, such as those proposed by NOAA/NESDIS and by EUMETSAT, are based on the infrared absorption properties of the ash particles, and the refractive index of a typical volcanic rock (i.e. andesite) has been used in the forward radiative transfer calculations. Because of a variety of the absorption properties for real volcanic ash particles at infrared wavelengths (9-13 micron), a large retrieval error may occur if the refractive index of the observed ash particles was different from that assumed in the retrieval algorithm. Satellite infrared sounder provides spectral information for the volcanic ash clouds. If we can estimate the refractive index of the ash particles from the infrared sounder data, a dataset of the optical properties for similar rock type of the volcanic ash can be prepared for the ash retrieval algorithms of geostationary/polar-orbiting satellites in advance. Furthermore, the estimated refractive index can be used for a diagnostic and a correction of the ash particle model in the retrieval algorithm within a period of the volcanic activities. In this work, optimal estimation of the volcanic ash parameters was conducted through the radiative transfer calculations for the window channels of the atmospheric infrared sounder (AIRS). The estimated refractive indices are proposed for the volcanic ash particles of some eruption events.

  17. ROBUST AND FAST FREQUENCY OFFSET ESTIMATION FOR OFDM BASED SATELLITE COMMUNICATION

    Institute of Scientific and Technical Information of China (English)

    Wei Li; Xu Youyun; Cai Yueming

    2009-01-01

    A pilot-aided Orthogonal Frequency Division Multiplexing (OFDM) frequency offset estimator designed for satellite communication system is proposed in the paper. The estimator focuses on the acquisition of the integer part of carrier frequency offset and the acquisition range is as large as the whole signal bandwidth. Making full use of the phase difference between received pilot and local referential pilot, a fast estimation is obtained. Compared with existing method, our method can also work well even in SNR as low as 0dB. Simulations verify the good performance of our method.

  18. Combining Satellite and Ground Magnetic Measurements to Improve Estimates of Electromagnetic Induction Transfer Functions

    Science.gov (United States)

    Balasis, G.; Egbert, G. D.

    2005-12-01

    Electromagnetic (EM) induction studies using satellite and ground-based magnetic data may ultimately provide critical new constraints on the electrical conductivity of Earth's mantle. Unlike ground-based observatories, which leave large areas of the Earth (especially the ocean basins) unsampled, satellites have the potential for nearly complete global coverage. However, because the number of operating satellites is limited, spatially complex (especially non-zonal) external current sources are sampled relatively poorly by satellites at any fixed time. The comparatively much larger number of ground-based observatories provides more complete synoptic sampling of external source structure. By combining data from both satellites and observatories models of external sources can be improved, leading to more reliable global mapping of Earth conductivity. For example, estimates of EM induction transfer functions estimated from night-side CHAMP data have been previously shown to have biases which depend systematically on local time (LT). This pattern of biases suggests that a purely zonal model does not adequately describe magnetospheric sources. As a first step toward improved modeling of spatial complexity in sources, we have applied empirical orthogonal function (EOF) methods to exploratory analysis of night-side observatory data. After subtraction of the predictions of the CM4 comprehensive model, which includes a zonally symmetric storm-time correction based on Dst, we find significant non-axisymmetric, but large scale coherent variability in the mid-latitude night-side observatory residuals. Over the restricted range of local times (18:00-6:00) and latitudes (50°S to 50°N) considered, the dominant spatial mode of variability is reasonably approximated by a q21 quadrupole spherical harmonic. Temporal variability of this leading EOF mode is well correlated with Dst. Strategies for moving beyond this initial exploratory EOF analysis to combine observatory data with

  19. Multi-technique combination of space geodesy observations: Impact of the Jason-2 satellite on the GPS satellite orbits estimation

    Science.gov (United States)

    Zoulida, Myriam; Pollet, Arnaud; Coulot, David; Perosanz, Félix; Loyer, Sylvain; Biancale, Richard; Rebischung, Paul

    2016-10-01

    In order to improve the Precise Orbit Determination (POD) of the GPS constellation and the Jason-2 Low Earth Orbiter (LEO), we carry out a simultaneous estimation of GPS satellite orbits along with Jason-2 orbits, using GINS software. Along with GPS station observations, we use Jason-2 GPS, SLR and DORIS observations, over a data span of 6 months (28/05/2011-03/12/2011). We use the Geophysical Data Records-D (GDR-D) orbit estimation standards for the Jason-2 satellite. A GPS-only solution is computed as well, where only the GPS station observations are used. It appears that adding the LEO GPS observations results in an increase of about 0.7% of ambiguities fixed, with respect to the GPS-only solution. The resulting GPS orbits from both solutions are of equivalent quality, agreeing with each other at about 7 mm on Root Mean Square (RMS). Comparisons of the resulting GPS orbits to the International GNSS Service (IGS) final orbits show the same level of agreement for both the GPS-only orbits, at 1.38 cm in RMS, and the GPS + Jason2 orbits at 1.33 cm in RMS. We also compare the resulting Jason-2 orbits with the 3-technique Segment Sol multi-missions d'ALTimétrie, d'orbitographie et de localisation précise (SSALTO) POD products. The orbits show good agreement, with 2.02 cm of orbit differences global RMS, and 0.98 cm of orbit differences RMS on the radial component.

  20. Rainfall estimation for real time flood monitoring using geostationary meteorological satellite data

    Science.gov (United States)

    Veerakachen, Watcharee; Raksapatcharawong, Mongkol

    2015-09-01

    Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared Threshold Rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapor imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.

  1. A Comparison of Satellite and Emnpirical Formula Techniques for Estimating Insolation over the Oceans.

    Science.gov (United States)

    Frouin, Robert; Gautier, Catherine; Katsaros, Kristina B.; Lind, Richard J.

    1988-09-01

    Surface insulation data collected during the Mixed Layer Dynamiccs Experiment are used to intercompare the satellite technique of Gautier et al. (1980) and five commonly referenced empirical formulas for estimating daily insulation over the oceans. The results demonstrate the superiority of the satellite technique, which exhibits a 0.97 correlation coefficient, a 12.0 W m M2 error of estimate, and a 4.9 W m2 bias error, and which is also able to account for water vapor, ozone, and dust amount variations in the atmosphere and monitor quasi-instantaneously vast extents of ocean. Among the empirical formulas, Mosby's (1936) yields the best predictions with a 0.84 correlation coefficient, a 19.1 W m2 standard error of estimate, and a 3.4 W m2 bias. Kimball'(1928) and Reed's (1977) formulas however, perform nearly as well. The largest biases are obtained with Berliand's (1960) and Laevastu' (1960) formulas, which overestimate insolation by 15.2 and 24.5 W m2, respectively. It is suggested the empirical formulas, even though established from visual cloud cover observations, would provide useful insolation estimates if employed with satellite-derived cloud cover.

  2. The Role of Satellite Imagery to Improve Pastureland Estimates in South America

    Science.gov (United States)

    Graesser, J.

    2015-12-01

    Agriculture has changed substantially across the globe over the past half century. While much work has been done to improve spatial-temporal estimates of agricultural changes, we still know more about the extent of row-crop agriculture than livestock-grazed land. The gap between cropland and pastureland estimates exists largely because it is challenging to characterize natural versus grazed grasslands from a remote sensing perspective. However, the impasse of pastureland estimates is set to break, with an increasing number of spaceborne sensors and freely available satellite data. The Landsat satellite archive in particular provides researchers with immense amounts of data to improve pastureland information. Here we focus on South America, where pastureland expansion has been scrutinized for the past few decades. We explore the challenges of estimating pastureland using temporal Landsat imagery and focus on key agricultural countries, regions, and ecosystems. We focus on the suggested shift of pastureland from the Argentine Pampas to northern Argentina, and the mixing of small-scale and large-scale ranching in eastern Paraguay and how it could impact the Chaco forest to the west. Further, the Beni Savannahs of northern Bolivia and the Colombian Llanos—both grassland and savannah regions historically used for livestock grazing—have been hinted at as future areas for cropland expansion. There are certainly environmental concerns with pastureland expansion into forests; but what are the environmental implications when well-managed pasture systems are converted to intensive soybean or palm oil plantation? Tropical, grazed grasslands are important habitats for biodiversity, and pasturelands can mitigate soil erosion when well managed. Thus, we must improve estimates of grazed land before we can make informed policy and conservation decisions. This talk presents insights into pastureland estimates in South America and discusses the feasibility to improve current

  3. BeiDou satellite's differential code biases estimation based on uncombined precise point positioning with triple-frequency observable

    Science.gov (United States)

    Fan, Lei; Li, Min; Wang, Cheng; Shi, Chuang

    2017-02-01

    The differential code bias (DCB) of BeiDou satellite is an important topic to make better use of BeiDou system (BDS) for many practical applications. This paper proposes a new method to estimate the BDS satellite DCBs based on triple-frequency uncombined precise point positioning (UPPP). A general model of both triple-frequency UPPP and Geometry-Free linear combination of Phase-Smoothed Range (GFPSR) is presented, in which, the ionospheric observable and the combination of triple-frequency satellite and receiver DCBs (TF-SRDCBs) are derived. Then the satellite and receiver DCBs (SRDCBs) are estimated together with the ionospheric delay that is modeled at each individual station in a weighted least-squares estimator, and the satellite DCBs are determined by introducing the zero-mean condition of all available BDS satellites. To validate the new method, 90 day's real tracking GNSS data (from January to March in 2014) collected from 9 Multi-GNSS Experiment (MGEX) stations (equipped with Trimble NETR9 receiver) is used, and the BDS satellite DCB products from German Aerospace Center (DLR) are taken as reference values for comparison. Results show that the proposed method is able to precisely estimate BDS satellite DCBs: (1) the mean value of the day-to-day scattering for all available BDS satellites is about 0.24 ns, which is reduced in average by 23% when compared with the results derived by only GFPSR. Moreover, the mean value of the day-to-day scattering of IGSO satellites is lower than that of GEO and MEO satellites; (2) the mean value of RMS of the difference with respect to DLR DCB products is about 0.39 ns, which is improved by an average of 11% when compared with the results derived by only GFPSR. Besides, the RMS of IGSO and MEO satellites is at the same level which is better than that of GEO satellites.

  4. Estimating Reliability of Disturbances in Satellite Time Series Data Based on Statistical Analysis

    Science.gov (United States)

    Zhou, Z.-G.; Tang, P.; Zhou, M.

    2016-06-01

    Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with "Change/ No change" by most of the present methods, while few methods focus on estimating reliability (or confidence level) of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1) Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST). (2) Forecasting and detecting disturbances in new time series data. (3) Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI) and Confidence Levels (CL). The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.

  5. A quantitative method for estimating cloud cover over tropical cyclones from satellite data

    OpenAIRE

    BALOGUN, E. E.

    2011-01-01

    A photometric method for quantifying cloud cover over tropical cyclones as observed from satellite photographs is presented. Two gridded photographs of tropical cyclones are analyzed by this method. On each photograph, nine concentric circles are drawn. The observed or reported centre of the cyclones is used as the centre for each set of concentric circles. Photometric estimates of cloud cover are made along the nine concentric circles. The principle of harmonic analysis is applied to the cl...

  6. A strategy for merging objective estimates of global daily precipitation from gauge observations, satellite estimates, and numerical predictions

    Science.gov (United States)

    Nie, Suping; Wu, Tongwen; Luo, Yong; Deng, Xueliang; Shi, Xueli; Wang, Zaizhi; Liu, Xiangwen; Huang, Jianbin

    2016-07-01

    This paper describes a strategy for merging daily precipitation information from gauge observations, satellite estimates (SEs), and numerical predictions at the global scale. The strategy is designed to remove systemic bias and random error from each individual daily precipitation source to produce a better gridded global daily precipitation product through three steps. First, a cumulative distribution function matching procedure is performed to remove systemic bias over gauge-located land areas. Then, the overall biases in SEs and model predictions (MPs) over ocean areas are corrected using a rescaled strategy based on monthly precipitation. Third, an optimal interpolation (OI)-based merging scheme (referred as the HL-OI scheme) is used to combine unbiased gauge observations, SEs, and MPs to reduce random error from each source and to produce a gauge—satellite-model merged daily precipitation analysis, called BMEP-d (Beijing Climate Center Merged Estimation of Precipitation with daily resolution), with complete global coverage. The BMEP-d data from a four-year period (2011-14) demonstrate the ability of the merging strategy to provide global daily precipitation of substantially improved quality. Benefiting from the advantages of the HL-OI scheme for quantitative error estimates, the better source data can obtain more weights during the merging processes. The BMEP-d data exhibit higher consistency with satellite and gauge source data at middle and low latitudes, and with model source data at high latitudes. Overall, independent validations against GPCP-1DD (GPCP one-degree daily) show that the consistencies between BMEP-d and GPCP-1DD are higher than those of each source dataset in terms of spatial pattern, temporal variability, probability distribution, and statistical precipitation events.

  7. Distributed Extended Kalman Filter for Position, Velocity, Time, Estimation in Satellite Navigation Receivers

    Directory of Open Access Journals (Sweden)

    O. Jakubov

    2013-09-01

    Full Text Available Common techniques for position-velocity-time estimation in satellite navigation, iterative least squares and the extended Kalman filter, involve matrix operations. The matrix inversion and inclusion of a matrix library pose requirements on a computational power and operating platform of the navigation processor. In this paper, we introduce a novel distributed algorithm suitable for implementation in simple parallel processing units each for a tracked satellite. Such a unit performs only scalar sum, subtraction, multiplication, and division. The algorithm can be efficiently implemented in hardware logic. Given the fast position-velocity-time estimator, frequent estimates can foster dynamic performance of a vector tracking receiver. The algorithm has been designed from a factor graph representing the extended Kalman filter by splitting vector nodes into scalar ones resulting in a cyclic graph with few iterations needed. Monte Carlo simulations have been conducted to investigate convergence and accuracy. Simulation case studies for a vector tracking architecture and experimental measurements with a real-time software receiver developed at CTU in Prague were conducted. The algorithm offers compromises in stability, accuracy, and complexity depending on the number of iterations. In scenarios with a large number of tracked satellites, it can outperform the traditional methods at low complexity.

  8. Estimation of snow cover distribution in Beas basin, Indian Himalaya using satellite data and ground measurements

    Indian Academy of Sciences (India)

    H S Negi; A V Kulkarni; B S Semwal

    2009-10-01

    In the present paper,a methodology has been developed for the mapping of snow cover in Beas basin,Indian Himalaya using AWiFS (IRS-P6)satellite data.The complexities in the mapping of snow cover in the study area are snow under vegetation,contaminated snow and patchy snow. To overcome these problems,field measurements using spectroradiometer were carried out and reflectance/snow indices trend were studied.By evaluation and validation of different topographic correction models,it was observed that,the normalized difference snow index (NDSI)values remain constant with the variations in slope and aspect and thus NDSI can take care of topography effects.Different snow cover mapping methods using snow indices are compared to find the suitable mapping technique.The proposed methodology for snow cover mapping uses the NDSI (estimated using planetary re flectance),NIR band reflectance and forest/vegetation cover information.The satellite estimated snow or non-snow pixel information using proposed methodology was validated with the snow cover information collected at three observatory locations and it was found that the algorithm classify all the sample points correctly,once that pixel is cloud free.The snow cover distribution was estimated using one year (2004 –05)cloud free satellite data and good correlation was observed between increase/decrease areal extent of seasonal snow cover and ground observed fresh snowfall and standing snow data.

  9. Signal Conditioning for the Kalman Filter: Application to Satellite Attitude Estimation with Magnetometer and Sun Sensors.

    Science.gov (United States)

    Esteban, Segundo; Girón-Sierra, Jose M; Polo, Óscar R; Angulo, Manuel

    2016-10-31

    Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework.

  10. Signal Conditioning for the Kalman Filter: Application to Satellite Attitude Estimation with Magnetometer and Sun Sensors

    Directory of Open Access Journals (Sweden)

    Segundo Esteban

    2016-10-01

    Full Text Available Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework.

  11. Differences in estimating terrestrial water flux from three satellite-based Priestley-Taylor algorithms

    Science.gov (United States)

    Yao, Yunjun; Liang, Shunlin; Yu, Jian; Zhao, Shaohua; Lin, Yi; Jia, Kun; Zhang, Xiaotong; Cheng, Jie; Xie, Xianhong; Sun, Liang; Wang, Xuanyu; Zhang, Lilin

    2017-04-01

    Accurate estimates of terrestrial latent heat of evaporation (LE) for different biomes are essential to assess energy, water and carbon cycles. Different satellite- based Priestley-Taylor (PT) algorithms have been developed to estimate LE in different biomes. However, there are still large uncertainties in LE estimates for different PT algorithms. In this study, we evaluated differences in estimating terrestrial water flux in different biomes from three satellite-based PT algorithms using ground-observed data from eight eddy covariance (EC) flux towers of China. The results reveal that large differences in daily LE estimates exist based on EC measurements using three PT algorithms among eight ecosystem types. At the forest (CBS) site, all algorithms demonstrate high performance with low root mean square error (RMSE) (less than 16 W/m2) and high squared correlation coefficient (R2) (more than 0.9). At the village (HHV) site, the ATI-PT algorithm has the lowest RMSE (13.9 W/m2), with bias of 2.7 W/m2 and R2 of 0.66. At the irrigated crop (HHM) site, almost all models algorithms underestimate LE, indicating these algorithms may not capture wet soil evaporation by parameterization of the soil moisture. In contrast, the SM-PT algorithm shows high values of R2 (comparable to those of ATI-PT and VPD-PT) at most other (grass, wetland, desert and Gobi) biomes. There are no obvious differences in seasonal LE estimation using MODIS NDVI and LAI at most sites. However, all meteorological or satellite-based water-related parameters used in the PT algorithm have uncertainties for optimizing water constraints. This analysis highlights the need to improve PT algorithms with regard to water constraints.

  12. Temporal and Spatial Assessment of Four Satellite Rainfall Estimates over French Guiana and North Brazil

    Directory of Open Access Journals (Sweden)

    Justine Ringard

    2015-12-01

    Full Text Available Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis V7 (Version 7 and RT (real time, CMORPH (Climate Prediction Center (CPC MORPHing technique and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network, for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio. Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and

  13. Monte-Carlo Estimation of the Inflight Performance of the GEMS Satellite X-Ray Polarimeter

    Science.gov (United States)

    Kitaguchi, Takao; Tamagawa, Toru; Hayato, Asami; Enoto, Teruaki; Yoshikawa, Akifumi; Kaneko, Kenta; Takeuchi, Yoko; Black, Kevin; Hill, Joanne; Jahoda, Keith; Krizmanic, John; Sturner, Steve; Griffiths, Scott; Kaaret, Philip; Marlowe, Hannah

    2014-01-01

    We report a Monte-Carlo estimation of the in-orbit performance of a cosmic X-ray polarimeter designed to be installed on the focal plane of a small satellite. The simulation uses GEANT for the transport of photons and energetic particles and results from Magboltz for the transport of secondary electrons in the detector gas. We validated the simulation by comparing spectra and modulation curves with actual data taken with radioactive sources and an X-ray generator. We also estimated the in-orbit background induced by cosmic radiation in low Earth orbit.

  14. Simultaneous state and actuator fault estimation for satellite attitude control systems

    Institute of Scientific and Technical Information of China (English)

    Cheng Yao; Wang Rixin; Xu Minqiang; Li Yuqing

    2016-01-01

    In this paper, a new nonlinear augmented observer is proposed and applied to satellite attitude control systems. The observer can estimate system state and actuator fault simultaneously. It can enhance the performances of rapidly-varying faults estimation. Only original system matrices are adopted in the parameter design. The considered faults can be unbounded, and the proposed augmented observer can estimate a large class of faults. Systems without disturbances and the fault whose finite times derivatives are zero piecewise are initially considered, followed by a discussion of a general situation where the system is subject to disturbances and the finite times derivatives of the faults are not null but bounded. For the considered nonlinear system, convergence conditions of the observer are provided and the stability analysis is performed using Lyapunov direct method. Then a feasible algorithm is explored to compute the observer parameters using linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed approach is illustrated by considering an example of a closed-loop satellite attitude control system. The simulation results show satisfactory perfor-mance in estimating states and actuator faults. It also shows that multiple faults can be estimated successfully.

  15. Satellite-rainfall estimation for identification of rainfall thresholds used for landslide/debris flow prediction

    Science.gov (United States)

    Maggioni, Viviana; Nikolopoulos, Efthymios I.; Marra, Francesco; Destro, Elisa; Borga, Marco

    2016-04-01

    Rainfall-induced landslides and debris flows pose a significant and widespread hazard, resulting in a large number of casualties and enormous economic damages worldwide. Rainfall thresholds are often used to identify the local or regional rainfall conditions that, when reached or exceeded, are likely to result in landslides or debris flows. Rain gauge data are the typical source of information for the definition of these rainfall thresholds. However, in-situ observations over mountainous areas, where these hazards mainly occur, are very sparse or inexistent. Therefore identification and use of gauge-based rainfall thresholds is impossible in many landslide prone areas over the globe. The vast advancements in satellite-based precipitation estimation over the last couple of decades have lead to the creation of a number of global precipitation datasets at various spatiotemporal resolutions. Although several investigations have shown that these datasets can be associated with considerable uncertainty, they provide the only source of precipitation information over many areas around the globe. Therefore it is important to assess their performance in the context of landslide/debris flow prediction and investigate how we can potentially benefit from the information they provide. In this work, we evaluate the performance of three widely used quasi-global satellite precipitation products (3B42v7, PERSIANN and CMORPH) for the identification of rainfall threshold for landslide/debris flow triggering. Products are available at 0.25deg/3h resolution. The study region is focused over the Upper Adige river basin, northern Italy where a detailed database of more than 400 identified debris flows (during period 2000-2015) and a raingauge network of 95 stations, is available. Rain-gauge based rainfall thresholds are compared against satellite-based thresholds to evaluate strengths and limitations in using satellite precipitation estimates for defining rainfall thresholds. Analysis of

  16. Inversion Technique for Estimating Emissions of Volcanic Ash from Satellite Imagery

    Science.gov (United States)

    Pelley, Rachel; Cooke, Michael; Manning, Alistair; Thomson, David; Witham, Claire; Hort, Matthew

    2014-05-01

    When using dispersion models such as NAME (Numerical Atmospheric-dispersion Modelling Environment) to predict the dispersion of volcanic ash, a source term defining the mass release rate of ash is required. Inversion modelling using observations of the ash plume provides a method of estimating the source term for use in NAME. Our inversion technique makes use of satellite retrievals, calculated using data from the SEVIRI (Spinning Enhanced Visible and Infrared Imager) instrument on-board the MSG (Meteosat Second Generation) satellite, as the ash observations. InTEM (Inversion Technique for Emission Modelling) is the UK Met Office's inversion modelling system. Recently the capability to estimate time and height varying source terms has been implemented and applied to volcanic ash. InTEM uses a probabilistic approach to fit NAME model concentrations to satellite retrievals. This is achieved by applying Bayes Theorem to give a cost function for the source term. Source term profiles with lower costs generate model concentrations that better fit the satellite retrievals. InTEM uses the global optimisation technique, simulated annealing, to find the minimum of the cost function. The use of a probabilistic approach allows the uncertainty in the satellite retrievals to be incorporated into the inversion technique. InTEM makes use of satellite retrievals of both ash column loadings and of cloud free regions. We present a system that allows InTEM to be used during an eruption. The system is automated and can produce source term updates up to four times a day. To allow automation hourly satellite retrievals of ash are routinely produced using conservative detection limits. The conservative detection limits provide good detection of the ash plume while limiting the number of false alarms. Regions which are flagged as ash contaminated or free from cloud (both meteorological and ash) are used in the InTEM system. This approach is shown to improve the concentrations in the

  17. Fast emission estimates in China and South Africa constrained by satellite observations

    Science.gov (United States)

    Mijling, Bas; van der A, Ronald

    2013-04-01

    Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Unfortunately, bottom-up emission inventories, compiled from large quantities of statistical data, are easily outdated for emerging economies such as China and South Africa, where rapid economic growth change emissions accordingly. Alternatively, top-down emission estimates from satellite observations of air constituents have important advantages of being spatial consistent, having high temporal resolution, and enabling emission updates shortly after the satellite data become available. However, constraining emissions from observations of concentrations is computationally challenging. Within the GlobEmission project (part of the Data User Element programme of ESA) a new algorithm has been developed, specifically designed for fast daily emission estimates of short-lived atmospheric species on a mesoscopic scale (0.25 × 0.25 degree) from satellite observations of column concentrations. The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates in East China and South Africa, using the CHIMERE chemical transport model together with tropospheric NO2 column retrievals of the OMI and GOME-2 satellite instruments. The observations are used to construct a monthly emission time series, which reveal important emission trends such as the emission reduction measures during the Beijing Olympic Games, and the impact and recovery from the global economic crisis. The algorithm is also able to detect emerging sources (e.g. new power plants) and improve emission information for areas where proxy data are not or badly known (e

  18. REKF and RUKF for pico satellite attitude estimation in the presence of measurement faults

    Institute of Scientific and Technical Information of China (English)

    Halil Ersin Söken; Chingiz Hajiyev

    2014-01-01

    When a pico satel ite is under normal operational condi-tions, whether it is extended or unscented, a conventional Kalman filter gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunc-tions in the estimation system, the Kalman filter gives inaccurate results and diverges by time. This study compares two different robust Kalman filtering algorithms, robust extended Kalman filter (REKF) and robust unscented Kalman filter (RUKF), for the case of measurement malfunctions. In both filters, by the use of de-fined variables named as the measurement noise scale factor, the faulty measurements are taken into the consideration with a smal weight, and the estimations are corrected without affecting the characteristic of the accurate ones. The proposed robust Kalman filters are applied for the attitude estimation process of a pico satel-lite, and the results are compared.

  19. Toward the Estimation of Surface Soil Moisture Content Using Geostationary Satellite Data over Sparsely Vegetated Area

    Directory of Open Access Journals (Sweden)

    Pei Leng

    2015-04-01

    Full Text Available Based on a novel bare surface soil moisture (SSM retrieval model developed from the synergistic use of the diurnal cycles of land surface temperature (LST and net surface shortwave radiation (NSSR (Leng et al. 2014. “Bare Surface Soil Moisture Retrieval from the Synergistic Use of Optical and Thermal Infrared Data”. International Journal of Remote Sensing 35: 988–1003., this paper mainly investigated the model’s capability to estimate SSM using geostationary satellite observations over vegetated area. Results from the simulated data primarily indicated that the previous bare SSM retrieval model is capable of estimating SSM in the low vegetation cover condition with fractional vegetation cover (FVC ranging from 0 to 0.3. In total, the simulated data from the Common Land Model (CoLM on 151 cloud-free days at three FLUXNET sites that with different climate patterns were used to describe SSM estimates with different underlying surfaces. The results showed a strong correlation between the estimated SSM and the simulated values, with a mean Root Mean Square Error (RMSE of 0.028 m3·m−3 and a coefficient of determination (R2 of 0.869. Moreover, diurnal cycles of LST and NSSR derived from the Meteosat Second Generation (MSG satellite data on 59 cloud-free days were utilized to estimate SSM in the REMEDHUS soil moisture network (Spain. In particular, determination of the model coefficients synchronously using satellite observations and SSM measurements was explored in detail in the cases where meteorological data were not available. A preliminary validation was implemented to verify the MSG pixel average SSM in the REMEDHUS area with the average SSM calculated from the site measurements. The results revealed a significant R2 of 0.595 and an RMSE of 0.021 m3·m−3.

  20. Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data

    Directory of Open Access Journals (Sweden)

    Sandra Eckert

    2012-03-01

    Full Text Available Accurate estimation of aboveground biomass and carbon stock has gained importance in the context of the United Nations Framework Convention on Climate Change (UNFCCC and the Kyoto Protocol. In order to develop improved forest stratum–specific aboveground biomass and carbon estimation models for humid rainforest in northeast Madagascar, this study analyzed texture measures derived from WorldView-2 satellite data. A forest inventory was conducted to develop stratum-specific allometric equations for dry biomass. On this basis, carbon was calculated by applying a conversion factor. After satellite data preprocessing, vegetation indices, principal components, and texture measures were calculated. The strength of their relationships with the stratum-specific plot data was analyzed using Pearson’s correlation. Biomass and carbon estimation models were developed by performing stepwise multiple linear regression. Pearson’s correlation coefficients revealed that (a texture measures correlated more with biomass and carbon than spectral parameters, and (b correlations were stronger for degraded forest than for non-degraded forest. For degraded forest, the texture measures of Correlation, Angular Second Moment, and Contrast, derived from the red band, contributed to the best estimation model, which explained 84% of the variability in the field data (relative RMSE = 6.8%. For non-degraded forest, the vegetation index EVI and the texture measures of Variance, Mean, and Correlation, derived from the newly introduced coastal blue band, both NIR bands, and the red band, contributed to the best model, which explained 81% of the variability in the field data (relative RMSE = 11.8%. These results indicate that estimation of tropical rainforest biomass/carbon, based on very high resolution satellite data, can be improved by (a developing and applying forest stratum–specific models, and (b including textural information in addition to spectral information.

  1. Model of a neural network inertial satellite navigation system capable of estimating the earth's gravitational field gradient

    Science.gov (United States)

    Devyatisil'nyi, A. S.

    2016-09-01

    A model for recognizing inertial and satellite data on an object's motion that are delivered by a set of distributed onboard sensors (newtonmeters, gyros, satellite receivers) has been described. Specifically, the model is capable of estimating the parameters of the gravitational field.

  2. An improved technique for global daily sunshine duration estimation using satellite imagery

    Institute of Scientific and Technical Information of China (English)

    Muhammad Ali SHAMIM; Renji REMESAN; Da-wei HAN; Naeem EJAZ; Ayub ELAHI

    2012-01-01

    This paper presents an improved model for global sunshine duration estimation.The methodology incorporates geostationary satellite images by including snow cover information,sun and satellite angles and a trend correction factor for seasons,for the determination of cloud cover index.The effectiveness of the proposed methodology has been tested using Meteosat geostationary satellite images in the visible band with a temporal resolution of 1 h and spatial resolution of 2.5 km×2.5 km,for the Brue Catchment in the southwest of England.Validation results show a significant improvement in the estimation of global sunshine duration by the proposed method as compared to its predecessor (R2 is improved from 0.68 to 0.83,root mean squared error (RMSE) from 2.37 h/d to 1.19 h/d and the mean biased error (MBE) from 0.21 h/d to 0.08 h/d).Further studies are needed to test this method in other parts of the world with different climate and geographical conditions.

  3. Estimating Ground-Level Particulate Matter (PM) Concentration using Satellite-derived Aerosol Optical Depth (AOD)

    Science.gov (United States)

    Park, Seohui; Im, Jungho

    2017-04-01

    Atmospheric aerosols are strongly associated with adverse human health effects. In particular, particulate matter less than 10 micrometers and 2.5 micrometers (i.e., PM10 and PM2.5, respectively) can cause cardiovascular and lung diseases such as asthma and chronic obstructive pulmonary disease (COPD). Air quality including PM has typically been monitored using station-based in-situ measurements over the world. However, in situ measurements do not provide spatial continuity over large areas. An alternative approach is to use satellite remote sensing as it provides data over vast areas at high temporal resolution. The literature shows that PM concentrations are related with Aerosol Optical Depth (AOD) that is derived from satellite observations, but it is still difficult to identify PM concentrations directly from AOD. Some studies used statistical approaches for estimating PM concentrations from AOD while some others combined numerical models and satellite-derived AOD. In this study, satellite-derived products were used to estimate ground PM concentrations based on machine learning over South Korea. Satellite-derived products include AOD from Geostationary Ocean Color Imager (GOCI), precipitation from Tropical Rainfall Measuring Mission (TRMM), soil moisture from AMSR-2, elevation from Shuttle Radar Topography Mission (SRTM), and land cover, land surface temperature and normalized difference vegetation index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS). PM concentrations data were collected from 318 stations. A statistical ordinary least squares (OLS) approach was also tested and compared with the machine learning approach (i.e., random forest). PM concentration was estimated during spring season (from March to May) in 2015 that typically shows high concentration of PM. The randomly selected 80% of data were used for model calibration and the remaining 20% were used for validation. The developed models were further tested for prediction of PM

  4. Eliminating Obliquity Error from the Estimation of Ionospheric Delay in a Satellite-Based Augmentation System

    Science.gov (United States)

    Sparks, Lawrence

    2013-01-01

    Current satellite-based augmentation systems estimate ionospheric delay using algorithms that assume the electron density of the ionosphere is non-negligible only in a thin shell located near the peak of the actual profile. In its initial operating capability, for example, the Wide Area Augmentation System incorporated the thin shell model into an estimation algorithm that calculates vertical delay using a planar fit. Under disturbed conditions or at low latitude where ionospheric structure is complex, however, the thin shell approximation can serve as a significant source of estimation error. A recent upgrade of the system replaced the planar fit algorithm with an algorithm based upon kriging. The upgrade owes its success, in part, to the ability of kriging to mitigate the error due to this approximation. Previously, alternative delay estimation algorithms have been proposed that eliminate the need for invoking the thin shell model altogether. Prior analyses have compared the accuracy achieved by these methods to the accuracy achieved by the planar fit algorithm. This paper extends these analyses to include a comparison with the accuracy achieved by kriging. It concludes by examining how a satellite-based augmentation system might be implemented without recourse to the thin shell approximation.

  5. A preliminary estimate of geoid-induced variations in repeat orbit satellite altimeter observations

    Science.gov (United States)

    Brenner, Anita C.; Beckley, B. D.; Koblinsky, C. J.

    1990-01-01

    Altimeter satellites are often maintained in a repeating orbit to facilitate the separation of sea-height variations from the geoid. However, atmospheric drag and solar radiation pressure cause a satellite orbit to drift. For Geosat this drift causes the ground track to vary by + or - 1 km about the nominal repeat path. This misalignment leads to an error in the estimates of sea surface height variations because of the local slope in the geoid. This error has been estimated globally for the Geosat Exact Repeat Mission using a mean sea surface constructed from Geos 3 and Seasat altimeter data. Over most of the ocean the geoid gradient is small, and the repeat-track misalignment leads to errors of only 1 to 2 cm. However, in the vicinity of trenches, continental shelves, islands, and seamounts, errors can exceed 20 cm. The estimated error is compared with direct estimates from Geosat altimetry, and a strong correlation is found in the vicinity of the Tonga and Aleutian trenches. This correlation increases as the orbit error is reduced because of the increased signal-to-noise ratio.

  6. Fault estimation of satellite reaction wheels using covariance based adaptive unscented Kalman filter

    Science.gov (United States)

    Rahimi, Afshin; Kumar, Krishna Dev; Alighanbari, Hekmat

    2017-05-01

    Reaction wheels, as one of the most commonly used actuators in satellite attitude control systems, are prone to malfunction which could lead to catastrophic failures. Such malfunctions can be detected and addressed in time if proper analytical redundancy algorithms such as parameter estimation and control reconfiguration are employed. Major challenges in parameter estimation include speed and accuracy of the employed algorithm. This paper presents a new approach for improving parameter estimation with adaptive unscented Kalman filter. The enhancement in tracking speed of unscented Kalman filter is achieved by systematically adapting the covariance matrix to the faulty estimates using innovation and residual sequences combined with an adaptive fault annunciation scheme. The proposed approach provides the filter with the advantage of tracking sudden changes in the system non-measurable parameters accurately. Results showed successful detection of reaction wheel malfunctions without requiring a priori knowledge about system performance in the presence of abrupt, transient, intermittent, and incipient faults. Furthermore, the proposed approach resulted in superior filter performance with less mean squared errors for residuals compared to generic and adaptive unscented Kalman filters, and thus, it can be a promising method for the development of fail-safe satellites.

  7. A preliminary estimate of geoid-induced variations in repeat orbit satellite altimeter observations

    Science.gov (United States)

    Brenner, Anita C.; Beckley, B. D.; Koblinsky, C. J.

    1990-01-01

    Altimeter satellites are often maintained in a repeating orbit to facilitate the separation of sea-height variations from the geoid. However, atmospheric drag and solar radiation pressure cause a satellite orbit to drift. For Geosat this drift causes the ground track to vary by + or - 1 km about the nominal repeat path. This misalignment leads to an error in the estimates of sea surface height variations because of the local slope in the geoid. This error has been estimated globally for the Geosat Exact Repeat Mission using a mean sea surface constructed from Geos 3 and Seasat altimeter data. Over most of the ocean the geoid gradient is small, and the repeat-track misalignment leads to errors of only 1 to 2 cm. However, in the vicinity of trenches, continental shelves, islands, and seamounts, errors can exceed 20 cm. The estimated error is compared with direct estimates from Geosat altimetry, and a strong correlation is found in the vicinity of the Tonga and Aleutian trenches. This correlation increases as the orbit error is reduced because of the increased signal-to-noise ratio.

  8. Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

    Science.gov (United States)

    Husak, G.J.; Marshall, M. T.; Michaelsen, J.; Pedreros, Diego; Funk, Christopher C.; Galu, G.

    2008-01-01

    Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

  9. Elastic thickness and heat flux estimates for the uranian satellite Ariel

    Science.gov (United States)

    Peterson, G.; Nimmo, F.; Schenk, P.

    2015-04-01

    The surface of Ariel, an icy satellite orbiting Uranus, shows extensional tectonic features suggesting an episode of endogenic heating in the satellite's past. Using topography derived from stereo-photoclinometry, we identified flexural uplift at a rift zone suggesting elastic thickness values in the range 3.8-4.4 km. We estimate the temperature at the base of the lithosphere to be in the range 99-146 K, depending on the strain rate assumed, with corresponding heat fluxes of 28-92 mW/m2. Neither tidal heating, assuming Ariel's current eccentricity, nor radiogenic heat production from the silicate core are enough to cause the inferred heat fluxes. None of three proposed ancient mean-motion resonances produce equilibrium tidal heating values in excess of 4.3 mW/m2. Thus, the origin of the inferred high heat fluxes is currently mysterious.

  10. Satellite Estimates of Crop Area and Maize Yield in Zambia's Agricultural Districts

    Science.gov (United States)

    Azzari, G.; Lobell, D. B.

    2015-12-01

    Predicting crop yield and area from satellite is a valuable tool to monitor different aspects of productivity dynamics and food security. In Sub-Saharan Africa, where the agricultural landscape is complex and dominated by smallholder systems, such dynamics need to be investigated at the field scale. We leveraged the large data pool and computational power of Google Earth Engine to 1) generate 30 m resolution cover maps of selected provinces of Zambia, 2) estimate crop area, and 3) produce yearly maize yield maps using the recently developed SCYM (Scalable satellite-based Crop Yield Mapper) algorithm. We will present our results and their validation against a ground survey dataset collected yearly by the Zambia Ministry of Agriculture from about 12,500 households.

  11. Leveraging Machine Learning to Estimate Soil Salinity through Satellite-Based Remote Sensing

    Science.gov (United States)

    Welle, P.; Ravanbakhsh, S.; Póczos, B.; Mauter, M.

    2016-12-01

    Human-induced salinization of agricultural soils is a growing problem which now affects an estimated 76 million hectares and causes billions of dollars of lost agricultural revenues annually. While there are indications that soil salinization is increasing in extent, current assessments of global salinity levels are outdated and rely heavily on expert opinion due to the prohibitive cost of a worldwide sampling campaign. A more practical alternative to field sampling may be earth observation through remote sensing, which takes advantage of the distinct spectral signature of salts in order to estimate soil conductivity. Recent efforts to map salinity using remote sensing have been met with limited success due to tractability issues of managing the computational load associated with large amounts of satellite data. In this study, we use Google Earth Engine to create composite satellite soil datasets, which combine data from multiple sources and sensors. These composite datasets contain pixel-level surface reflectance values for dates in which the algorithm is most confident that the surface contains bare soil. We leverage the detailed soil maps created and updated by the United States Geological Survey as label data and apply machine learning regression techniques such as Gaussian processes to learn a smooth mapping from surface reflection to noisy estimates of salinity. We also explore a semi-supervised approach using deep generative convolutional networks to leverage the abundance of unlabeled satellite images in producing better estimates for salinity values where we have relatively fewer measurements across the globe. The general method results in two significant contributions: (1) an algorithm that can be used to predict levels of soil salinity in regions without detailed soil maps and (2) a general framework that serves as an example for how remote sensing can be paired with extensive label data to generate methods for prediction of physical phenomenon.

  12. Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting

    Science.gov (United States)

    Wardah, T.; Abu Bakar, S. H.; Bardossy, A.; Maznorizan, M.

    2008-07-01

    SummaryFrequent flash-floods causing immense devastation in the Klang River Basin of Malaysia necessitate an improvement in the real-time forecasting systems being used. The use of meteorological satellite images in estimating rainfall has become an attractive option for improving the performance of flood forecasting-and-warning systems. In this study, a rainfall estimation algorithm using the infrared (IR) information from the Geostationary Meteorological Satellite-5 (GMS-5) is developed for potential input in a flood forecasting system. Data from the records of GMS-5 IR images have been retrieved for selected convective cells to be trained with the radar rain rate in a back-propagation neural network. The selected data as inputs to the neural network, are five parameters having a significant correlation with the radar rain rate: namely, the cloud-top brightness-temperature of the pixel of interest, the mean and the standard deviation of the temperatures of the surrounding five by five pixels, the rate of temperature change, and the sobel operator that indicates the temperature gradient. In addition, three numerical weather prediction (NWP) products, namely the precipitable water content, relative humidity, and vertical wind, are also included as inputs. The algorithm is applied for the areal rainfall estimation in the upper Klang River Basin and compared with another technique that uses power-law regression between the cloud-top brightness-temperature and radar rain rate. Results from both techniques are validated against previously recorded Thiessen areal-averaged rainfall values with coefficient correlation values of 0.77 and 0.91 for the power-law regression and the artificial neural network (ANN) technique, respectively. An extra lead time of around 2 h is gained when the satellite-based ANN rainfall estimation is coupled with a rainfall-runoff model to forecast a flash-flood event in the upper Klang River Basin.

  13. Satellite-based estimate of aerosol direct radiative effect over the South-East Atlantic

    Directory of Open Access Journals (Sweden)

    L. Costantino

    2013-09-01

    Full Text Available The net effect of aerosol Direct Radiative Forcing (DRF is the balance between the scattering effect that reflects solar radiation back to space (cooling, and the absorption that decreases the reflected sunlight (warming. The amplitude of these two effects and their balance depends on the aerosol load, its absorptivity, the cloud fraction and the respective position of aerosol and cloud layers. In this study, we use the information provided by CALIOP (CALIPSO satellite and MODIS (AQUA satellite instruments as input data to a Rapid Radiative Transfer Model (RRTM and quantify the shortwave (SW aerosol direct atmospheric forcing, over the South-East Atlantic. The combination of the passive and active measurements allows estimates of the horizontal and vertical distributions of the aerosol and cloud parameters. We use a parametrization of the Single Scattering Albedo (SSA based on the satellite-derived Angstrom coefficient. The South East Atlantic is a particular region, where bright stratocumulus clouds are often topped by absorbing smoke particles. Results from radiative transfer simulations confirm the similar amplitude of the cooling effect, due to light scattering by the aerosols, and the warming effect, due to the absorption by the same particles. Over six years of satellite retrievals, from 2005 to 2010, the South-East Atlantic all-sky SW DRF is −0.03 W m−2, with a spatial standard deviation of 8.03 W m−2. In good agreement with previous estimates, statistics show that a cloud fraction larger than 0.5 is generally associated with positive all-sky DRF. In case of cloudy-sky and aerosol located only above the cloud top, a SSA larger than 0.91 and cloud optical thickness larger than 4 can be considered as threshold values, beyond which the resulting radiative forcing becomes positive.

  14. Stratified estimation of forest area using satellite imagery, inventory data, and the k-nearest neighbors technique

    Science.gov (United States)

    Ronald E. McRoberts; Mark D. Nelson; Daniel G. Wendt

    2002-01-01

    For two large study areas in Minnesota, USA, stratified estimation using classified Landsat Thematic Mapper satellite imagery as the basis for stratification was used to estimate forest area. Measurements of forest inventory plots obtained for a 12-month period in 1998 and 1999 were used as the source of data for within-stratum estimates. These measurements further...

  15. An Efficient Two-Fold Marginalized Bayesian Filter for Multipath Estimation in Satellite Navigation Receivers

    Directory of Open Access Journals (Sweden)

    Robertson Patrick

    2010-01-01

    Full Text Available Multipath is today still one of the most critical problems in satellite navigation, in particular in urban environments, where the received navigation signals can be affected by blockage, shadowing, and multipath reception. Latest multipath mitigation algorithms are based on the concept of sequential Bayesian estimation and improve the receiver performance by exploiting the temporal constraints of the channel dynamics. In this paper, we specifically address the problem of estimating and adjusting the number of multipath replicas that is considered by the receiver algorithm. An efficient implementation via a two-fold marginalized Bayesian filter is presented, in which a particle filter, grid-based filters, and Kalman filters are suitably combined in order to mitigate the multipath channel by efficiently estimating its time-variant parameters in a track-before-detect fashion. Results based on an experimentally derived set of channel data corresponding to a typical urban propagation environment are used to confirm the benefit of our novel approach.

  16. Frequency Estimation in Iterative Interference Cancellation Applied to Multibeam Satellite Systems

    Directory of Open Access Journals (Sweden)

    Ducasse A

    2007-01-01

    Full Text Available This paper deals with interference cancellation techniques to mitigate cochannel interference on the reverse link of multibeam satellite communication systems. The considered system takes as a starting point the DVB-RCS standard with the use of convolutional coding. The considered algorithm consists of an iterative parallel interference cancellation scheme which includes estimation of beamforming coefficients. This algorithm is first derived in the case of a symbol asynchronous channel with time-invariant carrier phases. The aim of this article is then to study possible extensions of this algorithm to the case of frequency offsets affecting user terminals. The two main approaches evaluated and discussed here are based on (1 the use of block processing for estimation of beamforming coefficients in order to follow carrier phase variations and (2 the use of single-user frequency offset estimations.

  17. A Simple Statistical Model to Estimate Incident Solar Radiation at the Surface from NOAA AVHRR Satellite Data

    Directory of Open Access Journals (Sweden)

    Mst. Ashrafunnahar Hena

    2013-01-01

    Full Text Available Processing of meteorological satellite image data provides a wealth of information useful in earth surface and environmental applications. Particularly, it is important for the estimation of different parameters of surface energy budget. In this work, a method has been developed to estimation of hourly incoming solar radiation on the surface of Bangladesh using NOAA-AVHRR satellite digital images. The model is based on the statistical regressions between the ground truth and satellite estimated values. Hundreds of full resolution images (1.1 km for two months of the year have been processed using ERDAS IMAGINE software. Ground solar global irradiation for one place has been estimated for two months through this application. The efficiency of this method for calculating surface insolation has been checked by estimating the relative deviation between the estimated Irradiation and measured Irradiation. The method can be used for calculation of hourly irradiation over areas in a tropical environment.

  18. Global lightning NOx production estimated by an assimilation of multiple satellite data sets

    Science.gov (United States)

    Miyazaki, K.; Eskes, H. J.; Sudo, K.; Zhang, C.

    2014-04-01

    The global source of lightning-produced NOx (LNOx) is estimated by assimilating observations of NO2, O3, HNO3, and CO measured by multiple satellite measurements into a chemical transport model. Included are observations from the Ozone Monitoring Instrument (OMI), Microwave Limb Sounder (MLS), Tropospheric Emission Spectrometer (TES), and Measurements of Pollution in the Troposphere (MOPITT) instruments. The assimilation of multiple chemical data sets with different vertical sensitivity profiles provides comprehensive constraints on the global LNOx source while improving the representations of the entire chemical system affecting atmospheric NOx, including surface emissions and inflows from the stratosphere. The annual global LNOx source amount and NO production efficiency are estimated at 6.3 Tg N yr-1 and 310 mol NO flash-1, respectively. Sensitivity studies with perturbed satellite data sets, model and data assimilation settings lead to an error estimate of about 1.4 Tg N yr-1 on this global LNOx source. These estimates are significantly different from those estimated from a parameter inversion that optimizes only the LNOx source from NO2 observations alone, which may lead to an overestimate of the source adjustment. The total LNOx source is predominantly corrected by the assimilation of OMI NO2 observations, while TES and MLS observations add important constraints on the vertical source profile. The results indicate that the widely used lightning parameterization based on the C-shape assumption underestimates the source in the upper troposphere and overestimates the peak source height by up to about 1 km over land and the tropical western Pacific. Adjustments are larger over ocean than over land, suggesting that the cloud height dependence is too weak over the ocean in the Price and Rind (1992) approach. The significantly improved agreement between the analyzed ozone fields and independent observations gives confidence in the performance of the LNOx source

  19. ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS

    Directory of Open Access Journals (Sweden)

    Z.-G. Zhou

    2016-06-01

    Full Text Available Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with “Change/ No change” by most of the present methods, while few methods focus on estimating reliability (or confidence level of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1 Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST. (2 Forecasting and detecting disturbances in new time series data. (3 Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI and Confidence Levels (CL. The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.

  20. Net primary productivity of forest stands in New Hampshire estimated from Landsat and MODIS satellite data

    Directory of Open Access Journals (Sweden)

    Genovese Vanessa

    2007-10-01

    Full Text Available Abstract Background A simulation model that relies on satellite observations of vegetation cover from the Landsat 7 sensor and from the Moderate Resolution Imaging Spectroradiometer (MODIS was used to estimate net primary productivity (NPP of forest stands at the Bartlett Experiment Forest (BEF in the White Mountains of New Hampshire. Results Net primary production (NPP predicted from the NASA-CASA model using 30-meter resolution Landsat inputs showed variations related to both vegetation cover type and elevational effects on mean air temperatures. Overall, the highest predicted NPP from the NASA-CASA model was for deciduous forest cover at low to mid-elevation locations over the landscape. Comparison of the model-predicted annual NPP to the plot-estimated values showed a significant correlation of R2 = 0.5. Stepwise addition of 30-meter resolution elevation data values explained no more than 20% of the residual variation in measured NPP patterns at BEF. Both the Landsat 7 and the 250-meter resolution MODIS derived mean annual NPP predictions for the BEF plot locations were within ± 2.5% of the mean of plot estimates for annual NPP. Conclusion Although MODIS imagery cannot capture the spatial details of NPP across the network of closely spaced plot locations as well as Landsat, the MODIS satellite data as inputs to the NASA-CASA model does accurately predict the average annual productivity of a site like the BEF.

  1. Minimum Number of Observation Points for LEO Satellite Orbit Estimation by OWL Network

    Science.gov (United States)

    Park, Maru; Jo, Jung Hyun; Cho, Sungki; Choi, Jin; Kim, Chun-Hwey; Park, Jang-Hyun; Yim, Hong-Suh; Choi, Young-Jun; Moon, Hong-Kyu; Bae, Young-Ho; Park, Sun-Youp; Kim, Ji-Hye; Roh, Dong-Goo; Jang, Hyun-Jung; Park, Young-Sik; Jeong, Min-Ji

    2015-12-01

    By using the Optical Wide-field Patrol (OWL) network developed by the Korea Astronomy and Space Science Institute (KASI) we generated the right ascension and declination angle data from optical observation of Low Earth Orbit (LEO) satellites. We performed an analysis to verify the optimum number of observations needed per arc for successful estimation of orbit. The currently functioning OWL observatories are located in Daejeon (South Korea), Songino (Mongolia), and Oukaïmeden (Morocco). The Daejeon Observatory is functioning as a test bed. In this study, the observed targets were Gravity Probe B, COSMOS 1455, COSMOS 1726, COSMOS 2428, SEASAT 1, ATV-5, and CryoSat-2 (all in LEO). These satellites were observed from the test bed and the Songino Observatory of the OWL network during 21 nights in 2014 and 2015. After we estimated the orbit from systematically selected sets of observation points (20, 50, 100, and 150) for each pass, we compared the difference between the orbit estimates for each case, and the Two Line Element set (TLE) from the Joint Space Operation Center (JSpOC). Then, we determined the average of the difference and selected the optimal observation points by comparing the average values.

  2. TRMM Satellite Algorithm Estimates to Represent the Spatial Distribution of Rainstorms

    Directory of Open Access Journals (Sweden)

    Patrick Marina

    2017-01-01

    Full Text Available On-site measurements from rain gauge provide important information for the design, construction, and operation of water resources engineering projects, groundwater potentials, and the water supply and irrigation systems. A dense gauging network is needed to accurately characterize the variation of rainfall over a region, unfitting for conditions with limited networks, such as in Sarawak, Malaysia. Hence, satellite-based algorithm estimates are introduced as an innovative solution to these challenges. With accessibility to dataset retrievals from public domain websites, it has become a useful source to measure rainfall for a wider coverage area at finer temporal resolution. This paper aims to investigate the rainfall estimates prepared by Tropical Rainfall Measuring Mission (TRMM to explain whether it is suitable to represent the distribution of extreme rainfall in Sungai Sarawak Basin. Based on the findings, more uniform correlations for the investigated storms can be observed for low to medium altitude (>40 MASL. It is found for the investigated events of Jan 05-11, 2009: the normalized root mean square error (NRMSE = 36.7 %; and good correlation (CC = 0.9. These findings suggest that satellite algorithm estimations from TRMM are suitable to represent the spatial distribution of extreme rainfall.

  3. Solar resources estimation combining digital terrain models and satellite images techniques

    Energy Technology Data Exchange (ETDEWEB)

    Bosch, J.L.; Batlles, F.J. [Universidad de Almeria, Departamento de Fisica Aplicada, Ctra. Sacramento s/n, 04120-Almeria (Spain); Zarzalejo, L.F. [CIEMAT, Departamento de Energia, Madrid (Spain); Lopez, G. [EPS-Universidad de Huelva, Departamento de Ingenieria Electrica y Termica, Huelva (Spain)

    2010-12-15

    One of the most important steps to make use of any renewable energy is to perform an accurate estimation of the resource that has to be exploited. In the designing process of both active and passive solar energy systems, radiation data is required for the site, with proper spatial resolution. Generally, a radiometric stations network is used in this evaluation, but when they are too dispersed or not available for the study area, satellite images can be utilized as indirect solar radiation measurements. Although satellite images cover wide areas with a good acquisition frequency they usually have a poor spatial resolution limited by the size of the image pixel, and irradiation must be interpolated to evaluate solar irradiation at a sub-pixel scale. When pixels are located in flat and homogeneous areas, correlation of solar irradiation is relatively high, and classic interpolation can provide a good estimation. However, in complex topography zones, data interpolation is not adequate and the use of Digital Terrain Model (DTM) information can be helpful. In this work, daily solar irradiation is estimated for a wide mountainous area using a combination of Meteosat satellite images and a DTM, with the advantage of avoiding the necessity of ground measurements. This methodology utilizes a modified Heliosat-2 model, and applies for all sky conditions; it also introduces a horizon calculation of the DTM points and accounts for the effect of snow covers. Model performance has been evaluated against data measured in 12 radiometric stations, with results in terms of the Root Mean Square Error (RMSE) of 10%, and a Mean Bias Error (MBE) of +2%, both expressed as a percentage of the mean value measured. (author)

  4. Parametric fault estimation based on H∞ optimization in a satellite launch vehicle

    DEFF Research Database (Denmark)

    Soltani, Mohsen; Izadi-Zamanabadi, Roozbeh; Stoustrup, Jakob

    2008-01-01

    Correct diagnosis under harsh environmental conditions is crucial for space vehiclespsila health management systems to avoid possible hazardous situations. Consequently, the diagnosis methods are required to be robust toward these conditions. Design of a parametric fault detector, where the fault...... estimation is formulated in the so-called standard set-up for Hinfin control design problem, is addressed in this paper. In particular, we investigate the tunability of the design through the dedicated choice of the fault model. The method is applied to the model of turbopump as a subsystem of the jet engine...... for the satellite launch vehicle and the results are discussed....

  5. Bias reduction for Satellite Based Precipitation Estimates using statistical transformations in Guiana Shield

    Science.gov (United States)

    Ringard, Justine; Becker, Melanie; Seyler, Frederique; Linguet, Laurent

    2016-04-01

    Currently satellite-based precipitation estimates exhibit considerable biases, and there have been many efforts to reduce these biases by merging surface gauge measurements with satellite-based estimates. In Guiana Shield all products exhibited better performances during the dry season (August- December). All products greatly overestimate very low intensities (50 mm). Moreover the responses of each product are different according to hydro climatic regimes. The aim of this study is to correct spatially the bias of precipitation, and compare various correction methods to define the best methods depending on the rainfall characteristic correcting (intensity, frequency). Four satellites products are used: Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product (3B42V7) and real time product (3B42RT), the Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network (PERSIANN) and the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH), for six hydro climatic regimes between 2001 and 2012. Several statistical transformations are used to correct the bias. Statistical transformations attempt to find a function h that maps a simulated variable Ps such that its new distribution equals the distribution of the observed variable Po. The first is the use of a distribution derived transformations which is a mixture of the Bernoulli and the Gamma distribution, where the Bernoulli distribution is used to model the probability of precipitation occurrence and the Gamma distribution used to model precipitation intensities. The second a quantile-quantile relation using parametric transformation, and the last one is a common approach using the empirical CDF of observed and modelled values instead of assuming parametric distributions. For each correction 30% of both, simulated and observed data sets, are used to calibrate and the other part used to validate. The validation are test with statistical

  6. Ionospheric TEC Estimations with the Signals of Various Geostationary Navigational Satellites

    Science.gov (United States)

    Kurbatov, G. A.; Padokhin, A. M.; Kunitsyn, V.; Yasyukevich, Y.

    2015-12-01

    The development of GNSS and SBAS systems provides the possibility to retrieve ionospheric TEC from the dual frequency observations from a number of geostationary satellites using the same approach as for dual frequency GPS/GLONASS observations. In this connection, the quality of geostationary data, first of all the level of noise in TEC estimations is of great interest and importance. In this work we present the results of the comparison of the noise patterns in TEC estimations using signals of geostationary satellites of augumentation systems - indian GAGAN, european EGNOS and american WAAS, as well as the signals of chinees COMPASS/Beidou navigational system. We show that among above mentioned systems geostationary COMPASS/Beidou satellites provide best noise level in TEC estimations (RMS~0.1TECU), which corresponds to those of GPS/GLONASS, while GAGAN and WAAS TEC RMS could reach up to 1.5 TECU with typical values of 0.25-0.5 TECU which is up to one order greater than for common GPS/GLONASS observations. EGNOS TEC estimations being even more noisy (TEC RMS up to 10TECU) than WAAS and GAGAN ones at present time are not suitable for ionospheric studies. We also present geostationary TEC response to increasing solar X-Ray and EUV ionizing radiation during several recent X-class flares. Good correlation was found between TEC and EUV flux for the stations at the sunlit hemisphere. We also present geostationary TEC response to geomagnetic field variations during strong and moderate geomagnetic storms (including G4 St. Patricks Day Storm of 2015) showing examples of both positive and negative TEC anomalies of order of tens of TECU during main storm phase. Our results show the capability of geostationary GNSS and SBAS observations for continuous monitoring of ionospheric TEC. Intensively growing networks of dedicated receivers (for example MGEX network) and increasing number of dual-frequency geostationary satellites in SBAS and GNSS constellations potentially make it a

  7. Estimation of fossil-fuel CO2 emissions using satellite measurements of "proxy" species

    Science.gov (United States)

    Konovalov, Igor B.; Berezin, Evgeny V.; Ciais, Philippe; Broquet, Grégoire; Zhuravlev, Ruslan V.; Janssens-Maenhout, Greet

    2016-11-01

    Fossil-fuel (FF) burning releases carbon dioxide (CO2) together with many other chemical species, some of which, such as nitrogen dioxide (NO2) and carbon monoxide (CO), are routinely monitored from space. This study examines the feasibility of estimation of FF CO2 emissions from large industrial regions by using NO2 and CO column retrievals from satellite measurements in combination with simulations by a mesoscale chemistry transport model (CTM). To this end, an inverse modeling method is developed that allows estimating FF CO2 emissions from different sectors of the economy, as well as the total CO2 emissions, in a given region. The key steps of the method are (1) inferring "top-down" estimates of the regional budget of anthropogenic NOx and CO emissions from satellite measurements of proxy species (NO2 and CO in the case considered) without using formal a priori constraints on these budgets, (2) the application of emission factors (the NOx-to-CO2 and CO-to-CO2 emission ratios in each sector) that relate FF CO2 emissions to the proxy species emissions and are evaluated by using data of "bottom-up" emission inventories, and (3) cross-validation and optimal combination of the estimates of CO2 emission budgets derived from measurements of the different proxy species. Uncertainties in the top-down estimates of the NOx and CO emissions are evaluated and systematic differences between the measured and simulated data are taken into account by using original robust techniques validated with synthetic data. To examine the potential of the method, it was applied to the budget of emissions for a western European region including 12 countries by using NO2 and CO column amounts retrieved from, respectively, the OMI and IASI satellite measurements and simulated by the CHIMERE mesoscale CTM, along with the emission conversion factors based on the EDGAR v4.2 emission inventory. The analysis was focused on evaluation of the uncertainty levels for the top-down NOx and CO emission

  8. Analytic Perturbation Method for Estimating Ground Flash Fraction from Satellite Lightning Observations

    Science.gov (United States)

    Koshak, William; Solakiewicz, Richard

    2013-01-01

    An analytic perturbation method is introduced for estimating the lightning ground flash fraction in a set of N lightning flashes observed by a satellite lightning mapper. The value of N is large, typically in the thousands, and the observations consist of the maximum optical group area produced by each flash. The method is tested using simulated observations that are based on Optical Transient Detector (OTD) and Lightning Imaging Sensor (LIS) data. National Lightning Detection NetworkTM (NLDN) data is used to determine the flash-type (ground or cloud) of the satellite-observed flashes, and provides the ground flash fraction truth for the simulation runs. It is found that the mean ground flash fraction retrieval errors are below 0.04 across the full range 0-1 under certain simulation conditions. In general, it is demonstrated that the retrieval errors depend on many factors (i.e., the number, N, of satellite observations, the magnitude of random and systematic measurement errors, and the number of samples used to form certain climate distributions employed in the model).

  9. Evaluation of a physically-based snow model with infrared and microwave satellite-derived estimates

    Science.gov (United States)

    Wang, L.

    2013-05-01

    Snow (with high albedo, as well as low roughness and thermal conductivity) has significant influence on the land-atmosphere interactions in the cold climate and regions of high elevation. The spatial and temporal variability of the snow distribution on a basin scale greatly determines the timing and magnitude of spring snowmelt runoff. For improved water resources management, a physically-based distributed snow model has been developed and applied to the upper Yellow River Basin to provide the outputs of snow variables as well as streamflows from 2001 to 2005. Remotely-sensed infrared information from MODIS satellites has been used to evaluate the model's outputs of spatially-distributed snow cover extent (SCE) and land surface temperature (LST); while the simulated snow depth (SD) and snow water equivalent (SWE) have been compared with the microwave information from SSM/I and AMSR-E satellites. In general, the simulated streamflows (including spring snowmelt) agree fairly well with the gauge-based observations; while the modeled snow variables show acceptable accuracies through comparing to various satellite-derived estimates from infrared or microwave information.;

  10. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization

    Science.gov (United States)

    Smith, W. Kolby; Reed, Sasha C.; Cleveland, Cory C.; Ballantyne, Ashley P; Anderegg, William R. L.; Wieder, William R.; Liu, Yi Y; Running, Steven W.

    2015-01-01

    Atmospheric mass balance analyses suggest that terrestrial carbon (C) storage is increasing, partially abating the atmospheric [CO2] growth rate, although the continued strength of this important ecosystem service remains uncertain. Some evidence suggests that these increases will persist owing to positive responses of vegetation growth (net primary productivity; NPP) to rising atmospheric [CO2] (that is, ‘CO2 fertilization’). Here, we present a new satellite-derived global terrestrial NPP data set, which shows a significant increase in NPP from 1982 to 2011. However, comparison against Earth system model (ESM) NPP estimates reveals a significant divergence, with satellite-derived increases (2.8 ± 1.50%) less than half of ESM-derived increases (7.6  ±  1.67%) over the 30-year period. By isolating the CO2 fertilization effect in each NPP time series and comparing it against a synthesis of available free-air CO2 enrichment data, we provide evidence that much of the discrepancy may be due to an over-sensitivity of ESMs to atmospheric [CO2], potentially reflecting an under-representation of climatic feedbacks and/or a lack of representation of nutrient constraints. Our understanding of CO2 fertilization effects on NPP needs rapid improvement to enable more accurate projections of future C cycle–climate feedbacks; we contend that better integration of modelling, satellite and experimental approaches offers a promising way forward.

  11. Exploiting the power law distribution properties of satellite fire radiative power retrievals: A method to estimate fire radiative energy and biomass burned from sparse satellite observations

    Science.gov (United States)

    Kumar, S. S.; Roy, D. P.; Boschetti, L.; Kremens, R.

    2011-10-01

    Instantaneous estimates of the power released by fire (fire radiative power, FRP) are available with satellite active fire detection products. The temporal integral of FRP provides an estimate of the fire radiative energy (FRE) that is related linearly to the amount of biomass burned needed by the atmospheric emissions modeling community. The FRE, however, is sensitive to satellite temporal and spatial FRP undersampling due to infrequent satellite overpasses, cloud and smoke obscuration, and failure to detect cool and/or small fires. Satellite FRPs derived over individual burned areas and fires have been observed to exhibit power law distributions. This property is exploited to develop a new way to derive FRE, as the product of the fire duration and the expected FRP value derived from the FRP power law probability distribution function. The method is demonstrated and validated by the use of FRP data measured with a dual-band radiometer over prescribed fires in the United States and by the use of FRP data retrieved from moderate resolution imaging spectroradiometer (MODIS) active-fire detections over Brazilian deforestation and Australian savanna fires. The biomass burned derived using the conventional FRP temporal integration and power law FRE estimation methods is compared with biomass burned measurements (prescribed fires) and available fuel load information reported in the literature (Australian and Brazilian fires). The results indicate that the FRE power law derivation method may provide more reliable burned biomass estimates under sparse satellite FRP sampling conditions and correct for satellite active-fire detection omission errors if the FRP power law distribution parameters and the fire duration are known.

  12. Validity and feasibility of a satellite imagery-based method for rapid estimation of displaced populations

    Directory of Open Access Journals (Sweden)

    Checchi Francesco

    2013-01-01

    Full Text Available Abstract Background Estimating the size of forcibly displaced populations is key to documenting their plight and allocating sufficient resources to their assistance, but is often not done, particularly during the acute phase of displacement, due to methodological challenges and inaccessibility. In this study, we explored the potential use of very high resolution satellite imagery to remotely estimate forcibly displaced populations. Methods Our method consisted of multiplying (i manual counts of assumed residential structures on a satellite image and (ii estimates of the mean number of people per structure (structure occupancy obtained from publicly available reports. We computed population estimates for 11 sites in Bangladesh, Chad, Democratic Republic of Congo, Ethiopia, Haiti, Kenya and Mozambique (six refugee camps, three internally displaced persons’ camps and two urban neighbourhoods with a mixture of residents and displaced ranging in population from 1,969 to 90,547, and compared these to “gold standard” reference population figures from census or other robust methods. Results Structure counts by independent analysts were reasonably consistent. Between one and 11 occupancy reports were available per site and most of these reported people per household rather than per structure. The imagery-based method had a precision relative to reference population figures of Conclusions In settings with clearly distinguishable individual structures, the remote, imagery-based method had reasonable accuracy for the purposes of rapid estimation, was simple and quick to implement, and would likely perform better in more current application. However, it may have insurmountable limitations in settings featuring connected buildings or shelters, a complex pattern of roofs and multi-level buildings. Based on these results, we discuss possible ways forward for the method’s development.

  13. Monitoring Niger River Floods from satellite Rainfall Estimates : overall skill and rainfall uncertainty propagation.

    Science.gov (United States)

    Gosset, Marielle; Casse, Claire; Peugeot, christophe; boone, aaron; pedinotti, vanessa

    2015-04-01

    Global measurement of rainfall offers new opportunity for hydrological monitoring, especially for some of the largest Tropical river where the rain gauge network is sparse and radar is not available. Member of the GPM constellation, the new French-Indian satellite Mission Megha-Tropiques (MT) dedicated to the water and energy budget in the tropical atmosphere contributes to a better monitoring of rainfall in the inter-tropical zone. As part of this mission, research is developed on the use of satellite rainfall products for hydrological research or operational application such as flood monitoring. A key issue for such applications is how to account for rainfall products biases and uncertainties, and how to propagate them into the end user models ? Another important question is how to choose the best space-time resolution for the rainfall forcing, given that both model performances and rain-product uncertainties are resolution dependent. This paper analyses the potential of satellite rainfall products combined with hydrological modeling to monitor the Niger river floods in the city of Niamey, Niger. A dramatic increase of these floods has been observed in the last decades. The study focuses on the 125000 km2 area in the vicinity of Niamey, where local runoff is responsible for the most extreme floods recorded in recent years. Several rainfall products are tested as forcing to the SURFEX-TRIP hydrological simulations. Differences in terms of rainfall amount, number of rainy days, spatial extension of the rainfall events and frequency distribution of the rain rates are found among the products. Their impacts on the simulated outflow is analyzed. The simulations based on the Real time estimates produce an excess in the discharge. For flood prediction, the problem can be overcome by a prior adjustment of the products - as done here with probability matching - or by analysing the simulated discharge in terms of percentile or anomaly. All tested products exhibit some

  14. Evaluation of satellite-based precipitation estimates in winter season using an object-based approach

    Science.gov (United States)

    Li, J.; Hsu, K.; AghaKouchak, A.; Sorooshian, S.

    2012-12-01

    Verification has become an integral component of satellite precipitation algorithms and products. A number of object-based verification methods have been proposed to provide diagnostic information regarding the precipitation products' ability to capture the spatial pattern, intensity, and placement of precipitation. However, most object-based methods are not capable of investigating precipitation objects at the storm-scale. In this study, an image processing approach known as watershed segmentation was adopted to detect the storm-scale rainfall objects. Then, a fuzzy logic-based technique was utilized to diagnose and analyze storm-scale object attributes, including centroid distance, area ratio, intersection area ratio and orientation angle difference. Three verification metrics (i.e., false alarm ratio, missing ratio and overall membership score) were generated for validation and verification. Three satellite-based precipitation products, including PERSIANN, CMORPH, 3B42RT, were evaluated against NOAA stage IV MPE multi-sensor composite rain analysis at 0.25° by 0.25° on a daily scale in the winter season of 2010 over the contiguous United States. Winter season is dominated by frontal systems which usually have larger area coverage. All three products and the stage IV observation tend to find large size storm objects. With respect to the evaluation attributes, PERSIANN tends to obtain larger area ratio and consequently has larger centroid distance to the stage IV observations, while 3B42RT are found to be closer to the stage IV for the object size. All evaluation products give small orientation angle differences but vary significantly for the missing ratio and false alarm ratio. This implies that satellite estimates can fail to detect storms in winter. The overall membership scores are close for all three different products which indicate that all three satellite-based precipitation products perform well for capturing the spatial and geometric characteristics of

  15. Estimation of micrometeorites and satellite dust flux surrounding Mars in the light of MAVEN results

    Science.gov (United States)

    Pabari, J. P.; Bhalodi, P. J.

    2017-05-01

    Recently, MAVEN observed dust around Mars from ∼150 km to ∼1000 km and it is a puzzling question to the space scientists about the presence of dust at orbital altitudes and about its source. A continuous supply of dust from various sources could cause existence of dust around Mars and it is expected that the dust could mainly be from either the interplanetary source or the Phobos/Deimos. We have studied incident projectiles or micrometeorites at Mars using the existing model, in this article. Comparison of results with the MAVEN results gives a new value of the population index S, which is reported here. The index S has been referred in a power law model used to describe the number of impacting particles on Mars. In addition, the secondary ejecta from natural satellites of Mars can cause a dust ring or torus around Mars and remain present for its lifetime. The dust particles whose paths are altered by the solar wind over its lifetime, could present a second plausible source of dust around Mars. We have investigated escaping particles from natural satellites of Mars and compared with the interplanetary dust flux estimation. It has been found that flux rate at Mars is dominated (∼2 orders of magnitude higher) by interplanetary particles in comparison with the satellite originated dust. It is inferred that the dust at high altitudes of Mars could be interplanetary in nature and our expectation is in agreement with the MAVEN observation. As a corollary, the mass loss from Martian natural satellites is computed based on the surface erosion by incident projectiles.

  16. Evaluation of satellite rainfall estimates for drought and flood monitoring in Mozambique

    Science.gov (United States)

    Tote, Carolien; Patricio, Domingos; Boogaard, Hendrik; van der Wijngaart, Raymond; Tarnavsky, Elena; Funk, Christopher C.

    2015-01-01

    Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT) v2.0, Famine Early Warning System NETwork (FEWS NET) Rainfall Estimate (RFE) v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)) are compared to independent gauge data (2001–2012). This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

  17. Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique

    Directory of Open Access Journals (Sweden)

    Carolien Toté

    2015-02-01

    Full Text Available Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT v2.0, Famine Early Warning System NETwork (FEWS NET Rainfall Estimate (RFE v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS are compared to independent gauge data (2001–2012. This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

  18. Assimilating satellite-based canopy height within an ecosystem model to estimate aboveground forest biomass

    Science.gov (United States)

    Joetzjer, E.; Pillet, M.; Ciais, P.; Barbier, N.; Chave, J.; Schlund, M.; Maignan, F.; Barichivich, J.; Luyssaert, S.; Hérault, B.; von Poncet, F.; Poulter, B.

    2017-07-01

    Despite advances in Earth observation and modeling, estimating tropical biomass remains a challenge. Recent work suggests that integrating satellite measurements of canopy height within ecosystem models is a promising approach to infer biomass. We tested the feasibility of this approach to retrieve aboveground biomass (AGB) at three tropical forest sites by assimilating remotely sensed canopy height derived from a texture analysis algorithm applied to the high-resolution Pleiades imager in the Organizing Carbon and Hydrology in Dynamic Ecosystems Canopy (ORCHIDEE-CAN) ecosystem model. While mean AGB could be estimated within 10% of AGB derived from census data in average across sites, canopy height derived from Pleiades product was spatially too smooth, thus unable to accurately resolve large height (and biomass) variations within the site considered. The error budget was evaluated in details, and systematic errors related to the ORCHIDEE-CAN structure contribute as a secondary source of error and could be overcome by using improved allometric equations.

  19. Application of satellite estimates of rainfall distribution to simulate the potential for malaria transmission in Africa

    Science.gov (United States)

    Yamana, T. K.; Eltahir, E. A.

    2009-12-01

    The Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS) is a mechanistic model developed to assess malaria risk in areas where the disease is water-limited. This model relies on precipitation inputs as its primary forcing. Until now, applications of the model have used ground-based precipitation observations. However, rain gauge networks in the areas most affected by malaria are often sparse. The increasing availability of satellite based rainfall estimates could greatly extend the range of the model. The minimum temporal resolution of precipitation data needed was determined to be one hour. The CPC Morphing technique (CMORPH ) distributed by NOAA fits this criteria, as it provides 30-minute estimates at 8km resolution. CMORPH data were compared to ground observations in four West African villages, and calibrated to reduce overestimation and false alarm biases. The calibrated CMORPH data were used to force HYDREMATS, resulting in outputs for mosquito populations, vectorial capacity and malaria transmission.

  20. Global Assessment of Land Surface Temperature From Geostationary Satellites and Model Estimates

    Science.gov (United States)

    Reichle, Rolf H.; Liu, Q.; Minnis, P.; daSilva, A. M., Jr.; Palikonda, R.; Yost, C. R.

    2012-01-01

    Land surface (or 'skin') temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. In this research we compare two global and independent data sets: (i) LST retrievals from five geostationary satellites generated at the NASA Langley Research Center (LaRC) and (ii) LST estimates from the quasi-operational NASA GEOS-5 global modeling and assimilation system. The objective is to thoroughly understand both data sets and their systematic differences in preparation for the assimilation of the LaRC LST retrievals into GEOS-5. As expected, mean differences (MD) and root-mean-square differences (RMSD) between modeled and retrieved LST vary tremendously by region and time of day. Typical (absolute) MD values range from 1-3 K in Northern Hemisphere mid-latitude regions to near 10 K in regions where modeled clouds are unrealistic, for example in north-eastern Argentina, Uruguay, Paraguay, and southern Brazil. Typically, model estimates of LST are higher than satellite retrievals during the night and lower during the day. RMSD values range from 1-3 K during the night to 2-5 K during the day, but are larger over the 50-120 W longitude band where the LST retrievals are derived from the FY2E platform

  1. GPS satellite and receiver instrumental biases estimation using least squares method for accurate ionosphere modelling

    Indian Academy of Sciences (India)

    G Sasibhushana Rao

    2007-10-01

    The positional accuracy of the Global Positioning System (GPS)is limited due to several error sources.The major error is ionosphere.By augmenting the GPS,the Category I (CAT I)Precision Approach (PA)requirements can be achieved.The Space-Based Augmentation System (SBAS)in India is known as GPS Aided Geo Augmented Navigation (GAGAN).One of the prominent errors in GAGAN that limits the positional accuracy is instrumental biases.Calibration of these biases is particularly important in achieving the CAT I PA landings.In this paper,a new algorithm is proposed to estimate the instrumental biases by modelling the TEC using 4th order polynomial.The algorithm uses values corresponding to a single station for one month period and the results confirm the validity of the algorithm.The experimental results indicate that the estimation precision of the satellite-plus-receiver instrumental bias is of the order of ± 0.17 nsec.The observed mean bias error is of the order − 3.638 nsec and − 4.71 nsec for satellite 1 and 31 respectively.It is found that results are consistent over the period.

  2. Tracking and Data Relay Satellite (TDRS) Orbit Estimation Using an Extended Kalman Filter

    Science.gov (United States)

    Ward, Douglas T.; Dang, Ket D.; Slojkowski, Steve; Blizzard, Mike; Jenkins, Greg

    2007-01-01

    Alternatives to the Tracking and Data Relay Satellite (TDRS) orbit estimation procedure were studied to develop a technique that both produces more reliable results and is more amenable to automation than the prior procedure. The Earth Observing System (EOS) Terra mission has TDRS ephemeris prediction 3(sigma) requirements of 75 meters in position and 5.5 millimeters per second in velocity over a 1.5-day prediction span. Meeting these requirements sometimes required reruns of the prior orbit determination (OD) process, with manual editing of tracking data to get an acceptable solution. After a study of the available alternatives, the Flight Dynamics Facility (FDF) began using the Real-Time Orbit Determination (RTOD(Registered TradeMark)) Kalman filter program for operational support of TDRSs in February 2007. This extended Kalman filter (EKF) is used for daily support, including within hours after most thrusting, to estimate the spacecraft position, velocity, and solar radiation coefficient of reflectivity (C(sub R)). The tracking data used are from the Bilateration Ranging Transponder System (BRTS), selected TDRS System (TDRSS) User satellite tracking data, and Telemetry, Tracking, and Command (TT&C) data. Degraded filter results right after maneuvers and some momentum unloads provided incentive for a hybrid OD technique. The results of combining EKF strengths with the Goddard Trajectory Determination System (GTDS) Differential Correction (DC) program batch-least-squares solutions, as recommended in a 2005 paper on the chain-bias technique, are also presented.

  3. Evaluation and Application of Satellite-Based Latent Heating Profile Estimation Methods

    Science.gov (United States)

    Olson, William S.; Grecu, Mircea; Yang, Song; Tao, Wei-Kuo

    2004-01-01

    In recent years, methods for estimating atmospheric latent heating vertical structure from both passive and active microwave remote sensing have matured to the point where quantitative evaluation of these methods is the next logical step. Two approaches for heating algorithm evaluation are proposed: First, application of heating algorithms to synthetic data, based upon cloud-resolving model simulations, can be used to test the internal consistency of heating estimates in the absence of systematic errors in physical assumptions. Second, comparisons of satellite-retrieved vertical heating structures to independent ground-based estimates, such as rawinsonde-derived analyses of heating, provide an additional test. The two approaches are complementary, since systematic errors in heating indicated by the second approach may be confirmed by the first. A passive microwave and combined passive/active microwave heating retrieval algorithm are evaluated using the described approaches. In general, the passive microwave algorithm heating profile estimates are subject to biases due to the limited vertical heating structure information contained in the passive microwave observations. These biases may be partly overcome by including more environment-specific a priori information into the algorithm s database of candidate solution profiles. The combined passive/active microwave algorithm utilizes the much higher-resolution vertical structure information provided by spaceborne radar data to produce less biased estimates; however, the global spatio-temporal sampling by spaceborne radar is limited. In the present study, the passive/active microwave algorithm is used to construct a more physically-consistent and environment-specific set of candidate solution profiles for the passive microwave algorithm and to help evaluate errors in the passive algorithm s heating estimates. Although satellite estimates of latent heating are based upon instantaneous, footprint- scale data, suppression

  4. Estimating Field Scale Crop Evapotranspiration using Landsat and MODIS Satellite Observations

    Science.gov (United States)

    Wong, A.; Jin, Y.; Snyder, R. L.; Daniele, Z.; Gao, F.

    2016-12-01

    Irrigation accounts for 80% of human freshwater consumption, and most of it return to the atmosphere through Evapotranspiration (ET). Given the challenges of already-stressed water resources and ground water regulation in California, a cost-effective, timely, and consistent spatial estimate of crop ET, from the farm to watershed level, is becoming increasingly important. The Priestley-Taylor (PT) approach, calibrated with field data and driven by satellite observations, shows great promise for accurate ET estimates across diverse ecosystems. We here aim to improve the robustness of the PT approach in agricultural lands, to enable growers and farm managers to tailor irrigation management based on in-field spatial variability and in-season variation. We optimized the PT coefficients for each crop type with available ET measurements from eddy covariance towers and/or surface renewal stations at six crop fields (Alfalfa, Almond, Citrus, Corn, Pistachio and Rice) in California. Good agreement was found between satellite-based estimates and field measurements of net radiation, with a RMSE of less than 36 W m-2. The crop type specific optimization performed well, with a RMSE of 30 W m-2 and a correlation of 0.81 for predicted daily latent heat flux. The calibrated algorithm was used to estimate ET at 30 m resolution over the Sacramento-San Joaquin Delta region for 2015 water year. It captures well the seasonal dynamics and spatial distribution of ET in Sacramento-San Joaquin Delta. A continuous monitoring of the dynamics and spatial heterogeneity of canopy and consumptive water use at a field scale, will help the growers to be well prepared and informed to adaptively manage water, canopy, and grove density to maximize the yield with the least amount of water.

  5. An empirical method of RH correction for satellite estimation of ground-level PM concentrations

    Science.gov (United States)

    Wang, Zifeng; Chen, Liangfu; Tao, Jinhua; Liu, Yang; Hu, Xuefei; Tao, Minghui

    2014-10-01

    A hygroscopic growth model suitable for local aerosol characteristics and their temporal variations is necessary for accurate satellite retrieval of ground-level particulate matters (PM). This study develops an empirical method to correct the relative humidity (RH) impact on aerosol extinction coefficient and to further derive PM concentrations from satellite observations. Not relying on detailed information of aerosol chemical and microphysical properties, this method simply uses the in-situ observations of visibility (VIS), RH and PM concentrations to characterize aerosol hygroscopicity, and thus makes the RH correction capable of supporting the satellite PM estimations with large spatial and temporal coverage. In this method, the aerosol average mass extinction efficiency (αext) is used to describe the general hygroscopic growth behaviors of the total aerosol populations. The association between αext and RH is obtained through empirical model fitting, and is then applied to carry out RH correction. Nearly one year of in-situ measurements of VIS, RH and PM10 in Beijing urban area are collected for this study and RH correction is made for each of the months with sufficient data samples. The correlations between aerosol extinction coefficients and PM10 concentrations are significantly improved, with the monthly correlation R2 increasing from 0.26-0.63 to 0.49-0.82, as well as the whole dataset's R2 increasing from 0.36 to 0.68. PM10 concentrations are retrieved through RH correction and validated for each season individually. Good agreements between the retrieved and observed PM10 concentrations are found in all seasons, with R2 ranging from 0.54 in spring to 0.73 in fall, and the mean relative errors ranging from -2.5% in winter to -10.8% in spring. Based on the satellite AOD and the model simulated aerosol profiles, surface PM10 over Beijing area is retrieved through the RH correction. The satellite retrieved PM10 and those observed at ground sites agree well

  6. MITRA Virtual laboratory for operative application of satellite time series for land degradation risk estimation

    Science.gov (United States)

    Nole, Gabriele; Scorza, Francesco; Lanorte, Antonio; Manzi, Teresa; Lasaponara, Rosa

    2015-04-01

    This paper aims to present the development of a tool to integrate time series from active and passive satellite sensors (such as of MODIS, Vegetation, Landsat, ASTER, COSMO, Sentinel) into a virtual laboratory to support studies on landscape and archaeological landscape, investigation on environmental changes, estimation and monitoring of natural and anthropogenic risks. The virtual laboratory is composed by both data and open source tools specifically developed for the above mentioned applications. Results obtained for investigations carried out using the implemented tools for monitoring land degradation issues and subtle changes ongoing on forestry and natural areas are herein presented. In detail MODIS, SPOT Vegetation and Landsat time series were analyzed comparing results of different statistical analyses and the results integrated with ancillary data and evaluated with field survey. The comparison of the outputs we obtained for the Basilicata Region from satellite data analyses and independent data sets clearly pointed out the reliability for the diverse change analyses we performed, at the pixel level, using MODIS, SPOT Vegetation and Landsat TM data. Next steps are going to be implemented to further advance the current Virtual Laboratory tools, by extending current facilities adding new computational algorithms and applying to other geographic regions. Acknowledgement This research was performed within the framework of the project PO FESR Basilicata 2007/2013 - Progetto di cooperazione internazionale MITRA "Remote Sensing tecnologies for Natural and Cultural heritage Degradation Monitoring for Preservation and valorization" funded by Basilicata Region Reference 1. A. Lanorte, R Lasaponara, M Lovallo, L Telesca 2014 Fisher-Shannon information plane analysis of SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series to characterize vegetation recovery after fire disturbance International Journal of Applied Earth Observation and

  7. Estimating forest aboveground biomass using HJ-1 Satellite CCD and ICESat GLAS waveform data

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The ecosystem in northeastern China and the Russian Far East is a hotspot of scientific research into the global carbon balance.Forest aboveground biomass(AGB) is an important component in the land surface carbon cycle.In this study,using forest inventory data and forest distribution data,the AGB was estimated for forest in Daxinganlin in northeastern China by combining charge-coupled device(CCD) data from the Small Satellite for Disaster and Environment Monitoring and Forecast(HJ-1) and Geoscience Laser Altimeter System(GLAS) waveform data from the Ice,Cloud and land Elevation Satellite(ICESat).The forest AGB prediction models were separately developed for different forest types in the research area at GLAS footprint level from GLAS waveform parameters and field survey plot biomass in the Changqing(CQ) Forest Center,which was calculated from forest inventory data.The resulted statistical regression models have a R2=0.68 for conifer and R2=0.71 for broadleaf forests.These models were used to estimate biomass for all GLAS footprints of forest located in the study area.All GLAS footprint biomass coupled with various spectral reflectivity parameters and vegetation indices derived from HJ-1 satellite CCD data were used in multiple regression analyses to establish biomass prediction models(R2=0.55 and R2=0.52 for needle and broadleaf respectively).Then the models were used to produce a forest AGB map for the whole study area using the HJ-1 data.Biomass data obtained from forest inventory data of the Zhuanglin(ZL) Forest Center were used as independent field measurements to validate the AGB estimated from HJ-1 CCD data(R2=0.71).About 80% of biomass samples had an error less than 20 t ha-1,and the mean error of all validation samples is 5.74 t ha-1.The pixel-level biomass map was then stratified into different biomass levels to illustrate the AGB spatial distribution pattern in this area.It was found that HJ-1 wide-swath data and GLAS waveform data can be combined to

  8. Multiple satellite estimates of urban fractions and climate effects at regional scale

    Science.gov (United States)

    Jia, G.; Xu, R.; He, Y.

    2014-12-01

    Regional climate is controlled by large scale forcing at lateral boundary and physical processes within the region. Landuse in East Asia has been changed substantially in the last three decades, featured with expansion of urban built-up at unprecedented scale and speed. The fast expansion of urban areas could contribute to local even regional climate change. However, current spatial datasets of urban fractions do not well represent extend and expansion of urban areas in the regions, and the best available satellite data and remote sensing techniques have not been well applied to serve regional modeling of urbanization impacts on near surface temperature and other climate variables. Better estimates of localized urban fractions and urban climate effects are badly needed. Here we use high and mid resolution satellite data to estimate urban fractions and to assess effects of urban heat islands at local and regional scales. With our fractional cover, data fusion, and differentiated threshold approaches, estimated urban extent was greater than previously reported in many global datasets. Many city clusters were merging into each other, with gradual blurring boundaries and disappearing of gaps among member cities. Cities and towns were more connected with roads and commercial corridors, while wildland and urban greens became more isolated as patches among built-up areas. Those new estimates are expected to effectively improve climate simulation at local and regional scales in East Asia. There were significant positive relations between urban fraction and urban heat island effects as demonstrated by VNIR and TIR data from multiple satellites. Stronger warming was detected at the meteorological stations that experienced greater urbanization, i.e., those with a higher urbanization rate. While the total urban area affects the absolute temperature values, the change of the urban area (urbanization rate) likely affects the temperature trend. Increases of approximately 10% in

  9. A Fault-tolerance Estimating Method for Ionosphere Corrections in Satellite Navigation System

    Institute of Scientific and Technical Information of China (English)

    GAO Shuliang; LI Rui; HUANG Zhigang

    2011-01-01

    Aiming to the reliable estimates of the ionosphere differential corrections for the satellite navigation system in the presence of the ionosphere anomaly,a fault-tolerance estimating method,which is based on the distributed Kalman filtering,is proposed.The method utilizes the parallel sub-filters for estimating the ionosphere differential corrections.Meanwhile,an infinite norm (IN) method is proposed for the detection of the ionosphere irregularity in the filter processing.Once the anomaly is detected,the sub-filter contaminated by the anomaly measurements will be excluded to ensure the reliability of the estimates.The simulation is conducted to validate the method and the results indicate that the anomaly can be found timely due to the novel fault detection method based on the infinite norm.Because of the parallel sub-filter architecture,the measurements are classified by the spatial distribution so that the ionosphere anomaly can be positioned and excluded more easily.Thus,the method can provide the robust and accurate ionosphere differential corrections.

  10. RICE YIELD ESTIMATION THROUGH ASSIMILATING SATELLITE DATA INTO A CROP SIMUMLATION MODEL

    Directory of Open Access Journals (Sweden)

    N. T. Son

    2016-06-01

    Full Text Available Rice is globally the most important food crop, feeding approximately half of the world’s population, especially in Asia where around half of the world’s poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government’s yield statistics indicated the root mean square error (RMSE of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.

  11. The electrical conductivity of the upper mantle as estimated from satellite magnetic field data

    Science.gov (United States)

    Didwall, E. M.

    1984-01-01

    The electrical conductivity of the upper mantle is estimated from low-latitude magnetic field variations caused by large fluctuations in the equatorial ring current. The data base is derived from magnetic field magnitude data measured by satellites OGO 2, 4, and 6, which offer better global coverage than land-based observatories. The procedures of analysis consist of: (1) separation of the disturbance field into internal and external parts relative to the surface of the earth, (2) estimation of an electromagnetic response function Q(omega) which relates the internally generated magnetic field variations to the external variations due to the ring current, and (3) interpretation of the estimated response function using theoretical response functions for assumed conductivity profiles. Special consideration is given to possible oceanic and ionospheric effects. Best estimates of the geomagnetic response function Q(omega) for 0.2 to 2.0 cpd indicate an upper mantle conductivity of the order of 0.01 S/m.

  12. Rice Yield Estimation Through Assimilating Satellite Data Into a Crop Simumlation Model

    Science.gov (United States)

    Son, N. T.; Chen, C. F.; Chen, C. R.; Chang, L. Y.; Chiang, S. H.

    2016-06-01

    Rice is globally the most important food crop, feeding approximately half of the world's population, especially in Asia where around half of the world's poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT) for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO) algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI) and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount) in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government's yield statistics indicated the root mean square error (RMSE) of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.

  13. The electrical conductivity of the upper mantle as estimated from satellite magnetic field data

    Science.gov (United States)

    Didwall, E. M.

    1984-01-01

    The electrical conductivity of the upper mantle is estimated from low-latitude magnetic field variations caused by large fluctuations in the equatorial ring current. The data base is derived from magnetic field magnitude data measured by satellites OGO 2, 4, and 6, which offer better global coverage than land-based observatories. The procedures of analysis consist of: (1) separation of the disturbance field into internal and external parts relative to the surface of the earth, (2) estimation of an electromagnetic response function Q(omega) which relates the internally generated magnetic field variations to the external variations due to the ring current, and (3) interpretation of the estimated response function using theoretical response functions for assumed conductivity profiles. Special consideration is given to possible oceanic and ionospheric effects. Best estimates of the geomagnetic response function Q(omega) for 0.2 to 2.0 cpd indicate an upper mantle conductivity of the order of 0.01 S/m.

  14. Steady state estimation of soil organic carbon using satellite-derived canopy leaf area index

    Science.gov (United States)

    Fang, Yilin; Liu, Chongxuan; Huang, Maoyi; Li, Hongyi; Leung, L. Ruby

    2014-12-01

    Estimation of soil organic carbon (SOC) stock using models typically requires long term spin-up of the carbon-nitrogen (CN) models, which has become a bottleneck for global modeling. We report a new numerical approach to estimate global SOC stock that can alleviate long spin-up. The approach uses satellite-based canopy leaf area index (LAI) and takes advantage of a reaction-based biogeochemical module—Next Generation BioGeoChemical Module (NGBGC) that was recently developed and incorporated in version 4 of the Community Land Model (CLM4). Although NGBGC uses the same CN mechanisms as in CLM4CN, it can be easily configured to run prognostic or steady state simulations. The new approach was applied at point and global scales and compared with SOC derived from spin-up by running NGBGC in the prognostic mode, and SOC from the Harmonized World Soil Database (HWSD). The steady state solution is comparable to the spin-up value when the satellite LAI is close to that from the spin-up solution, and largely captured the global variability of the HWSD SOC across the different dominant plant functional types (PFTs). The correlation between the simulated and HWSD SOC was, however, weak at both point and global scales, suggesting the needs for improving the biogeochemical processes described in CLM4 and updating HWSD. Besides SOC, the steady state solution also includes all other state variables simulated by a spin-up run, which makes the tested approach a promising tool to efficiently estimate global SOC distribution and evaluate and compare multiple aspects simulated by different CN mechanisms in the model.

  15. Estimating stream discharge from a Himalayan Glacier using coupled satellite sensor data

    Science.gov (United States)

    Child, S. F.; Stearns, L. A.; van der Veen, C. J.; Haritashya, U. K.; Tarpanelli, A.

    2015-12-01

    The 4th IPCC report highlighted our limited understanding of Himalayan glacier behavior and contribution to the region's hydrology. Seasonal snow and glacier melt in the Himalayas are important sources of water, but estimates greatly differ about the actual contribution of melted glacier ice to stream discharge. A more comprehensive understanding of the contribution of glaciers to stream discharge is needed because streams being fed by glaciers affect the livelihoods of a large part of the world's population. Most of the streams in the Himalayas are unmonitored because in situ measurements are logistically difficult and costly. This necessitates the use of remote sensing platforms to obtain estimates of river discharge for validating hydrological models. In this study, we estimate stream discharge using cost-effective methods via repeat satellite imagery from Landsat-8 and SENTINEL-1A sensors. The methodology is based on previous studies, which show that ratio values from optical satellite bands correlate well with measured stream discharge. While similar, our methodology relies on significantly higher resolution imagery (30 m) and utilizes bands that are in the blue and near-infrared spectrum as opposed to previous studies using 250 m resolution imagery and spectral bands only in the near-infrared. Higher resolution imagery is necessary for streams where the source is a glacier's terminus because the width of the stream is often only 10s of meters. We validate our methodology using two rivers in the state of Kansas, where stream gauges are plentiful. We then apply our method to the Bhagirathi River, in the North-Central Himalayas, which is fed by the Gangotri Glacier and has a well monitored stream gauge. The analysis will later be used to couple river discharge and glacier flow and mass balance through an integrated hydrologic model in the Bhagirathi Basin.

  16. Coastal water quality estimation from Geostationary Ocean Color Imager (GOCI) satellite data using machine learning approaches

    Science.gov (United States)

    Im, Jungho; Ha, Sunghyun; Kim, Yong Hoon; Ha, Hokyung; Choi, Jongkuk; Kim, Miae

    2014-05-01

    It is important to monitor coastal water quality using key parameters such as chlorophyll-a concentration and suspended sediment to better manage coastal areas as well as to better understand the nature of biophysical processes in coastal seawater. Remote sensing technology has been commonly used to monitor coastal water quality due to its ability of covering vast areas at high temporal resolution. While it is relatively straightforward to estimate water quality in open ocean (i.e., Case I water) using remote sensing, coastal water quality estimation is still challenging as many factors can influence water quality, including various materials coming from inland water systems and tidal circulation. There are continued efforts to accurately estimate water quality parameters in coastal seawater from remote sensing data in a timely manner. In this study, two major water quality indicators, chlorophyll-a concentration and the amount of suspended sediment, were estimated using Geostationary Ocean Color Imager (GOCI) satellite data. GOCI, launched in June 2010, is the first geostationary ocean color observation satellite in the world. GOCI collects data hourly for 8 hours a day at 6 visible and 2 near-infrared bands at a 500 m resolution with 2,500 x 2,500 km square around Korean peninsula. Along with conventional statistical methods (i.e., various linear and non-linear regression), three machine learning approaches such as random forest, Cubist, and support vector regression were evaluated for coastal water quality estimation. In situ measurements (63 samples; including location, two water quality parameters, and the spectra of surface water using a hand-held spectroradiometer) collected during four days between 2011 and 2012 were used as reference data. Due to the small sample size, leave-one-out cross validation was used to assess the performance of the water quality estimation models. Atmospherically corrected radiance data and selected band-ratioed images were used

  17. Estimate Landslide Volume with Genetic Algorithms and Image Similarity Method from Single Satellite Image

    Science.gov (United States)

    Yu, Ting-To

    2013-04-01

    It is important to acquire the volume of landslide in short period of time. For hazard mitigation and also emergency response purpose, the traditional method takes much longer time than expected. Due to the weather limit, traffic accessibility and many regulations of law, it take months to handle these process before the actual carry out of filed work. Remote sensing imagery can get the data as long as the visibility allowed, which happened only few day after the event. While traditional photometry requires a stereo pairs images to produce the post event DEM for calculating the change of volume. Usually have to wait weeks or even months for gathering such data, LiDAR or ground GPS measurement might take even longer period of time with much higher cost. In this study we use one post event satellite image and pre-event DTM to compare the similarity between these by alter the DTM with genetic algorithms. The outcome of smartest guess from GAs shall remove or add exact values of height at each location, which been converted into shadow relief viewgraph to compare with satellite image. Once the similarity threshold been make then the guessing work stop. It takes only few hours to finish the entire task, the computed accuracy is around 70% by comparing to the high resolution LiDAR survey at a landslide, southern Taiwan. With extra GCPs, the estimate accuracy can improve to 85% and also within few hours after the receiving of satellite image. Data of this demonstration case is a 5 m DTM at 2005, 2M resolution FormoSat optical image at 2009 and 5M LiDAR at 2010. The GAs and image similarity code is developed on Matlab at windows PC.

  18. Interannual Variability of Tropical Precipitation: How Well Do Climate Models Agree With Current Satellite Estimates?

    Science.gov (United States)

    Robertson, Franklin R.; Marshall, Susan; Roads, John; Oglesby, Robert J.; Fitzjarrald, Dan; Goodman, H. Michael (Technical Monitor)

    2001-01-01

    Since the beginning of the World Climate Research Program's Global Precipitation Climatology Project (GPCP) satellite remote sensing of precipitation has made dramatic improvements, particularly for tropical regions. Data from microwave and infrared sensors now form the most critical input to precipitation data sets and can be calibrated with surface gauges to so that the strengths of each data source can be maximized in some statistically optimal sense. Recent availability of the TRMM (Tropical Rainfall Measuring Mission) has further aided in narrowing uncertainties in rainfall over die tropics and subtropics. Although climate modeling efforts have long relied on space-based precipitation estimates for validation, we now are in a position to make more quantitative assessments of model performance, particularly in tropical regions. An integration of the CCM3 using observed SSTs as a lower boundary condition is used to examine how well this model responds to ENSO forcing in terms of anomalous precipitation. An integration of the NCEP spectral model used for the Reanalysis-H effort is also examined. This integration is run with specified SSTs, but with no data assimilation. Our analysis focuses on two aspects of inter-annual variability. First are the spatial anomalies that are indicative of dislocations in Hadley and Walker circulations. Second, we consider the ability of models to replicate observed increases in oceanic precipitation that are noted in satellite observations for large ENSO events. Finally, we consider a slab ocean version of the CCM3 model with prescribed ocean beat transports that mimic upwelling anomalies, but which still allows the surface energy balance to be predicted. This less restrictive experiment is used to understand why model experiments with specified SSTs seem to have noticeably less interannual variability in precipitation than do the satellite observations.

  19. Employing satellite retrieved soil moisture for parameter estimation of the hydrologic model mHM

    Science.gov (United States)

    Zink, Matthias; Mai, Juliane; Rakovec, Oldrich; Schrön, Martin; Kumar, Rohini; Schäfer, David; Samaniego, Luis

    2016-04-01

    Hydrological models are usually calibrated against observed streamflow at the catchment outlet and thus they are conditioned by an integral catchment signal. Rakovec et al. 2016 (JHM) recently demonstrated that constraining model parameters against river discharge is a necessary, but not a sufficient condition. Such a procedure ensures the fulfillment of the catchment's water balance but can lead to high predictive uncertainties of model internal states, like soil moisture, or a lack in spatial representativeness of the model. However, some hydrologic applications, as e.g. soil drought monitoring and prediction, rely on this information. Within this study we propose a framework in which the mesoscale Hydrologic Model (mHM) is calibrated with soil moisture retrievals from various sources. The aim is to condition the model on soil moisture (SM), while preserving good performance in streamflow estimation. We identify the most appropriate objective functions by conducting synthetic experiments. The best objective function is determined based on: 1) deviation between synthetic and simulated soil moisture, 2) nonparametric comparison of SM fields (e.g. copulas), and 3) by euclidian distance of model parameters, which is zero if the parameters of the synthetic data are recovered. Those objective functions performing best are used to calibrate mHM against different satellite soil moisture products, e.g. ESA-CCI, H-SAF, and in situ observations. This procedure is tested in three distinct European basins (upper Sava, Neckar, and upper Guadalquivir basin) ranging from snow domination to semi arid climatic conditions. Results obtained with the synthetic experiment indicate that objective functions focusing on the temporal dynamics of SM are preferable to objective functions aiming at spatial patterns or catchment averages. Since the deviation of soil moisture fields (1) and their copulas (2) don't lead to conclusive results, the decision of the best performing objective

  20. Combined evaluation of optical and microwave satellite dataset for soil moisture deficit estimation

    Science.gov (United States)

    Srivastava, Prashant K.; Han, Dawei; Islam, Tanvir; Singh, Sudhir Kumar; Gupta, Manika; Gupta, Dileep Kumar; Kumar, Pradeep

    2016-04-01

    Soil moisture is a key variable responsible for water and energy exchanges from land surface to the atmosphere (Srivastava et al., 2014). On the other hand, Soil Moisture Deficit (or SMD) can help regulating the proper use of water at specified time to avoid any agricultural losses (Srivastava et al., 2013b) and could help in preventing natural disasters, e.g. flood and drought (Srivastava et al., 2013a). In this study, evaluation of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and soil moisture from Soil Moisture and Ocean Salinity (SMOS) satellites are attempted for prediction of Soil Moisture Deficit (SMD). Sophisticated algorithm like Adaptive Neuro Fuzzy Inference System (ANFIS) is used for prediction of SMD using the MODIS and SMOS dataset. The benchmark SMD estimated from Probability Distributed Model (PDM) over the Brue catchment, Southwest of England, U.K. is used for all the validation. The performances are assessed in terms of Nash Sutcliffe Efficiency, Root Mean Square Error and the percentage of bias between ANFIS simulated SMD and the benchmark. The performance statistics revealed a good agreement between benchmark and the ANFIS estimated SMD using the MODIS dataset. The assessment of the products with respect to this peculiar evidence is an important step for successful development of hydro-meteorological model and forecasting system. The analysis of the satellite products (viz. SMOS soil moisture and MODIS LST) towards SMD prediction is a crucial step for successful hydrological modelling, agriculture and water resource management, and can provide important assistance in policy and decision making. Keywords: Land Surface Temperature, MODIS, SMOS, Soil Moisture Deficit, Fuzzy Logic System References: Srivastava, P.K., Han, D., Ramirez, M.A., Islam, T., 2013a. Appraisal of SMOS soil moisture at a catchment scale in a temperate maritime climate. Journal of Hydrology 498, 292-304. Srivastava, P.K., Han, D., Rico

  1. SACRA – global data sets of satellite-derived crop calendars for agricultural simulations: an estimation of a high-resolution crop calendar using satellite-sensed NDVI

    Directory of Open Access Journals (Sweden)

    S. Kotsuki

    2015-01-01

    Full Text Available To date, many studies have performed numerical estimations of food production and agricultural water demand to understand the present and future supply–demand relationship. A crop calendar (CC is an essential input datum to estimate food production and agricultural water demand accurately with the numerical estimations. CC defines the date or month when farmers plant and harvest in cropland. This study aims to develop a new global data set of a satellite-derived crop calendar for agricultural simulations (SACRA and reveal advantages and disadvantages of the satellite-derived CC compared to other global products. We estimate global CC at a spatial resolution of 5 min (≈10 km using the satellite-sensed NDVI data, which corresponds well to vegetation growth and death on the land surface. We first demonstrate that SACRA shows similar spatial pattern in planting date compared to a census-based product. Moreover, SACRA reflects a variety of CC in the same administrative unit, since it uses high-resolution satellite data. However, a disadvantage is that the mixture of several crops in a grid is not considered in SACRA. We also address that the cultivation period of SACRA clearly corresponds to the time series of NDVI. Therefore, accuracy of SACRA depends on the accuracy of NDVI used for the CC estimation. Although SACRA shows different CC from a census-based product in some regions, multiple usages of the two products are useful to take into consideration the uncertainty of the CC. An advantage of SACRA compared to the census-based products is that SACRA provides not only planting/harvesting dates but also a peak date from the time series of NDVI data.

  2. Assessing the utility of satellite-based whitecap fraction to estimate sea spray production and CO2 transfer velocity

    Science.gov (United States)

    Anguelova, M. D.

    2016-05-01

    The utility of a satellite-based whitecap database for estimates of surface sea spray production and bubble-mediated gas transfer on a global scale is presented. Existing formulations of sea spray production and bubble-mediated CO2 transfer velocity involve whitecap fraction parametrization as a function of wind speed at 10 m reference height W(U 10) based on photographic measurements of whitecaps. Microwave radiometric measurements of whitecaps from satellites provide whitecap fraction data over the world oceans for all seasons. Parametrizations W(U 10) based on such radiometric data are thus applicable for a wide range of conditions and can account for influences secondary to the primary forcing factor, the wind speed. Radiometric satellite-based W(U 10) relationship was used as input to: (i) the Coupled Ocean-Atmosphere Response Experiment Gas transfer (COAREG) algorithm to obtain CO2 transfer velocity and total CO2 flux; and (ii) the sea spray source function (SSSF) recommended by Andreas in 2002 to obtain fluxes of sea spray number and mass. The outputs of COAREG and SSSF obtained with satellite-based W(U 10) are compared with respective outputs obtained with the nominal W(U 10) relationship based on photographic data. Good comparisons of the gas and sea spray fluxes with direct measurements and previous estimates imply that the satellite- based whitecap database can be useful to obtain surface fluxes of particles and gases in regions and conditions difficult to access and sample in situ. Satellite and in situ estimates of surface sea spray production and bubble-mediated gas transfer thus complement each other: accurate in situ observations can constrain radiometric whitecap fraction and mass flux estimates, while satellite observations can provide global coverage of whitecap fraction and mass flux estimates.

  3. Evaluating Satellite Products for Precipitation Estimation in Mountain Regions: A Case Study for Nepal

    Directory of Open Access Journals (Sweden)

    Tarendra Lakhankar

    2013-08-01

    Full Text Available Precipitation in mountain regions is often highly variable and poorly observed, limiting abilities to manage water resource challenges. Here, we evaluate remote sensing and ground station-based gridded precipitation products over Nepal against weather station precipitation observations on a monthly timescale. We find that the Tropical Rainfall Measuring Mission (TRMM 3B-43 precipitation product exhibits little mean bias and reasonable skill in giving precipitation over Nepal. Compared to station observations, the TRMM precipitation product showed an overall Nash-Sutcliffe efficiency of 0.49, which is similar to the skill of the gridded station-based product Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE. The other satellite precipitation products considered (Global Satellite Mapping of Precipitation (GSMaP, the Climate Prediction Center Morphing technique (CMORPH, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS were less skillful, as judged by Nash-Sutcliffe efficiency, and, on average, substantially underestimated precipitation compared to station observations, despite their, in some cases, higher nominal spatial resolution compared to TRMM. None of the products fully captured the dependence of mean precipitation on elevation seen in the station observations. Overall, the TRMM product is promising for use in water resources applications.

  4. Evaluation of satellite and reanalysis-based global net surface energy flux and uncertainty estimates

    Science.gov (United States)

    Allan, Richard; Liu, Chunlei

    2017-04-01

    The net surface energy flux is central to the climate system yet observational limitations lead to substantial uncertainty (Trenberth and Fasullo, 2013; Roberts et al., 2016). A combination of satellite-derived radiative fluxes at the top of atmosphere (TOA) adjusted using the latest estimation of the net heat uptake of the Earth system, and the atmospheric energy tendencies and transports from the ERA-Interim reanalysis are used to estimate surface energy flux globally (Liu et al., 2015). Land surface fluxes are adjusted through a simple energy balance approach using relations at each grid point with the consideration of snowmelt to improve regional realism. The energy adjustment is redistributed over the oceans using a weighting function to avoid meridional discontinuities. Uncertainties in surface fluxes are investigated using a variety of approaches including comparison with a range of atmospheric reanalysis input data and products. Zonal multiannual mean surface flux uncertainty is estimated to be less than 5 Wm-2 but much larger uncertainty is likely for regional monthly values. The meridional energy transport is calculated using the net surface heat fluxes estimated in this study and the result shows better agreement with observations in Atlantic than before. The derived turbulent fluxes (difference between the net heat flux and the CERES EBAF radiative flux at surface) also have good agreement with those from OAFLUX dataset and buoy observations. Decadal changes in the global energy budget and the hemisphere energy imbalances are quantified and present day cross-equator heat transports is re-evaluated as 0.22±0.15 PW southward by the atmosphere and 0.32±0.16 PW northward by the ocean considering the observed ocean heat sinks (Roemmich et al., 2006) . Liu et al. (2015) Combining satellite observations and reanalysis energy transports to estimate global net surface energy fluxes 1985-2012. J. Geophys. Res., Atmospheres. ISSN 2169-8996 doi: 10.1002/2015JD

  5. Estimation of evapotranspiration over heterogeneous surfaces based on HJ1B satellite data in China

    Science.gov (United States)

    Xin, Xiaozhou; Jiao, Jingjun

    2014-05-01

    The HJ1B satellite of China is equipped with two CCD cameras with 30m resolution and one infrared multispectral camera with 300m resolution. And the revisit period of HJ1B satellite is 4 days. Compared to MODIS or TM, HJ1B data has the advantage of high spatial-temporal resolution. Methodology based on the one-source energy balance model was developed for net radiation (Rn), soil heat flux (G), sensible heat flux (H) and latent heat flux (LE) estimation from HI1B data. The core procedure is a scheme that was designed for correcting the spatial scale error over heterogeneous surfaces by taking advantage of the HJ1B data characteristics, i.e., high resolution CCD data (30m) along with thermal data (300m). First of all, a regression relationship between Ts and NDVI was built up at 300m resolution based on the data of Ts and NDVI of the selected "pure" pixels. And then the relationship function was applied at 30m resolution to derive Ts at high resolution, i.e., at the subpixel level. Furthermore, the 30m land class data was also used in the parameterization of surface energy balance and surface aerodynamic transfer, which is important since significant error may be resulted by using one land class type to represent the whole mixed pixel. By using high resolution NDVI and land class data, we are able to mitigate the spatial scale error of the mixed pixels at 300m resolution. At last, the 300m surface energy fluxes were obtained by aggregation of the 30m estimation. HJ1B data at Hai river basin in north China in 2010 were used to verify this method. The eddy-correlation system data were used as validation. The results of the method were compared with the results of a simple method that estimates the fluxes at 300m by aggregating all of the input parameters to 300m. It is shown that the method proposed in this study shows higher agreement with in-suit measurement, and the fluxes maps also show much more details of the spatial variation. By using this method, it can be

  6. Comparison of Historical Satellite-Based Estimates of Solar Radiation Resources with Recent Rotating Shadowband Radiometer Measurements: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Myers, D. R.

    2009-03-01

    The availability of rotating shadow band radiometer measurement data at several new stations provides an opportunity to compare historical satellite-based estimates of solar resources with measurements. We compare mean monthly daily total (MMDT) solar radiation data from eight years of NSRDB and 22 years of NASA hourly global horizontal and direct beam solar estimates with measured data from three stations, collected after the end of the available resource estimates.

  7. Estimation of the rice-planting field in Bangladesh by satellite remote sensing

    Science.gov (United States)

    Furuta, E.; Suzuki, G.; Yamassaki, M.; Teraoka, T.; Fujiwara, H.; Ogino, Y.; Akashi, M.; Lahrita, L.; Naruse, N.; Takahashi, Y.

    2016-12-01

    In Bangladesh, price of rice has been unstable due to a large increase in production. To control the price can become a political issue, because rice agriculture is one of the most important industries in Bangladesh, whereas the total area of the paddy field is accurately unknown, owing to unsustainable and on-site surveys for the area (1). Satellite remote sensing is an effective solution to research the all area of domestic paddy field. Microwave satellite imaging has a large merit to be observable regardless of the weather conditions, however, research institutions have been limited to observing continuously since the cost is high for developing countries, such as Bangladesh. This study aims to establish the way to grasp the paddy field using optical satellite images for free of charge (Landsat-8). We have focused on seasonal changes in the water and the vegetation indices obtained from paddy fields. We have performed image calculations of Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) of the well-known paddy field in Bangladesh Rice Research Institute. We found that there are seasonal changes of NDVI and NDWI calculated from paddy field. The characteristics are as follows; the NDVI and the NDWI values varies by 0.17-0.25 up and 0.11-0.19 down, respectively, at the transition from the dry to the rainy season, on the other hand, the NDVI and the NDWI changes by 0.21-0.29 down and 0.09-0.17 up from the rainy to the dry season. These features make us to distinguish the paddy field from the other cultivated area. The decrease of NDVI means that rice bares, The increase of NDWI can be interpreted that the paddy field is covered with water for the preparation for planting it. Our estimated area of paddy field in Bangladesh (85,900km ) corresponds well with the previous reported value of 117,700km (1). We have established the way to grasp the paddy field using optical satellite images for free of charge, on the bases of the

  8. Lava discharge rate estimates from thermal infrared satellite data for Pacaya Volcano during 2004-2010

    Science.gov (United States)

    Morgan, Hilary A.; Harris, Andrew J. L.; Gurioli, Lucia

    2013-08-01

    Pacaya is one of the most active volcanoes in Central America and has produced lava flows frequently since 1961. All effusive activity between 1961 and 2009 was confined by an arcuate collapse scarp surrounding the northern and eastern flanks. However, the recent breaching of this topographic barrier, and the eruption of a large lava flow outside of the main center of activity, have allowed lava to extend into nearby populated areas, indicating the need for assessment and monitoring of lava flow hazards. We investigated whether a commonly used satellite-based model could produce accurate lava discharge rates for the purpose of near-real-time assessment of hazards during future eruptions and to assess the dynamics of this persistently degassing system. The model assumes a linear relationship between active lava flow area and time-averaged discharge rate (TADR) via a simple conversion factor. We calculated the conversion factor via two methods: (1) best-fitting of satellite-derived flow areas to ground-based estimates of lava flow volume, and (2) theoretically via a parameterized model that takes into account the physical properties of the lava. To apply the latter method, we sampled four lava flows and measured density, vesicularity, crystal content, and major element composition. We found the best agreement of conversion factors in the eruption with the most complete satellite coverage, and used data for these flows to define the linear relationship between area and discharge rate. The physical properties of the sampled flows were essentially identical, so that any discrepancy between the two methods of calculating conversion factors must be due to modeling errors or environmental factors unaccounted for by the parameterized model. However, our best-fitting method provides a new means to set the conversion appropriately, and to obtain self-consistent TADRs. We identified two distinct types of effusive activity at Pacaya: Type 1 activity characterized by initially

  9. Global top-down smoke aerosol emissions estimation using satellite fire radiative power measurements

    Directory of Open Access Journals (Sweden)

    C. Ichoku

    2013-10-01

    Full Text Available Biomass burning occurs seasonally in most vegetated parts of the world, consuming large amounts of biomass fuel, generating intense heat energy, and emitting corresponding amounts of smoke plumes that comprise different species of aerosols and trace gases. Accurate estimates of these emissions are required as model inputs to evaluate and forecast smoke plume transport and impacts on air quality, human health, clouds, weather, radiation, and climate. Emissions estimates have long been based on bottom-up approaches that are not only complex, but also fraught with compounding uncertainties. Fortunately, a series of recent studies have revealed that both the rate of biomass consumption and the rate of emission of aerosol particulate matter (PM by open biomass burning are directly proportional to the rate of release of fire radiative energy (FRE, which is fire radiative power (FRP that is measurable from satellite. This direct relationship enables the determination of coefficients of emission (Ce, which can be used to convert FRP or FRE to smoke aerosol emissions in the same manner as emission factors (EFs are used to convert burned biomass to emissions. We have leveraged this relationship to generate the first global 1° × 1° gridded Ce product for smoke aerosol or total particulate matter (TPM emissions using coincident measurements of FRP and aerosol optical thickness (AOT from the Moderate-resolution Imaging Spectro-radiometer (MODIS sensors aboard the Terra and Aqua satellites. This new Fire Energetics and Emissions Research version 1.0 (FEER.v1 Ce product has now been released to the community and can be obtained from http://feer.gsfc.nasa.gov/, along with the corresponding 1-to-1 mapping of their quality assurance (QA flags that will enable the Ce values to be filtered by quality for use in various applications. The regional averages of Ce values for different ecosystem types were found to be in the ranges of: 16–21 g MJ−1 for savanna

  10. Estimating crop yield using a satellite-based light use efficiency model

    DEFF Research Database (Denmark)

    Yuan, Wenping; Chen, Yang; Xia, Jiangzhou

    2016-01-01

    for simulating crops’ GPP. At both irrigated and rainfed sites, the EC-LUE model exhibits a similar level of performance. However, large errors are found when simulating yield based on crop harvest index. This analysis highlights the need to improve the representation of the harvest index and carbon allocation...... primary production (GPP) and yield of crops. The EC-LUE model can explain on average approximately 90% of the variability in GPP for 36 FLUXNET sites globally. The results indicate that a universal set of parameters, independent of crop species (except for C4 crops), can be adopted in the EC-LUE model...... for improving crop yield estimations from satellite-based methods....

  11. Dynamics modeling for sugar cane sucrose estimation using time series satellite imagery

    Science.gov (United States)

    Zhao, Yu; Justina, Diego Della; Kazama, Yoriko; Rocha, Jansle Vieira; Graziano, Paulo Sergio; Lamparelli, Rubens Augusto Camargo

    2016-10-01

    Sugarcane, as one of the most mainstay crop in Brazil, plays an essential role in ethanol production. To monitor sugarcane crop growth and predict sugarcane sucrose content, remote sensing technology plays an essential role while accurate and timely crop growth information is significant, in particularly for large scale farming. We focused on the issues of sugarcane sucrose content estimation using time-series satellite image. Firstly, we calculated the spectral features and vegetation indices to make them be correspondence to the sucrose accumulation biological mechanism. Secondly, we improved the statistical regression model considering more other factors. The evaluation was performed and we got precision of 90% which is about 20% higher than the conventional method. The validation results showed that prediction accuracy using our sugarcane growth modeling and improved mix model is satisfied.

  12. Efficient estimation algorithms for a satellite-aided search and rescue mission

    Science.gov (United States)

    Argentiero, P.; Garza-Robles, R.

    1977-01-01

    It has been suggested to establish a search and rescue orbiting satellite system as a means for locating distress signals from downed aircraft, small boats, and overland expeditions. Emissions from Emergency Locator Transmitters (ELT), now available in most U.S. aircraft are to be utilized in the positioning procedure. A description is presented of a set of Doppler navigation algorithms for extracting ELT position coordinates from Doppler data. The algorithms have been programmed for a small computing machine and the resulting system has successfully processed both real and simulated Doppler data. A software system for solving the Doppler navigation problem must include an orbit propagator, a first guess algorithm, and an algorithm for estimating longitude and latitude from Doppler data. Each of these components is considered.

  13. Single Tree Vegetation Depth Estimation Tool for Satellite Services Link Design

    Directory of Open Access Journals (Sweden)

    Z. Hasirci

    2016-04-01

    Full Text Available Attenuation caused by tree shadowing is an important factor for describing the propagation channel of satellite services. Thus, vegetation effects should be determined by experimental studies or empirical formulations. In this study, tree types in the Black Sea Region of Turkey are classified based on their geometrical shapes into four groups such as conic, ellipsoid, spherical and hemispherical. The variations of the vegetation depth according to different tree shapes are calculated with ray tracing method. It is showed that different geometrical shapes have different vegetation depths even if they have same foliage volume for different elevation angles. The proposed method is validated with the related literature in terms of average single tree attenuation. On the other hand, due to decrease system requirements (speed, memory usage etc. of ray tracing method, an artificial neural network is proposed as an alternative. A graphical user interface is created for the above processes in MATLAB environment named vegetation depth estimation tool (VdET.

  14. Estimate solar contribution to the global surface warming using the ACRIM TSI satellite composite.

    Science.gov (United States)

    Scafetta, N.; West, B. J.

    2005-12-01

    We study, by using a wavelet decomposition methodology, the solar signature on global surface temperature data using the ACRIM total solar irradiance satellite composite by Willson and Mordvinov. These data present a +0.047% per decade trend between minima during solar cycles 21-23 (1980-2002). By using the phenomenological climate sensitivity to a 22-year cycle, we estimate that the ACRIM upward trend might have contributed 10-30% of the global surface temperature warming over the period 1980-2002. Moreover, by comparing the phenomenological climate sensitivity to the 11-year solar cycle with those hypothesized by some energy balance models we conclude that the former is 1.5-3 times stronger than the latter. Finally, we study the climate sensitivity in different regions of the Earth.

  15. Improved global high resolution precipitation estimation using multi-satellite multi-spectral information

    Science.gov (United States)

    Behrangi, Ali

    In respond to the community demands, combining microwave (MW) and infrared (IR) estimates of precipitation has been an active area of research since past two decades. The anticipated launching of NASA's Global Precipitation Measurement (GPM) mission and the increasing number of spectral bands in recently launched geostationary platforms will provide greater opportunities for investigating new approaches to combine multi-source information towards improved global high resolution precipitation retrievals. After years of the communities' efforts the limitations of the existing techniques are: (1) Drawbacks of IR-only techniques to capture warm rainfall and screen out no-rain thin cirrus clouds; (2) Grid-box- only dependency of many algorithms with not much effort to capture the cloud textures whether in local or cloud patch scale; (3) Assumption of indirect relationship between rain rate and cloud-top temperature that force high intensity precipitation to any cold cloud; (4) Neglecting the dynamics and evolution of cloud in time; (5) Inconsistent combination of MW and IR-based precipitation estimations due to the combination strategies and as a result of above described shortcomings. This PhD dissertation attempts to improve the combination of data from Geostationary Earth Orbit (GEO) and Low-Earth Orbit (LEO) satellites in manners that will allow consistent high resolution integration of the more accurate precipitation estimates, directly observed through LEO's PMW sensors, into the short-term cloud evolution process, which can be inferred from GEO images. A set of novel approaches are introduced to cope with the listed limitations and is consist of the following four consecutive components: (1) starting with the GEO part and by using an artificial-neural network based method it is demonstrated that inclusion of multi-spectral data can ameliorate existing problems associated with IR-only precipitating retrievals; (2) through development of Precipitation Estimation

  16. Estimating Zenith Tropospheric Delays from BeiDou Navigation Satellite System Observations

    Directory of Open Access Journals (Sweden)

    Xin Sui

    2013-04-01

    Full Text Available The GNSS derived Zenith Tropospheric Delay (ZTD plays today a very critical role in meteorological study and weather forecasts, as ZTDs of thousands of GNSS stations are operationally assimilated into numerical weather prediction models. Recently, the Chinese BeiDou Navigation Satellite System (BDS was officially announced to provide operational services around China and its neighborhood and it was demonstrated to be very promising for precise navigation and positioning. In this contribution, we concentrate on estimating ZTD using BDS observations to assess its capacity for troposphere remote sensing. A local network which is about 250 km from Beijing and comprised of six stations equipped with GPS- and BDS-capable receivers is utilized. Data from 5 to 8 November 2012 collected on the network is processed in network mode using precise orbits and in Precise Point Positioning mode using precise orbits and clocks. The precise orbits and clocks are generated from a tracking network with most of the stations in China and several stations around the world. The derived ZTDs are compared with that estimated from GPS data using the final products of the International GNSS Service (IGS. The comparison shows that the bias and the standard deviation of the ZTD differences are about 2 mm and 5 mm, respectively, which are very close to the differences of GPS ZTD estimated using different software packages.

  17. Estimating zenith tropospheric delays from BeiDou navigation satellite system observations.

    Science.gov (United States)

    Xu, Aigong; Xu, Zongqiu; Ge, Maorong; Xu, Xinchao; Zhu, Huizhong; Sui, Xin

    2013-04-03

    The GNSS derived Zenith Tropospheric Delay (ZTD) plays today a very critical role in meteorological study and weather forecasts, as ZTDs of thousands of GNSS stations are operationally assimilated into numerical weather prediction models. Recently, the Chinese BeiDou Navigation Satellite System (BDS) was officially announced to provide operational services around China and its neighborhood and it was demonstrated to be very promising for precise navigation and positioning. In this contribution, we concentrate on estimating ZTD using BDS observations to assess its capacity for troposphere remote sensing. A local network which is about 250 km from Beijing and comprised of six stations equipped with GPS- and BDS-capable receivers is utilized. Data from 5 to 8 November 2012 collected on the network is processed in network mode using precise orbits and in Precise Point Positioning mode using precise orbits and clocks. The precise orbits and clocks are generated from a tracking network with most of the stations in China and several stations around the world. The derived ZTDs are compared with that estimated from GPS data using the final products of the International GNSS Service (IGS). The comparison shows that the bias and the standard deviation of the ZTD differences are about 2 mm and 5 mm, respectively, which are very close to the differences of GPS ZTD estimated using different software packages.

  18. Estimating the global surface area of rivers and streams using satellite imagery

    Science.gov (United States)

    Allen, George; Pavelsky, Tamlin

    2017-04-01

    Global observational assessments of river and stream systems are based largely on gauge station data, which are fragmented and often limited to country-level statistics. This limitation severely impedes our understanding of global-scale hydrologic, geomorphic, and biogeochemical fluvial processes. In contrast, satellite remote sensing data provide a globally-consistent and spatially-continuous tool for studying rivers. Here we present a novel method estimate the total surface area of all rivers and stream globally using measurements from the recently-developed Global River Widths from Landsat (GRWL) database and field surveys. The surface area of rivers and streams is a key model parameter in global evaluations of greenhouse gas emissions from inland waters. Preliminary analysis suggests that rivers occupy a total area of 80 thousand square kilometers, or 0.58% of Earth's land surface. This result is 30% greater than the previous best estimate that is based on digital elevation models and gauge station measurements. Compared to previous regional assessments, we find that rivers and streams occupy a greater proportion of the land surface in the arctic and in the tropics, and a lower proportion of land surface in the United States and in Europe. Our results suggest that current estimates of greenhouse gas emissions from inland waters should be revised upwards to account for the greater abundance of river and stream surface area.

  19. Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

    Directory of Open Access Journals (Sweden)

    C. A. Poulsen

    2011-04-01

    Full Text Available Clouds play an important role in balancing the Earth's radiation budget. Clouds reflect sunlight which cools the Earth, and also trap infrared radiation in the same manner as greenhouse gases. Changes in cloud cover and cloud properties over time can have important consequences for climate. The Intergovernmental Panel for Climate Change (IPCC has identified current gaps in the understanding of clouds and related climate feedback processes as a leading cause of uncertainty in forecasting climate change. In this paper we present an algorithm that uses optimal estimation to retrieve cloud parameters from satellite multi-spectral imager data, in particular the Along-Track Scanning Radiometers ATSR-2 and AATSR. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase. Importantly, the technique also provides estimated errors along with the retrieved values and quantifies the consistency between retrieval representation of cloud and satellite radiances. This should enable the effective use of the products for comparison with climate models or for exploitation via data assimilation. The technique is evaluated by performing retrieval simulations for a variety of simulated single layer and multi-layer conditions. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed. This algorithm has been applied to both ATSR-2 and AATSR visible and infrared measurements in the context of the GRAPE (Global Retrieval and cloud Product Evaluation project to produce a 14 year consistent record for climate research (Sayer et al., 2010.

  20. Using pan-sharpened high resolution satellite data to improve impervious surfaces estimation

    Science.gov (United States)

    Xu, Ru; Zhang, Hongsheng; Wang, Ting; Lin, Hui

    2017-05-01

    Impervious surface is an important environmental and socio-economic indicator for numerous urban studies. While a large number of researches have been conducted to estimate the area and distribution of impervious surface from satellite data, the accuracy for impervious surface estimation (ISE) is insufficient due to high diversity of urban land cover types. This study evaluated the use of panchromatic (PAN) data in very high resolution satellite image for improving the accuracy of ISE by various pan-sharpening approaches, with a further comprehensive analysis of its scale effects. Three benchmark pan-sharpening approaches, Gram-Schmidt (GS), PANSHARP and principal component analysis (PCA) were applied to WorldView-2 in three spots of Hong Kong. The on-screen digitization were carried out based on Google Map and the results were viewed as referenced impervious surfaces. The referenced impervious surfaces and the ISE results were then re-scaled to various spatial resolutions to obtain the percentage of impervious surfaces. The correlation coefficient (CC) and root mean square error (RMSE) were adopted as the quantitative indicator to assess the accuracy. The accuracy differences between three research areas were further illustrated by the average local variance (ALV) which was used for landscape pattern analysis. The experimental results suggested that 1) three research regions have various landscape patterns; 2) ISE accuracy extracted from pan-sharpened data was better than ISE from original multispectral (MS) data; and 3) this improvement has a noticeable scale effects with various resolutions. The improvement was reduced slightly as the resolution became coarser.

  1. Estimating the impact of satellite observations on large-scale river flood forecasting

    Science.gov (United States)

    Andreadis, Konstantinos; Schumann, Guy

    2014-05-01

    Floods are one of the costliest natural disasters, posing severe risks to human population. Hydraulic models are able to predict flood characteristics, such as water surface elevations and inundated area, and are being used for forecasting operationally although there are many uncertainties. In this work, the potential value of satellite observations to initialize these hydraulic models (and their forecasts correspondingly) is explored. The Ensemble Sensitivity method is adapted to evaluate the impact of potential satellite observations on the forecasting of flood characteristics. The estimation of the impact is based on the Local Ensemble Transform Kalman Filter, allowing for the forecast error reductions to be computed without additional model runs. The study area was located in the Ohio River basin, and the model used was the LISFLOOD-FP hydrodynamic model. The experimental design consisted of two configurations of the LISFLOOD-FP model. The first (baseline) simulation represents a calibrated 'best effort' model based on a sub-grid channel structure using observations for parameters and boundary conditions, whereas the second (background) simulation consists of estimated parameters and SRTM-based boundary conditions. Results showed that the forecast skill was improved for water heights up to lead times of 11 days, while even partial observations of the river contained information for the entire river's water surface profile and allowed forecasting 5 to 7 days ahead. On the other hand, discharge forecasts were not improved as much when assimilating water height observations although forecast errors were reduced. Finally, the potential for identifying errors in the model structure and parameterizations via the ensemble sensitivity method is discussed.

  2. River discharge estimation at daily resolution from satellite altimetry over an entire river basin

    Science.gov (United States)

    Tourian, M. J.; Schwatke, C.; Sneeuw, N.

    2017-03-01

    One of the main challenges of hydrological modeling is the poor spatiotemporal coverage of in situ discharge databases which have steadily been declining over the past few decades. It has been demonstrated that water heights over rivers from satellite altimetry can sensibly be used to deal with the growing lack of in situ discharge data. However, the altimetric discharge is often estimated from a single virtual station suffering from coarse temporal resolution, sometimes with data outages, poor modeling and inconsistent sampling. In this study, we propose a method to estimate daily river discharge using altimetric time series of an entire river basin including its tributaries. Here, we implement a linear dynamic model to (1) provide a scheme for data assimilation of multiple altimetric discharge along a river; (2) estimate daily discharge; (3) deal with data outages, and (4) smooth the estimated discharge. The model consists of a stochastic process model that benefits from the cyclostationary behavior of discharge. Our process model comprises the covariance and cross-covariance information of river discharge at different gauges. Combined with altimetric discharge time series, we solve the linear dynamic system using the Kalman filter and smoother providing unbiased discharge with minimum variance. We evaluate our method over the Niger basin, where we generate altimetric discharge using water level time series derived from missions ENVISAT, SARAL/AltiKa, and Jason-2. Validation against in situ discharge shows that our method provides daily river discharge with an average correlation of 0.95, relative RMS error of 12%, relative bias of 10% and NSE coefficient of 0.7. Using a modified NSE-metric, that assesses the non-cyclostationary behavior, we show that our estimated discharge outperforms available legacy mean daily discharge.

  3. Lower satellite-gravimetry estimates of Antarctic sea-level contribution.

    Science.gov (United States)

    King, Matt A; Bingham, Rory J; Moore, Phil; Whitehouse, Pippa L; Bentley, Michael J; Milne, Glenn A

    2012-11-22

    Recent estimates of Antarctica's present-day rate of ice-mass contribution to changes in sea level range from 31 gigatonnes a year (Gt yr(-1); ref. 1) to 246 Gt yr(-1) (ref. 2), a range that cannot be reconciled within formal errors. Time-varying rates of mass loss contribute to this, but substantial technique-specific systematic errors also exist. In particular, estimates of secular ice-mass change derived from Gravity Recovery and Climate Experiment (GRACE) satellite data are dominated by significant uncertainty in the accuracy of models of mass change due to glacial isostatic adjustment (GIA). Here we adopt a new model of GIA, developed from geological constraints, which produces GIA rates systematically lower than those of previous models, and an improved fit to independent uplift data. After applying the model to 99 months (from August 2002 to December 2010) of GRACE data, we estimate a continent-wide ice-mass change of -69 ± 18 Gt yr(-1) (+0.19 ± 0.05 mm yr(-1) sea-level equivalent). This is about a third to a half of the most recently published GRACE estimates, which cover a similar time period but are based on older GIA models. Plausible GIA model uncertainties, and errors relating to removing longitudinal GRACE artefacts ('destriping'), confine our estimate to the range -126 Gt yr(-1) to -29 Gt yr(-1) (0.08-0.35 mm yr(-1) sea-level equivalent). We resolve 26 independent drainage basins and find that Antarctic mass loss, and its acceleration, is concentrated in basins along the Amundsen Sea coast. Outside this region, we find that West Antarctica is nearly in balance and that East Antarctica is gaining substantial mass.

  4. Uncertainty Estimates of NASA Satellite LST over the Greenland and Antarctic Plateau: 2003-2015

    Science.gov (United States)

    Knuteson, R.; Borbas, E. E.; Burgess, G.

    2015-12-01

    Jin and Dickinson (2010) identify three reasons why LST has not been adopted as a climate variable. Paraphrasing the authors, the three roadblocks for use of satellite LST products in climate studies are; 1) unknown accuracy (What are surface emissivity and atmospheric correction uncertainties?)2) spatial scale ambiguity (Are satellite footprints too large to be physically meaningful?)3) lack of consistency over decadal time scales (How far backward/forward can we go in time?). These issues apply particularly to the cryosphere where the lack of surface measurement sites make the proper use of satellite observations critical for monitoring climate change. This paper will address each of these three issues but with a focus on the high and dry Greenland and Antarctic plateaus and the contrast in trends between the two. Recent comparisons of MODIS LST products with AIRS version 6 LST products show large differences over Greenland (Lee et al. 2014). In this paper we take the logical next step of creating a bottoms up uncertainty budget for a new synergistic AIRS/MODIS LST product for ice and snow conditions. This new product will address the issue of unknown accuracy by providing a local LST uncertainty along with each estimate of surface temperature. The combination of the high spatial resolution of the MODIS and the high spectral resolution of the AIRS observations of radiance allow the combination of the two sensors to provide information with lower uncertainty than what is possible from the current separate operational products. The issue of surface emissivity and atmospheric correction uncertainties will be addressed explicitly using spectrally resolved models that cover the infrared region. The issue of spatial scale ambiguity is overcome by creating a classification of the results based on the spatial homogenity of surface temperatures. The issue of lack of consistency over long time scales is addressed by demonstrating an algorithm using collocated NASA MODIS

  5. Contribution of MODIS Derived Snow Cover Satellite Data into Artificial Neural Network for Streamflow Estimation

    Science.gov (United States)

    Uysal, Gokcen; Arda Sorman, Ali; Sensoy, Aynur

    2014-05-01

    Contribution of snowmelt and correspondingly snow observations are highly important in mountainous basins for modelers who deal with conceptual, physical or soft computing models in terms of effective water resources management. Long term archived continuous data are needed for appropriate training and testing of data driven approaches like artificial neural networks (ANN). Data is scarce at the upper elevations due to the difficulty of installing sufficient automated SNOTEL stations; thus in literatures many attempts are made on the rainfall dominated basins for streamflow estimation studies. On the other hand, optical satellites can easily detect snow because of its high reflectance property. MODIS (Moderate Resolution Imaging Spectroradiometer) satellite that has two platforms (Terra and Aqua) provides daily and 8-daily snow images for different time periods since 2000, therefore snow cover data (SCA) may be useful as an input layer for ANN applications. In this study, a multi-layer perceptron (MLP) model is trained and tested with precipitation, temperature, radiation, previous day discharges as well as MODIS daily SCA data. The weights and biases are optimized with fastest and robust Levenberg-Marquardt backpropagation algorithm. MODIS snow cover images are removed from cloud coverage using certain filtering techniques. The Upper Euphrates River Basin in eastern part of Turkey (10 250 km2) is selected as the application area since it is fed by snowmelt approximately 2/3 of total annual volume during spring and early summer. Several input models and ANN structures are investigated to see the effect of the contributions using 10 years of data (2001-2010) for training and validation. The accuracy of the streamflow estimations is checked with statistical criteria (coefficient of determination, Nash-Sutcliffe model efficiency, root mean square error, mean absolute error) and the results seem to improve when SCA data is introduced. Furthermore, a forecast study is

  6. Discharge estimation in arid areas with the help of optical satellite data

    Science.gov (United States)

    Mett, M.; Aufleger, M.

    2009-04-01

    The MENA region is facing severe water scarcity. Overexploitation of groundwater resources leads to an ongoing drawdown of the water tables, salinisation and desertification of vast areas. To make matters worse enormous birth-rates, economic growth and refugees from conflict areas let the need for water explode. In the context of climate change this situation will even worsen and armed conflicts are within the bounds of possibility. To ease water scarcity many innovative techniques like artificial groundwater recharge are being developed or already state of the art. But missing hydrological information (for instance discharge data) often prevents design and efficient operation of such measures. Especially in poor countries hydrological measuring devices like gage stations are often missing, in a bad status or professionals of the water sector are absent. This leads to the paradox situation that in many arid regions water resources are indeed available but they cannot be utilised because they are not known. Nowadays different approaches are being designed to obtain hydrological information from perennial river systems with the help of satellite techniques. Mostly they are based on hydraulic parameters like river dimensions, roughness and water levels which can be derived from satellite data. By using conventional flow formulas and additional field investigations the discharge can be estimated. Another methodology derived information about maximum flow depth and flow width from optical sensors of high resolution to calculate discharge of the rivers whilst the flood. Attempts to derive discharge information from structural components of the river and fluviomorphologic changes due to changing flow regimes are in the focus of recent research. One attempt used Synthetic Aperture Radar (SAR) data to estimate discharge in braided river systems. Other attempts used airborne SAR imagery to obtain information about sinuosity and total river width of perennial braided river

  7. Estimation of hydraulic conductivity of a coastal aquifer using satellite imagery

    Science.gov (United States)

    Rebolledo-Vieyra, M.; Iglesias-Prieto, R.; Marino-Tapia, I.

    2012-12-01

    The northern Yucatan Peninsula is characterized by a young and dynamic karstic system that yields very high secondary porosity and permeability. However, we have little, if none, knowledge about the hydraulic conductivity and the amount of groundwater being discharged in to ocean. Here we present and estimation of the hydraulic conductivity and quantity of groundwater being discharged by the northern Yucatan Peninsula coastal aquifer into the Gulf of Mexico, using the Sea Surface Temperature (SST) Images offshore the Yucatan coast, where we have detected a thermal anomaly that appears few hours after heavy rainfall in northern Yucatan. We associated these thermal anomalies of the SST to the groundwater being discharged into the ocean. To test our hypothesis we conducted a review of extreme rainfall events in the last 10 years; in parallel we used data from pressure and flow direction gauges installed in a known submarine groundwater discharge (SGD) to estimate the hydraulic conductivity and the quantity of groundwater being discharged. The satellite imagery and the rainfall data, allowed us to estimate the time lag between the rainfall and the SGD beginning, along with the hydraulic data from the gauges we have estimated the hydrogeological parameters of the coastal aquifer. This data is very important to contribute to the understanding the hydrogeological setting of the Yucatan coastal aquifer and its implications of the impact of human activities on the water quality. July 29th, 2005, NOAA's Sea Surface Temperature (SST) image of the Gulf of Mexico taken a week after hurricane Emily (2005). A thermal low is present offshore northern Yucatan.

  8. Reconstruction of temporal variations of evapotranspiration using instantaneous estimates at the time of satellite overpass

    Directory of Open Access Journals (Sweden)

    E. Delogu

    2012-08-01

    Full Text Available Evapotranspiration estimates can be derived from remote sensing data and ancillary, mostly meterorological, information. For this purpose, two types of methods are classically used: the first type estimates a potential evapotranspiration rate from vegetation indices, and adjusts this rate according to water availability derived from either a surface temperature index or a first guess obtained from a rough estimate of the water budget, while the second family of methods relies on the link between the surface temperature and the latent heat flux through the surface energy budget. The latter provides an instantaneous estimate at the time of satellite overpass. In order to compute daily evapotranspiration, one needs an extrapolation algorithm. Since no image is acquired during cloudy conditions, these methods can only be applied during clear sky days. In order to derive seasonal evapotranspiration, one needs an interpolation method. Two combined interpolation/extrapolation methods based on the self preservation of evaporative fraction and the stress factor are compared to reconstruct seasonal evapotranspiration from instantaneous measurements acquired in clear sky conditions. Those measurements are taken from instantaneous latent heat flux from 11 datasets in Southern France and Morocco. Results show that both methods have comparable performances with a clear advantage for the evaporative fraction for datasets with several water stress events. Both interpolation algorithms tend to underestimate evapotranspiration due to the energy limiting conditions that prevail during cloudy days. Taking into account the diurnal variations of the evaporative fraction according to an empirical relationship derived from a previous study improved the performance of the extrapolation algorithm and therefore the retrieval of the seasonal evapotranspiration for all but one datasets.

  9. Exploring the uncertainty associated with satellite-based estimates of premature mortality due to exposure to fine particulate matter

    Directory of Open Access Journals (Sweden)

    B. Ford

    2015-09-01

    Full Text Available The negative impacts of fine particulate matter (PM2.5 exposure on human health are a primary motivator for air quality research. However, estimates of the air pollution health burden vary considerably and strongly depend on the datasets and methodology. Satellite observations of aerosol optical depth (AOD have been widely used to overcome limited coverage from surface monitoring and to assess the global population exposure to PM2.5 and the associated premature mortality. Here we quantify the uncertainty in determining the burden of disease using this approach, discuss different methods and datasets, and explain sources of discrepancies among values in the literature. For this purpose we primarily use the MODIS satellite observations in concert with the GEOS-Chem chemical transport model. We contrast results in the United States and China for the years 2004–2011. We estimate that in the United States, exposure to PM2.5 accounts for approximately 4 % of total deaths compared to 22 % in China (using satellite-based exposure, which falls within the range of previous estimates. The difference in estimated mortality burden based solely on a global model vs. that derived from satellite is approximately 9 % for the US and 4 % for China on a nationwide basis, although regionally the differences can be much greater. This difference is overshadowed by the uncertainty in the methodology for deriving PM2.5 burden from satellite observations, which we quantify to be on order of 20 % due to uncertainties in the AOD-to-surface-PM2.5 relationship, 10 % due to the satellite observational uncertainty, and 30 % or greater uncertainty associated with the application of concentration response functions to estimated exposure.

  10. Exploring the uncertainty associated with satellite-based estimates of premature mortality due to exposure to fine particulate matter

    Science.gov (United States)

    Ford, Bonne; Heald, Colette L.

    2016-03-01

    The negative impacts of fine particulate matter (PM2.5) exposure on human health are a primary motivator for air quality research. However, estimates of the air pollution health burden vary considerably and strongly depend on the data sets and methodology. Satellite observations of aerosol optical depth (AOD) have been widely used to overcome limited coverage from surface monitoring and to assess the global population exposure to PM2.5 and the associated premature mortality. Here we quantify the uncertainty in determining the burden of disease using this approach, discuss different methods and data sets, and explain sources of discrepancies among values in the literature. For this purpose we primarily use the MODIS satellite observations in concert with the GEOS-Chem chemical transport model. We contrast results in the United States and China for the years 2004-2011. Using the Burnett et al. (2014) integrated exposure response function, we estimate that in the United States, exposure to PM2.5 accounts for approximately 2 % of total deaths compared to 14 % in China (using satellite-based exposure), which falls within the range of previous estimates. The difference in estimated mortality burden based solely on a global model vs. that derived from satellite is approximately 14 % for the US and 2 % for China on a nationwide basis, although regionally the differences can be much greater. This difference is overshadowed by the uncertainty in the methodology for deriving PM2.5 burden from satellite observations, which we quantify to be on the order of 20 % due to uncertainties in the AOD-to-surface-PM2.5 relationship, 10 % due to the satellite observational uncertainty, and 30 % or greater uncertainty associated with the application of concentration response functions to estimated exposure.

  11. Operational Estimation of Accumulated Precipitation using Satellite Observation, by Eumetsat Satellite Application facility in Support to Hydrology (H-SAF Consortium).

    Science.gov (United States)

    di Diodato, A.; de Leonibus, L.; Zauli, F.; Biron, D.; Melfi, D.

    2009-04-01

    Operational Estimation of Accumulated Precipitation using Satellite Observation, by Eumetsat Satellite Application facility in Support to Hydrology (H-SAF Consortium). Cap. Attilio DI DIODATO(*), T.Col. Luigi DE LEONIBUS(*), T.Col Francesco ZAULI(*), Cap. Daniele BIRON(*), Ten. Davide Melfi(*) Satellite Application Facilities (SAFs) are specialised development and processing centres of the EUMETSAT Distributed Ground Segment. SAFs process level 1b data from meteorological satellites (geostationary and polar ones) in conjunction with all other relevant sources of data and appropriate models to generate services and level 2 products. Each SAF is a consortium of EUMETSAT European partners lead by a host institute responsible for the management of the complete SAF project. The Meteorological Service of Italian Air Force is the host Institute for the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF). HSAF has the commitment to develop and to provide, operationally after 2010, products regarding precipitation, soil moisture and snow. HSAF is going to provide information on error structure of its products and validation of the products via their impacts into Hydrological models. To that purpose it has been structured a specific subgroups. Accumulated precipitation is computed by temporal integration of the instantaneous rain rate achieved by the blended LEO/MW and GEO/IR precipitation rate products generated by Rapid Update method available every 15 minutes. The algorithm provides four outputs, consisting in accumulated precipitation in 3, 6, 12 and 24 hours, delivered every 3 hours at the synoptic hours. These outputs are our precipitation background fields. Satellite estimates can cover most of the globe, however, they suffer from errors due to lack of a direct relationship between observation parameters and precipitation, the poor sampling and algorithm imperfections. For this reason the 3 hours accumulated precipitation is

  12. Estimating water storage changes and sink terms in Volta Basin from satellite missions

    Directory of Open Access Journals (Sweden)

    Vagner G. FERREIRA

    2014-01-01

    Full Text Available The insufficiency of distributed in situ hydrological measurements is a major challenge for hydrological studies in many regions of the world. Satellite missions such as the Gravity Recovery and Climate Experiment (GRACE and the Tropical Rainfall Measurement Mission (TRMM can be used to improve our understanding of water resources beyond surface water in poorly gauged basins. In this study we combined GRACE and TRMM to investigate monthly estimates of evaporation plus runoff (sink terms using the water balance equation for the period from January 2005 to December 2010 within the Volta Basin. These estimates have been validated by comparison with time series of sink terms (evaporation plus surface and subsurface runoff from the Global Land Data Assimilation System (GLDAS. The results, for the period under consideration, show strong agreement between both time series, with a root mean square error (RMSE of 20.2 mm/month (0.67 mm/d and a correlation coefficient of 0.85. This illustrates the ability of GRACE to predict hydrological quantities, e.g. evaporation, in the Volta Basin. The water storage change data from GRACE and precipitation data from TRMM all show qualitative agreement, with evidence of basin saturation at approximately 73 mm in the equivalent water column at the annual and semi-annual time scales.

  13. Comparative Study of Ground Measured, Satellite-Derived, and Estimated Global Solar Radiation Data in Nigeria

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    Boluwaji M. Olomiyesan

    2016-01-01

    Full Text Available In this study, the performance of three global solar radiation models and the accuracy of global solar radiation data derived from three sources were compared. Twenty-two years (1984–2005 of surface meteorological data consisting of monthly mean daily sunshine duration, minimum and maximum temperatures, and global solar radiation collected from the Nigerian Meteorological (NIMET Agency, Oshodi, Lagos, and the National Aeronautics Space Agency (NASA for three locations in North-Western region of Nigeria were used. A new model incorporating Garcia model into Angstrom-Prescott model was proposed for estimating global radiation in Nigeria. The performances of the models used were determined by using mean bias error (MBE, mean percentage error (MPE, root mean square error (RMSE, and coefficient of determination (R2. Based on the statistical error indices, the proposed model was found to have the best accuracy with the least RMSE values (0.376 for Sokoto, 0.463 for Kaduna, and 0.449 for Kano and highest coefficient of determination, R2 values of 0.922, 0.938, and 0.961 for Sokoto, Kano, and Kaduna, respectively. Also, the comparative study result indicates that the estimated global radiation from the proposed model has a better error range and fits the ground measured data better than the satellite-derived data.

  14. Estimation of Supraglacial Dust and Debris Geochemical Composition via Satellite Reflectance and Emissivity

    Science.gov (United States)

    Casey, Kimberly Ann; Kaab, Andreas

    2012-01-01

    We demonstrate spectral estimation of supraglacial dust, debris, ash and tephra geochemical composition from glaciers and ice fields in Iceland, Nepal, New Zealand and Switzerland. Surface glacier material was collected and analyzed via X-ray fluorescence spectroscopy (XRF) and X-ray diffraction (XRD) for geochemical composition and mineralogy. In situ data was used as ground truth for comparison with satellite derived geochemical results. Supraglacial debris spectral response patterns and emissivity-derived silica weight percent are presented. Qualitative spectral response patterns agreed well with XRF elemental abundances. Quantitative emissivity estimates of supraglacial SiO2 in continental areas were 67% (Switzerland) and 68% (Nepal), while volcanic supraglacial SiO2 averages were 58% (Iceland) and 56% (New Zealand), yielding general agreement. Ablation season supraglacial temperature variation due to differing dust and debris type and coverage was also investigated, with surface debris temperatures ranging from 5.9 to 26.6 C in the study regions. Applications of the supraglacial geochemical reflective and emissive characterization methods include glacier areal extent mapping, debris source identification, glacier kinematics and glacier energy balance considerations.

  15. Exposure Estimation from Multi-Resolution Optical Satellite Imagery for Seismic Risk Assessment

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    Jochen Zschau

    2012-05-01

    Full Text Available Given high urbanization rates and increasing spatio-temporal variability in many present-day cities, exposure information is often out-of-date, highly aggregated or spatially fragmented, increasing the uncertainties associated with seismic risk assessments. This work therefore aims at using space-based technologies to estimate, complement and extend exposure data at multiple scales, over large areas and at a comparatively low cost for the case of the city of Bishkek, Kyrgyzstan. At a neighborhood scale, an analysis of urban structures using medium-resolution optical satellite images is performed. Applying image classification and change-detection analysis to a time-series of Landsat images, the urban environment can be delineated into areas of relatively homogeneous urban structure types, which can provide a first estimate of an exposed building stock (e.g., approximate age of structures, composition and distribution of predominant building types. At a building-by-building scale, a more detailed analysis of the exposed building stock is carried out using a high-resolution Quickbird image. Furthermore, the multi-resolution datasets are combined with census data to disaggregate population statistics. The tools used within this study are being developed on a free- and open-source basis and aim at being transparent, usable and transferable.

  16. Estimation of Supraglacial Dust and Debris Geochemical Composition via Satellite Reflectance and Emissivity

    Directory of Open Access Journals (Sweden)

    Kimberly Casey

    2012-09-01

    Full Text Available We demonstrate spectral estimation of supraglacial dust, debris, ash and tephra geochemical composition from glaciers and ice fields in Iceland, Nepal, New Zealand and Switzerland. Surface glacier material was collected and analyzed via X-ray fluorescence spectroscopy (XRF and X-ray diffraction (XRD for geochemical composition and mineralogy. In situ data was used as ground truth for comparison with satellite derived geochemical results. Supraglacial debris spectral response patterns and emissivity-derived silica weight percent are presented. Qualitative spectral response patterns agreed well with XRF elemental abundances. Quantitative emissivity estimates of supraglacial SiO2 in continental areas were 67% (Switzerland and 68% (Nepal, while volcanic supraglacial SiO2 averages were 58% (Iceland and 56% (New Zealand, yielding general agreement. Ablation season supraglacial temperature variation due to differing dust and debris type and coverage was also investigated, with surface debris temperatures ranging from 5.9 to 26.6 C in the study regions. Applications of the supraglacial geochemical reflective and emissive characterization methods include glacier areal extent mapping, debris source identification, glacier kinematics and glacier energy balance considerations.

  17. Bayesian Estimation of Precipitation from Satellite Passive Microwave Observations Using Combined Radar-Radiometer Retrievals

    Science.gov (United States)

    Grecu, Mircea; Olson, William S.

    2006-01-01

    Precipitation estimation from satellite passive microwave radiometer observations is a problem that does not have a unique solution that is insensitive to errors in the input data. Traditionally, to make this problem well posed, a priori information derived from physical models or independent, high-quality observations is incorporated into the solution. In the present study, a database of precipitation profiles and associated brightness temperatures is constructed to serve as a priori information in a passive microwave radiometer algorithm. The precipitation profiles are derived from a Tropical Rainfall Measuring Mission (TRMM) combined radar radiometer algorithm, and the brightness temperatures are TRMM Microwave Imager (TMI) observed. Because the observed brightness temperatures are consistent with those derived from a radiative transfer model embedded in the combined algorithm, the precipitation brightness temperature database is considered to be physically consistent. The database examined here is derived from the analysis of a month-long record of TRMM data that yields more than a million profiles of precipitation and associated brightness temperatures. These profiles are clustered into a tractable number of classes based on the local sea surface temperature, a radiometer-based estimate of the echo-top height (the height beyond which the reflectivity drops below 17 dBZ), and brightness temperature principal components. For each class, the mean precipitation profile, brightness temperature principal components, and probability of occurrence are determined. The precipitation brightness temperature database supports a radiometer-only algorithm that incorporates a Bayesian estimation methodology. In the Bayesian framework, precipitation estimates are weighted averages of the mean precipitation values corresponding to the classes in the database, with the weights being determined according to the similarity between the observed brightness temperature principal

  18. South African Weather Service operational satellite based precipitation estimation technique: applications and improvements

    Directory of Open Access Journals (Sweden)

    E. de Coning

    2010-11-01

    Full Text Available Extreme weather related to heavy or more frequent precipitation events seem to be a likely possibility for the future of our planet. While precipitation measurements can be done by means of rain gauges, the obvious disadvantages of point measurements are driving meteorologists towards remotely sensed precipitation methods. In South Africa more sophisticated and expensive nowcasting technology such as radar and lightning networks are available, supported by a fairly dense rain gauge network of about 1500 gauges. In the rest of southern Africa rainfall measurements are more difficult to obtain. The availability of the local version of the Unified Model and the Meteosat Second Generation satellite data make these products ideal components of precipitation measurement in data sparse regions such as Africa. In this article the local version of the Hydroestimator (originally from NOAA/NESDIS is discussed as well as its applications for precipitation measurement in this region. Hourly accumulations of the Hydroestimator are currently used as a satellite based precipitation estimator for the South African Flash Flood Guidance system. However, the Hydroestimator is by no means a perfect representation of the real rainfall. In this study the Hydroestimator and the stratiform rainfall field from the Unified Model are both bias corrected and then combined into a new precipitation field which can feed into the South African Flash Flood Guidance system. This new product should provide a more accurate and comprehensive input to the Flash Flood Guidance systems in South Africa as well as southern Africa. In this way the southern African region where data is sparse and very few radars are available can have access to more accurate flash flood guidance.

  19. Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

    Directory of Open Access Journals (Sweden)

    C. A. Poulsen

    2012-08-01

    Full Text Available Clouds play an important role in balancing the Earth's radiation budget. Hence, it is vital that cloud climatologies are produced that quantify cloud macro and micro physical parameters and the associated uncertainty. In this paper, we present an algorithm ORAC (Oxford-RAL retrieval of Aerosol and Cloud which is based on fitting a physically consistent cloud model to satellite observations simultaneously from the visible to the mid-infrared, thereby ensuring that the resulting cloud properties provide both a good representation of the short-wave and long-wave radiative effects of the observed cloud. The advantages of the optimal estimation method are that it enables rigorous error propagation and the inclusion of all measurements and any a priori information and associated errors in a rigorous mathematical framework. The algorithm provides a measure of the consistency between retrieval representation of cloud and satellite radiances. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase.

    The algorithm can be applied to most visible/infrared satellite instruments. In this paper, we demonstrate the applicability to the Along-Track Scanning Radiometers ATSR-2 and AATSR. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed, in particular the algorithm is evaluated for a number of simulated single-layer and multi-layer conditions. The algorithm was found to perform well for single-layer cloud except when the cloud was very thin; i.e., less than 1 optical depths. For the multi-layer cloud, the algorithm was robust except when the upper ice cloud layer is less than five optical depths. In these cases the retrieved cloud top pressure and cloud effective radius become a weighted average of the 2 layers. The sum of optical depth of multi-layer cloud is retrieved well until the cloud becomes thick

  20. Testing estimation of water surface in Italian rice district from MODIS satellite data

    Science.gov (United States)

    Ranghetti, Luigi; Busetto, Lorenzo; Crema, Alberto; Fasola, Mauro; Cardarelli, Elisa; Boschetti, Mirco

    2016-10-01

    Recent changes in rice crop management within Northern Italy rice district led to a reduction of seeding in flooding condition, which may have an impact on reservoir water management and on the animal and plant communities that depend on the flooded paddies. Therefore, monitoring and quantifying the spatial and temporal variability of water presence in paddy fields is becoming important. In this study we present a method to estimate dynamics of presence of standing water (i.e. fraction of flooded area) in rice fields using MODIS data. First, we produced high resolution water presence maps from Landsat by thresholding the Normalised Difference Flood Index (NDFI) made: we made it by comparing five Landsat 8 images with field-obtained information about rice field status and water presence. Using these data we developed an empirical model to estimate the flooding fraction of each MODIS cell. Finally we validated the MODIS-based flooding maps with both Landsat and ground information. Results showed a good predictability of water surface from Landsat (OA = 92%) and a robust usability of MODIS data to predict water fraction (R2 = 0.73, EF = 0.57, RMSE = 0.13 at 1 × 1 km resolution). Analysis showed that the predictive ability of the model decreases with the greening up of rice, so we used NDVI to automatically discriminate estimations for inaccurate cells in order to provide the water maps with a reliability flag. Results demonstrate that it is possible to monitor water dynamics in rice paddies using moderate resolution multispectral satellite data. The achievement is a proof of concept for the analysis of MODIS archives to investigate irrigation dynamics in the last 15 years to retrieve information for ecological and hydrological studies.

  1. Surface energy balance and actual evapotranspiration of the transboundary Indus Basin estimated from satellite measurements and the ETLook model

    NARCIS (Netherlands)

    Bastiaansen, W.G.M.; Cheema, M.J.M.; Immerzeel, W.W.; Miltenburg, I.J.; Pelgrum, H.

    2012-01-01

    The surface energy fluxes and related evapotranspiration processes across the Indus Basin were estimated for the hydrological year 2007 using satellite measurements. The new ETLook remote sensing model (version 1) infers information on actual Evaporation (E) and actual Transpiration (T) from combine

  2. Errors of Mean Dynamic Topography and Geostrophic Current Estimates in China's Marginal Seas from GOCE and Satellite Altimetry

    DEFF Research Database (Denmark)

    Jin, Shuanggen; Feng, Guiping; Andersen, Ole Baltazar

    2014-01-01

    and geostrophic current estimates from satellite gravimetry and altimetry are investigated and evaluated in China's marginal seas. The cumulative error in MDT from GOCE is reduced from 22.75 to 9.89 cm when compared to the Gravity Recovery and Climate Experiment (GRACE) gravity field model ITG-Grace2010 results...

  3. Estimation of Reservoir Discharges from Lake Nasser and Roseires Reservoir in the Nile Basin Using Satellite Altimetry and Imagery Data

    NARCIS (Netherlands)

    Muala, E.; Mohamed, Y.A.; Duan, Z.; Van der Zaag, P.

    2014-01-01

    This paper presents the feasibility of estimating discharges from Roseires Reservoir (Sudan) for the period from 2002 to 2010 and Aswan High Dam/Lake Nasser (Egypt) for the periods 1999–2002 and 2005–2009 using satellite altimetry and imagery with limited in situ data. Discharges were computed using

  4. A scalable satellite-based crop yield mapper: Integrating satellites and crop models for field-scale estimation in India

    Science.gov (United States)

    Jain, M.; Singh, B.; Srivastava, A.; Lobell, D. B.

    2015-12-01

    Food security will be challenged over the upcoming decades due to increased food demand, natural resource degradation, and climate change. In order to identify potential solutions to increase food security in the face of these changes, tools that can rapidly and accurately assess farm productivity are needed. With this aim, we have developed generalizable methods to map crop yields at the field scale using a combination of satellite imagery and crop models, and implement this approach within Google Earth Engine. We use these methods to examine wheat yield trends in Northern India, which provides over 15% of the global wheat supply and where over 80% of farmers rely on wheat as a staple food source. In addition, we identify the extent to which farmers are shifting sow date in response to heat stress, and how well shifting sow date reduces the negative impacts of heat stress on yield. To identify local-level decision-making, we map wheat sow date and yield at a high spatial resolution (30 m) using Landsat satellite imagery from 1980 to the present. This unique dataset allows us to examine sow date decisions at the field scale over 30 years, and by relating these decisions to weather experienced over the same time period, we can identify how farmers learn and adapt cropping decisions based on weather through time.

  5. Advances in regional crop yield estimation over the United States using satellite remote sensing data

    Science.gov (United States)

    Johnson, D. M.; Dorn, M. F.; Crawford, C.

    2015-12-01

    Since the dawn of earth observation imagery, particularly from systems like Landsat and the Advanced Very High Resolution Radiometer, there has been an overarching desire to regionally estimate crop production remotely. Research efforts integrating space-based imagery into yield models to achieve this need have indeed paralleled these systems through the years, yet development of a truly useful crop production monitoring system has been arguably mediocre in coming. As a result, relatively few organizations have yet to operationalize the concept, and this is most acute in regions of the globe where there are not even alternative sources of crop production data being collected. However, the National Agricultural Statistics Service (NASS) has continued to push for this type of data source as a means to complement its long-standing, traditional crop production survey efforts which are financially costly to the government and create undue respondent burden on farmers. Corn and soybeans, the two largest field crops in the United States, have been the focus of satellite-based production monitoring by NASS for the past decade. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been seen as the most pragmatic input source for modeling yields primarily based on its daily revisit capabilities and reasonable ground sample resolution. The research methods presented here will be broad but provides a summary of what is useful and adoptable with satellite imagery in terms of crop yield estimation. Corn and soybeans will be of particular focus but other major staple crops like wheat and rice will also be presented. NASS will demonstrate that while MODIS provides a slew of vegetation related products, the traditional normalized difference vegetation index (NDVI) is still ideal. Results using land surface temperature products, also generated from MODIS, will also be shown. Beyond the MODIS data itself, NASS research has also focused efforts on understanding a

  6. Estimating seasonal variations in cloud droplet number concentration over the boreal forest from satellite observations

    Directory of Open Access Journals (Sweden)

    R. H. H. Janssen

    2011-08-01

    Full Text Available Seasonal variations in cloud droplet number concentration (NCD in low-level stratiform clouds over the boreal forest are estimated from MODIS observations of cloud optical and microphysical properties, using a sub-adiabatic cloud model to interpret vertical profiles of cloud properties. An uncertainty analysis of the cloud model is included to reveal the main sensitivities of the cloud model. We compared the seasonal cycle in NCD, obtained using 9 yr of satellite data, to surface concentrations of potential cloud activating aerosols, measured at the SMEAR II station at Hyytiälä in Finland. The results show that NCD and cloud condensation nuclei (CCN concentrations have no clear correlation at seasonal time scale. The fraction of aerosols that actually activate as cloud droplet decreases sharply with increasing aerosol concentrations. Furthermore, information on the stability of the atmosphere shows that low NCD is linked to stable atmospheric conditions. Combining these findings leads to the conclusion that cloud droplet activation for the studied clouds over the boreal forest is limited by convection. Our results suggest that it is important to take the strength of convection into account when studying the influence of aerosols from the boreal forest on cloud formation, although they do not rule out the possibility that aerosols from the boreal forest affect other types of clouds with a closer coupling to the surface.

  7. Statistical theory for estimating sampling errors of regional radiation averages based on satellite measurements

    Science.gov (United States)

    Smith, G. L.; Bess, T. D.; Minnis, P.

    1983-01-01

    The processes which determine the weather and climate are driven by the radiation received by the earth and the radiation subsequently emitted. A knowledge of the absorbed and emitted components of radiation is thus fundamental for the study of these processes. In connection with the desire to improve the quality of long-range forecasting, NASA is developing the Earth Radiation Budget Experiment (ERBE), consisting of a three-channel scanning radiometer and a package of nonscanning radiometers. A set of these instruments is to be flown on both the NOAA-F and NOAA-G spacecraft, in sun-synchronous orbits, and on an Earth Radiation Budget Satellite. The purpose of the scanning radiometer is to obtain measurements from which the average reflected solar radiant exitance and the average earth-emitted radiant exitance at a reference level can be established. The estimate of regional average exitance obtained will not exactly equal the true value of the regional average exitance, but will differ due to spatial sampling. A method is presented for evaluating this spatial sampling error.

  8. Comparison of satellite-based evapotranspiration estimates over the Tibetan Plateau

    Science.gov (United States)

    Peng, Jian; Loew, Alexander; Chen, Xuelong; Ma, Yaoming; Su, Zhongbo

    2016-08-01

    The Tibetan Plateau (TP) plays a major role in regional and global climate. The understanding of latent heat (LE) flux can help to better describe the complex mechanisms and interactions between land and atmosphere. Despite its importance, accurate estimation of evapotranspiration (ET) over the TP remains challenging. Satellite observations allow for ET estimation at high temporal and spatial scales. The purpose of this paper is to provide a detailed cross-comparison of existing ET products over the TP. Six available ET products based on different approaches are included for comparison. Results show that all products capture the seasonal variability well with minimum ET in the winter and maximum ET in the summer. Regarding the spatial pattern, the High resOlution Land Atmosphere surface Parameters from Space (HOLAPS) ET demonstrator dataset is very similar to the LandFlux-EVAL dataset (a benchmark ET product from the Global Energy and Water Cycle Experiment), with decreasing ET from the south-east to north-west over the TP. Further comparison against the LandFlux-EVAL over different sub-regions that are decided by different intervals of normalised difference vegetation index (NDVI), precipitation, and elevation reveals that HOLAPS agrees best with LandFlux-EVAL having the highest correlation coefficient (R) and the lowest root mean square difference (RMSD). These results indicate the potential for the application of the HOLAPS demonstrator dataset in understanding the land-atmosphere-biosphere interactions over the TP. In order to provide more accurate ET over the TP, model calibration, high accuracy forcing dataset, appropriate in situ measurements as well as other hydrological data such as runoff measurements are still needed.

  9. On the scale estimation using truncated swath measurements from low Earth orbiting satellites

    Science.gov (United States)

    Liu, Qi

    2013-05-01

    Truncation effect caused by limited swath width of low Earth orbiting (LEO) satellites results in inevitable underestimation of object scale when using pixel-counting methods. A new approach is proposed to obtain more accurate object scale through truncated measurements. The approach is based upon the mean object area fraction (MOAF), which depicts the relative population of object points in a varying-size domain and proves to be less sensitive to truncation effect. The MOAF-equivalent radius (MER) is deduced by comparing the actual MOAF with the standard one inferred from a circle object. Numerical simulations are implemented to demonstrate the MER characteristics. In contrast to area-equivalent radius (AER) that is merely determined by the absolute amount of object points, MER relies on the overall spatial structure of the object. For objects with irregular shapes, the MER value is generally smaller than AER in the absence of truncation. Nevertheless, taking the actual AER as true scale, MER has significantly reduced biases compared to AER once the object is truncated. This advantage can be reinforced when focusing on size statistics of analogous objects, because negative and positive biases associated with various truncation situations coexist in MER, against the uniform negative biases of AER. When applied to MODIS cloud mask data that are restricted in individual granules, MER has consistently larger values than AER for most truncated clouds. Compared with the explicitly problematic estimation from AER due to truncation, MER offers a notable elevation on the estimated cloud size and gets closer to the truth.

  10. Estimation of land surface evapotranspiration with A satellite remote sensing procedure

    Science.gov (United States)

    Irmak, A.; Ratcliffe, I.; Ranade, P.; Hubbard, K.G.; Singh, R.K.; Kamble, B.; Kjaersgaard, J.

    2011-01-01

    There are various methods available for estimating magnitude and trends of evapotranspiration. Bowen ratio energy balance system and eddy correlation techniques offer powerful alternatives for measuring land surface evapotranspiration. In spite of the elegance, high accuracy, and theoretical attractions of these techniques for measuring evapotranspiration, their practical use over large areas can be limited due to the number of sites needed and the related expense. Application of evapotranspiration mapping from satellite measurements can overcome the limitations. The objective of this study was to utilize the METRICTM (Mapping Evapotranspiration at High Resolution using Internalized Calibration) model in Great Plains environmental settings to understand water use in managed ecosystems on a regional scale. We investigated spatiotemporal distribution of a fraction of reference evapotranspiration (ETrF) using eight Landsat 5 images during the 2005 and 2006 growing season for path 29, row 32. The ETrF maps generated by METRICTM allowed us to follow the magnitude and trend in ETrF for major land-use classes during the growing season. The ETrF was lower early in the growing season for agricultural crops and gradually increased as the normalized difference vegetation index of crops increased, thus presenting more surface area over which water could transpire toward the midseason. Comparison of predictions with Bowen ratio energy balance system measurements at Clay Center, NE, showed that METRICTM performed well at the field scale for predicting evapotranspiration from a cornfield. If calibrated properly, the model could be a viable tool to estimate water use in managed ecosystems in subhumid climates at a large scale.

  11. Estimation efficiency of usage satellite derived and modelled biophysical products for yield forecasting

    Science.gov (United States)

    Kolotii, Andrii; Kussul, Nataliia; Skakun, Sergii; Shelestov, Andrii; Ostapenko, Vadim; Oliinyk, Tamara

    2015-04-01

    Efficient and timely crop monitoring and yield forecasting are important tasks for ensuring of stability and sustainable economic development [1]. As winter crops pay prominent role in agriculture of Ukraine - the main focus of this study is concentrated on winter wheat. In our previous research [2, 3] it was shown that usage of biophysical parameters of crops such as FAPAR (derived from Geoland-2 portal as for SPOT Vegetation data) is far more efficient for crop yield forecasting to NDVI derived from MODIS data - for available data. In our current work efficiency of usage such biophysical parameters as LAI, FAPAR, FCOVER (derived from SPOT Vegetation and PROBA-V data at resolution of 1 km and simulated within WOFOST model) and NDVI product (derived from MODIS) for winter wheat monitoring and yield forecasting is estimated. As the part of crop monitoring workflow (vegetation anomaly detection, vegetation indexes and products analysis) and yield forecasting SPIRITS tool developed by JRC is used. Statistics extraction is done for landcover maps created in SRI within FP-7 SIGMA project. Efficiency of usage satellite based and modelled with WOFOST model biophysical products is estimated. [1] N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "Sensor Web approach to Flood Monitoring and Risk Assessment", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 815-818. [2] F. Kogan, N. Kussul, T. Adamenko, S. Skakun, O. Kravchenko, O. Kryvobok, A. Shelestov, A. Kolotii, O. Kussul, and A. Lavrenyuk, "Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models," International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 192-203, 2013. [3] Kussul O., Kussul N., Skakun S., Kravchenko O., Shelestov A., Kolotii A, "Assessment of relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 3235 - 3238.

  12. Air-sea fluxes and satellite-based estimation of water masses formation

    Science.gov (United States)

    Sabia, Roberto; Klockmann, Marlene; Fernandez-Prieto, Diego; Donlon, Craig

    2015-04-01

    and monthly water mass formation rates for different SST and SSS ranges are presented. The formation peaks are remapped geographically, to analyze the extent of the formation area. Water mass formation derived from SMOS and OSTIA compares well with the results obtained from in-situ data, although slight differences in magnitude and peak location occur. Known water masses can then be identified. Ongoing/future work aims at extending this study along different avenues by: 1) expand systematically the spatial and temporal domain of the study to additional ocean basins and to the entire time period of available SSS observations from SMOS/Aquarius; 2) perform a thorough error propagation to assess how errors in satellite SSS and SST translate into errors in water masses formation rates and geographical areas extent; and 3) explore the different options to connect the surface information to the vertical buoyancy structure to assess potential density instability (e.g., Turner angle). References [1] Sabia, R., M. Klockmann, D. Fernández-Prieto, and C. Donlon (2014), A first estimation of SMOS-based ocean surface T-S diagrams, J. Geophys. Res. Oceans, 119, 7357-7371, doi:10.1002/2014JC010120. [2] Klockmann, M., R. Sabia, D. Fernández-Prieto, C. Donlon, J. Font; Towards an estimation of water masses formation areas from SMOS-based T-S diagrams; EGU general assembly 2014, April 27-May 2, 2014. [3] Klockmann, M., R. Sabia, D. Fernández-Prieto, C. Donlon, Linking satellite SSS and SST to water mass formation; Ocean salinity science and salinity remote sensing workshop, Exeter, UK, November 26-28, 2014. [4] Font, J., A. Camps, A. Borges, M. Martín-Neira, J. Boutin, N. Reul, Y. H. Kerr, A. Hahne, and S. Mecklenburg, "SMOS: The challenging sea surface salinity measurement from space," Proceedings of the IEEE, vol. 98, pp. 649-665, 2010. [5] Le Vine, D.M.; Lagerloef, G.S.E.; Torrusio, S.E.; "Aquarius and Remote Sensing of Sea Surface Salinity from Space," Proceedings of the IEEE

  13. Assessment of the global monthly mean surface insolation estimated from satellite measurements using global energy balance archive data

    Science.gov (United States)

    Li, Zhanqing; Whitlock, Charles H.; Charlock, Thomas P.

    1995-01-01

    Global sets of surface radiation budget (SRB) have been obtained from satellite programs. These satellite-based estimates need validation with ground-truth observations. This study validates the estimates of monthly mean surface insolation contained in two satellite-based SRB datasets with the surface measurements made at worldwide radiation stations from the Global Energy Balance Archive (GEBA). One dataset was developed from the Earth Radiation Budget Experiment (ERBE) using the algorithm of Li et al. (ERBE/SRB), and the other from the International Satellite Cloud Climatology Project (ISCCP) using the algorithm of Pinker and Laszlo and that of Staylor (GEWEX/SRB). Since the ERBE/SRB data contain the surface net solar radiation only, the values of surface insolation were derived by making use of the surface albedo data contained GEWEX/SRB product. The resulting surface insolation has a bias error near zero and a root-mean-square error (RMSE) between 8 and 28 W/sq m. The RMSE is mainly associated with poor representation of surface observations within a grid cell. When the number of surface observations are sufficient, the random error is estimated to be about 5 W/sq m with present satellite-based estimates. In addition to demonstrating the strength of the retrieving method, the small random error demonstrates how well the ERBE derives from the monthly mean fluxes at the top of the atmosphere (TOA). A larger scatter is found for the comparison of transmissivity than for that of insolation. Month to month comparison of insolation reveals a weak seasonal trend in bias error with an amplitude of about 3 W/sq m. As for the insolation data from the GEWEX/SRB, larger bias errors of 5-10 W/sq m are evident with stronger seasonal trends and almost identical RMSEs.

  14. Using daily satellite observations to estimate emissions of short-lived air pollutants on a mesoscopic scale

    Science.gov (United States)

    Mijling, B.; van der A, R. J.

    2012-09-01

    Emission inventories of air pollutants are crucial information for policy makers and form important input data for air quality models. Using satellite observations for emission estimates has important advantages over bottom-up emission inventories: they are spatially consistent, have high temporal resolution, and enable updates shortly after the satellite data become available. We present a new algorithm specifically designed to use daily satellite observations of column concentrations for fast updates of emission estimates of short-lived atmospheric constituents on a mesoscopic scale (˜25 × 25 km2). The algorithm needs only one forward model run from a chemical transport model to calculate the sensitivity of concentration to emission, using trajectory analysis to account for transport away from the source. By using a Kalman filter in the inverse step, optimal use of the a priori knowledge and the newly observed data is made. We apply the algorithm for NOx emission estimates of East China, using the CHIMERE model on a 0.25 degree resolution together with tropospheric NO2column retrievals of the OMI and GOME-2 satellite instruments. Closed loop tests show that the algorithm is capable of reproducing new emission scenarios. Applied with real satellite data, the algorithm is able to detect emerging sources (e.g., new power plants), and improves emission information for areas where proxy data are not or badly known (e.g., shipping emissions). Chemical transport model runs with the daily updated emission estimates provide better spatial and temporal agreement between observed and simulated concentrations, facilitating improved air quality forecasts.

  15. Satellite-based Estimates of Ambient Air Pollution and Global Variations in Childhood Asthma Prevalence

    Science.gov (United States)

    Anderson, H. Ross; Butland, Barbara K.; Donkelaar, Aaron Matthew Van; Brauer, Michael; Strachan, David P.; Clayton, Tadd; van Dingenen, Rita; Amann, Marcus; Brunekreef, Bert; Cohen, Aaron; Dentener, Frank; Lai, Christopher; Lamsal, Lok N.; Martin, Randall V.

    2012-01-01

    Background: The effect of ambient air pollution on global variations and trends in asthma prevalence is unclear. Objectives: Our goal was to investigate community-level associations between asthma prevalence data from the International Study of Asthma and Allergies in Childhood (ISAAC) and satellite-based estimates of particulate matter with aerodynamic diameter prevalence of severe asthma as the outcome and multilevel models to adjust for gross national income (GNI) and center- and country-level sex, climate, and population density. We examined associations (adjusting for GNI) between air pollution and asthma prevalence over time in centers with data from ISAAC Phase One (mid-1900s) and Phase Three (2001-2003). Results: For the 13- to 14-year age group (128 centers in 28 countries), the estimated average within-country change in center-level asthma prevalence per 100 children per 10% increase in center-level PM2.5 and NO2 was -0.043 [95% confidence interval (CI): -0.139, 0.053] and 0.017 (95% CI: -0.030, 0.064) respectively. For ozone the estimated change in prevalence per parts per billion by volume was -0.116 (95% CI: -0.234, 0.001). Equivalent results for the 6- to 7-year age group (83 centers in 20 countries), though slightly different, were not significantly positive. For the 13- to 14-year age group, change in center-level asthma prevalence over time per 100 children per 10% increase in PM2.5 from Phase One to Phase Three was -0.139 (95% CI: -0.347, 0.068). The corresponding association with ozone (per ppbV) was -0.171 (95% CI: -0.275, -0.067). Conclusion: In contrast to reports from within-community studies of individuals exposed to traffic pollution, we did not find evidence of a positive association between ambient air pollution and asthma prevalence as measured at the community level.

  16. Oceanic primary production 2. Estimation at global scale from satellite (coastal zone color scanner) chlorophyll

    Science.gov (United States)

    Antoine, David; André, Jean-Michel; Morel, André

    A fast method has been proposed [Antoine and Morel, this issue] to compute the oceanic primary production from the upper ocean chlorophyll-like pigment concentration, as it can be routinely detected by a spaceborne ocean color sensor. This method is applied here to the monthly global maps of the photosynthetic pigments that were derived from the coastal zone color scanner (CZCS) data archive [Feldman et al., 1989]. The photosynthetically active radiation (PAR) field is computed from the astronomical constant and by using an atmospheric model, thereafter combined with averaged cloud information, derived from the International Satellite Cloud Climatology Project (ISCCP). The aim is to assess the seasonal evolution, as well as the spatial distribution of the photosynthetic carbon fixation within the world ocean and for a ``climatological year,'' to the extent that both the chlorophyll information and the cloud coverage statistics actually are averages obtained over several years. The computed global annual production actually ranges between 36.5 and 45.6 Gt C yr-1 according to the assumption which is made (0.8 or 1) about the ratio of active-to-total pigments (recall that chlorophyll and pheopigments are not radiometrically resolved by CZCS). The relative contributions to the global productivity of the various oceans and zonal belts are examined. By considering the hypotheses needed in such computations, the nature of the data used as inputs, and the results of the sensitivity studies, the global numbers have to be cautiously considered. Improving the reliability of the primary production estimates implies (1) new global data sets allowing a higher temporal resolution and a better coverage, (2) progress in the knowledge of physiological responses of phytoplankton and therefore refinements of the time and space dependent parameterizations of these responses.

  17. SACRA - a method for the estimation of global high-resolution crop calendars from a satellite-sensed NDVI

    Science.gov (United States)

    Kotsuki, S.; Tanaka, K.

    2015-11-01

    To date, many studies have performed numerical estimations of biomass production and agricultural water demand to understand the present and future supply-demand relationship. A crop calendar (CC), which defines the date or month when farmers sow and harvest crops, is an essential input for the numerical estimations. This study aims to present a new global data set, the SAtellite-derived CRop calendar for Agricultural simulations (SACRA), and to discuss advantages and disadvantages compared to existing census-based and model-derived products. We estimate global CC at a spatial resolution of 5 arcmin using satellite-sensed normalized difference vegetation index (NDVI) data, which corresponds to vegetation vitality and senescence on the land surface. Using the time series of the NDVI averaged from three consecutive years (2004-2006), sowing/harvesting dates are estimated for six crops (temperate-wheat, snow-wheat, maize, rice, soybean and cotton). We assume time series of the NDVI represent the phenology of one dominant crop and estimate CCs of the dominant crop in each grid. The dominant crops are determined using harvested areas based on census-based data. The cultivation period of SACRA is identified from the time series of the NDVI; therefore, SACRA considers current effects of human decisions and natural disasters. The difference between the estimated sowing dates and other existing products is less than 2 months (disadvantage of our method is that the mixture of several crops in a grid is not considered in SACRA. The assumption of one dominant crop in each grid is a major source of discrepancy in crop calendars between SACRA and other products. The disadvantages of our approach may be reduced with future improvements based on finer satellite sensors and crop-type classification studies to consider several dominant crops in each grid. The comparison of the CC also demonstrates that identification of wheat type (sowing in spring or fall) is a major source of

  18. Assessment of the accuracy of global geodetic satellite laser ranging observations and estimated impact on ITRF scale: estimation of systematic errors in LAGEOS observations 1993-2014

    Science.gov (United States)

    Appleby, Graham; Rodríguez, José; Altamimi, Zuheir

    2016-12-01

    Satellite laser ranging (SLR) to the geodetic satellites LAGEOS and LAGEOS-2 uniquely determines the origin of the terrestrial reference frame and, jointly with very long baseline interferometry, its scale. Given such a fundamental role in satellite geodesy, it is crucial that any systematic errors in either technique are at an absolute minimum as efforts continue to realise the reference frame at millimetre levels of accuracy to meet the present and future science requirements. Here, we examine the intrinsic accuracy of SLR measurements made by tracking stations of the International Laser Ranging Service using normal point observations of the two LAGEOS satellites in the period 1993 to 2014. The approach we investigate in this paper is to compute weekly reference frame solutions solving for satellite initial state vectors, station coordinates and daily Earth orientation parameters, estimating along with these weekly average range errors for each and every one of the observing stations. Potential issues in any of the large number of SLR stations assumed to have been free of error in previous realisations of the ITRF may have been absorbed in the reference frame, primarily in station height. Likewise, systematic range errors estimated against a fixed frame that may itself suffer from accuracy issues will absorb network-wide problems into station-specific results. Our results suggest that in the past two decades, the scale of the ITRF derived from the SLR technique has been close to 0.7 ppb too small, due to systematic errors either or both in the range measurements and their treatment. We discuss these results in the context of preparations for ITRF2014 and additionally consider the impact of this work on the currently adopted value of the geocentric gravitational constant, GM.

  19. Using Sentinel-1 and Landsat 8 satellite images to estimate surface soil moisture content.

    Science.gov (United States)

    Mexis, Philippos-Dimitrios; Alexakis, Dimitrios D.; Daliakopoulos, Ioannis N.; Tsanis, Ioannis K.

    2016-04-01

    Nowadays, the potential for more accurate assessment of Soil Moisture (SM) content exploiting Earth Observation (EO) technology, by exploring the use of synergistic approaches among a variety of EO instruments has emerged. This study is the first to investigate the potential of Synthetic Aperture Radar (SAR) (Sentinel-1) and optical (Landsat 8) images in combination with ground measurements to estimate volumetric SM content in support of water management and agricultural practices. SAR and optical data are downloaded and corrected in terms of atmospheric, geometric and radiometric corrections. SAR images are also corrected in terms of roughness and vegetation with the synergistic use of Oh and Topp models using a dataset consisting of backscattering coefficients and corresponding direct measurements of ground parameters (moisture, roughness). Following, various vegetation indices (NDVI, SAVI, MSAVI, EVI, etc.) are estimated to record diachronically the vegetation regime within the study area and as auxiliary data in the final modeling. Furthermore, thermal images from optical data are corrected and incorporated to the overall approach. The basic principle of Thermal InfraRed (TIR) method is that Land Surface Temperature (LST) is sensitive to surface SM content due to its impact on surface heating process (heat capacity and thermal conductivity) under bare soil or sparse vegetation cover conditions. Ground truth data are collected from a Time-domain reflectometer (TRD) gauge network established in western Crete, Greece, during 2015. Sophisticated algorithms based on Artificial Neural Networks (ANNs) and Multiple Linear Regression (MLR) approaches are used to explore the statistical relationship between backscattering measurements and SM content. Results highlight the potential of SAR and optical satellite images to contribute to effective SM content detection in support of water resources management and precision agriculture. Keywords: Sentinel-1, Landsat 8, Soil

  20. Estimation of Land Surface Energy Balance Using Satellite Data of Spatial Reduced Resolution

    Science.gov (United States)

    Vintila, Ruxandra; Radnea, Cristina; Savin, Elena; Poenaru, Violeta

    2010-12-01

    The paper presents preliminary results concerning the monitoring at national level of several geo-biophysical variables retrieved by remote sensing, in particular those related to drought or aridisation. The study, which is in progress, represents also an exercise for to the implementation of a Land Monitoring Core Service for Romania, according to the Kopernikus Program and in compliance with the INSPIRE Directive. The SEBS model has been used to retrieve land surface energy balance variables, such as turbulent heat fluxes, evaporative fraction and daily evaporation, based on three information types: (1) surface albedo, emissivity, temperature, fraction of vegetation cover (fCover), leaf area index (LAI) and vegetation height; (2) air pressure, temperature, humidity and wind speed at the planetary boundary layer (PBL) height; (3) downward solar radiation and downward longwave radiation. AATSR and MERIS archived reprocessed images have provided several types of information. Thus, surface albedo, emissivity, and land surface temperature have been retrieved from AATSR, while LAI and fCover have been estimated from MERIS. The vegetation height has been derived from CORINE Land Cover and PELCOM Land Use databases, while the meteorological information at the height of PBL have been estimated from the measurements provided by the national weather station network. Other sources of data used during this study have been the GETASSE30 digital elevation model with 30" spatial resolution, used for satellite image orthorectification, and the SIGSTAR-200 geographical information system of soil resources of Romania, used for water deficit characterisation. The study will continue by processing other AATSR and MERIS archived images, complemented by the validation of SEBS results with ground data collected on the most important biomes for Romania at various phenological stages, and the transformation of evaporation / evapotranspiration into a drought index using the soil texture

  1. Evapotranspiration Estimation over Yangtze River Basin from GRACE satellite measurement and in situ data

    Science.gov (United States)

    Li, Qiong; Luo, Zhicai; Zhong, Bo; Wang, Haihong; Zhou, Zebing

    2016-04-01

    As the critical component of hydrologic cycle, evapotranspiration (ET) plays an important role in global water exchanges and energy flow across the hydrosphere, atmosphere and biosphere. Influenced by the Asian monsoon, the Yangtze River Basin (YRB) suffer from the several severe floods and droughts over the last decades due to the significant difference between temporal and spatial distribution terrestrial water storages. As an indispensable part, it is practically important to assessment ET in the YRB accompany with increased population and rapid economic and agriculture development. Average ET over the YRB is computed as the residual of terrestrial water budget using the Gravity Recovery and Climate Experiment (GRACE) satellite-based measurements and the ground-based observations. The GRACE-based ET were well coincidence with the ET from MODIS, with the correlation coefficient of 0.853, and the correlation coefficient is 0.696 while comparing with the ET ground-based observation. The mean monthly average of ET from these various estimates is 56.9 mm/month over the whole YRB, and peak between June and August. Monthly variations of ET reach a maximum in Wujiang with 69.11 mm/month and a minimum in Jinshajiang with 39.01 mm/month. Based on the correlation between ET and independent estimates of near-surface temperature and soil moisture, it is showed that as the temperature increased, the ET of the seven sub-catchment were rising except for the Poyang Lake and Donting Lake. And we also can infer that the midstream of YRB is significant correlated with ESON especially in the Hanjiang basin. The Surface Humidity Index over the YRB was gradually decreased and its variations in each sub-catchment showed a significant decreasing trend in Jinshajiang and Mingjiang. This research has important potential for use in large-scale water budget assessments and intercomparison studies. Acknowledgements: This research is supported by the National Natural Science Foundation of

  2. Analysis of TRMM 3-Hourly Multi-Satellite Precipitation Estimates Computed in Both Real and Post-Real Time

    Science.gov (United States)

    Huffman, George J.; Adler, Robert F.; Stocker, Erich; Bolvin, David T.; Nelkin, Eric J.

    2002-01-01

    Satellite data form the core of the information available for estimating precipitation on a global basis. While it is possible to create such estimates solely from one sensor, researchers have increasingly moved to using combinations of sensors in an attempt to improve accuracy, coverage, and resolution. This poster updates a long-term project in which the authors are working to provide routine combined-sensor estimates of precipitation over the entire globe at relatively fine time and space intervals. The goal is to produce these globally complete precipitation estimates on a 25-km grid every 3 hours. Since late January 2002 we have been estimating precipitation for the latitude band 50 degrees N-S within about 6 hours of observation time. This work is 1 of only 2 or 3 such efforts in the world. Now we are preparing to provide similar estimates for the last 5 years. All of this work is being carried out as part of the Tropical Rainfall Measuring Mission (TRMM). Initially, TRMM was focused on providing excellent long-term averages of precipitation in tropical regions, but since its launch in November 1997 continued research has allowed the same satellite and data system to be used for addressing weather-scale problems as well.

  3. Combining METEOSAT-10 satellite image data with GPS tropospheric path delays to estimate regional Integrated Water Vapor (IWV) distribution

    OpenAIRE

    2016-01-01

    Using GPS satellites signals, we can study different processes and coupling mechanisms that can help us understand the physical conditions in the upper atmosphere, which might lead or act as proxies for severe weather events such as extreme storms and flooding. GPS signals received by ground stations are multi-purpose and can also provide estimates of tropospheric zenith delays, which can be converted into mm-accuracy Precipitable Water Vapor (PWV) using collocated pressure and temperature me...

  4. Propagation Models for Dimensioning and Estimation of Performance and Availability of New Satellite Communication Systems

    OpenAIRE

    2001-01-01

    A rapid growth of new satellite systems utilizing the Ka-band (27 – 40 Ghz) and even higher frequencies is expected in the coming years. The services offered will include broadband communication, interactive broadcasting, multimedia applications, interconnection of local area networks and Internet connectivity. Many of the new systems will use technologies as multiple spot-beams, onboard processing, and switching of packets between beams and inter satellite links. Because of congestion in the...

  5. Detecting tents to estimate the displaced populations for post-disaster relief using high resolution satellite imagery

    Science.gov (United States)

    Wang, Shifeng; So, Emily; Smith, Pete

    2015-04-01

    Estimating the number of refugees and internally displaced persons is important for planning and managing an efficient relief operation following disasters and conflicts. Accurate estimates of refugee numbers can be inferred from the number of tents. Extracting tents from high-resolution satellite imagery has recently been suggested. However, it is still a significant challenge to extract tents automatically and reliably from remote sensing imagery. This paper describes a novel automated method, which is based on mathematical morphology, to generate a camp map to estimate the refugee numbers by counting tents on the camp map. The method is especially useful in detecting objects with a clear shape, size, and significant spectral contrast with their surroundings. Results for two study sites with different satellite sensors and different spatial resolutions demonstrate that the method achieves good performance in detecting tents. The overall accuracy can be up to 81% in this study. Further improvements should be possible if over-identified isolated single pixel objects can be filtered. The performance of the method is impacted by spectral characteristics of satellite sensors and image scenes, such as the extent of area of interest and the spatial arrangement of tents. It is expected that the image scene would have a much higher influence on the performance of the method than the sensor characteristics.

  6. Development of Deep Learning Based Data Fusion Approach for Accurate Rainfall Estimation Using Ground Radar and Satellite Precipitation Products

    Science.gov (United States)

    Chen, H.; Chandra, C. V.; Tan, H.; Cifelli, R.; Xie, P.

    2016-12-01

    Rainfall estimation based on onboard satellite measurements has been an important topic in satellite meteorology for decades. A number of precipitation products at multiple time and space scales have been developed based upon satellite observations. For example, NOAA Climate Prediction Center has developed a morphing technique (i.e., CMORPH) to produce global precipitation products by combining existing space based rainfall estimates. The CMORPH products are essentially derived based on geostationary satellite IR brightness temperature information and retrievals from passive microwave measurements (Joyce et al. 2004). Although the space-based precipitation products provide an excellent tool for regional and global hydrologic and climate studies as well as improved situational awareness for operational forecasts, its accuracy is limited due to the sampling limitations, particularly for extreme events such as very light and/or heavy rain. On the other hand, ground-based radar is more mature science for quantitative precipitation estimation (QPE), especially after the implementation of dual-polarization technique and further enhanced by urban scale radar networks. Therefore, ground radars are often critical for providing local scale rainfall estimation and a "heads-up" for operational forecasters to issue watches and warnings as well as validation of various space measurements and products. The CASA DFW QPE system, which is based on dual-polarization X-band CASA radars and a local S-band WSR-88DP radar, has demonstrated its excellent performance during several years of operation in a variety of precipitation regimes. The real-time CASA DFW QPE products are used extensively for localized hydrometeorological applications such as urban flash flood forecasting. In this paper, a neural network based data fusion mechanism is introduced to improve the satellite-based CMORPH precipitation product by taking into account the ground radar measurements. A deep learning system is

  7. Comparing near-earth and satellite remote sensing based phenophase estimates: an analysis using multiple webcams and MODIS (Invited)

    Science.gov (United States)

    Hufkens, K.; Richardson, A. D.; Migliavacca, M.; Frolking, S. E.; Braswell, B. H.; Milliman, T.; Friedl, M. A.

    2010-12-01

    In recent years several studies have used digital cameras and webcams to monitor green leaf phenology. Such "near-surface" remote sensing has been shown to be a cost effective means of accurately capturing phenology. Specifically, it allows for accurate tracking of intra- and inter-annual phenological dynamics at high temporal frequency and over broad spatial scales compared to visual observations or tower-based fAPAR and broadband NDVI measurements. Near surface remote sensing measurements therefore show promise for bridging the gap between traditional in-situ measurements of phenology and satellite remote sensing data. For this work, we examined the relationship between phenophase estimates derived from satellite remote sensing (MODIS) and near-earth remote sensing derived from webcams for a select set of sites with high-quality webcam data. A logistic model was used to characterize phenophases for both the webcam and MODIS data. We documented model fit accuracy, phenophase estimates, and model biases for both data sources. Our results show that different vegetation indices (VI's) derived from MODIS produce significantly different phenophase estimates compared to corresponding estimates derived from webcam data. Different VI's showed markedly different radiometric properties, and as a result, influenced phenophase estimates. The study shows that phenophase estimates are not only highly dependent on the algorithm used but also depend on the VI used by the phenology retrieval algorithm. These results highlight the need for a better understanding of how near-earth and satellite remote data relate to eco-physiological and canopy changes during different parts of the growing season.

  8. Measurement-based perturbation theory and differential equation parameter estimation for high-precision high-resolution reconstruction of the Earth's gravitational field from satellite tracking measurements

    CERN Document Server

    Xu, Peiliang

    2016-01-01

    The numerical integration method has been routinely used to produce global standard gravitational models from satellite tracking measurements of CHAMP/GRACE types. It is implemented by solving the differential equations of the partial derivatives of a satellite orbit with respect to the unknown harmonic coefficients under the conditions of zero initial values. From the mathematical point of view, satellite gravimetry from satellite tracking is the problem of estimating unknown parameters in the Newton's nonlinear differential equations from satellite tracking measurements. We prove that zero initial values for the partial derivatives are incorrect mathematically and not permitted physically. The numerical integration method, as currently implemented and used in satellite gravimetry and statistics, is groundless. We use three different methods to derive new local solutions to the Newton's nonlinear governing differential equations of motion with a nominal reference orbit. Bearing in mind that satellite orbits ...

  9. Lunar gravitational field estimation and the effects of mismodeling upon lunar satellite orbit prediction. M.S. Thesis

    Science.gov (United States)

    Davis, John H.

    1993-01-01

    Lunar spherical harmonic gravity coefficients are estimated from simulated observations of a near-circular low altitude polar orbiter disturbed by lunar mascons. Lunar gravity sensing missions using earth-based nearside observations with and without satellite-based far-side observations are simulated and least squares maximum likelihood estimates are developed for spherical harmonic expansion fit models. Simulations and parameter estimations are performed by a modified version of the Smithsonian Astrophysical Observatory's Planetary Ephemeris Program. Two different lunar spacecraft mission phases are simulated to evaluate the estimated fit models. Results for predicting state covariances one orbit ahead are presented along with the state errors resulting from the mismodeled gravity field. The position errors from planning a lunar landing maneuver with a mismodeled gravity field are also presented. These simulations clearly demonstrate the need to include observations of satellite motion over the far side in estimating the lunar gravity field. The simulations also illustrate that the eighth degree and order expansions used in the simulated fits were unable to adequately model lunar mascons.

  10. Unscented predictive variable structure filter for satellite attitude estimation with model errors when using low precision sensors

    Science.gov (United States)

    Cao, Lu; Li, Hengnian

    2016-10-01

    For the satellite attitude estimation problem, the serious model errors always exist and hider the estimation performance of the Attitude Determination and Control System (ACDS), especially for a small satellite with low precision sensors. To deal with this problem, a new algorithm for the attitude estimation, referred to as the unscented predictive variable structure filter (UPVSF) is presented. This strategy is proposed based on the variable structure control concept and unscented transform (UT) sampling method. It can be implemented in real time with an ability to estimate the model errors on-line, in order to improve the state estimation precision. In addition, the model errors in this filter are not restricted only to the Gaussian noises; therefore, it has the advantages to deal with the various kinds of model errors or noises. It is anticipated that the UT sampling strategy can further enhance the robustness and accuracy of the novel UPVSF. Numerical simulations show that the proposed UPVSF is more effective and robustness in dealing with the model errors and low precision sensors compared with the traditional unscented Kalman filter (UKF).

  11. Use of satellite images in snow water equivalent (SWE) estimation in the Upper Vistula drainage basin in southern Poland

    Science.gov (United States)

    Kasina, M.; Chamerlinska, A.; Lasek, J.; Przeniczny, P.

    2013-12-01

    Snow-water equivalent (SWE) and snow depth are the two most important parameters used in hydrological forecasting offices to estimate the amount of water stored in the form of snow. These two parameters are essential during snowmelt periods when rapid snowmelt (often combined with rainfall) causes slow-onset floods. In this case, the water equivalent can be used as an indicator of the amount of water expected in runoff. So far the Institute of Meteorology and Water Management - National Research Institute (IMGW-PIB) produces this estimate based only on SWE and snow depth data obtained from meteorological stations at five day intervals. The two measurements are performed at shorter intervals either when heavy snowfall occurs or when the mean daily air temperature exceeds 0οC. The main aim of this project is to improve the existing method of snow water equivalent estimation by using satellite-derived data. Snow-water equivalent data and the extent of snow cover were derived from satellite products prepared within the H-SAF Project, which is the EUMETSAT Network of Satellite Application Facility dedicated to support Operational Hydrology and Water Management. Both H10 - Snow mask by VIS/IR radiometry and H11 - Snow water equivalent by MW radiometry products are derived in a daily time step. It then becomes possible to compare and further assimilate ground-truth data. Other analyses included Bayesian Kriging spatial interpolation, which is suitable for non-stationary data. The method described in this paper - same as other methods based on satellite derived data - is weather dependent and it is limited to cloud-free periods. A comparison of results obtained using this method and results obtained using ground-based data calculations proved its applicability in lowland areas. In mountainous regions, where differences in SWE values strongly depend on elevation, the obtained SWE values were underestimated.

  12. Growth in NOx emissions from power plants in China: bottom-up estimates and satellite observations

    Directory of Open Access Journals (Sweden)

    Y. Lei

    2012-01-01

    Full Text Available Using OMI (Ozone Monitoring Instrument tropospheric NO2 columns and a nested-grid 3-D global chemical transport model (GEOS-Chem, we investigated the growth in NOx emissions from coal-fired power plants and their contributions to the growth in NO2 columns in 2005–2007 in China. We first developed a unit-based power plant NOx emission inventory for 2005–2007 to support this investigation. The total capacities of coal-fired power generation have increased by 48.8% in 2005–2007, with 92.2% of the total capacity additions coming from generator units with size ≥300 MW. The annual NOx emissions from coal-fired power plants were estimated to be 8.11 Tg NO2 for 2005 and 9.58 Tg NO2 for 2007, respectively. The modeled summer average tropospheric NO2 columns were highly correlated (R2 = 0.79–0.82 with OMI measurements over grids dominated by power plant emissions, with only 7–14% low bias, lending support to the high accuracy of the unit-based power plant NOx emission inventory. The ratios of OMI-derived annual and summer average tropospheric NO2 columns between 2007 and 2005 indicated that most of the grids with significant NO2 increases were related to power plant construction activities. OMI had the capability to trace the changes of NOx emissions from individual large power plants in cases where there is less interference from other NOx sources. Scenario runs from GEOS-Chem model suggested that the new power plants contributed 18.5% and 10% to the annual average NO2 columns in 2007 in Inner Mongolia and North China, respectively. The massive new power plant NOx emissions significantly changed the local NO2 profiles, especially in less polluted areas. A sensitivity study found that changes of NO2 shape factors due to including new power plant emissions increased the summer average OMI tropospheric NO2 columns by 3.8–17.2% for six selected locations, indicating that the updated emission information could help to improve the satellite

  13. Dust indicator maps for improving solar radiation estimation from satellite data

    Science.gov (United States)

    Marpu, P. R.; Eissa, Y.; Al Meqbali, N.; Ghedira, H.

    2012-12-01

    Measurement of solar radiation from ground-based sensors is an expensive process as it requires large number of ground measurement stations to account for the spatial variability. Moreover, the instruments require regular maintenance. Satellite data can be used to model solar radiation and produce maps in regular intervals, which can be used for solar resource assessment. The models can either be empirical, physics-based or statistical models. However, in environments such as the United Arab Emirates (UAE) which are characterized by heavy dust, the results obtained by the models will lead to lower accuracies. In this study, we build on the model developed in [1], where ensembles of ANNs are used separately for cloudy and cloud-free pixels to derive solar radiation maps using the data acquired in the thermal channels of the Meteosat SEVIRI instrument. The model showed good accuracies for the estimation of direct normal irradiance (DNI), diffuse horizontal irradiance (DHI) and global horizontal irradiance (GHI); where the relative root mean square error (rRMSE) values for the DNI, DHI and GHI were 15.7, 23.6 and 7.2%, respectively, while the relative mean bias error (rMBE) values were +0.8, +8.3 and +1.9%, respectively. However, an analysis of the results on different dusty days showed varying accuracy. To further improve the model, we propose to use the dust indicator maps as inputs to the model. An interception index was proposed in [2] to detect dust over desert regions using visible channels of the SEVIRI instrument. The index has a range of 0 to 1 where the value of 1 corresponds to heavy dust and 0 corresponds to clear conditions. There is ongoing work to use the measurements from AERONET stations to derive dust indicator maps based on canonical correlation analysis, which relates the thermal channels to the aerosol optical depth (AOD) derived at different wavelengths from the AERONET measurements. There is also an ongoing work to analyze the time series of the

  14. Growth in NOx emissions from power plants in China: bottom-up estimates and satellite observations

    Directory of Open Access Journals (Sweden)

    Y. Lei

    2012-05-01

    Full Text Available Using OMI (Ozone Monitoring Instrument tropospheric NO2 columns and a nested-grid 3-D global chemical transport model (GEOS-Chem, we investigated the growth in NOx emissions from coal-fired power plants and their contributions to the growth in NO2 columns in 2005–2007 in China. We first developed a unit-based power plant NOx emission inventory for 2005–2007 to support this investigation. The total capacities of coal-fired power generation have increased by 48.8% in 2005–2007, with 92.2% of the total capacity additions coming from generator units with size ≥300 MW. The annual NOx emissions from coal-fired power plants were estimated to be 8.11 Tg NO2 for 2005 and 9.58 Tg NO2 for 2007, respectively. The modeled summer average tropospheric NO2 columns were highly correlated (R2 = 0.79–0.82 with OMI measurements over grids dominated by power plant emissions, with only 7–14% low bias, lending support to the high accuracy of the unit-based power plant NOx emission inventory. The ratios of OMI-derived annual and summer average tropospheric NO2 columns between 2007 and 2005 indicated that most of the grids with significant NO2 increases were related to power plant construction activities. OMI had the capability to trace the changes of NOx emissions from individual large power plants in cases where there is less interference from other NOx sources. Scenario runs from GEOS-Chem model suggested that the new power plants contributed 18.5% and 10% to the annual average NO2 columns in 2007 in Inner Mongolia and North China, respectively. The massive new power plant NOx emissions significantly changed the local NO2 profiles, especially in less polluted areas. A sensitivity study found that changes of NO2 shape factors due to including new power plant emissions increased the summer average OMI tropospheric NO2 columns by 3.8–17.2% for six selected locations, indicating that the updated emission information could help to improve the satellite

  15. Estimation of residual microaccelerations on board an artificial earth satellite in the monoaxial solar orientation mode

    Science.gov (United States)

    Ignatov, A. I.; Sazonov, V. V.

    2013-09-01

    The mode of monoaxial solar orientation of a designed artificial Earth satellite (AES), intended for microgravitational investigations, is studied. In this mode the normal line to the plane of satellite’s solar batteries is permanently directed at the Sun, the absolute angular velocity of a satellite is virtually equal to zero. The mode is implemented by means of an electromechanical system of powered flywheels or gyrodynes. The calculation of the level of microaccelerations arising on board in such a mode, was carried out by mathematical modeling of satellite motion with respect to the center of masses under an effect of gravitational and restoring aerodynamic moments, as well as of the moment produced by the gyrosystem. Two versions of a law for controlling the characteristic angular momentum of a gyrosystem are considered. The first version provides only attenuation of satellite’s perturbed motion in the vicinity of the position of rest with the required velocity. The second version restricts, in addition, the increase in the accumulated angular momentum of a gyrosystem by controlling the angle of rotation of the satellite around the normal to the light-sensitive side of the solar batteries. Both control law versions are shown to maintain the monoaxial orientation mode to a required accuracy and provide a very low level of quasistatic microaccelerations on board the satellite.

  16. Do agrometeorological data improve optical satellite-based estimations of the herbaceous yield in Sahelian semi-arid ecosystems?

    DEFF Research Database (Denmark)

    Diouf, Abdoul Aziz; Hiernaux, Pierre; Brandt, Martin Stefan;

    2016-01-01

    Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed Photosyn......Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed...... evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal. It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R2 = 0.69; RMSE = 483...

  17. Burst Format Design for Optimum Joint Estimation of Doppler-Shift and Doppler-Rate in Packet Satellite Communications

    Directory of Open Access Journals (Sweden)

    Luca Giugno

    2007-05-01

    Full Text Available This paper considers the problem of optimizing the burst format of packet transmission to perform enhanced-accuracy estimation of Doppler-shift and Doppler-rate of the carrier of the received signal, due to relative motion between the transmitter and the receiver. Two novel burst formats that minimize the Doppler-shift and the Doppler-rate Cramér-Rao bounds (CRBs for the joint estimation of carrier phase/Doppler-shift and of the Doppler-rate are derived, and a data-aided (DA estimation algorithm suitable for each optimal burst format is presented. Performance of the newly derived estimators is evaluated by analysis and by simulation, showing that such algorithms attain their relevant CRBs with very low complexity, so that they can be directly embedded into new-generation digital modems for satellite communications at low SNR.

  18. Antarctic ice-mass balance 2003 to 2012: regional reanalysis of GRACE satellite gravimetry measurements with improved estimate of glacial-isostatic adjustment based on GPS uplift rates

    NARCIS (Netherlands)

    Sasgen, I.; Konrad, H.; Ivins, E.R.; van den Broeke, M.R.|info:eu-repo/dai/nl/073765643; Bamber, J.L.; Martinec, Z.; Klemann, V.

    2013-01-01

    We present regional-scale mass balances for 25 drainage basins of the Antarctic Ice Sheet (AIS) from satellite observations of the Gravity and Climate Experiment (GRACE) for time period January 2003 to September 2012. Satellite gravimetry estimates of the AIS mass balance are strongly influenced by

  19. Antarctic ice-mass balance 2003 to 2012: regional reanalysis of GRACE satellite gravimetry measurements with improved estimate of glacial-isostatic adjustment based on GPS uplift rates

    NARCIS (Netherlands)

    Sasgen, I.; Konrad, H.; Ivins, E.R.; van den Broeke, M.R.; Bamber, J.L.; Martinec, Z.; Klemann, V.

    2013-01-01

    We present regional-scale mass balances for 25 drainage basins of the Antarctic Ice Sheet (AIS) from satellite observations of the Gravity and Climate Experiment (GRACE) for time period January 2003 to September 2012. Satellite gravimetry estimates of the AIS mass balance are strongly influenced by

  20. Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall-runoff model

    Science.gov (United States)

    Roy, Tirthankar; Gupta, Hoshin V.; Serrat-Capdevila, Aleix; Valdes, Juan B.

    2017-02-01

    Daily, quasi-global (50° N-S and 180° W-E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchment-scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making diagnostic structural improvements to hydrologic models whenever possible.

  1. Estimation Model and Accuracy Analysis of BeiDou/GPS Real-time Precise Satellite Clock Error Integrated Resolving

    Directory of Open Access Journals (Sweden)

    CHEN Liang

    2016-09-01

    Full Text Available Real-time high-precise satellite orbit and clock products are needed in real-time GNSS precise point positioning (PPP. In this paper, Estimation model and strategy of multi-GNSS precise satellite clock integrated resolving are researched and BeiDou/GPS real-time precise clock integrated estimation algorithm is realized by filter. Real-time simulation test results show: the STD accuracy of BeiDou/GPS real-time clock estimated in this paper compared to GFZ multi-GNSS precise clock(GBM is about 0.15ns; horizontal accuracy after convergence of GPS kinematic PPP using simulation real-time clock products estimated in this paper is better than 5cm and vertical accuracy is better than 10cm, respectively; in BeiDou kinematic PPP test, horizontal and vertical accuracy results are same as the results using GFZ multi-GNSS precise clock(GBM products, and the decimeter positioning can be realized.

  2. Evaluation of satellite based indices for primary production estimates in a sparse savanna in the Sudan

    Directory of Open Access Journals (Sweden)

    M. Sjöström

    2008-07-01

    Full Text Available One of the more frequently applied methods for integrating controls on primary production through satellite data is the Light Use Efficiency (LUE approach. Satellite indices such as the Enhanced Vegetation Index (EVI and the Shortwave Infrared Water Stress Index (SIWSI have previously shown promise as predictors of primary production in several different environments. In this study, we evaluate EVI and SIWSI derived from the Moderate Resolution Imaging Spectroradiometer (MODIS satellite sensor against in-situ measurements from central Sudan in order to asses their applicability in LUE-based primary production modelling within a water limited environment. Results show a strong correlation between EVI against gross primary production (GPP, demonstrating the significance of EVI for deriving information on primary production with relatively high accuracy at similar areas. Evaluation of SIWSI however, reveal that the fraction of vegetation apparently is to low for the index to provide accurate information on canopy water content, indicating that the use of SIWSI as a predictor of water stress in satellite data-driven primary production modelling in similar semi-arid ecosystems is limited.

  3. Estimates of radiative flux divergence in the atmosphere from satellite data

    Science.gov (United States)

    Smith, G. L.; Charlock, Thomas P.; Bess, T. D.; Gupta, Shashi; Rutan, David; Rose, Fred G.

    1990-01-01

    Several options for the inference of the atmospheric radiative flux divergence (ARD) on the basis of satellite data are discussed. Attention is given to the clear-sky case and the cloudy-sky case. LW ARD profiles for different climatological regimes are presented and the effect of cloud base height on LW ARD divergence at various heights is illustrated.

  4. Water storage variations in the Poyang Lake Basin estimated from GRACE and satellite altimetry

    Institute of Scientific and Technical Information of China (English)

    Yang Zhou; Shuanggen Jin; Robert Tenzer; Jialiang Feng

    2016-01-01

    The Gravity Recovery and Climate Experiment (GRACE) satellite mission provides a unique opportunity to quantitatively study terrestrial water storage (TWS) variations. In this paper, the terrestrial water storage variations in the Poyang Lake Basin are recovered from the GRACE gravity data from January 2003 to March 2014 and compared with the Global Land Data Assimilation System (GLDAS) hydrological models and satellite altimetry. Further-more, the impact of soil moisture content from GLDAS and rainfall from the Tropical Rainfall Measuring Mission (TRMM) on TWS variations are investigated. Our results indi-cate that the TWS variations from GRACE, GLDAS and satellite altimetry have a general consistency. The TWS trends in the Poyang Lake Basin determined from GRACE, GLDAS and satellite altimetry are increasing at 0.0141 km3/a, 0.0328 km3/a and 0.0238 km3/a, respectively during the investigated time period. The TWS is governed mainly by the soil moisture content and dominated primarily by the precipitation but also modulated by the flood season of the Yangtze River as well as the lake and river exchange water.

  5. Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique

    NARCIS (Netherlands)

    Tote, C.; Patricio, D.; Boogaard, H.L.; Wijngaart, van der R.; Tarnavsky, E.; Funk, C.

    2015-01-01

    Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and

  6. The Contribution Of Sampling Errors In Satellite Precipitation Estimates To High Flood Uncertainty In Subtropical South America

    Science.gov (United States)

    Demaria, E. M.; Valdes, J. B.; Nijssen, B.; Rodriguez, D.; Su, F.

    2009-12-01

    Satellite precipitation estimates are becoming increasingly available at temporal and spatial scales of interest for hydrological applications. Unfortunately precipitation estimated from global satellites is prone to errors hailing from different sources. The impact of sampling errors on the hydrological cycle of a large-size basin was assessed with a macroscale hydrological model. Synthetic precipitation fields were generated in a Monte Carlo fashion by perturbing observed precipitation fields with sampling errors. Three sampling intervals were chosen to generate the precipitation fields: one-hour, three-hours which is the canonical Global Precipitation Mission (GPM) sampling interval, and six-hours. The Variable Infiltration Capacity (VIC) model was used to assess the impact of sampling errors on hydrological fluxes and states in the Iguazu basin in South America for the period 1982-2005. The propagation of sampling errors through the hydrological cycle was evaluated for high flow events that have the 2% chance of being exceeded in any given time. Results show that observed event volumes are underestimated for small volumes for the three and six-hours sampling intervals but for the one-hour sampling interval the difference is almost negligible.The timing of the hydrograph is not affected by uncertainty existent in satellite-derived precipitation when it propagates through the hydrological cycle. Results of two non-parametric tests: the Kruskal-Wallis test on the mean ranks of the population and the Ansari-Bradley test on the equality of the variances indicate that sampling errors do no affect the occurrence of high flows since their probability distribution is not affected. The applicability of these results is limited to a humid climate. However the Iguazu basin is representative of several basins located in subtropical regions around the world, many of which are under-instrumented catchments, where satellite precipitation might be one of the few available data

  7. Multiannual changes of CO2 emissions in China: indirect estimates derived from satellite measurements of tropospheric NO2 columns

    Directory of Open Access Journals (Sweden)

    M. Beekmann

    2013-01-01

    Full Text Available Multi-annual satellite measurements of tropospheric NO2 columns are used for evaluation of CO2 emission changes in China in the period from 1996 to 2008. Indirect annual top-down estimates of CO2 emissions are derived from the satellite NO2 columns measurements by means of a simple inverse modeling procedure involving simulations performed with the CHIMERE mesoscale chemistry transport model and the CO2 to NOx emission ratios from the Emission Database for Global Atmospheric Research version 4.2 (EDGAR v4.2 global anthropogenic emission inventory. Exponential trends in the normalized time series of annual emission are evaluated separately for the periods from 1996 to 2001 and from 2001 to 2008. The results indicate that the both periods manifest strong positive trends in the CO2 emissions, and that the trend in the second period was significantly larger than the trend in the first period. Specifically, the trends in the first and second periods are estimated to be in the range from 3.7 to 8.0 and from 9.5 to 13.0 percent per year, respectively, taking into account both statistical and probable systematic uncertainties. Comparison of our top-down estimates of the CO2 emission changes with the corresponding bottom-up estimates provided by EDGAR v4.2 and Global Carbon Project (GCP emission inventories reveals that while acceleration of the CO2 emission growth in the considered period is a common feature of the both kinds of estimates, nonlinearity in the CO2 emission changes may be strongly exaggerated in the emission inventories. Specifically, the atmospheric NO2 observations do not confirm the existence of a sharp bend in the emission inventory data time series in the period from 2000 to 2002. A significant quantitative difference is revealed between the bottom-up and top-down estimates of the CO2 emission trend in the period from 1996 to 2001 (specifically, the trend was not positive according to the emission inventories, but is strongly

  8. Estimation of Reservoir Discharges from Lake Nasser and Roseires Reservoir in the Nile Basin Using Satellite Altimetry and Imagery Data

    Directory of Open Access Journals (Sweden)

    Eric Muala

    2014-08-01

    Full Text Available This paper presents the feasibility of estimating discharges from Roseires Reservoir (Sudan for the period from 2002 to 2010 and Aswan High Dam/Lake Nasser (Egypt for the periods 1999–2002 and 2005–2009 using satellite altimetry and imagery with limited in situ data. Discharges were computed using the water balance of the reservoirs. Rainfall and evaporation data were obtained from public domain data sources. In situ measurements of inflow and outflow (for validation were obtained, as well. The other water balance components, such as the water level and surface area, for derivation of the change of storage volume were derived from satellite measurements. Water levels were obtained from Hydroweb for Roseires Reservoir and Hydroweb and Global Reservoir and Lake Monitor (GRLM for Lake Nasser. Water surface areas were derived from Landsat TM/ETM+ images using the Normalized Difference Water Index (NDWI. The water volume variations were estimated by integrating the area-level relationship of each reservoir. For Roseires Reservoir, the water levels from Hydroweb agreed well with in situ water levels (RMSE = 0.92 m; R2 = 0.96. Good agreement with in situ measurements were also obtained for estimated water volume (RMSE = 23%; R2 = 0.94 and computed discharge (RMSE = 18%; R2 = 0.98. The accuracy of the computed discharge was considered acceptable for typical reservoir operation applications. For Lake Nasser, the altimetry water levels also agreed well with in situ levels, both for Hydroweb (RMSE = 0.72 m; R2 = 0.81 and GRLM (RMSE = 0.62 m; R2 = 0.96 data. Similar agreements were also observed for the estimated water volumes (RMSE = 10%–15%. However, the estimated discharge from satellite data agreed poorly with observed discharge, Hydroweb (RMSE = 70%; R2 = 0.09 and GRLM (RMSE = 139%; R2 = 0.36. The error could be attributed to the high sensitivity of discharge to errors in storage volume because of the immense reservoir compared to inflow

  9. Solar radiation estimation using images form geostationary satellites; Estimacao de radiacao solar usando imagens de satelites geoestacionarios

    Energy Technology Data Exchange (ETDEWEB)

    Santos, Cicero Barbosa dos [Centro Federal de Educacao Tecnologica do Parana, PR (Brazil); Zuern, Hans Helmut [Santa Catarina Univ., Florianopolis, SC (Brazil). LABSPOT

    1996-12-31

    The opportunity of potential measurement Photovoltaic through of studies of solar radiation maximum incidence, aimed the generation of electrical energy, contained in either radial systems or stand-alone or even grid-connected to conventional system, preceded by a study of solar radiation estimation technique, which is the main fuel the Photovoltaic generation. This work presents a technique of solar radiation estimation, using images from satellite geo-stationary in a visible field. The city of Curitiba was an implementation and the results are compared to the estimation made by researches from INPE - National Institute Space Research, whose observations were based on the solar shine duration, obtained in a network of 187 meteorological station for period of 10 years (1961-1970). (author) 24 refs., 2 figs., 2 tabs.; e-mail: cicero at labspot.ufsc.br; eellhh at ibm.ufsc.br

  10. Carbon export fluxes in the Southern Ocean: results from inverse modeling and comparison with satellite-based estimates

    Science.gov (United States)

    Schlitzer, Reiner

    The use of dissolved nutrients and carbon for photosynthesis in the euphotic zone and the subsequent downward transport of particulate and dissolved organic material strongly affect carbon concentrations in surface water and thus the air-sea exchange of CO 2. Efforts to quantify the downward carbon flux for the whole ocean or on basin-scales are hampered by the sparseness of direct productivity or flux measurements. Here, a global ocean circulation, biogeochemical model is used to determine rates of export production and vertical carbon fluxes in the Southern Ocean. The model exploits the existing large sets of hydrographic, oxygen, nutrient and carbon data that contain information on the underlying biogeochemical processes. The model is fitted to the data by systematically varying circulation, air-sea fluxes, production, and remineralization rates simultaneously. Use of the adjoint method yields model property simulations that are in very good agreement with measurements. In the model, the total integrated export flux of particulate organic matter necessary for the realistic reproduction of nutrient data is significantly larger than export estimates derived from primary productivity maps. Of the 10,000 TgC yr -1(10 GtC yr -1) required globally, the Southern Ocean south of 30°S contributes about 3000 TgC yr -1 (33%), most of it occurring in a zonal belt along the Antarctic Circumpolar Current and in the Peru, Chile and Namibia coastal upwelling regions. The export flux of POC for the area south of 50°S amounts to 1000±210 TgC yr -1, and the particle flux in 1000 m for the same area is 115±20 TgC yr -1. Unlike for the global ocean, the contribution of the downward flux of dissolved organic carbon is significant in the Southern Ocean in the top 500 m of the water column. Comparison with satellite-based productivity estimates (CZCS and SeaWiFS) shows a relatively good agreement over most of the ocean except for the Southern Ocean south of 50°S, where the model

  11. Understanding satellite-based monthly-to-seasonal reservoir outflow estimation as a function of hydrologic controls

    Science.gov (United States)

    Bonnema, Matthew; Sikder, Safat; Miao, Yabin; Chen, Xiaodong; Hossain, Faisal; Ara Pervin, Ismat; Mahbubur Rahman, S. M.; Lee, Hyongki

    2016-05-01

    Growing population and increased demand for water is causing an increase in dam and reservoir construction in developing nations. When rivers cross international boundaries, the downstream stakeholders often have little knowledge of upstream reservoir operation practices. Satellite remote sensing in the form of radar altimetry and multisensor precipitation products can be used as a practical way to provide downstream stakeholders with the fundamentally elusive upstream information on reservoir outflow needed to make important and proactive water management decisions. This study uses a mass balance approach of three hydrologic controls to estimate reservoir outflow from satellite data at monthly and annual time scales: precipitation-induced inflow, evaporation, and reservoir storage change. Furthermore, this study explores the importance of each of these hydrologic controls to the accuracy of outflow estimation. The hydrologic controls found to be unimportant could potentially be neglected from similar future studies. Two reservoirs were examined in contrasting regions of the world, the Hungry Horse Reservoir in a mountainous region in northwest U.S. and the Kaptai Reservoir in a low-lying, forested region of Bangladesh. It was found that this mass balance method estimated the annual outflow of both reservoirs with reasonable skill. The estimation of monthly outflow from both reservoirs was however less accurate. The Kaptai basin exhibited a shift in basin behavior resulting in variable accuracy across the 9 year study period. Monthly outflow estimation from Hungry Horse Reservoir was compounded by snow accumulation and melt processes, reflected by relatively low accuracy in summer and fall, when snow processes control runoff. Furthermore, it was found that the important hydrologic controls for reservoir outflow estimation at the monthly time scale differs between the two reservoirs, with precipitation-induced inflow being the most important control for the Kaptai

  12. Estimation of net surface shortwave radiation over the tropical Indian Ocean using geostationary satellite observations: Algorithm and validation

    Science.gov (United States)

    Shahi, Naveen R.; Thapliyal, Pradeep K.; Sharma, Rashmi; Pal, Pradip K.; Sarkar, Abhijit

    2011-09-01

    This paper presents the development of a methodology to estimate the net surface shortwave radiation (SWR) over tropical oceans using half-hourly geostationary satellite estimates of outgoing longwave radiation (OLR). The collocated data set of SWR measured at 13 buoy locations over the Indian Ocean and a Meteosat-derived OLR for the period of 2002-2009 have been used to derive an empirical relationship. The information from the solar zenith angle that determines the amount of solar radiation received at a particular location is used to normalize the SWR to nadir observation in order to make the empirical relationship location independent. As the relationship between SWR and OLR is valid mostly over the warm-pool regions, the present study restricts SWR estimation in the tropical Indian Ocean domain (30°E-110°E, 30°S-30°N). The SWR estimates are validated with an independent collocated data set and subsequently compared with the SWR estimates from the Global Energy and Water Cycle Experiment-Surface Radiation Budget V3.0 (GEWEX-SRB), International Satellite Cloud Climatology Project-Flux Data (ISCCP-FD), and National Centers for Environmental Prediction (NCEP) reanalysis for the year 2007. The present algorithm provides significantly better accuracy of SWR estimates, with a root-mean-square error of 27.3 W m-2 as compared with the values of 32.7, 37.5, and 59.6 W m-2 obtained from GEWEX-SRB, ISCCP-FD, and NCEP, respectively. The present algorithm also provides consistently better SWR compared with other available products under different sky conditions and seasons over Indian Ocean warm-pool regions.

  13. Modeling of groundwater draft based on satellite-derived crop acreage estimation over an arid region of northwest India

    Science.gov (United States)

    Bhadra, Bidyut Kumar; Kumar, Sanjay; Paliwal, Rakesh; Jeyaseelan, A. T.

    2016-11-01

    Over-exploitation of groundwater for agricultural crops puts stress on the sustainability of natural resources in the arid region of Rajasthan state, India. Hydrogeological study of groundwater levels of the study area during the pre-monsoon (May to June), post-monsoon (October to November) and post-irrigation (February to March) seasons of 2004-2005 to 2011-2012 shows a steady decline of groundwater levels at the rate of 1.28-1.68 m/year, mainly due to excessive groundwater draft for irrigation. Due to the low density of the groundwater observation-well network in the study area, assessment of groundwater draft, and thus groundwater resource management, becomes a difficult task. To overcome the situation, a linear groundwater draft model (LGDM) has been developed based on the empirical relationship between satellite-derived crop acreage and the observed groundwater draft for the year 2003-2004. The model has been validated for a decade, during three year-long intervals (2005-2006, 2008-2009 and 2011-2012) using groundwater draft, estimated through a discharge factor method. Further, the estimated draft was validated through observed pumping data from random sampled villages (2011-2012). The results suggest that the developed LGDM model provides a good alternative to the estimation of groundwater draft based on satellite-based crop area in the absence of groundwater observation wells in arid regions of northwest India.

  14. The key role of Satellite Laser Ranging towards the integrated estimation of geometry, rotation and gravitational field of the Earth

    Science.gov (United States)

    Blossfeld, Mathis

    2015-01-01

    In 2007, the Global Geodetic Observing System (GGOS) was installed as a full component of the International Association of Geodesy (IAG). One primary goal of GGOS is the integration of geometric and gravimetric observation techniques to estimate consistent geodetic-geophysical parameters. Thereby, GGOS is based on the data and services of the IAG. Besides the combination of different geodetic techniques, also the common estimation of the station coordinates (TRF), Earth Orientation Parameters (EOP) and coefficients of the Earth's gravitational field (Stokes coefficients) is necessary in order to reach this goal. However, the combination of all geometric and gravimetric observation techniques is not yet fully realized. A major step towards the GGOS idea of parameter integration would be the understanding of the existing correlations between the above mentioned fundamental geodetic parameter groups. This topic is the major objective of this thesis. One possibility to study the interactions is the use of Satellite Laser Ranging (SLR) in an intertechnique combination with Global Navigation Satellite Systems (GNSS) and Very Long Baseline Interferometry (VLBI) or the intra-technique combination of multiple SLR-tracked satellites. SLR plays a key role in this thesis since it is the unique technique which is sensitive to all parameter groups and allows an integrated parameter estimation with very high accuracy. The present work is based on five first-author publications which are supplemented by four co-author publications. In this framework, for the first time an extensive discussion of a refined global Terrestrial Reference Frame (TRF) estimation procedure, the estimation of so-called Epoch Reference Frames (ERFs) is presented. In contrast to the conventional linear station motion model, the ERFs provide frequently estimated station coordinates and Earth Orientation Parameters (EOP) which allow to approximate not modeled non-linear station motions very accurately

  15. Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

    Directory of Open Access Journals (Sweden)

    Sanggyun Lee

    2016-08-01

    Full Text Available Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011–2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM-bird data collected over two days during the CryoSat Validation experiment (CryoVex field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE = 0.29 m compared to thickness estimation based on existing lead detection methods (RMSE = 0.86–0.93 m. Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011–2013 and rebounded in 2014.

  16. Estimating soil moisture from satellite microwave observations: Past and ongoing projects, and relevance to GCIP

    Science.gov (United States)

    Owe, M.; Van de Griend, A. A.; de Jeu, R.; de Vries, J. J.; Seyhan, E.; Engman, E. T.

    1999-08-01

    On the basis of a series of studies conducted in Botswana and preliminary results from an ongoing study in Spain, developments in microwave remote sensing by satellite, which can be used to monitor near-real-time surface moisture and also study long-term soil moisture climatology, are described. A progression of methodologies beginning with single-polarization studies and leading to both dual polarization and multiple frequency techniques are described. Continuing analysis of a 9 year data set of satellite-derived surface moisture in Spain is ongoing. Preliminary results from this study appear to provide some evidence of long-term desertification in certain parts of this region. The methodologies developed during these investigations can be applied easily to other regions such as the GCIP area and could provide useful databases for simulation and validation studies. Additionally, they have strong potential for global applications such as climate change studies.

  17. Satellite microwave estimates of soil moisture and applications for desertification studies

    Science.gov (United States)

    Owe, Manfred; Van de Griend, Adriaan A.; de Jeu, Richard A.; de Vries, Jorrit; Seyhan, E.

    1998-12-01

    Based on a series of studies conducted in Botswana and preliminary results from an ongoing study in Spain, developments in microwave remote sensing by satellite which can be used to monitor near real-time surface moisture and also study long term soil moisture climatology are described. A progression of methodologies beginning with single polarization studies and leading to both dual polarization and multiple frequency techniques are described. Continuing analysis of a nine year data set of satellite-derived surface moisture in Spain is ongoing. Preliminary results from this study appear to provide some evidence of long term decertification in certain parts of this region. The methodologies developed during these investigations can be applied to other regions, and have the potential for providing modelers with extended data sets of independently derived surface moisture for simulation and validation studies, and climate change studies at the global scale.

  18. Multiscale Estimation of Leaf Area Index from Satellite Observations Based on an Ensemble Multiscale Filter

    Directory of Open Access Journals (Sweden)

    Jingyi Jiang

    2016-03-01

    Full Text Available Currently, multiple leaf area index (LAI products retrieved from remote sensing data are widely used in crop growth monitoring, land-surface process simulation and studies of climate change. However, most LAI products are only retrieved from individual satellite observations, which may result in spatial-temporal discontinuities and low accuracy in these products. In this paper, a new method was developed to simultaneously retrieve multiscale LAI data from satellite observations with different spatial resolutions based on an ensemble multiscale filter (EnMsF. The LAI average values corresponding to the date of satellite observations were calculated from the multi-year Moderate Resolution Imaging Spectroradiometer (MODIS LAI product and were used as a priori knowledge for LAI in order to construct an initial ensemble multiscale tree (EnMsT. Satellite observations obtained at different spatial resolutions were then applied to update the LAI values at each node of the EnMsT using a two-sweep filtering procedure. Next, the retrieved LAI values at the finest scale were used as a priori knowledge for LAI for the new round of construction and updating of the EnMsT, until the sum of the difference of LAI values at each node of the EnMsT between two adjacent updates is less than a given threshold. The method was tested using Thematic Mapper (TM or Enhanced Thematic Mapper Plus (ETM+ surface reflectance data and MODIS surface reflectance data from five sites that have different vegetation types. The results demonstrate that the retrieved LAI values for each spatial resolution were in good agreement with the aggregated LAI reference map values for the corresponding spatial resolution. The retrieved LAI values at the coarsest scale provided better accuracy with the aggregated LAI reference map values (root mean square error (RMSE = 0.45 compared with that obtained from the MODIS LAI values (RMSE = 1.30.

  19. Ozone depletion in the upper stratosphere estimated from satellite and Space Shuttle data

    Science.gov (United States)

    Hilsenrath, Ernest; Cebula, Richard P.; Jackman, Charles H.

    1992-01-01

    Shuttle Solar Backscatter Ultraviolet (SSBUV) spectrometer observations of ozone concentrations in the upper stratosphere made in October 1989 are combined here with measurements made in October 1980 by the similar SBUV instruments on NASA's Nimbus-7 satellite. It is shown that the ozone concentration near 45 km has decreased during this period by about 7 +/- 2 percent. The trend is consistent with predictions of a 2D photochemical model.

  20. Estimating Advective Near-surface Currents from Ocean Color Satellite Images

    Science.gov (United States)

    2015-01-01

    K., Arnone, R.A., et al. (2014). Forecasting the ocean’s optical environment using the BioCast system. Oceanography , 27, 46–57. 14 H. Yang et al...satellite images 0602435N 73-9358-09-5 Haoping Yang, Robert Arnone, Jason Jolliff Naval Research Laboratory Oceanography Division Stennis Space Center...U.S. East and Gulf coasts. The MCC calculation is validated in a series of Bio- Optical Forecasting (BioCast) experiments with predetermined synthetic

  1. Single Tree Vegetation Depth Estimation Tool for Satellite Services Link Design

    OpenAIRE

    HASIRCI, Z.; Cavdar, I. H.; Ozturk, M

    2016-01-01

    Attenuation caused by tree shadowing is an important factor for describing the propagation channel of satellite services. Thus, vegetation effects should be determined by experimental studies or empirical formulations. In this study, tree types in the Black Sea Region of Turkey are classified based on their geometrical shapes into four groups such as conic, ellipsoid, spherical and hemispherical. The variations of the vegetation depth according to different tree shapes are calculated with ...

  2. REVISITING THE DOPPLER FILTER OF LEO SATELLITE GNSS RECEIVERS FOR PRECISE VELOCITY ESTIMATION

    Institute of Scientific and Technical Information of China (English)

    Chen Xi; Gao Wenyun; Wan Yunheng

    2013-01-01

    The theoretical aspects of the precise velocity determination of Low Earth Orbit (LEO) satellites' onboard Global Navigation Satellite Systems (GNSS) receivers are derived.It shows that the receiver's Phase Lock Loop (PLL) is required to feature extremely small group delay within its low frequency band,which is in contrast to existing work that proposed wide band linear phase filters.Following this theory,a Finite Impulse Response (FⅠR) filter is proposed.To corroborate,the proposed FIR filter and an Infinite Impulse Response (ⅡR) filter lately proposed in literals are implemented in a LEO satellite onboard GNSS receiver.Tests are conducted using a third party commercial GPS,signal generator.The results show that the GNSS receiver with the proposed FⅠR achieves 11 mm/s R.M.S precision,while the GNSS receiver with the ⅡR filter has a filter-caused velocity error that can not be ignored for space borne GNSS receivers.

  3. Development and validation of satellite-based estimates of surface visibility

    Science.gov (United States)

    Brunner, J.; Pierce, R. B.; Lenzen, A.

    2016-02-01

    A satellite-based surface visibility retrieval has been developed using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements as a proxy for Advanced Baseline Imager (ABI) data from the next generation of Geostationary Operational Environmental Satellites (GOES-R). The retrieval uses a multiple linear regression approach to relate satellite aerosol optical depth, fog/low cloud probability and thickness retrievals, and meteorological variables from numerical weather prediction forecasts to National Weather Service Automated Surface Observing System (ASOS) surface visibility measurements. Validation using independent ASOS measurements shows that the GOES-R ABI surface visibility retrieval (V) has an overall success rate of 64.5 % for classifying clear (V ≥ 30 km), moderate (10 km ≤ V skill during June through September, when Heidke skill scores are between 0.2 and 0.4. We demonstrate that the aerosol (clear-sky) component of the GOES-R ABI visibility retrieval can be used to augment measurements from the United States Environmental Protection Agency (EPA) and National Park Service (NPS) Interagency Monitoring of Protected Visual Environments (IMPROVE) network and provide useful information to the regional planning offices responsible for developing mitigation strategies required under the EPA's Regional Haze Rule, particularly during regional haze events associated with smoke from wildfires.

  4. Estimation of the Perturbing Accelerations Induced on the LARES Satellite by Neutral Atmosphere Drag

    CERN Document Server

    Pardini, Carmen; Lucchesi, David Massimo; Peron, Roberto

    2016-01-01

    The laser-ranged satellite LARES is expected to provide new refined measurements of relativistic physics, as well as significant contributions to space geodesy and geophysics. The very low area-to-mass ratio of this passive and dense satellite was chosen to reduce as much as possible the disturbing effects of non-gravitational perturbations. However, because of its height, about 1450 km compared with about 5800-5900 km for the two LAGEOS satellites, LARES is exposed to a much stronger drag due to neutral atmosphere. From a precise orbit determination, analyzing the laser ranging normal points of LARES over a time span of about 3.7 years, it was found an average semi-major axis decay rate of -0.999 m per year, corresponding to a non-conservative net force with a mean along-track acceleration of -1.444 x 10^-11 m/s^2. By means of a modified version of the SATRAP (ISTI/CNR) code, the neutral drag perturbation acting on LARES was evaluated over the same time span, taking into account the real evolution of solar a...

  5. The first estimates of global nucleation mode aerosol concentrations based on satellite measurements

    Directory of Open Access Journals (Sweden)

    M. Kulmala

    2011-11-01

    Full Text Available Atmospheric aerosols play a key role in the Earth's climate system by scattering and absorbing solar radiation and by acting as cloud condensation nuclei. Satellites are increasingly used to obtain information on properties of aerosol particles with a diameter larger than about 100 nm. However, new aerosol particles formed by nucleation are initially much smaller and grow into the optically active size range on time scales of many hours. In this paper we derive proxies, based on process understanding and ground-based observations, to determine the concentrations of these new particles and their spatial distribution using satellite data. The results are applied to provide seasonal variation of nucleation mode concentration. The proxies describe the concentration of nucleation mode particles over continents. The source rates are related to both regional nucleation and nucleation associated with more restricted sources. The global pattern of nucleation mode particle number concentration predicted by satellite data using our proxies is compared qualitatively against both observations and global model simulations.

  6. Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA, using multi-satellite data fusion

    Science.gov (United States)

    Yang, Yun; Anderson, Martha C.; Gao, Feng; Hain, Christopher R.; Semmens, Kathryn A.; Kustas, William P.; Noormets, Asko; Wynne, Randolph H.; Thomas, Valerie A.; Sun, Ge

    2017-02-01

    As a primary flux in the global water cycle, evapotranspiration (ET) connects hydrologic and biological processes and is directly affected by water and land management, land use change and climate variability. Satellite remote sensing provides an effective means for diagnosing ET patterns over heterogeneous landscapes; however, limitations on the spatial and temporal resolution of satellite data, combined with the effects of cloud contamination, constrain the amount of detail that a single satellite can provide. In this study, we describe an application of a multi-sensor ET data fusion system over a mixed forested/agricultural landscape in North Carolina, USA, during the growing season of 2013. The fusion system ingests ET estimates from the Two-Source Energy Balance Model (TSEB) applied to thermal infrared remote sensing retrievals of land surface temperature from multiple satellite platforms: hourly geostationary satellite data at 4 km resolution, daily 1 km imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) and biweekly Landsat thermal data sharpened to 30 m. These multiple ET data streams are combined using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to estimate daily ET at 30 m resolution to investigate seasonal water use behavior at the level of individual forest stands and land cover patches. A new method, also exploiting the STARFM algorithm, is used to fill gaps in the Landsat ET retrievals due to cloud cover and/or the scan-line corrector (SLC) failure on Landsat 7. The retrieved daily ET time series agree well with observations at two AmeriFlux eddy covariance flux tower sites in a managed pine plantation within the modeling domain: US-NC2 located in a mid-rotation (20-year-old) loblolly pine stand and US-NC3 located in a recently clear-cut and replanted field site. Root mean square errors (RMSEs) for NC2 and NC3 were 0.99 and 1.02 mm day-1, respectively, with mean absolute errors of approximately 29 % at the

  7. Does GPM-based multi-satellite precipitation enhance rainfall estimates over Pakistan and Bolivia arid regions?

    Science.gov (United States)

    Hussain, Y.; Satgé, F.; Bonnet, M. P.; Pillco, R.; Molina, J.; Timouk, F.; Roig, H.; Martinez-Carvajal, H., Sr.; Gulraiz, A.

    2016-12-01

    Arid regions are sensitive to rainfall variations which are expressed in the form of flooding and droughts. Unfortunately, those regions are poorly monitored and high quality rainfall estimates are still needed. The Global Precipitation Measurement (GPM) mission released two new satellite rainfall products named Integrated Multisatellite Retrievals GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) bringing the possibility of accurate rainfall monitoring over these countries. This study assessed both products at monthly scale over Pakistan considering dry and wet season over the 4 main climatic zones from 2014 to 2016. With similar climatic conditions, the Altiplano region of Bolivia is considered to quantify the influence of big lakes (Titicaca and Poopó) in rainfall estimates. For comparison, the widely used TRMM-Multisatellite Precipitation Analysis 3B43 (TMPA-3B43) version 7 is also involved in the analysis to observe the potential enhancement in rainfall estimate brought by GPM products. Rainfall estimates derived from 110 rain-gauges are used as reference to compare IMERG, GSMaP-v6 and TMPA-3B43 at the 0.1° and 0.25° spatial resolution. Over both regions, IMERG and GSMaP-v6 capture the spatial pattern of precipitation as well as TMPA-3B43. All products tend to over estimates rainfall over very arid regions. This feature is even more marked during dry season. However, during this season, both reference and estimated rainfall remain very low and do not impact seasonal water budget computation. On a general way, IMERG slightly outperforms TMPA-3B43 and GSMaP-v6 which provides the less accurate rainfall estimate. The TMPA-3B43 rainfall underestimation previously found over Lake Titicaca is still observed in IMERG estimates. However, GSMaP-v6 considerably decreases the underestimation providing the most accurate rainfall estimate over the lake. MOD11C3 Land Surface Temperature (LST) and ASTER Global Emissivity Dataset reveal strong

  8. Applications of TRMM-based Multi-Satellite Precipitation Estimation for Global Runoff Simulation: Prototyping a Global Flood Monitoring System

    Science.gov (United States)

    Hong, Yang; Adler, Robert F.; Huffman, George J.; Pierce, Harold

    2008-01-01

    Advances in flood monitoring/forecasting have been constrained by the difficulty in estimating rainfall continuously over space (catchment-, national-, continental-, or even global-scale areas) and flood-relevant time scale. With the recent availability of satellite rainfall estimates at fine time and space resolution, this paper describes a prototype research framework for global flood monitoring by combining real-time satellite observations with a database of global terrestrial characteristics through a hydrologically relevant modeling scheme. Four major components included in the framework are (1) real-time precipitation input from NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA); (2) a central geospatial database to preprocess the land surface characteristics: water divides, slopes, soils, land use, flow directions, flow accumulation, drainage network etc.; (3) a modified distributed hydrological model to convert rainfall to runoff and route the flow through the stream network in order to predict the timing and severity of the flood wave, and (4) an open-access web interface to quickly disseminate flood alerts for potential decision-making. Retrospective simulations for 1998-2006 demonstrate that the Global Flood Monitor (GFM) system performs consistently at both station and catchment levels. The GFM website (experimental version) has been running at near real-time in an effort to offer a cost-effective solution to the ultimate challenge of building natural disaster early warning systems for the data-sparse regions of the world. The interactive GFM website shows close-up maps of the flood risks overlaid on topography/population or integrated with the Google-Earth visualization tool. One additional capability, which extends forecast lead-time by assimilating QPF into the GFM, also will be implemented in the future.

  9. The electrical conductivity of the Earth's upper mantle as estimated from satellite measured magnetic field variations. Ph.D. Thesis

    Science.gov (United States)

    Didwall, E. M.

    1981-01-01

    Low latitude magnetic field variations (magnetic storms) caused by large fluctuations in the equatorial ring current were derived from magnetic field magnitude data obtained by OGO 2, 4, and 6 satellites over an almost 5 year period. Analysis procedures consisted of (1) separating the disturbance field into internal and external parts relative to the surface of the Earth; (2) estimating the response function which related to the internally generated magnetic field variations to the external variations due to the ring current; and (3) interpreting the estimated response function using theoretical response functions for known conductivity profiles. Special consideration is given to possible ocean effects. A temperature profile is proposed using conductivity temperature data for single crystal olivine. The resulting temperature profile is reasonable for depths below 150-200 km, but is too high for shallower depths. Apparently, conductivity is not controlled solely by olivine at shallow depths.

  10. Estimating boundary currents from satellite altimetry: A case study for the east coast of India

    Digital Repository Service at National Institute of Oceanography (India)

    Durand, F.; Shankar, D.; Birol, F.; Shenoi, S.S.C.

    in our area (Chelton et al., 1998). We can see that the other dynamical effects not accounted for by the linear theory tend to spread the coastal trapping of the energy in the offshore direction. About 200 km off the coast, the power of the annual... by means of a 3.notdef.g0002 filter, where .notdef.g0002 is the standard deviation of the original along track record. One value of .notdef.g0002 is computed per satellite cycle and per corrective parameter, so as to account for the natural temporal...

  11. In-Orbit Trend Analysis of Galileo Satellites for Power Sources Degradation Estimation

    Directory of Open Access Journals (Sweden)

    Bard Frederic

    2017-01-01

    The results are in all cases better than the predictions, which is expected due to the usage of conservatives assumptions in the design to cover (for both IOV and FOC worst case scenario for the entire constellation. It should be noted that the FOC GSAT201 and GSAT202 batteries are degrading slightly faster than the 6 others FOC batteries identified GSAT203, GSAT204, GSAT205, GSAT206, GSAT208 and GSAT209, but still below predictions due to their peculiar unexpected orbit reached after launch (higher DoD up to 42% measured due to longer eclipses. These 2 satellites will require specific degradation monitoring.

  12. Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting.

    Science.gov (United States)

    Yu, Wenxi; Liu, Yang; Ma, Zongwei; Bi, Jun

    2017-08-01

    Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM2.5 is a promising way to fill the areas that are not covered by ground PM2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R(2) = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM2.5 estimates.

  13. Remote Estimation of Greenland Ice Sheet Supraglacial River Discharge using GIS Modeling and WorldView-2 Satellite Imagery

    Science.gov (United States)

    Chu, V. W.; Smith, L. C.; Yang, K.; Gleason, C. J.; Rennermalm, A. K.; Pitcher, L. H.; Legleiter, C. J.; Forster, R. R.

    2014-12-01

    Increasing surface melting on the Greenland ice sheet and rising sea level have heightened the need for understanding the complex pathways transporting meltwater from the ice sheet surface to the ice edge and the ocean. Satellite images show supraglacial rivers abundantly covering the western ablation zone throughout the melt season, transporting large volumes of meltwater into moulins and to the ice edge, yet these rivers remain poorly studied. Here, a GIS modeling framework is developed to estimate supraglacial river discharge by spatially adapting Manning's equation for use with remotely sensed imagery and is applied to supraglacial rivers on the Greenland Ice Sheet. This framework incorporates high-resolution visible/near-infrared WorldView-2 (WV2) satellite imagery, the Greenland Ice Mapping Project (GIMP) DEM, and a field-calibrated WV2 river bathymetry retrieval algorithm and channel roughness parameter. Orthogonal cross-sections are simulated along river centerlines to extract cross-sectional discharge using Manning's equation for open channel flow. A total of 1,629,502 reach-averaged points were retrieved over 465 river networks of western Greenland in 2012, including attributes of width, depth, velocity, slope, wetted perimeter, hydraulic radius, and discharge. This work provides a method for producing spatially extensive, high-resolution estimates of supraglacial meltwater flux in river networks and into the ice sheet.

  14. A New Temperature-Vegetation Triangle Algorithm with Variable Edges (TAVE for Satellite-Based Actual Evapotranspiration Estimation

    Directory of Open Access Journals (Sweden)

    Hua Zhang

    2016-09-01

    Full Text Available The estimation of spatially-variable actual evapotranspiration (AET is a critical challenge to regional water resources management. We propose a new remote sensing method, the Triangle Algorithm with Variable Edges (TAVE, to generate daily AET estimates based on satellite-derived land surface temperature and the vegetation index NDVI. The TAVE captures heterogeneity in AET across elevation zones and permits variability in determining local values of wet and dry end-member classes (known as edges. Compared to traditional triangle methods, TAVE introduces three unique features: (i the discretization of the domain as overlapping elevation zones; (ii a variable wet edge that is a function of elevation zone; and (iii variable values of a combined-effect parameter (that accounts for aerodynamic and surface resistance, vapor pressure gradient, and soil moisture availability along both wet and dry edges. With these features, TAVE effectively addresses the combined influence of terrain and water stress on semi-arid environment AET estimates. We demonstrate the effectiveness of this method in one of the driest countries in the world—Jordan, and compare it to a traditional triangle method (TA and a global AET product (MOD16 over different land use types. In irrigated agricultural lands, TAVE matched the results of the single crop coefficient model (−3%, in contrast to substantial overestimation by TA (+234% and underestimation by MOD16 (−50%. In forested (non-irrigated, water consuming regions, TA and MOD16 produced AET average deviations 15.5 times and −3.5 times of those based on TAVE. As TAVE has a simple structure and low data requirements, it provides an efficient means to satisfy the increasing need for evapotranspiration estimation in data-scarce semi-arid regions. This study constitutes a much needed step towards the satellite-based quantification of agricultural water consumption in Jordan.

  15. Satellite-based estimates of light-use efficiency in a subtropical mangrove forest equipped with CO2 eddy covariance

    Directory of Open Access Journals (Sweden)

    D. O. Fuller

    2012-11-01

    Full Text Available Despite the importance of mangrove ecosystems in the global carbon budget, the relationships between environmental drivers and carbon dynamics in these forests remain poorly understood. This limited understanding is partly a result of the challenges associated with in situ flux studies. Tower-based carbon dioxide eddy covariance (EC systems are installed in only a few mangrove forests worldwide and the longest EC record from the Florida Everglades contains less than 9 yr of observations. A primary goal of the present study was to develop a methodology to estimate canopy-scale photosynthetic light use efficiency in this forest. These tower-based observations represent a basis for associating CO2 fluxes with canopy light use properties, and thus provide the means for utilizing satellite-based reflectance data for larger-scale investigations. We present a model for mangrove canopy light use efficiency utilizing the enhanced green vegetation index (EVI derived from the Moderate Resolution Imaging Spectroradiometer (MODIS that is capable of predicting changes in mangrove forest CO2 fluxes caused by a hurricane disturbance and changes in regional environmental conditions, including temperature and salinity. Model parameters are solved for in a Bayesian framework. The model structure requires estimates of ecosystem respiration (RE and we present the first-ever tower-based estimates of mangrove forest RE derived from night-time CO2 fluxes. Our investigation is also the first to show the effects of salinity on mangrove forest CO2 uptake, which declines 5% per each 10 parts per thousand (ppt increases in salinity. Light use efficiency in this forest declines with increasing daily photosynthetic active radiation, which is an important departure from the assumption of constant light use efficiency typically applied in satellite-driven models. The model developed here provides a framework for estimating CO2 uptake by these forests from reflectance data and

  16. How reliable are satellite precipitation estimates for driving hydrological models: a verification study over the Mediterranean area

    Science.gov (United States)

    Camici, Stefania; Ciabatta, Luca; Massari, Christian; Brocca, Luca

    2017-04-01

    Floods are one of the most common and dangerous natural hazards, causing every year thousands of casualties and damages worldwide. The main tool for assessing flood risk and reducing damages is represented by hydrologic early warning systems that allow to forecast flood events by using real time data obtained through ground monitoring networks (e.g., raingauges and radars). However, the use of such data, mainly rainfall, presents some issues firstly related to the network density and to the limited spatial representativeness of local measurements. A way to overcome these issues may be the use of satellite-based rainfall products (SRPs) that nowadays are available on a global scale at ever increasing spatial/temporal resolution and accuracy. However, despite the large availability and increased accuracy of SRPs (e.g., the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA); the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF); and the recent Global Precipitation Measurement (GPM) mission), remotely sensed rainfall data are scarcely used in hydrological modeling and only a small number of studies have been carried out to outline some guidelines for using satellite data as input for hydrological modelling. Reasons may be related to: 1) the large bias characterizing satellite precipitation estimates, which is dependent on rainfall intensity and season, 2) the spatial/temporal resolution, 3) the timeliness, which is often insufficient for operational purposes, and 4) a general (often not justified) skepticism of the hydrological community in the use of satellite products for land applications. The objective of this study is to explore the feasibility of using SRPs in a lumped hydrologic model (MISDc, "Modello Idrologico Semi-Distribuito in continuo", Masseroni et al., 2017) over 10 basins in the Mediterranean area with different sizes and physiographic characteristics. Specifically

  17. Air quality estimates in Mediterranean cities using high resolution satellite technologies

    Science.gov (United States)

    Chudnovsky, Alexandra; Lyapustin, Alexei; Wang, Yujie

    2016-04-01

    Satellite imaging is an essential tool for monitoring air pollution because, unlike ground observations, it supplies continuous data with global coverage of terrestrial and atmospheric components. Satellite-based Aerosol Optical Depth (AOD) retrievals reflect particle abundance in the atmospheric column. This data provide some indication on the extent of particle concentrations. However, it is difficult to retrieve AOD at high spatial resolution above areas with high surface reflectance and heterogeneous land cover, such as urban areas. Therefore, many crowded regions worldwide including Israel, AOD climatology are still uncertain because of the high ground reflectance and coarse spatial resolution. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. This study aims to investigate the spatial variability of AOD within Israeli and several other Mediterranean cities. In addition, we aim to characterize the impact of climatic condition on pollution patterns in-and-between cities and to identify days when cities exhibit the highest variability in AOD. Furthermore, we assessed the differences in pollution levels between adjacent locations. We will report on spatial variability in AOD levels derived from high 1km resolution MAIAC AOD algorithm on a temporal basis, in relation to season and synoptic-meteorological conditions.

  18. Kinematics of Milky Way Satellites: Mass Estimates, Rotation Limits, and Proper Motions

    Directory of Open Access Journals (Sweden)

    Louis E. Strigari

    2010-01-01

    Full Text Available In the past several years kinematic data sets from Milky Way satellite galaxies have greatly improved, furthering the evidence that these systems are the most dark matter dominated objects known. This paper discusses a maximum likelihood formalism that extracts important quantities from these kinematic data sets, including the amplitude of a rotational signal, proper motions, and the mass distributions. Using a simple model for galaxy rotation it is shown that the expected error on the amplitude of a rotational signal is ∼0.5 km s−1 with ∼103 stars from either classical or ultra-faint satellites. As an example Sculptor is analyzed for the presence of a rotational signal; no significant detection of rotation is found, with a 90% c.l. upper limit of ∼2 km s−1. A criterion for model selection is presented that determines the parameters required to describe the dark matter halo density profiles and the stellar velocity anisotropy. Applied to four data sets with a wide range of velocities, models with variable velocity anisotropy are preferred relative to those with constant velocity anisotropy, and that central dark matter profiles both less cuspy and more cuspy than Lambda-Cold Dark Matter-based fits are equally acceptable.

  19. Traceable quality assurance for independent reference data used in the validation of satellite ECV estimates.

    Science.gov (United States)

    Lanconelli, Christian; Gobron, Nadine; Adams, Jennifer; Disney, Mathias; Govaerts, Yves

    2016-04-01

    This contribution presents the methodology and first results of the Quality Assurance for Essential Climate Variables (QA4ECV) project for ensuring how trustable assessments of satellite land Essential Climate Variables (ECVs) quality can facilitate users in judging the fitness-for-purpose of the ECV Climate Data Record (CDR). This aims to bring a major step forward in providing quality assured long-term CDRs that are relevant for policy and climate change assessments. The main goal here is to provide algorithms/products developers with several simulated satellite sensors data from a 3-D radiative transfer soil and canopy model coupled with an atmospheric one using 6S RTC. Atmospheric water vapour and ozone are represented using ERA Interim reanalysis, while aerosol properties are assumed from both AERONET and MODIS archives. With the aim to enlighten the contribution of bi-directional reflectance (BRF) on top-of-atmosphere (TOA) values, simulations are performed assuming i) the anisotropic surface reflectance arising from 3-D Raytran simulations and ii) an lambertian surface albedo equal to that provided by the 3-D diffuse sky simulation. We summarize the 3-D canopies scenes as well as the atmospheric properties. Results arising from first simulations and concerning the radiative models performances in reproducing real BRF measurements (MERIS, AVHRR, MODIS) are then presented.

  20. Estimating Carbon STOCK Changes of Mangrove Forests Using Satellite Imagery and Airborne LiDAR Data in the South Sumatra State, Indonesia

    Science.gov (United States)

    Maeda, Y.; Fukushima, A.; Imai, Y.; Tanahashi, Y.; Nakama, E.; Ohta, S.; Kawazoe, K.; Akune, N.

    2016-06-01

    The purposes of this study were 1) to estimate the biomass in the mangrove forests using satellite imagery and airborne LiDAR data, and 2) to estimate the amount of carbon stock changes using biomass estimated. The study area is located in the coastal area of the South Sumatra state, Indonesia. This area is approximately 66,500 ha with mostly flat land features. In this study, the following procedures were carried out: (1) Classification of types of tree species using Satellite imagery in the study area, (2) Development of correlation equations between spatial volume based on LiDAR data and biomass stock based on field survey for each types of tree species, and estimation of total biomass stock and carbon stock using the equation, and (3) Estimation of carbon stock change using Chronological Satellite Imageries. The result showed the biomass and the amount of carbon stock changes can be estimated with high accuracy, by combining the spatial volume based on airborne LiDAR data with the tree species classification based on satellite imagery. Quantitative biomass monitoring is in demand for projects related to REDD+ in developing countries, and this study showed that combining airborne LiDAR data with satellite imagery is one of the effective methods of monitoring for REDD+ projects.

  1. ESTIMATING CARBON STOCK CHANGES OF MANGROVE FORESTS USING SATELLITE IMAGERY AND AIRBORNE LiDAR DATA IN THE SOUTH SUMATRA STATE, INDONESIA

    Directory of Open Access Journals (Sweden)

    Y. Maeda

    2016-06-01

    Full Text Available The purposes of this study were 1 to estimate the biomass in the mangrove forests using satellite imagery and airborne LiDAR data, and 2 to estimate the amount of carbon stock changes using biomass estimated. The study area is located in the coastal area of the South Sumatra state, Indonesia. This area is approximately 66,500 ha with mostly flat land features. In this study, the following procedures were carried out: (1 Classification of types of tree species using Satellite imagery in the study area, (2 Development of correlation equations between spatial volume based on LiDAR data and biomass stock based on field survey for each types of tree species, and estimation of total biomass stock and carbon stock using the equation, and (3 Estimation of carbon stock change using Chronological Satellite Imageries. The result showed the biomass and the amount of carbon stock changes can be estimated with high accuracy, by combining the spatial volume based on airborne LiDAR data with the tree species classification based on satellite imagery. Quantitative biomass monitoring is in demand for projects related to REDD+ in developing countries, and this study showed that combining airborne LiDAR data with satellite imagery is one of the effective methods of monitoring for REDD+ projects.

  2. Estimation of differential code biases for Beidou navigation system using multi-GNSS observations: How stable are the differential satellite and receiver code biases?

    Science.gov (United States)

    Xue, Junchen; Song, Shuli; Zhu, Wenyao

    2016-04-01

    Differential code biases (DCBs) are important parameters that must be estimated accurately and reliably for high-precision GNSS applications. For optimal operational service performance of the Beidou navigation system (BDS), continuous monitoring and constant quality assessment of the BDS satellite DCBs are crucial. In this study, a global ionospheric model was constructed based on a dual system BDS/GPS combination. Daily BDS DCBs were estimated together with the total electron content from 23 months' multi-GNSS observations. The stability of the resulting BDS DCB estimates was analyzed in detail. It was found that over a long period, the standard deviations (STDs) for all satellite B1-B2 DCBs were within 0.3 ns (average: 0.19 ns) and for all satellite B1-B3 DCBs, the STDs were within 0.36 ns (average: 0.22 ns). For BDS receivers, the STDs were greater than for the satellites, with most values BDS satellite DCBs between two consecutive days was BDS DCBs, they only require occasional estimation or calibration. Furthermore, the 30-day averaged satellite DCBs can be used reliably for the most demanding BDS applications.

  3. Optimal estimation retrieval of aerosol microphysical properties from SAGE II satellite observations in the volcanically unperturbed lower stratosphere

    Directory of Open Access Journals (Sweden)

    T. Deshler

    2010-05-01

    Full Text Available Stratospheric aerosol particles under non-volcanic conditions are typically smaller than 0.1 μm. Due to fundamental limitations of the scattering theory in the Rayleigh limit, these tiny particles are hard to measure by satellite instruments. As a consequence, current estimates of global aerosol properties retrieved from spectral aerosol extinction measurements tend to be strongly biased. Aerosol surface area densities, for instance, are observed to be about 40% smaller than those derived from correlative in situ measurements (Deshler et al., 2003. An accurate knowledge of the global distribution of aerosol properties is, however, essential to better understand and quantify the role they play in atmospheric chemistry, dynamics, radiation and climate. To address this need a new retrieval algorithm was developed, which employs a nonlinear Optimal Estimation (OE method to iteratively solve for the monomodal size distribution parameters which are statistically most consistent with both the satellite-measured multi-wavelength aerosol extinction data and a priori information. By thus combining spectral extinction measurements (at visible to near infrared wavelengths with prior knowledge of aerosol properties at background level, even the smallest particles are taken into account which are practically invisible to optical remote sensing instruments. The performance of the OE retrieval algorithm was assessed based on synthetic spectral extinction data generated from both monomodal and small-mode-dominant bimodal sulphuric acid aerosol size distributions. For monomodal background aerosol, the new algorithm was shown to fairly accurately retrieve the particle sizes and associated integrated properties (surface area and volume densities, even in the presence of large extinction uncertainty. The associated retrieved uncertainties are a good estimate of the true errors. In the case of bimodal background aerosol, where the retrieved (monomodal size

  4. Discrepant estimates of primary and export production from satellite algorithms, a biogeochemical model, and geochemical tracer measurements in the North Pacific Ocean

    Science.gov (United States)

    Palevsky, Hilary I.; Quay, Paul D.; Nicholson, David P.

    2016-08-01

    Estimates of primary and export production (PP and EP) based on satellite remote sensing algorithms and global biogeochemical models are widely used to provide year-round global coverage not available from direct observations. However, observational data to validate these approaches are limited. We find that no single satellite algorithm or model can reproduce seasonal and annual geochemically determined PP, export efficiency (EP/PP), and EP rates throughout the North Pacific basin, based on comparisons throughout the full annual cycle at time series stations in the subarctic and subtropical gyres and basin-wide regions sampled by container ship transects. The high-latitude regions show large PP discrepancies in winter and spring and strong effects of deep winter mixed layers on annual EP that cannot be accounted for in current satellite-based approaches. These results underscore the need to evaluate satellite- and model-based estimates using multiple productivity parameters measured over broad ocean regions throughout the annual cycle.

  5. Performance estimation and design of group demodulator for satellite FDMA/TDM transmission

    Science.gov (United States)

    Loo, Chun; Umehira, Masahiro

    The authors describe a Monte Carlo simulation of QPSK (quadrature phase shift keying) and offset QPSK group modems which take into account the effect of the nonlinearity of each ground terminal HPA. The effect of uplink fading due to rain, as encountered in satellite links operated in the Ka and Ku bands, is included. Results show that a normalized channel spacing with respect to a symbol rate of 2.5 or greater is required to reduce the effect of adjacent channel interference. At this spacing the performance of the modem will still incur a Eb/N0 (energy per bit/noise density) degradation of about 1.0 dB. In addition, design criteria for major components such as digital subfilter, rate conversion filter, carrier recovery circuits, and quantization are given.

  6. EPSAT-SG: a satellite method for precipitation estimation; its concepts and implementation for the AMMA experiment

    Directory of Open Access Journals (Sweden)

    J. C. Bergès

    2010-01-01

    Full Text Available This paper presents a new rainfall estimation method, EPSAT-SG which is a frame for method design. The first implementation has been carried out to meet the requirement of the AMMA database on a West African domain. The rainfall estimation relies on two intermediate products: a rainfall probability and a rainfall potential intensity. The first one is computed from MSG/SEVIRI by a feed forward neural network. First evaluation results show better properties than direct precipitation intensity assessment by geostationary satellite infra-red sensors. The second product can be interpreted as a conditional rainfall intensity and, in the described implementation, it is extracted from GPCP-1dd. Various implementation options are discussed and comparison of this embedded product with 3B42 estimates demonstrates the importance of properly managing the temporal discontinuity. The resulting accumulated rainfall field can be presented as a GPCP downscaling. A validation based on ground data supplied by AGRHYMET (Niamey indicates that the estimation error has been reduced in this process. The described method could be easily adapted to other geographical area and operational environment.

  7. Satellite altimetry in sea ice regions - detecting open water for estimating sea surface heights

    Science.gov (United States)

    Müller, Felix L.; Dettmering, Denise; Bosch, Wolfgang

    2017-04-01

    The Greenland Sea and the Farm Strait are transporting sea ice from the central Arctic ocean southwards. They are covered by a dynamic changing sea ice layer with significant influences on the Earth climate system. Between the sea ice there exist various sized open water areas known as leads, straight lined open water areas, and polynyas exhibiting a circular shape. Identifying these leads by satellite altimetry enables the extraction of sea surface height information. Analyzing the radar echoes, also called waveforms, provides information on the surface backscatter characteristics. For example waveforms reflected by calm water have a very narrow and single-peaked shape. Waveforms reflected by sea ice show more variability due to diffuse scattering. Here we analyze altimeter waveforms from different conventional pulse-limited satellite altimeters to separate open water and sea ice waveforms. An unsupervised classification approach employing partitional clustering algorithms such as K-medoids and memory-based classification methods such as K-nearest neighbor is used. The classification is based on six parameters derived from the waveform's shape, for example the maximum power or the peak's width. The open-water detection is quantitatively compared to SAR images processed while accounting for sea ice motion. The classification results are used to derive information about the temporal evolution of sea ice extent and sea surface heights. They allow to provide evidence on climate change relevant influences as for example Arctic sea level rise due to enhanced melting rates of Greenland's glaciers and an increasing fresh water influx into the Arctic ocean. Additionally, the sea ice cover extent analyzed over a long-time period provides an important indicator for a globally changing climate system.

  8. Multi-annual changes of NOx emissions in megacity regions: nonlinear trend analysis of satellite measurement based estimates

    Directory of Open Access Journals (Sweden)

    J. P. Burrows

    2010-09-01

    Full Text Available Hazardous impact of air pollutant emissions from megacities on atmospheric composition on regional and global scales is currently an important issue in atmospheric research. However, the quantification of emissions and related effects is frequently a difficult task, especially in the case of developing countries, due to the lack of reliable data and information. This study examines possibilities to retrieve multi-annual NOx emissions changes in megacity regions from satellite measurements of nitrogen dioxide and to quantify them in terms of linear and nonlinear trends. By combining the retrievals of the GOME and SCIAMACHY satellite instrument data with simulations performed by the CHIMERE chemistry transport model, we obtain the time series of NOx emission estimates for the 12 largest urban agglomerations in Europe and the Middle East in the period from 1996 to 2008. We employ then a novel method allowing estimation of a nonlinear trend in a noisy time series of an observed variable. The method is based on the probabilistic approach and the use of artificial neural networks; it does not involve any quantitative a priori assumptions. As a result, statistically significant nonlinearities in the estimated NOx emission trends are detected in 5 megacities (Bagdad, Madrid, Milan, Moscow and Paris. Statistically significant upward linear trends are detected in Istanbul and Tehran, while downward linear trends are revealed in Berlin, London and the Ruhr agglomeration. The presence of nonlinearities in NOx emission changes in Milan, Paris and Madrid is confirmed by comparison of simulated NOx concentrations with independent air quality monitoring data. A good quantitative agreement between the linear trends in the simulated and measured near surface NOx concentrations is found in London.

  9. Satellite Angular Velocity Estimation Based on Star Images and Optical Flow Techniques

    Directory of Open Access Journals (Sweden)

    Giancarmine Fasano

    2013-09-01

    Full Text Available An optical flow-based technique is proposed to estimate spacecraft angular velocity based on sequences of star-field images. It does not require star identification and can be thus used to also deliver angular rate information when attitude determination is not possible, as during platform de tumbling or slewing. Region-based optical flow calculation is carried out on successive star images preprocessed to remove background. Sensor calibration parameters, Poisson equation, and a least-squares method are then used to estimate the angular velocity vector components in the sensor rotating frame. A theoretical error budget is developed to estimate the expected angular rate accuracy as a function of camera parameters and star distribution in the field of view. The effectiveness of the proposed technique is tested by using star field scenes generated by a hardware-in-the-loop testing facility and acquired by a commercial-off-the shelf camera sensor. Simulated cases comprise rotations at different rates. Experimental results are presented which are consistent with theoretical estimates. In particular, very accurate angular velocity estimates are generated at lower slew rates, while in all cases the achievable accuracy in the estimation of the angular velocity component along boresight is about one order of magnitude worse than the other two components.

  10. Analysis and Mitigation of Tropospheric Effects on Ka Band Satellite Signals and Estimation of Ergodic Capacity and Outage Probability for Terrestrial Links

    OpenAIRE

    Enserink, Scott Warren

    2012-01-01

    The first part of this work covers the effect of the troposphere onKa band (20-30 GHz) satellite signals. The second part deals withthe estimation of the capacity and outage probability forterrestrial links when constrained to quadrature amplitudemodulations.The desire for higher data rates and the need for availablebandwidth has pushed satellite communications into the Ka band(20-30 GHz). At these higher carrier frequencies the effects ofscintillation and rain attenuation are increased. In...

  11. Combined Use of Satellite Observations with Urban Surface Characteristics to Estimate PM Concentrations by Employing Mixed-Effects Models

    Science.gov (United States)

    Beloconi, Anton; Benas, Nikolaos; Chrysoulakis, Nektarios; Kamarianakis, Yiannis

    2008-11-01

    Linear mixed effects models were developed for the estimation of the average daily Particulate Matter (PM) concentration spatial distribution over the area of Greater London (UK). Both fine (PM2.5) and coarse (PM10) concentrations were predicted for the 2002- 2012 time period, based on satellite data. The latter included Aerosol Optical Thickness (AOT) at 3×3 km spatial resolution, as well as the Surface Relative Humidity, Surface Temperature and K-Index derived from MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. For a meaningful interpretation of the association among these variables, all data were homogenized with regard to spatial support and geographic projection, thus addressing the change of support problem and leading to a valid statistical inference. To this end, spatial (2D) and spatio- temporal (3D) kriging techniques were applied to in-situ particulate matter concentrations and the leave-one- station-out cross-validation was performed on a daily level to gauge the quality of the predictions. Satellite- derived covariates displayed clear seasonal patterns; in order to work with data which is stationary in mean, for each covariate, deviations from its estimated annual profiles were computed using nonlinear least squares and nonlinear absolute deviations. High-resolution land- cover and morphology static datasets were additionally incorporated in the analysis in order to catch the effects of nearby emission sources and sequestration sites. For pairwise comparisons of the particulate matter concentration means at distinct land-cover classes, the pairwise comparisons method for unequal sample sizes, known as Tukey's method, was performed. The use of satellite-derived products allowed better assessment of space-time interactions of PM, since these daily spatial measurements were able to capture differences in PM concentrations between grid cells, while the use of high- resolution land-cover and morphology static datasets allowed accounting for

  12. Comparison of surface energy fluxes with satellite-derived surface energy flux estimates from a shrub-steppe

    Energy Technology Data Exchange (ETDEWEB)

    Kirkham, Randy R. [Univ. of Washington, Seattle, WA (United States)

    1993-12-01

    This thesis relates the components of the surface energy balance (i.e., net radiation, sensible and latent heat flux densities, soil heat flow) to remotely sensed data for native vegetation in a semi-arid environment. Thematic mapper data from Landsat 4 and 5 were used to estimate net radiation, sensible heat flux (H), and vegetation amount. Several sources of ground truth were employed. They included soil water balance using the neutron thermalization method and weighing lysimeters, and the measurement of energy fluxes with the Bowen ratio energy balance (BREB) technique. Sensible and latent heat flux were measured at four sites on the U.S. Department of Energy`s Hanford Site using a weighing lysimeter and/or BREB stations. The objective was to calibrate an aerodynamic transport equation that related H to radiant surface temperature. The transport equation was then used with Landsat thermal data to generate estimates of H and compare these estimates against H values obtained with BREB/lysimeters at the time of overflight. Landsat and surface meteorologic data were used to estimate the radiation budget terms at the surface. Landsat estimates of short-wave radiation reflected from the surface correlate well with reflected radiation measured using inverted Eppley pyranometers. Correlation of net radiation estimates determined from satellite data, pyranometer, air temperature, and vapor pressure compared to net radiometer values obtained at time of overflight were excellent for a single image, but decrease for multiple images. Soil heat flux, GT, is a major component of the energy balance in arid systems and G{sub T} generally decreases as vegetation cover increases. Normalized difference vegetation index (NDVI) values generated from Landsat thermatic mapper data were representative of field observations of the presence of green vegetation, but it was not possible to determine a single relationship between NDVI and GT for all sites.

  13. Solar absorption estimated from surface radiation measurements and collocated satellite products over Europe

    Science.gov (United States)

    Zyta Hakuba, Maria; Folini, Doris; Wild, Martin; Sanchez-Lorenzo, Arturo

    2013-04-01

    Anthropogenic climate change is physically speaking a perturbation of the atmospheric energy budget through the insertion of constituents such as greenhouse gases or aerosols. Changes in the atmospheric energy budget largely affect the global climate and hydrological cycle, but the quantification of the different energy balance components is still afflicted with large uncertainties. The overall aim of the present study is the assessment of the mean state and the spatio-temporal variations in the solar energy disposition, in which we focus on obtaining an accurate partitioning of absorbed solar radiation between the surface and the atmosphere. Surface based measurements of solar radiation (GEBA, BSRN) are combined with collocated satellite-retrieved surface albedo (MODIS, CERES FSW, or CM SAF GAC-SAL) and top-of-atmosphere net incoming solar radiation (CERES EBAF) to quantify the absorbed solar radiation (ASR) at the surface and within the atmosphere over Europe for the period 2001-2005. In a first step, we examine the quality and temporal homogeneity of the monthly time series beyond 2000 provided by GEBA in order to identify a subset of sufficient quality. We find the vast majority of monthly time series to be suitable for our purposes. Using the satellite-derived CM SAF surface solar radiation product at 0.03° spatial resolution, we assess the spatial representativeness of the GEBA and BSRN sites for their collocated 1° grid cells as we intend to combine the point measurements with the coarser resolved CERES EBAF products (1° resolution), and we find spatial sampling errors of on average 3 Wm-2 or 2% (normalized by point values). Based on the combination of 134 GEBA surface solar radiation (SSR) time series with MODIS white-sky albedo and CERES EBAF top-of-atmosphere net radiation (TOAnet), we obtain a European mean partitioning (2001-2005) of absorbed solar radiation (relative to total incoming radiation) of: ASRsurf= 41% and ASRatm= 25%, together equaling

  14. A probabilistic approach for estimating monthly catchment water balances from satellite and ground data

    Science.gov (United States)

    Schoups, Gerrit

    2017-04-01

    A probabilistic model is developed to estimate monthly basin-scale precipitation, evaporation, storage and river discharge from open-source data and water balance constraints. Both random and systematic deviations between observed and "true" water balance components are included in the model to account for measurement/processing errors and differences in scale. Model parameters comprise data standard deviations (random noise) and scaling factors (systematic bias). Water balance terms and parameters are estimated using Bayesian inference, yielding posterior distributions for all unknowns. The model is applied to MOPEX basins across the continental US using the following data sources: TRMM-3B43 (precipitation), SSEBop (evaporation), GRACE (storage), and USGS stream gauges (river discharge). Results provide optimal estimates and uncertainty of water balance components and data errors across a range of basin characteristics (size, wetness, etc).

  15. Estimating vegetation dryness to optimize fire risk assessment with spot vegetation satellite data in savanna ecosystems

    Science.gov (United States)

    Verbesselt, J.; Somers, B.; Lhermitte, S.; van Aardt, J.; Jonckheere, I.; Coppin, P.

    2005-10-01

    The lack of information on vegetation dryness prior to the use of fire as a management tool often leads to a significant deterioration of the savanna ecosystem. This paper therefore evaluated the capacity of SPOT VEGETATION time-series to monitor the vegetation dryness (i.e., vegetation moisture content per vegetation amount) in order to optimize fire risk assessment in the savanna ecosystem of Kruger National Park in South Africa. The integrated Relative Vegetation Index approach (iRVI) to quantify the amount of herbaceous biomass at the end of the rain season and the Accumulated Relative Normalized Difference vegetation index decrement (ARND) related to vegetation moisture content were selected. The iRVI and ARND related to vegetation amount and moisture content, respectively, were combined in order to monitor vegetation dryness and optimize fire risk assessment in the savanna ecosystems. In situ fire activity data was used to evaluate the significance of the iRVI and ARND to monitor vegetation dryness for fire risk assessment. Results from the binary logistic regression analysis confirmed that the assessment of fire risk was optimized by integration of both the vegetation quantity (iRVI) and vegetation moisture content (ARND) as statistically significant explanatory variables. Consequently, the integrated use of both iRVI and ARND to monitor vegetation dryness provides a more suitable tool for fire management and suppression compared to other traditional satellite-based fire risk assessment methods, only related to vegetation moisture content.

  16. Karstic water storage response to the recent droughts in Southwest China estimated from satellite gravimetry

    Science.gov (United States)

    Yao, Chaolong; Luo, Zhicai

    2015-12-01

    The water resources crisis is intensifying in Southwest China (SWC), which includes the world's largest continuous coverage of karst landforms, due to recent severe drought events. However, because of the special properties of karstic water system, such as strong heterogeneity, monitoring the variation of karstic water resources at large scales remains still difficult. Satellite gravimetry has emerged as an effective tool for investigating the global and regional water cycles. In this study, we used GRACE (Gravity Recovery and Climate Experiment) data from January 2003 to January 2013 to investigate karstic water storage variability over the karst region of SWC. We assessed the impacts of the recent severe droughts on karst water resources, including two heavy droughts in September 2010 to May 2010 and August 2011 to January 2012. Results show a slightly water increase tend during the studied period, but these two severe droughts have resulted in significant water depletion in the studied karst region. The latter drought during 2011 and 2012 caused more water deficits than that of the drought in 2010. Strong correlation between the variations of GRACE-based total water storage and precipitation suggests that climate change is the main driving force for the significant water absent over the studied karst region. As the world's largest continuous coverage karst aquifer, the karst region of SWC offers an example of GRACE applications to a karst system incisively and will benefit for water management from a long-term perspective in karst systems throughout the world.

  17. A biophysical process based approach for estimating net primary production using satellite and ground observations

    Science.gov (United States)

    Choudhury, Bhaskar J.

    An approach is presented for calculating interannual variation of net primary production (C) of terrestrial plant communities at regional scale using satellite and ground measurements. C has been calculated as the difference of gross photosynthesis (A g) and respiration (R), recognizing that different biophysical factors exert major control on these two processes. A g has been expressed as the product of radiation use efficiency for gross photosynthesis by an unstressed canopy and intercepted photosynthetically active radiation, which is then adjusted for stresses due to soil water shortage and temperature away from optimum. R has been calculated as the sum of growth and maintenance components (respectively, R g and R m. The R m has been determined from nitrogen content of plant tissue per unit ground area, while R g has been obtained as a fraction of the difference of A g and R m. Model parameters have not been determined by matching the calculated fluxes against observations at any location. Results are presented for cultivated and temperate deciduous forest areas over North America for five consecutive years (1986-1990) and compared with observations.

  18. Improvement of dem Generation from Aster Images Using Satellite Jitter Estimation and Open Source Implementation

    Science.gov (United States)

    Girod, L.; Nuth, C.; Kääb, A.

    2015-12-01

    The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a source of stereoscopic images covering the whole globe at a 15m resolution at a consistent quality for over 15 years. The potential of this data in terms of geomorphological analysis and change detection in three dimensions is unrivaled and needs to be exploited. However, the quality of the DEMs and ortho-images currently delivered by NASA (ASTER DMO products) is often of insufficient quality for a number of applications such as mountain glacier mass balance. For this study, the use of Ground Control Points (GCPs) or of other ground truth was rejected due to the global "big data" type of processing that we hope to perform on the ASTER archive. We have therefore developed a tool to compute Rational Polynomial Coefficient (RPC) models from the ASTER metadata and a method improving the quality of the matching by identifying and correcting jitter induced cross-track parallax errors. Our method outputs more accurate DEMs with less unmatched areas and reduced overall noise. The algorithms were implemented in the open source photogrammetric library and software suite MicMac.

  19. Surface net solar radiation estimated from satellite measurements - Comparisons with tower observations

    Science.gov (United States)

    Li, Zhanqing; Leighton, H. G.; Cess, Robert D.

    1993-01-01

    A parameterization that relates the reflected solar flux at the top of the atmosphere to the net solar flux at the surface in terms of only the column water vapor amount and the solar zenith angle was tested against surface observations. Net surface fluxes deduced from coincidental collocated satellite-measured radiances and from measurements from towers in Boulder during summer and near Saskatoon in winter have mean differences of about 2 W/sq m, regardless of whether the sky is clear or cloudy. Furthermore, comparisons between the net fluxes deduced from the parameterization and from surface measurements showed equally good agreement when the data were partitioned into morning and afternoon observations. This is in contrast to results from an empirical clear-sky algorithm that is unable to account adequately for the effects of clouds and that shows, at Boulder, a distinct morning to afternoon variation. It is also demonstrated that the parameterization may be applied to irradiances at the top of the atmosphere that have been temporally averaged. The good agreement between the results of the parameterization and surface measurements suggests that the algorithm is a useful tool for a variety of climate studies.

  20. Surface Net Solar Radiation Estimated from Satellite Measurements: Comparisons with Tower Observations

    Science.gov (United States)

    Li, Zhanqing; Leighton, H. G.; Cess, Robert D.

    1993-01-01

    A parameterization that relates the reflected solar flux at the top of the atmosphere to the net solar flux at the surface in terms of only the column water vapor amount and the solar zenith angle was tested against surface observations. Net surface fluxes deduced from coincidental collocated satellite-measured radiances and from measurements from towers in Boulder during summer and near Saskatoon in winter have mean differences of about 2 W/sq m, regardless of whether the sky is clear or cloudy. Furthermore, comparisons between the net fluxes deduced from the parameterization and from surface measurements showed equally good agreement when the data were partitioned into morning and afternoon observations. This is in contrast to results from an empirical clear-sky algorithm that is unable to account adequately for the effects of clouds and that shows, at Boulder, a distinct morning to afternoon variation, which is presumably due to the predominance of different cloud types throughout the day. It is also demonstrated that the parameterization may be applied to irradiances at the top of the atmosphere that have been temporally averaged by using the temporally averaged column water vapor amount and the temporally averaged cosine of the solar zenith angle. The good agreement between the results of the parameterization and surface measurements suggests that the algorithm is a useful tool for a variety of climate studies.

  1. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites

    NARCIS (Netherlands)

    Mitchard, Edward T. A.; Feldpausch, Ted R.; Brienen, Roel J. W.; Lopez-Gonzalez, Gabriela; Monteagudo, Abel; Baker, Timothy R.; Lewis, Simon L.; Lloyd, Jon; Quesada, Carlos A.; Gloor, Manuel; ter Steege, Hans|info:eu-repo/dai/nl/075217120; Meir, Patrick; Alvarez, Esteban; Araujo-Murakami, Alejandro; Aragao, Luiz E. O. C.; Arroyo, Luzmila; Aymard, Gerardo; Banki, Olaf; Bonal, Damien; Brown, Sandra; Brown, Foster I.; Ceron, Carlos E.; Chama Moscoso, Victor; Chave, Jerome; Comiskey, James A.; Cornejo, Fernando; Corrales Medina, Massiel; Da Costa, Lola; Costa, Flavia R. C.; Di Fiore, Anthony; Domingues, Tomas F.; Erwin, Terry L.; Frederickson, Todd; Higuchi, Niro; Honorio Coronado, Euridice N.; Levis, Carolina; Killeen, Tim J.; Laurance, William F.; Magnusson, William E.; Marimon, Beatriz S.; Marimon Junior, Ben Hur; Mendoza Polo, Irina; Mishra, Piyush; Nascimento, Marcelo T.; Neill, David; Nunez Vargas, Mario P.; Palacios, Walter A.; Parada, Alexander; Pardo Molina, Guido; Pena-Claros, Marielos; Pitman, Nigel; Peres, Carlos A.; Prieto, Adriana; Poorter, Lourens; Ramirez-Angulo, Hirma; Restrepo Correa, Zorayda; Roopsind, Anand; Roucoux, Katherine H.; Rudas, Agustin; Salomao, Rafael P.; Schietti, Juliana; Silveira, Marcos; de Souza, Priscila F.; Steininger, Marc K.; Stropp, Juliana; Terborgh, John; Thomas, Raquel; Toledo, Marisol; Torres-Lezama, Armando; van Andel, Tinde R.|info:eu-repo/dai/nl/205284868; van der Heijden, Geertje M. F.; Vieira, Ima C. G.; Vieira, Simone; Vilanova-Torre, Emilio; Vos, Vincent A.; Wang, Ophelia; Zartman, Charles E.; Malhi, Yadvinder; Phillips, Oliver L.; Cruz, A.P.; Cuenca, W.P.; Espejo, J.E.; Ferreira, L.; Germaine, A.; Penuela, M.C.; Silva, N.; Valenzuela Gamarra, L.

    Aim The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass

  2. Hawksbill satellite-tracking case study: Implications for remigration interval and population estimates

    Science.gov (United States)

    Sartain-Iverson, Autumn R.; Hart, Kristen M.; Fujisaki, Ikuko; Cherkiss, Michael S.; Pollock, Clayton; Lundgren, Ian; Hillis-Starr, Zandy

    2016-01-01

    Hawksbill sea turtles (Eretmochelys imbricata) are circumtropically distributed and listed as Critically Endangered by the IUCN (Meylan & Donnelly 1999; NMFS & USFWS 1993). To aid in population recovery and protection, the Hawksbill Recovery Plan identified the need to determine demographic information for hawksbills, such as distribution, abundance, seasonal movements, foraging areas (sections 121 and 2211), growth rates, and survivorship (section 2213, NMFS & USFWS 1993). Mark-recapture analyses are helpful in estimating demographic parameters and have been used for hawksbills throughout the Caribbean (e.g., Richardson et al. 1999; Velez-Zuazo et al. 2008); integral to these studies are recaptures at the nesting site as well as remigration interval estimates (Hays 2000). Estimates of remigration intervals (the duration between nesting seasons) are critical to marine turtle population estimates and measures of nesting success (Hays 2000; Richardson et al. 1999). Although hawksbills in the Caribbean generally show natal philopatry and nesting-site fidelity (Bass et al. 1996; Bowen et al. 2007), exceptions to this have been observed for hawksbills and other marine turtles (Bowen & Karl 2007; Diamond 1976; Esteban et al. 2015; Hart et al. 2013). This flexibility in choosing a nesting beach could therefore affect the apparent remigration interval and subsequently, region-wide population counts.

  3. Uncertainties in Steric Sea Level Change Estimation During the Satellite Altimeter Era: Concepts and Practices

    Science.gov (United States)

    MacIntosh, C. R.; Merchant, C. J.; von Schuckmann, K.

    2016-10-01

    This article presents a review of current practice in estimating steric sea level change, focussed on the treatment of uncertainty. Steric sea level change is the contribution to the change in sea level arising from the dependence of density on temperature and salinity. It is a significant component of sea level rise and a reflection of changing ocean heat content. However, tracking these steric changes still remains a significant challenge for the scientific community. We review the importance of understanding the uncertainty in estimates of steric sea level change. Relevant concepts of uncertainty are discussed and illustrated with the example of observational uncertainty propagation from a single profile of temperature and salinity measurements to steric height. We summarise and discuss the recent literature on methodologies and techniques used to estimate steric sea level in the context of the treatment of uncertainty. Our conclusions are that progress in quantifying steric sea level uncertainty will benefit from: greater clarity and transparency in published discussions of uncertainty, including exploitation of international standards for quantifying and expressing uncertainty in measurement; and the development of community "recipes" for quantifying the error covariances in observations and from sparse sampling and for estimating and propagating uncertainty across spatio-temporal scales.

  4. Estimating forest variables from top-of-atmosphere radiance satellite measurements using coupled radiative transfer models

    NARCIS (Netherlands)

    Laurent, V.C.E.; Verhoef, W.; Clevers, J.G.P.W.; Schaepman, M.E.

    2011-01-01

    Traditionally, it is necessary to pre-process remote sensing data to obtain top of canopy (TOC) reflectances before applying physically-based model inversion techniques to estimate forest variables. Corrections for atmospheric, adjacency, topography, and surface directional effects are applied

  5. Estimating Canopy Structure in an Amazon Forest from Laser Range Finder and IKONOS Satellite Observations

    Science.gov (United States)

    Gregory P. Asner; Michael Palace; Michael Keller; Rodrigo Pereira Jr.; Jose N. M. Silva; Johan C. Zweede

    2002-01-01

    Canopy structural data can be used for biomass estimation and studies of carbon cycling, disturbance, energy balance, and hydrological processes in tropical forest ecosystems. Scarce information on canopy dimensions reflects the difficulties associated with measuring crown height, width, depth, and area in tall, humid tropical forests. New field and spaceborne...

  6. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites

    NARCIS (Netherlands)

    Mitchard, Edward T. A.; Feldpausch, Ted R.; Brienen, Roel J. W.; Lopez-Gonzalez, Gabriela; Monteagudo, Abel; Baker, Timothy R.; Lewis, Simon L.; Lloyd, Jon; Quesada, Carlos A.; Gloor, Manuel; ter Steege, Hans; Meir, Patrick; Alvarez, Esteban; Araujo-Murakami, Alejandro; Aragao, Luiz E. O. C.; Arroyo, Luzmila; Aymard, Gerardo; Banki, Olaf; Bonal, Damien; Brown, Sandra; Brown, Foster I.; Ceron, Carlos E.; Chama Moscoso, Victor; Chave, Jerome; Comiskey, James A.; Cornejo, Fernando; Corrales Medina, Massiel; Da Costa, Lola; Costa, Flavia R. C.; Di Fiore, Anthony; Domingues, Tomas F.; Erwin, Terry L.; Frederickson, Todd; Higuchi, Niro; Honorio Coronado, Euridice N.; Levis, Carolina; Killeen, Tim J.; Laurance, William F.; Magnusson, William E.; Marimon, Beatriz S.; Marimon Junior, Ben Hur; Mendoza Polo, Irina; Mishra, Piyush; Nascimento, Marcelo T.; Neill, David; Nunez Vargas, Mario P.; Palacios, Walter A.; Parada, Alexander; Pardo Molina, Guido; Pena-Claros, Marielos; Pitman, Nigel; Peres, Carlos A.; Prieto, Adriana; Poorter, Lourens; Ramirez-Angulo, Hirma; Restrepo Correa, Zorayda; Roopsind, Anand; Roucoux, Katherine H.; Rudas, Agustin; Salomao, Rafael P.; Schietti, Juliana; Silveira, Marcos; de Souza, Priscila F.; Steininger, Marc K.; Stropp, Juliana; Terborgh, John; Thomas, Raquel; Toledo, Marisol; Torres-Lezama, Armando; van Andel, Tinde R.; van der Heijden, Geertje M. F.; Vieira, Ima C. G.; Vieira, Simone; Vilanova-Torre, Emilio; Vos, Vincent A.; Wang, Ophelia; Zartman, Charles E.; Malhi, Yadvinder; Phillips, Oliver L.; Cruz, A.P.; Cuenca, W.P.; Espejo, J.E.; Ferreira, L.; Germaine, A.; Penuela, M.C.; Silva, N.; Valenzuela Gamarra, L.

    2014-01-01

    Aim The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directl

  7. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites

    NARCIS (Netherlands)

    Mitchard, Edward T. A.; Feldpausch, Ted R.; Brienen, Roel J. W.; Lopez-Gonzalez, Gabriela; Monteagudo, Abel; Baker, Timothy R.; Lewis, Simon L.; Lloyd, Jon; Quesada, Carlos A.; Gloor, Manuel; ter Steege, Hans|info:eu-repo/dai/nl/075217120; Meir, Patrick; Alvarez, Esteban; Araujo-Murakami, Alejandro; Aragao, Luiz E. O. C.; Arroyo, Luzmila; Aymard, Gerardo; Banki, Olaf; Bonal, Damien; Brown, Sandra; Brown, Foster I.; Ceron, Carlos E.; Chama Moscoso, Victor; Chave, Jerome; Comiskey, James A.; Cornejo, Fernando; Corrales Medina, Massiel; Da Costa, Lola; Costa, Flavia R. C.; Di Fiore, Anthony; Domingues, Tomas F.; Erwin, Terry L.; Frederickson, Todd; Higuchi, Niro; Honorio Coronado, Euridice N.; Levis, Carolina; Killeen, Tim J.; Laurance, William F.; Magnusson, William E.; Marimon, Beatriz S.; Marimon Junior, Ben Hur; Mendoza Polo, Irina; Mishra, Piyush; Nascimento, Marcelo T.; Neill, David; Nunez Vargas, Mario P.; Palacios, Walter A.; Parada, Alexander; Pardo Molina, Guido; Pena-Claros, Marielos; Pitman, Nigel; Peres, Carlos A.; Prieto, Adriana; Poorter, Lourens; Ramirez-Angulo, Hirma; Restrepo Correa, Zorayda; Roopsind, Anand; Roucoux, Katherine H.; Rudas, Agustin; Salomao, Rafael P.; Schietti, Juliana; Silveira, Marcos; de Souza, Priscila F.; Steininger, Marc K.; Stropp, Juliana; Terborgh, John; Thomas, Raquel; Toledo, Marisol; Torres-Lezama, Armando; van Andel, Tinde R.|info:eu-repo/dai/nl/205284868; van der Heijden, Geertje M. F.; Vieira, Ima C. G.; Vieira, Simone; Vilanova-Torre, Emilio; Vos, Vincent A.; Wang, Ophelia; Zartman, Charles E.; Malhi, Yadvinder; Phillips, Oliver L.; Cruz, A.P.; Cuenca, W.P.; Espejo, J.E.; Ferreira, L.; Germaine, A.; Penuela, M.C.; Silva, N.; Valenzuela Gamarra, L.

    2014-01-01

    Aim The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directl

  8. Estimating forest variables from top-of-atmosphere radiance satellite measurements using coupled radiative transfer models

    NARCIS (Netherlands)

    Laurent, V.C.E.; Verhoef, W.; Clevers, J.G.P.W.; Schaepman, M.E.

    2011-01-01

    Traditionally, it is necessary to pre-process remote sensing data to obtain top of canopy (TOC) reflectances before applying physically-based model inversion techniques to estimate forest variables. Corrections for atmospheric, adjacency, topography, and surface directional effects are applied seque

  9. Estimating Evapotranspiration from an Improved Two-Source Energy Balance Model Using ASTER Satellite Imagery

    Directory of Open Access Journals (Sweden)

    Qifeng Zhuang

    2015-11-01

    Full Text Available Reliably estimating the turbulent fluxes of latent and sensible heat at the Earth’s surface by remote sensing is important for research on the terrestrial hydrological cycle. This paper presents a practical approach for mapping surface energy fluxes using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER images from an improved two-source energy balance (TSEB model. The original TSEB approach may overestimate latent heat flux under vegetative stress conditions, as has also been reported in recent research. We replaced the Priestley-Taylor equation used in the original TSEB model with one that uses plant moisture and temperature constraints based on the PT-JPL model to obtain a more accurate canopy latent heat flux for model solving. The collected ASTER data and field observations employed in this study are over corn fields in arid regions of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER area, China. The results were validated by measurements from eddy covariance (EC systems, and the surface energy flux estimates of the improved TSEB model are similar to the ground truth. A comparison of the results from the original and improved TSEB models indicates that the improved method more accurately estimates the sensible and latent heat fluxes, generating more precise daily evapotranspiration (ET estimate under vegetative stress conditions.

  10. Uncertainties in Steric Sea Level Change Estimation During the Satellite Altimeter Era: Concepts and Practices

    Science.gov (United States)

    MacIntosh, C. R.; Merchant, C. J.; von Schuckmann, K.

    2017-01-01

    This article presents a review of current practice in estimating steric sea level change, focussed on the treatment of uncertainty. Steric sea level change is the contribution to the change in sea level arising from the dependence of density on temperature and salinity. It is a significant component of sea level rise and a reflection of changing ocean heat content. However, tracking these steric changes still remains a significant challenge for the scientific community. We review the importance of understanding the uncertainty in estimates of steric sea level change. Relevant concepts of uncertainty are discussed and illustrated with the example of observational uncertainty propagation from a single profile of temperature and salinity measurements to steric height. We summarise and discuss the recent literature on methodologies and techniques used to estimate steric sea level in the context of the treatment of uncertainty. Our conclusions are that progress in quantifying steric sea level uncertainty will benefit from: greater clarity and transparency in published discussions of uncertainty, including exploitation of international standards for quantifying and expressing uncertainty in measurement; and the development of community "recipes" for quantifying the error covariances in observations and from sparse sampling and for estimating and propagating uncertainty across spatio-temporal scales.

  11. Vision-Based 3D Motion Estimation for On-Orbit Proximity Satellite Tracking and Navigation

    Science.gov (United States)

    2015-06-01

    Network .....................................................................................58 3. Telemetry Computer...screenshot of the telemetry software and the SSH terminals. ...........61 Figure 25. View of the VICON cameras above the granite flat floor of the FSS...point-wise kinematic models. The pose of the 3D structure is then estimated using a dual quaternion method [19]. The robustness and validity of this

  12. Phase Residual Estimations for PCVs of Spaceborne GPS Receiver Antenna and Their Impacts on Precise Orbit Determination of GRACE Satellites

    Institute of Scientific and Technical Information of China (English)

    TU Jia; GU Defeng; WU Yi; YI Dongyun

    2012-01-01

    In-flight phase center systematic errors of global positioning system (GPS) receiver antenna are the main restriction for improving the precision of precise orbit determination using dual-frequency GPS.Residual approach is one of the valid methods for in-flight calibration of GPS receiver antenna phase center variations (PCVs) from ground calibration.In this paper,followed by the correction model of spaceborne GPS receiver antenna phase center,ionosphere-free PCVs can be directly estimated by ionosphere-free carrier phase post-fit residuals of reduced dynamic orbit determination.By the data processing of gravity recovery and climate experiment (GRACE) satellites,the following conclusions are drawn.Firstly,the distributions of ionosphere-free carrier phase post-fit residuals from different periods have the similar systematic characteristics.Secondly,simulations show that the influence of phase residual estimations for ionosphere-free PCVs on orbit determination can reach the centimeter level.Finally,it is shown by in-flight data processing that phase residual estimations of current period could not only be used for the calibration for GPS receiver antenna phase center of foretime and current period,but also be used for the forecast of ionosphere-free PCVs in future period,and the accuracy of orbit determination can be well improved.

  13. On estimating the basin-scale ocean circulation from satellite altimetry. Part 1: Straightforward spherical harmonic expansion

    Science.gov (United States)

    Tai, Chang-Kou

    1988-01-01

    Direct estimation of the absolute dynamic topography from satellite altimetry has been confined to the largest scales (basically the basin-scale) owing to the fact that the signal-to-noise ratio is more unfavorable everywhere else. But even for the largest scales, the results are contaminated by the orbit error and geoid uncertainties. Recently a more accurate Earth gravity model (GEM-T1) became available, providing the opportunity to examine the whole question of direct estimation under a more critical limelight. It is found that our knowledge of the Earth's gravity field has indeed improved a great deal. However, it is not yet possible to claim definitively that our knowledge of the ocean circulation has improved through direct estimation. Yet, the improvement in the gravity model has come to the point that it is no longer possible to attribute the discrepancy at the basin scales between altimetric and hydrographic results as mostly due to geoid uncertainties. A substantial part of the difference must be due to other factors; i.e., the orbit error, or the uncertainty of the hydrographically derived dynamic topography.

  14. Developing a Near-Continuous Estimation of Volumetric Fluctuations in Tropical Lakes and Reservoirs Using Satellite Remote Sensing

    Science.gov (United States)

    Keys, T.; Scott, D.

    2015-12-01

    Lakes and reservoirs play an integral role in water resources management by storing large quantities of water commonly used for irrigation, hydroelectric power, water supply, and flood mitigation. Knowing the exact quantity of stored water and necessary water for each of these usages is a critical component of sustainable water resources management. However, limited amounts of hydrologic data in developing nations, most of which are located in the tropics, hinders the accurate monitoring of water storage and allocation. Recent improvements in remote sensing have greatly enhanced the ability to calculate volumetric fluctuations of lakes and reservoirs at given points through time but are limited by temporal resolution as well as the computational time required for image processing. This study utilizes the newly developed MODISTools package for the programming language R in conjunction with satellite altimetry from three different altimetry databases to estimate lake and reservoir volumes at eight day intervals over a 15 year period. The study specifically examines three large lakes and reservoirs: Balbina Reservoir in the Amazon River Basin, Lake Tana in the Nile River Basin, and Tonle Sap Lake in the Mekong River Basin. Altimetry-based water level estimations are validated by in situ water level data from monitoring stations while surface area estimations are validated by Sound Navigation and Ranging (SONAR) generated bathymetric maps with corresponding stage-area relationships. Preliminary results indicate that both remotely sensed water levels and surface areas agree well with in situ measurements, supporting the appropriateness of this methodology.

  15. Estimation of an eartquake focal mechanism from a satellite radar interferogram:Application to the December 4, 1992 Landers aftershock

    Science.gov (United States)

    Feigl, Kurt L.; Sergent, Arnaud; Jacq, Dominique

    1995-05-01

    Interferometric fringes generated by the phase difference between a pair of synthetic-aperture radar images acquired by the ERS-1 satellite were used to estimate the focal mechanism of a small, shallow thrust earthquake. The inversion procedure is an iterative, linerarized least-squares algorithm based on a standard elastic dislocation formulation for coseismic displacements. The preferred estimate is a thrust focal mechanism with its hypocenter at (N34.35 deg +/- 0.4 km, W 116.91 deg +/- 0.2 km, 2.6 +/- 0.3 km depth) on a plane dipping southward beneath the San Bernardino Mountains, with a moment magnitude of 5.4. The strike, dip and rake are N106 deg E +/- 7 deg, 28 deg +/- 4 deg, and 93 deg +/- 4deg, respectively on a fault 3.1 +/- 0.5 km wide and 2.9 +/- 0.4 km long. The precision of these estimates is competitive with seismological determinations.

  16. An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations.

    Science.gov (United States)

    Feng, Fei; Li, Xianglan; Yao, Yunjun; Liang, Shunlin; Chen, Jiquan; Zhao, Xiang; Jia, Kun; Pintér, Krisztina; McCaughey, J Harry

    2016-01-01

    Accurate estimation of latent heat flux (LE) based on remote sensing data is critical in characterizing terrestrial ecosystems and modeling land surface processes. Many LE products were released during the past few decades, but their quality might not meet the requirements in terms of data consistency and estimation accuracy. Merging multiple algorithms could be an effective way to improve the quality of existing LE products. In this paper, we present a data integration method based on modified empirical orthogonal function (EOF) analysis to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16) and the Priestley-Taylor LE algorithm of Jet Propulsion Laboratory (PT-JPL) estimate. Twenty-two eddy covariance (EC) sites with LE observation were chosen to evaluate our algorithm, showing that the proposed EOF fusion method was capable of integrating the two satellite data sets with improved consistency and reduced uncertainties. Further efforts were needed to evaluate and improve the proposed algorithm at larger spatial scales and time periods, and over different land cover types.

  17. Temporal disaggregation of satellite-derived monthly precipitation estimates and the resulting propagation of error in partitioning of water at the land surface

    Directory of Open Access Journals (Sweden)

    S.A. Margulis

    2001-01-01

    Full Text Available Global estimates of precipitation can now be made using data from a combination of geosynchronous and low earth-orbit satellites. However, revisit patterns of polar-orbiting satellites and the need to sample mixed-clouds scenes from geosynchronous satellites leads to the coarsening of the temporal resolution to the monthly scale. There are prohibitive limitations to the applicability of monthly-scale aggregated precipitation estimates in many hydrological applications. The nonlinear and threshold dependencies of surface hydrological processes on precipitation may cause the hydrological response of the surface to vary considerably based on the intermittent temporal structure of the forcing. Therefore, to make the monthly satellite data useful for hydrological applications (i.e. water balance studies, rainfall-runoff modelling, etc., it is necessary to disaggregate the monthly precipitation estimates into shorter time intervals so that they may be used in surface hydrology models. In this study, two simple statistical disaggregation schemes are developed for use with monthly precipitation estimates provided by satellites. The two techniques are shown to perform relatively well in introducing a reasonable temporal structure into the disaggregated time series. An ensemble of disaggregated realisations was routed through two land surface models of varying complexity so that the error propagation that takes place over the course of the month could be characterised. Results suggest that one of the proposed disaggregation schemes can be used in hydrological applications without introducing significant error. Keywords: precipitation, temporal disaggregation, hydrological modelling, error propagation

  18. Biomass burning losses of carbon estimated from ecosystem modeling and satellite data analysis for the Brazilian Amazon region

    Science.gov (United States)

    Potter, Christopher; Brooks Genovese, Vanessa; Klooster, Steven; Bobo, Matthew; Torregrosa, Alicia

    To produce a new daily record of gross carbon emissions from biomass burning events and post-burning decomposition fluxes in the states of the Brazilian Legal Amazon (Instituto Brasileiro de Geografia e Estatistica (IBGE), 1991. Anuario Estatistico do Brasil, Vol. 51. Rio de Janeiro, Brazil pp. 1-1024). We have used vegetation greenness estimates from satellite images as inputs to a terrestrial ecosystem production model. This carbon allocation model generates new estimates of regional aboveground vegetation biomass at 8-km resolution. The modeled biomass product is then combined for the first time with fire pixel counts from the advanced very high-resolution radiometer (AVHRR) to overlay regional burning activities in the Amazon. Results from our analysis indicate that carbon emission estimates from annual region-wide sources of deforestation and biomass burning in the early 1990s are apparently three to five times higher than reported in previous studies for the Brazilian Legal Amazon (Houghton et al., 2000. Nature 403, 301-304; Fearnside, 1997. Climatic Change 35, 321-360), i.e., studies which implied that the Legal Amazon region tends toward a net-zero annual source of terrestrial carbon. In contrast, our analysis implies that the total source fluxes over the entire Legal Amazon region range from 0.2 to 1.2 Pg C yr -1, depending strongly on annual rainfall patterns. The reasons for our higher burning emission estimates are (1) use of combustion fractions typically measured during Amazon forest burning events for computing carbon losses, (2) more detailed geographic distribution of vegetation biomass and daily fire activity for the region, and (3) inclusion of fire effects in extensive areas of the Legal Amazon covered by open woodland, secondary forests, savanna, and pasture vegetation. The total area of rainforest estimated annually to be deforested did not differ substantially among the previous analyses cited and our own.

  19. Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI AMSR-E, SSM/I, AMSU-B and the TRMM PR

    Science.gov (United States)

    Fisher, Brad; Wolff, David B.

    2010-01-01

    Passive and active microwave rain sensors onboard earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscure the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different space-borne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25 resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite and the regional climatology. The most significant result from this study found that each of the satellites incurred negative longterm oceanic retrieval biases of 10 to 30%.

  20. Gravity wave driving of the QBO estimated from satellite observations and ERA-Interim

    Science.gov (United States)

    Ern, Manfred; Preusse, Peter; Kalisch, Silvio; Ploeger, Felix; Riese, Martin

    2015-04-01

    The quasi-biennial oscillation (QBO) of the zonal wind in the tropical stratosphere is an important process in atmospheric dynamics. The QBO has effect on atmospheric dynamics over a large range of altitudes and latitudes. Effects of the QBO are found, for example, in the mesosphere, and selective filtering of upward propagating waves plays an important role for the stratopause semiannual oscillation (SAO). The QBO also influences the extratropics and even surface weather and climate. Still, climate models have large difficulties in reproducing a realistic QBO. Atmospheric waves play an important role in the driving of the QBO. Both global scale waves and mesoscale gravity waves (GWs) contribute. We derive GW temperature variances, GW momentum fluxes and potential GW drag from three years of High Resolution Dynamics Limb Sounder (HIRDLS) and from 11 years of Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) satellite data. These observations are compared with the drag that is still missing in the tropical momentum budget of the ECMWF ERA-Interim (ERAI) reanalysis after considering zonal wind tendency, Coriolis force, advection terms, and the drag due to resolved global-scale waves. Being strongly constrained by data assimilation, the meteorological fields of ERAI are quite realistic. Therefore this missing drag can be attributed to small scale GWs not resolved by the model. We find good qualitative agreement between observed GW drag and the missing drag due to waves not resolved in ERAI. During eastward QBO wind shear even the magnitude of observed and ERAI missing drag are in good agreement. During westward shear, however, observed drag is much weaker than the ERAI missing drag. This asymmetry might hint at uncertainties in the advection terms of ERAI. Further, observed GW spectra indicate that QBO-related GW dissipation is mainly due to critical level filtering.

  1. A Monocular SLAM Method to Estimate Relative Pose During Satellite Proximity Operations

    Science.gov (United States)

    2015-03-26

    HOMER video sequence in Figure 17) provides the relative rotation and translation required from the perspective of the camera for successful...shows the SLAM results, with the camera models representing the relative pose estimates of the camera on the arm. Figure 40 shows another perspective of...ensures initialization is performed with adequate relative motion. If no additional perspective information is obtained in an initialization attempt, a

  2. Soil moisture deficit estimation using satellite multi-angle brightness temperature

    Science.gov (United States)

    Zhuo, Lu; Han, Dawei; Dai, Qiang

    2016-08-01

    Accurate soil moisture information is critically important for hydrological modelling. Although remote sensing soil moisture measurement has become an important data source, it cannot be used directly in hydrological modelling. A novel study based on nonlinear techniques (a local linear regression (LLR) and two feedforward artificial neural networks (ANNs)) is carried out to estimate soil moisture deficit (SMD), using the Soil Moisture and Ocean Salinity (SMOS) multi-angle brightness temperatures (Tbs) with both horizontal (H) and vertical (V) polarisations. The gamma test is used for the first time to determine the optimum number of Tbs required to construct a reliable smooth model for SMD estimation, and the relationship between model input and output is achieved through error variance estimation. The simulated SMD time series in the study area is from the Xinanjiang hydrological model. The results have shown that LLR model is better at capturing the interrelations between SMD and Tbs than ANNs, with outstanding statistical performances obtained during both training (NSE = 0.88, r = 0.94, RMSE = 0.008 m) and testing phases (NSE = 0.85, r = 0.93, RMSE = 0.009 m). Nevertheless, both ANN training algorithms (radial BFGS and conjugate gradient) have performed well in estimating the SMD data and showed excellent performances compared with those derived directly from the SMOS soil moisture products. This study has also demonstrated the informative capability of the gamma test in the input data selection for model development. These results provide interesting perspectives for data-assimilation in flood-forecasting.

  3. Satellite detection of land-use change and effects on regional forest aboveground biomass estimates.

    Science.gov (United States)

    Zheng, Daolan; Heath, Linda S; Ducey, Mark J

    2008-09-01

    We used remote-sensing-driven models to detect land-cover change effects on forest aboveground biomass (AGB) density (Mg.ha(-1), dry weight) and total AGB (Tg) in Minnesota, Wisconsin, and Michigan USA, between the years 1992-2001, and conducted an evaluation of the approach. Inputs included remotely-sensed 1992 reflectance data and land-cover map (University of Maryland) from Advanced Very High Resolution Radiometer (AVHRR) and 2001 products from Moderate Resolution Imaging Spectroradiometer (MODIS) at 1-km resolution for the region; and 30-m resolution land-cover maps from the National Land Cover Data (NLCD) for a subarea to conduct nine simulations to address our questions. Sensitivity analysis showed that (1) AVHRR data tended to underestimate AGB density by 11%, on average, compared to that estimated using MODIS data; (2) regional mean AGB density increased slightly from 124 (1992) to 126 Mg ha(-1) (2001) by 1.6%; (3) a substantial decrease in total forest AGB across the region was detected, from 2,507 (1992) to 1,961 Tg (2001), an annual rate of -2.4%; and (4) in the subarea, while NLCD-based estimates suggested a 26% decrease in total AGB from 1992 to 2001, AVHRR/MODIS-based estimates indicated a 36% increase. The major source of uncertainty in change detection of total forest AGB over large areas was due to area differences from using land-cover maps produced by different sources. Scaling up 30-m land-cover map to 1-km resolution caused a mean difference of 8% (in absolute value) in forest area estimates at the county-level ranging from 0 to 17% within a 95% confidence interval.

  4. TOTAL WOOD VOLUME ESTIMATION OF EUCALYPTUS SPECIES BY IMAGES OF LANDSAT SATELLITE

    Directory of Open Access Journals (Sweden)

    Elias Fernando Berra

    2012-12-01

    Full Text Available http://dx.doi.org/10.5902/198050987566Models relating spectral answers with biophysical parameters aim estimate variables, like wood volume, without the necessity of frequent field measurements. The objective was to develop models to estimate wood volume by Landsat 5 TM images, supported by regional forest inventory data. The image was geo-referenced and converted to spectral reflectance. After, the images-index NDVI (Normalized Difference Vegetation Index and SR (Simple Ratio was generated. The reflectance values of the bands (TM1, TM2, TM3 e TM4 and of the indices (NDVI and SR was related with the wood volume. The biggest correlation with volume was with the NDVI and SR indices. The variables selection was made by Stepwise method, which returned three regression models as significant to explain the variation in volume. Finally, the best fitted model was selected (volume = -830,95 + 46,05 (SR + 107,47 (TM2, which was applied on the Landsat image where the pixels had started to represent the estimated volume in m³/ha on the Eucalyptus sp. production units. This model, significant at 95% confidence level, explains 68% of the wood volume variation.

  5. Fast converging with high accuracy estimates of satellite attitude and orbit based on magnetometer augmented with gyro, star sensor and GPS via extended Kalman filter

    Directory of Open Access Journals (Sweden)

    Tamer Mekky Ahmed Habib

    2011-12-01

    Full Text Available The primary goal of this work is to extend the work done in, Tamer (2009, to provide high accuracy satellite attitude and orbit estimates needed for imaging purposes and also before execution of spacecraft orbital maneuvers for the next Egyptian scientific satellite. The problem of coarse satellite attitude and orbit estimation based on magnetometer measurements has been treated in the literature. The current research expands the field of application from coarse and slow converging estimates to accurate and fast converging attitude and orbit estimates within 0.1°, and 10 m for attitude angles and spacecraft location respectively (1-σ. The magnetometer is used for both spacecraft attitude and orbit estimation, aided with gyro to provide angular velocity measurements, star sensor to provide attitude quaternion, and GPS receiver to provide spacecraft location. The spacecraft under consideration is subject to solar radiation pressure forces and moments, aerodynamics forces and moments, earth’s oblateness till the fourth order (i.e. J4, gravity gradient moments, and residual magnetic dipole moments. The estimation algorithm developed is powerful enough to converge quickly (actually within 10 s despite very large initial estimation errors with sufficiently high accuracy estimates.

  6. Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia

    Science.gov (United States)

    Kamarul Zaman, Nurul Amalin Fatihah; Kanniah, Kasturi Devi; Kaskaoutis, Dimitris G.

    2017-09-01

    The insufficient number of ground-based stations for measuring Particulate Matter training are performed via comparison with measured PM10 at 29 stations over Malaysia and reveal that the ANN provides slightly higher accuracy with R2 = 0.71 and RMSE = 11.61 μg m- 3 compared to the MLR method (R2 = 0.66 and RMSE = 12.39 μg m- 3). Stepwise regression analysis performed on the MLR method reveals that the MODIS AOD550 is the most important parameter for PM10 estimations (R2 = 0.59 and RMSE = 13.61 μg m- 3); however, the inclusion of the meteorological parameters in the MLR increases the accuracy of the retrievals (R2 = 0.66, RMSE = 12.39 μg m- 3). The estimated PM10 concentrations are finally validated against surface measurements at 16 stations resulting in similar performance from the ANN model (R2 = 0.58, RMSE = 10.16 μg m- 3) and MLR technique (R2 = 0.56, RMSE = 10.58 μg m- 3). The significant accuracy that has been attained in PM10 estimations from space allows us to assess the pollution levels in Malaysia and map the PM10 distribution at large spatial and temporal scales. Supplementary Figure 2: Seasonal-mean Terra-MODIS AOD550 values at 10 x 10 km over the 45 air-pollution monitoring stations in Malaysia during 2007 - 2011 for (a) dry season (June - September), (b) wet season (November - March), (c) April - May and (d) October.

  7. Estimating irrigated areas from satellite and model soil moisture data over the contiguous US

    Science.gov (United States)

    Zaussinger, Felix; Dorigo, Wouter; Gruber, Alexander

    2017-04-01

    Information about irrigation is crucial for a number of applications such as drought- and yield management and contributes to a better understanding of the water-cycle, land-atmosphere interactions as well as climate projections. Currently, irrigation is mainly quantified by national agricultural statistics, which do not include spatial information. The digital Global Map of Irrigated Areas (GMIA) has been the first effort to quantify irrigation at the global scale by merging these statistics with remote sensing data. Also, the MODIS-Irrigated Agriculture Dataset (MirAD-US) was created by merging annual peak MODIS-NDVI with US county level irrigation statistics. In this study we aim to map irrigated areas by confronting time series of various satellite soil moisture products with soil moisture from the ERA-Interim/Land reanalysis product. We follow the assumption that irrigation signals are not modelled in the reanalysis product, nor contributing to its forcing data, but affecting the spatially continuous remote sensing observations. Based on this assumption, spatial patterns of irrigation are derived from differences between the temporal slopes of the modelled and remotely sensed time series during the irrigation season. Results show that a combination of ASCAT and ERA-Interim/Land show spatial patterns which are in good agreement with the MIrAD-US, particularly within the Mississippi Delta, Texas and eastern Nebraska. In contrast, AMSRE shows weak agreements, plausibly due to a higher vegetation dependency of the soil moisture signal. There is no significant agreement to the MIrAD-US in California, which is possibly related to higher crop-diversity and lower field sizes. Also, a strong signal in the region of the Great Corn Belt is observed, which is generally not outlined as an irrigated area. It is not yet clear to what extent the signal obtained in the Mississippi Delta is related to re-reflection effects caused by standing water due to flood or furrow

  8. Estimates of changes of structural parameters of forest ecosystems in decoding high resolution satellite images

    Directory of Open Access Journals (Sweden)

    Yuri F. Rozhkov

    2016-05-01

    Full Text Available Aim of this study was to assess the possibility of using of the parameter of symmetry of pixel distribution in the forest condition monitoring. Multispectral satellite imagery and their fragments of high and medium resolution (Landsat TM/ЕТМ+, Aster, Spot, IRS, which have been made in 1995–2011, were processed in two stages. At the first stage, uncontrolled classification has been carried out using the method ISODATA (Iterative Self-Organizing Data Analysis Technigue. At the second stage, parameter of symmetry of pixel distribution was calculated. The results of classification were divided into two halves. Classes with lower optical density of the reflected light were concentrated in the upper half while classes with higher optical density of the reflected light were concentrated in the bottom half. The prospects for using the parameter symmetry of pixel distribution aim to assess the degree of forest disturbance after the fire impact was demonstrated. Disturbed forest areas a have larger sum of pixels in the bottom half of the classification results compared with the upper half. In contrast, undisturbed forest areas have a larger or equal sum of pixels in the upper half of the classification results compared with the bottom half. The prospects for using the parameter symmetry of pixel distribution in monitoring of seasonal changes of forest status were demonstrated. Comparison of two forest fragments with dominance of larch and Siberian pine showed that during the autumn months (September, October after needle fall and leaf fall, there is a sharp decrease of the parameter "symmetry of pixel distribution" within the fragment with dominance of larch due to the increase of the proportion of pixels with high optical density. Seasonal changes in the parameter of symmetry of pixels distribution were less pronounced for the forest fragment with dominance of Siberian pine. We considered the prospect to use the parameter of symmetry of pixel

  9. Developing an Ice Volume Estimate of Jarvis Glacier, Alaska, using Ground-Penetrating Radar and High Resolution Satellite Imagery

    Science.gov (United States)

    Wu, N. L.; Campbell, S. W.; Douglas, T. A.; Osterberg, E. C.

    2013-12-01

    Jarvis Glacier is an important water source for Fort Greely and Delta Junction, Alaska. Yet with warming summer temperatures caused by climate change, the glacier is melting rapidly. Growing concern of a dwindling water supply has caused significant research efforts towards determining future water resources from spring melt and glacier runoff which feeds the community on a yearly basis. The main objective of this project was to determine the total volume of the Jarvis Glacier. In April 2012, a centerline profile of the Jarvis Glacier and 15 km of 100 MHz ground-penetrating radar (GPR) profiles were collected in cross sections to provide ice depth measurements. These depth measurements were combined with an interpreted glacier boundary (depth = 0 m) from recently collected high resolution WorldView satellite imagery to estimate total ice volume. Ice volume was calculated at 0.62 km3 over a surface area of 8.82 km2. However, it is likely that more glacier-ice exists within Jarvis Glacier watershed considering the value calculated with GPR profiles accounts for only the glacier ice within the valley and not for the valley side wall ice. The GLIMS glacier area database suggests that the valley accounts for approximately 50% of the total ice covered watershed. Hence, we are currently working to improve total ice volume estimates which incorporate the surrounding valley walls. Results from this project will be used in conjunction with climate change estimates and hydrological properties downstream of the glacier to estimate future water resources available to Fort Greely and Delta Junction.

  10. Tree canopy light interception estimates in almond and a walnut orchards using ground, low flying aircraft, and satellite based methods to improve irrigation scheduling programs.

    Science.gov (United States)

    Rosecrance, R. C.; Johnson, L.; Soderstrom, D.

    2016-12-01

    Canopy light interception is a main driver of water use and crop yield in almond and walnut production. Fractional green canopy cover (Fc) is a good indicator of light interception and can be estimated remotely from satellite using the normalized difference vegetation index (NDVI) data. Satellite-based Fc estimates could be used to inform crop evapotranspiration models, and hence support improvements in irrigation evaluation and management capabilities. Satellite estimates of Fc in almond and walnut orchards, however, need to be verified before incorporating them into irrigation scheduling or other crop water management programs. In this study, Landsat-based NDVI and Fc from NASA's Satellite Irrigation Management Support (SIMS) were compared with four estimates of canopy cover: 1. light bar measurement, 2. in-situ and image-based dimensional tree-crown analyses, 3. high-resolution NDVI data from low flying aircraft, and 4. orchard photos obtained via Google Earth and processed by an Image J thresholding routine. Correlations between the various estimates are discussed.

  11. Tree Canopy Light Interception Estimates in Almond and a Walnut Orchards Using Ground, Low Flying Aircraft, and Satellite Based Methods to Improve Irrigation Scheduling Programs

    Science.gov (United States)

    Rosecrance, Richard C.; Johnson, Lee; Soderstrom, Dominic

    2016-01-01

    Canopy light interception is a main driver of water use and crop yield in almond and walnut production. Fractional green canopy cover (Fc) is a good indicator of light interception and can be estimated remotely from satellite using the normalized difference vegetation index (NDVI) data. Satellite-based Fc estimates could be used to inform crop evapotranspiration models, and hence support improvements in irrigation evaluation and management capabilities. Satellite estimates of Fc in almond and walnut orchards, however, need to be verified before incorporating them into irrigation scheduling or other crop water management programs. In this study, Landsat-based NDVI and Fc from NASA's Satellite Irrigation Management Support (SIMS) were compared with four estimates of canopy cover: 1. light bar measurement, 2. in-situ and image-based dimensional tree-crown analyses, 3. high-resolution NDVI data from low flying aircraft, and 4. orchard photos obtained via Google Earth and processed by an Image J thresholding routine. Correlations between the various estimates are discussed.

  12. Estimating carbon and showing impacts of drought using satellite data in regression-tree models

    Science.gov (United States)

    Boyte, Stephen; Wylie, Bruce K.; Howard, Danny; Dahal, Devendra; Gilmanov, Tagir G.

    2018-01-01

    Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, allowing a better understanding of broad-scale ecosystem processes. The current study presents annual gross primary production (GPP) and annual ecosystem respiration (RE) for 2000–2013 in several short-statured vegetation types using carbon flux data from towers that are located strategically across the conterminous United States (CONUS). We calculate carbon fluxes (annual net ecosystem production [NEP]) for each year in our study period, which includes 2012 when drought and higher-than-normal temperatures influence vegetation productivity in large parts of the study area. We present and analyse carbon flux dynamics in the CONUS to better understand how drought affects GPP, RE, and NEP. Model accuracy metrics show strong correlation coefficients (r) (r ≥ 94%) between training and estimated data for both GPP and RE. Overall, average annual GPP, RE, and NEP are relatively constant throughout the study period except during 2012 when almost 60% less carbon is sequestered than normal. These results allow us to conclude that this modelling method effectively estimates carbon dynamics through time and allows the exploration of impacts of meteorological anomalies and vegetation types on carbon dynamics.

  13. Impact of Spatial LAI Heterogeneity on Estimate of Directional Gap Fraction from SPOT-Satellite Data

    Directory of Open Access Journals (Sweden)

    Zhao-Liang Li

    2008-06-01

    Full Text Available Directional gap probability or gap fraction is a basic parameter in the optical remote sensing modeling. Although some approaches have been proposed to estimate this gap probability from remotely sensed measurements, few efforts have been made to investigate the scaling effects of this parameter. This paper analyzes the scaling effect through aggregating the high-resolution directional gap probability (pixel size of 20 meters estimated from leaf area index (LAI images of VALERI database by means of Beer's law and introduces an extension of clumping index, Ĉ, to compensate the scaling bias. The results show that the scaling effect depends on both the surface heterogeneity and the nonlinearity degree of the retrieved function. Analytical expressions for the scaling bias of gap probability and Ĉ are established in function of the variance of LAI and the mean value of LAI in a coarse pixel. With the VALERI dataset, the study in this paper shows that relative scaling bias of gap probability increases with decreasing spatial resolution for most of land cover types. Large relative biases are found for most of crops sites and a mixed forest site due to their relative large variance of LAI, while very small biases occur over grassland and shrubs sites. As for Ĉ, it varies slowly in the pure forest, grassland and shrubs sites, while more significantly in crops and mixed forest.

  14. Estimating Total Discharge in the Yangtze River Basin Using Satellite-Based Observations

    Directory of Open Access Journals (Sweden)

    Samuel A. Andam‑Akorful

    2013-07-01

    Full Text Available The measurement of total basin discharge along coastal regions is necessary for understanding the hydrological and oceanographic issues related to the water and energy cycles. However, only the observed streamflow (gauge-based observation is used to estimate the total fluxes from the river basin to the ocean, neglecting the portion of discharge that infiltrates to underground and directly discharges into the ocean. Hence, the aim of this study is to assess the total discharge of the Yangtze River (Chang Jiang basin. In this study, we explore the potential response of total discharge to changes in precipitation (from the Tropical Rainfall Measuring Mission—TRMM, evaporation (from four versions of the Global Land Data Assimilation—GLDAS, namely, CLM, Mosaic, Noah and VIC, and water-storage changes (from the Gravity Recovery and Climate Experiment—GRACE by using the terrestrial water budget method. This method has been validated by comparison with the observed streamflow, and shows an agreement with a root mean square error (RMSE of 14.30 mm/month for GRACE-based discharge and 20.98 mm/month for that derived from precipitation minus evaporation (P − E. This improvement of approximately 32% indicates that monthly terrestrial water-storage changes, as estimated by GRACE, cannot be considered negligible over Yangtze basin. The results for the proposed method are more accurate than the results previously reported in the literature.

  15. Satellite based estimates of reduced CO and CO2 emissions due to traffic restrictions during the 2008 Beijing Olympics

    Science.gov (United States)

    Worden, H. M.; Cheng, Y.; Pfister, G.; Carmichael, G. R.; Zhang, Q.; Streets, D. G.; Deeter, M. N.; Edwards, D. P.; Gille, J. C.; Worden, J.

    2012-12-01

    We present estimates of the reductions in CO and CO2 emissions resulting from the control measures on the Beijing transportation sector taken during the 2008 Beijing Olympics. This study used MOPITT (Measurements Of Pollution In The Troposphere) multispectral satellite measurements of near surface CO along with WRF Chem (Weather Research and Forecasting model with Chemistry) simulations for Beijing during August, 2007 and 2008 to estimate changes in CO due to meteorology and emissions. Using fractional changes in the emissions inventory transportation sector along with a reported CO/CO2 emission ratio for Beijing vehicles, we find the corresponding reduction in CO2 emissions. We then compare this reduction to target CO2 emissions in the RCP (representative concentration pathway) scenarios being considered for the IPCC AR5 (Intergovernmental Panel on Climate Change, 5th Assessment Report). Our result suggests that urban traffic reductions could play a significant role in meeting target cuts for global CO2 emissions, even for the most aggressive control scenario (RCP2.6).

  16. Satellite-based estimates of reduced CO and CO2 emissions due to traffic restrictions during the 2008 Beijing Olympics

    Science.gov (United States)

    Worden, Helen M.; Cheng, Yafang; Pfister, Gabriele; Carmichael, Gregory R.; Zhang, Qiang; Streets, David G.; Deeter, Merritt; Edwards, David P.; Gille, John C.; Worden, John R.

    2012-07-01

    During the 2008 Olympics, the Chinese government made a significant effort to improve air quality in Beijing, including restrictions on traffic. Here we estimate the reductions in carbon monoxide (CO) and carbon dioxide (CO2) emissions resulting from the control measures on Beijing transportation. Using MOPITT (Measurements Of Pollution In The Troposphere) multispectral satellite observations of near-surface CO along with WRF-Chem (Weather Research and Forecasting model with Chemistry) simulations for Beijing during August, 2007 and 2008, we estimate changes in CO due to meteorology and transportation sector emissions. Applying a reported CO/CO2 emission ratio for fossil fuels, we find the corresponding reduction in CO2, 60 ± 36 Gg[CO2]/day. As compared to emission scenarios being considered for the IPCC AR5 (Intergovernmental Panel on Climate Change, 5th Assessment Report), this result suggests that urban traffic controls on the Beijing Olympics scale could play a significant role in meeting target reductions for global CO2 emissions.

  17. A comparative study of satellite estimation for solar insolation in Albania with ground measurements

    Energy Technology Data Exchange (ETDEWEB)

    Mitrushi, Driada, E-mail: driadamitrushi@yahoo.com; Berberi, Pëllumb, E-mail: pellumb.berberi@gmail.com; Muda, Valbona, E-mail: vmuda@hotmail.com; Buzra, Urim, E-mail: rimibuzra@yahoo.com [Department of Engineering Physics, Faculty of Engineering Mathematics and Engineering Physics, Polytechnic University of Tirana, Tirana (Albania); Bërdufi, Irma, E-mail: irmaberdufi@gmail.com [Institute of Applied Nuclear Physics, Tirana University, Street “Th. Filipeu”, Tirana (Albania); Topçiu, Daniela, E-mail: topciudaniela@yahoo.com [Department of Physics, Faculty of Natural Physics, “Aleksander Xhuvani” University, Elbasan (Albania)

    2016-03-25

    The main objective of this study is to compare data provided by Database of NASA with available ground data for regions covered by national meteorological net NASA estimates that their measurements of average daily solar radiation have a root-mean-square deviation RMSD error of 35 W/m{sup 2} (roughly 20% inaccuracy). Unfortunately valid data from meteorological stations for regions of interest are quite rare in Albania. In these cases, use of Solar Radiation Database of NASA would be a satisfactory solution for different case studies. Using a statistical method allows to determine most probable margins between to sources of data. Comparison of mean insulation data provided by NASA with ground data of mean insulation provided by meteorological stations show that ground data for mean insolation results, in all cases, to be underestimated compared with data provided by Database of NASA. Converting factor is 1.149.

  18. Soil sail content estimation in the yellow river delta with satellite hyperspectral data

    Science.gov (United States)

    Weng, Yongling; Gong, Peng; Zhu, Zhi-Liang

    2008-01-01

    Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl2. Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487-1527, 1971-1991, 2032-2092, and 2163-2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSEC = 0.753 (R2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSEV = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 (R2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area. ?? 2008 CASI.

  19. Estimating babassu palm density using automatic palm tree detection with very high spatial resolution satellite images.

    Science.gov (United States)

    Dos Santos, Alessio Moreira; Mitja, Danielle; Delaître, Eric; Demagistri, Laurent; de Souza Miranda, Izildinha; Libourel, Thérèse; Petit, Michel

    2017-05-15

    High spatial resolution images as well as image processing and object detection algorithms are recent technologies that aid the study of biodiversity and commercial plantations of forest species. This paper seeks to contribute knowledge regarding the use of these technologies by studying randomly dispersed native palm tree. Here, we analyze the automatic detection of large circular crown (LCC) palm tree using a high spatial resolution panchromatic GeoEye image (0.50 m) taken on the area of a community of small agricultural farms in the Brazilian Amazon. We also propose auxiliary methods to estimate the density of the LCC palm tree Attalea speciosa (babassu) based on the detection results. We used the "Compt-palm" algorithm based on the detection of palm tree shadows in open areas via mathematical morphology techniques and the spatial information was validated using field methods (i.e. structural census and georeferencing). The algorithm recognized individuals in life stages 5 and 6, and the extraction percentage, branching factor and quality percentage factors were used to evaluate its performance. A principal components analysis showed that the structure of the studied species differs from other species. Approximately 96% of the babassu individuals in stage 6 were detected. These individuals had significantly smaller stipes than the undetected ones. In turn, 60% of the stage 5 babassu individuals were detected, showing significantly a different total height and a different number of leaves from the undetected ones. Our calculations regarding resource availability indicate that 6870 ha contained 25,015 adult babassu palm tree, with an annual potential productivity of 27.4 t of almond oil. The detection of LCC palm tree and the implementation of auxiliary field methods to estimate babassu density is an important first step to monitor this industry resource that is extremely important to the Brazilian economy and thousands of families over a large scale.

  20. Effect of Bias Correction of Satellite-Rainfall Estimates on Runoff Simulations at the Source of the Upper Blue Nile

    Directory of Open Access Journals (Sweden)

    Emad Habib

    2014-07-01

    Full Text Available Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applications. In the present study, we explore a rainfall-runoff modeling application using the Climate Prediction Center-MORPHing (CMORPH satellite rainfall product. The study area is the Gilgel Abbay catchment situated at the source basin of the Upper Blue Nile basin in Ethiopia, Eastern Africa. Rain gauge networks in such area are typically sparse. We examine different bias correction schemes applied locally to the CMORPH product. These schemes vary in the degree to which spatial and temporal variability in the CMORPH bias fields are accounted for. Three schemes are tested: space and time-invariant, time-variant and spatially invariant, and space and time variant. Bias-corrected CMORPH products were used to calibrate and drive the Hydrologiska Byråns Vattenbalansavdelning (HBV rainfall-runoff model. Applying the space and time-fixed bias correction scheme resulted in slight improvement of the CMORPH-driven runoff simulations, but in some instances caused deterioration. Accounting for temporal variation in the bias reduced the rainfall bias by up to 50%. Additional improvements were observed when both the spatial and temporal variability in the bias was accounted for. The rainfall bias was found to have a pronounced effect on model calibration. The calibrated model parameters changed significantly when using rainfall input from gauges alone, uncorrected, and bias-corrected CMORPH estimates. Changes of up to 81% were obtained for model parameters controlling the stream flow volume.

  1. Satellite Image-based Estimates of Snow Water Equivalence in Restored Ponderosa Pine Forests in Northern Arizona

    Science.gov (United States)

    Sankey, T.; Springer, A. E.; O'Donnell, F. C.; Donald, J.; McVay, J.; Masek Lopez, S.

    2014-12-01

    accumulation and this increase can be efficiently estimated at a landscape scale using satellite data.

  2. Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue, France

    Science.gov (United States)

    Manfron, Giacinto; Delmotte, Sylvestre; Busetto, Lorenzo; Hossard, Laure; Ranghetti, Luigi; Brivio, Pietro Alessandro; Boschetti, Mirco

    2017-05-01

    Crop simulation models are commonly used to forecast the performance of cropping systems under different hypotheses of change. Their use on a regional scale is generally constrained, however, by a lack of information on the spatial and temporal variability of environment-related input variables (e.g., soil) and agricultural practices (e.g., sowing dates) that influence crop yields. Satellite remote sensing data can shed light on such variability by providing timely information on crop dynamics and conditions over large areas. This paper proposes a method for analyzing time series of MODIS satellite data in order to estimate the inter-annual variability of winter wheat sowing dates. A rule-based method was developed to automatically identify a reliable sample of winter wheat field time series, and to infer the corresponding sowing dates. The method was designed for a case study in the Camargue region (France), where winter wheat is characterized by vernalization, as in other temperate regions. The detection criteria were chosen on the grounds of agronomic expertise and by analyzing high-confidence time-series vegetation index profiles for winter wheat. This automatic method identified the target crop on more than 56% (four-year average) of the cultivated areas, with low commission errors (11%). It also captured the seasonal variability in sowing dates with errors of ±8 and ±16 days in 46% and 66% of cases, respectively. Extending the analysis to the years 2002-2012 showed that sowing in the Camargue was usually done on or around November 1st (±4 days). Comparing inter-annual sowing date variability with the main local agro-climatic drivers showed that the type of preceding crop and the weather conditions during the summer season before the wheat sowing had a prominent role in influencing winter wheat sowing dates.

  3. Estimation of soil moisture-thermal infrared emissivity relation in arid and semi-arid environments using satellite observations

    Science.gov (United States)

    Grazia Blasi, Maria; Masiello, Guido; Serio, Carmine; Venafra, Sara; Liuzzi, Giuliano; Dini, Luigi

    2016-04-01

    The retrieval of surface parameters is very important for various aspects concerning the climatological and meteorological context. At this purpose surface emissivity represents one of the most important parameters useful for different applications such as the estimation of climate changes and land cover features. It is known that thermal infrared (TIR) emissivity is affected by soil moisture, but there are very few works in literature on this issue. This study is aimed to analyze and find a relation between satellite soil moisture data and TIR emissivity focusing on arid and semi-arid environments. These two parameters, together with the land surface temperature, are fundamental for a better understanding of the physical phenomena implied in the soil-atmosphere interactions and the surface energy balance. They are also important in several fields of study, such as climatology, meteorology, hydrology and agriculture. In particular, there are several studies stating a correlation between soil moisture and the emissivity at 8-9 μm in desertic soils, which corresponds to the quartz Reststrahlen, a feature which is typical of sandy soils. We investigated several areas characterized by arid or semi-arid environments, focusing our attention on the Dahra desert (Senegal), and on the Negev desert (Israel). For the Dahra desert we considered both in situ, provided by the International Soil Moisture Network, and satellite soil moisture data, from ASCAT and AMSR-E sensors, for the whole year 2011. In the case of the Negev desert soil moisture data are derived from ASCAT observations and we computed a soil moisture index from a temporal series of SAR data acquired by the Cosmo-SkyMed constellation covering a period of six months, from June 2015 to November 2015. For both cases soil moisture data were related to the retrieved TIR emissivity from the geostationary satellite SEVIRI in three different spectral channels, at 8.7 μm, 10.8 μm and 12 μm. A Kalman filter physical

  4. Satellite based classification (haze, fog) and affected area estimation over Indo - Pak Sub-Continent

    Science.gov (United States)

    Ghauri, Badar; Zafar, Sumaira

    2016-07-01

    Northern Pakistan and bordering Indian Punjab experience intense smog and fog during fall and winters. Environmentalists have been raising their voices over the situation and demanded control over regional emissions to save the livelihood of millions of dwellers whose trade, commerce and agriculture is at stake because of long smog/ fog spells.. This paper estimates the area affected by haze, smog and fog during 2006- 2010. MODIS (geo-referenced MODIS subsets India1, 2 &3) of the area in Pakistan and India from 2006 to 2010 for the period October to February) were analyzed using state of the art software ENVI 4.2 and ArcGIS 10.2. This process resulted in area belonging to each class that is; haze, smog and fog. On the basis of density, haze and fog cover was determined. Variations in fog cover, its density and identification of location of fog initiation process were also determined using near real time (30 minutes) METEOSAT-7 IODC data where actually fog formation started and then extended to the area of favorable conditions. Haze has been noticed to intensify due to massive burning of agricultural waste (rice husk) in India and Pakistan towards the end of October each year. MODIS thermal anomalies/fire data (MYD 14) were also used to verify this activity on the ground, which results in hazy conditions at regional level during fall months. Haze-affected area during 2006 to 2010 in Pakistan ranged from 155,000 Km2 to 354,000 Km2 and in India it ranged from 333,000 Km2 to 846,000 Km2. Similarly winter fog cover during this period in Pakistan varied from 136,000 Km2 to 381,000 Km2 and in India it was estimated at 327,000 Km2 to 566,000 Km2. This phenomenon was more prominent in India than in Pakistan where and fog cover was at least twice than that was observed in Pakistan. It has been noted that area covered by fog, smog and haze doubled during the study period in the region. Atmospheric dimming during autumn/ fall also reduces the mixing height leading to greater

  5. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites

    Science.gov (United States)

    Mitchard, Edward T A; Feldpausch, Ted R; Brienen, Roel J W; Lopez-Gonzalez, Gabriela; Monteagudo, Abel; Baker, Timothy R; Lewis, Simon L; Lloyd, Jon; Quesada, Carlos A; Gloor, Manuel; ter Steege, Hans; Meir, Patrick; Alvarez, Esteban; Araujo-Murakami, Alejandro; Aragão, Luiz E O C; Arroyo, Luzmila; Aymard, Gerardo; Banki, Olaf; Bonal, Damien; Brown, Sandra; Brown, Foster I; Cerón, Carlos E; Chama Moscoso, Victor; Chave, Jerome; Comiskey, James A; Cornejo, Fernando; Corrales Medina, Massiel; Da Costa, Lola; Costa, Flavia R C; Di Fiore, Anthony; Domingues, Tomas F; Erwin, Terry L; Frederickson, Todd; Higuchi, Niro; Honorio Coronado, Euridice N; Killeen, Tim J; Laurance, William F; Levis, Carolina; Magnusson, William E; Marimon, Beatriz S; Marimon Junior, Ben Hur; Mendoza Polo, Irina; Mishra, Piyush; Nascimento, Marcelo T; Neill, David; Núñez Vargas, Mario P; Palacios, Walter A; Parada, Alexander; Pardo Molina, Guido; Peña-Claros, Marielos; Pitman, Nigel; Peres, Carlos A; Poorter, Lourens; Prieto, Adriana; Ramirez-Angulo, Hirma; Restrepo Correa, Zorayda; Roopsind, Anand; Roucoux, Katherine H; Rudas, Agustin; Salomão, Rafael P; Schietti, Juliana; Silveira, Marcos; de Souza, Priscila F; Steininger, Marc K; Stropp, Juliana; Terborgh, John; Thomas, Raquel; Toledo, Marisol; Torres-Lezama, Armando; van Andel, Tinde R; van der Heijden, Geertje M F; Vieira, Ima C G; Vieira, Simone; Vilanova-Torre, Emilio; Vos, Vincent A; Wang, Ophelia; Zartman, Charles E; Malhi, Yadvinder; Phillips, Oliver L

    2014-01-01

    Aim The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. Location Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/2014_1 Methods Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons. Results The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by > 25%, whereas regional uncertainties for the maps were reported to be < 5%. Main conclusions Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities

  6. Analysis of solar radiation on the surface estimated from GWNU solar radiation model with temporal resolution of satellite cloud fraction

    Science.gov (United States)

    Zo, Il-Sung; Jee, Joon-Bum; Lee, Kyu-Tae; Kim, Bu-Yo

    2016-08-01

    Preliminary analysis with a solar radiation model is generally performed for photovoltaic power generation projects. Therefore, model accuracy is extremely important. The temporal and spatial resolutions used in previous studies of the Korean Peninsula were 1 km × 1 km and 1-h, respectively. However, calculating surface solar radiation at 1-h intervals does not ensure the accuracy of the geographical effects, and this parameter changes owing to atmospheric elements (clouds, aerosol, ozone, etc.). Thus, a change in temporal resolution is required. In this study, one-year (2013) analysis was conducted using Chollian geostationary meteorological satellite data from observations recorded at 15-min intervals. Observation data from the intensive solar site at Gangneung-Wonju National University (GWNU) showed that the coefficient of determination (R²), which was estimated for each month and season, increased, whereas the standard error (SE) decreased when estimated in 15-min intervals over those obtained in 1-h intervals in 2013. When compared with observational data from 22 solar sites of the Korean Meteorological Administration (KMA), R2 was 0.9 or higher on average, and over- or under-simulated sites did not exceed 3 sites. The model and 22 solar sites showed similar values of annual accumulated solar irradiation, and their annual mean was similar at 4,998 MJ m-2 (3.87 kWh m-2). These results show a difference of approximately ± 70 MJ m-2 (± 0.05 kWh m-2) from the distribution of the Korean Peninsula estimated in 1-h intervals and a higher correlation at higher temporal resolution.

  7. Predictive estimation of upward pollutant migration during shale gas production using satellite image processing

    Science.gov (United States)

    Lyalko, Vadim; Azimov, Oleksandr; Yakovlev, Yevgen

    2016-07-01

    The report considers the relevance of the application of modern remote aerospace and hydrogeological methods in the problems of the ecological safety for the hydrosphere during shale gas production in Ukraine. Case studies of pilot implementation of these methods are present for the Bilyaivska area adjacent to the Yuzivka licensed site within the Dnieper-Donets Depression. A number of the hydrogeological filtration parameters and the thematic processing for remote sensing data of the Earth enable to obtain the rough estimate of the temporal indices for the upward pollutant migration from the fracturing zone to the groundwater aquifers in the potential process of shale gas production (as an example the 400-Bilyaivska well). It is found that the possible variety of the active permeability in tectonic zone, which may be predicted by using remote sensing of the Earth image interpretation in vicinity of the well, is responsible for the passage time of pollution from the fracturing zone level to the groundwater aquifers one and this time interval spans 50˜5 years.

  8. Contribution of Modis Satellite Image to Estimate the Daily Air Temperature in the Casablanca City, Morocco

    Science.gov (United States)

    Bahi, Hicham; Rhinane, Hassan; Bensalmia, Ahmed

    2016-10-01

    Air temperature is considered to be an essential variable for the study and analysis of meteorological regimes and chronics. However, the implementation of a daily monitoring of this variable is very difficult to achieve. It requires sufficient of measurements stations density, meteorological parks and favourable logistics. The present work aims to establish relationship between day and night land surface temperatures from MODIS data and the daily measurements of air temperature acquired between [2011-20112] and provided by the Department of National Meteorology [DMN] of Casablanca, Morocco. The results of the statistical analysis show significant interdependence during night observations with correlation coefficient of R2=0.921 and Root Mean Square Error RMSE=1.503 for Tmin while the physical magnitude estimated from daytime MODIS observation shows a relatively coarse error with R2=0.775 and RMSE=2.037 for Tmax. A method based on Gaussian process regression was applied to compute the spatial distribution of air temperature from MODIS throughout the city of Casablanca.

  9. CONTRIBUTION OF MODIS SATELLITE IMAGE TO ESTIMATE THE DAILY AIR TEMPERATURE IN THE CASABLANCA CITY, MOROCCO

    Directory of Open Access Journals (Sweden)

    H. Bahi

    2016-10-01

    Full Text Available Air temperature is considered to be an essential variable for the study and analysis of meteorological regimes and chronics. However, the implementation of a daily monitoring of this variable is very difficult to achieve. It requires sufficient of measurements stations density, meteorological parks and favourable logistics. The present work aims to establish relationship between day and night land surface temperatures from MODIS data and the daily measurements of air temperature acquired between [2011-20112] and provided by the Department of National Meteorology [DMN] of Casablanca, Morocco. The results of the statistical analysis show significant interdependence during night observations with correlation coefficient of R2=0.921 and Root Mean Square Error RMSE=1.503 for Tmin while the physical magnitude estimated from daytime MODIS observation shows a relatively coarse error with R2=0.775 and RMSE=2.037 for Tmax. A method based on Gaussian process regression was applied to compute the spatial distribution of air temperature from MODIS throughout the city of Casablanca.

  10. Use of spectral channels and vegetation indices from satellite VEGETATION time series for the Post-Fire vegetation recovery estimation

    Science.gov (United States)

    Coluzzi, Rosa; Lasaponara, Rosa; Montesano, Tiziana; Lanorte, Antonio; de Santis, Fortunato

    2010-05-01

    Satellite data can help monitoring the dynamics of vegetation in burned and unburned areas. Several methods can be used to perform such kind of analysis. This paper is focused on the use of different satellite-based parameters for fire recovery monitoring. In particular, time series of single spectral channels and vegetation indices from SPOT-VEGETATION have investigated. The test areas is the Mediterranean ecosystems of Southern Italy. For this study we considered: 1) the most widely used index to follow the process of recovery after fire: normalized difference vegetation index (NDVI) obtained from the visible (Red) and near infrared (NIR) by using the following formula NDVI = (NIR_Red)/(NIR + Red), 2) moisture index MSI obtained from the near infrared and Mir for characterization of leaf and canopy water content. 3) NDWI obtained from the near infrared and Mir as in the case of MSI, but with the normalization (as the NDVI) to reduce the atmospheric effects. All analysis for this work was performed on ten-daily normalized difference vegetation index (NDVI) image composites (S10) from the SPOT- VEGETATION (VGT) sensor. The final data set consisted of 279 ten-daily, 1 km resolution NDVI S1O composites for the period 1 April 1998 to 31 December 2005 with additional surface reflectance values in the blue (B; 0.43-0.47,um), red (R; 0.61-0.68,um), near-infrared (NIR; 0.78-0.89,um) and shortwave-infrared (SWIR; 1.58-1.75,um) spectral bands, and information on the viewing geometry and pixel status. Preprocessing of the data was performed by the Vlaamse Instelling voor Technologisch Onderzoek (VITO) in the framework of the Global Vegetation Monitoring (GLOVEG) preprocessing chain. It consisted of the Simplified Method for Atmospheric Correction (SMAC) and compositing at ten-day intervals based on the Maximum Value Compositing (MVC) criterion. All the satellite time series were analysed using the Detrended Fluctuation Analysis (DFA) to estimate post fire vegetation recovery

  11. Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations

    Science.gov (United States)

    We analyzed 10 established and 4 new satellite reflectance algorithms for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense water truth collected within one hour of image acquisition to develop si...

  12. Toward a Satellite-Based System of Sugarcane Yield Estimation and Forecasting in Smallholder Farming Conditions: A Case Study on Reunion Island

    Directory of Open Access Journals (Sweden)

    Julien Morel

    2014-07-01

    Full Text Available Estimating sugarcane biomass is difficult to achieve when working with highly variable spatial distributions of growing conditions, like on Reunion Island. We used a dataset of in-farm fields with contrasted climatic conditions and farming practices to compare three methods of yield estimation based on remote sensing: (1 an empirical relationship method with a growing season-integrated Normalized Difference Vegetation Index NDVI, (2 the Kumar-Monteith efficiency model, and (3 a forced-coupling method with a sugarcane crop model (MOSICAS and satellite-derived fraction of absorbed photosynthetically active radiation. These models were compared with the crop model alone and discussed to provide recommendations for a satellite-based system for the estimation of yield at the field scale. Results showed that the linear empirical model produced the best results (RMSE = 10.4 t∙ha−1. Because this method is also the simplest to set up and requires less input data, it appears that it is the most suitable for performing operational estimations and forecasts of sugarcane yield at the field scale. The main limitation is the acquisition of a minimum of five satellite images. The upcoming open-access Sentinel-2 Earth observation system should overcome this limitation because it will provide 10-m resolution satellite images with a 5-day frequency.

  13. Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations

    Science.gov (United States)

    We analyzed 10 established and 4 new satellite reflectance algorithms for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense water truth collected within one hour of image acquisition to develop si...

  14. Consistency of Estimated Global Water Cycle Variations Over the Satellite Era

    Science.gov (United States)

    Robertson, F. R.; Bosilovich, M. G.; Roberts, J. B.; Reichle, R. H.; Adler, R.; Ricciardulli, L.; Berg, W.; Huffman, G. J.

    2013-01-01

    Motivated by the question of whether recent indications of decadal climate variability and a possible "climate shift" may have affected the global water balance, we examine evaporation minus precipitation (E-P) variability integrated over the global oceans and global land from three points of view-remotely sensed retrievals / objective analyses over the oceans, reanalysis vertically-integrated moisture convergence (MFC) over land, and land surface models forced with observations-based precipitation, radiation and near-surface meteorology. Because monthly variations in area-averaged atmospheric moisture storage are small and the global integral of moisture convergence must approach zero, area-integrated E-P over ocean should essentially equal precipitation minus evapotranspiration (P-ET) over land (after adjusting for ocean and land areas). Our analysis reveals considerable uncertainty in the decadal variations of ocean evaporation when integrated to global scales. This is due to differences among datasets in 10m wind speed and near-surface atmospheric specific humidity (2m qa) used in bulk aerodynamic retrievals. Precipitation variations, all relying substantially on passive microwave retrievals over ocean, still have uncertainties in decadal variability, but not to the degree present with ocean evaporation estimates. Reanalysis MFC and P-ET over land from several observationally forced diagnostic and land surface models agree best on interannual variations. However, upward MFC (i.e. P-ET) reanalysis trends are likely related in part to observing system changes affecting atmospheric assimilation models. While some evidence for a low-frequency E-P maximum near 2000 is found, consistent with a recent apparent pause in sea-surface temperature (SST) rise, uncertainties in the datasets used here remain significant. Prospects for further reducing uncertainties are discussed. The results are interpreted in the context of recent climate variability (Pacific Decadal

  15. Estimating Agricultural Land Use Change in Karamoja, NE. Uganda Using Very High Resolution Satellite Data

    Science.gov (United States)

    Nakalembe, C. L.

    2013-12-01

    Land use information is useful for deriving biophysical variables for effective planning and management of natural resources. Land use information is also needed to understand negative environmental impacts of land use while maintaining economic and social benefits. Recent maps of land cover and land use have been generated for Africa at the continental scale from coarse resolution data (e.g. MODIS, Spot Vegetation, MERIS, and Landsat). In these map products, croplands and rangelands are generally poorly represented, particularly in semi-arid regions like Karamoja. Products derived from coarse resolution data also fail at mapping subsistence croplands and are limited in their use for extraction of land-cover specific temporal profiles for agricultural monitoring in the study area (Fritz, See, & Rembold, 2010). Given the subsistence nature of agriculture, most fields in Karamoja are very small that care not discernible from other land uses in coarse resolution data and data products such as FAO Africover2000. product derived from 30m Landsat data is one such product. There is a high level of disagreement and large errors of omission and omission due to the coarse resolution of the data used to derive the product. In addition population growth and policy changes in the region have resulted in a shift to agro-pastoralism and systematic expansion of cropland area since 2000. This research will produce an updated agricultural land use map for Karamoja. The land cover map will be used to estimate agricultural land use change in the region and as a filter to extract agricultural land use specific temporal profiles specific to agriculture to compare to crop statistics.

  16. Thicknesses and volumes of glaciers in the Andes of Peru estimated with satellite data and digital terrain information

    Science.gov (United States)

    Torres, Judith; Colonia, Daniel; Haeberli, Wilfried; Giráldez, Claudia; Frey, Holger; Huggel, Christian

    2014-05-01

    The glaciers in the tropical Andes of Peru have been melting at an unprecedented rate in recent years and generally after the Little Ice Age, a cold period that lasted from the 16th to the 19th century. Knowledge of glacier thicknesses and volumes is necessary for evaluating possible future scenarios of glacier shrinkage and of water supply to the Andean populations under conditions of continued warming. Calculation of glacier volumes for 19 mountain ranges in Perú has been based on two ice- thickness modeling methods including an area-related approach with different parameterizations and a slope-dependent approach. Both methods allow for rapid treatment of regional data obtained from satellite imagery and a Digital Elevation Model, integrated into a Geographic Information System. In addition, glacier outlines were obtained from the glacier inventory compiled by the Unit of Glaciology and Water Resources (UGRH) - National Water Authority (ANA) that used satellite imagery (ASTER, SPOT and LISS III from 2003 to 2010) and topographic information acquired from the cartography of the National Geographical Institute (IGN). The volume-area scaling approach resulted in glacier volume of 35.00 km3 and a total volume of 34.39 km3 resulted from the slope-dependent thickness with a thickness approximately 30 m. Estimated results also show a loss of the total ice surface ~42% and glacier volume loss about ~38% in both methods based on the first Glacier Inventory of Peru (from aerial photographs 1962 -1970) performed by HIDRANDINA SA. The results also indicate that volume estimations are subject to large uncertainties. Field measurements of glacier thickness are scarce and locally restricted due to rugged topography, high altitude and heavy crevassing of glaciers. Possibilities of calibrating and validating the applied model approaches are therefore limited. New possibilities nevertheless come into play with slope-dependent approaches, which lead beyond area-related average

  17. Towards a Quantitative Use of Satellite Remote Sensing in Crop Growth Models for Large Scale Agricultural Production Estimate (Invited)

    Science.gov (United States)

    Defourny, P.

    2013-12-01

    The development of better agricultural monitoring capabilities is clearly considered as a critical step for strengthening food production information and market transparency thanks to timely information about crop status, crop area and yield forecasts. The documentation of global production will contribute to tackle price volatility by allowing local, national and international operators to make decisions and anticipate market trends with reduced uncertainty. Several operational agricultural monitoring systems are currently operating at national and international scales. Most are based on the methods derived from the pioneering experiences completed some decades ago, and use remote sensing to qualitatively compare one year to the others to estimate the risks of deviation from a normal year. The GEO Agricultural Monitoring Community of Practice described the current monitoring capabilities at the national and global levels. An overall diagram summarized the diverse relationships between satellite EO and agriculture information. There is now a large gap between the current operational large scale systems and the scientific state of the art in crop remote sensing, probably because the latter mainly focused on local studies. The poor availability of suitable in-situ and satellite data over extended areas hampers large scale demonstrations preventing the much needed up scaling research effort. For the cropland extent, this paper reports a recent research achievement using the full ENVISAT MERIS 300 m archive in the context of the ESA Climate Change Initiative. A flexible combination of classification methods depending to the region of the world allows mapping the land cover as well as the global croplands at 300 m for the period 2008 2012. This wall to wall product is then compared with regards to the FP 7-Geoland 2 results obtained using as Landsat-based sampling strategy over the IGADD countries. On the other hand, the vegetation indices and the biophysical variables

  18. Top-of-Atmosphere Albedo Estimation from Angular Distribution Models using Scene Identification from Satellite Cloud Property Retrievals

    Science.gov (United States)

    Loeb, N. G.; Parol, F.; Buriez, J.-C.; Vanbauce, C.

    2000-01-01

    The next generation of Earth radiation budget satellite instruments will routinely merge estimates of global top-of-atmosphere radiative fluxes with cloud properties. This information will offer many new opportunities for validating radiative transfer models and cloud parameterizations in climate models. In this study, five months of POLarization and Directionality of the Earth's Reflectances (POLDER) 670 nm radiance measurements are considered in order to examine how satellite cloud property retrievals can be used to define empirical Angular Distribution Models (ADMs) for estimating top-of-atmosphere (TOA) albedo. ADMs are defined for 19 scene types defined by satellite retrievals of cloud fraction and cloud optical depth. Two approaches are used to define the ADM scene types: The first assumes there are no biases in the retrieved cloud properties and defines ADMs for fixed discrete intervals of cloud fraction and cloud optical depth (fixed-tau approach). The second approach involves the same cloud fraction intervals, but uses percentile intervals of cloud optical depth instead (percentile-tau approach). Albedos generated using these methods are compared with albedos inferred directly from the mean observed reflectance field. Albedos based on ADMs that assume cloud properties are unbiased (fixed-tau approach) show a strong systematic dependence on viewing geometry. This dependence becomes more pronounced with increasing solar zenith angle, reaching approximately equals 12% (relative) between near-nadir and oblique viewing zenith angles for solar zenith angles between 60 deg and 70 deg. The cause for this bias is shown to be due to biases in the cloud optical depth retrievals. In contrast, albedos based on ADMs built using percentile intervals of cloud optical depth (percentile-tau approach) show very little viewing zenith angle dependence and are in good agreement with albedos obtained by direct integration of the mean observed reflectance field (less than 1

  19. Investigation of Adaptive-threshold Approaches for Determining Area-Time Integrals from Satellite Infrared Data to Estimate Convective Rain Volumes

    Science.gov (United States)

    Smith, Paul L.; VonderHaar, Thomas H.

    1996-01-01

    The principal goal of this project is to establish relationships that would allow application of area-time integral (ATI) calculations based upon satellite data to estimate rainfall volumes. The research is being carried out as a collaborative effort between the two participating organizations, with the satellite data analysis to determine values for the ATIs being done primarily by the STC-METSAT scientists and the associated radar data analysis to determine the 'ground-truth' rainfall estimates being done primarily at the South Dakota School of Mines and Technology (SDSM&T). Synthesis of the two separate kinds of data and investigation of the resulting rainfall-versus-ATI relationships is then carried out jointly. The research has been pursued using two different approaches, which for convenience can be designated as the 'fixed-threshold approach' and the 'adaptive-threshold approach'. In the former, an attempt is made to determine a single temperature threshold in the satellite infrared data that would yield ATI values for identifiable cloud clusters which are closely related to the corresponding rainfall amounts as determined by radar. Work on the second, or 'adaptive-threshold', approach for determining the satellite ATI values has explored two avenues: (1) attempt involved choosing IR thresholds to match the satellite ATI values with ones separately calculated from the radar data on a case basis; and (2) an attempt involved a striaghtforward screening analysis to determine the (fixed) offset that would lead to the strongest correlation and lowest standard error of estimate in the relationship between the satellite ATI values and the corresponding rainfall volumes.

  20. Retrospective Analog Year Analyses Using NASA Satellite Data to Improve USDA's World Agricultural Supply and Demand Estimates

    Science.gov (United States)

    Teng, William; Shannon, Harlan

    2011-01-01

    The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted, including maps, charts, and time series of recent weather, climate, and crop observations; numerical output from weather and crop models; and reports from the press, USDA attach s, and foreign governments. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. Because both the amount and timing of precipitation significantly affect crop yields, WAOB often uses precipitation time series to identify growing seasons with similar weather patterns and help estimate crop yields for the current growing season, based on observed yields in analog years. Historically, these analog years are visually identified; however, the qualitative nature of this method sometimes precludes the definitive identification of the best analog year. Thus, one goal of this study is to derive a more rigorous, statistical approach for identifying analog years, based on a modified coefficient of determination, termed the analog index (AI). A second goal is to compare the performance of AI for time series derived from surface-based observations vs. satellite-based measurements (NASA TRMM and other data).

  1. Improving snow process modeling with satellite-based estimation of near-surface-air-temperature lapse rate

    Science.gov (United States)

    Wang, Lei; Sun, Litao; Shrestha, Maheswor; Li, Xiuping; Liu, Wenbin; Zhou, Jing; Yang, Kun; Lu, Hui; Chen, Deliang

    2016-10-01

    In distributed hydrological modeling, surface air temperature (Tair) is of great importance in simulating cold region processes, while the near-surface-air-temperature lapse rate (NLR) is crucial to prepare Tair (when interpolating Tair from site observations to model grids). In this study, a distributed biosphere hydrological model with improved snow physics (WEB-DHM-S) was rigorously evaluated in a typical cold, large river basin (e.g., the upper Yellow River basin), given a mean monthly NLRs. Based on the validated model, we have examined the influence of the NLR on the simulated snow processes and streamflows. We found that the NLR has a large effect on the simulated streamflows, with a maximum difference of greater than 24% among the various scenarios for NLRs considered. To supplement the insufficient number of monitoring sites for near-surface-air-temperature at developing/undeveloped mountain regions, the nighttime Moderate Resolution Imaging Spectroradiometer land surface temperature is used as an alternative to derive the approximate NLR at a finer spatial scale (e.g., at different elevation bands, different land covers, different aspects, and different snow conditions). Using satellite-based estimation of NLR, the modeling of snow processes has been greatly refined. Results show that both the determination of rainfall/snowfall and the snowpack process were significantly improved, contributing to a reduced summer evapotranspiration and thus an improved streamflow simulation.

  2. Observed and blended gauge-satellite precipitation estimates perspective on meteorological drought intensity over South Sulawesi, Indonesia

    Science.gov (United States)

    Setiawan, A. M.; Koesmaryono, Y.; Faqih, A.; Gunawan, D.

    2017-01-01

    South Sulawesi province as one of the rice production center for national food security are highly influenced by climate phenomenon that lead to drought condition. This paper quantifies meteorological drought based on Standardized Precipitation Index (SPI) recommended by the World Meteorological Organization (WMO) and Consecutive Dry Days (CDD) as one of the extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices were calculated by using (i) quality controlled daily and monthly observational precipitation data from 23 weather stations of various record lengths within 1967-2015 periods, and (ii) 0.05o x 0.05o blended gauge-satellite of daily and monthly precipitation estimates of the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset. Meteorological drought intensity represented by Average Duration of Drought Intensity (ADI) from three-monthly SPI (SPI3) show spatial differences characteristic between eastern and western region. Observed and CHIRPS have relatively similar perspective on meteorological drought intensity over South Sulawesi. Relatively high values of ADI and longest CDD observed mainly over south western part of study area.

  3. Combining Meteosat-10 satellite image data with GPS tropospheric path delays to estimate regional integrated water vapor (IWV) distribution

    Science.gov (United States)

    Leontiev, Anton; Reuveni, Yuval

    2017-02-01

    Using GPS satellites signals, we can study different processes and coupling mechanisms that can help us understand the physical conditions in the lower atmosphere, which might lead or act as proxies for severe weather events such as extreme storms and flooding. GPS signals received by ground stations are multi-purpose and can also provide estimates of tropospheric zenith delays, which can be converted into accurate integrated water vapor (IWV) observations using collocated pressure and temperature measurements on the ground. Here, we present for the first time the use of Israel's dense regional GPS network for extracting tropospheric zenith path delays combined with near-real-time Meteosat-10 water vapor (WV) and surface temperature pixel intensity values (7.3 and 10.8 µm channels, respectively) in order to assess whether it is possible to obtain absolute IWV (kg m-2) distribution. The results show good agreement between the absolute values obtained from our triangulation strategy based solely on GPS zenith total delays (ZTD) and Meteosat-10 surface temperature data compared with available radiosonde IWV absolute values. The presented strategy can provide high temporal and special IWV resolution, which is needed as part of the accurate and comprehensive observation data integrated in modern data assimilation systems and is required for increasing the accuracy of regional numerical weather prediction systems forecast.

  4. Global Increase in UV Irradiance during the Past 30 Years (1979-2008) Estimated from Satellite Data

    Science.gov (United States)

    Herman, Jay R.

    2010-01-01

    Zonal average ultraviolet irradiance (flux ultraviolet, F(sub uv)) reaching the Earth's surface has significantly increased since 1979 at all latitudes except the equatorial zone. Changes are estimated in zonal average F(sub uv) caused by ozone and cloud plus aerosol reflectivity using an approach based on Beer's law for monochromatic and action spectrum weighted irradiances. For four different cases, it is shown that Beer's Law leads to a power law form similar to that applied to erythemal action spectrum weighted irradiances. Zonal and annual average increases in F(sub uv) were caused by decreases in ozone amount from 1979 to 1998. After 1998, midlatitude annual average ozone amounts and UV irradiance levels have been approximately constant. In the Southern Hemisphere, zonal and annual average UV increase is partially offset by tropospheric cloud and aerosol transmission decreases (hemispherical dimming), and to a lesser extent in the Northern Hemisphere. Ozone and 340 nm reflectivity changes have been obtained from multiple joined satellite time series from 1978 to 2008. The largest zonal average increases in F(sub uv) have occurred in the Southern Hemisphere. For clear-sky conditions at 50 S, zonal average F(sub uv) changes are estimated (305 nm, 23%; erythemal, 8.5%; 310 nm, 10%; vitamin D production, 12%). These are larger than at 50 N (305 nm, 9%; erythemal, 4%; 310 nm, 4%; vitamin D production, 6%). At the latitude of Buenos Aires, Argentina (34.6 S), the clear-sky Fuv increases are comparable to the increases near Washington, D. C. (38.9 N): 305 nm, 9% and 7%; erythemal, 6% and 4%; and vitamin D production, 7% and 5%, respectively.

  5. The Use of Satellite Imagery to Guide Field Plot Sampling Scheme for Biomass Estimation in Ghanaian Forest

    Science.gov (United States)

    Sah, B. P.; Hämäläinen, J. M.; Sah, A. K.; Honji, K.; Foli, E. G.; Awudi, C.

    2012-07-01

    Accurate and reliable estimation of biomass in tropical forest has been a challenging task because a large proportion of forests are difficult to access or inaccessible. So, for effective implementation of REDD+ and fair benefit sharing, the proper designing of field plot sampling schemes plays a significant role in achieving robust biomass estimation. The existing forest inventory protocols using various field plot sampling schemes, including FAO's regular grid concept of sampling for land cover inventory at national level, are time and human resource intensive. Wall to wall LiDAR scanning is, however, a better approach to assess biomass with high precision and spatial resolution even though this approach suffers from high costs. Considering the above, in this study a sampling design based on a LiDAR strips sampling scheme has been devised for Ghanaian forests to support field plot sampling. Using Top-of-Atmosphere (TOA) reflectance value of satellite data, Land Use classification was carried out in accordance with IPCC definitions and the resulting classes were further stratified, incorporating existing GIS data of ecological zones in the study area. Employing this result, LiDAR sampling strips were allocated using systematic sampling techniques. The resulting LiDAR strips represented all forest categories, as well as other Land Use classes, with their distribution adequately representing the areal share of each category. In this way, out of at total area of 15,153km2 of the study area, LiDAR scanning was required for only 770 km2 (sampling intensity being 5.1%). We conclude that this systematic LiDAR sampling design is likely to adequately cover variation in above-ground biomass densities and serve as sufficient a-priori data, together with the Land Use classification produced, for designing efficient field plot sampling over the seven ecological zones.

  6. Surface energy balance and actual evapotranspiration of the transboundary Indus Basin estimated from satellite measurements and the ETLook model

    Science.gov (United States)

    Bastiaanssen, W. G. M.; Cheema, M. J. M.; Immerzeel, W. W.; Miltenburg, I. J.; Pelgrum, H.

    2012-11-01

    The surface energy fluxes and related evapotranspiration processes across the Indus Basin were estimated for the hydrological year 2007 using satellite measurements. The new ETLook remote sensing model (version 1) infers information on actual Evaporation (E) and actual Transpiration (T) from combined optical and passive microwave sensors, which can observe the land-surface even under persistent overcast conditions. A two-layer Penman-Monteith equation was applied for quantifying soil and canopy evaporation. The novelty of the paper is the computation of E and T across a vast area (116.2 million ha) by using public domain microwave data that can be applied under all weather conditions, and for which no advanced input data are required. The average net radiation for the basin was estimated as being 112 Wm-2. The basin average sensible, latent and soil heat fluxes were estimated to be 80, 32, and 0 Wm-2, respectively. The average evapotranspiration (ET) and evaporative fraction were 1.2 mm d-1 and 0.28, respectively. The basin wide ET was 496 ± 16.8 km3 yr-1. Monte Carlo analysis have indicated 3.4% error at 95% confidence interval for a dominant land use class. Results compared well with previously conducted soil moisture, lysimeter and Bowen ratio measurements at field scale (R2 = 0.70; RMSE = 0.45 mm d-1; RE = -11.5% for annual ET). ET results were also compared against earlier remote sensing and modeling studies for various regions and provinces in Pakistan (R2 = 0.76; RMSE = 0.29 mmd-1; RE = 6.5% for annual ET). The water balance for all irrigated areas together as one total system in Pakistan and India (26.02 million ha) show a total ET value that is congruent with the ET value from the ETLook surface energy balance computations. An unpublished validation of the same ETLook model for 23 jurisdictional areas covering the entire Australian continent showed satisfactory results given the quality of the watershed data and the diverging physiographic and climatic

  7. Estimation of snowpack matching ground-truth data and MODIS satellite-based observations by using regression kriging

    Science.gov (United States)

    Juan Collados-Lara, Antonio; Pardo-Iguzquiza, Eulogio; Pulido-Velazquez, David

    2016-04-01

    The estimation of Snow Water Equivalent (SWE) is essential for an appropriate assessment of the available water resources in Alpine catchment. The hydrologic regime in these areas is dominated by the storage of water in the snowpack, which is discharged to rivers throughout the melt season. An accurate estimation of the resources will be necessary for an appropriate analysis of the system operation alternatives using basin scale management models. In order to obtain an appropriate estimation of the SWE we need to know the spatial distribution snowpack and snow density within the Snow Cover Area (SCA). Data for these snow variables can be extracted from in-situ point measurements and air-borne/space-borne remote sensing observations. Different interpolation and simulation techniques have been employed for the estimation of the cited variables. In this paper we propose to estimate snowpack from a reduced number of ground-truth data (1 or 2 campaigns per year with 23 observation point from 2000-2014) and MODIS satellite-based observations in the Sierra Nevada Mountain (Southern Spain). Regression based methodologies has been used to study snowpack distribution using different kind of explicative variables: geographic, topographic, climatic. 40 explicative variables were considered: the longitude, latitude, altitude, slope, eastness, northness, radiation, maximum upwind slope and some mathematical transformation of each of them [Ln(v), (v)^-1; (v)^2; (v)^0.5). Eight different structure of regression models have been tested (combining 1, 2, 3 or 4 explicative variables). Y=B0+B1Xi (1); Y=B0+B1XiXj (2); Y=B0+B1Xi+B2Xj (3); Y=B0+B1Xi+B2XjXl (4); Y=B0+B1XiXk+B2XjXl (5); Y=B0+B1Xi+B2Xj+B3Xl (6); Y=B0+B1Xi+B2Xj+B3XlXk (7); Y=B0+B1Xi+B2Xj+B3Xl+B4Xk (8). Where: Y is the snow depth; (Xi, Xj, Xl, Xk) are the prediction variables (any of the 40 variables); (B0, B1, B2, B3) are the coefficients to be estimated. The ground data are employed to calibrate the multiple regressions. In

  8. An Original Processing Method of Satellite Altimetry for Estimating Water Levels and Volume Fluctuations in a Series of Small Lakes of the Pantanal Wetland Complex in Brazil

    Science.gov (United States)

    Henrique Costa, Paulo; Oliveira Pereira, Eric; Maillard, Philippe

    2016-06-01

    Satellite altimetry is becoming a major tool for measuring water levels in rivers and lakes offering accuracies compatible with many hydrological applications, especially in uninhabited regions of difficult access. The Pantanal is considered the largest tropical wetland in the world and the sparsity of in situ gauging station make remote methods of water level measurements an attractive alternative. This article describes how satellites altimetry data from Envisat and Saral was used to determine water level in two small lakes in the Pantanal. By combining the water level with the water surface area extracted from satellite imagery, water volume fluctuations were also estimated for a few periods. The available algorithms (retrackers) that compute a range solution from the raw waveforms do not always produce reliable measurements in small lakes. This is because the return signal gets often "contaminated" by the surrounding land. To try to solve this, we created a "lake" retracker that rejects waveforms that cannot be attributed to "calm water" and convert them to altitude. Elevation data are stored in a database along with the water surface area to compute the volume fluctuations. Satellite water level time series were also produced and compared with the only nearby in situ gauging station. Although the "lake" retracker worked well with calm water, the presence of waves and other factors was such that the standard "ice1" retracker performed better on the overall. We estimate our water level accuracy to be around 75 cm. Although the return time of both satellites is only 35 days, the next few years promise to bring new altimetry satellite missions that will significantly increase this frequency.

  9. Utilization of satellite-derived estimates of meteorological and land surface characteristics in the Land Surface Model for vast agricultural region territory

    Science.gov (United States)

    Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Volkova, Elena

    2015-04-01

    The method has been elaborated to evaluate the water and heat regime characteristics of the territory on a regional scale for the vegetation season based on a physical-mathematical model of water and heat exchange between vegetation covered land surface and atmosphere (LSM, Land Surface Model) appropriate for using satellite information on land surface and meteorological conditions. The developed model is intended for calculating soil water content, evapotranspiration (evaporation from bare soil and transpiration by vegetation), vertical water and heat fluxes as well as land surface and vegetation cover temperatures and vertical distributions of temperature and moisture in the active soil layer. Parameters of the model are soil and vegetation characteristics and input variables are meteorological characteristics. Their values have been obtained from ground-based observations at agricultural meteorological stations and satellite-based measurements by scanning radiometers AVHRR/NOAA, MODIS/EOS Terra and Aqua and SEVIRI (geostationary satellites Meteosat-9, -10). The AVHRR data have been used to build the estimates of three types of land surface temperature (LST): land skin temperature Tsg, air temperature at a level of vegetation cover Ta and efficient radiation temperature Tseff, emissivity E, normalized vegetation index NDVI, vegetation cover fraction B, leaf area index LAI, and precipitation. The set of estimates derived from MODIS data has comprised values of LST Tls, E, NDVI and LAI. The SEVIRI-based retrievals have included Tls, Ta, Е at daylight and nighttime, LAI (daily) and precipitation. The case study has been carried out for agricultural Central Black Earth region of the European Russia of 227,300 sq.km containing 7 regions of the Russian Federation for years 2009-2013 vegetation seasons. Estimates of described characteristics have been built with the help of the developed original and improved pre-existing methods and technologies of thematic processing

  10. Using the Global Navigation Satellite System (GNSS) data for Hazard Estimation in Some Active Regions in Egypt

    Science.gov (United States)

    Sayed Mohamed, Abdel-Monem

    2016-07-01

    Egypt rapidly growing development is accompanied by increasing levels of standard living particular in its urban areas. However, there is a limited experience in quantifying the sources of risk management in Egypt and in designing efficient strategies to keep away serious impacts of earthquakes. From the historical point of view and recent instrumental records, there are some seismo-active regions in Egypt, where some significant earthquakes had occurred in different places. The special tectonic features in Egypt: Aswan, Greater Cairo, Red Sea and Sinai Peninsula regions are the territories of a high seismic risk, which have to be monitored by up-to date technologies. The investigations of the seismic events and interpretations led to evaluate the seismic hazard for disaster prevention and for the safety of the dense populated regions and the vital national projects as the High Dam. In addition to the monitoring of the recent crustal movements, the most powerful technique of satellite geodesy GNSS are used where geodetic networks are covering such seismo-active regions. The results from the data sets are compared and combined in order to determine the main characteristics of the deformation and hazard estimation for specified regions. The final compiled output from the seismological and geodetic analysis threw lights upon the geodynamical regime of these seismo-active regions and put Aswan and Greater Cairo under the lowest class according to horizontal crustal strains classifications. This work will serve a basis for the development of so-called catastrophic models and can be further used for catastrophic risk management. Also, this work is trying to evaluate risk of large catastrophic losses within the important regions including the High Dam, strategic buildings and archeological sites. Studies on possible scenarios of earthquakes and losses are a critical issue for decision making in insurance as a part of mitigation measures.

  11. Satellite estimate of freshwater exchange between the Indonesian Seas and the Indian Ocean via the Sunda Strait

    Science.gov (United States)

    Potemra, James T.; Hacker, Peter W.; Melnichenko, Oleg; Maximenko, Nikolai

    2016-07-01

    The straits in Indonesia allow for low-latitude exchange of water between the Pacific and Indian Oceans. Collectively known as the Indonesian Throughflow (ITF), this exchange is thought to occur primarily via the Makassar Strait and downstream via Lombok Strait, Ombai Strait, and Timor Passage. The Sunda Strait, between the islands of Sumatra and Java, is a very narrow (≈10 km) and shallow (≈20 m) gap, but it connects the Java Sea directly to the Indian Ocean. Flow through this strait is presumed to be small, given the size of the passage; however, recent observations from the Aquarius satellite indicate periods of significant freshwater transport, suggesting the Sunda Strait may play a more important role in Pacific to Indian Ocean exchange. The nature of this exchange is short-duration (several days) bursts of freshwater injected into the eastern Indian Ocean superimposed on a mean seasonal cycle. The mean volume transport is small averaging about 0.1 Sv toward the Indian Ocean, but the freshwater transport is nonnegligible (estimated at 5.8 mSv). Transport through the strait is hydraulically controlled and directly correlates to the along-strait pressure difference. The episodic low-salinity plumes observed by Aquarius do not, however, appear to be forced by this same mechanism but are instead controlled by convergence of flow at the exit of the Strait in the Indian Ocean. Numerical model results show the fate of this freshwater plume varies with season and is either advected to the northwest along the coast of Sumatra or southerly into the ITF pathway.

  12. A probabilistic approach for assessing landslide-triggering event rainfall in Papua New Guinea, using TRMM satellite precipitation estimates

    Science.gov (United States)

    Robbins, J. C.

    2016-10-01

    Large and numerous landslides can result in widespread impacts which are felt particularly strongly in the largely subsistence-orientated communities residing in the most landslide-prone areas of Papua New Guinea (PNG). Understanding the characteristics of rainfall preceding these landslide events is essential for the development of appropriate early warning systems and forecasting models. Relationships between rainfall and landslides are frequently complex and uncertainties tend to be amplified by inconsistent and incomplete landslide catalogues and sparse rainfall data availability. To address some of these uncertainties a modified Bayesian technique has been used, in conjunction with the multiple time frames method, to produce thresholds of landslide probability associated with rainfall events of specific magnitude and duration. Satellite-derived precipitation estimates have been used to derive representative rainfall accumulations and intensities over a range of different rainfall durations (5, 10, 15, 30, 45, 60, 75 and 90 days) for rainfall events which resulted in landslides and those which did not result in landslides. Of the two parameter combinations (accumulation-duration and intensity-duration) analysed, rainfall accumulation and duration provide the best scope for identifying probabilistic thresholds for use in landslide warning and forecasting in PNG. Analysis of historical events and rainfall characteristics indicates that high accumulation (>250 mm), shorter duration (75 days), high accumulation (>1200 mm) rainfall events are more likely to lead to moderate- to high-impact landslides. This analysis has produced the first proxy probability thresholds for landslides in PNG and their application within an early warning framework has been discussed.

  13. A High-Resolution Two-Stage Satellite Model to Estimate PM2.5 Concentrations in China

    Science.gov (United States)

    Liu, Y.; Ma, Z.; Hu, X.; Yang, K.

    2014-12-01

    With the rapid economic development and urbanization, severe and widespread PM2.5 pollution in China has attracted nationwide attention. Study of the health impact of PM2.5 exposure has been hindered, however, by the limited coverage of ground measurements from recently established regulatory monitoring networks. Estimating ground-level PM2.5 from satellite remote sensing is a promising new method to evaluate the spatial and temporal patterns of PM2.5 exposure. We developed a two-stage spatial statistical model to estimate daily mean PM2.5 concentrations at 10 km resolution in 2013 in China using MODIS Collection 6 AOD, assimilated meteorology, population density, and land use parameters. A custom inverse variance weighting approach was developed to combine MODIS Dark Target (DT) and Deep Blue (DB) AOD to optimize coverage. Compared with the AERONET AOD measurements, our combined AOD (R2=0.80, mean bias = 0.07) performs similarly to MODIS' combined AOD (R2=0.81, mean bias =0.07), but has 90% greater coverage. We used the first-stage linear mixed effect model to represent the temporal variability of PM2.5 and the second-stage generalized additive model to represent its spatial contrast. The overall model cross-validation R2 and relative prediction error are 0.80 and 30%, respectively. PM2.5 levels exhibit strong seasonal patterns, with the highest national mean concentrations in winter (75 µg/m3) and the lowest in summer (30 µg/m3). Elevated annual mean PM2.5 levels are predicted in North China Plain and Sichuan Basin, with the maximum annual PM2.5 concentrations higher than 130 µg/m3 and 110 µg/m3, respectively. Our results also indicates that over 94% of the Chinese population lives in areas that exceed the WHO Air Quality Interim Target-1 standard (35 μg/m3). The exceptions include Taiwan, Hainan, Yunnan, Tibet, and North Inner Mongolia.

  14. Estimation of the demand for public services communications. [market research and economic analysis for a communications satellite system

    Science.gov (United States)

    1976-01-01

    Market analyses and economic studies are presented to support NASA planning for a communications satellite system to provide public services in health, education, mobile communications, data transfer, and teleconferencing.

  15. Optimizing Satellite-Based Precipitation Estimation for Nowcasting of Rainfall and Flash Flood Events over the South African Domain

    OpenAIRE

    Estelle de Coning

    2013-01-01

    The South African Weather Service is mandated to issue warnings of hazardous weather events, including those related to heavy precipitation, in order to safeguard life and property. Flooding and flash flood events are common in South Africa. Frequent updates and real-time availability of precipitation data are crucial to support hydrometeorological warning services. Satellite rainfall estimation provides a very important data source for flash flood guidance systems as well as nowcasting of pr...

  16. Estimating Uncertainties in Bio-Optical Products Derived from Satellite Ocean Color Imagery Using an Ensemble Approach

    Science.gov (United States)

    2011-01-01

    We propose a methodology to quantify errors and produce uncertainty maps for satellite-derived ocean color bio -optical products using ensemble...retrievals of bio -optical properties from satellite ocean color imagery are related to a variety of factors, including sensor calibration, atmospheric...correction, and the bio -optical inversion algorithms. Errors propagate, amplify, and intertwine along the processing path, so it is important to

  17. Detection, emission estimation and risk prediction of forest fires in China using satellite sensors and simulation models in the past three decades--an overview.

    Science.gov (United States)

    Zhang, Jia-Hua; Yao, Feng-Mei; Liu, Cheng; Yang, Li-Min; Boken, Vijendra K

    2011-08-01

    Forest fires have major impact on ecosystems and greatly impact the amount of greenhouse gases and aerosols in the atmosphere. This paper presents an overview in the forest fire detection, emission estimation, and fire risk prediction in China using satellite imagery, climate data, and various simulation models over the past three decades. Since the 1980s, remotely-sensed data acquired by many satellites, such as NOAA/AVHRR, FY-series, MODIS, CBERS, and ENVISAT, have been widely utilized for detecting forest fire hot spots and burned areas in China. Some developed algorithms have been utilized for detecting the forest fire hot spots at a sub-pixel level. With respect to modeling the forest burning emission, a remote sensing data-driven Net Primary productivity (NPP) estimation model was developed for estimating forest biomass and fuel. In order to improve the forest fire risk modeling in China, real-time meteorological data, such as surface temperature, relative humidity, wind speed and direction, have been used as the model input for improving prediction of forest fire occurrence and its behavior. Shortwave infrared (SWIR) and near infrared (NIR) channels of satellite sensors have been employed for detecting live fuel moisture content (FMC), and the Normalized Difference Water Index (NDWI) was used for evaluating the forest vegetation condition and its moisture status.

  18. Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

    Energy Technology Data Exchange (ETDEWEB)

    Li, Zhengpeng; Liu, Shuguang; Tan, Zhengxi; Bliss, Norman B.; Young, Claudia J.; West, Tristram O.; Ogle, Stephen M.

    2014-04-01

    Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687 g C m-2 yr-1 and total NPP in the range of 318–490 Tg C yr-1 for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650 g C m-2 yr-1 while the MODIS NPP product estimated the mean NPP was less than 500 g C m-2 yr-1. MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. Finally, we suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.

  19. Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

    Science.gov (United States)

    Li, Zhengpeng; Liu, Shuguang; Tan, Zhengxi; Bliss, Norman B.; Young, Claudia J.; West, Tristram O.; Ogle, Stephen M.

    2014-01-01

    Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687 g C m−2 yr−1and total NPP in the range of 318–490 Tg C yr−1 for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650 g C m−2 yr−1 while the MODIS NPP product estimated the mean NPP was less than 500 g C m−2 yr−1. MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. We suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.

  20. Brief communication: The challenge and benefit of using sea ice concentration satellite data products with uncertainty estimates in summer sea ice data assimilation

    Science.gov (United States)

    Yang, Qinghua; Losch, Martin; Losa, Svetlana N.; Jung, Thomas; Nerger, Lars; Lavergne, Thomas

    2016-04-01

    Data assimilation experiments that aim at improving summer ice concentration and thickness forecasts in the Arctic are carried out. The data assimilation system used is based on the MIT general circulation model (MITgcm) and a local singular evolutive interpolated Kalman (LSEIK) filter. The effect of using sea ice concentration satellite data products with appropriate uncertainty estimates is assessed by three different experiments using sea ice concentration data of the European Space Agency Sea Ice Climate Change Initiative (ESA SICCI) which are provided with a per-grid-cell physically based sea ice concentration uncertainty estimate. The first experiment uses the constant uncertainty, the second one imposes the provided SICCI uncertainty estimate, while the third experiment employs an elevated minimum uncertainty to account for a representation error. Using the observation uncertainties that are provided with the data improves the ensemble mean forecast of ice concentration compared to using constant data errors, but the thickness forecast, based on the sparsely available data, appears to be degraded. Further investigating this lack of positive impact on the sea ice thicknesses leads us to a fundamental mismatch between the satellite-based radiometric concentration and the modeled physical ice concentration in summer: the passive microwave sensors used for deriving the vast majority of the sea ice concentration satellite-based observations cannot distinguish ocean water (in leads) from melt water (in ponds). New data assimilation methodologies that fully account or mitigate this mismatch must be designed for successful assimilation of sea ice concentration satellite data in summer melt conditions. In our study, thickness forecasts can be slightly improved by adopting the pragmatic solution of raising the minimum observation uncertainty to inflate the data error and ensemble spread.

  1. A Satellite-Based Estimation of Evapotranspiration Using Vegetation Index-Temperature Trapezoid Concept: A Case Study in Southern Florida, U.S.A.

    Science.gov (United States)

    Yagci, A. L.; Santanello, J. A., Jr.; Jones, J. W.

    2015-12-01

    One of the key surface variables for hydrological applications, monitoring of natural and anthropogenic water consumption, closing energy balance and water budgets and drought identification is evapotranspiration (ET). There is currently a strong need for high temporal and spatial resolution ET products for climate and hydrological modelers. A satellite-based retrieval method based on vegetation index-temperature trapezoid (VITT) concept has been developed. This model has the ability to generate accurate ET estimates at high temporal and spatial resolutions by taking advantage of key remotely sensed parameters such as vegetation indices (VIs) and land surface temperature (LST) acquired by satellites as well as routinely-measured meteorological variables such as air temperature (Ta) and net radiation. For local-scale applications, the model has been successfully implemented in Python programming language and tested using Landsat satellite products at an eddy covariance flux tower in Florida. It is fully functional and automated such that there is no need of user intervention to run the model. The model development for continental-scale applications using VI and LST products from NASA satellites such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) is currently in progress. The results for local-scale application and early results for continental-scale (US) will be presented and discussed.

  2. Evaluation of Hyperspectral Multi-Band Indices to Estimate Chlorophyll-A Concentration Using Field Spectral Measurements and Satellite Data in Dianshan Lake, China

    Directory of Open Access Journals (Sweden)

    Linna Li

    2013-04-01

    Full Text Available Chlorophyll-a (Chl-a concentration is considered as a key indicator of the eutrophic status of inland water bodies. Various algorithms have been developed for estimating Chl-a in order to improve the accuracy of predictive models. The objective of this study is to assess the potential of hyperspectral multi-band indices to estimate the Chl-a concentration in Dianshan Lake, which is the largest lake in Shanghai, an international metropolis of China. Based on field spectral measurements and in-situ Chl-a concentration collected on 7–8 September 2010, hyperspectral multi-band indices were calibrated to estimate the Chl-a concentration with optimal wavelengths selected by model tuning. A three-band index accounts for 87.36% (R2 = 0.8736 of the Chl-a variation. A four-band index, which adds a wavelength in the near infrared (NIR region, results in a higher R2 (0.8997 by removing the absorption and backscattering effects of suspended solids. To test the applicability of the proposed indices for routinely monitoring of Chl-a in inland lakes, simulated Hyperion and real HJ-1A satellite data were selected to estimate the Chl-a concentration. The results show that the explanatory powers of these satellite hyperspectral multi-band indices are relatively high with R2 = 0.8559, 0.8945, 0.7969, and 0.8241 for simulated Hyperion and real HJ-1A satellite data, respectively. All of the results provide strong evidence that hyperspectral multi-band indices are promising and applicable to estimate Chl-a in eutrophic inland lakes.

  3. Comparison of global irradiance measurements of the official Spanish radiometric network for 2006 with satellite estimated data

    Directory of Open Access Journals (Sweden)

    J. M. Sancho

    2011-01-01

    Full Text Available The monthly average values of daily global irradiance measured in broadband at 40 stations of the National Radiometric Network of the Spanish Meteorological Agency have been compared with the monthly values of SIS (Surface Incoming Shortwave radiation of the Climate Monitoring-Satellite Application Facility for 2006. It is calculated by the data from the instrument Spinning Enhanced Visible and Infrared Imager of the Meteosat Second Generation satellite and of the Advanced Very High Resolution Radiometer of the NOAA polar satellites. The results show a great similarity between the data from both sources of information, and the discrepancies found are around 5%. The aim of such a comparison is to evaluate the suitability of the use of the SIS data for the elaboration of an atlas of solar irradiance available in Spain.

  4. Can Airborne Laser Scanning (ALS and Forest Estimates Derived from Satellite Images Be Used to Predict Abundance and Species Richness of Birds and Beetles in Boreal Forest?

    Directory of Open Access Journals (Sweden)

    Eva Lindberg

    2015-04-01

    Full Text Available In managed landscapes, conservation planning requires effective methods to identify high-biodiversity areas. The objective of this study was to evaluate the potential of airborne laser scanning (ALS and forest estimates derived from satellite images extracted at two spatial scales for predicting the stand-scale abundance and species richness of birds and beetles in a managed boreal forest landscape. Multiple regression models based on forest data from a 50-m radius (i.e., corresponding to a homogenous forest stand had better explanatory power than those based on a 200-m radius (i.e., including also parts of adjacent stands. Bird abundance and species richness were best explained by the ALS variables “maximum vegetation height” and “vegetation cover between 0.5 and 3 m” (both positive. Flying beetle abundance and species richness, as well as epigaeic (i.e., ground-living beetle richness were best explained by a model including the ALS variable “maximum vegetation height” (positive and the satellite-derived variable “proportion of pine” (negative. Epigaeic beetle abundance was best explained by “maximum vegetation height” at 50 m (positive and “stem volume” at 200 m (positive. Our results show that forest estimates derived from satellite images and ALS data provide complementary information for explaining forest biodiversity patterns. We conclude that these types of remote sensing data may provide an efficient tool for conservation planning in managed boreal landscapes.

  5. Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?

    DEFF Research Database (Denmark)

    Schumacher, Paul; Mislimshoeva, Bunafsha; Brenning, Alexander;

    2016-01-01

    Remote sensing-based woody biomass quantification in sparsely-vegetated areas is often limited when using only common broadband vegetation indices as input data for correlation with ground-based measured biomass information. Red edge indices and texture attributes are often suggested as a means...... to overcome this issue. However, clear recommendations on the suitability of specific proxies to provide accurate biomass information in semi-arid to arid environments are still lacking. This study contributes to the understanding of using multispectral high-resolution satellite data (RapidEye), specifically...... red edge and texture attributes, to estimate wood volume in semi-arid ecosystems characterized by scarce vegetation. LASSO (Least Absolute Shrinkage and Selection Operator) and random forest were used as predictive models relating in situ-measured aboveground standing wood volume to satellite data...

  6. Estimation of surface heat and moisture fluxes over a prairie grassland. IV - Impact of satellite remote sensing of slow canopy variables on performance of a hybrid biosphere model

    Science.gov (United States)

    Crosson, William L.; Smith, Eric A.; Cooper, Harry J.

    1993-01-01

    Numerical experiments are conducted using the Ex-BATS model of Crosson and Weng (1993), which is an adaptation the Dickinson (1983, 1984) and Dickinson et al. (1986) biosphere model BATS. The purpose of these experiments is the assessment of the Ex-BATS performance when using remotely sensed data for the estimation of three key canopy variables retrieved from NOAA-AVHRR measurements: the total surface albedo, the leaf area index (LAI), and the nondiurnally varying component of stomatal resistance, r(s). The results of the simulations, which cover the entire FIFE 1987 time period, show that the satellite retrievals of r(s) are only 20 to 30 percent less accurate than the idealized results of the control experiment. The performance of the model which used satellite retrieval of the surface albedo and LAI was essentially equivalent to the hypothetical version.

  7. Estimation of glacier mass balance: An approach based on satellite-derived transient snowlines and a temperature index driven by meteorological observations

    Science.gov (United States)

    Tawde, S. A.; Kulkarni, A. V.; Bala, G.

    2015-12-01

    In the Himalaya, large area is comprised of glaciers and seasonal snow, mainly due to its high elevated mountain ranges. Long term and continuous assessment of glaciers in this region is important for climatological and hydrological applications. However, rugged terrains and severe weather conditions in the Himalaya lead to paucity in field observations. Therefore, in recent decades, glacier dynamics are extensively monitored using remote sensing in inaccessible terrain like Himalaya. Estimation of glacier mass balance using empirical relationship between mass balance and area accumulation ratio (AAR) requires an accurate estimate of equilibrium-line altitude (ELA). ELA is defined as the snowline at the end of the hydrological year. However, identification of ELA, using remote sensing is difficult because of temporal gaps, cloud cover and intermediate snowfall on glaciers. This leads to large uncertainty in glacier mass-balance estimates by the conventional AAR method that uses satellite-derived highest snowline in ablation season as an ELA. The present study suggests a new approach to improve estimates of ELA location. First, positions of modelled snowlines are optimized using satellite-derived snowlines in the early melt season. Secondly, ELA at the end of the glaciological year is estimated by the melt and accumulation models driven using in situ temperature and precipitation records. From the modelled ELA, mass balance is estimated using the empirical relationship between AAR and mass balance. The modelled mass balance is validated using field measurements on Chhota Shigri and Hamtah glaciers, Himachal Pradesh, India. The new approach shows a substantial improvement in glacier mass-balance estimation, reducing bias by 46% and 108% for Chhota Shigiri and Hamtah glaciers respectively. The cumulative mass loss reconstructed from our approach is 0.85 Gt for nine glaciers in the Chandra basin from 2001 to 2009. The result of the present study is in agreement with

  8. Estimated total emissions of trace gases from the Canberra Wildfires of 2003: a new method using satellite measurements of aerosol optical depth & the MOZART chemical transport model

    Directory of Open Access Journals (Sweden)

    C. Paton-Walsh

    2010-06-01

    Full Text Available In this paper we describe a new method for estimating trace gas emissions from large vegetation fires using satellite measurements of aerosol optical depth (AOD at 550 nm, combined with an atmospheric chemical transport model. The method uses a threshold value to screen out normal levels of AOD that may be caused by raised dust, sea salt aerosols or diffuse smoke transported from distant fires. Using this method we infer an estimated total emission of 15±5 Tg of carbon monoxide, 0.05±0.02 Tg of hydrogen cyanide, 0.11±0.03 Tg of ammonia, 0.25±0.07 Tg of formaldehyde, 0.03±0.01 of acetylene, 0.10±0.03 Tg of ethylene, 0.03±0.01 Tg of ethane, 0.21±0.06 Tg of formic acid and 0.28±0.09 Tg of methanol released to the atmosphere from the Canberra fires of 2003. An assessment of the uncertainties in the new method is made and we show that our estimate agrees (within expected uncertainties with estimates made using current conventional methods of multiplying together factors for the area burned, fuel load, the combustion efficiency and the emission factor for carbon monoxide. A simpler estimate derived directly from the satellite AOD measurements is also shown to be in agreement with conventional estimates, suggesting that the method may, under certain meteorological conditions, be applied without the complication of using a chemical transport model. The new method is suitable for estimating emissions from distinct large fire episodes and although it has some significant uncertainties, these are largely independent of the uncertainties inherent in conventional techniques. Thus we conclude that the new method is a useful additional tool for characterising emissions from vegetation fires.

  9. Satellite based radar interferometry to estimate large-scale soil water depletion from clay shrinkage: possibilities and limitations

    NARCIS (Netherlands)

    Brake, te B.; Hanssen, R.F.; Ploeg, van der M.J.; Rooij, de G.H.

    2013-01-01

    Satellite-based radar interferometry is a technique capable of measuring small surface elevation changes at large scales and with a high resolution. In vadose zone hydrology, it has been recognized for a long time that surface elevation changes due to swell and shrinkage of clayey soils can serve as

  10. Evaluation of satellite based indices for gross primary production estimates in a sparse savanna in the Sudan

    Directory of Open Access Journals (Sweden)

    M. Sjöström

    2009-01-01

    Full Text Available One of the more frequently applied methods for integrating controls on primary production through satellite data is the Light Use Efficiency (LUE approach. Satellite indices such as the Normalized Difference Vegetation Index (NDVI, Enhanced Vegetation Index (EVI and the Shortwave Infrared Water Stress Index (SIWSI have previously shown promise as predictors of primary production in several different environments. In this study, we evaluate NDVI, EVI and SIWSI derived from the Moderate Resolution Imaging Spectroradiometer (MODIS satellite sensor against in-situ measurements from central Sudan in order to asses their applicability in LUE-based primary production modeling within a water limited environment. Results show a strong correlation between vegetation indices and gross primary production (GPP, demonstrating the significance of vegetation indices for deriving information on primary production with relatively high accuracy at similar areas. Evaluation of SIWSI however, reveal that the fraction of vegetation apparently is to low for the index to provide accurate information on canopy water content, indicating that the use of SIWSI as a predictor of water stress in satellite data-driven primary production modeling in similar semi-arid ecosystems is limited.

  11. Daily Landsat-scale evapotranspiration estimation over a forested landscape in North Carolina, USA, using multi-satellite data fusion

    Science.gov (United States)

    Yun Yang; Martha C. Anderson; Feng Gao; Christopher R. Hain; Kathryn A. Semmens; William P. Kustas; Asko Noormets; Randolph H. Wynne; Valerie A. Thomas; Ge Sun

    2017-01-01

    As a primary flux in the global water cycle, evapotranspiration (ET) connects hydrologic and biological processes and is directly affected by water and land management, land use change and climate variability. Satellite remote sensing provides an effective means for diagnosing ET patterns over heterogeneous landscapes; however, limitations on the spatial and temporal...

  12. Satellite based radar interferometry to estimate large-scale soil water depletion from clay shrinkage: possibilities and limitations

    NARCIS (Netherlands)

    Brake, te B.; Hanssen, R.F.; Ploeg, van der M.J.; Rooij, de G.H.

    2013-01-01

    Satellite-based radar interferometry is a technique capable of measuring small surface elevation changes at large scales and with a high resolution. In vadose zone hydrology, it has been recognized for a long time that surface elevation changes due to swell and shrinkage of clayey soils can serve as

  13. Orbits of massive satellite galaxies - II. Bayesian estimates of the Milky Way and Andromeda masses using high-precision astrometry and cosmological simulations

    Science.gov (United States)

    Patel, Ekta; Besla, Gurtina; Mandel, Kaisey

    2017-07-01

    In the era of high-precision astrometry, space observatories like the Hubble Space Telescope (HST) and Gaia are providing unprecedented 6D phase-space information of satellite galaxies. Such measurements can shed light on the structure and assembly history of the Local Group, but improved statistical methods are needed to use them efficiently. Here we illustrate such a method using analogues of the Local Group's two most massive satellite galaxies, the Large Magellanic Cloud (LMC) and Triangulum (M33), from the Illustris dark-matter-only cosmological simulation. We use a Bayesian inference scheme combining measurements of positions, velocities and specific orbital angular momenta (j) of the LMC/M33 with importance sampling of their simulated analogues to compute posterior estimates of the Milky Way (MW) and Andromeda's (M31) halo masses. We conclude that the resulting host halo mass is more susceptible to bias when using measurements of the current position and velocity of satellites, especially when satellites are at short-lived phases of their orbits (i.e. at pericentre). Instead, the j value of a satellite is well conserved over time and provides a more reliable constraint on host mass. The inferred virial mass of the MW (M31) using j of the LMC (M33) is {{M}}_{vir, MW} = 1.02^{+0.77}_{-0.55} × 10^{12} M⊙ ({{M}}_{vir, M31} = 1.37^{+1.39}_{-0.75} × 10^{12} M⊙). Choosing simulated analogues whose j values are consistent with the conventional picture of a previous (<3 Gyr ago), close encounter (<100 kpc) of M33 about M31 results in a very low virial mass for M31 (˜1012 M⊙). This supports the new scenario put forth in Patel, Besla & Sohn, wherein M33 is on its first passage about M31 or on a long-period orbit. We conclude that this Bayesian inference scheme, utilizing satellite j, is a promising method to reduce the current factor of 2 spread in the mass range of the MW and M31. This method is easily adaptable to include additional satellites as new 6D

  14. Japanese Advanced Meteorological Imager

    Science.gov (United States)

    Puschell, Jeffery J.; Lowe, Howard A.; Jeter, James W.; Kus, Steven M.; Osgood, Roderic; Hurt, W. Todd; Gilman, David; Rogers, David L.; Hoelter, Roger L.; Kamel, Ahmed

    2005-01-01

    The Japanese Advanced Meteorological Imager (JAMI) was developed by Raytheon and delivered to Space Systems/Loral as the Imager Subsystem for Japan's MTSAT-1R satellite. Due to Japan's urgent need to replace MTSAT-1, which was destroyed in a launch failure in 1999, JAMI was developed on an expeditious 39-month schedule. Raytheon's success in responding to the needs of MTSAT-1R and delivering an excellent operational geosynchronous Earth orbit (GEO) imager was enabled by an elegant instrument architecture and use of newer but proven technology that simplified design, assembly and test of the Imager while simultaneously supplying superior performance. JAMI breaks through limitations of earlier three-axis stabilized GEO instruments with significant improvements in many areas, including spatial sampling, radiometric sensitivity, calibration and performance around local midnight.

  15. Estimation of volcanic ash emissions with satellite data: The inclusion of mass loading and plume height information in modified 4D-Var

    Science.gov (United States)

    Lu, Sha; Lin, Hai Xiang; Heemink, Arnold; Segers, Arjo; Fu, Guangliang

    2015-04-01

    Volcanic ash forecasting is a critical tool in hazard assessment and operational volcano monitoring. Emission parameters such as injection height, total emission mass and vertical distribution of the emission plume rate are essential and important in the implementation of volcanic ash models. Satellite instrument is a powerful tool to monitor volcanic aerosol evolution and satellite total-column data has been integrated in the modeling process to achieve a better initial condition for the forecasting. However, the use of total-column data,which has no vertical resolution, usually leads to an ill-conditioned problem and ineffective estimation of emission parameters. Fortunately, techniques to retrieve the information of total ash mass loading and injection height from satellite data has been developed recently. It provides a new possibility to increase the accuracy of estimation results by integrating them into data assimilation systems. In this work we propose a modified 4D-Var approach which seek the vertical emission distribution by observing ash cloud transport patterns from satellite total-ash-columns data, and two ways of including the information of mass loading and plume height in the assimilation process. The modified 4D-Var based on trajectory statistics forms a reformulated cost function which computes the total difference between observed ash columns and a linear combination of simulated ensemble columns coupled with a priori emission knowledge ('background' term). The ensembles are generated by a volcanic ash transport model with the tracer released form different layers. Experiment shows such straightforward method does not always guarantee the identification of injection height with a short assimilation time window, and additional information of injection height is needed to correct the solution. We propose two tricks to incorporate the information: 1. add extra terms containing the information to the cost function as restriction term; 2. generate a

  16. Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile

    Science.gov (United States)

    Zambrano-Bigiarini, Mauricio; Nauditt, Alexandra; Birkel, Christian; Verbist, Koen; Ribbe, Lars

    2017-03-01

    Accurate representation of the real spatio-temporal variability of catchment rainfall inputs is currently severely limited. Moreover, spatially interpolated catchment precipitation is subject to large uncertainties, particularly in developing countries and regions which are difficult to access. Recently, satellite-based rainfall estimates (SREs) provide an unprecedented opportunity for a wide range of hydrological applications, from water resources modelling to monitoring of extreme events such as droughts and floods.This study attempts to exhaustively evaluate - for the first time - the suitability of seven state-of-the-art SRE products (TMPA 3B42v7, CHIRPSv2, CMORPH, PERSIANN-CDR, PERSIAN-CCS-Adj, MSWEPv1.1, and PGFv3) over the complex topography and diverse climatic gradients of Chile. Different temporal scales (daily, monthly, seasonal, annual) are used in a point-to-pixel comparison between precipitation time series measured at 366 stations (from sea level to 4600 m a.s.l. in the Andean Plateau) and the corresponding grid cell of each SRE (rescaled to a 0.25° grid if necessary). The modified Kling-Gupta efficiency was used to identify possible sources of systematic errors in each SRE. In addition, five categorical indices (PC, POD, FAR, ETS, fBIAS) were used to assess the ability of each SRE to correctly identify different precipitation intensities.Results revealed that most SRE products performed better for the humid South (36.4-43.7° S) and Central Chile (32.18-36.4° S), in particular at low- and mid-elevation zones (0-1000 m a.s.l.) compared to the arid northern regions and the Far South. Seasonally, all products performed best during the wet seasons (autumn and winter; MAM-JJA) compared to summer (DJF) and spring (SON). In addition, all SREs were able to correctly identify the occurrence of no-rain events, but they presented a low skill in classifying precipitation intensities during rainy days. Overall, PGFv3 exhibited the best performance everywhere

  17. Estimating carbon flux phenology with satellite-derived land surface phenology and climate drivers for different biomes: a synthesis of AmeriFlux observations.

    Directory of Open Access Journals (Sweden)

    Wenquan Zhu

    Full Text Available Carbon Flux Phenology (CFP can affect the interannual variation in Net Ecosystem Exchange (NEE of carbon between terrestrial ecosystems and the atmosphere. In this study, we proposed a methodology to estimate CFP metrics with satellite-derived Land Surface Phenology (LSP metrics and climate drivers for 4 biomes (i.e., deciduous broadleaf forest, evergreen needleleaf forest, grasslands and croplands, using 159 site-years of NEE and climate data from 32 AmeriFlux sites and MODIS vegetation index time-series data. LSP metrics combined with optimal climate drivers can explain the variability in Start of Carbon Uptake (SCU by more than 70% and End of Carbon Uptake (ECU by more than 60%. The Root Mean Square Error (RMSE of the estimations was within 8.5 days for both SCU and ECU. The estimation performance for this methodology was primarily dependent on the optimal combination of the LSP retrieval methods, the explanatory climate drivers, the biome types, and the specific CFP metric. This methodology has a potential for allowing extrapolation of CFP metrics for biomes with a distinct and detectable seasonal cycle over large areas, based on synoptic multi-temporal optical satellite data and climate data.

  18. Estimating carbon flux phenology with satellite-derived land surface phenology and climate drivers for different biomes: a synthesis of AmeriFlux observations.

    Science.gov (United States)

    Zhu, Wenquan; Chen, Guangsheng; Jiang, Nan; Liu, Jianhong; Mou, Minjie

    2013-01-01

    Carbon Flux Phenology (CFP) can affect the interannual variation in Net Ecosystem Exchange (NEE) of carbon between terrestrial ecosystems and the atmosphere. In this study, we proposed a methodology to estimate CFP metrics with satellite-derived Land Surface Phenology (LSP) metrics and climate drivers for 4 biomes (i.e., deciduous broadleaf forest, evergreen needleleaf forest, grasslands and croplands), using 159 site-years of NEE and climate data from 32 AmeriFlux sites and MODIS vegetation index time-series data. LSP metrics combined with optimal climate drivers can explain the variability in Start of Carbon Uptake (SCU) by more than 70% and End of Carbon Uptake (ECU) by more than 60%. The Root Mean Square Error (RMSE) of the estimations was within 8.5 days for both SCU and ECU. The estimation performance for this methodology was primarily dependent on the optimal combination of the LSP retrieval methods, the explanatory climate drivers, the biome types, and the specific CFP metric. This methodology has a potential for allowing extrapolation of CFP metrics for biomes with a distinct and detectable seasonal cycle over large areas, based on synoptic multi-temporal optical satellite data and climate data.

  19. Advantages of using satellite soil moisture estimates over precipitation products to assess regional vegetation water availability and activity

    Science.gov (United States)

    Chen, Tiexi

    2017-04-01

    To improve the understanding of water-vegetation relationships, direct comparative studies assessing the utility of satellite remotely sensed soil moisture, gridded precipitation products, and land surface model output are needed. A case study was investigated for a water-limited, lateral inflow receiving area in northeastern Australia during December 2008 to May 2009. In January 2009, monthly precipitation showed strong positive anomalies, which led to strong positive soil moisture anomalies. The precipitation anomalies disappeared within a month. In contrast, the soil moisture anomalies persisted for months. Positive anomalies of Normalized Difference Vegetation Index (NDVI) appeared in February, in response to water supply, and then persisted for several months. In addition to these temporal characteristics, the spatial patterns of NDVI anomalies were more similar to soil moisture patterns than to those of precipitation and land surface model output. The long memory of soil moisture mainly relates to the presence of clay-rich soils. Modeled soil moisture from four of five global land surface models failed to capture the memory length of soil moisture and all five models failed to present the influence of lateral inflow. This case study indicates that satellite-based soil moisture is a better predictor of vegetation water availability than precipitation in environments having a memory of several months and thus is able to persistently affect vegetation dynamics. These results illustrate the usefulness of satellite remotely sensed soil moisture in ecohydrology studies. This case study has the potential to be used as a benchmark for global land surface model evaluations. The advantages of using satellite remotely sensed soil moisture over gridded precipitation products are mainly expected in lateral-inflow and/or clay-rich regions worldwide.

  20. Evaluating Biosphere Model Estimates of the Start of the Vegetation Active Season in Boreal Forests by Satellite Observations

    Directory of Open Access Journals (Sweden)

    Kristin Böttcher

    2016-07-01

    Full Text Available The objective of this study was to assess the performance of the simulated start of the photosynthetically active season by a large-scale biosphere model in boreal forests in Finland with remote sensing observations. The start of season for two forest types, evergreen needle- and deciduous broad-leaf, was obtained for the period 2003–2011 from regional JSBACH (Jena Scheme for Biosphere–Atmosphere Hamburg runs, driven with climate variables from a regional climate model. The satellite-derived start of season was determined from daily Moderate Resolution Imaging Spectrometer (MODIS time series of Fractional Snow Cover and the Normalized Difference Water Index by applying methods that were targeted to the two forest types. The accuracy of the satellite-derived start of season in deciduous forest was assessed with bud break observations of birch and a root mean square error of seven days was obtained. The evaluation of JSBACH modelled start of season dates with satellite observations revealed high spatial correspondence. The bias was less than five days for both forest types but showed regional differences that need further consideration. The agreement with satellite observations was slightly better for the evergreen than for the deciduous forest. Nonetheless, comparison with gross primary production (GPP determined from CO2 flux measurements at two eddy covariance sites in evergreen forest revealed that the JSBACH-simulated GPP was higher in early spring and led to too-early simulated start of season dates. Photosynthetic activity recovers differently in evergreen and deciduous forests. While for the deciduous forest calibration of phenology alone could improve the performance of JSBACH, for the evergreen forest, changes such as seasonality of temperature response, would need to be introduced to the photosynthetic capacity to improve the temporal development of gross primary production.

  1. Non-algal particles spatial-temporal distribution at global scale: a first estimation from satellite data

    Science.gov (United States)

    Bellacicco, Marco; Volpe, Gianluca; Colella, Simone; Pitarch, Jaime; Brando, Vittorio; Marullo, Salvatore; Santoleri, Rosalia

    2016-04-01

    Phytoplankton, heterotrophic bacteria and viruses contribute to the definition of the trophic regime of the oceans. While phytoplankton has been extensively studied from space, satellite studies of the autochthonous non-algal particles (NAP, i.e. bacteria and viruses) are relatively recent. Dedicated studies of the NAP distribution and dynamics can help to improve the understanding of marine ecosystem change, globally. Using the 18 years of Glob-Colour monthly satellite data, from the satellite particulate backscattering coefficient (bbp) the NAP global climatology was derived. High NAP values were found in productive regions like polar seas, the North Atlantic and the equatorial Pacific, as well as shelf regions affected by upwelling currents. In contrast, oligotrophic areas like the sub-tropical gyres displayed low NAP values. The annual and seasonal distribution as well as the temporal evolution will be discussed. In the future, improved understanding of the phytoplankton dynamics and physiology will benefit from accurate NAP calculations for different regions and seasons in relation to climate change studies.

  2. Estimating daily surface NO2 concentrations from satellite data - a case study over Hong Kong using land use regression models

    Science.gov (United States)

    Anand, Jasdeep S.; Monks, Paul S.

    2017-07-01

    Land use regression (LUR) models have been used in epidemiology to determine the fine-scale spatial variation in air pollutants such as nitrogen dioxide (NO2) in cities and larger regions. However, they are often limited in their temporal resolution, which may potentially be rectified by employing the synoptic coverage provided by satellite measurements. In this work a mixed-effects LUR model is developed to model daily surface NO2 concentrations over the Hong Kong SAR during the period 2005-2015. In situ measurements from the Hong Kong Air Quality Monitoring Network, along with tropospheric vertical column density (VCD) data from the OMI, GOME-2A, and SCIAMACHY satellite instruments were combined with fine-scale land use parameters to provide the spatiotemporal information necessary to predict daily surface concentrations. Cross-validation with the in situ data shows that the mixed-effects LUR model using OMI data has a high predictive power (adj. R2 = 0. 84), especially when compared with surface concentrations derived using the MACC-II reanalysis model dataset (adj. R2 = 0. 11). Time series analysis shows no statistically significant trend in NO2 concentrations during 2005-2015, despite a reported decline in NOx emissions. This study demonstrates the utility in combining satellite data with LUR models to derive daily maps of ambient surface NO2 for use in exposure studies.

  3. Chlorophyll-a Estimation Around the Antarctica Peninsula Using Satellite Algorithms: Hints from Field Water Leaving Reflectance

    Directory of Open Access Journals (Sweden)

    Chen Zeng

    2016-12-01

    Full Text Available Ocean color remote sensing significantly contributes to our understanding of phytoplankton distribution and abundance and primary productivity in the Southern Ocean (SO. However, the current SO in situ optical database is still insufficient and unevenly distributed. This limits the ability to produce robust and accurate measurements of satellite-based chlorophyll. Based on data collected on cruises around the Antarctica Peninsula (AP on January 2014 and 2016, this research intends to enhance our knowledge of SO water and atmospheric optical characteristics and address satellite algorithm deficiency of ocean color products. We collected high resolution in situ water leaving reflectance (±1 nm band resolution, simultaneous in situ chlorophyll-a concentrations and satellite (MODIS and VIIRS water leaving reflectance. Field samples show that clouds have a great impact on the visible green bands and are difficult to detect because NASA protocols apply the NIR band as a cloud contamination threshold. When compared to global case I water, water around the AP has lower water leaving reflectance and a narrower blue-green band ratio, which explains chlorophyll-a underestimation in high chlorophyll-a regions and overestimation in low chlorophyll-a regions. VIIRS shows higher spatial coverage and detection accuracy than MODIS. After coefficient improvement, VIIRS is able to predict chlorophyll a with 53% accuracy.

  4. Galileo satellite antenna modeling

    Science.gov (United States)

    Steigenberger, Peter; Dach, Rolf; Prange, Lars; Montenbruck, Oliver

    2015-04-01

    The space segment of the European satellite navigation system Galileo currently consists of six satellites. Four of them belong to the first generation of In-Orbit Validation (IOV) satellites whereas the other two are Full Operational Capability (FOC) satellites. High-precision geodetic applications require detailed knowledge about the actual phase center of the satellite and receiver antenna. The deviation of this actual phase center from a well-defined reference point is described by phase center offsets (PCOs) and phase center variations (PCVs). Unfortunately, no public information is available about the Galileo satellite antenna PCOs and PCVs, neither for the IOV, nor the FOC satellites. Therefore, conventional values for the IOV satellite antenna PCOs have been adopted for the Multi-GNSS experiment (MGEX) of the International GNSS Service (IGS). The effect of the PCVs is currently neglected and no PCOs for the FOC satellites are available yet. To overcome this deficiency in GNSS observation modeling, satellite antenna PCOs and PCVs are estimated for the Galileo IOV satellites based on global GNSS tracking data of the MGEX network and additional stations of the legacy IGS network. Two completely independent solutions are computed with the Bernese and Napeos software packages. The PCO and PCV values of the individual satellites are analyzed and the availability of two different solutions allows for an accuracy assessment. The FOC satellites are built by a different manufacturer and are also equipped with another type of antenna panel compared to the IOV satellites. Signal transmission of the first FOC satellite has started in December 2014 and activation of the second satellite is expected for early 2015. Based on the available observations PCO estimates and, optionally PCVs of the FOC satellites will be presented as well. Finally, the impact of the new antenna model on the precision and accuracy of the Galileo orbit determination is analyzed.

  5. Lessons Learned from the Deployment and Integration of a Microwave Sounder Based Tropical Cyclone Intensity and Surface Wind Estimation Algorithm into NOAA/NESDIS Satellite Product Operations

    Science.gov (United States)

    Longmore, S. P.; Knaff, J. A.; Schumacher, A.; Dostalek, J.; DeMaria, R.; Chirokova, G.; Demaria, M.; Powell, D. C.; Sigmund, A.; Yu, W.

    2014-12-01

    The Colorado State University (CSU) Cooperative Institute for Research in the Atmosphere (CIRA) has recently deployed a tropical cyclone (TC) intensity and surface wind radii estimation algorithm that utilizes Suomi National Polar-orbiting Partnership (S-NPP) satellite Advanced Technology Microwave Sounder (ATMS) and Advanced Microwave Sounding Unit (AMSU) from the NOAA18, NOAA19 and METOPA polar orbiting satellites for testing, integration and operations for the Product System Development and Implementation (PSDI) projects at NOAA's National Environmental Satellite, Data, and Information Service (NESDIS). This presentation discusses the evolution of the CIRA NPP/AMSU TC algorithms internally at CIRA and its migration and integration into the NOAA Data Exploitation (NDE) development and testing frameworks. The discussion will focus on 1) the development cycle of internal NPP/AMSU TC algorithms components by scientists and software engineers, 2) the exchange of these components into the NPP/AMSU TC software systems using the subversion version control system and other exchange methods, 3) testing, debugging and integration of the NPP/AMSU TC systems both at CIRA/NESDIS and 4) the update cycle of new releases through continuous integration. Lastly, a discussion of the methods that were effective and those that need revision will be detailed for the next iteration of the NPP/AMSU TC system.

  6. Integrating Indian remote sensing multi-spectral satellite and field data to estimate seagrass cover change in the Andaman and Nicobar Islands, India

    Science.gov (United States)

    Paulose, Nobi Elavumkudi; Dilipan, Elangovan; Thangaradjou, Thirunavukarassu

    2013-06-01

    Environmental resource managers and policy makers require a reliable tool to quickly assess the spatial extent of any natural resources, including seagrasses, in order to develop management plans. Even small natural or anthropogenic disturbances can cause severe changes in the distributional pattern of seagrass meadows. Satellite imageries provide a suitable means to detect and assess such changes in space and time in remote and inaccessible areas. Present study aims to understand the distribution pattern of seagrasses after the Indian Ocean Tsunami in 2004 with the help of Indian Remote Sensing satellite data and in situ ground surveys with hand held GPS. As no geospatial data bases were available for the pre-tsunami period, the changes in seagrass cover were compared with the ground estimates available in the literature and also using pre-tsunami satellite data sets. The study found severe loss of seagrasses in the northern Andaman particularly in the Interview and North reef islands and in the Nicobar group of islands including Great Nicobar and Trinket islands. The investigation revealed the presence of 2,943.38 ha of seagrass covering the entire Andaman and Nicobar islands, and that 1,619.41 ha of seagrasses had been denuded during this period. The earthquake and subsequent tsunami in 2004 was the major reason for the loss of seagrasses in these islands. The seagrass spatial map generated in the present study can be used for the development of conservation and management plans and also to restore the denuded seagrasses of this region.

  7. Antarctic ice-mass balance 2002 to 2011: regional re-analysis of GRACE satellite gravimetry measurements with improved estimate of glacial-isostatic adjustment

    Directory of Open Access Journals (Sweden)

    I. Sasgen

    2012-09-01

    Full Text Available We present regional-scale mass balances for 25 drainage basins of the Antarctic Ice Sheet (AIS from satellite observations of the Gravity and Climate Experiment (GRACE for the years 2002–2011. Satellite gravimetry estimates of the AIS mass balance are strongly influenced by mass movement in the Earth interior caused by ice advance and retreat during the last glacial cycle. Here, we develop an improved glacial-isostatic adjustment (GIA estimate for Antarctica using newly available GPS uplift rates, allowing us to more accurately separate GIA-induced trends in the GRACE gravity fields from those caused by current imbalances of the AIS. Our revised GIA estimate is considerably lower than previous predictions, yielding an (upper estimate of apparent mass change of 48 ± 18 Gt yr−1. Therefore, our AIS mass balance of −103 ± 23 Gt yr−1 is considerably less negative than previous GRACE estimates. The Northern Antarctic Peninsula and the Amundsen Sea Sector exhibit the largest mass loss (−25 ± 6 Gt yr−1 and −126 ± 11 Gt yr−1, respectively. In contrast, East Antarctica exhibits a slightly positive mass balance (19 ± 16 Gt yr−1, which is, however, mostly the consequence of compensating mass anomalies in Dronning Maud and Enderby Land (positive and Wilkes and George V Land (negative due to interannual accumulation variations. In total, 7% of the area constitute more than half of the AIS imbalance (53%, contributing −151 ± 9 Gt yr−1 to global mean sea-level change. Most of this imbalance is caused by long-term ice-dynamic speed up expected to prevail in the future.

  8. SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications

    Directory of Open Access Journals (Sweden)

    G. Snider

    2014-07-01

    Full Text Available Ground-based observations have insufficient spatial coverage to assess long-term human exposure to fine particulate matter (PM2.5 at the global scale. Satellite remote sensing offers a promising approach to provide information on both short- and long-term exposure to PM2.5 at local-to-global scales, but there are limitations and outstanding questions about the accuracy and precision with which ground-level aerosol mass concentrations can be inferred from satellite remote sensing alone. A key source of uncertainty is the global distribution of the relationship between annual average PM2.5 and discontinuous satellite observations of columnar aerosol optical depth (AOD. We have initiated a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing estimates for application in health effects research and risk assessment. This Surface PARTiculate mAtter Network (SPARTAN includes a global federation of ground-level monitors of hourly PM2.5 situated primarily in highly populated regions and collocated with existing ground-based sun photometers that measure AOD. The instruments, a three-wavelength nephelometer and impaction filter sampler for both PM2.5 and PM10, are highly autonomous. Hourly PM2.5 concentrations are inferred from the combination of weighed filters and nephelometer data. Data from existing networks were used to develop and evaluate network sampling characteristics. SPARTAN filters are analyzed for mass, black carbon, water-soluble ions, and metals. These measurements provide, in a variety of global regions, the key data required to evaluate and enhance satellite-based PM2.5 estimates used for assessing the health effects of aerosols. Mean PM2.5 concentrations across sites vary by an order of magnitude. Initial measurements indicate that the AOD column to PM2.5 ratio is driven temporally primarily by the vertical profile of aerosol scattering; and spatially by a~ more complex interaction

  9. Assessing the Sensitivity of Satellite-Derived Estimates of Ice Sheet Mass Balance to Regional Climate Model Simulations of Snow Accumulation and Firn Compaction

    Science.gov (United States)

    Briggs, K.; Shepherd, A.; Horwath, M.; Horvath, A.; Nagler, T.; Wuite, J.; Muir, A.; Gilbert, L.; Mouginot, J.

    2015-12-01

    Surface mass balance (SMB) estimates from Regional Climate Models (RCMs) are fundamental for assessing and understanding ice sheet mass trends. Mass budget and altimetry assessments rely on RCMs both directly for estimates of the SMB contribution to the total mass trend, and indirectly for ancillary data in the form of firn compaction corrections. As such, mass balance assessments can be highly sensitive to RCM outputs and therefore their accuracy. Here we assess the extent to which geodetic measurements of mass balance are sensitive to RCM model outputs at different resolutions. We achieve this by comparing SMB dependent estimates of mass balance from the mass budget method and altimetry, with those from satellite gravimetry that are independent of SMB estimates. Using the outputs of the RACMO/ANT 2.3 model at 5.5 km and 27 km horizontal spatial resolution, we generate estimates of mass balance using the mass budget method and altimetry for the Western Palmer Land region of the Antarctic Peninsula between 2003 and 2014. We find a 19% increase in the long-term (1980 to 2014) mean annual SMB for the region when enhancing the model resolution to 5.5 km. This translates into an approximate 50% reduction in the total mass loss from 2003 to 2014 calculated with the mass budget method and a 15% increase in the altimetry estimate. The use of the enhanced resolution product leads to consistency between the estimates of mass loss from the altimetry and the mass budget method that is not observed with the coarser resolution product, in which estimates of cumulative mass fall beyond the relative errors. Critically, when using the 5.5 km product, we find excellent agreement, both in pattern and magnitude, with the independent estimate derived from gravimetry. Our results point toward the crucial need for high resolution SMB products from RCMs for mass balance assessments, particularly in regions of high mass turnover and complex terrain as found over the Antarctic Peninsula.

  10. Comparison of Satellite-Derived TOA Shortwave Clear-Sky Fluxes to Estimates from GCM Simulations Constrained by Satellite Observations of Land Surface Characteristics

    Science.gov (United States)

    Anantharaj, Valentine G.; Nair, Udaysankar S.; Lawrence, Peter; Chase, Thomas N.; Christopher, Sundar; Jones, Thomas

    2010-01-01

    Clear-sky, upwelling shortwave flux at the top of the atmosphere (S(sub TOA raised arrow)), simulated using the atmospheric and land model components of the Community Climate System Model 3 (CCSM3), is compared to corresponding observational estimates from the Clouds and Earth's Radiant Energy System (CERES) sensor. Improvements resulting from the use of land surface albedo derived from Moderate Resolution Imaging Spectroradiometer (MODIS) to constrain the simulations are also examined. Compared to CERES observations, CCSM3 overestimates global, annual averaged S(sub TOA raised arrow) over both land and oceans. However, regionally, CCSM3 overestimates S(sub TOA raised arrow) over some land and ocean areas while underestimating it over other sites. CCSM3 underestimates S(sub TOA raised arrow) over the Saharan and Arabian Deserts and substantial differences exist between CERES observations and CCSM3 over agricultural areas. Over selected sites, after using groundbased observations to remove systematic biases that exist in CCSM computation of S(sub TOA raised arrow), it is found that use of MODIS albedo improves the simulation of S(sub TOA raised arrow). Inability of coarse resolution CCSM3 simulation to resolve spatial heterogeneity of snowfall over high altitude sites such as the Tibetan Plateau causes overestimation of S(sub TOA raised arrow) in these areas. Discrepancies also exist in the simulation of S(sub TOA raised arrow) over ocean areas as CCSM3 does not account for the effect of wind speed on ocean surface albedo. This study shows that the radiative energy budget at the TOA is improved through the use of MODIS albedo in Global Climate Models.

  11. AQA-PM: Extension of the Air-Quality Model For Austria with Satellite based Particulate Matter Estimates

    Science.gov (United States)

    Hirtl, Marcus; Mantovani, Simone; Krüger, Bernd C.; Triebnig, Gerhard; Flandorfer, Claudia

    2013-04-01

    Air quality is a key element for the well-being and quality of life of European citizens. Air pollution measurements and modeling tools are essential for assessment of air quality according to EU legislation. The responsibilities of ZAMG as the national weather service of Austria include the support of the federal states and the public in questions connected to the protection of the environment in the frame of advisory and counseling services as well as expert opinions. The Air Quality model for Austria (AQA) is operated at ZAMG in cooperation with the University of Natural Resources and Life Sciences in Vienna (BOKU) by order of the regional governments since 2005. AQA conducts daily forecasts of gaseous and particulate (PM10) air pollutants over Austria. In the frame of the project AQA-PM (funded by FFG), satellite measurements of the Aerosol Optical Thickness (AOT) and ground-based PM10-measurements are combined to highly-resolved initial fields using regression- and assimilation techniques. For the model simulations WRF/Chem is used with a resolution of 3 km over the alpine region. Interfaces have been developed to account for the different measurements as input data. The available local emission inventories provided by the different Austrian regional governments were harmonized and used for the model simulations. An episode in February 2010 is chosen for the model evaluation. During that month exceedances of PM10-thresholds occurred at many measurement stations of the Austrian network. Different model runs (only model/only ground stations assimilated/satellite and ground stations assimilated) are compared to the respective measurements. The goal of this project is to improve the PM10-forecasts for Austria with the integration of satellite based measurements and to provide a comprehensive product-platform.

  12. Real-Time Global Flood Estimation Using Satellite-Based Precipitation and a Coupled Land Surface and Routing Model

    Science.gov (United States)

    Wu, Huan; Adler, Robert F.; Tian, Yudong; Huffman, George J.; Li, Hongyi; Wang, JianJian

    2014-01-01

    A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing-based runoff-routing model to form the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real-time Global Flood Monitoring System (GFMS). The GFMS uses real-time satellite-based precipitation to derive flood monitoring parameters for the latitude band 50 deg. N - 50 deg. S at relatively high spatial (approximately 12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real-time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research-quality and real-time satellite precipitation products. Evaluation results are slightly better for the research-quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is approximately 0.9 and the false alarm ratio is approximately 0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi-global domain. Validation using real-time precipitation across the tropics (30 deg. S - 30 deg. N) gives positive daily Nash-Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. There were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.

  13. Predicting global landslide spatiotemporal distribution: Integrating landslide susceptibility zoning techniques and real-time satellite rainfall estimates

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of sufficient ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing a preliminary real-time prediction system to identify where rainfall-triggered landslides will occur is proposed by combining two necessary components: surface landslide susceptibility and a real-time space-based rainfall analysis system (http://trmm.gsfc.nasa.govV First, a global landslide susceptibility map is derived from a combination of semi-static global surface characteristics (digital elevation topography, slope, soil types, soil texture, land cover classification, etc.) using a GIS weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide hazards at areas with high susceptibility. A major outcome of this work is the availability for the first time of a global assessment of landslide hazards, which is only possible because of the utilization of global satellite remote sensing products. This preliminary system can be updated continuously using the new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and mitigation activities across the world.

  14. Spatial estimation of air PM2.5 emissions using activity data, local emission factors and land cover derived from satellite imagery

    Directory of Open Access Journals (Sweden)

    H. P. Gibe

    2017-09-01

    Full Text Available Exposure to particulate matter (PM is a serious environmental problem in many urban areas on Earth. In the Philippines, most existing studies and emission inventories have mainly focused on point and mobile sources, while research involving human exposures to particulate pollutants is rare. This paper presents a method for estimating the amount of fine particulate (PM2.5 emissions in a test study site in the city of Cabanatuan, Nueva Ecija, in the Philippines, by utilizing local emission factors, regionally procured data, and land cover/land use (activity data interpreted from satellite imagery. Geographic information system (GIS software was used to map the estimated emissions in the study area. The present results suggest that vehicular emissions from motorcycles and tricycles, as well as fuels used by households (charcoal and burning of agricultural waste, largely contribute to PM2.5 emissions in Cabanatuan. Overall, the method used in this study can be applied in other small urbanizing cities, as long as on-site specific activity, emission factor, and satellite-imaged land cover data are available.

  15. Estimating daily time series of streamflow using hydrological model calibrated based on satellite observations of river water surface width: Toward real world applications.

    Science.gov (United States)

    Sun, Wenchao; Ishidaira, Hiroshi; Bastola, Satish; Yu, Jingshan

    2015-05-01

    Lacking observation data for calibration constrains applications of hydrological models to estimate daily time series of streamflow. Recent improvements in remote sensing enable detection of river water-surface width from satellite observations, making possible the tracking of streamflow from space. In this study, a method calibrating hydrological models using river width derived from remote sensing is demonstrated through application to the ungauged Irrawaddy Basin in Myanmar. Generalized likelihood uncertainty estimation (GLUE) is selected as a tool for automatic calibration and uncertainty analysis. Of 50,000 randomly generated parameter sets, 997 are identified as behavioral, based on comparing model simulation with satellite observations. The uncertainty band of streamflow simulation can span most of 10-year average monthly observed streamflow for moderate and high flow conditions. Nash-Sutcliffe efficiency is 95.7% for the simulated streamflow at the 50% quantile. These results indicate that application to the target basin is generally successful. Beyond evaluating the method in a basin lacking streamflow data, difficulties and possible solutions for applications in the real world are addressed to promote future use of the proposed method in more ungauged basins. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  16. Spatial estimation of air PM2.5 emissions using activity data, local emission factors and land cover derived from satellite imagery

    Science.gov (United States)

    Gibe, Hezron P.; Cayetano, Mylene G.

    2017-09-01

    Exposure to particulate matter (PM) is a serious environmental problem in many urban areas on Earth. In the Philippines, most existing studies and emission inventories have mainly focused on point and mobile sources, while research involving human exposures to particulate pollutants is rare. This paper presents a method for estimating the amount of fine particulate (PM2.5) emissions in a test study site in the city of Cabanatuan, Nueva Ecija, in the Philippines, by utilizing local emission factors, regionally procured data, and land cover/land use (activity data) interpreted from satellite imagery. Geographic information system (GIS) software was used to map the estimated emissions in the study area. The present results suggest that vehicular emissions from motorcycles and tricycles, as well as fuels used by households (charcoal) and burning of agricultural waste, largely contribute to PM2.5 emissions in Cabanatuan. Overall, the method used in this study can be applied in other small urbanizing cities, as long as on-site specific activity, emission factor, and satellite-imaged land cover data are available.

  17. Real-Time Estimation of Satellite-Derived PM2.5 Based on a Semi-Physical Geographically Weighted Regression Model

    Science.gov (United States)

    Zhang, Tianhao; Liu, Gang; Zhu, Zhongmin; Gong, Wei; Ji, Yuxi; Huang, Yusi

    2016-01-01

    The real-time estimation of ambient particulate matter with diameter no greater than 2.5 μm (PM2.5) is currently quite limited in China. A semi-physical geographically weighted regression (GWR) model was adopted to estimate PM2.5 mass concentrations at national scale using the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth product fused by the Dark Target (DT) and Deep Blue (DB) algorithms, combined with meteorological parameters. The fitting results could explain over 80% of the variability in the corresponding PM2.5 mass concentrations, and the estimation tends to overestimate when measurement is low and tends to underestimate when measurement is high. Based on World Health Organization standards, results indicate that most regions in China suffered severe PM2.5 pollution during winter. Seasonal average mass concentrations of PM2.5 predicted by the model indicate that residential regions, namely Jing-Jin-Ji Region and Central China, were faced with challenge from fine particles. Moreover, estimation deviation caused primarily by the spatially uneven distribution of monitoring sites and the changes of elevation in a relatively small region has been discussed. In summary, real-time PM2.5 was estimated effectively by the satellite-based semi-physical GWR model, and the results could provide reasonable references for assessing health impacts and offer guidance on air quality management in China. PMID:27706054

  18. Entrainment rate diurnal cycle in marine stratiform clouds estimated from geostationary satellite retrievals and a meteorological forecast model

    Science.gov (United States)

    Painemal, David; Xu, Kuan-Man; Palikonda, Rabindra; Minnis, Patrick

    2017-07-01

    The mean diurnal cycle of cloud entrainment rate (we) over the northeast Pacific region is for the first time computed by combining, in a mixed-layer model framework, the hourly composited GOES-15 satellite-based cloud top height (HT) tendency, advection, and large-scale vertical velocity (w) during May to September 2013, with horizontal winds and w taken from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The tendency term dominates the magnitude and phase of the we diurnal cycle, with a secondary role of w, and a modest advective contribution. The peak and minimum in we occur between 20:00-22:00 LT and 9:00-11:00 LT, respectively, in close agreement with the diurnal cycle of turbulence driven by cloud top longwave cooling. Uncertainties in HT and ECMWF fields are assessed with in situ observations and three meteorological reanalysis data sets. This study provides the basis for constructing nearly global climatologies of we by combining a suite of well-calibrated geostationary satellites.

  19. Improved frequency and time of arrival estimation methods in search and rescue system based on MEO satellites

    Science.gov (United States)

    Lin, Mo; Li, Rui; Li, Jilin

    2007-11-01

    This paper deals with several key points including parameter estimation such as frequency of arrival (FOA), time of arrival (TOA) estimation algorithm and signal processing techniques in Medium-altitude Earth Orbit Local User Terminals (MEOLUT) based on Cospas-Sarsat Medium-altitude Earth Orbit Search and Rescue system (MEOSAR). Based on an analytical description of distress beacon, improved TOA and FOA estimation methods have been proposed. An improved FOA estimation method which integrates bi-FOA measurement, FFT method, Rife algorithm and Gaussian window is proposed to improve the accuracy of FOA estimation. In addition, TPD algorithm and signal correlation techniques are used to achieve a high performance of TOA estimation. Parameter estimation problems are solved by proposed FOA/TOA methods under quite poor Carrier-to-Noise (C/N0). A number of simulations are done to show the improvements. FOA and TOA estimation error are lower than 0.1Hz and 11μs respectively which is very high system requirement for MEOSAR system MEOLUT.

  20. Assessing the uncertainty of biomass change estimates obtained using multi-temporal field, lidar sampling, and satellite imagery on the Kenai Peninsula of Alaska (Invited)

    Science.gov (United States)

    Andersen, H.

    2013-12-01

    There is increasing interest in the development of statistical sampling designs for aboveground biomass (and carbon) inventory and monitoring programs that can make efficient use of a variety of available data sources, including field plots, airborne lidar sampling, and satellite imagery. While the use of multiple sources, or levels, of remote sensing data can significantly increase the precision of biomass change estimates, especially in remote areas (such as interior Alaska) where it is extremely expensive to establish field plots, it can be challenging to accurately characterize the uncertainty (i.e. variance and bias) of the estimates obtained from these complex multi-level designs. In this study we evaluate a model-based approach to estimate changes in biomass over the western lowlands of the Kenai Peninsula of Alaska during the period 2004-2009 using a combination of field plots, lidar sampling, and satellite imagery. The model-based approach -- where all inferences are conditioned on the model relating the remote-sensing measurements to the inventory parameter of interest (e.g. biomass) - is appropriate for cases where it is cost-prohibitive, or infeasible, to establish a probability sample of field plots that are both spatially and temporally coincident with each remote sensing data set. For example, a model-based approach can be used to obtain biomass estimates over a period of time, even when field data is only available for the current time period. In this study, lidar data were collected in 2004 and 2009 over single swaths that covered 130 Forest Inventory and Analysis (FIA) plots distributed on a regular grid over the entire western Kenai. Field measurements on FIA plots were initially acquired over the period 1999-2003 and fifty-percent of these plots were remeasured in the period 2004-2009. In addition, high-accuracy coordinates (GPS equipment. Changes in biomass (and associated uncertainty) estimated from field remeasurements alone were compared to

  1. Daily estimates of fire danger using multitemporal satellite MODIS data: the experience of FIRE-SAT in the Basilicata Region (Italy)

    Science.gov (United States)

    Lanorte, R.; Lasaponara, R.; De Santis, F.; Aromando, A.; Nole, G.

    2012-04-01

    Daily estimates of fire danger using multitemporal satellite MODIS data: the experience of FIRE-SAT in the Basilicata Region (Italy) A. Lanorte, F. De Santis , A. Aromando, G. Nolè, R. Lasaponara, CNR-IMAA, Potenza, Italy In the recent years the Basilicata Region (Southern Italy) has been characterized by an increasing incidence of fire disturbance which also tends to affect protected (Regional and national parks) and natural vegetated areas. FIRE_SAT project has been funded by the Civil Protection of the Basilicata Region in order to set up a low cost methodology for fire danger/risk monitoring based on satellite Earth Observation techniques. To this aim, NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data were used. The spectral capability and daily availability makes MODIS products especially suitable for estimating the variations of fuel characteristics. This work presents new significant results obtained in the context of FIRE-SAT project. In order to obtain a dynamical indicator of fire susceptibility based on multitemporal MODIS satellite data, up-datable in short-time periods (daily), we used the spatial/temporal variations of following parameters: (1) Relative Greenness Index (2) Live and dead fuel moisture content (3) Temperature In particular, the dead fuel moisture content is a key factor in fire ignition. Dead fuel moisture dynamics are significantly faster than those observed for live fuel. Dead fine vegetation exhibits moisture and density values dependent on rapid atmospheric changes and strictly linked to local meteorological conditions. For this reason, commonly, the estimation of dead fuel moisture content is based on meteorological variables. In this study we propose to use MODIS data to estimate meteorological data (specifically Relative Humidity) at an adequate spatial and temporal resolution. The assessment of dead fuel moisture content plays a decisive role in determining a fire dynamic danger index in combination with other

  2. Improving Quantitative Precipitation Estimation via Data Fusion of High-Resolution Ground-based Radar Network and CMORPH Satellite-based Product

    Science.gov (United States)

    Cifelli, R.; Chen, H.; Chandrasekar, V.; Xie, P.

    2015-12-01

    A large number of precipitation products at multi-scales have been developed based upon satellite, radar, and/or rain gauge observations. However, how to produce optimal rainfall estimation for a given region is still challenging due to the spatial and temporal sampling difference of different sensors. In this study, we develop a data fusion mechanism to improve regional quantitative precipitation estimation (QPE) by utilizing satellite-based CMORPH product, ground radar measurements, as well as numerical model simulations. The CMORPH global precipitation product is essentially derived based on retrievals from passive microwave measurements and infrared observations onboard satellites (Joyce et al. 2004). The fine spatial-temporal resolution of 0.05o Lat/Lon and 30-min is appropriate for regional hydrologic and climate studies. However, it is inadequate for localized hydrometeorological applications such as urban flash flood forecasting. Via fusion of the Regional CMORPH product and local precipitation sensors, the high-resolution QPE performance can be improved. The area of interest is the Dallas-Fort Worth (DFW) Metroplex, which is the largest land-locked metropolitan area in the U.S. In addition to an NWS dual-polarization S-band WSR-88DP radar (i.e., KFWS radar), DFW hosts the high-resolution dual-polarization X-band radar network developed by the center for Collaborative Adaptive Sensing of the Atmosphere (CASA). This talk will present a general framework of precipitation data fusion based on satellite and ground observations. The detailed prototype architecture of using regional rainfall instruments to improve regional CMORPH precipitation product via multi-scale fusion techniques will also be discussed. Particularly, the temporal and spatial fusion algorithms developed for the DFW Metroplex will be described, which utilizes CMORPH product, S-band WSR-88DP, and X-band CASA radar measurements. In order to investigate the uncertainties associated with each

  3. Assessing the potential of satellite-based precipitation estimates for flood frequency analysis in ungauged or poorly gauged tributaries of China's Yangtze River basin

    Science.gov (United States)

    Gao, Zhen; Long, Di; Tang, Guoqiang; Zeng, Chao; Huang, Jiesheng; Hong, Yang

    2017-07-01

    Flood frequency analysis (FFA) is critical for water resources engineering projects, particularly the design of hydraulic structures such as dams and reservoirs. However, it is often difficult to implement FFA in ungauged or poorly gauged basins because of the lack of consistent and long-term records of streamflow observations. The objective of this study was to evaluate the utility of satellite-based precipitation estimates for performing FFA in two presumably ungauged tributaries, the Jialing and Tuojiang Rivers, of the upper Yangtze River. Annual peak flow series were simulated using the Coupled Routing and Excess STorage (CREST) hydrologic model. Flood frequency was estimated by fitting the Pearson type III distribution of both observed and modeled streamflow with historic floods. Comparison of satellite-based precipitation products with a ground-based daily precipitation dataset for the period 2002-2014 reveals that 3B42V7 outperformed 3B42RT. The 3B42V7 product also shows consistent reliability in streamflow simulation and FFA (e.g., relative errors -20%-5% in the Jialing River). The results also indicate that complex terrain, drainage area, and reservoir construction are important factors that impact hydrologic model performance. The larger basin (156,736 km2) is more likely to produce satisfactory results than the small basin (19,613 km2) under similar circumstances (e.g., Jialing/Tuojiang calibrated by 3B42V7 for the calibration period: NSCE = 0.71/0.56). Using the same calibrated parameter sets from the entire Jialing River basin, the 3B42V7/3B42RT-driven hydrologic model performs better for two tributaries of the Jialing River (e.g., for the calibration period, NSCE = 0.71/0.60 in the Qujiang River basin and 0.54/0.38 in the Fujiang River basin) than for the upper mainstem of the Jialing River (NSCE = 0.34/0.32), which has more cascaded reservoirs with all these tributaries treated as ungauged basins for model validation. Overall, this study underscores

  4. A multi-scale analysis of Namibian rainfall over the recent decade – comparing TMPA satellite estimates and ground observations

    Directory of Open Access Journals (Sweden)

    Xuefei Lu

    2016-12-01

    New hydrological insights for the region: The agreement between ground and satellite rainfall data was generally good at annual/monthly scales but large variations were observed at the daily scale. Results showed a spatial variability of rainfall trends across the rainfall gradient. We observed significant changes in frequency along with insignificant changes in intensity and no changes in total amount for the driest location, but no changes in any of the rainfall parameters were observed for the three wetter locations. The results also showed increased rainfall variability for the driest location. This study provided a useful approach of using TMPA data associated with trend analysis to extend the data record for ecohydrological studies for similar data scarce conditions. The results of this study will also help constrain IPCC predictions in this region.

  5. Comparison of Two Methods for Estimating the Sampling-Related Uncertainty of