Satellite-derived land surface parameters for mesoscale modelling of the Mexico City basin

Directory of Open Access Journals (Sweden)

B. de Foy

2006-01-01

Full Text Available Mesoscale meteorological modelling is an important tool to help understand air pollution and heat island effects in urban areas. Accurate wind simulations are difficult to obtain in areas of weak synoptic forcing. Local factors have a dominant role in the circulation and include land surface parameters and their interaction with the atmosphere. This paper examines an episode during the MCMA-2003 field campaign held in the Mexico City Metropolitan Area (MCMA in April of 2003. Because the episode has weak synoptic forcing, there is the potential for the surface heat budget to influence the local meteorology. High resolution satellite observations are used to specify the land use, vegetation fraction, albedo and surface temperature in the MM5 model. Making use of these readily available data leads to improved meteorological simulations in the MCMA, both for the wind circulation patterns and the urban heat island. Replacing values previously obtained from land-use tables with actual measurements removes the number of unknowns in the model and increases the accuracy of the energy budget. In addition to improving the understanding of local meteorology, this sets the stage for the use of advanced urban modules.

A Land Product Characterization System for Comparative Analysis of Satellite Data and Products

Directory of Open Access Journals (Sweden)

Kevin Gallo

2017-12-01

Full Text Available A Land Product Characterization System (LPCS has been developed to provide land data and products to the community of individuals interested in validating space-based land products by comparing them with similar products available from other sensors or surface-based observations. The LPCS facilitates the application of global multi-satellite and in situ data for characterization and validation of higher-level, satellite-derived, land surface products (e.g., surface reflectance, normalized difference vegetation index, and land surface temperature. The LPCS includes data search, inventory, access, and analysis functions that will permit data to be easily identified, retrieved, co-registered, and compared statistically through a single interface. The system currently includes data and products available from Landsat 4 through 8, Moderate Resolution Imaging Spectroradiometer (MODIS Terra and Aqua, Suomi National Polar-Orbiting Partnership (S-NPP/Joint Polar Satellite System (JPSS Visible Infrared Imaging Radiometer Suite (VIIRS, and simulated data for the Geostationary Operational Environmental Satellite (GOES-16 Advanced Baseline Imager (ABI. In addition to the future inclusion of in situ data, higher-level land products from the European Space Agency (ESA Sentinel-2 and -3 series of satellites, and other high and medium resolution spatial sensors, will be included as available. When fully implemented, any of the sensor data or products included in the LPCS would be available for comparative analysis.

Downscaling Satellite Land Surface Temperatures in Urban Regions for Surface Energy Balance Study and Heat Index Development

Science.gov (United States)

Norouzi, H.; Bah, A.; Prakash, S.; Nouri, N.; Blake, R.

2017-12-01

A great percentage of the world's population reside in urban areas that are exposed to the threats of global and regional climate changes and associated extreme weather events. Among them, urban heat islands have significant health and economic impacts due to higher thermal gradients of impermeable surfaces in urban regions compared to their surrounding rural areas. Therefore, accurate characterization of the surface energy balance in urban regions are required to predict these extreme events. High spatial resolution Land surface temperature (LST) in the scale of street level in the cities can provide wealth of information to study surface energy balance and eventually providing a reliable heat index. In this study, we estimate high-resolution LST maps using combination of LandSat 8 and infrared based satellite products such as Moderate Resolution Imaging Spectroradiometer (MODIS) and newly launched Geostationary Operational Environmental Satellite-R Series (GOES-R). Landsat 8 provides higher spatial resolution (30 m) estimates of skin temperature every 16 days. However, MODIS and GOES-R have lower spatial resolution (1km and 4km respectively) with much higher temporal resolution. Several statistical downscaling methods were investigated to provide high spatiotemporal LST maps in urban regions. The results reveal that statistical methods such as Principal Component Analysis (PCA) can provide reliable estimations of LST downscaling with 2K accuracy. Other methods also were tried including aggregating (up-scaling) the high-resolution data to a coarse one to examine the limitations and to build the model. Additionally, we deployed flux towers over distinct materials such as concrete, asphalt, and rooftops in New York City to monitor the sensible and latent heat fluxes through eddy covariance method. To account for the incoming and outgoing radiation, a 4-component radiometer is used that can observe both incoming and outgoing longwave and shortwave radiation. This

Analysing Regional Land Surface Temperature Changes by Satellite Data, a Case Study of Zonguldak, Turkey

Science.gov (United States)

Sekertekin, A.; Kutoglu, S.; Kaya, S.; Marangoz, A. M.

2014-12-01

In recent years, climate change is one of the most important problems that the ecological system of the world has been encountering. Global warming and climate change have been studied frequently by all disciplines all over the world and Geomatics Engineering also contributes to such studies by means of remote sensing, global positioning system etc. Monitoring Land Surface Temperature (LST) via remote sensing satellites is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and there are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. Generally three algorithms are used to obtain LST by using Landsat 5 TM data. These algorithms are radiative transfer equation method, single channel method and mono-window algorithm. Radiative transfer equation method is not applicable because during the satellite pass, atmospheric parameters must be measured in-situ. In this research, mono window algorithm was implemented to Landsat 5 TM image. Besides, meteorological data such as humidity and temperature are used in the algorithm. Acquisition date of the image is 28.08.2011 and our study area is Zonguldak, Turkey. High resolution images are used to investigate the relationships between LST and land cover type. As a result of these analyses, area with vegetation cover has approximately 5 ºC lower temperature than the city center and arid land. Because different surface properties like reinforced concrete construction, green zones and sandbank are all together in city center, LST differs about 10 ºC in the city center. The temperature around some places in thermal power plant region Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature because of land cover structure. Thanks to this

Correction of sub-pixel topographical effects on land surface albedo retrieved from geostationary satellite (FengYun-2D) observations

International Nuclear Information System (INIS)

Roupioz, L; Nerry, F; Jia, L; Menenti, M

2014-01-01

The Qinghai-Tibetan Plateau is characterised by a very strong relief which affects albedo retrieval from satellite data. The objective of this study is to highlight the effects of sub-pixel topography and to account for those effects when retrieving land surface albedo from geostationary satellite FengYun-2D (FY-2D) data with 1.25km spatial resolution using the high spatial resolution (30 m) data of the Digital Elevation Model (DEM) from ASTER. The methodology integrates the effects of sub-pixel topography on the estimation of the total irradiance received at the surface, allowing the computation of the topographically corrected surface reflectance. Furthermore, surface albedo is estimated by applying the parametric BRDF (Bidirectional Reflectance Distribution Function) model called RPV (Rahman-Pinty-Verstraete) to the terrain corrected surface reflectance. The results, evaluated against ground measurements collected over several experimental sites on the Qinghai-Tibetan Plateau, document the advantage of integrating the sub-pixel topography effects in the land surface reflectance at 1km resolution to estimate the land surface albedo. The results obtained after using sub-pixel topographic correction are compared with the ones obtained after using pixel level topographic correction. The preliminary results imply that, in highly rugged terrain, the sub-pixel topography correction method gives more accurate results. The pixel level correction tends to overestimate surface albedo

Land surface temperature distribution and development for green open space in Medan city using imagery-based satellite Landsat 8

Science.gov (United States)

Sulistiyono, N.; Basyuni, M.; Slamet, B.

2018-03-01

Green open space (GOS) is one of the requirements where a city is comfortable to stay. GOS might reduce land surface temperature (LST) and air pollution. Medan is one of the biggest towns in Indonesia that experienced rapid development. However, the early development tends to neglect the GOS existence for the city. The objective of the study is to determine the distribution of land surface temperature and the relationship between the normalized difference vegetation index (NDVI) and the priority of GOS development in Medan City using imagery-based satellite Landsat 8. The method approached to correlate the distribution of land surface temperature derived from the value of digital number band 10 with the NDVI which was from the ratio of groups five and four on satellite images of Landsat 8. The results showed that the distribution of land surface temperature in the Medan City in 2016 ranged 20.57 - 33.83 °C. The relationship between the distribution of LST distribution with NDVI was reversed with a negative correlation of -0.543 (sig 0,000). The direction of GOS in Medan City is therefore developed on the allocation of LST and divided into three priority classes namely first priority class had 5,119.71 ha, the second priority consisted of 16,935.76 ha, and third priority of 6,118.50 ha.

An Overview of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE)

Science.gov (United States)

Sellers, P. J.; Hall, F. G.; Asrar, G.; Strebel, D. E.; Murphy, R. E.

1992-11-01

In the summer of 1983 a group of scientists working in the fields of meteorology, biology, and remote sensing met to discuss methods for modeling and observing land-surface—atmosphere interactions on regional and global scales. They concluded, first, that the existing climate models contained poor representations of the processes controlling the exchanges of energy, water, heat, and carbon between the land surface and the atmosphere and, second, that satellite remote sensing had been underutilized as a means of specifying global fields of the governing biophysical parameters. Accordingly, a multiscale, multidisciplinary experiment, FIFE, was initiated to address these two issues. The objectives of FIFE were specified as follows: (1) Upscale integration of models: The experiment was designed to test the soil-plant-atmosphere models developed by biometeorologists for small-scale applications (millimeters to meters) and to develop methods to apply them at the larger scales (kilometers) appropriate to atmospheric models and satellite remote sensing. (2) Application of satellite remote sensing: Even if the first goal were achieved to yield a "perfect" model of vegetation-atmosphere exchanges, it would have very limited applications without a global observing system for initialization and validation. As a result, the experiment was tasked with exploring methods for using satellite data to quantify important biophysical states and rates for model input. The experiment was centered on a 15 × 15 km grassland site near Manhattan, Kansas. This area became the focus for an extended monitoring program of satellite, meteorological, biophysical, and hydrological data acquisition from early 1987 through October 1989 and a series of 12- to 20-day intensive field campaigns (IFCs), four in 1987 and one in 1989. During the IFCs the fluxes of heat, moisture, carbon dioxide, and radiation were measured with surface and airborne equipment in coordination with measurements of surface

Integrating satellite retrieved leaf chlorophyll into land surface models for constraining simulations of water and carbon fluxes

KAUST Repository

Houborg, Rasmus

2013-07-01

In terrestrial biosphere models, key biochemical controls on carbon uptake by vegetation canopies are typically assigned fixed literature-based values for broad categories of vegetation types although in reality significant spatial and temporal variability exists. Satellite remote sensing can support modeling efforts by offering distributed information on important land surface characteristics, which would be very difficult to obtain otherwise. This study investigates the utility of satellite based retrievals of leaf chlorophyll for estimating leaf photosynthetic capacity and for constraining model simulations of water and carbon fluxes. © 2013 IEEE.

Land Data Assimilation of Satellite-Based Soil Moisture Products Using the Land Information System Over the NLDAS Domain

Science.gov (United States)

Mocko, David M.; Kumar, S. V.; Peters-Lidard, C. D.; Tian, Y.

2011-01-01

This presentation will include results from data assimilation simulations using the NASA-developed Land Information System (LIS). Using the ensemble Kalman filter in LIS, two satellite-based soil moisture products from the AMSR-E instrument were assimilated, one a NASA-based product and the other from the Land Parameter Retrieval Model (LPRM). The domain and land-surface forcing data from these simulations were from the North American Land Data Assimilation System Phase-2, over the period 2002-2008. The Noah land-surface model, version 3.2, was used during the simulations. Changes to estimates of land surface states, such as soil moisture, as well as changes to simulated runoff/streamflow will be presented. Comparisons over the NLDAS domain will also be made to two global reference evapotranspiration (ET) products, one an interpolated product based on FLUXNET tower data and the other a satellite- based algorithm from the MODIS instrument. Results of an improvement metric show that assimilating the LPRM product improved simulated ET estimates while the NASA-based soil moisture product did not.

Land Surface Temperature- Comparing Data from Polar Orbiting and Geostationary Satellites

Science.gov (United States)

Comyn-Platt, E.; Remedios, J. J.; Good, E. J.; Ghent, D.; Saunders, R.

2012-04-01

Land Surface Temperature (LST) is a vital parameter in Earth climate science, driving long-wave radiation exchanges that control the surface energy budget and carbon fluxes, which are important factors in Numerical Weather Prediction (NWP) and the monitoring of climate change. Satellites offer a convenient way to observe LST consistently and regularly over large areas. A comparison between LST retrieved from a Geostationary Instrument, the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), and a Polar Orbiting Instrument, the Advanced Along Track Scanning Radiometer (AATSR) is presented. Both sensors offer differing benefits. AATSR offers superior precision and spatial resolution with global coverage but given its sun-synchronous platform only observes at two local times, ~10am and ~10pm. SEVIRI provides the high-temporal resolution (every 15 minutes) required for observing diurnal variability of surface temperatures but given its geostationary platform has a poorer resolution, 3km at nadir, which declines at higher latitudes. A number of retrieval methods are applied to the raw satellite data: First order coefficient based algorithms provided on an operational basis by the LandSAF (for SEVIRI) and the University of Leicester (for AATSR); Second order coefficient based algorithms put forward by the University of Valencia; and an optimal estimation method using the 1DVar software provided by the NWP SAF. Optimal estimation is an iterative technique based upon inverse theory, thus is very useful for expanding into data assimilation systems. The retrievals are assessed and compared on both a fine scale using in-situ data from recognised validation sites and on a broad scale using two 100x100 regions such that biases can be better understood. Overall, the importance of LST lies in monitoring daily temperature extremes, e.g. for estimating permafrost thawing depth or risk of crop damage due to frost, hence the ideal dataset would use a combination of observations

Using Satellite Data and Land Surface Models to Monitor and Forecast Drought Conditions in Africa and Middle East

Science.gov (United States)

Arsenault, K. R.; Shukla, S.; Getirana, A.; Peters-Lidard, C. D.; Kumar, S.; McNally, A.; Zaitchik, B. F.; Badr, H. S.; Funk, C. C.; Koster, R. D.; Narapusetty, B.; Jung, H. C.; Roningen, J. M.

2017-12-01

Drought and water scarcity are among the important issues facing several regions within Africa and the Middle East. In addition, these regions typically have sparse ground-based data networks, where sometimes remotely sensed observations may be the only data available. Long-term satellite records can help with determining historic and current drought conditions. In recent years, several new satellites have come on-line that monitor different hydrological variables, including soil moisture and terrestrial water storage. Though these recent data records may be considered too short for the use in identifying major droughts, they do provide additional information that can better characterize where water deficits may occur. We utilize recent satellite data records of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and the European Space Agency's Advanced Scatterometer (ASCAT) soil moisture retrievals. Combining these records with land surface models (LSMs), NASA's Catchment and the Noah Multi-Physics (MP), is aimed at improving the land model states and initialization for seasonal drought forecasts. The LSMs' total runoff is routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics, which can provide an additional means of validation against in situ streamflow data. The NASA Land Information System (LIS) software framework drives the LSMs and HyMAP and also supports the capability to assimilate these satellite retrievals, such as soil moisture and TWS. The LSMs are driven for 30+ years with NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS/UCSB Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) rainfall dataset. The seasonal water deficit forecasts are generated using downscaled and bias-corrected versions of NASA's Goddard Earth Observing System Model (GEOS-5), and NOAA's Climate Forecast System (CFSv2) forecasts

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.

Land Surface Data Assimilation

Science.gov (United States)

Houser, P. R.

2012-12-01

Information about land surface water, energy and carbon conditions is of critical importance to real-world applications such as agricultural production, water resource management, flood prediction, water supply, weather and climate forecasting, and environmental preservation. While ground-based observational networks are improving, the only practical way to observe these land surface states on continental to global scales is via satellites. Remote sensing can make spatially comprehensive measurements of various components of the terrestrial system, but it cannot provide information on the entire system (e.g. evaporation), and the observations represent only an instant in time. Land surface process models may be used to predict temporal and spatial terrestrial dynamics, but these predictions are often poor, due to model initialization, parameter and forcing, and physics errors. Therefore, an attractive prospect is to combine the strengths of land surface models and observations (and minimize the weaknesses) to provide a superior terrestrial state estimate. This is the goal of land surface data assimilation. Data Assimilation combines observations into a dynamical model, using the model's equations to provide time continuity and coupling between the estimated fields. Land surface data assimilation aims to utilize both our land surface process knowledge, as embodied in a land surface model, and information that can be gained from observations. Both model predictions and observations are imperfect and we wish to use both synergistically to obtain a more accurate result. Moreover, both contain different kinds of information, that when used together, provide an accuracy level that cannot be obtained individually. Model biases can be mitigated using a complementary calibration and parameterization process. Limited point measurements are often used to calibrate the model(s) and validate the assimilation results. This presentation will provide a brief background on land

Validation of Land Surface Temperature from Sentinel-3

Science.gov (United States)

Ghent, D.

2017-12-01

One of the main objectives of the Sentinel-3 mission is to measure sea- and land-surface temperature with high-end accuracy and reliability in support of environmental and climate monitoring in an operational context. Calibration and validation are thus key criteria for operationalization within the framework of the Sentinel-3 Mission Performance Centre (S3MPC). Land surface temperature (LST) has a long heritage of satellite observations which have facilitated our understanding of land surface and climate change processes, such as desertification, urbanization, deforestation and land/atmosphere coupling. These observations have been acquired from a variety of satellite instruments on platforms in both low-earth orbit and in geostationary orbit. Retrieval accuracy can be a challenge though; surface emissivities can be highly variable owing to the heterogeneity of the land, and atmospheric effects caused by the presence of aerosols and by water vapour absorption can give a bias to the underlying LST. As such, a rigorous validation is critical in order to assess the quality of the data and the associated uncertainties. Validation of the level-2 SL_2_LST product, which became freely available on an operational basis from 5th July 2017 builds on an established validation protocol for satellite-based LST. This set of guidelines provides a standardized framework for structuring LST validation activities. The protocol introduces a four-pronged approach which can be summarised thus: i) in situ validation where ground-based observations are available; ii) radiance-based validation over sites that are homogeneous in emissivity; iii) intercomparison with retrievals from other satellite sensors; iv) time-series analysis to identify artefacts on an interannual time-scale. This multi-dimensional approach is a necessary requirement for assessing the performance of the LST algorithm for the Sea and Land Surface Temperature Radiometer (SLSTR) which is designed around biome

Evaluating the use of sharpened land surface temperature for daily evapotranspiration estimation over irrigated crops in arid lands

KAUST Repository

Rosas, Jorge

2014-12-01

Satellite remote sensing provides data on land surface characteristics, useful for mapping land surface energy fluxes and evapotranspiration (ET). Land-surface temperature (LST) derived from thermal infrared (TIR) satellite data has been reliably used as a remote indicator of ET and surface moisture status. However, TIR imagery usually operates at a coarser resolution than that of shortwave sensors on the same satellite platform, making it sometimes unsuitable for monitoring of field-scale crop conditions. This study applies the data mining sharpener (DMS; Gao et al., 2012) technique to data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which sharpens the 1 km thermal data down to the resolution of the optical data (250-500 m) based on functional LST and reflectance relationships established using a flexible regression tree approach. The DMS approach adopted here has been enhanced/refined for application over irrigated farming areas located in harsh desert environments in Saudi Arabia. The sharpened LST data is input to an integrated modeling system that uses the Atmosphere-Land Exchange Inverse (ALEXI) model and associated flux disaggregation scheme (DisALEXI) in conjunction with model reanalysis data and remotely sensed data from polar orbiting (MODIS) and geostationary (MSG; Meteosat Second Generation) satellite platforms to facilitate daily estimates of evapotranspiration. Results are evaluated against available flux tower observations over irrigated maize near Riyadh in Saudi Arabia. Successful monitoring of field-scale changes in surface fluxes are of importance towards an efficient water use in areas where fresh water resources are scarce and poorly monitored. Gao, F.; Kustas, W.P.; Anderson, M.C. A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land. Remote Sens. 2012, 4, 3287-3319.

Satellite Estimation of Daily Land Surface Water Vapor Pressure Deficit from AMSR- E

Science.gov (United States)

Jones, L. A.; Kimball, J. S.; McDonald, K. C.; Chan, S. K.; Njoku, E. G.; Oechel, W. C.

2007-12-01

Vapor pressure deficit (VPD) is a key variable for monitoring land surface water and energy exchanges, and estimating plant water stress. Multi-frequency day/night brightness temperatures from the Advanced Microwave Scanning Radiometer on EOS Aqua (AMSR-E) were used to estimate daily minimum and average near surface (2 m) air temperatures across a North American boreal-Arctic transect. A simple method for determining daily mean VPD (Pa) from AMSR-E air temperature retrievals was developed and validated against observations across a regional network of eight study sites ranging from boreal grassland and forest to arctic tundra. The method assumes that the dew point and minimum daily air temperatures tend to equilibrate in areas with low night time temperatures and relatively moist conditions. This assumption was tested by comparing the VPD algorithm results derived from site daily temperature observations against results derived from AMSR-E retrieved temperatures alone. An error analysis was conducted to determine the amount of error introduced in VPD estimates given known levels of error in satellite retrieved temperatures. Results indicate that the assumption generally holds for the high latitude study sites except for arid locations in mid-summer. VPD estimates using the method with AMSR-E retrieved temperatures compare favorably with site observations. The method can be applied to land surface temperature retrievals from any sensor with day and night surface or near-surface thermal measurements and shows potential for inferring near-surface wetness conditions where dense vegetation may hinder surface soil moisture retrievals from low-frequency microwave sensors. This work was carried out at The University of Montana, at San Diego State University, and at the Jet Propulsion Laboratory, California Institute of Technology, under contract to the National Aeronautics and Space Administration.

Rain detection over land surfaces using passive microwave satellite data

NARCIS (Netherlands)

Bauer, P.; Burose, D.; Schulz, J.

2002-01-01

An algorithm is presented for the detection of surface rainfall using passive microwave measurements by satellite radiometers. The technique consists of a two-stage approach to distinguish precipitation signatures from other effects: (1) Contributions from slowly varying parameters (surface type and

On the sensitivity of Land Surface Temperature estimates in arid irrigated lands using MODTRAN

KAUST Repository

Rosas, Jorge; Houborg, Rasmus; McCabe, Matthew

2015-01-01

Land surface temperature (LST) derived from thermal infrared (TIR) satellite data has been reliably used as a remote indicator of evapotranspiration (ET) and surface moisture status. However, in order to retrieve the ET with an accuracy approaching

Modelling the angular effects on satellite retrieved LST at global scale using a land surface classification

Science.gov (United States)

Ermida, Sofia; DaCamara, Carlos C.; Trigo, Isabel F.; Pires, Ana C.; Ghent, Darren

2017-04-01

Land Surface Temperature (LST) is a key climatological variable and a diagnostic parameter of land surface conditions. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Although LST estimation from remote sensing instruments operating in the Infrared (IR) is widely used and has been performed for nearly 3 decades, there is still a list of open issues. One of these is the LST dependence on viewing and illumination geometry. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should represent to the ensemble of directional radiometric temperature of all surface elements within the FOV. Angular effects on LST are here conveniently estimated by means of a kernel model of the surface thermal emission, which describes the angular dependence of LST as a function of viewing and illumination geometry. The model is calibrated using LST data as provided by a wide range of sensors to optimize spatial coverage, namely: 1) a LEO sensor - the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board NASA's TERRA and AQUA; and 2) 3 GEO sensors - the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board EUMETSAT's Meteosat Second Generation (MSG), the Japanese Meteorological Imager (JAMI) on-board the Japanese Meteorological Association (JMA) Multifunction Transport SATellite (MTSAT-2), and NASA's Geostationary Operational Environmental Satellites (GOES). As shown in our previous feasibility studies the sampling of illumination and view angles has a high impact on the obtained model parameters. This impact may be mitigated when the sampling size is increased by aggregating pixels with similar surface conditions. Here we propose a methodology where land surface is

Hydrological land surface modelling

DEFF Research Database (Denmark)

Ridler, Marc-Etienne Francois

Recent advances in integrated hydrological and soil-vegetation-atmosphere transfer (SVAT) modelling have led to improved water resource management practices, greater crop production, and better flood forecasting systems. However, uncertainty is inherent in all numerical models ultimately leading...... temperature are explored in a multi-objective calibration experiment to optimize the parameters in a SVAT model in the Sahel. The two satellite derived variables were effective at constraining most land-surface and soil parameters. A data assimilation framework is developed and implemented with an integrated...... and disaster management. The objective of this study is to develop and investigate methods to reduce hydrological model uncertainty by using supplementary data sources. The data is used either for model calibration or for model updating using data assimilation. Satellite estimates of soil moisture and surface...

Using satellite data on meteorological and vegetation characteristics and soil surface humidity in the Land Surface Model for the vast territory of agricultural destination

Science.gov (United States)

Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Vasilenko, Eugene; Volkova, Elena; Kukharsky, Alexander

2017-04-01

The model of water and heat exchange between vegetation covered territory and atmosphere (LSM, Land Surface Model) for vegetation season has been developed to calculate soil water content, evapotranspiration, infiltration of water into the soil, vertical latent and sensible heat fluxes and other water and heat balances components as well as soil surface and vegetation cover temperatures and depth distributions of moisture and temperature. The LSM is suited for utilizing satellite-derived estimates of precipitation, land surface temperature and vegetation characteristics and soil surface humidity for each pixel. Vegetation and meteorological characteristics being the model parameters and input variables, correspondingly, have been estimated by ground observations and thematic processing measurement data of scanning radiometers AVHRR/NOAA, SEVIRI/Meteosat-9, -10 (MSG-2, -3) and MSU-MR/Meteor-M № 2. Values of soil surface humidity has been calculated from remote sensing data of scatterometers ASCAT/MetOp-A, -B. The case study has been carried out for the territory of part of the agricultural Central Black Earth Region of European Russia with area of 227300 km2 located in the forest-steppe zone for years 2012-2015 vegetation seasons. The main objectives of the study have been: - to built estimates of precipitation, land surface temperatures (LST) and vegetation characteristics from MSU-MR measurement data using the refined technologies (including algorithms and programs) of thematic processing satellite information matured on AVHRR and SEVIRI data. All technologies have been adapted to the area of interest; - to investigate the possibility of utilizing satellite-derived estimates of values above in the LSM including verification of obtained estimates and development of procedure of their inputting into the model. From the AVHRR data there have been built the estimates of precipitation, three types of LST: land skin temperature Tsg, air temperature at a level of

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

Science.gov (United States)

Maxwell, S.K.; Meliker, J.R.; Goovaerts, P.

2010-01-01

In recent years, geographic information systems (GIS) have increasingly been used for reconstructing individual-level exposures to environmental contaminants in epidemiological research. Remotely sensed data can be useful in creating space-time models of environmental measures. The primary advantage of using remotely sensed data is that it allows for study at the local scale (e.g., residential level) without requiring expensive, time-consuming monitoring campaigns. The purpose of our study was to identify how land surface remotely sensed data are currently being used to study the relationship between cancer and environmental contaminants, focusing primarily on agricultural chemical exposure assessment applications. We present the results of a comprehensive literature review of epidemiological research where remotely sensed imagery or land cover maps derived from remotely sensed imagery were applied. We also discuss the strengths and limitations of the most commonly used imagery data (aerial photographs and Landsat satellite imagery) and land cover maps.

Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data

Directory of Open Access Journals (Sweden)

Tao Yu

2018-02-01

Full Text Available Accurately estimating vegetation productivity is important in research on terrestrial ecosystems, carbon cycles and climate change. Eight-day gross primary production (GPP and annual net primary production (NPP are contained in MODerate Resolution Imaging Spectroradiometer (MODIS products (MOD17, which are considered the first operational datasets for monitoring global vegetation productivity. However, the cloud-contaminated MODIS leaf area index (LAI and Fraction of Photosynthetically Active Radiation (FPAR retrievals may introduce some considerable errors to MODIS GPP and NPP products. In this paper, global eight-day GPP and eight-day NPP were first estimated based on Global LAnd Surface Satellite (GLASS LAI and FPAR products. Then, GPP and NPP estimates were validated by FLUXNET GPP data and BigFoot NPP data and were compared with MODIS GPP and NPP products. Compared with MODIS GPP, a time series showed that estimated GLASS GPP in our study was more temporally continuous and spatially complete with smoother trajectories. Validated with FLUXNET GPP and BigFoot NPP, we demonstrated that estimated GLASS GPP and NPP achieved higher precision for most vegetation types.

Analysis of Anomaly in Land Surface Temperature Using MODIS Products

Science.gov (United States)

Yorozu, K.; Kodama, T.; Kim, S.; Tachikawa, Y.; Shiiba, M.

2011-12-01

Atmosphere-land surface interaction plays a dominant role on the hydrologic cycle. Atmospheric phenomena cause variation of land surface state and land surface state can affect on atmosphereic conditions. Widely-known article related in atmospheric-land interaction was published by Koster et al. in 2004. The context of this article is that seasonal anomaly in soil moisture or soil surface temperature can affect summer precipitation generation and other atmospheric processes especially in middle North America, Sahel and south Asia. From not only above example but other previous research works, it is assumed that anomaly of surface state has a key factor. To investigate atmospheric-land surface interaction, it is necessary to analyze anomaly field in land surface state. In this study, soil surface temperature should be focused because it can be globally and continuously observed by satellite launched sensor. To land surface temperature product, MOD11C1 and MYD11C1 products which are kinds of MODIS products are applied. Both of them have 0.05 degree spatial resolution and daily temporal resolution. The difference of them is launched satellite, MOD11C1 is Terra and MYD11C1 is Aqua. MOD11C1 covers the latter of 2000 to present and MYD11C1 covers the early 2002 to present. There are unrealistic values on provided products even if daily product was already calibrated or corrected. For pre-analyzing, daily data is aggregated into 8-days data to remove irregular values for stable analysis. It was found that there are spatial and temporal distribution of 10-years average and standard deviation for each 8-days term. In order to point out extreme anomaly in land surface temperature, standard score for each 8-days term is applied. From the analysis of standard score, it is found there are large anomaly in land surface temperature around north China plain in early April 2005 and around Bangladesh in early May 2009.

THE EFFECT OF LAND USE CHANGE ON LAND SURFACE TEMPERATURE IN THE NETHERLANDS

Directory of Open Access Journals (Sweden)

S. Youneszadeh

2015-12-01

Full Text Available The Netherlands is a small country with a relatively large population which experienced a rapid rate of land use changes from 2000 to 2008 years due to the industrialization and population increase. Land use change is especially related to the urban expansion and open agriculture reduction due to the enhanced economic growth. This research reports an investigation into the application of remote sensing and geographical information system (GIS in combination with statistical methods to provide a quantitative information on the effect of land use change on the land surface temperature. In this study, remote sensing techniques were used to retrieve the land surface temperature (LST by using the MODIS Terra (MOD11A2 Satellite imagery product. As land use change alters the thermal environment, the land surface temperature (LST could be a proper change indicator to show the thermal changes in relation with land use changes. The Geographical information system was further applied to extract the mean yearly land surface temperature (LST for each land use type and each province in the 2003, 2006 and 2008 years, by using the zonal statistic techniques. The results show that, the inland water and offshore area has the highest night land surface temperature (LST. Furthermore, the Zued (South-Holland province has the highest night LST value in the 2003, 2006 and 2008 years. The result of this research will be helpful tool for urban planners and environmental scientists by providing the critical information about the land surface temperature.

GEONEX: Land Monitoring From a New Generation of Geostationary Satellite Sensors

Science.gov (United States)

Nemani, Ramakrishna; Lyapustin, Alexei; Wang, Weile; Wang, Yujie; Hashimoto, Hirofumi; Li, Shuang; Ganguly, Sangram; Michaelis, Andrew; Higuchi, Atsushi; Takaneka, Hideaki;

GEONEX: Land monitoring from a new generation of geostationary satellite sensors

Science.gov (United States)

Nemani, R. R.; Lyapustin, A.; Wang, W.; Ganguly, S.; Wang, Y.; Michaelis, A.; Hashimoto, H.; Li, S.; Higuchi, A.; Huete, A. R.; Yeom, J. M.; camacho De Coca, F.; Lee, T. J.; Takenaka, H.

2017-12-01

The latest generation of geostationary satellites carry sensors such as ABI (Advanced Baseline Imager on GOES-16) and the AHI (Advanced Himawari Imager on Himawari) that closely mimic the spatial and spectral characteristics of Earth Observing System flagship MODIS for monitoring land surface conditions. More importantly they provide observations at 5-15 minute intervals. Such high frequency data offer exciting possibilities for producing robust estimates of land surface conditions by overcoming cloud cover, enabling studies of diurnally varying local-to-regional biosphere-atmosphere interactions, and operational decision-making in agriculture, forestry and disaster management. But the data come with challenges that need special attention. For instance, geostationary data feature changing sun angle at constant view for each pixel, which is reciprocal to sun-synchronous observations, and thus require careful adaptation of EOS algorithms. Our goal is to produce a set of land surface products from geostationary sensors by leveraging NASA's investments in EOS algorithms and in the data/compute facility NEX. The land surface variables of interest include atmospherically corrected surface reflectances, snow cover, vegetation indices and leaf area index (LAI)/fraction of photosynthetically absorbed radiation (FPAR), as well as land surface temperature and fires. In order to get ready to produce operational products over the US from GOES-16 starting 2018, we have utilized 18 months of data from Himawari AHI over Australia to test the production pipeline and the performance of various algorithms for our initial tests. The end-to-end processing pipeline consists of a suite of modules to (a) perform calibration and automatic georeference correction of the AHI L1b data, (b) adopt the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to produce surface spectral reflectances along with compositing schemes and QA, and (c) modify relevant EOS retrieval

Surface Characterization for Land-Atmosphere Studies of CLASIC

Science.gov (United States)

Jackson, T. J.; Kustas, W.; Torn, M. S.; Meyers, T.; Prueger, J.; Fischer, M. L.; Avissar, R.; Yueh, S.; Anderson, M.; Miller, M.

2006-12-01

The Cloud and Land Surface Interaction Campaign will focus on interactions between the land surface, convective boundary layer, and cumulus clouds. It will take place in the Southern Great Plains (SGP) area of the U.S, specifically within the US DOE ARM Climate Research Facility. The intensive observing period will be June of 2007, which typically covers the winter wheat harvest in the region. This region has been the focus of several related experiments that include SGP97, SGP99, and SMEX03. For the land surface, some of the specific science questions include 1) how do spatial variations in land cover along this trajectory modulate the cloud structure and the low-level water vapor budget, 2) what are the relationships between land surface characteristics (i.e., soil texture, vegetation type and fractional cover) and states (particularly soil moisture and surface temperature) and the resulting impact of the surface energy balance on boundary layer and cloud structure and dynamics and aerosol loading; and 3) what is the interplay between cumulus cloud development and surface energy balance partitioning between latent and sensible heat, and implications for the carbon flux? Most of these objectives will require flux and state measurements throughout the dominant land covers and distributed over the geographic domain. These observations would allow determining the level of up- scaling/aggregation required in order to understand the impact of landscape changes affecting energy balance/flux partitioning and impact on cloud/atmospheric dynamics. Specific contributions that are planned to be added to CLASIC include continuous tower-based monitoring of surface fluxes for key land cover types prior to, during, and post-IOP, replicate towers to quantify flux variance within each land cover, boundary layer properties and fluxes from a helicopter-based system, airplane- and satellite-based flux products throughout the region, aircraft- and tower-based concentration data for

Assimilation of Feng-Yun-3B satellite microwave humidity sounder data over land

Science.gov (United States)

Chen, Keyi; Bormann, Niels; English, Stephen; Zhu, Jiang

2018-03-01

The ECMWF has been assimilating Feng-Yun-3B (FY-3B) satellite microwave humidity sounder (MWHS) data over ocean in an operational forecasting system since 24 September 2014. It is more difficult, however, to assimilate microwave observations over land and sea ice than over the open ocean due to higher uncertainties in land surface temperature, surface emissivity and less effective cloud screening. We compare approaches in which the emissivity is retrieved dynamically from MWHS channel 1 [150 GHz (vertical polarization)] with the use of an evolving emissivity atlas from 89 GHz observations from the MWHS onboard NOAA and EUMETSAT satellites. The assimilation of the additional data over land improves the fit of short-range forecasts to other observations, notably ATMS (Advanced Technology Microwave Sounder) humidity channels, and the forecast impacts are mainly neutral to slightly positive over the first five days. The forecast impacts are better in boreal summer and the Southern Hemisphere. These results suggest that the techniques tested allow for effective assimilation of MWHS/FY-3B data over land.

Monitoring Snow and Land Ice Using Satellite data in the GMES Project CryoLand

Science.gov (United States)

Bippus, Gabriele; Nagler, Thomas

2013-04-01

The main objectives of the project "CryoLand - GMES Service Snow and Land Ice" are to develop, implement and validate services for snow, glaciers and lake and river ice products as a Downstream Service within the Global Monitoring for Environment and Security (GMES) program of the European Commission. CryoLand exploits Earth Observation data from current optical and microwave sensors and of the upcoming GMES Sentinel satellite family. The project prepares also the basis for the cryospheric component of the GMES Land Monitoring services. The CryoLand project team consists of 10 partner organisations from Austria, Finland, Norway, Sweden, Switzerland and Romania and is funded by the 7th Framework Program of the European Commission. The CryoLand baseline products for snow include fractional snow extent from optical satellite data, the extent of melting snow from SAR data, and coarse resolution snow water equivalent maps from passive microwave data. Experimental products include maps of snow surface wetness and temperature. The products range from large scale coverage at medium resolution to regional products with high resolution, in order to address a wide user community. Medium resolution optical data (e.g. MODIS, in the near future Sentinel-3) and SAR (ENVISAT ASAR, in the near future Sentinel-1) are the main sources of EO data for generating large scale products in near real time. For generation of regional products high resolution satellite data are used. Glacier products are based on high resolution optical (e.g. SPOT-5, in the near future Sentinel-2) and SAR (TerraSAR-X, in the near future Sentinel-1) data and include glacier outlines, mapping of glacier facies, glacier lakes and ice velocity. The glacier products are generated on users demand. Current test areas are located in the Alps, Norway, Greenland and the Himalayan Mountains. The lake and river ice products include ice extent and its temporal changes and snow extent on ice. The algorithms for these

Determination of the Impact of Urbanization on Agricultural Lands using Multi-temporal Satellite Sensor Images

Science.gov (United States)

Kaya, S.; Alganci, U.; Sertel, E.; Ustundag, B.

2015-12-01

Throughout the history, agricultural activities have been performed close to urban areas. Main reason behind this phenomenon is the need of fast marketing of the agricultural production to urban residents and financial provision. Thus, using the areas nearby cities for agricultural activities brings out advantage of easy transportation of productions and fast marketing. For decades, heavy migration to cities has directly and negatively affected natural grasslands, forests and agricultural lands. This pressure has caused agricultural lands to be changed into urban areas. Dense urbanization causes increase in impervious surfaces, heat islands and many other problems in addition to destruction of agricultural lands. Considering the negative impacts of urbanization on agricultural lands and natural resources, a periodic monitoring of these changes becomes indisputably important. At this point, satellite images are known to be good data sources for land cover / use change monitoring with their fast data acquisition, large area coverages and temporal resolution properties. Classification of the satellite images provides thematic the land cover / use maps of the earth surface and changes can be determined with GIS based analysis multi-temporal maps. In this study, effects of heavy urbanization over agricultural lands in Istanbul, metropolitan city of Turkey, were investigated with use of multi-temporal Landsat TM satellite images acquired between 1984 and 2011. Images were geometrically registered to each other and classified using supervised maximum likelihood classification algorithm. Resulting thematic maps were exported to GIS environment and destructed agricultural lands by urbanization were determined using spatial analysis.

Identification of soil erosion land surfaces by Landsat data analysis and processing

International Nuclear Information System (INIS)

Lo Curzio, S.

2009-01-01

In this paper, we outline the typical relationship between the spectral reflectance of aileron's on newly-formed land surfaces and the geo morphological features of the land surfaces at issue. These latter represent the products of superficial erosional processes due to the action of the gravity and/or water; thus, such land surfaces are highly representative of the strong soil degradation occurring in a wide area located on the boundary between Molise and Puglia regions (Southern Italy). The results of this study have been reported on thematic maps; on such maps, the detected erosional land surfaces have been mapped on the basis of their typical spectral signature. The study has been performed using Landsat satellite imagery data which have been then validated by means of field survey data. The satellite data have been processed using remote sensing techniques, such as: false colour composite, contrast stretching, principal component analysis and decorrelation stretching. The study has permitted to produce, in a relatively short time and at low expense, a map of the eroded land surfaces. Such a result represents a first and fundamental step in evaluating and monitoring the erosional processes in the study area [it

Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

Science.gov (United States)

Reichle, Rolf H.; De Lannoy, Gabrielle J. M.; Forman, Barton A.; Draper, Clara S.; Liu, Qing

2013-01-01

A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Translation of Land Surface Model Accuracy and Uncertainty into Coupled Land-Atmosphere Prediction

Science.gov (United States)

Santanello, Joseph A.; Kumar, Sujay; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Zhou, Shuija

2012-01-01

Land-atmosphere (L-A) Interactions playa critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (US-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF Simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

Translation of Land Surface Model Accuracy and Uncertainty into Coupled Land-Atmosphere Prediction

Science.gov (United States)

Santanello, J. A.; Kumar, S.; Peters-Lidard, C. D.; Harrison, K. W.; Zhou, S.

2012-12-01

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (LIS-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

Modifying a dynamic global vegetation model for simulating large spatial scale land surface water balances

Science.gov (United States)

Tang, G.; Bartlein, P. J.

2012-08-01

Satellite-based data, such as vegetation type and fractional vegetation cover, are widely used in hydrologic models to prescribe the vegetation state in a study region. Dynamic global vegetation models (DGVM) simulate land surface hydrology. Incorporation of satellite-based data into a DGVM may enhance a model's ability to simulate land surface hydrology by reducing the task of model parameterization and providing distributed information on land characteristics. The objectives of this study are to (i) modify a DGVM for simulating land surface water balances; (ii) evaluate the modified model in simulating actual evapotranspiration (ET), soil moisture, and surface runoff at regional or watershed scales; and (iii) gain insight into the ability of both the original and modified model to simulate large spatial scale land surface hydrology. To achieve these objectives, we introduce the "LPJ-hydrology" (LH) model which incorporates satellite-based data into the Lund-Potsdam-Jena (LPJ) DGVM. To evaluate the model we ran LH using historical (1981-2006) climate data and satellite-based land covers at 2.5 arc-min grid cells for the conterminous US and for the entire world using coarser climate and land cover data. We evaluated the simulated ET, soil moisture, and surface runoff using a set of observed or simulated data at different spatial scales. Our results demonstrate that spatial patterns of LH-simulated annual ET and surface runoff are in accordance with previously published data for the US; LH-modeled monthly stream flow for 12 major rivers in the US was consistent with observed values respectively during the years 1981-2006 (R2 > 0.46, p 0.52). The modeled mean annual discharges for 10 major rivers worldwide also agreed well (differences day method for snowmelt computation, the addition of the solar radiation effect on snowmelt enabled LH to better simulate monthly stream flow in winter and early spring for rivers located at mid-to-high latitudes. In addition, LH

Estimation of the Land Surface Temperature over the Tibetan Plateau by Using Chinese FY-2C Geostationary Satellite Data.

Science.gov (United States)

Hu, Yuanyuan; Zhong, Lei; Ma, Yaoming; Zou, Mijun; Xu, Kepiao; Huang, Ziyu; Feng, Lu

2018-01-28

During the process of land-atmosphere interaction, one of the essential parameters is the land surface temperature (LST). The LST has high temporal variability, especially in its diurnal cycle, which cannot be acquired by polar-orbiting satellites. Therefore, it is of great practical significance to retrieve LST data using geostationary satellites. According to the data of FengYun 2C (FY-2C) satellite and the measurements from the Enhanced Observing Period (CEOP) of the Asia-Australia Monsoon Project (CAMP) on the Tibetan Plateau (CAMP/Tibet), a regression approach was utilized in this research to optimize the split window algorithm (SWA). The thermal infrared data obtained by the Chinese geostationary satellite FY-2C over the Tibetan Plateau (TP) was used to estimate the hourly LST time series. To decrease the effects of cloud, the 10-day composite hourly LST data were obtained through the approach of maximal value composite (MVC). The derived LST was used to compare with the product of MODIS LST and was also validated by the field observation. The results show that the LST retrieved through the optimized SWA and in situ data has a better consistency (with correlation coefficient (R), mean absolute error (MAE), mean bias (MB), and root mean square error (RMSE) values of 0.987, 1.91 K, 0.83 K and 2.26 K, respectively) than that derived from Becker and Li's SWA and MODIS LST product, which means that the modified SWA can be applied to achieve plateau-scale LST. The diurnal variation of the LST and the hourly time series of the LST over the Tibetan Plateau were also obtained. The diurnal range of LST was found to be clearly affected by the influence of the thawing and freezing process of soil and the summer monsoon evolution. The comparison between the seasonal and diurnal variations of LST at four typical underlying surfaces over the TP indicate that the variation of LST is closely connected with the underlying surface types as well. The diurnal variation of LST is

A Spatio-Temporal Analysis of the Relationship Between Near-Surface Air Temperature and Satellite Land Surface Temperatures Using 17 Years of Data from the ATSR Series

Science.gov (United States)

Ghent, D.; Good, E.; Bulgin, C.; Remedios, J. J.

2017-12-01

Surface temperatures (ST) over land have traditionally been measured at weather stations. There are many parts of the globe with very few stations, e.g. across much of Africa, leading to gaps in ST datasets, affecting our understanding of how ST is changing, and the impacts of extreme events. Satellites can provide global ST data but these observations represent how hot the land ST (LST; including the uppermost parts of e.g. trees, buildings) is to touch, whereas stations measure the air temperature just above the surface (T2m). Satellite LST data may only be available in cloud-free conditions and data records are frequently climate studies. In this study, the relationship between clear-sky satellite LST and all-sky T2m is characterised in space and time using >17 years of data. The analysis uses a new monthly LST climate data record (CDR) based on the Along-Track Scanning Radiometer (ATSR) series, which has been produced within the European Space Agency GlobTemperature project. The results demonstrate the dependency of the global LST-T2m differences on location, land cover, vegetation and elevation. LSTnight ( 10 pm local solar time) is found to be closely coupled with minimum T2m (Tmin) and the two temperatures generally consistent to within ±5 °C (global median LSTnight- Tmin= 1.8 °C, interquartile range = 3.8 °C). The LSTday ( 10 am local time)-maximum T2m (Tmax) variability is higher because LST is strongly influenced by insolation and surface regime (global median LSTday-Tmax= -0.1 °C, interquartile range = 8.1 °C). Correlations for both temperature pairs are typically >0.9 outside of the tropics. A crucial aspect of this study is a comparison between the monthly global anomaly time series of LST and CRUTEM4 T2m. The time series agree remarkably well, with a correlation of 0.9 and 90% of the CDR anomalies falling within the T2m 95% confidence limits (see figure). This analysis provides independent verification of the 1995-2012 T2m anomaly time series

Generating Land Surface Reflectance for the New Generation of Geostationary Satellite Sensors with the MAIAC Algorithm

Science.gov (United States)

Wang, W.; Wang, Y.; Hashimoto, H.; Li, S.; Takenaka, H.; Higuchi, A.; Lyapustin, A.; Nemani, R. R.

2017-12-01

The latest generation of geostationary satellite sensors, including the GOES-16/ABI and the Himawari 8/AHI, provide exciting capability to monitor land surface at very high temporal resolutions (5-15 minute intervals) and with spatial and spectral characteristics that mimic the Earth Observing System flagship MODIS. However, geostationary data feature changing sun angles at constant view geometry, which is almost reciprocal to sun-synchronous observations. Such a challenge needs to be carefully addressed before one can exploit the full potential of the new sources of data. Here we take on this challenge with Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, recently developed for accurate and globally robust applications like the MODIS Collection 6 re-processing. MAIAC first grids the top-of-atmosphere measurements to a fixed grid so that the spectral and physical signatures of each grid cell are stacked ("remembered") over time and used to dramatically improve cloud/shadow/snow detection, which is by far the dominant error source in the remote sensing. It also exploits the changing sun-view geometry of the geostationary sensor to characterize surface BRDF with augmented angular resolution for accurate aerosol retrievals and atmospheric correction. The high temporal resolutions of the geostationary data indeed make the BRDF retrieval much simpler and more robust as compared with sun-synchronous sensors such as MODIS. As a prototype test for the geostationary-data processing pipeline on NASA Earth Exchange (GEONEX), we apply MAIAC to process 18 months of data from Himawari 8/AHI over Australia. We generate a suite of test results, including the input TOA reflectance and the output cloud mask, aerosol optical depth (AOD), and the atmospherically-corrected surface reflectance for a variety of geographic locations, terrain, and land cover types. Comparison with MODIS data indicates a general agreement between the retrieved surface reflectance

Sentinel-2: next generation satellites for optical land observation from space

Science.gov (United States)

Lautenschläger, G.; Gessner, R.; Gockel, W.; Haas, C.; Schweickert, G.; Bursch, S.; Welsch, M.; Sontag, H.

2013-10-01

The first Sentinel-2 satellites, which constitute the next generation of operational Earth observation satellites for optical land monitoring from space, are undergoing completion in the facilities at Astrium ready for launch end 2014. Sentinel-2 will feature a major breakthrough in the area of optical land observation since it will for the first time enable continuous and systematic acquisition of all land surfaces world-wide with the Multi-Spectral Instrument (MSI), thus providing the basis for a truly operational service. Flying in the same orbital plane and spaced at 180°, the constellation of two satellites, designed for an in-orbit nominal operational lifetime of 7 years each, will acquire all land surfaces in only 5 days at the equator. In order to support emergency operations, the satellites can further be operated in an extended observation mode allowing to image any point on Earth even on a daily basis. MSI acquires images in 13 spectral channels from Visible-to-Near Infrared (VNIR) to Short Wave Infrared (SWIR) with a swath of almost 300 km on ground and a spatial resolution up to 10 m. The data ensure continuity to the existing data sets produced by the series of Landsat and SPOT satellites, and will further provide detailed spectral information to enable derivation of biophysical or geophysical products. Excellent geometric image quality performances are achieved with geolocation better than 16 m, thanks to an innovative instrument design in conjunction with a high-performance satellite AOCS subsystem centered around a 2-band GPS receiver, high-performance star trackers and a fiberoptic gyro. To cope with the high data volume on-board, data are compressed using a state-of-the-art wavelet compression scheme. Thanks to a powerful mission data handling system built around a newly developed very large solid-state mass memory based on flash technology, on-board compression losses will be kept to a minimum. The Sentinel-2 satellite design features a highly

National Satellite Land Remote Sensing Data Archive

Science.gov (United States)

Faundeen, John L.; Longhenry, Ryan

2018-06-13

The National Satellite Land Remote Sensing Data Archive is managed on behalf of the Secretary of the Interior by the U.S. Geological Survey’s Earth Resources Observation and Science Center. The Land Remote Sensing Policy Act of 1992 (51 U.S.C. §601) directed the U.S. Department of the Interior to establish a permanent global archive consisting of imagery over land areas obtained from satellites orbiting the Earth. The law also directed the U.S. Department of the Interior, delegated to the U.S. Geological Survey, to ensure proper storage and preservation of imagery, and timely access for all parties. Since 2008, these images have been available at no cost to the user.

Modifying a dynamic global vegetation model for simulating large spatial scale land surface water balances

Directory of Open Access Journals (Sweden)

G. Tang

2012-08-01

Full Text Available Satellite-based data, such as vegetation type and fractional vegetation cover, are widely used in hydrologic models to prescribe the vegetation state in a study region. Dynamic global vegetation models (DGVM simulate land surface hydrology. Incorporation of satellite-based data into a DGVM may enhance a model's ability to simulate land surface hydrology by reducing the task of model parameterization and providing distributed information on land characteristics. The objectives of this study are to (i modify a DGVM for simulating land surface water balances; (ii evaluate the modified model in simulating actual evapotranspiration (ET, soil moisture, and surface runoff at regional or watershed scales; and (iii gain insight into the ability of both the original and modified model to simulate large spatial scale land surface hydrology. To achieve these objectives, we introduce the "LPJ-hydrology" (LH model which incorporates satellite-based data into the Lund-Potsdam-Jena (LPJ DGVM. To evaluate the model we ran LH using historical (1981–2006 climate data and satellite-based land covers at 2.5 arc-min grid cells for the conterminous US and for the entire world using coarser climate and land cover data. We evaluated the simulated ET, soil moisture, and surface runoff using a set of observed or simulated data at different spatial scales. Our results demonstrate that spatial patterns of LH-simulated annual ET and surface runoff are in accordance with previously published data for the US; LH-modeled monthly stream flow for 12 major rivers in the US was consistent with observed values respectively during the years 1981–2006 (R² > 0.46, p < 0.01; Nash-Sutcliffe Coefficient > 0.52. The modeled mean annual discharges for 10 major rivers worldwide also agreed well (differences < 15% with observed values for these rivers. Compared to a degree-day method for snowmelt computation, the addition of the solar radiation effect on snowmelt

Remote sensing of land surface temperature: The directional viewing effect

International Nuclear Information System (INIS)

Smith, J.A.; Schmugge, T.J.; Ballard, J.R. Jr.

1997-01-01

Land Surface Temperature (LST) is an important parameter in understanding global environmental change because it controls many of the underlying processes in the energy budget at the surface and heat and water transport between the surface and the atmosphere. The measurement of LST at a variety of spatial and temporal scales and extension to global coverage requires remote sensing means to achieve these goals. Land surface temperature and emissivity products are currently being derived from satellite and aircraft remote sensing data using a variety of techniques to correct for atmospheric effects. Implicit in the commonly employed approaches is the assumption of isotropy in directional thermal infrared exitance. The theoretical analyses indicate angular variations in apparent infrared temperature will typically yield land surface temperature errors ranging from 1 to 4 C unless corrective measures are applied

The retrieval of land surface albedo in rugged terrain

NARCIS (Netherlands)

Gao, B.; Jia, L.; Menenti, M.

2012-01-01

Land surface albedo may be derived from the satellite data through the estimation of a bidirectional reflectance distribution function (BRDF) model and angular integration. However many BRDF models do not consider explicitly the topography. In rugged terrain, the topography influences the observed

Land-atmosphere interaction patterns in southeastern South America using satellite products and climate models

Science.gov (United States)

Spennemann, P. C.; Salvia, M.; Ruscica, R. C.; Sörensson, A. A.; Grings, F.; Karszenbaum, H.

2018-02-01

In regions of strong Land-Atmosphere (L-A) interaction, soil moisture (SM) conditions can impact the atmosphere through modulating the land surface fluxes. The importance of the identification of L-A interaction regions lies in the potential improvement of the weather/seasonal forecast and the better understanding of the physical mechanisms involved. This study aims to compare the terrestrial segment of the L-A interaction from satellite products and climate models, motivated by previous modeling studies pointing out southeastern South America (SESA) as a L-A hotspot during austral summer. In addition, the L-A interaction under dry or wet anomalous conditions over SESA is analyzed. To identify L-A hotspots the AMSRE-LPRM SM and MODIS land surface temperature products; coupled climate models and uncoupled land surface models were used. SESA highlights as a strong L-A interaction hotspot when employing different metrics, temporal scales and independent datasets, showing consistency between models and satellite estimations. Both AMSRE-LPRM bands (X and C) are consistent showing a strong L-A interaction hotspot over the Pampas ecoregion. Intensification and a larger spatial extent of the L-A interaction for dry summers was observed in both satellite products and models compared to wet summers. These results, which were derived from measured physical variables, are encouraging and promising for future studies analyzing L-A interactions. L-A interaction analysis is proposed here as a meeting point between remote sensing and climate modelling communities of Argentina, within a region with the highest agricultural and livestock production of the continent, but with an important lack of in-situ SM observations.

Modifying a dynamic global vegetation model for simulating large spatial scale land surface water balance

Science.gov (United States)

Tang, G.; Bartlein, P. J.

2012-01-01

Water balance models of simple structure are easier to grasp and more clearly connect cause and effect than models of complex structure. Such models are essential for studying large spatial scale land surface water balance in the context of climate and land cover change, both natural and anthropogenic. This study aims to (i) develop a large spatial scale water balance model by modifying a dynamic global vegetation model (DGVM), and (ii) test the model's performance in simulating actual evapotranspiration (ET), soil moisture and surface runoff for the coterminous United States (US). Toward these ends, we first introduced development of the "LPJ-Hydrology" (LH) model by incorporating satellite-based land covers into the Lund-Potsdam-Jena (LPJ) DGVM instead of dynamically simulating them. We then ran LH using historical (1982-2006) climate data and satellite-based land covers at 2.5 arc-min grid cells. The simulated ET, soil moisture and surface runoff were compared to existing sets of observed or simulated data for the US. The results indicated that LH captures well the variation of monthly actual ET (R2 = 0.61, p 0.46, p 0.52) with observed values over the years 1982-2006, respectively. The modeled spatial patterns of annual ET and surface runoff are in accordance with previously published data. Compared to its predecessor, LH simulates better monthly stream flow in winter and early spring by incorporating effects of solar radiation on snowmelt. Overall, this study proves the feasibility of incorporating satellite-based land-covers into a DGVM for simulating large spatial scale land surface water balance. LH developed in this study should be a useful tool for studying effects of climate and land cover change on land surface hydrology at large spatial scales.

Evaluation of Land Surface Models in Reproducing Satellite-Derived LAI over the High-Latitude Northern Hemisphere. Part I: Uncoupled DGVMs

Directory of Open Access Journals (Sweden)

Ning Zeng

2013-10-01

Full Text Available Leaf Area Index (LAI represents the total surface area of leaves above a unit area of ground and is a key variable in any vegetation model, as well as in climate models. New high resolution LAI satellite data is now available covering a period of several decades. This provides a unique opportunity to validate LAI estimates from multiple vegetation models. The objective of this paper is to compare new, satellite-derived LAI measurements with modeled output for the Northern Hemisphere. We compare monthly LAI output from eight land surface models from the TRENDY compendium with satellite data from an Artificial Neural Network (ANN from the latest version (third generation of GIMMS AVHRR NDVI data over the period 1986–2005. Our results show that all the models overestimate the mean LAI, particularly over the boreal forest. We also find that seven out of the eight models overestimate the length of the active vegetation-growing season, mostly due to a late dormancy as a result of a late summer phenology. Finally, we find that the models report a much larger positive trend in LAI over this period than the satellite observations suggest, which translates into a higher trend in the growing season length. These results highlight the need to incorporate a larger number of more accurate plant functional types in all models and, in particular, to improve the phenology of deciduous trees.

Afforestation in China cools local land surface temperature

OpenAIRE

Peng, Shu-Shi; Piao, Shilong; Zeng, Zhenzhong; Ciais, Philippe; Zhou, Liming; Li, Laurent Z. X.; Myneni, Ranga B.; Yin, Yi; Zeng, Hui

2014-01-01

International audience; China has the largest afforested area in the world (~62 million hectares in 2008), and these forests are carbon sinks. The climatic effect of these new forests depends on how radiant and turbulent energy fluxes over these plantations modify surface temperature. For instance, a lower albedo may cause warming, which negates the climatic benefits of carbon sequestration. Here, we used satellite measurements of land surface temperature (LST) from planted forests and adjace...

Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs

Science.gov (United States)

Chatterjee, R. S.; Singh, Narendra; Thapa, Shailaja; Sharma, Dravneeta; Kumar, Dheeraj

2017-06-01

The present study proposes land surface temperature (LST) retrieval from satellite-based thermal IR data by single channel radiative transfer algorithm using atmospheric correction parameters derived from satellite-based and in-situ data and land surface emissivity (LSE) derived by a hybrid LSE model. For example, atmospheric transmittance (τ) was derived from Terra MODIS spectral radiance in atmospheric window and absorption bands, whereas the atmospheric path radiance and sky radiance were estimated using satellite- and ground-based in-situ solar radiation, geographic location and observation conditions. The hybrid LSE model which is coupled with ground-based emissivity measurements is more versatile than the previous LSE models and yields improved emissivity values by knowledge-based approach. It uses NDVI-based and NDVI Threshold method (NDVITHM) based algorithms and field-measured emissivity values. The model is applicable for dense vegetation cover, mixed vegetation cover, bare earth including coal mining related land surface classes. The study was conducted in a coalfield of India badly affected by coal fire for decades. In a coal fire affected coalfield, LST would provide precise temperature difference between thermally anomalous coal fire pixels and background pixels to facilitate coal fire detection and monitoring. The derived LST products of the present study were compared with radiant temperature images across some of the prominent coal fire locations in the study area by graphical means and by some standard mathematical dispersion coefficients such as coefficient of variation, coefficient of quartile deviation, coefficient of quartile deviation for 3rd quartile vs. maximum temperature, coefficient of mean deviation (about median) indicating significant increase in the temperature difference among the pixels. The average temperature slope between adjacent pixels, which increases the potential of coal fire pixel detection from background pixels, is

Quantifying the Terrestrial Surface Energy Fluxes Using Remotely-Sensed Satellite Data

Science.gov (United States)

Siemann, Amanda Lynn

The dynamics of the energy fluxes between the land surface and the atmosphere drive local and regional climate and are paramount to understand the past, present, and future changes in climate. Although global reanalysis datasets, land surface models (LSMs), and climate models estimate these fluxes by simulating the physical processes involved, they merely simulate our current understanding of these processes. Global estimates of the terrestrial, surface energy fluxes based on observations allow us to capture the dynamics of the full climate system. Remotely-sensed satellite data is the source of observations of the land surface which provide the widest spatial coverage. Although net radiation and latent heat flux global, terrestrial, surface estimates based on remotely-sensed satellite data have progressed, comparable sensible heat data products and ground heat flux products have not progressed at this scale. Our primary objective is quantifying and understanding the terrestrial energy fluxes at the Earth's surface using remotely-sensed satellite data with consistent development among all energy budget components [through the land surface temperature (LST) and input meteorology], including validation of these products against in-situ data, uncertainty assessments, and long-term trend analysis. The turbulent fluxes are constrained by the available energy using the Bowen ratio of the un-constrained products to ensure energy budget closure. All final products are within uncertainty ranges of literature values, globally. When validated against the in-situ estimates, the sensible heat flux estimates using the CFSR air temperature and constrained with the products using the MODIS albedo produce estimates closest to the FLUXNET in-situ observations. Poor performance over South America is consistent with the largest uncertainties in the energy budget. From 1984-2007, the longwave upward flux increase due to the LST increase drives the net radiation decrease, and the

LEAST SQUARE APPROACH FOR ESTIMATING OF LAND SURFACE TEMPERATURE FROM LANDSAT-8 SATELLITE DATA USING RADIATIVE TRANSFER EQUATION

Directory of Open Access Journals (Sweden)

Y. Jouybari-Moghaddam

2017-09-01

Full Text Available Land Surface Temperature (LST is one of the significant variables measured by remotely sensed data, and it is applied in many environmental and Geoscience studies. The main aim of this study is to develop an algorithm to retrieve the LST from Landsat-8 satellite data using Radiative Transfer Equation (RTE. However, LST can be retrieved from RTE, but, since the RTE has two unknown parameters including LST and surface emissivity, estimating LST from RTE is an under the determined problem. In this study, in order to solve this problem, an approach is proposed an equation set includes two RTE based on Landsat-8 thermal bands (i.e.: band 10 and 11 and two additional equations based on the relation between the Normalized Difference Vegetation Index (NDVI and emissivity of Landsat-8 thermal bands by using simulated data for Landsat-8 bands. The iterative least square approach was used for solving the equation set. The LST derived from proposed algorithm is evaluated by the simulated dataset, built up by MODTRAN. The result shows the Root Mean Squared Error (RMSE is less than 1.18°K. Therefore; the proposed algorithm can be a suitable and robust method to retrieve the LST from Landsat-8 satellite data.

Least Square Approach for Estimating of Land Surface Temperature from LANDSAT-8 Satellite Data Using Radiative Transfer Equation

Science.gov (United States)

Jouybari-Moghaddam, Y.; Saradjian, M. R.; Forati, A. M.

2017-09-01

Land Surface Temperature (LST) is one of the significant variables measured by remotely sensed data, and it is applied in many environmental and Geoscience studies. The main aim of this study is to develop an algorithm to retrieve the LST from Landsat-8 satellite data using Radiative Transfer Equation (RTE). However, LST can be retrieved from RTE, but, since the RTE has two unknown parameters including LST and surface emissivity, estimating LST from RTE is an under the determined problem. In this study, in order to solve this problem, an approach is proposed an equation set includes two RTE based on Landsat-8 thermal bands (i.e.: band 10 and 11) and two additional equations based on the relation between the Normalized Difference Vegetation Index (NDVI) and emissivity of Landsat-8 thermal bands by using simulated data for Landsat-8 bands. The iterative least square approach was used for solving the equation set. The LST derived from proposed algorithm is evaluated by the simulated dataset, built up by MODTRAN. The result shows the Root Mean Squared Error (RMSE) is less than 1.18°K. Therefore; the proposed algorithm can be a suitable and robust method to retrieve the LST from Landsat-8 satellite data.

Analysing the advantages of high temporal resolution geostationary MSG SEVIRI data compared to Polar operational environmental satellite data for land surface monitoring in Africa

DEFF Research Database (Denmark)

Fensholt, Rasmus; Anyamba, Assaf; Huber Gharib, Silvia

2011-01-01

Since 1972, satellite remote sensing of the environment has been dominated by polar-orbiting sensors providing useful data for monitoring the earth’s natural resources. However their observation and monitoring capacity are inhibited by daily to monthly looks for any given ground surface which often...... is obscured by frequent and persistent cloud cover creating large gaps in time series measurements. The launch of the Meteosat Second Generation (MSG) satellite into geostationary orbit has opened new opportunities for land surface monitoring. The Spinning Enhanced Visible and Infrared Imager (SEVIRI...... affected by droughts and floods in 2008 caused by periods of abnormally low and high rainfall, respectively. Based on the effectiveness of monitoring these events from Earth Observation (EO) data the current analyses show that the new generation of geostationary remote sensing data can provide higher...

ANALYSING THE EFFECTS OF DIFFERENT LAND COVER TYPES ON LAND SURFACE TEMPERATURE USING SATELLITE DATA

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A. Şekertekin

2015-12-01

Full Text Available Monitoring Land Surface Temperature (LST via remote sensing images is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and it also helps us to understand the behavior of urban heat islands. There are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. In this research, mono window algorithm was implemented to Landsat 5 TM image acquired on 28.08.2011. Besides, meteorological data such as humidity and temperature are used in the algorithm. Moreover, high resolution Geoeye-1 and Worldview-2 images acquired on 29.08.2011 and 12.07.2013 respectively were used to investigate the relationships between LST and land cover type. As a result of the analyses, area with vegetation cover has approximately 5 ºC lower temperatures than the city center and arid land., LST values change about 10 ºC in the city center because of different surface properties such as reinforced concrete construction, green zones and sandbank. The temperature around some places in thermal power plant region (ÇATES and ZETES Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature due to the land cover structure.

Analysing the Effects of Different Land Cover Types on Land Surface Temperature Using Satellite Data

Science.gov (United States)

Şekertekin, A.; Kutoglu, Ş. H.; Kaya, S.; Marangoz, A. M.

2015-12-01

Monitoring Land Surface Temperature (LST) via remote sensing images is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and it also helps us to understand the behavior of urban heat islands. There are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. In this research, mono window algorithm was implemented to Landsat 5 TM image acquired on 28.08.2011. Besides, meteorological data such as humidity and temperature are used in the algorithm. Moreover, high resolution Geoeye-1 and Worldview-2 images acquired on 29.08.2011 and 12.07.2013 respectively were used to investigate the relationships between LST and land cover type. As a result of the analyses, area with vegetation cover has approximately 5 ºC lower temperatures than the city center and arid land., LST values change about 10 ºC in the city center because of different surface properties such as reinforced concrete construction, green zones and sandbank. The temperature around some places in thermal power plant region (ÇATES and ZETES) Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature due to the land cover structure.

Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

Science.gov (United States)

Reichle, Rolf H.; De Lannoy, Gabrielle J. M.

2012-01-01

The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters

The impact of climatic and non-climatic factors on land surface temperature in southwestern Romania

Science.gov (United States)

Roşca, Cristina Florina; Harpa, Gabriela Victoria; Croitoru, Adina-Eliza; Herbel, Ioana; Imbroane, Alexandru Mircea; Burada, Doina Cristina

2017-11-01

Land surface temperature is one of the most important parameters related to global warming. It depends mainly on soil type, discontinuous vegetation cover, or lack of precipitation. The main purpose of this paper is to investigate the relationship between high LST, synoptic conditions and air masses trajectories, vegetation cover, and soil type in one of the driest region in Romania. In order to calculate the land surface temperature and normalized difference vegetation index, five satellite images of LANDSAT missions 5 and 7, covering a period of 26 years (1986-2011), were selected, all of them collected in the month of June. The areas with low vegetation density were derived from normalized difference vegetation index, while soil types have been extracted from Corine Land Cover database. HYSPLIT application was employed to identify the air masses origin based on their backward trajectories for each of the five study cases. Pearson, logarithmic, and quadratic correlations were used to detect the relationships between land surface temperature and observed ground temperatures, as well as between land surface temperature and normalized difference vegetation index. The most important findings are: strong correlation between land surface temperature derived from satellite images and maximum ground temperature recorded in a weather station located in the area, as well as between areas with land surface temperature equal to or higher than 40.0 °C and those with lack of vegetation; the sandy soils are the most prone to high land surface temperature and lack of vegetation, followed by the chernozems and brown soils; extremely severe drought events may occur in the region.

Climate Impacts of Fire-Induced Land-Surface Changes

Science.gov (United States)

Liu, Y.; Hao, X.; Qu, J. J.

2017-12-01

One of the consequences of wildfires is the changes in land-surface properties such as removal of vegetation. This will change local and regional climate through modifying the land-air heat and water fluxes. This study investigates mechanism by developing and a parameterization of fire-induced land-surface property changes and applying it to modeling of the climate impacts of large wildfires in the United States. Satellite remote sensing was used to quantitatively evaluate the land-surface changes from large fires provided from the Monitoring Trends in Burning Severity (MTBS) dataset. It was found that the changes in land-surface properties induced by fires are very complex, depending on vegetation type and coverage, climate type, season and time after fires. The changes in LAI are remarkable only if the actual values meet a threshold. Large albedo changes occur in winter for fires in cool climate regions. The signs are opposite between the first post-fire year and the following years. Summer day-time temperature increases after fires, while nigh-time temperature changes in various patterns. The changes are larger in forested lands than shrub / grassland lands. In the parameterization scheme, the detected post-fire changes are decomposed into trends using natural exponential functions and fluctuations of periodic variations with the amplitudes also determined by natural exponential functions. The final algorithm is a combination of the trends, periods, and amplitude functions. This scheme is used with Earth system models to simulate the local and regional climate effects of wildfires.

Characterizing Temporal and Spatial Changes in Land Surface Temperature across the Amazon Basin using Thermal and Infrared Satellite Data

Science.gov (United States)

Cak, A. D.

2017-12-01

The Amazon Basin has faced innumerable pressures in recent years, including logging, mining and resource extraction, agricultural expansion, road building, and urbanization. These changes have drastically altered the landscape, transforming a predominantly forested environment into a mosaic of different types of land cover. The resulting fragmentation has caused dramatic and negative impacts on its structure and function, including on biodiversity and the transfer of water and energy to and from soil, vegetation, and the atmosphere (e.g., evapotranspiration). Because evapotranspiration from forested areas, which is affected by factors including temperature and water availability, plays a significant role in water dynamics in the Amazon Basin, measuring land surface temperature (LST) across the region can provide a dynamic assessment of hydrological, vegetation, and land use and land cover changes. It can also help to identify widespread urban development, which often has a higher LST signal relative to surrounding vegetation. Here, we discuss results from work to measure and identify drivers of change in LST across the entire Amazon Basin through analysis of past and current thermal and infrared satellite imagery. We leverage cloud computing resources in new ways to allow for more efficient analysis of imagery over the Amazon Basin across multiple years and multiple sensors. We also assess potential drivers of change in LST using spatial and multivariate statistical analyses with additional data sources of land cover, urban development, and demographics.

Utilization of satellite remote sensing data on land surface characteristics in water and heat balance component modeling for vegetation covered territories

Science.gov (United States)

Muzylev, Eugene; Uspensky, Alexander; Startseva, Zoya; Volkova, Elena; Kukharsky, Alexander; Uspensky, Sergey

2010-05-01

The model of vertical water and heat transfer in the "soil-vegetation-atmosphere" system (SVAT) for vegetation covered territory has been developed, allowing assimilating satellite remote sensing data on land surface condition as well as accounting for heterogeneities of vegetation and meteorological characteristics. The model provides the calculation of water and heat balance components (such as evapotranspiration Ev, soil water content W, sensible and latent heat fluxes and others ) as well as vertical soil moisture and temperature distributions, temperatures of soil surface and foliage, land surface brightness temperature for any time interval within vegetation season. To describe the landscape diversity soil constants and leaf area index LAI, vegetation cover fraction B, and other vegetation characteristics are used. All these values are considered to be the model parameters. Territory of Kursk region with square about 15 thousands km2 situated in the Black Earth zone of Central Russia was chosen for investigation. Satellite-derived estimates of land surface characteristics have been constructed under cloud-free condition basing AVHRR/NOAA, MODIS/EOS Terra and EOS Aqua, SEVIRI/Meteosat-8, -9 data. The developed technologies of AVHRR data thematic processing have been refined providing the retrieval of surface skin brightness temperature Tsg, air foliage temperature Ta, efficient surface temperature Ts.eff and emissivity E, as well as derivation of vegetation index NDVI, B, and LAI. The linear regression estimators for Tsg, Ta and LAI have been built using representative training samples for 2003-2009 vegetation seasons. The updated software package has been applied for AVHRR data thematic processing to generate named remote sensing products for various dates of the above vegetation seasons. The error statistics of Ta, Ts.eff and Тsg derivation has been investigated for various samples using comparison with in-situ measurements that has given RMS errors in the

Updating representation of land surface-atmosphere feedbacks in airborne campaign modeling analysis

Science.gov (United States)

Huang, M.; Carmichael, G. R.; Crawford, J. H.; Chan, S.; Xu, X.; Fisher, J. A.

2017-12-01

An updated modeling system to support airborne field campaigns is being built at NASA Ames Pleiades, with focus on adjusting the representation of land surface-atmosphere feedbacks. The main updates, referring to previous experiences with ARCTAS-CARB and CalNex in the western US to study air pollution inflows, include: 1) migrating the WRF (Weather Research and Forecasting) coupled land surface model from Noah to improved/more complex models especially Noah-MP and Rapid Update Cycle; 2) enabling the WRF land initialization with suitably spun-up land model output; 3) incorporating satellite land cover, vegetation dynamics, and soil moisture data (i.e., assimilating Soil Moisture Active Passive data using the ensemble Kalman filter approach) into WRF. Examples are given of comparing the model fields with available aircraft observations during spring-summer 2016 field campaigns taken place at the eastern side of continents (KORUS-AQ in South Korea and ACT-America in the eastern US), the air pollution export regions. Under fair weather and stormy conditions, air pollution vertical distributions and column amounts, as well as the impact from land surface, are compared. These help identify challenges and opportunities for LEO/GEO satellite remote sensing and modeling of air quality in the northern hemisphere. Finally, we briefly show applications of this system on simulating Australian conditions, which would explore the needs for further development of the observing system in the southern hemisphere and inform the Clean Air and Urban Landscapes (https://www.nespurban.edu.au) modelers.

LAND SURFACE TEMPERATURES ESTIMATED ON GROUNDOBSERVED DATA AND SATELLITE IMAGES, DURING THE VEGETATION PERIOD IN THE OLTENIA PLAIN

Directory of Open Access Journals (Sweden)

ONŢEL IRINA

2015-03-01

Full Text Available The purpose of this study is to analyze the land surface temperatures by using climatological and remote sensing data during the vegetation period in the Oltenia Plain. The data used in this study refer both to climatological data (namely monthly and seasonal air and soil temperatures, and to remote sensing data delivered by MODIS Land Surface Temperature (LST, with a spatial resolution of 1 km. The analyzed period spans from 2000 to 2013 and the vegetation period considered is April-September. As main results, there were observed four years with high temperatures, namely 2000 (20.4oC-air T, 24.6oC soil T, and 26oC LST, 2003 (20.2oC air T, 23.9oC soil T and 24.5oC LST, 2007 (20.5oC air T, 24.3oC soil T and 25oC LST and 2012 (21.3oC air T, 25.7oC soil T and 26.5oC LST. The correlations between air temperature, soil temperature and LST were statisticaly significant. The diference between air temperature and soil temperature values ranked within 3-4oC, while the difference between soil temperature and land surface temperature obtained from MODIS images was about 0.8oC. Spatially, the highest temperatures were recorded on the Leu-Rotunda Field, the Caracal Plain and the Nedeia Field, and pretty high variations of observed temperatures seemed to depend on vegetation cover. The MODIS images represent one of the most important types of satellite data available for free, which can be successfully used in determining the climatic parameters and can help to predict the changes in plant activity, due to weather phenomena.

A satellite based scheme for predicting the effects of land cover change on local microclimate and surface hydrology: Development of an operational regional planning tool

Science.gov (United States)

Arthur, Sandra Traci

Humans have diverse goals for their use of land: mining, water supply, aesthetic enjoyment, recreation, transportation, housing, etc. Any individual living within an actively developing community can look back in time and note how, perhaps slowly but nonetheless dramatically, the total land area dedicated to human use has increased. As our society's basic functioning intensifies, the disappearance of "free" open space is apparent---today, even conservation areas are carefully designated, mapped and controlled. This transition in land use is a result of many individual decisions that occur throughout space and time, often with little concern for the potential impacts on the local environment. Two specific environmental components---the microclimate and surface hydrology---are the focus of this thesis. This study, as well as related tools and bodies of knowledge, should be used to broaden the scientific basis behind land use management decisions. It will be shown that development can induce predictable changes in measures of the local radiant surface temperature and evapotranspiration fraction---as long as certain features of the development are known. Specifically, the vegetation changes that accompany the development must be noted, as well as the initial climatic state of the land parcel. Additionally, plots of runoff vs. rainfall for gauged basins will be interpreted in terms of the proportion of the basin contributing to a storm event's runoff signal. For a particular basin, four distinct runoff responses, separated by season and antecedent moisture conditions, will be distinguished. The response for the non-summer months under typical antecedent moisture conditions will be shown to be the most representative of and responsive to a basin's land use patterns. A scheme that makes use of satellite-derived land cover patterns and other physical attributes of the basin in order to determine this particular runoff response will be presented. The Soil Conservation

Land mobile satellite services in Europe

Science.gov (United States)

Bartholomé, P.; Berretta, G.; Rogard, R.

The demand for land-mobile communications on a Europe-wide basis is an important and pressing problem. The pan-European cellular network now in the planning stage will be slow in coming and it will have its limitations. A regional satellite system for Europe to complement the cellular network is the only practical way to satisfy a specialised market that encompasses a population of several hundred thousand mobiles, including road vehicles, merchant shipping, fishing boats, and trains. The deployment of a regional system would take place in a number of phases, the first being based on a simple payload embarked on a host satellite belonging to a European organisation. Further phases will involve the development of more advanced payloads on dedicated satellites. For the long-term future, the use of satellites in highly inclined orbits is being considered as a means of improving their visibility and hence the service quality.

The CEOS-Land Surface Imaging Constellation Portal for GEOSS: A resource for land surface imaging system information and data access

Science.gov (United States)

Holm, Thomas; Gallo, Kevin P.; Bailey, Bryan

2010-01-01

The Committee on Earth Observation Satellites is an international group that coordinates civil space-borne observations of the Earth, and provides the space component of the Global Earth Observing System of Systems (GEOSS). The CEOS Virtual Constellations concept was implemented in an effort to engage and coordinate disparate Earth observing programs of CEOS member agencies and ultimately facilitate their contribution in supplying the space-based observations required to satisfy the requirements of the GEOSS. The CEOS initially established Study Teams for four prototype constellations that included precipitation, land surface imaging, ocean surface topography, and atmospheric composition. The basic mission of the Land Surface Imaging (LSI) Constellation [1] is to promote the efficient, effective, and comprehensive collection, distribution, and application of space-acquired image data of the global land surface, especially to meet societal needs of the global population, such as those addressed by the nine Group on Earth Observations (GEO) Societal Benefit Areas (SBAs) of agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather. The LSI Constellation Portal is the result of an effort to address important goals within the LSI Constellation mission and provide resources to assist in planning for future space missions that might further contribute to meeting those goals.

Two-Layer Variable Infiltration Capacity Land Surface Representation for General Circulation Models

Science.gov (United States)

Xu, L.

1994-01-01

A simple two-layer variable infiltration capacity (VIC-2L) land surface model suitable for incorporation in general circulation models (GCMs) is described. The model consists of a two-layer characterization of the soil within a GCM grid cell, and uses an aerodynamic representation of latent and sensible heat fluxes at the land surface. The effects of GCM spatial subgrid variability of soil moisture and a hydrologically realistic runoff mechanism are represented in the soil layers. The model was tested using long-term hydrologic and climatalogical data for Kings Creek, Kansas to estimate and validate the hydrological parameters. Surface flux data from three First International Satellite Land Surface Climatology Project Field Experiments (FIFE) intensive field compaigns in the summer and fall of 1987 in central Kansas, and from the Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) in Brazil were used to validate the mode-simulated surface energy fluxes and surface temperature.

Advantages of Using Microwave Satellite Soil Moisture over Gridded Precipitation Products and Land Surface Model Output in Assessing Regional Vegetation Water Availability and Growth Dynamics for a Lateral Inflow Receiving Landscape

NARCIS (Netherlands)

Chen, T.; McVicar, T.R.; Wang, G.J.; Chen, X.; de Jeu, R.A.M.; Liu, Y.; Shen, H.; Zhang, F.; Dolman, A.J.

2016-01-01

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

Diurnal Cycles of High Resolution Land Surface Temperatures (LSTs) Determined from UAV Platforms Across a Range of Surface Types

Science.gov (United States)

McCabe, M.; Rosas Aguilar, J.; Parkes, S. D.; Aragon, B.

2017-12-01

Observation of land surface temperature (LST) has many practical uses, from studying boundary layer dynamics and land-atmosphere coupling, to investigating surface properties such as soil moisture status, heat stress and surface heat fluxes. Typically, LST is observed via satellite based sensors such as LandSat or via point measurements using IR radiometers. These measurements provide either good spatial coverage and resolution or good temporal coverage. However, neither are able to provide the needed spatial and temporal resolution for many of the research applications described above. Technological developments in the use of Unmanned Aerial Vehicles (UAVs), together with small thermal frame cameras, has enabled a capacity to overcome this spatiotemporal constraint. Utilising UAV platforms to collect LST measurements across diurnal cycles provides an opportunity to study how meteorological and surface properties vary in both space and time. Here we describe the collection of LST data from a multi-rotor UAV across a study domain that is observed multiple times throughout the day. Flights over crops of Rhodes grass and alfalfa, along with a bare desert surface, were repeated with between 8 and 11 surveys covering the period from early morning to sunset. Analysis of the collected thermal imagery shows that the constructed LST maps illustrate a strong diurnal cycle consistent with expected trends, but with considerable spatial and temporal variability observed within and between the different domains. These results offer new insights into the dynamics of land surface behavior in both dry and wet soil conditions and at spatiotemporal scales that are unable to be replicated using traditional satellite platforms.

Estimating Global Impervious Surface based on Social-economic Data and Satellite Observations

Science.gov (United States)

Zeng, Z.; Zhang, K.; Xue, X.; Hong, Y.

2016-12-01

Impervious surface areas around the globe are expanding and significantly altering the surface energy balance, hydrology cycle and ecosystem services. Many studies have underlined the importance of impervious surface, r from hydrological modeling to contaminant transport monitoring and urban development estimation. Therefore accurate estimation of the global impervious surface is important for both physical and social sciences. Given the limited coverage of high spatial resolution imagery and ground survey, using satellite remote sensing and geospatial data to estimate global impervious areas is a practical approach. Based on the previous work of area-weighted imperviousness for north branch of the Chicago River provided by HDR, this study developed a method to determine the percentage of impervious surface using latest global land cover categories from multi-source satellite observations, population density and gross domestic product (GDP) data. Percent impervious surface at 30-meter resolution were mapped. We found that 1.33% of the CONUS (105,814 km2) and 0.475% of the land surface (640,370km2) are impervious surfaces. To test the utility and practicality of the proposed method, National Land Cover Database (NLCD) 2011 percent developed imperviousness for the conterminous United States was used to evaluate our results. The average difference between the derived imperviousness from our method and the NLCD data across CONUS is 1.14%, while difference between our results and the NLCD data are within ±1% over 81.63% of the CONUS. The distribution of global impervious surface map indicates that impervious surfaces are primarily concentrated in China, India, Japan, USA and Europe where are highly populated and/or developed. This study proposes a straightforward way of mapping global imperviousness, which can provide useful information for hydrologic modeling and other applications.

Impacts of surface gold mining on land use systems in Western Ghana.

Science.gov (United States)

Schueler, Vivian; Kuemmerle, Tobias; Schröder, Hilmar

2011-07-01

Land use conflicts are becoming increasingly apparent from local to global scales. Surface gold mining is an extreme source of such a conflict, but mining impacts on local livelihoods often remain unclear. Our goal here was to assess land cover change due to gold surface mining in Western Ghana, one of the world's leading gold mining regions, and to study how these changes affected land use systems. We used Landsat satellite images from 1986-2002 to map land cover change and field interviews with farmers to understand the livelihood implications of mining-related land cover change. Our results showed that surface mining resulted in deforestation (58%), a substantial loss of farmland (45%) within mining concessions, and widespread spill-over effects as relocated farmers expand farmland into forests. This points to rapidly eroding livelihood foundations, suggesting that the environmental and social costs of Ghana's gold boom may be much higher than previously thought.

A spatiotemporal analysis of the relationship between near-surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series

Science.gov (United States)

Good, Elizabeth J.; Ghent, Darren J.; Bulgin, Claire E.; Remedios, John J.

2017-09-01

The relationship between satellite land surface temperature (LST) and ground-based observations of 2 m air temperature (T2m) is characterized in space and time using >17 years of data. The analysis uses a new monthly LST climate data record (CDR) based on the Along-Track Scanning Radiometer series, which has been produced within the European Space Agency GlobTemperature project (http://www.globtemperature.info/). Global LST-T2m differences are analyzed with respect to location, land cover, vegetation fraction, and elevation, all of which are found to be important influencing factors. LSTnight ( 10 P.M. local solar time, clear-sky only) is found to be closely coupled with minimum T2m (Tmin, all-sky) and the two temperatures generally consistent to within ±5°C (global median LSTnight-Tmin = 1.8°C, interquartile range = 3.8°C). The LSTday ( 10 A.M. local solar time, clear-sky only)-maximum T2m (Tmax, all-sky) variability is higher (global median LSTday-Tmax = -0.1°C, interquartile range = 8.1°C) because LST is strongly influenced by insolation and surface regime. Correlations for both temperature pairs are typically >0.9 outside of the tropics. The monthly global and regional anomaly time series of LST and T2m—which are completely independent data sets—compare remarkably well. The correlation between the data sets is 0.9 for the globe with 90% of the CDR anomalies falling within the T2m 95% confidence limits. The results presented in this study present a justification for increasing use of satellite LST data in climate and weather science, both as an independent variable, and to augment T2m data acquired at meteorological stations.

Global Drought Monitoring and Forecasting based on Satellite Data and Land Surface Modeling

Science.gov (United States)

Sheffield, J.; Lobell, D. B.; Wood, E. F.

2010-12-01

Monitoring drought globally is challenging because of the lack of dense in-situ hydrologic data in many regions. In particular, soil moisture measurements are absent in many regions and in real time. This is especially problematic for developing regions such as Africa where water information is arguably most needed, but virtually non-existent on the ground. With the emergence of remote sensing estimates of all components of the water cycle there is now the potential to monitor the full terrestrial water cycle from space to give global coverage and provide the basis for drought monitoring. These estimates include microwave-infrared merged precipitation retrievals, evapotranspiration based on satellite radiation, temperature and vegetation data, gravity recovery measurements of changes in water storage, microwave based retrievals of soil moisture and altimetry based estimates of lake levels and river flows. However, many challenges remain in using these data, especially due to biases in individual satellite retrieved components, their incomplete sampling in time and space, and their failure to provide budget closure in concert. A potential way forward is to use modeling to provide a framework to merge these disparate sources of information to give physically consistent and spatially and temporally continuous estimates of the water cycle and drought. Here we present results from our experimental global water cycle monitor and its African drought monitor counterpart (http://hydrology.princeton.edu/monitor). The system relies heavily on satellite data to drive the Variable Infiltration Capacity (VIC) land surface model to provide near real-time estimates of precipitation, evapotranspiraiton, soil moisture, snow pack and streamflow. Drought is defined in terms of anomalies of soil moisture and other hydrologic variables relative to a long-term (1950-2000) climatology. We present some examples of recent droughts and how they are identified by the system, including

Detection of land cover change using an Artificial Neural Network on a time-series of MODIS satellite data

CSIR Research Space (South Africa)

Olivier, JC

2007-11-01

Full Text Available An Artificial Neural Network (ANN) is proposed to detect human-induced land cover change using a sliding window through a time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite surface reflectance pixel values. Training...

Spatio-temporal Assessment of Land Use/ Land Cover Dynamics and Urban Heat Island of Jaipur City using Satellite Data

Science.gov (United States)

Jalan, S.; Sharma, K.

2014-11-01

Urban Heat Island (UHI) refers to the phenomena of higher surface temperature occurring in urban areas as compared to the surrounding countryside attributable to urbanization. Spatio-temporal changes in UHI can be quantified through Land Surface Temperature (LST) derived from satellite imageries. Spatial variations in LST occur due to complexity of land surface - combination of impervious surface materials, vegetation, exposed soils as well as water surfaces. Jaipur city has observed rapid urbanization over the last decade. Due to rising population pressure the city has expanded considerably in areal extent and has also observed substantial land use/land cover (LULC) changes. The paper aims to determine changes in the LST and UHI phenomena for Jaipur city over the period from 2000 to 2011 and analyzes the spatial distribution and temporal variation of LST in context of changes in LULC. Landsat 7 ETM+ (2000) and Landsat 5 TM (2011) images of summer season have been used. Results reveal that Jaipur city has witnessed considerable growth in built up area at the cost of greener patches over the last decade, which has had clear impact on variation in LST. There has been an average rise of 2.99 °C in overall summer temperature. New suburbs of the city record 2° to 4 °C increase in LST. LST change is inversely related to change in vegetation cover and positively related to extent of built up area. The study concludes that UHI of Jaipur city has intensified and extended over new areas.

Seasonal evaluation of the land surface sheme HTESSEL against remote sensing derived energy fluxes of the Transdanubian regions in Hungary

NARCIS (Netherlands)

Wipfler, E.L.; Metselaar, K.; Dam, van J.C.; Feddes, R.A.; Meijgaard, van E.; Ulft, van L.H.; Hurk, van den B.; Zwart, S.J.; Bastiaanssen, W.G.M.

2011-01-01

The skill of the land surface model HTESSEL is assessed to reproduce evaporation in response to land surface characteristics and atmospheric forcing, both being spatially variable. Evaporation estimates for the 2005 growing season are inferred from satellite observations of the Western part of

Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

NARCIS (Netherlands)

Romaguera, M.; Vaughan, R. G.; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F. D.

This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of

Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

NARCIS (Netherlands)

Romaguera, M.; Vaughan, R. G.; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F. D.

2017-01-01

This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of

A new digital land mobile satellite system

Science.gov (United States)

Schneider, Philip

A description is given of the different digital services planned to be carried over existing and planned mobile satellite systems. These systems are then compared with analog services in terms of bandwidth and power efficiency. This comparison provides the rationale for the establishment of a digital land mobile satellite service (DLMSS) to use frequencies that are currently available but not yet assigned to a domestic mobile satellite system in the United States. The focus here is on the expected advantages of digital transmission techniques in accommodating additional mobile satellite systems in this portion of the spectrum, and how such techniques can fully satisfy voice, data and facsimile mobile communications requirements in a cost effective manner. A description is given of the system architecture of the DMLSS service proposed by the Geostar Messaging Corporation (GMC) and the market potential of DLMSS.

Estimation of surface air temperature over central and eastern Eurasia from MODIS land surface temperature

International Nuclear Information System (INIS)

Shen Suhung; Leptoukh, Gregory G

2011-01-01

Surface air temperature (T a ) is a critical variable in the energy and water cycle of the Earth–atmosphere system and is a key input element for hydrology and land surface models. This is a preliminary study to evaluate estimation of T a from satellite remotely sensed land surface temperature (T s ) by using MODIS-Terra data over two Eurasia regions: northern China and fUSSR. High correlations are observed in both regions between station-measured T a and MODIS T s . The relationships between the maximum T a and daytime T s depend significantly on land cover types, but the minimum T a and nighttime T s have little dependence on the land cover types. The largest difference between maximum T a and daytime T s appears over the barren and sparsely vegetated area during the summer time. Using a linear regression method, the daily maximum T a were estimated from 1 km resolution MODIS T s under clear-sky conditions with coefficients calculated based on land cover types, while the minimum T a were estimated without considering land cover types. The uncertainty, mean absolute error (MAE), of the estimated maximum T a varies from 2.4 °C over closed shrublands to 3.2 °C over grasslands, and the MAE of the estimated minimum T a is about 3.0 °C.

Error estimates for near-Real-Time Satellite Soil Moisture as Derived from the Land Parameter Retrieval Model

NARCIS (Netherlands)

Parinussa, R.M.; Meesters, A.G.C.A.; Liu, Y.Y.; Dorigo, W.; Wagner, W.; de Jeu, R.A.M.

2011-01-01

A time-efficient solution to estimate the error of satellite surface soil moisture from the land parameter retrieval model is presented. The errors are estimated using an analytical solution for soil moisture retrievals from this radiative-transfer-based model that derives soil moisture from

Quantifying Uncertainty in Satellite-Retrieved Land Surface Temperature from Cloud Detection Errors

Directory of Open Access Journals (Sweden)

Claire E. Bulgin

2018-04-01

Full Text Available Clouds remain one of the largest sources of uncertainty in remote sensing of surface temperature in the infrared, but this uncertainty has not generally been quantified. We present a new approach to do so, applied here to the Advanced Along-Track Scanning Radiometer (AATSR. We use an ensemble of cloud masks based on independent methodologies to investigate the magnitude of cloud detection uncertainties in area-average Land Surface Temperature (LST retrieval. We find that at a grid resolution of 625 km 2 (commensurate with a 0.25 ∘ grid size at the tropics, cloud detection uncertainties are positively correlated with cloud-cover fraction in the cell and are larger during the day than at night. Daytime cloud detection uncertainties range between 2.5 K for clear-sky fractions of 10–20% and 1.03 K for clear-sky fractions of 90–100%. Corresponding night-time uncertainties are 1.6 K and 0.38 K, respectively. Cloud detection uncertainty shows a weaker positive correlation with the number of biomes present within a grid cell, used as a measure of heterogeneity in the background against which the cloud detection must operate (e.g., surface temperature, emissivity and reflectance. Uncertainty due to cloud detection errors is strongly dependent on the dominant land cover classification. We find cloud detection uncertainties of a magnitude of 1.95 K over permanent snow and ice, 1.2 K over open forest, 0.9–1 K over bare soils and 0.09 K over mosaic cropland, for a standardised clear-sky fraction of 74.2%. As the uncertainties arising from cloud detection errors are of a significant magnitude for many surface types and spatially heterogeneous where land classification varies rapidly, LST data producers are encouraged to quantify cloud-related uncertainties in gridded products.

Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-Cover Classification

OpenAIRE

Xueke Li; Taixia Wu; Kai Liu; Yao Li; Lifu Zhang

2016-01-01

The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1) opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, ...

Physical characteristics of satellite surfaces

International Nuclear Information System (INIS)

Veverka, J.; Thomas, P.; Johnson, T.V.; Matson, D.; Housen, K.

1986-01-01

Both exogenic and endogenic effects have been proposed to explain the major observed characteristics of satellite surfaces. The current view is that the basic properties of most surfaces result from the intrinsic composition of a body and its geologic history. Exogenic effects have, however, played a role in modifying the appearance of nearly all surfaces. The most important exogenic effect is impact cratering, one manifestation of which is the production of micrometeoroid gardened regoliths on airless bodies. On large, silicate bodies the micrometeoroid bombardment can produce an optically mature, dark agglutinate-rich soil; the nature of regoliths on predominantly icy satellites remains uncertain. Direct accumulation of infalling material does not appear to play a major role in modifying most surfaces. Solar wind radiation effects have not altered greatly the optical properties of solar system objects; magnetospheric charged particles may have modified the optical properties of some outer planet satellites (e.g., sulfur ion bombardment in the case of some of the satellites of Jupiter). Other effects, such as aeolian and liquid/solid chemical weathering, may be important on satellites with atmospheres like Titan and Triton

Impact of highway construction on land surface energy balance and local climate derived from LANDSAT satellite data.

Science.gov (United States)

Nedbal, Václav; Brom, Jakub

2018-08-15

Extensive construction of highways has a major impact on the landscape and its structure. They can also influence local climate and heat fluxes in the surrounding area. After the removal of vegetation due to highway construction, the amount of solar radiation energy used for plant evapotranspiration (latent heat flux) decreases, bringing about an increase in landscape surface temperature, changing the local climate and increasing surface run-off. In this study, we evaluated the impact of the D8 highway construction (Central Bohemia, Czech Republic) on the distribution of solar radiation energy into the various heat fluxes (latent, sensible and ground heat flux) and related surface functional parameters (surface temperature and surface wetness). The aim was to describe the severity of the impact and the distance from the actual highway in which it can be observed. LANDSAT multispectral satellite images and field meteorological measurements were used to calculate surface functional parameters and heat balance before and during the highway construction. Construction of a four-lane highway can influence the heat balance of the landscape surface as far as 90m in the perpendicular direction from the highway axis, i.e. up to 75m perpendicular from its edge. During a summer day, the decrease in evapotranspired water can reach up to 43.7m 3 per highway kilometre. This means a reduced cooling effect, expressed as the decrease in latent heat flux, by an average of 29.7MWh per day per highway kilometre and its surroundings. The loss of the cooling ability of the land surface by evaporation can lead to a rise in surface temperature by as much as 7°C. Thus, the results indicate the impact of extensive line constructions on the local climate. Copyright © 2018 Elsevier B.V. All rights reserved.

Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products

Directory of Open Access Journals (Sweden)

Ying Qu

2015-01-01

Full Text Available Surface albedo is one of the key controlling geophysical parameters in the surface energy budget studies, and its temporal and spatial variation is closely related to the global climate change and regional weather system due to the albedo feedback mechanism. As an efficient tool for monitoring the surfaces of the Earth, remote sensing is widely used for deriving long-term surface broadband albedo with various geostationary and polar-orbit satellite platforms in recent decades. Moreover, the algorithms for estimating surface broadband albedo from satellite observations, including narrow-to-broadband conversions, bidirectional reflectance distribution function (BRDF angular modeling, direct-estimation algorithm and the algorithms for estimating albedo from geostationary satellite data, are developed and improved. In this paper, we present a comprehensive literature review on algorithms and products for mapping surface broadband albedo with satellite observations and provide a discussion of different algorithms and products in a historical perspective based on citation analysis of the published literature. This paper shows that the observation technologies and accuracy requirement of applications are important, and long-term, global fully-covered (including land, ocean, and sea-ice surfaces, gap-free, surface broadband albedo products with higher spatial and temporal resolution are required for climate change, surface energy budget, and hydrological studies.

Global mobile satellite communications theory for maritime, land and aeronautical applications

CERN Document Server

Ilčev, Stojče Dimov

2017-01-01

This book discusses current theory regarding global mobile satellite communications (GMSC) for maritime, land (road and rail), and aeronautical applications. It covers how these can enable connections between moving objects such as ships, road and rail vehicles and aircrafts on one hand, and on the other ground telecommunications subscribers through the medium of communications satellites, ground earth stations, Terrestrial Telecommunication Networks (TTN), Internet Service Providers (ISP) and other wireless and landline telecommunications providers. This new edition covers new developments and initiatives that have resulted in land and aeronautical applications and the introduction of new satellite constellations in non-geostationary orbits and projects of new hybrid satellite constellations. The book presents current GMSC trends, mobile system concepts and network architecture using a simple mode of style with understandable technical information, characteristics, graphics, illustrations and mathematics equ...

Global observation-based diagnosis of soil moisture control on land surface flux partition

Science.gov (United States)

Gallego-Elvira, Belen; Taylor, Christopher M.; Harris, Phil P.; Ghent, Darren; Veal, Karen L.; Folwell, Sonja S.

2016-04-01

Soil moisture plays a central role in the partition of available energy at the land surface between sensible and latent heat flux to the atmosphere. As soils dry out, evapotranspiration becomes water-limited ("stressed"), and both land surface temperature (LST) and sensible heat flux rise as a result. This change in surface behaviour during dry spells directly affects critical processes in both the land and the atmosphere. Soil water deficits are often a precursor in heat waves, and they control where feedbacks on precipitation become significant. State-of-the-art global climate model (GCM) simulations for the Coupled Model Intercomparison Project Phase 5 (CMIP5) disagree on where and how strongly the surface energy budget is limited by soil moisture. Evaluation of GCM simulations at global scale is still a major challenge owing to the scarcity and uncertainty of observational datasets of land surface fluxes and soil moisture at the appropriate scale. Earth observation offers the potential to test how well GCM land schemes simulate hydrological controls on surface fluxes. In particular, satellite observations of LST provide indirect information about the surface energy partition at 1km resolution globally. Here, we present a potentially powerful methodology to evaluate soil moisture stress on surface fluxes within GCMs. Our diagnostic, Relative Warming Rate (RWR), is a measure of how rapidly the land warms relative to the overlying atmosphere during dry spells lasting at least 10 days. Under clear skies, this is a proxy for the change in sensible heat flux as soil dries out. We derived RWR from MODIS Terra and Aqua LST observations, meteorological re-analyses and satellite rainfall datasets. Globally we found that on average, the land warmed up during dry spells for 97% of the observed surface between 60S and 60N. For 73% of the area, the land warmed faster than the atmosphere (positive RWR), indicating water stressed conditions and increases in sensible heat flux

Estimation of Surface Air Temperature Over Central and Eastern Eurasia from MODIS Land Surface Temperature

Science.gov (United States)

Shen, Suhung; Leptoukh, Gregory G.

2011-01-01

Surface air temperature (T(sub a)) is a critical variable in the energy and water cycle of the Earth.atmosphere system and is a key input element for hydrology and land surface models. This is a preliminary study to evaluate estimation of T(sub a) from satellite remotely sensed land surface temperature (T(sub s)) by using MODIS-Terra data over two Eurasia regions: northern China and fUSSR. High correlations are observed in both regions between station-measured T(sub a) and MODIS T(sub s). The relationships between the maximum T(sub a) and daytime T(sub s) depend significantly on land cover types, but the minimum T(sub a) and nighttime T(sub s) have little dependence on the land cover types. The largest difference between maximum T(sub a) and daytime T(sub s) appears over the barren and sparsely vegetated area during the summer time. Using a linear regression method, the daily maximum T(sub a) were estimated from 1 km resolution MODIS T(sub s) under clear-sky conditions with coefficients calculated based on land cover types, while the minimum T(sub a) were estimated without considering land cover types. The uncertainty, mean absolute error (MAE), of the estimated maximum T(sub a) varies from 2.4 C over closed shrublands to 3.2 C over grasslands, and the MAE of the estimated minimum Ta is about 3.0 C.

The esa earth explorer land surface processes and interactions mission

Science.gov (United States)

Labandibar, Jean-Yves; Jubineau, Franck; Silvestrin, Pierluigi; Del Bello, Umberto

2017-11-01

The European Space Agency (ESA) is defining candidate missions for Earth Observation. In the class of the Earth Explorer missions, dedicated to research and pre-operational demonstration, the Land Surface Processes and Interactions Mission (LSPIM) will acquire the accurate quantitative measurements needed to improve our understanding of the nature and evolution of biosphere-atmosphere interactions and to contribute significantly to a solution of the scaling problems for energy, water and carbon fluxes at the Earth's surface. The mission is intended to provide detailed observations of the surface of the Earth and to collect data related to ecosystem processes and radiation balance. It is also intended to address a range of issues important for environmental monitoring, renewable resources assessment and climate models. The mission involves a dedicated maneuvering satellite which provides multi-directional observations for systematic measurement of Land Surface BRDF (BiDirectional Reflectance Distribution Function) of selected sites on Earth. The satellite carries an optical payload : PRISM (Processes Research by an Imaging Space Mission), a multispectral imager providing reasonably high spatial resolution images (50 m over 50 km swath) in the whole optical spectral domain (from 450 nm to 2.35 μm with a resolution close to 10 nm, and two thermal bands from 8.1 to 9.1 μm). This paper presents the results of the Phase A study awarded by ESA, led by ALCATEL Space Industries and concerning the design of LSPIM.

Analysis of high resolution satellite digital data for land use studies ...

African Journals Online (AJOL)

High-resolution satellite data can give vital information about land cover, which can lead to better interpretation and classification of land resources. This study examined the relationship between Systeme Probatoire d'Observation de la Terre (SPOT) digital data and land use types in the derived savanna ecosystem of ...

Evaluation of the global MODIS 30 arc-second spatially and temporally complete snow-free land surface albedo and reflectance anisotropy dataset

Science.gov (United States)

Sun, Qingsong; Wang, Zhuosen; Li, Zhan; Erb, Angela; Schaaf, Crystal B.

2017-06-01

Land surface albedo is an essential variable for surface energy and climate modeling as it describes the proportion of incident solar radiant flux that is reflected from the Earth's surface. To capture the temporal variability and spatial heterogeneity of the land surface, satellite remote sensing must be used to monitor albedo accurately at a global scale. However, large data gaps caused by cloud or ephemeral snow have slowed the adoption of satellite albedo products by the climate modeling community. To address the needs of this community, we used a number of temporal and spatial gap-filling strategies to improve the spatial and temporal coverage of the global land surface MODIS BRDF, albedo and NBAR products. A rigorous evaluation of the gap-filled values shows good agreement with original high quality data (RMSE = 0.027 for the NIR band albedo, 0.020 for the red band albedo). This global snow-free and cloud-free MODIS BRDF and albedo dataset (established from 2001 to 2015) offers unique opportunities to monitor and assess the impact of the changes on the Earth's land surface.

[An operational remote sensing algorithm of land surface evapotranspiration based on NOAA PAL dataset].

Science.gov (United States)

Hou, Ying-Yu; He, Yan-Bo; Wang, Jian-Lin; Tian, Guo-Liang

2009-10-01

Based on the time series 10-day composite NOAA Pathfinder AVHRR Land (PAL) dataset (8 km x 8 km), and by using land surface energy balance equation and "VI-Ts" (vegetation index-land surface temperature) method, a new algorithm of land surface evapotranspiration (ET) was constructed. This new algorithm did not need the support from meteorological observation data, and all of its parameters and variables were directly inversed or derived from remote sensing data. A widely accepted ET model of remote sensing, i. e., SEBS model, was chosen to validate the new algorithm. The validation test showed that both the ET and its seasonal variation trend estimated by SEBS model and our new algorithm accorded well, suggesting that the ET estimated from the new algorithm was reliable, being able to reflect the actual land surface ET. The new ET algorithm of remote sensing was practical and operational, which offered a new approach to study the spatiotemporal variation of ET in continental scale and global scale based on the long-term time series satellite remote sensing images.

Analysing the Advantages of High Temporal Resolution Geostationary MSG SEVIRI Data Compared to Polar Operational Environmental Satellite Data for Land Surface Monitoring in Africa

Science.gov (United States)

Fensholt, R.; Anyamba, A.; Huber, S.; Proud, S. R.; Tucker, C. J.; Small, J.; Pak, E.; Rasmussen, M. O.; Sandholt, I.; Shisanya, C.

2011-01-01

Since 1972, satellite remote sensing of the environment has been dominated by polar-orbiting sensors providing useful data for monitoring the earth s natural resources. However their observation and monitoring capacity are inhibited by daily to monthly looks for any given ground surface which often is obscured by frequent and persistent cloud cover creating large gaps in time series measurements. The launch of the Meteosat Second Generation (MSG) satellite into geostationary orbit has opened new opportunities for land surface monitoring. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on-board MSG with an imaging capability every 15 minutes which is substantially greater than any temporal resolution that can be obtained from existing polar operational environmental satellites (POES) systems currently in use for environmental monitoring. Different areas of the African continent were affected by droughts and floods in 2008 caused by periods of abnormally low and high rainfall, respectively. Based on the effectiveness of monitoring these events from Earth Observation (EO) data the current analyses show that the new generation of geostationary remote sensing data can provide higher temporal resolution cloud-free (less than 5 days) measurements of the environment as compared to existing POES systems. SEVIRI MSG 5-day continental scale composites will enable rapid assessment of environmental conditions and improved early warning of disasters for the African continent such as flooding or droughts. The high temporal resolution geostationary data will complement existing higher spatial resolution polar-orbiting satellite data for various dynamic environmental and natural resource applications of terrestrial ecosystems.

Variations in global land surface phenology: a comparison of satellite optical and passive microwave data

Science.gov (United States)

Tong, X.; Tian, F.; Brandt, M.; Zhang, W.; Liu, Y.; Fensholt, R.

2017-12-01

Changes in vegetation phenological events are among the most sensitive biological responses to climate change. In last decades, facilitating by satellite remote sensing techniques, land surface phenology (LSP) have been monitored at global scale using proxy approaches as tracking the temporal change of a satellite-derived vegetation index. However, the existing global assessments of changes in LSP are all established on the basis of leaf phenology using NDVI derived from optical sensors, being responsive to vegetation canopy cover and greenness. Instead, the vegetation optical depth (VOD) parameter from passive microwave sensors, which is sensitive to the aboveground vegetation water content by including as well the woody components in the observations, provides an alternative, independent and comprehensive means for global vegetation phenology monitoring. We used the unique long-term global VOD record available for the period 1992-2012 to monitoring the dynamics of LSP metrics (length of season, start of season and end of season) in comparison with the dynamics of LSP metrics derived from the latest GIMMS NDVI3G V1. We evaluated the differences in the linear trends of LSP metrics between two datasets. Currently, our results suggest that the level of seasonality variation of vegetation water content is less than the vegetation greenness. We found significant phenological changes in vegetation water content in African woodlands, where has been reported with little leaf phenological change regardless of the delays in rainfall onset. Therefore, VOD might allow us to detect temporal shifts in the timing difference of vegetation water storage vs. leaf emergence and to see if some ecophysiological thresholds seem to be reached, that could cause species turnover as climate change-driven alterations to the African monsoon proceed.

Satellite image simulations for model-supervised, dynamic retrieval of crop type and land use intensity

Science.gov (United States)

Bach, H.; Klug, P.; Ruf, T.; Migdall, S.; Schlenz, F.; Hank, T.; Mauser, W.

2015-04-01

To support food security, information products about the actual cropping area per crop type, the current status of agricultural production and estimated yields, as well as the sustainability of the agricultural management are necessary. Based on this information, well-targeted land management decisions can be made. Remote sensing is in a unique position to contribute to this task as it is globally available and provides a plethora of information about current crop status. M4Land is a comprehensive system in which a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal satellite images. SLC uses modelled surface state parameters from PROMET, such as leaf area index or phenology of different crops to simulate spatially distributed surface reflectance spectra. This is the basis for generating artificial satellite images considering sensor specific configurations (spectral bands, solar and observation geometries). Ensembles of model runs are used to represent different crop types, fertilization status, soil colour and soil moisture. By multi-temporal comparisons of simulated and real satellite images, the land cover/crop type can be classified in a dynamically, model-supervised way and without in-situ training data. The method is demonstrated in an agricultural test-site in Bavaria. Its transferability is studied by analysing PROMET model results for the rest of Germany. Especially the simulated phenological development can be verified on this scale in order to understand whether PROMET is able to adequately simulate spatial, as well as temporal (intra- and inter-season) crop growth conditions, a prerequisite for the model-supervised approach. This sophisticated new technology allows monitoring of management decisions on the field-level using high resolution optical data (presently RapidEye and Landsat). The M4Land analysis system is designed to integrate multi-mission data and is

Mapping Global Ocean Surface Albedo from Satellite Observations: Models, Algorithms, and Datasets

Science.gov (United States)

Li, X.; Fan, X.; Yan, H.; Li, A.; Wang, M.; Qu, Y.

2018-04-01

Ocean surface albedo (OSA) is one of the important parameters in surface radiation budget (SRB). It is usually considered as a controlling factor of the heat exchange among the atmosphere and ocean. The temporal and spatial dynamics of OSA determine the energy absorption of upper level ocean water, and have influences on the oceanic currents, atmospheric circulations, and transportation of material and energy of hydrosphere. Therefore, various parameterizations and models have been developed for describing the dynamics of OSA. However, it has been demonstrated that the currently available OSA datasets cannot full fill the requirement of global climate change studies. In this study, we present a literature review on mapping global OSA from satellite observations. The models (parameterizations, the coupled ocean-atmosphere radiative transfer (COART), and the three component ocean water albedo (TCOWA)), algorithms (the estimation method based on reanalysis data, and the direct-estimation algorithm), and datasets (the cloud, albedo and radiation (CLARA) surface albedo product, dataset derived by the TCOWA model, and the global land surface satellite (GLASS) phase-2 surface broadband albedo product) of OSA have been discussed, separately.

The influence of land surface parameters on energy flux densities derived from remote sensing data

Energy Technology Data Exchange (ETDEWEB)

Tittebrand, A.; Schwiebus, A. [Inst. for Hydrology und Meteorology, TU Dresden (Germany); Berger, F.H. [Observatory Lindenberg, German Weather Service, Lindenberg (Germany)

2005-04-01

Knowledge of the vegetation properties surface reflectance, normalised difference vegetation index (NDVI) and leaf area index (LAI) are essential for the determination of the heat and water fluxes between terrestrial ecosystems and the atmosphere. Remote sensing data can be used to derive spatial estimates of the required surface properties. The determination of land surface parameters and their influence on radiant and energy flux densities is investigated with data of different remote sensing systems. Sensitivity studies show the importance of correctly derived land surface properties to estimate the key quantity of the hydrological cycle, the evapotranspiration (L.E), most exactly. In addition to variable parameters like LAI or NDVI there are also parameters which are can not be inferred from satellite data but needed for the Penman-Monteith approach. Fixed values are assumed for these variables because they have little influence on L.E. Data of Landsat-7 ETM+ and NOAA-16 AVHRR are used to show results in different spatial resolution. The satellite derived results are compared with ground truth data provided by the Observatory Lindenberg of the German Weather Service. (orig.)

Retrieval of the Land Surface Reflectance for Landsat-8 and Sentinel-2 and its validation.

Science.gov (United States)

Roger, J. C.; Vermote, E.; Skakun, S.; Franch, B.; Holben, B. N.; Justice, C. O.

2017-12-01

The land surface reflectance is a fundamental climate data record at the basis of the derivation of other climate data records (Albedo, LAI/Fpar, Vegetation indices) and a key parameter in the understanding of the land-surface-climate processes. For 25 years, Vermote and al. develop atmospheric corrections methods to define a land surface reflectance product for various satellites (AVHRR, MODIS, VIIRS…). This presentation highlights the algorithms developed both for Landsant-8 and Sentinel-2. We also emphasize the validation of the "Land surface reflectance" satellite products, which is a very important step to be done. For that purpose, we compared the surface reflectance products to a reference determined by using the accurate radiative transfer code 6S and very detailed measurements of the atmosphere obtained over the AERONET network (which allows to test for a large range of aerosol characteristics); formers being important inputs for atmospheric corrections. However, the application of this method necessitates the definition of a very detailed protocol for the use of AERONET data especially as far as size distribution and absorption are concerned, so that alternative validation methods or protocols could be compared. We describe here the protocol we have been working on based on our experience with the AERONET data and its application to Landsat-8 and Sentinel-2). We also derive a detailed error budget in relation to this approach. For a mean loaded atmosphere, t550 less than 0.25, the maximum uncertainty is 0.0025 corresponding to a relative uncertainty (in the RED channels): U 0.10, and 1% rsurf > 0.04.

The impact of urban morphology and land cover on the sensible heat flux retrieved by satellite and in-situ observations

Science.gov (United States)

Gawuc, L.; Łobocki, L.; Kaminski, J. W.

2017-12-01

Land surface temperature (LST) is a key parameter in various applications for urban environments research. However, remotely-sensed radiative surface temperature is not equivalent to kinetic nor aerodynamic surface temperature (Becker and Li, 1995; Norman and Becker, 1995). Thermal satellite observations of urban areas are also prone to angular anisotropy which is directly connected with the urban structure and relative sun-satellite position (Hu et al., 2016). Sensible heat flux (Qh) is the main component of surface energy balance in urban areas. Retrieval of Qh, requires observations of, among others, a temperature gradient. The lower level of temperature measurement is commonly replaced by remotely-sensed radiative surface temperature (Chrysoulakis, 2003; Voogt and Grimmond, 2000; Xu et al., 2008). However, such replacement requires accounting for the differences between aerodynamic and radiative surface temperature (Chehbouni et al., 1996; Sun and Mahrt, 1995). Moreover, it is important to avoid micro-scale processes, which play a major role in the roughness sublayer. This is due to the fact that Monin-Obukhov similarity theory is valid only in dynamic sublayer. We will present results of the analyses of the impact of urban morphology and land cover on the seasonal changes of sensible heat flux (Qh). Qh will be retrieved by two approaches. First will be based on satellite observations of radiative surface temperature and second will be based on in-situ observations of kinetic road temperature. Both approaches will utilize wind velocity, and air temperature observed in-situ. We will utilize time series of MODIS LST observations for the period of 2005-2014 as well as simultaneous in-situ observations collected by road weather network (9 stations). Ground stations are located across the city of Warsaw, outside the city centre in low-rise urban structure. We will account for differences in urban morphology and land cover in the proximity of ground stations. We will

Does quality control matter? Surface urban heat island intensity variations estimated by satellite-derived land surface temperature products

Science.gov (United States)

Lai, Jiameng; Zhan, Wenfeng; Huang, Fan; Quan, Jinling; Hu, Leiqiu; Gao, Lun; Ju, Weimin

2018-05-01

The temporally regular and spatially comprehensive monitoring of surface urban heat islands (SUHIs) have been extremely difficult, until the advent of satellite-based land surface temperature (LST) products. However, these LST products have relatively higher errors compared to in situ measurements. This has resulted in comparatively inaccurate estimations of SUHI indicators and, consequently, may have distorted interpretations of SUHIs. Although reports have shown that LST qualities are important for SUHI interpretations, systematic investigations of the response of SUHI indicators to LST qualities across cities with dissimilar bioclimates are rare. To address this issue, we chose eighty-six major cities across mainland China and analyzed SUHI intensity (SUHII) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) LST data. The LST-based SUHII differences due to inclusion or exclusion of MODIS quality control (QC) flags (i.e., ΔSUHII) were evaluated. Our major findings included, but are not limited to, the following four aspects: (1) SUHIIs can be significantly impacted by MODIS QC flags, and the associated QC-induced ΔSUHIIs generally accounted for 24.3% (29.9%) of the total SUHII value during the day (night); (2) the ΔSUHIIs differed between seasons, with considerable differences between transitional (spring and autumn) and extreme (summer and winter) seasons; (3) significant discrepancies also appeared among cities located in northern and southern regions, with northern cities often possessing higher annual mean ΔSUHIIs. The internal variations of ΔSUHIIs within individual cities also showed high heterogeneity, with ΔSUHII variations that generally exceeded 5.0 K (3.0 K) in northern (southern) cities; (4) ΔSUHIIs were negatively related to SUHIIs and cloud cover percentages (mostly in transitional seasons). No significant relationship was found in the extreme seasons. Our findings highlight the need to be extremely cautious when using LST

Characterizing Land Surface Anisotropic Reflectance over Rugged Terrain: A Review of Concepts and Recent Developments

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Jianguang Wen

2018-02-01

Full Text Available Rugged terrain, including mountains, hills, and some high lands are typical land surfaces around the world. As a physical parameter for characterizing the anisotropic reflectance of the land surface, the importance of the bidirectional reflectance distribution function (BRDF has been gradually recognized in the remote sensing community, and great efforts have been dedicated to build BRDF models over various terrain types. However, on rugged terrain, the topography intensely affects the shape and magnitude of the BRDF and creates challenges in modeling the BRDF. In this paper, after a brief introduction of the theoretical background of the BRDF over rugged terrain, the status of estimating land surface BRDF properties over rugged terrain is comprehensively reviewed from a historical perspective and summarized in two categories: BRDFs describing solo slopes and those describing composite slopes. The discussion focuses on land surface reflectance retrieval over mountainous areas, the difference in solo slope and composite slope BRDF models, and suggested future research to improve the accuracy of BRDFs derived with remote sensing satellites.

Monitoring arid-land groundwater abstraction through optimization of a land surface model with remote sensing-based evaporation

KAUST Repository

Lopez Valencia, Oliver Miguel

2018-02-01

The increase in irrigated agriculture in Saudi Arabia is having a large impact on its limited groundwater resources. While large-scale water storage changes can be estimated using satellite data, monitoring groundwater abstraction rates is largely non-existent at either farm or regional level, so water management decisions remain ill-informed. Although determining water use from space at high spatiotemporal resolutions remains challenging, a number of approaches have shown promise, particularly in the retrieval of crop water use via evaporation. Apart from satellite-based estimates, land surface models offer a continuous spatial-temporal evolution of full land-atmosphere water and energy exchanges. In this study, we first examine recent trends in terrestrial water storage depletion within the Arabian Peninsula and explore its relation to increased agricultural activity in the region using satellite data. Next, we evaluate a number of large-scale remote sensing-based evaporation models, giving insight into the challenges of evaporation retrieval in arid environments. Finally, we present a novel method aimed to retrieve groundwater abstraction rates used in irrigated fields by constraining a land surface model with remote sensing-based evaporation observations. The approach is used to reproduce reported irrigation rates over 41 center-pivot irrigation fields presenting a range of crop dynamics over the course of one year. The results of this application are promising, with mean absolute errors below 3 mm:day-1, bias of -1.6 mm:day-1, and a first rough estimate of total annual abstractions of 65.8 Mm3 (close to the estimated value using reported farm data, 69.42 Mm3). However, further efforts to address the overestimation of bare soil evaporation in the model are required. The uneven coverage of satellite data within the study site allowed us to evaluate its impact on the optimization, with a better match between observed and obtained irrigation rates on fields with

Downscaling of Aircraft, Landsat, and MODIS-bases Land Surface Temperature Images with Support Vector Machines

Science.gov (United States)

High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at s...

Black Sea impact on its west-coast land surface temperature

Science.gov (United States)

Cheval, Sorin; Constantin, Sorin

2018-03-01

This study investigates the Black Sea influence on the thermal characteristics of its western hinterland based on satellite imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). The marine impact on the land surface temperature (LST) values is detected at daily, seasonal and annual time scales, and a strong linkage with the land cover is demonstrated. The remote sensing products used within the study supply LST data with complete areal coverage during clear sky conditions at 1-km spatial resolution, which is appropriate for climate studies. The sea influence is significant up to 4-5 km, by daytime, while the nighttime influence is very strong in the first 1-2 km, and it gradually decreases westward. Excepting the winter, the daytime temperature increases towards the plateau with the distance from the sea, e.g. with a gradient of 0.9 °C/km in the first 5 km in spring or with 0.7 °C/km in summer. By nighttime, the sea water usually remains warmer than the contiguous land triggering higher LST values in the immediate proximity of the coastline in all seasons, e.g. mean summer LST is 19.0 °C for the 1-km buffer, 16.6 °C for the 5-km buffer and 16.0 °C for the 10-km buffer. The results confirm a strong relationship between the land cover and thermal regime in the western hinterland of the Black Sea coast. The satellite-derived LST and air temperature values recorded at the meteorological stations are highly correlated for similar locations, but the marine influence propagates differently, pledging for distinct analysis. Identified anomalies in the general observed trends are investigated in correlation with sea surface temperature dynamics in the coastal area.

Land-mobile satellite excess path loss measurements

Science.gov (United States)

Hess, G. C.

1980-05-01

An experiment conducted with the ATS-6 satellite to determine the additional path loss over free-space loss experienced by land-mobile communication links is described. This excess path loss is measured as a function of 1) local environment, 2) vehicle heading, 3) link frequency, 4) satellite elevation angle, and 5) street side. A statistical description of excess loss developed from the data shows that the first two parameters dominate. Excess path loss on the order of 25 dB is typical in urban situations, but decreases to under 10 dB in suburban/rural areas. Spaced antenna selection diversity is found to provide only a slight decrease (4 dB, typically) in the urban excess path loss observed. Level crossing rates are depressed in satellite links relative to those of Rayleigh-faded terrestrial links, but increases in average fade durations tend to offset that advantage. The measurements show that the excess path loss difference between 860-MHz links and 1550-MHz links is generally negligible.

On land-use modeling: A treatise of satellite imagery data and misclassification error

Science.gov (United States)

Sandler, Austin M.

Recent availability of satellite-based land-use data sets, including data sets with contiguous spatial coverage over large areas, relatively long temporal coverage, and fine-scale land cover classifications, is providing new opportunities for land-use research. However, care must be used when working with these datasets due to misclassification error, which causes inconsistent parameter estimates in the discrete choice models typically used to model land-use. I therefore adapt the empirical correction methods developed for other contexts (e.g., epidemiology) so that they can be applied to land-use modeling. I then use a Monte Carlo simulation, and an empirical application using actual satellite imagery data from the Northern Great Plains, to compare the results of a traditional model ignoring misclassification to those from models accounting for misclassification. Results from both the simulation and application indicate that ignoring misclassification will lead to biased results. Even seemingly insignificant levels of misclassification error (e.g., 1%) result in biased parameter estimates, which alter marginal effects enough to affect policy inference. At the levels of misclassification typical in current satellite imagery datasets (e.g., as high as 35%), ignoring misclassification can lead to systematically erroneous land-use probabilities and substantially biased marginal effects. The correction methods I propose, however, generate consistent parameter estimates and therefore consistent estimates of marginal effects and predicted land-use probabilities.

Impacts of Satellite-Based Snow Albedo Assimilation on Offline and Coupled Land Surface Model Simulations.

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Tao Wang

Full Text Available Seasonal snow cover in the Northern Hemisphere is the largest component of the terrestrial cryosphere and plays a major role in the climate system through strong positive feedbacks related to albedo. The snow-albedo feedback is invoked as an important cause for the polar amplification of ongoing and projected climate change, and its parameterization across models is an important source of uncertainty in climate simulations. Here, instead of developing a physical snow albedo scheme, we use a direct insertion approach to assimilate satellite-based surface albedo during the snow season (hereafter as snow albedo assimilation into the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms and assess the influences of such assimilation on offline and coupled simulations. Our results have shown that snow albedo assimilation in both ORCHIDEE and ORCHIDEE-LMDZ (a general circulation model of Laboratoire de Météorologie Dynamique improve the simulation accuracy of mean seasonal (October throughout May snow water equivalent over the region north of 40 degrees. The sensitivity of snow water equivalent to snow albedo assimilation is more pronounced in the coupled simulation than the offline simulation since the feedback of albedo on air temperature is allowed in ORCHIDEE-LMDZ. We have also shown that simulations of air temperature at 2 meters in ORCHIDEE-LMDZ due to snow albedo assimilation are significantly improved during the spring in particular over the eastern Siberia region. This is a result of the fact that high amounts of shortwave radiation during the spring can maximize its snow albedo feedback, which is also supported by the finding that the spatial sensitivity of temperature change to albedo change is much larger during the spring than during the autumn and winter. In addition, the radiative forcing at the top of the atmosphere induced by snow albedo assimilation during the spring is estimated to be -2.50 W m-2, the

Mapping Impervious Surfaces Globally at 30m Resolution Using Landsat Global Land Survey Data

Science.gov (United States)

Brown de Colstoun, E.; Huang, C.; Wolfe, R. E.; Tan, B.; Tilton, J.; Smith, S.; Phillips, J.; Wang, P.; Ling, P.; Zhan, J.; Xu, X.; Taylor, M. P.

2013-12-01

Impervious surfaces, mainly artificial structures and roads, cover less than 1% of the world's land surface (1.3% over USA). Regardless of the relatively small coverage, impervious surfaces have a significant impact on the environment. They are the main source of the urban heat island effect, and affect not only the energy balance, but also hydrology and carbon cycling, and both land and aquatic ecosystem services. In the last several decades, the pace of converting natural land surface to impervious surfaces has increased. Quantitatively monitoring the growth of impervious surface expansion and associated urbanization has become a priority topic across both the physical and social sciences. The recent availability of consistent, global scale data sets at 30m resolution such as the Global Land Survey from the Landsat satellites provides an unprecedented opportunity to map global impervious cover and urbanization at this resolution for the first time, with unprecedented detail and accuracy. Moreover, the spatial resolution of Landsat is absolutely essential to accurately resolve urban targets such a buildings, roads and parking lots. With long term GLS data now available for the 1975, 1990, 2000, 2005 and 2010 time periods, the land cover/use changes due to urbanization can now be quantified at this spatial scale as well. In the Global Land Survey - Imperviousness Mapping Project (GLS-IMP), we are producing the first global 30 m spatial resolution impervious cover data set. We have processed the GLS 2010 data set to surface reflectance (8500+ TM and ETM+ scenes) and are using a supervised classification method using a regression tree to produce continental scale impervious cover data sets. A very large set of accurate training samples is the key to the supervised classifications and is being derived through the interpretation of high spatial resolution (~2 m or less) commercial satellite data (Quickbird and Worldview2) available to us through the unclassified

Estimation of land-atmosphere energy transfer over the Tibetan Plateau by a combination use of geostationary and polar-orbiting satellite data

Science.gov (United States)

Zhong, L.; Ma, Y.

2017-12-01

Land-atmosphere energy transfer is of great importance in land-atmosphere interactions and atmospheric boundary layer processes over the Tibetan Plateau (TP). The energy fluxes have high temporal variability, especially in their diurnal cycle, which cannot be acquired by polar-orbiting satellites alone because of their low temporal resolution. Therefore, it's of great practical significance to retrieve land surface heat fluxes by a combination use of geostationary and polar orbiting satellites. In this study, a time series of the hourly LST was estimated from thermal infrared data acquired by the Chinese geostationary satellite FengYun 2C (FY-2C) over the TP. The split window algorithm (SWA) was optimized using a regression method based on the observations from the Enhanced Observing Period (CEOP) of the Asia-Australia Monsoon Project (CAMP) on the Tibetan Plateau (CAMP/Tibet) and Tibetan observation and research platform (TORP), the land surface emissivity (LSE) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the water vapor content from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) project. The 10-day composite hourly LST data were generated via the maximum value composite (MVC) method to reduce the cloud effects. The derived LST was validated by the field observations of CAMP/Tibet and TORP. The results show that the retrieved LST and in situ data have a very good correlation (with root mean square error (RMSE), mean bias (MB), mean absolute error (MAE) and correlation coefficient (R) values of 1.99 K, 0.83 K, 1.71 K, and 0.991, respectively). Together with other characteristic parameters derived from polar-orbiting satellites and meteorological forcing data, the energy balance budgets have been retrieved finally. The validation results showed there was a good consistency between estimation results and in-situ measurements over the TP, which prove the robustness of the proposed estimation

Satellite constraints on surface concentrations of particulate matter

Science.gov (United States)

Ford Hotmann, Bonne

Because of the increasing evidence of the widespread adverse effects on human health from exposure to poor air quality and the recommendations of the World Health Organization to significantly reduce PM2.5 in order to reduce these risks, better estimates of surface air quality globally are required. However, surface measurements useful for monitoring particulate exposure are scarce, especially in developing countries which often experience the worst air pollution. Therefore, other methods are necessary to augment estimates in regions with limited surface observations. The prospect of using satellite observations to infer surface air quality is attractive; however, it requires knowledge of the complicated relationship between satellite-observed aerosol optical depth (AOD) and surface concentrations. This dissertation explores how satellite observations can be used in conjunction with a chemical transport model (GEOS-Chem) to better understand this relationship. First, we investigate the seasonality in aerosols over the Southeastern United States using observations from several satellite instruments (MODIS, MISR, CALIOP) and surface network sites (IMPROVE, SEARCH, AERONET). We find that the strong summertime enhancement in satellite-observed aerosol optical depth (factor 2-3 enhancement over wintertime AOD) is not present in surface mass concentrations (25-55% summertime enhancement). Goldstein et al. [2009] previously attributed this seasonality in AOD to biogenic organic aerosol; however, surface observations show that organic aerosol only accounts for ~35% of PM2.5 mass and exhibits similar seasonality to total surface PM2.5. The GEOS-Chem model generally reproduces these surface aerosol measurements, but under represents the AOD seasonality observed by satellites. We show that seasonal differences in water uptake cannot sufficiently explain the magnitude of AOD increase. As CALIOP profiles indicate the presence of additional aerosol in the lower troposphere

Towards automated statewide land cover mapping in Wisconsin using satellite remote sensing and GIS techniques

International Nuclear Information System (INIS)

Cosentino, B.L.; Lillesand, T.M.

1991-01-01

Attention is given to an initial research project being performed by the University of Wisconsin-Madison, Environmental Remote Sensing Center in conjunction with seven local, state, and federal agencies to implement automated statewide land cover mapping using satellite remote sensing and geographical information system (GIS) techniques. The basis, progress, and future research needs for this mapping program are presented. The research efforts are directed toward strategies that integrate satellite remote sensing and GIS techniques in the generation of land cover data for multiple users of land cover information. The project objectives are to investigate methodologies that integrate satellite data with other imagery and spatial data resident in emerging GISs in the state for particular program needs, and to develop techniques that can improve automated land cover mapping efficiency and accuracy. 10 refs

Internal Physical Features of a Land Surface Model Employing a Tangent Linear Model

Science.gov (United States)

Yang, Runhua; Cohn, Stephen E.; daSilva, Arlindo; Joiner, Joanna; Houser, Paul R.

1997-01-01

variables are predicted imperfectly due to inherent uncertainties in the modeling process, our study suggests how satellite observations can be combined with the model, through land surface data assimilation, to improve their prediction.

A European Land Mobile Satellite System via EMS

Science.gov (United States)

Ananasso, Fulvio; Mistretta, Ignazio

1991-10-01

The paper analyzes the technical and market issues that influence the strategy of implementation of a Land Mobile Satellite System via the payload EMS (European Mobile System) embarked on ITALSAT F-2. The final goal is to determine services, network architecture, and added value chain that make LMSS via EMS profitable for a typical telecommunication company.

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.

Enhancing the representation of subgrid land surface characteristics in land surface models

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Y. Ke

2013-09-01

Full Text Available Land surface heterogeneity has long been recognized as important to represent in the land surface models. In most existing land surface models, the spatial variability of surface cover is represented as subgrid composition of multiple surface cover types, although subgrid topography also has major controls on surface processes. In this study, we developed a new subgrid classification method (SGC that accounts for variability of both topography and vegetation cover. Each model grid cell was represented with a variable number of elevation classes and each elevation class was further described by a variable number of vegetation types optimized for each model grid given a predetermined total number of land response units (LRUs. The subgrid structure of the Community Land Model (CLM was used to illustrate the newly developed method in this study. Although the new method increases the computational burden in the model simulation compared to the CLM subgrid vegetation representation, it greatly reduced the variations of elevation within each subgrid class and is able to explain at least 80% of the total subgrid plant functional types (PFTs. The new method was also evaluated against two other subgrid methods (SGC1 and SGC2 that assigned fixed numbers of elevation and vegetation classes for each model grid (SGC1: M elevation bands–N PFTs method; SGC2: N PFTs–M elevation bands method. Implemented at five model resolutions (0.1°, 0.25°, 0.5°, 1.0°and 2.0° with three maximum-allowed total number of LRUs (i.e., NLRU of 24, 18 and 12 over North America (NA, the new method yielded more computationally efficient subgrid representation compared to SGC1 and SGC2, particularly at coarser model resolutions and moderate computational intensity (NLRU = 18. It also explained the most PFTs and elevation variability that is more homogeneously distributed spatially. The SGC method will be implemented in CLM over the NA continent to assess its impacts on

Generalized Split-Window Algorithm for Estimate of Land Surface Temperature from Chinese Geostationary FengYun Meteorological Satellite (FY-2C Data

Directory of Open Access Journals (Sweden)

Jun Xia

2008-02-01

Full Text Available On the basis of the radiative transfer theory, this paper addressed the estimate ofLand Surface Temperature (LST from the Chinese first operational geostationarymeteorological satellite-FengYun-2C (FY-2C data in two thermal infrared channels (IR1,10.3-11.3 μ m and IR2, 11.5-12.5 μ m , using the Generalized Split-Window (GSWalgorithm proposed by Wan and Dozier (1996. The coefficients in the GSW algorithmcorresponding to a series of overlapping ranging of the mean emissivity, the atmosphericWater Vapor Content (WVC, and the LST were derived using a statistical regressionmethod from the numerical values simulated with an accurate atmospheric radiativetransfer model MODTRAN 4 over a wide range of atmospheric and surface conditions.The simulation analysis showed that the LST could be estimated by the GSW algorithmwith the Root Mean Square Error (RMSE less than 1 K for the sub-ranges with theViewing Zenith Angle (VZA less than 30° or for the sub-rangs with VZA less than 60°and the atmospheric WVC less than 3.5 g/cm2 provided that the Land Surface Emissivities(LSEs are known. In order to determine the range for the optimum coefficients of theGSW algorithm, the LSEs could be derived from the data in MODIS channels 31 and 32 provided by MODIS/Terra LST product MOD11B1, or be estimated either according tothe land surface classification or using the method proposed by Jiang et al. (2006; and theWVC could be obtained from MODIS total precipitable water product MOD05, or beretrieved using Li et al.’ method (2003. The sensitivity and error analyses in term of theuncertainty of the LSE and WVC as well as the instrumental noise were performed. Inaddition, in order to compare the different formulations of the split-window algorithms,several recently proposed split-window algorithms were used to estimate the LST with thesame simulated FY-2C data. The result of the intercomparsion showed that most of thealgorithms give

Integration of satellite-induced fluorescence and vegetation optical depth to improve the retrieval of land evaporation

Science.gov (United States)

Pagán, B. R.; Martens, B.; Maes, W. H.; Miralles, D. G.

2017-12-01

Global satellite-based data sets of land evaporation overcome limitations in coverage of in situ measurements while retaining some observational nature. Although their potential for real world applications are promising, their value during dry conditions is still poorly understood. Most evaporation retrieval algorithms are not directly sensitive to soil moisture. An exception is the Global Land Evaporation Amsterdam Model (GLEAM), which uses satellite surface soil moisture and precipitation to account for land water availability. The existing methodology may greatly benefit from the optimal integration of novel observations of the land surface. Microwave vegetation optical depth (VOD) and near-infrared solar-induced fluorescence (SIF) are expected to reflect different aspects of evaporative stress. While the former is considered to be a proxy of vegetation water content, the latter is indicative of the activity of photosynthetic machinery. As stomata regulate both photosynthesis and transpiration, we expect a relationship between SIF and transpiration. An important motivation to incorporate observations in land evaporation calculations is that plant transpiration - usually the largest component of the flux - is extremely challenging to model due to species-dependent responses to drought. Here we present an innovative integration of VOD and SIF into the GLEAM evaporative stress function. VOD is utilized as a measurement of isohydricity to improve the representation of species specific drought responses. SIF is used for transpiration modelling, a novel application, and standardized by incoming solar radiation to better account for radiation-limited periods. Results are validated with global FLUXNET and International Soil Moisture Network data and demonstrate that the incorporation of VOD and SIF can yield accurate estimates of transpiration over large-scales, which are essential to further understand ecosystem-atmosphere feedbacks and the response of terrestrial

Integration between terrestrial-based and satellite-based land mobile communications systems

Science.gov (United States)

Arcidiancono, Antonio

1990-01-01

A survey is given of several approaches to improving the performance and marketability of mobile satellite systems (MSS). The provision of voice/data services in the future regional European Land Mobile Satellite System (LMSS), network integration between the Digital Cellular Mobile System (GSM) and LMSS, the identification of critical areas for the implementation of integrated GSM/LMSS areas, space segment scenarios, LMSS for digital trunked private mobile radio (PMR) services, and code division multiple access (CDMA) techniques for a terrestrial/satellite system are covered.

Comparison of Hyperspectral and Multispectral Satellites for Discriminating Land Cover in Northern California

Science.gov (United States)

Clark, M. L.; Kilham, N. E.

2015-12-01

Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Most land-cover maps at regional to global scales are produced with remote sensing techniques applied to multispectral satellite imagery with 30-500 m pixel sizes (e.g., Landsat, MODIS). Hyperspectral, or imaging spectrometer, imagery measuring the visible to shortwave infrared regions (VSWIR) of the spectrum have shown impressive capacity to map plant species and coarser land-cover associations, yet techniques have not been widely tested at regional and greater spatial scales. The Hyperspectral Infrared Imager (HyspIRI) mission is a VSWIR hyperspectral and thermal satellite being considered for development by NASA. The goal of this study was to assess multi-temporal, HyspIRI-like satellite imagery for improved land cover mapping relative to multispectral satellites. We mapped FAO Land Cover Classification System (LCCS) classes over 22,500 km2 in the San Francisco Bay Area, California using 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery simulated from data acquired by NASA's AVIRIS airborne sensor. Random Forests (RF) and Multiple-Endmember Spectral Mixture Analysis (MESMA) classifiers were applied to the simulated images and accuracies were compared to those from real Landsat 8 images. The RF classifier was superior to MESMA, and multi-temporal data yielded higher accuracy than summer-only data. With RF, hyperspectral data had overall accuracy of 72.2% and 85.1% with full 20-class and reduced 12-class schemes, respectively. Multispectral imagery had lower accuracy. For example, simulated and real Landsat data had 7.5% and 4.6% lower accuracy than HyspIRI data with 12 classes, respectively. In summary, our results indicate increased mapping accuracy using HyspIRI multi-temporal imagery, particularly in discriminating different natural vegetation types, such as

Landsat—Earth observation satellites

Science.gov (United States)

,

2015-11-25

Since 1972, Landsat satellites have continuously acquired space-based images of the Earth’s land surface, providing data that serve as valuable resources for land use/land change research. The data are useful to a number of applications including forestry, agriculture, geology, regional planning, and education. Landsat is a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). NASA develops remote sensing instruments and the spacecraft, then launches and validates the performance of the instruments and satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground reception, data archiving, product generation, and data distribution. The result of this program is an unprecedented continuing record of natural and human-induced changes on the global landscape.

Land surface phenology of Northeast China during 2000-2015: temporal changes and relationships with climate changes.

Science.gov (United States)

Zhang, Yue; Li, Lin; Wang, Hongbin; Zhang, Yao; Wang, Naijia; Chen, Junpeng

2017-10-01

As an important crop growing area, Northeast China (NEC) plays a vital role in China's food security, which has been severely affected by climate change in recent years. Vegetation phenology in this region is sensitive to climate change, and currently, the relationship between the phenology of NEC and climate change remains unclear. In this study, we used a satellite-derived normalized difference vegetation index (NDVI) to obtain the temporal patterns of the land surface phenology in NEC from 2000 to 2015 and validated the results using ground phenology observations. We then explored the relationships among land surface phenology, temperature, precipitation, and sunshine hours for relevant periods. Our results showed that the NEC experienced great phenological changes in terms of spatial heterogeneity during 2000-2015. The spatial patterns of land surface phenology mainly changed with altitude and land cover type. In most regions of NEC, the start date of land surface phenology had advanced by approximately 1.0 days year -1 , and the length of land surface phenology had been prolonged by approximately 1.0 days year -1 except for the needle-leaf and cropland areas, due to the warm conditions. We found that a distinct inter-annual variation in land surface phenology related to climate variables, even if some areas presented non-significant trends. Land surface phenology was coupled with climate variables and distinct responses at different combinations of temperature, precipitation, sunshine hours, altitude, and anthropogenic influence. These findings suggest that remote sensing and our phenology extracting methods hold great potential for helping to understand how land surface phenology is sensitive to global climate change.

Global Land Surface Temperature From the Along-Track Scanning Radiometers

Science.gov (United States)

Ghent, D. J.; Corlett, G. K.; Göttsche, F.-M.; Remedios, J. J.

2017-11-01

The Leicester Along-Track Scanning Radiometer (ATSR) and Sea and Land Surface Temperature Radiometer (SLSTR) Processor for LAnd Surface Temperature (LASPLAST) provides global land surface temperature (LST) products from thermal infrared radiance data. In this paper, the state-of-the-art version of LASPLAST, as deployed in the GlobTemperature project, is described and applied to data from the Advanced Along-Track Scanning Radiometer (AATSR). The LASPLAST retrieval formulation for LST is a nadir-only, two-channel, split-window algorithm, based on biome classification, fractional vegetation, and across-track water vapor dependences. It incorporates globally robust retrieval coefficients derived using highly sampled atmosphere profiles. LASPLAST benefits from appropriate spatial resolution auxiliary information and a new probabilistic-based cloud flagging algorithm. For the first time for a satellite-derived LST product, pixel-level uncertainties characterized in terms of random, locally correlated, and systematic components are provided. The new GlobTemperature GT_ATS_2P Version 1.0 product has been validated for 1 year of AATSR data (2009) against in situ measurements acquired from "gold standard reference" stations: Gobabeb, Namibia, and Evora, Portugal; seven Surface Radiation Budget stations, and the Atmospheric Radiation Measurement station at Southern Great Plains. These data show average absolute biases for the GT_ATS_2P Version 1.0 product of 1.00 K in the daytime and 1.08 K in the nighttime. The improvements in data provenance including better accuracy, fully traceable retrieval coefficients, quantified uncertainty, and more detailed information in the new harmonized format of the GT_ATS_2P product will allow for more significant exploitation of the historical LST data record from the ATSRs and a valuable near-real-time service from the Sea and Land Surface Temperature Radiometers (SLSTRs).

Applications of satellite data to the studies of agricultural meteorology, 2: Relationship between air temperature and surface temperature measured by infrared thermal radiometer

International Nuclear Information System (INIS)

Horiguchi, I.; Tani, H.; Morikawa, S.

1985-01-01

Experiments were performed in order to establish interpretation keys for estimation of air temperature from satellite IR data. Field measurements were carried out over four kinds of land surfaces including seven different field crops on the university campus at Sapporo. The air temperature was compared with the surface temperature measured by infrared thermal radiometer (National ER2007, 8.5-12.5μm) and, also with other meteorological parameters (solar radiation, humidity and wind speed). Also perpendicular vegetation index (PVI) was measured to know vegetation density of lands by ho radio-spectralmeter (Figs. 1 & 2). Table 1 summarizes the measurements taken in these experiments.The correlation coefficients between air temperature and other meteorological parameters for each area are shown in Table 2. The best correlation coefficient for total data was obtained with surface temperature, and it suggests the possibility that air temperature may be estimated by satellite IR data since they are related to earth surface temperatures.Further analyses were done between air temperature and surface temperature measured with thermal infrared radiometer.The following conclusions may be drawn:(1) Air temperature from meteorological site was well correlated to surface temperature of lands that were covered with dense plant and water, for example, grass land, paddy field and rye field (Table 2).(2) The correlation coefficients and the regression equations on grass land, paddy field and rye field were almost the same (Fig. 3). The mean correlation coefficient for these three lands was 0.88 and the regression equation is given in Eq. (2).(3) There was good correlation on bare soil land also, but had large variations (Fig. 3).(4) The correlations on crop fields depend on the density of plant cover. Good correlation is obtained on dense vegetative fields.(5) Small variations about correlation coefficients were obtained for the time of day (Table 3).(6) On the other hand, large

Estimation of biogenic emissions with satellite-derived land use and land cover data for air quality modeling of Houston-Galveston ozone nonattainment area.

Science.gov (United States)

Byun, Daewon W; Kim, Soontae; Czader, Beata; Nowak, David; Stetson, Stephen; Estes, Mark

2005-06-01

The Houston-Galveston Area (HGA) is one of the most severe ozone non-attainment regions in the US. To study the effectiveness of controlling anthropogenic emissions to mitigate regional ozone nonattainment problems, it is necessary to utilize adequate datasets describing the environmental conditions that influence the photochemical reactivity of the ambient atmosphere. Compared to the anthropogenic emissions from point and mobile sources, there are large uncertainties in the locations and amounts of biogenic emissions. For regional air quality modeling applications, biogenic emissions are not directly measured but are usually estimated with meteorological data such as photo-synthetically active solar radiation, surface temperature, land type, and vegetation database. In this paper, we characterize these meteorological input parameters and two different land use land cover datasets available for HGA: the conventional biogenic vegetation/land use data and satellite-derived high-resolution land cover data. We describe the procedures used for the estimation of biogenic emissions with the satellite derived land cover data and leaf mass density information. Air quality model simulations were performed using both the original and the new biogenic emissions estimates. The results showed that there were considerable uncertainties in biogenic emissions inputs. Subsequently, ozone predictions were affected up to 10 ppb, but the magnitudes and locations of peak ozone varied each day depending on the upwind or downwind positions of the biogenic emission sources relative to the anthropogenic NOx and VOC sources. Although the assessment had limitations such as heterogeneity in the spatial resolutions, the study highlighted the significance of biogenic emissions uncertainty on air quality predictions. However, the study did not allow extrapolation of the directional changes in air quality corresponding to the changes in LULC because the two datasets were based on vastly different

Large-scale Validation of AMIP II Land-surface Simulations: Preliminary Results for Ten Models

Energy Technology Data Exchange (ETDEWEB)

Phillips, T J; Henderson-Sellers, A; Irannejad, P; McGuffie, K; Zhang, H

2005-12-01

This report summarizes initial findings of a large-scale validation of the land-surface simulations of ten atmospheric general circulation models that are entries in phase II of the Atmospheric Model Intercomparison Project (AMIP II). This validation is conducted by AMIP Diagnostic Subproject 12 on Land-surface Processes and Parameterizations, which is focusing on putative relationships between the continental climate simulations and the associated models' land-surface schemes. The selected models typify the diversity of representations of land-surface climate that are currently implemented by the global modeling community. The current dearth of global-scale terrestrial observations makes exacting validation of AMIP II continental simulations impractical. Thus, selected land-surface processes of the models are compared with several alternative validation data sets, which include merged in-situ/satellite products, climate reanalyses, and off-line simulations of land-surface schemes that are driven by observed forcings. The aggregated spatio-temporal differences between each simulated process and a chosen reference data set then are quantified by means of root-mean-square error statistics; the differences among alternative validation data sets are similarly quantified as an estimate of the current observational uncertainty in the selected land-surface process. Examples of these metrics are displayed for land-surface air temperature, precipitation, and the latent and sensible heat fluxes. It is found that the simulations of surface air temperature, when aggregated over all land and seasons, agree most closely with the chosen reference data, while the simulations of precipitation agree least. In the latter case, there also is considerable inter-model scatter in the error statistics, with the reanalyses estimates of precipitation resembling the AMIP II simulations more than to the chosen reference data. In aggregate, the simulations of land-surface latent and

The development of a land use inventory for regional planning using satellite imagery

Science.gov (United States)

Hessling, A. H.; Mara, T. G.

1975-01-01

Water quality planning in Ohio, Kentucky, and Indiana is reviewed in terms of use of land use data and satellite imagery. A land use inventory applicable to water quality planning and developed through computer processing of LANDSAT-1 imagery is described.

Land Surface Verification Toolkit (LVT) - A Generalized Framework for Land Surface Model Evaluation

Science.gov (United States)

Kumar, Sujay V.; Peters-Lidard, Christa D.; Santanello, Joseph; Harrison, Ken; Liu, Yuqiong; Shaw, Michael

2011-01-01

Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

Land surface Verification Toolkit (LVT) - a generalized framework for land surface model evaluation

Science.gov (United States)

Kumar, S. V.; Peters-Lidard, C. D.; Santanello, J.; Harrison, K.; Liu, Y.; Shaw, M.

2012-06-01

Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it supports hydrological data products from non-LIS environments as well. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

Estimation of land surface temperature based on satellite image data. Paper no. IGEC-1-117

International Nuclear Information System (INIS)

Torii, S.; Yano, T.; Iino, N.

2005-01-01

The aim of the present study is to predict a ground surface temperature (GST) of Kagoshima Prefecture in Kyushu Island, Japan, by using LANDSAT-5/TM and NOAA-11/AVHRR digital data. Image processing procedure is employed to aid in evaluating GST, in which the thermal images of AVHRR bands 4 and 5 are analysed. Emphasis is placed on the prediction accuracy of the existing atmospheric correlation equations taking the atmospheric attenuation effect into account, which are proposed by Tamba et. al., Bates and Diaz, and Sakkaida and Kawamura. It is disclosed that (I) the factor of the zenith angle need to be taken into account when the accurate GST is evaluated by using the atmospheric correction equations, (ii) that of Sakaida and Kawamura has better accuracy, (iii) LANDSAT-5/TM data contains a very high resolution level of the land, the estimated land surface temperature is higher than the measured value, and (iv) improved accuracy of the surface temperature is achieved if LANDSAT-5/TM data are used together with NOAA-11/AVHRR. (author)

Generation and Evaluation of a Global Land Surface Phenology Product from Suomi-NPP VIIRS Observations

Science.gov (United States)

Zhang, X.; Liu, L.; Yan, D.; Moon, M.; Liu, Y.; Henebry, G. M.; Friedl, M. A.; Schaaf, C.

2017-12-01

Land surface phenology (LSP) datasets have been produced from a variety of coarse spatial resolution satellite observations at both regional and global scales and spanning different time periods since 1982. However, the LSP product generated from NASA's MODerate Resolution Imaging Spectroradiometer (MODIS) data at a spatial resolution of 500m, which is termed Land Cover Dynamics (MCD12Q2), is the only global product operationally produced and freely accessible at annual time steps from 2001. Because MODIS instrument is aging and will be replaced by the Visible Infrared Imaging Radiometer Suite (VIIRS), this research focuses on the generation and evaluation of a global LSP product from Suomi-NPP VIIRS time series observations that provide continuity with the MCD12Q2 product. Specifically, we generate 500m VIIRS global LSP data using daily VIIRS Nadir BRDF (bidirectional reflectance distribution function)-Adjusted reflectances (NBAR) in combination with land surface temperature, snow cover, and land cover type as inputs. The product provides twelve phenological metrics (seven phenological dates and five phenological greenness magnitudes), along with six quality metrics characterizing the confidence and quality associated with phenology retrievals at each pixel. In this paper, we describe the input data and algorithms used to produce this new product, and investigate the impact of VIIRS data time series quality on phenology detections across various climate regimes and ecosystems. As part of our analysis, the VIIRS LSP is evaluated using PhenoCam imagery in North America and Asia, and using higher spatial resolution satellite observations from Landsat 8 over an agricultural area in the central USA. We also explore the impact of high frequency cloud cover on the VIIRS LSP product by comparing with phenology detected from the Advanced Himawari Imager (AHI) onboard Himawari-8. AHI is a new geostationary sensor that observes land surface every 10 minutes, which increases

Use of geostationary satellite imagery in optical and thermal bands for the estimation of soil moisture status and land evapotranspiration

Science.gov (United States)

Ghilain, N.; Arboleda, A.; Gellens-Meulenberghs, F.

2009-04-01

For water and agricultural management, there is an increasing demand to monitor the soil water status and the land evapotranspiration. In the framework of the LSA-SAF project (http://landsaf.meteo.pt), we are developing an energy balance model forced by remote sensing products, i.e. radiation components and vegetation parameters, to monitor in quasi real-time the evapotranspiration rate over land (Gellens-Meulenberghs et al, 2007; Ghilain et al, 2008). The model is applied over the full MSG disk, i.e. including Europe and Africa. Meteorological forcing, as well as the soil moisture status, is provided by the forecasts of the ECMWF model. Since soil moisture is computed by a forecast model not dedicated to the monitoring of the soil water status, inadequate soil moisture input can occur, and can cause large effects on evapotranspiration rates, especially over semi-arid or arid regions. In these regions, a remotely sensed-based method for the soil moisture retrieval can therefore be preferable, to avoid too strong dependency in ECMWF model estimates. Among different strategies, remote sensing offers the advantage of monitoring large areas. Empirical methods of soil moisture assessment exist using remotely sensed derived variables either from the microwave bands or from the thermal bands. Mainly polar orbiters are used for this purpose, and little attention has been paid to the new possibilities offered by geosynchronous satellites. In this contribution, images of the SEVIRI instrument on board of MSG geosynchronous satellites are used. Dedicated operational algorithms were developed for the LSA-SAF project and now deliver images of land surface temperature (LST) every 15-minutes (Trigo et al, 2008) and vegetations indices (leaf area index, LAI; fraction of vegetation cover, FVC; fraction of absorbed photosynthetically active radiation, FAPAR) every day (Garcia-Haro et al, 2005) over Africa and Europe. One advantage of using products derived from geostationary

Determination of atmospheric aerosol properties over land using satellite measurements

NARCIS (Netherlands)

Kokhanovsky, A.A.; Leeuw, G. de

2009-01-01

Mostly, aerosol properties are poorly understood because the aerosol properties are very sparse. The first workshop on the determination of atmospheric aerosol properties over land using satellite measurements is convened in Bremen, Germany. In this workshop, the topics of discussions included a

Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm

Directory of Open Access Journals (Sweden)

Offer Rozenstein

2014-03-01

Full Text Available Land surface temperature (LST is one of the most important variables measured by satellite remote sensing. Public domain data are available from the newly operational Landsat-8 Thermal Infrared Sensor (TIRS. This paper presents an adjustment of the split window algorithm (SWA for TIRS that uses atmospheric transmittance and land surface emissivity (LSE as inputs. Various alternatives for estimating these SWA inputs are reviewed, and a sensitivity analysis of the SWA to misestimating the input parameters is performed. The accuracy of the current development was assessed using simulated Modtran data. The root mean square error (RMSE of the simulated LST was calculated as 0.93 °C. This SWA development is leading to progress in the determination of LST by Landsat-8 TIRS.

Developing first time-series of land surface temperature from AATSR with uncertainty estimates

Science.gov (United States)

Ghent, Darren; Remedios, John

2013-04-01

Land surface temperature (LST) is the radiative skin temperature of the land, and is one of the key parameters in the physics of land-surface processes on regional and global scales. Earth Observation satellites provide the opportunity to obtain global coverage of LST approximately every 3 days or less. One such source of satellite retrieved LST has been the Advanced Along-Track Scanning Radiometer (AATSR); with LST retrieval being implemented in the AATSR Instrument Processing Facility in March 2004. Here we present first regional and global time-series of LST data from AATSR with estimates of uncertainty. Mean changes in temperature over the last decade will be discussed along with regional patterns. Although time-series across all three ATSR missions have previously been constructed (Kogler et al., 2012), the use of low resolution auxiliary data in the retrieval algorithm and non-optimal cloud masking resulted in time-series artefacts. As such, considerable ESA supported development has been carried out on the AATSR data to address these concerns. This includes the integration of high resolution auxiliary data into the retrieval algorithm and subsequent generation of coefficients and tuning parameters, plus the development of an improved cloud mask based on the simulation of clear sky conditions from radiance transfer modelling (Ghent et al., in prep.). Any inference on this LST record is though of limited value without the accompaniment of an uncertainty estimate; wherein the Joint Committee for Guides in Metrology quote an uncertainty as "a parameter associated with the result of a measurement that characterizes the dispersion of the values that could reasonably be attributed to the measurand that is the value of the particular quantity to be measured". Furthermore, pixel level uncertainty fields are a mandatory requirement in the on-going preparation of the LST product for the upcoming Sea and Land Surface Temperature (SLSTR) instrument on-board Sentinel-3

Temporal and spatial assessments of minimum air temperature using satellite surface temperature measurements in Massachusetts, USA.

Science.gov (United States)

Kloog, Itai; Chudnovsky, Alexandra; Koutrakis, Petros; Schwartz, Joel

2012-08-15

Although meteorological stations provide accurate air temperature observations, their spatial coverage is limited and thus often insufficient for epidemiological studies. Satellite data expand spatial coverage, enhancing our ability to estimate near surface air temperature (Ta). However, the derivation of Ta from surface temperature (Ts) measured by satellites is far from being straightforward. In this study, we present a novel approach that incorporates land use regression, meteorological variables and spatial smoothing to first calibrate between Ts and Ta on a daily basis and then predict Ta for days when satellite Ts data were not available. We applied mixed regression models with daily random slopes to calibrate Moderate Resolution Imaging Spectroradiometer (MODIS) Ts data with monitored Ta measurements for 2003. Then, we used a generalized additive mixed model with spatial smoothing to estimate Ta in days with missing Ts. Out-of-sample tenfold cross-validation was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with available Ts and days without Ts observations (mean out-of-sample R(2)=0.946 and R(2)=0.941 respectively). Furthermore, based on the high quality predictions we investigated the spatial patterns of Ta within the study domain as they relate to urban vs. non-urban land uses. Copyright © 2012 Elsevier B.V. All rights reserved.

Land Surface Model Biases and their Impacts on the Assimilation of Snow-related Observations

Science.gov (United States)

Arsenault, K. R.; Kumar, S.; Hunter, S. M.; Aman, R.; Houser, P. R.; Toll, D.; Engman, T.; Nigro, J.

2007-12-01

Some recent snow modeling studies have employed a wide range of assimilation methods to incorporate snow cover or other snow-related observations into different hydrological or land surface models. These methods often include taking both model and observation biases into account throughout the model integration. This study focuses more on diagnosing the model biases and presenting their subsequent impacts on assimilating snow observations and modeled snowmelt processes. In this study, the land surface model, the Community Land Model (CLM), is used within the Land Information System (LIS) modeling framework to show how such biases impact the assimilation of MODIS snow cover observations. Alternative in-situ and satellite-based observations are used to help guide the CLM LSM in better predicting snowpack conditions and more realistic timing of snowmelt for a western US mountainous region. Also, MODIS snow cover observation biases will be discussed, and validation results will be provided. The issues faced with inserting or assimilating MODIS snow cover at moderate spatial resolutions (like 1km or less) will be addressed, and the impacts on CLM will be presented.

How you cannot find rain with changes in land surface temperature

Science.gov (United States)

Wanders, Niko

2017-04-01

Estimating precipitation from space-born sensors is valuable source of observation in poorly-gauged regions. For example, hydrological modelling and monitoring greatly benefits from the increased near-real time data availability for improved accuracy in the simulations of water resources. As is true for all satellite product, precipitation estimated from space are far from perfect and scientist have used many techniques to improve their accuracy. In this study, I tried to improve the space-born precipitation estimates by using remotely sensed soil moisture to observe sudden increases in soil wetness as a result of precipitation. After a month of massaging the data and applied methodology I realized that the gain was very marginal and I was drilling a dry hole. Driven by these disappointing results I tried some random other satellite products to see if they showed correlation with the precipitation signal. There I found a causality that I had not expected at the start of this study, linking land surface temperature to precipitation. It seemed that using changes in land surface temperature strongly correlated with precipitation totals, driven by a cooling of the soil as a result of increase wetness. This link could not only be modelled, but more surprisingly it could be observed from space and used to improve the satellite precipitation estimates. The reduction in the precipitation uncertainty was far better than for any of the three soil moisture products, contrary to what one might expect. This was far from the anticipated result but it showed me that sometimes you should think out of the box and not only use observations for their intended purpose. This experience has motivated me to not only use the obvious observation or method and try techniques and methods from other disciplines to see if we can improve our understanding of the hydrological cycle.

Wind Atlas for the Gulf of Suez Satellite Imagery and Analyses

DEFF Research Database (Denmark)

Hasager, Charlotte Bay

(SAR) data derived from the European Remote Sensing Satellite (ERS) have been used to make wind speed maps for the Gulf of Suez. 2. “Land cover from Landsat TM imagery”. Landsat Thematic Mapper(TM) data have been used to establish true- and false-colour land cover maps, as well as land cover...... classification maps. 3. “Reporting on satellite information for the Wind Atlas for Egypt”. Along-Track Scanning Radiometer (ATSR) data from the European Remote Sensing Satellite (ERS) have been used to map the sea- and land-surface temperatures and albedos....

Satellite information for wind energy applications

DEFF Research Database (Denmark)

Nielsen, M.; Astrup, Poul; Hasager, Charlotte Bay

2004-01-01

An introduction to satellite information relevant for wind energy applications is given. It includes digital elevation model (DEM) data based on satellite observations. The Shuttle Radar Topography Mission (SRTM) is useful for regional scale wind resourcestudies. Comparison results from complex...... terrain in Spain and flat terrain in Denmark are found to be acceptable for both sites. Also land cover type information can be retrieved from satellite observations. Land cover type maps have to be combined withroughness data from field observation or literature values. Land cover type maps constitute...... an aid to map larger regions within shorter time. Field site observations of obstacles and hedges are still necessary. The raster-based map information from DEMand land cover maps can be converted for use in WASP. For offshore locations it is possible to estimate the wind resources based on ocean surface...

Use of Landsat Land Surface Temperature and Vegetation Indices for Monitoring Drought in the Salt Lake Basin Area, Turkey

Directory of Open Access Journals (Sweden)

Osman Orhan

2014-01-01

Full Text Available The main purpose of this paper is to investigate multitemporal land surface temperature (LST changes by using satellite remote sensing data. The study included a real-time field work performed during the overpass of Landsat-5 satellite on 21/08/2011 over Salt Lake, Turkey. Normalized vegetation index (NDVI, vegetation condition index (VCI, and temperature vegetation index (TVX were used for evaluating drought impact over the region between 1984 and 2011. In the image processing step, geometric and radiometric correction procedures were conducted to make satellite remote sensing data comparable with in situ measurements carried out using thermal infrared thermometer supported by hand-held GPS. The results showed that real-time ground and satellite remote sensing data were in good agreement with correlation coefficient (R2 values of 0.90. The remotely sensed and treated satellite images and resulting thematic indices maps showed that dramatic land surface temperature changes occurred (about 2∘C in the Salt Lake Basin area during the 28-year period (1984–2011. Analysis of air temperature data also showed increases at a rate of 1.5–2∘C during the same period. Intensification of irrigated agriculture particularly in the southern basin was also detected. The use of water supplies, especially groundwater, should be controlled considering particularly summer drought impacts on the basin.

Mapping the global land surface using 1 km AVHRR data

Science.gov (United States)

Lauer, D.T.; Eidenshink, J.C.

1998-01-01

The scientific requirements for mapping the global land surface using 1 km advanced very high resolution radiometer (AVHRR) data have been set forth by the U.S. Global Change Research Program; the International Geosphere Biosphere Programme (IGBP); The United Nations; the National Oceanic and Atmospheric Administration (NOAA); the Committee on Earth Observations Satellites; and the National Aeronautics and Space Administration (NASA) mission to planet Earth (MTPE) program. Mapping the global land surface using 1 km AVHRR data is an international effort to acquire, archive, process, and distribute 1 km AVHRR data to meet the needs of the international science community. A network of AVHRR receiving stations, along with data recorded by NOAA, has been acquiring daily global land coverage since April 1, 1992. A data set of over 70,000 AVHRR images is archived and distributed by the United States Geological Survey (USGS) EROS Data Center, and the European Space Agency. Under the guidance of the IGBP, processing standards have been developed for calibration, atmospheric correction, geometric registration, and the production of global 10-day maximum normalized difference vegetation index (NDVI) composites. The major uses of the composites are for the study of surface vegetation condition, mapping land cover, and deriving biophysical characteristics of terrestrial ecosystems. A time-series of 54 10-day global vegetation index composites for the period of April 1, 1992 through September 1993 has been produced. The production of a time-series of 33 10-day global vegetation index composites using NOAA-14 data for the period of February 1, 1995 through December 31, 1995 is underway. The data products are available from the USGS, in cooperation with NASA's MTPE program and other international organizations.

Propagation considerations in land mobile satellite transmission

Science.gov (United States)

Vogel, W. J.; Smith, E. K.

1985-01-01

It appears likely that the Land Mobile Satellite Services (LMSS) will be authorized by the FCC for operation in the 800 to 900 MHz (UHF) and possibly near 1500 MHz (L-band). Propagation problems are clearly an important factor in the effectiveness of this service, but useful measurements are few, and produced contradictory interpretations. A first order overview of existing measurements is presented with particular attention to the first two NASA balloon to mobile vehicle propagation experiments. Some physical insight into the interpretation of propagation effects in LMSS transmissions is provided.

Estimating Daily Global Evapotranspiration Using Penman–Monteith Equation and Remotely Sensed Land Surface Temperature

Directory of Open Access Journals (Sweden)

Roozbeh Raoufi

2017-11-01

Full Text Available Daily evapotranspiration (ET is modeled globally for the period 2000–2013 based on the Penman–Monteith equation with radiation and vapor pressures derived using remotely sensed Land Surface Temperature (LST from the MODerate resolution Imaging Spectroradiometer (MODIS on the Aqua and Terra satellites. The ET for a given land area is based on four surface conditions: wet/dry and vegetated/non-vegetated. For each, the ET resistance terms are based on land cover, leaf area index (LAI and literature values. The vegetated/non-vegetated fractions of the land surface are estimated using land cover, LAI, a simplified version of the Beer–Lambert law for describing light transition through vegetation and newly derived light extension coefficients for each MODIS land cover type. The wet/dry fractions of the land surface are nonlinear functions of LST derived humidity calibrated using in-situ ET measurements. Results are compared to in-situ measurements (average of the root mean squared errors and mean absolute errors for 39 sites are 0.81 mm day−1 and 0.59 mm day−1, respectively and the MODIS ET product, MOD16, (mean bias during 2001–2013 is −0.2 mm day−1. Although the mean global difference between MOD16 and ET estimates is only 0.2 mm day−1, local temperature derived vapor pressures are the likely contributor to differences, especially in energy and water limited regions. The intended application for the presented model is simulating ET based on long-term climate forecasts (e.g., using only minimum, maximum and mean daily or monthly temperatures.

Relation between seasonally detrended shortwave infrared reflectance data and land surface moisture in semi-arid Sahel

DEFF Research Database (Denmark)

Olsen, Jørgen Lundegaard; Ceccato, Pietro; Proud, Simon Richard

2013-01-01

in vegetation moisture status, and is compared to detrended time series of the Normalized Difference Vegetation Index (NDVI). It was found that when plant available water is low, the SIWSI anomalies increase over time, while the NDVI anomalies decrease over time, but less systematically. Therefore SIWSI may......In the Sudano-Sahelian areas of Africa droughts can have serious impacts on natural resources, and therefore land surface moisture is an important factor. Insufficient conventional sites for monitoring land surface moisture make the use of Earth Observation data for this purpose a key issue...... Second Generation (MSG) satellite. We focused on responses in surface reflectance to soil- and surface moisture for bare soil and early to mid- growing season. A method for implementing detrended time series of the Shortwave Infrared Water Stress Index (SIWSI) is examined for detecting variations...

Investigating Land Surface Temperature Changes Using Landsat Data in Konya, Turkey

Directory of Open Access Journals (Sweden)

O. Orhan

2016-06-01

Full Text Available The main purpose of this paper is to investigate multi-temporal land surface temperature (LST and Normalized Difference Vegetation Index (NDVI changes of Konya in Turkey using remotely sensed data. Konya is located in the semi-arid central Anatolian region of Turkey and hosts many important wetland sites including Salt Lake. Six images taken by Landsat-5 TM and Landsat 8- OLI satellites were used as the basic data source. These raw images were taken in 1984, 2011 and 2014 intended as long-term and short-term. Firstly, those raw images was corrected radiometric and geometrically within the scope of project. Three mosaic images were obtained by using the full-frame images of Landsat-5 TM / 8- OLI which had been already transformed comparison each other. Then, Land Surface Temperature (LST, Normalized Difference Vegetation Index (NDVI maps have been produced to determine the dimension of the drought. The obtained results showed that surface temperature rates in the basin increased about 5°C between 1984 and 2014 as long periods, increased about 2-3°C between 2011and 2014 as short periods. Meteorological data supports the increase in temperature.

Interactions of planetary magnetospheres with icy satellite surfaces

International Nuclear Information System (INIS)

Cheng, A.F.; Haff, P.K.; Johnson, R.E.; Lanzerotti, L.J.

1986-01-01

When natural satellites and ring particles are embedded within magnetospheric plasmas, the charged particles interact with the surfaces of these solid bodies. These interactions have important implications for the surface, the atmosphere of the parent body, and the magnetosphere as a whole. Significant erosion of the surface by sputtering, as well as redeposition of sputter ejecta, can occur over geologic time. The surface can also be chemically modified. Sputter ejecta can make important contributions to the atmosphere; sputtering provides a lower limit to the atmospheric column density even for arbitrarily cold satellite surfaces. Sputter ejecta escaping from the parent body can form extensive neutral clouds within the magnetosphere. Ionization and dissociation within these neutral clouds can be dominant sources of low-energy plasma. The importance of these processes is discussed for the satellites and magnetospheres of Jupiter, Saturn and Uranus

Derivation of Land Surface Albedo at High Resolution by Combining HJ-1A/B Reflectance Observations with MODIS BRDF Products

Directory of Open Access Journals (Sweden)

Bo Gao

2014-09-01

Full Text Available Land surface albedo is an essential parameter for monitoring global/regional climate and land surface energy balance. Although many studies have been conducted on global or regional land surface albedo using various remote sensing data over the past few decades, land surface albedo product with a high spatio–temporal resolution is currently very scarce. This paper proposes a method for deriving land surface albedo with a high spatio–temporal resolution (space: 30 m and time: 2–4 days. The proposed method works by combining the land surface reflectance data at 30 m spatial resolution obtained from the charge-coupled devices in the Huanjing-1A and -1B (HJ-1A/B satellites with the Moderate Resolution Imaging Spectroradiometer (MODIS land surface bidirectional reflectance distribution function (BRDF parameters product (MCD43A1, which is at a spatial resolution of 500 m. First, the land surface BRDF parameters for HJ-1A/B land surface reflectance with a spatial–temporal resolutions of 30 m and 2–4 day are calculated on the basis of the prior knowledge from the MODIS BRDF product; then, the calculated high resolution BRDF parameters are integrated over the illuminating/viewing hemisphere to produce the white- and black-sky albedos at 30 m resolution. These results form the basis for the final land surface albedo derivation by accounting for the proportion of direct and diffuse solar radiation arriving at the ground. The albedo retrieved by this novel method is compared with MODIS land surface albedo products, as well as with ground measurements. The results show that the derived land surface albedo during the growing season of 2012 generally achieved a mean absolute accuracy of ±0.044, and a root mean square error of 0.039, confirming the effectiveness of the newly proposed method.

Asian Dust Weather Categorization with Satellite and Surface Observations

Science.gov (United States)

Lin, Tang-Huang; Hsu, N. Christina; Tsay, Si-Chee; Huang, Shih-Jen

2011-01-01

This study categorizes various dust weather types by means of satellite remote sensing over central Asia. Airborne dust particles can be identified by satellite remote sensing because of the different optical properties exhibited by coarse and fine particles (i.e. varying particle sizes). If a correlation can be established between the retrieved aerosol optical properties and surface visibility, the intensity of dust weather can be more effectively and consistently discerned using satellite rather than surface observations. In this article, datasets consisting of collocated products from Moderate Resolution Imaging Spectroradiometer Aqua and surface measurements are analysed. The results indicate an exponential relationship between the surface visibility and the satellite-retrieved aerosol optical depth, which is subsequently used to categorize the dust weather. The satellite-derived spatial frequency distributions in the dust weather types are consistent with China s weather station reports during 2003, indicating that dust weather classification using satellite data is highly feasible. Although the period during the springtime from 2004 to 2007 may be not sufficient for statistical significance, our results reveal an increasing tendency in both intensity and frequency of dust weather over central Asia during this time period.

The Application of Chinese High-Spatial Remote Sensing Satellite Image in Land Law Enforcement Information Extraction

Science.gov (United States)

Wang, N.; Yang, R.

2018-04-01

Chinese high -resolution (HR) remote sensing satellites have made huge leap in the past decade. Commercial satellite datasets, such as GF-1, GF-2 and ZY-3 images, the panchromatic images (PAN) resolution of them are 2 m, 1 m and 2.1 m and the multispectral images (MS) resolution are 8 m, 4 m, 5.8 m respectively have been emerged in recent years. Chinese HR satellite imagery has been free downloaded for public welfare purposes using. Local government began to employ more professional technician to improve traditional land management technology. This paper focused on analysing the actual requirements of the applications in government land law enforcement in Guangxi Autonomous Region. 66 counties in Guangxi Autonomous Region were selected for illegal land utilization spot extraction with fusion Chinese HR images. The procedure contains: A. Defines illegal land utilization spot type. B. Data collection, GF-1, GF-2, and ZY-3 datasets were acquired in the first half year of 2016 and other auxiliary data were collected in 2015. C. Batch process, HR images were collected for batch preprocessing through ENVI/IDL tool. D. Illegal land utilization spot extraction by visual interpretation. E. Obtaining attribute data with ArcGIS Geoprocessor (GP) model. F. Thematic mapping and surveying. Through analysing 42 counties results, law enforcement officials found 1092 illegal land using spots and 16 suspicious illegal mining spots. The results show that Chinese HR satellite images have great potential for feature information extraction and the processing procedure appears robust.

Advances in land modeling of KIAPS based on the Noah Land Surface Model

Science.gov (United States)

Koo, Myung-Seo; Baek, Sunghye; Seol, Kyung-Hee; Cho, Kyoungmi

2017-08-01

As of 2013, the Noah Land Surface Model (LSM) version 2.7.1 was implemented in a new global model being developed at the Korea Institute of Atmospheric Prediction Systems (KIAPS). This land surface scheme is further refined in two aspects, by adding new physical processes and by updating surface input parameters. Thus, the treatment of glacier land, sea ice, and snow cover are addressed more realistically. Inconsistencies in the amount of absorbed solar flux at ground level by the land surface and radiative processes are rectified. In addition, new parameters are available by using 1-km land cover data, which had usually not been possible at a global scale. Land surface albedo/emissivity climatology is newly created using Moderate-Resolution Imaging Spectroradiometer (MODIS) satellitebased data and adjusted parameterization. These updates have been applied to the KIAPS-developed model and generally provide a positive impact on near-surface weather forecasting.

Northern Everglades, Florida, satellite image map

Science.gov (United States)

Thomas, Jean-Claude; Jones, John W.

2002-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program with support from the Everglades National Park. The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

Linking Satellite Derived Land Surface Temperature with Cholera: A Case Study for South Sudan

Science.gov (United States)

Aldaach, H. S. V.; Jutla, A.; Akanda, A. S.; Colwell, R. R.

2014-12-01

A sudden onset of cholera in South Sudan, in April 2014 in Northern Bari in Juba town resulted in more than 400 cholera cases after four weeks of initial outbreak with a case of fatality rate of CFR 5.4%. The total number of reported cholera cases for the period of April to July, 2014 were 5,141 including 114 deaths. With the limited efficacy of cholera vaccines, it is necessary to develop mechanisms to predict cholera occurrence and thereafter devise intervention strategies for mitigating impacts of the disease. Hydroclimatic processes, primarily precipitation and air temperature are related to epidemic and episodic outbreak of cholera. However, due to coarse resolution of both datasets, it is not possible to precisely locate the geographical location of disease. Here, using Land Surface Temperature (LST) from MODIS sensors, we have developed an algorithm to identify regions susceptible for cholera. Conditions for occurrence of cholera were detectable at least one month in advance in South Sudan and were statistically sensitive to hydroclimatic anomalies of land surface and air temperature, and precipitation. Our results indicate significant spatial and temporal averaging required to infer usable information from LST over South Sudan. Preliminary results that geographically location of cholera outbreak was identifiable within 1km resolution of the LST data.

Understanding the life cycle surface land requirements of natural gas-fired electricity

Science.gov (United States)

Jordaan, Sarah M.; Heath, Garvin A.; Macknick, Jordan; Bush, Brian W.; Mohammadi, Ehsan; Ben-Horin, Dan; Urrea, Victoria; Marceau, Danielle

2017-10-01

The surface land use of fossil fuel acquisition and utilization has not been well characterized, inhibiting consistent comparisons of different electricity generation technologies. Here we present a method for robust estimation of the life cycle land use of electricity generated from natural gas through a case study that includes inventories of infrastructure, satellite imagery and well-level production. Approximately 500 sites in the Barnett Shale of Texas were sampled across five life cycle stages (production, gathering, processing, transmission and power generation). Total land use (0.62 m2 MWh-1, 95% confidence intervals ±0.01 m2 MWh-1) was dominated by midstream infrastructure, particularly pipelines (74%). Our results were sensitive to power plant heat rate (85-190% of the base case), facility lifetime (89-169%), number of wells per site (16-100%), well lifetime (92-154%) and pipeline right of way (58-142%). When replicated for other gas-producing regions and different fuels, our approach offers a route to enable empirically grounded comparisons of the land footprint of energy choices.

Impact of Soil Moisture Assimilation on Land Surface Model Spin-Up and Coupled LandAtmosphere Prediction

Science.gov (United States)

Santanello, Joseph A., Jr.; Kumar, Sujay V.; Peters-Lidard, Christa D.; Lawston, P.

2016-01-01

Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASAs Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land-atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS-WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spin-up can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux- PBL-ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research.

A data mining approach for sharpening satellite thermal imagery over land

Science.gov (United States)

Thermal infrared (TIR) imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes which are at significant...

Land use change detection based on multi-date imagery from different satellite sensor systems

Science.gov (United States)

Stow, Douglas A.; Collins, Doretta; Mckinsey, David

1990-01-01

An empirical study is conducted to assess the accuracy of land use change detection using satellite image data acquired ten years apart by sensors with differing spatial resolutions. The primary goals of the investigation were to (1) compare standard change detection methods applied to image data of varying spatial resolution, (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice versa in the registration process, (3) determine if Landsat/Thermatic Mapper or SPOT/High Resolution Visible multispectral data provide more accurate detection of land use changes when registered to historical Landsat/MSS data. It is concluded that image ratioing of multisensor, multidate satellite data produced higher change detection accuracies than did principal components analysis, and that it is useful as a land use change enhancement method.

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing.

Science.gov (United States)

Midekisa, Alemayehu; Holl, Felix; Savory, David J; Andrade-Pacheco, Ricardo; Gething, Peter W; Bennett, Adam; Sturrock, Hugh J W

2017-01-01

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth's land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

Analysis of streamflow response to land use and land cover changes using satellite data and hydrological modelling: case study of Dinder and Rahad tributaries of the Blue Nile (Ethiopia-Sudan)

Science.gov (United States)

Hassaballah, Khalid; Mohamed, Yasir; Uhlenbrook, Stefan; Biro, Khalid

2017-10-01

Understanding the land use and land cover changes (LULCCs) and their implication on surface hydrology of the Dinder and Rahad basins (D&R, approximately 77 504 km2) is vital for the management and utilization of water resources in the basins. Although there are many studies on LULCC in the Blue Nile Basin, specific studies on LULCC in the D&R are still missing. Hence, its impact on streamflow is unknown. The objective of this paper is to understand the LULCC in the Dinder and Rahad and its implications on streamflow response using satellite data and hydrological modelling. The hydrological model has been derived by different sets of land use and land cover maps from 1972, 1986, 1998 and 2011. Catchment topography, land cover and soil maps are derived from satellite images and serve to estimate model parameters. Results of LULCC detection between 1972 and 2011 indicate a significant decrease in woodland and an increase in cropland. Woodland decreased from 42 to 14 % and from 35 to 14 % for Dinder and Rahad, respectively. Cropland increased from 14 to 47 % and from 18 to 68 % in Dinder and Rahad, respectively. The model results indicate that streamflow is affected by LULCC in both the Dinder and the Rahad rivers. The effect of LULCC on streamflow is significant during 1986 and 2011. This could be attributed to the severe drought during the mid-1980s and the recent large expansion in cropland.

Harmonizing estimates of forest land area from national-level forest inventory and satellite imagery

Science.gov (United States)

Bonnie Ruefenacht; Mark D. Nelson; Mark Finco

2009-01-01

Estimates of forest land area are derived both from national-level forest inventories and satellite image-based map products. These estimates can differ substantially within subregional extents (e.g., states or provinces) primarily due to differences in definitions of forest land between inventory- and image-based approaches. We present a geospatial modeling approach...

Estimation of land surface temperature of Kaduna metropolis ...

African Journals Online (AJOL)

Estimation of land surface temperature of Kaduna metropolis, Nigeria using landsat images. Isa Zaharaddeen, Ibrahim I. Baba, Ayuba Zachariah. Abstract. Understanding the spatial variation of Land Surface Temperature (LST), will be helpful in urban micro climate studies. This study estimates the land surface temperature ...

Reconnoitering the effect of shallow groundwater on land surface temperature and surface energy balance using MODIS and SEBS

Directory of Open Access Journals (Sweden)

F. Alkhaier

2012-07-01

Full Text Available The possibility of observing shallow groundwater depth and areal extent using satellite measurements can support groundwater models and vast irrigation systems management. Moreover, these measurements can help to include the effect of shallow groundwater on surface energy balance within land surface models and climate studies, which broadens the methods that yield more reliable and informative results. To examine the capacity of MODIS in detecting the effect of shallow groundwater on land surface temperature and the surface energy balance in an area within Al-Balikh River basin in northern Syria, we studied the interrelationship between in-situ measured water table depths and land surface temperatures measured by MODIS. We, also, used the Surface Energy Balance System (SEBS to calculate surface energy fluxes, evaporative fraction and daily evaporation, and inspected their relationships with water table depths. We found out that the daytime temperature increased while the nighttime temperature decreased when the depth of the water table increased. And, when the water table depth increased, net radiation, latent and ground heat fluxes, evaporative fraction and daily evaporation decreased, while sensible heat flux increased. This concords with the findings of a companion paper (Alkhaier et al., 2012. The observed clear relationships were the result of meeting both conditions that were concluded in the companion paper, i.e. high potential evaporation and big contrast in day-night temperature. Moreover, the prevailing conditions in this study area helped SEBS to yield accurate estimates. Under bare soil conditions and under the prevailing weather conditions, we conclude that MODIS is suitable for detecting the effect of shallow groundwater because it has proper imaging times and adequate sensor accuracy; nevertheless, its coarse spatial resolution is disadvantageous.

The Effect of Landing Surface on the Plantar Kinetics of Chinese Paratroopers Using Half-Squat Landing

Science.gov (United States)

Li, Yi; Wu, Ji; Zheng, Chao; Huang, Rong Rong; Na, Yuhong; Yang, Fan; Wang, Zengshun; Wu, Di

2013-01-01

The objective of the study was to determine the effect of landing surface on plantar kinetics during a half-squat landing. Twenty male elite paratroopers with formal parachute landing training and over 2 years of parachute jumping experience were recruited. The subjects wore parachuting boots in which pressure sensing insoles were placed. Each subject was instructed to jump off a platform with a height of 60 cm, and land on either a hard or soft surface in a half-squat posture. Outcome measures were maximal plantar pressure, time to maximal plantar pressure (T-MPP), and pressure-time integral (PTI) upon landing on 10 plantar regions. Compared to a soft surface, hard surface produced higher maximal plantar pressure in the 1st to 4th metatarsal and mid-foot regions, but lower maximal plantar pressure in the 5th metatarsal region. Shorter T- MPP was found during hard surface landing in the 1st and 2nd metatarsal and medial rear foot. Landing on a hard surface landing resulted in a lower PTI than a soft surface in the 1stphalangeal region. For Chinese paratroopers, specific foot prosthesis should be designed to protect the1st to 4thmetatarsal region for hard surface landing, and the 1stphalangeal and 5thmetatarsal region for soft surface landing. Key Points Understanding plantar kinetics during the half-squat landing used by Chinese paratroopers can assist in the design of protective footwear. Compared to landing on a soft surface, a hard surface produced higher maximal plantar pressure in the 1st to 4th metatarsal and mid-foot regions, but lower maximal plantar pressure in the 5th metatarsal region. A shorter time to maximal plantar pressure was found during a hard surface landing in the 1st and 2nd metatarsals and medial rear foot. Landing on a hard surface resulted in a lower pressure-time integral than landing on a soft surface in the 1st phalangeal region. For Chinese paratroopers, specific foot prosthesis should be designed to protect the 1st to 4th metatarsal

Upper-soil moisture inter-comparison from SMOS's products and land surface models over the Iberian Peninsula

Science.gov (United States)

Polcher, Jan; Barella-Ortiz, Anaïs; Aires, Filipe; Balsamo, Gianpaolo; Gelati, Emiliano; Rodríguez-Fernández, Nemesio

2015-04-01

Soil moisture is a key state variable of the hydrological cycle. It conditions runoff, infiltration and evaporation over continental surfaces, and is key for forecasting droughts and floods. It plays thus an important role in surface-atmosphere interactions. Surface Soil Moisture (SSM) can be measured by in situ measurements, by satellite observations or modelled using land surface models. As a complementary tool, data assimilation can be used to combine both modelling and satellite observations. The work presented here is an inter-comparison of retrieved and modelled SSM data, for the 2010 - 2012 period, over the Iberian Peninsula. The region has been chosen because its vegetation cover is not very dense and includes strong contrasts in the rainfall regimes and thus a diversity of behaviours for SSM. Furthermore this semi-arid region is strongly dependent on a good management of its water resources. Satellite observations correspond to the Soil Moisture and Ocean Salinity (SMOS) retrievals: the L2 product from an optimal interpolation retrieval, and 3 other products using Neural Network retrievals with different input information: SMOS time indexes, purely SMOS data, or addition of the European Advanced Scaterometer (ASCAT) backscattering, and the Moderate-Resolution Imaging Spectrometer (MODIS) surface temperature information. The modelled soil moistures have been taken from the ORCHIDEE (ORganising Carbon and Hydrology In Dynamic EcosystEms) and the HTESSEL (Hydrology-Tiled ECMWF Scheme for Surface Exchanges over Land) land surface models. Both models are forced with the same atmospheric conditions (as part of the Earth2Observe FP7 project) over the period but they represent the surface soil moisture with very different degrees of complexity. ORCHIDEE has 5 levels in the top 5 centimetres of soil while in HTESSEL this variable is part of the top soil moisture level. The two types of SMOS retrievals are compared to the model outputs in their spatial and temporal

Evaluation of MODIS Land Surface Temperature with In Situ Snow Surface Temperature from CREST-SAFE

Science.gov (United States)

Perez Diaz, C. L.; Lakhankar, T.; Romanov, P.; Munoz, J.; Khanbilvardi, R.; Yu, Y.

2016-12-01

This paper presents the procedure and results of a temperature-based validation approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) product provided by the National Aeronautics and Space Administration (NASA) Terra and Aqua Earth Observing System satellites using in situ LST observations recorded at the Cooperative Remote Sensing Science and Technology Center - Snow Analysis and Field Experiment (CREST-SAFE) during the years of 2013 (January-April) and 2014 (February-April). A total of 314 day and night clear-sky thermal images, acquired by the Terra and Aqua satellites, were processed and compared to ground-truth data from CREST-SAFE with a frequency of one measurement every 3 min. Additionally, this investigation incorporated supplementary analyses using meteorological CREST-SAFE in situ variables (i.e. wind speed, cloud cover, incoming solar radiation) to study their effects on in situ snow surface temperature (T-skin) and T-air. Furthermore, a single pixel (1km2) and several spatially averaged pixels were used for satellite LST validation by increasing the MODIS window size to 5x5, 9x9, and 25x25 windows for comparison. Several trends in the MODIS LST data were observed, including the underestimation of daytime values and nighttime values. Results indicate that, although all the data sets (Terra and Aqua, diurnal and nocturnal) showed high correlation with ground measurements, day values yielded slightly higher accuracy ( 1°C), both suggesting that MODIS LST retrievals are reliable for similar land cover classes and atmospheric conditions. Results from the CREST-SAFE in situ variables' analyses indicate that T-air is commonly higher than T-skin, and that a lack of cloud cover results in: lower T-skin and higher T-air minus T-skin difference (T-diff). Additionally, the study revealed that T-diff is inversely proportional to cloud cover, wind speed, and incoming solar radiation. Increasing the MODIS window size

Surface ages of mid-size saturnian satellites

Science.gov (United States)

Di Sisto, Romina P.; Zanardi, Macarena

2016-01-01

The observations of the surfaces of the mid-sized saturnian satellites made by Cassini-Huygens mission have shown a variety of features that allows study of the processes that took place and are taking place on those worlds. Research of the saturnian satellite surfaces has clear implications not only for Saturn's history and Saturn's surroundings, but also for the Solar System. Crater counting from high definition images is very important and could serve for the determination of the age of the surfaces. In a recent paper, we have calculated the production of craters on the mid-sized saturnian satellites by Centaur objects considering the current configuration of the Solar System. Also, we have compared our results with crater counts from Cassini images by other authors and we have noted that the number of observed small craters is less than our calculated theoretical number. In this paper we estimate the age of the surface for each observed terrain on each mid-sized satellite of Saturn. All the surfaces analyzed appear to be old with the exception of Enceladus. However, we have noticed that since there are less observed small craters than calculated (except on Iapetus), this results in younger ages than expected. This could be the result of efficient endogenous or exogenous process(es) for erasing small craters and/or crater saturation at those sizes. The size limit from which the observed number of smaller craters is less than the calculated is different for each satellite, possibly indicating processes that are unique to each, but other potential common explanations for this paucity of small craters would be crater saturation and/or deposition of E-ring particles. These processes are also suggested by the findings that the smaller craters are being preferentially removed, and the erasure process is gradual. On Enceladus, only mid and high latitude plains have remnants of old terrains; the other regions could be young. In particular, the regions near the South

Analysis of relationships between land surface temperature and land use changes in the Yellow River Delta

Science.gov (United States)

Ning, Jicai; Gao, Zhiqiang; Meng, Ran; Xu, Fuxiang; Gao, Meng

2018-06-01

This study analyzed land use and land cover changes and their impact on land surface temperature using Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager and Thermal Infrared Sensor imagery of the Yellow River Delta. Six Landsat images comprising two time series were used to calculate the land surface temperature and correlated vegetation indices. The Yellow River Delta area has expanded substantially because of the deposited sediment carried from upstream reaches of the river. Between 1986 and 2015, approximately 35% of the land use area of the Yellow River Delta has been transformed into salterns and aquaculture ponds. Overall, land use conversion has occurred primarily from poorly utilized land into highly utilized land. To analyze the variation of land surface temperature, a mono-window algorithm was applied to retrieve the regional land surface temperature. The results showed bilinear correlation between land surface temperature and the vegetation indices (i.e., Normalized Difference Vegetation Index, Adjusted-Normalized Vegetation Index, Soil-Adjusted Vegetation Index, and Modified Soil-Adjusted Vegetation Index). Generally, values of the vegetation indices greater than the inflection point mean the land surface temperature and the vegetation indices are correlated negatively, and vice versa. Land surface temperature in coastal areas is affected considerably by local seawater temperature and weather conditions.

Status of Land Surface Temperature Product Development at NOAA/NESDIS/STAR for JPSS and GOES-R Missions

Science.gov (United States)

Yu, Yunyue; Liu, yuling; yu, peng; Casiszar, Ivan; Zhou, Lihang

2016-04-01

Land surface temperature (LST) is of fundamental importance to many aspects of the geosciences, e.g., net radiation budget at the Earth surface, monitoring state of crops and vegetation, as well as an important indicator of both the greenhouse effect and the physics of land-surface processes at local through global scales. Satellite LST measurements provide unique data sources for regional and global coverage in fairly good temporal, spatial resolution and time span. Therefore, LST is one of baseline products in both JPSS and GOES-R satellite missions. The Center for SaTellite Applications and Research (STAR) of NOAA/NESDIS is responsible for developing high quality LST products for a variety of satellite missions including JPSS and GOES-R. The JPSS LST data, which is produced for each swath of observations, has reached its beta, provisional and validated stage 1 status in October 2013, May 2014, and December 2015, respectively. A routine validation and monitoring toolkit has been developed and its results are available through a public web site. Our validation results against the U.S. SURFRAD ground stations show that uncertainty of the VIIRS LST is less than 2.35K (vs. the JPSS mission requirement of 2.5K). Improvement of the JPSS LST product is on-going, which counts surface emissive variation explicitly in retrieval algorithm. Further, a gridded daily global LST product will be available by end of 2016. In terms of the GOES-R LST product, we have evaluated the retrieval algorithm using SEVIRI and AHI data as proxies. The evaluation results show that the accuracy of GOES-R LST is expected to be less than 2.30K (GOES-R mission requirement). The validation toolkit developed for JPSS mission will be extended and applicable for the GOES-R mission as well. A detailed Readiness, Implementation and Management Plan (RIMP) of GOES-R LST beta and provisional validation has been developed for the GOES-R launch that is scheduled in October 2016.

System architecture and market aspects of an European Land Mobile Satellite System via EMS

Science.gov (United States)

Ananasso, F.; Mistretta, I.

1992-03-01

The paper describes an implementation scenario of a Land Mobile Satellite System via the EMS (European Mobile System) payload embarked on Italsat F-2. Some emphasis is given on market issues aiming at singling out business niches of Land Mobile Satellite Services (LMSS) in Europe. Other crucial issues exist such as: the alternate/competitive systems, the problems of interworking with other existing and/or planned systems, the definition of network architecture that better fits the user requirements, the marketing strategy and, last but not least, the financial evaluation of the project. The paper, on the basis of a study performed by Telespazio on behalf of ESA, discusses some of these issues with emphasis on competitive market aspects.

Numerical modeling and remote sensing of global water management systems: Applications for land surface modeling, satellite missions, and sustainable water resources

Science.gov (United States)

Solander, Kurt C.

The ability to accurately quantify water storages and fluxes in water management systems through observations or models is of increasing importance due to the expected impacts from climate change and population growth worldwide. Here, I describe three innovative techniques developed to better understand this problem. First, a model was created to represent reservoir storage and outflow with the objective of integration into a Land Surface Model (LSM) to simulate the impacts of reservoir management on the climate system. Given this goal, storage capacity represented the lone model input required that is not already available to an LSM user. Model parameterization was linked to air temperature to allow future simulations to adapt to a changing climate, making it the first such model to mimic the potential response of a reservoir operator to climate change. Second, spatial and temporal error properties of future NASA Surface Water and Ocean Topography (SWOT) satellite reservoir operations were quantified. This work invoked the use of the SWOTsim instrument simulator, which was run over a number of synthetic and actual reservoirs so the resulting error properties could be extrapolated to the global scale. The results provide eventual users of SWOT data with a blueprint of expected reservoir error properties so such characteristics can be determined a priori for a reservoir given knowledge about its topology and anticipated repeat orbit pass over its location. Finally, data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission was used in conjunction with in-situ water use records to evaluate sustainable water use at the two-digit HUC basin scale over the contiguous United States. Results indicate that the least sustainable water management region is centered in the southwest, where consumptive water use exceeded water availability by over 100% on average for some of these basins. This work represents the first attempt at evaluating sustainable

Titan's surface spectra at the Huygens landing site and Shangri-La

Science.gov (United States)

Rannou, P.; Toledo, D.; Lavvas, P.; D'Aversa, E.; Moriconi, M. L.; Adriani, A.; Le Mouélic, S.; Sotin, C.; Brown, R.

2016-05-01

Titan is an icy satellite of Saturn with a dense atmosphere and covered by a global photochemical organic haze. Ground based observations and the Huygens descent probe allowed to retrieve the main spectral signature of the water ice (Griffith, C.A. et al. [2003]. Science 300(5619), 628-630; Coustenis, A. et al. [2005]. Icarus 177, 89-105) at the surface, possibly covered by a layer of sedimented organic material (Tomasko, M.G. et al. [2005]. Nature 438(7069), 765-778). However, the spectrum of the surface is not yet understood. In this study, we find that the surface reflectivity at the Huygens Landing Site (HLS) is well modeled by a layer of water ice grains overlaid by a moist layer of weakly compacted photochemical aggregated aerosols. Moist soils have spectra shifted toward short wavelengths relatively to spectra of dry soils. Cassini observations of Shangri-La region from orbit also show a very dark surface with a reflectivity peak shifted toward short wavelengths in respect to the reflectivity peak of bright surfaces, revealing a dichotomy between terrains based to their spectra in visible.

Object-oriented classification of land use in urban areas applying very high resolution satellite data

International Nuclear Information System (INIS)

Bauer, T.B.

2001-08-01

The availability of the new very high resolution satellite imagery will offer a wide range of new applications in the field of remote sensing. Information about actual land use is an important task for the management and planning in urban areas. High resolution satellite data will be an alternative to aerial photographs for updating and maintaining cartographic and geographic databases at reduced costs. The aim of the research is to formalize the visual interpretation procedure in order to automate the whole process. The assumption underlying this approach is that the land use functions can be distinguished on the basis of the differences in spatial distribution and pattern of land cover forms. Therefore a two-stage classification procedure is applied. In a first stage a land cover map is produced. In a second stage the morphological properties and spatial patterns of the land cover objects are analyzed with the structural analyzing and mapping system leading to a characterization and description of distinct urban land use categories. This information is then used for building a rule system that is implemented in a new commercial software tool called eCognition. An object-oriented classifier applies the rules to the land cover objects resulting in the required land use map. The potential of this method is demonstrated in a case study using IKONOS data covering a part of the metropolitan area of Vienna. (author)

South Florida Everglades: satellite image map

Science.gov (United States)

Jones, John W.; Thomas, Jean-Claude; Desmond, G.B.

2001-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program (http://access.usgs.gov/) with support from the Everglades National Park (http://www.nps.gov/ever/). The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

On the sensitivity of Land Surface Temperature estimates in arid irrigated lands using MODTRAN

KAUST Repository

Rosas, Jorge

2015-11-29

Land surface temperature (LST) derived from thermal infrared (TIR) satellite data has been reliably used as a remote indicator of evapotranspiration (ET) and surface moisture status. However, in order to retrieve the ET with an accuracy approaching 10%, LST should be retrieved to within 1 ◦C or better, disregarding other elements of uncertainty. The removal of atmospheric effects is key towards achieving a precise estimation of LST and it requires detailed information on water vapor. The Thermal Infrared Sensor (TIRS) onboard Landsat 8 captures data in two long wave thermal bands with 100-meter resolution. However, the US Geological Survey has reported a calibration problem of TIRS bands caused by stray light, resulting in a higher bias in one of its two bands (4% in band 11, 2% in band 10). Therefore, split-window algorithms for the estimation of LST might not be reliable. Our work will focus on the impact of using different atmospheric profiles (e.g. weather prediction models, satellite) for the estimation of LST derived from MODTRAN by using one of the TIRS bands onboard Landsat 8 (band 10). Sites with in-situ measurements of LST are used as evaluation sources. Comparisons between the measured LST and LST derived based on different atmospheric profile inputs to MODTRAN are carried out from 2 Landsat-overpass days (DOY 153 and 160 2015). Preliminary results show a mean absolute error of around 3 ◦C between in-situ and estimated LST over two different crops (alfalfa and carrot) and bare soil.

Assessment of Satellite-Derived Surface Reflectances by NASA's CAR Airborne Radiometer over Railroad Valley, Nevada

Science.gov (United States)

Kharbouche, Said; Muller, Jan-Peter; Gatebe, Charles K.; Scanlon, Tracy; Banks, Andrew C.

2017-01-01

CAR (Cloud Absorption Radiometer) is a multi-angular and multi-spectral airborne radiometer instrument, whose radiometric and geometric characteristics are well calibrated and adjusted before and after each flight campaign. CAR was built by NASA (National Aeronautics and Space Administration) in 1984. On 16 May 2008, a CAR flight campaign took place over the well-known calibration and validation site of Railroad Valley in Nevada (38.504 deg N, 115.692 deg W).The campaign coincided with the overpasses of several key EO (Earth Observation) satellites such as Landsat-7, Envisat and Terra. Thus, there are nearly simultaneous measurements from these satellites and the CAR airborne sensor over the same calibration site. The CAR spectral bands are close to those of most EO satellites. CAR has the ability to cover the whole range of azimuth view angles and a variety of zenith angles depending on altitude and, as a consequence, the biases seen between satellite and CAR measurements due to both unmatched spectral bands and unmatched angles can be significantly reduced. A comparison is presented here between CARs land surface reflectance (BRF or Bidirectional Reflectance Factor) with those derived from Terra/MODIS (MOD09 and MAIAC), Terra/MISR, Envisat/MERIS and Landsat-7. In this study, we utilized CAR data from low altitude flights (approx. 180 m above the surface) in order to minimize the effects of the atmosphere on these measurements and then obtain a valuable ground-truth data set of surface reflectance. Furthermore, this study shows that differences between measurements caused by surface heterogeneity can be tolerated, thanks to the high homogeneity of the study site on the one hand, and on the other hand, to the spatial sampling and the large number of CAR samples. These results demonstrate that satellite BRF measurements over this site are in good agreement with CAR with variable biases across different spectral bands. This is most likely due to residual aerosol

Detection of open water dynamics with ENVISAT ASAR in support of land surface modelling at high latitudes

Directory of Open Access Journals (Sweden)

A. Bartsch

2012-02-01

Full Text Available Wetlands are generally accepted as being the largest but least well quantified single source of methane (CH₄. The extent of wetland or inundation is a key factor controlling methane emissions, both in nature and in the parameterisations used in large-scale land surface and climate models. Satellite-derived datasets of wetland extent are available on the global scale, but the resolution is rather coarse (>25 km. The purpose of the present study is to assess the capability of active microwave sensors to derive inundation dynamics for use in land surface and climate models of the boreal and tundra environments. The focus is on synthetic aperture radar (SAR operating in C-band since, among microwave systems, it has comparably high spatial resolution and data availability, and long-term continuity is expected.

C-band data from ENVISAT ASAR (Advanced SAR operating in wide swath mode (150 m resolution were investigated and an automated detection procedure for deriving open water fraction has been developed. More than 4000 samples (single acquisitions tiled onto 0.5° grid cells have been analysed for July and August in 2007 and 2008 for a study region in Western Siberia. Simple classification algorithms were applied and found to be robust when the water surface was smooth. Modification of input parameters results in differences below 1 % open water fraction. The major issue to address was the frequent occurrence of waves due to wind and precipitation, which reduces the separability of the water class from other land cover classes. Statistical measures of the backscatter distribution were applied in order to retrieve suitable classification data. The Pearson correlation between each sample dataset and a location specific representation of the bimodal distribution was used. On average only 40 % of acquisitions allow a separation of the open water class. Although satellite data are available every 2–3 days over the Western Siberian

Evaluation of the Chinese Fine Spatial Resolution Hyperspectral Satellite TianGong-1 in Urban Land-Cover Classification

Directory of Open Access Journals (Sweden)

Xueke Li

2016-05-01

Full Text Available The successful launch of the Chinese high spatial resolution hyperspectral satellite TianGong-1 (TG-1 opens up new possibilities for applications of remotely-sensed satellite imagery. One of the main goals of the TG-1 mission is to provide observations of surface attributes at local and landscape spatial scales to map urban land cover accurately using the hyperspectral technique. This study attempted to evaluate the TG-1 datasets for urban feature analysis, using existing data over Beijing, China, by comparing the TG-1 (with a spatial resolution of 10 m to EO-1 Hyperion (with a spatial resolution of 30 m. The spectral feature of TG-1 was first analyzed and, thus, finding out optimal hyperspectral wavebands useful for the discrimination of urban areas. Based on this, the pixel-based maximum likelihood classifier (PMLC, pixel-based support vector machine (PSVM, hybrid maximum likelihood classifier (HMLC, and hybrid support vector machine (HSVM were implemented, as well as compared in the application of mapping urban land cover types. The hybrid classifier approach, which integrates the pixel-based classifier and the object-based segmentation approach, was demonstrated as an effective alternative to the conventional pixel-based classifiers for processing the satellite hyperspectral data, especially the fine spatial resolution data. For TG-1 imagery, the pixel-based urban classification was obtained with an average overall accuracy of 89.1%, whereas the hybrid urban classification was obtained with an average overall accuracy of 91.8%. For Hyperion imagery, the pixel-based urban classification was obtained with an average overall accuracy of 85.9%, whereas the hybrid urban classification was obtained with an average overall accuracy of 86.7%. Overall, it can be concluded that the fine spatial resolution satellite hyperspectral data TG-1 is promising in delineating complex urban scenes, especially when using an appropriate classifier, such as the

Area-averaged evapotranspiration over a heterogeneous land surface: aggregation of multi-point EC flux measurements with a high-resolution land-cover map and footprint analysis

Directory of Open Access Journals (Sweden)

F. Xu

2017-08-01

Full Text Available The determination of area-averaged evapotranspiration (ET at the satellite pixel scale/model grid scale over a heterogeneous land surface plays a significant role in developing and improving the parameterization schemes of the remote sensing based ET estimation models and general hydro-meteorological models. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER flux matrix provided a unique opportunity to build an aggregation scheme for area-averaged fluxes. On the basis of the HiWATER flux matrix dataset and high-resolution land-cover map, this study focused on estimating the area-averaged ET over a heterogeneous landscape with footprint analysis and multivariate regression. The procedure is as follows. Firstly, quality control and uncertainty estimation for the data of the flux matrix, including 17 eddy-covariance (EC sites and four groups of large-aperture scintillometers (LASs, were carefully done. Secondly, the representativeness of each EC site was quantitatively evaluated; footprint analysis was also performed for each LAS path. Thirdly, based on the high-resolution land-cover map derived from aircraft remote sensing, a flux aggregation method was established combining footprint analysis and multiple-linear regression. Then, the area-averaged sensible heat fluxes obtained from the EC flux matrix were validated by the LAS measurements. Finally, the area-averaged ET of the kernel experimental area of HiWATER was estimated. Compared with the formerly used and rather simple approaches, such as the arithmetic average and area-weighted methods, the present scheme is not only with a much better database, but also has a solid grounding in physics and mathematics in the integration of area-averaged fluxes over a heterogeneous surface. Results from this study, both instantaneous and daily ET at the satellite pixel scale, can be used for the validation of relevant remote sensing models and land surface process models. Furthermore, this

Area-averaged evapotranspiration over a heterogeneous land surface: aggregation of multi-point EC flux measurements with a high-resolution land-cover map and footprint analysis

Science.gov (United States)

Xu, Feinan; Wang, Weizhen; Wang, Jiemin; Xu, Ziwei; Qi, Yuan; Wu, Yueru

2017-08-01

The determination of area-averaged evapotranspiration (ET) at the satellite pixel scale/model grid scale over a heterogeneous land surface plays a significant role in developing and improving the parameterization schemes of the remote sensing based ET estimation models and general hydro-meteorological models. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) flux matrix provided a unique opportunity to build an aggregation scheme for area-averaged fluxes. On the basis of the HiWATER flux matrix dataset and high-resolution land-cover map, this study focused on estimating the area-averaged ET over a heterogeneous landscape with footprint analysis and multivariate regression. The procedure is as follows. Firstly, quality control and uncertainty estimation for the data of the flux matrix, including 17 eddy-covariance (EC) sites and four groups of large-aperture scintillometers (LASs), were carefully done. Secondly, the representativeness of each EC site was quantitatively evaluated; footprint analysis was also performed for each LAS path. Thirdly, based on the high-resolution land-cover map derived from aircraft remote sensing, a flux aggregation method was established combining footprint analysis and multiple-linear regression. Then, the area-averaged sensible heat fluxes obtained from the EC flux matrix were validated by the LAS measurements. Finally, the area-averaged ET of the kernel experimental area of HiWATER was estimated. Compared with the formerly used and rather simple approaches, such as the arithmetic average and area-weighted methods, the present scheme is not only with a much better database, but also has a solid grounding in physics and mathematics in the integration of area-averaged fluxes over a heterogeneous surface. Results from this study, both instantaneous and daily ET at the satellite pixel scale, can be used for the validation of relevant remote sensing models and land surface process models. Furthermore, this work will be

Monitoring of Land-Surface Deformation in the Karamay Oilfield, Xinjiang, China, Using SAR Interferometry

Directory of Open Access Journals (Sweden)

Yusupujiang Aimaiti

2017-07-01

Full Text Available Synthetic Aperture Radar (SAR interferometry is a technique that provides high-resolution measurements of the ground displacement associated with various geophysical processes. To investigate the land-surface deformation in Karamay, a typical oil-producing city in the Xinjiang Uyghur Autonomous Region, China, Advanced Land Observing Satellite (ALOS Phased Array L-band Synthetic Aperture Radar (PALSAR data were acquired for the period from 2007 to 2009, and a two-pass differential SAR interferometry (D-InSAR process was applied. The experimental results showed that two sites in the north-eastern part of the city exhibit a clear indication of land deformation. For a further evaluation of the D-InSAR result, the Persistent Scatterer (PS and Small Baseline Subset (SBAS-InSAR techniques were applied for 21 time series Environmental Satellite (ENVISAT C-band Advanced Synthetic Aperture Radar (ASAR data from 2003 to 2010. The comparison between the D-InSAR and SBAS-InSAR measurements had better agreement than that from the PS-InSAR measurement. The maximum deformation rate attributed to subsurface water injection for the period from 2003 to 2010 was up to approximately 33 mm/year in the line of sight (LOS direction. The interferometric phase change from November 2007 to June 2010 showed a clear deformation pattern, and the rebound center has been expanding in scale and increasing in quantity.

Changes in Land Surface Water Dynamics since the 1990s and Relation to Population Pressure

Science.gov (United States)

Prigent, C.; Papa, F.; Aires, F.; Jimenez, C.; Rossow, W. B.; Matthews, E.

2012-01-01

We developed a remote sensing approach based on multi-satellite observations, which provides an unprecedented estimate of monthly distribution and area of land-surface open water over the whole globe. Results for 1993 to 2007 exhibit a large seasonal and inter-annual variability of the inundation extent with an overall decline in global average maximum inundated area of 6% during the fifteen-year period, primarily in tropical and subtropical South America and South Asia. The largest declines of open water are found where large increases in population have occurred over the last two decades, suggesting a global scale effect of human activities on continental surface freshwater: denser population can impact local hydrology by reducing freshwater extent, by draining marshes and wetlands, and by increasing water withdrawals. Citation: Prigent, C., F. Papa, F. Aires, C. Jimenez, W. B. Rossow, and E. Matthews (2012), Changes in land surface water dynamics since the 1990s and relation to population pressure, in section 4, insisting on the potential applications of the wetland dataset.

A one-layer satellite surface energy balance for estimating evapotranspiration rates and crop water stress indexes.

Science.gov (United States)

Barbagallo, Salvatore; Consoli, Simona; Russo, Alfonso

2009-01-01

Daily evapotranspiration fluxes over the semi-arid Catania Plain area (Eastern Sicily, Italy) were evaluated using remotely sensed data from Landsat Thematic Mapper TM5 images. A one-source parameterization of the surface sensible heat flux exchange using satellite surface temperature has been used. The transfer of sensible and latent heat is described by aerodynamic resistance and surface resistance. Required model inputs are brightness, temperature, fractional vegetation cover or leaf area index, albedo, crop height, roughness lengths, net radiation, air temperature, air humidity and wind speed. The aerodynamic resistance (r(ah)) is formulated on the basis of the Monin-Obukhov surface layer similarity theory and the surface resistance (r(s)) is evaluated from the energy balance equation. The instantaneous surface flux values were converted into evaporative fraction (EF) over the heterogeneous land surface to derive daily evapotranspiration values. Remote sensing-based assessments of crop water stress (CWSI) were also made in order to identify local irrigation requirements. Evapotranspiration data and crop coefficient values obtained from the approach were compared with: (i) data from the semi-empirical approach "K(c) reflectance-based", which integrates satellite data in the visible and NIR regions of the electromagnetic spectrum with ground-based measurements and (ii) surface energy flux measurements collected from a micrometeorological tower located in the experiment area. The expected variability associated with ET flux measurements suggests that the approach-derived surface fluxes were in acceptable agreement with the observations.

Landsat: A global land-imaging mission

Science.gov (United States)

,

2012-01-01

Across four decades since 1972, Landsat satellites have continuously acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space. NASA develops remote-sensing instruments and spacecraft, then launches and validates the performance of the instruments and satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground reception, data archiving, product generation, and distribution. The result of this program is a long-term record of natural and human induced changes on the global landscape.

Global Land Product Validation Protocols: An Initiative of the CEOS Working Group on Calibration and Validation to Evaluate Satellite-derived Essential Climate Variables

Science.gov (United States)

Guillevic, P. C.; Nickeson, J. E.; Roman, M. O.; camacho De Coca, F.; Wang, Z.; Schaepman-Strub, G.

2016-12-01

The Global Climate Observing System (GCOS) has specified the need to systematically produce and validate Essential Climate Variables (ECVs). The Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) and in particular its subgroup on Land Product Validation (LPV) is playing a key coordination role leveraging the international expertise required to address actions related to the validation of global land ECVs. The primary objective of the LPV subgroup is to set standards for validation methods and reporting in order to provide traceable and reliable uncertainty estimates for scientists and stakeholders. The Subgroup is comprised of 9 focus areas that encompass 10 land surface variables. The activities of each focus area are coordinated by two international co-leads and currently include leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR), vegetation phenology, surface albedo, fire disturbance, snow cover, land cover and land use change, soil moisture, land surface temperature (LST) and emissivity. Recent additions to the focus areas include vegetation indices and biomass. The development of best practice validation protocols is a core activity of CEOS LPV with the objective to standardize the evaluation of land surface products. LPV has identified four validation levels corresponding to increasing spatial and temporal representativeness of reference samples used to perform validation. Best practice validation protocols (1) provide the definition of variables, ancillary information and uncertainty metrics, (2) describe available data sources and methods to establish reference validation datasets with SI traceability, and (3) describe evaluation methods and reporting. An overview on validation best practice components will be presented based on the LAI and LST protocol efforts to date.

Satellite-derived land covers for runoff estimation using SCS-CN method in Chen-You-Lan Watershed, Taiwan

Science.gov (United States)

Zhang, Wen-Yan; Lin, Chao-Yuan

2017-04-01

The Soil Conservation Service Curve Number (SCS-CN) method, which was originally developed by the USDA Natural Resources Conservation Service, is widely used to estimate direct runoff volume from rainfall. The runoff Curve Number (CN) parameter is based on the hydrologic soil group and land use factors. In Taiwan, the national land use maps were interpreted from aerial photos in 1995 and 2008. Rapid updating of post-disaster land use map is limited due to the high cost of production, so the classification of satellite images is the alternative method to obtain the land use map. In this study, Normalized Difference Vegetation Index (NDVI) in Chen-You-Lan Watershed was derived from dry and wet season of Landsat imageries during 2003 - 2008. Land covers were interpreted from mean value and standard deviation of NDVI and were categorized into 4 groups i.e. forest, grassland, agriculture and bare land. Then, the runoff volume of typhoon events during 2005 - 2009 were estimated using SCS-CN method and verified with the measured runoff data. The result showed that the model efficiency coefficient is 90.77%. Therefore, estimating runoff by using the land cover map classified from satellite images is practicable.

evaluation of land surface temperature parameterization ...

African Journals Online (AJOL)

user

Surface temperature (Ts) is vital to the study of land-atmosphere interactions and ... representation of Ts in Global Climate Models using available ..... Obviously, the influence of the ambient .... diurnal cycle over land under clear and cloudy.

ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models

Science.gov (United States)

Faroux, S.; Kaptué Tchuenté, A. T.; Roujean, J.-L.; Masson, V.; Martin, E.; Le Moigne, P.

2013-04-01

The overall objective of the present study is to introduce the new ECOCLIMAP-II database for Europe, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, already implemented at global scale. The ECOCLIMAP programme is a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modelling (notably leaf area index and albedo). Hence, the aim of this innovative physiography is to enhance the quality of initialisation and impose some surface attributes within the scope of weather forecasting and climate related studies. The strategy for implementing ECOCLIMAP-II is to depart from prevalent land cover products such as CLC2000 (Corine Land Cover) and GLC2000 (Global Land Cover) by splitting existing classes into new classes that possess a better regional character by virtue of the climatic environment (latitude, proximity to the sea, topography). The leaf area index (LAI) from MODIS and normalized difference vegetation index (NDVI) from SPOT/Vegetation (a global monitoring system of vegetation) yield the two proxy variables that were considered here in order to perform a multi-year trimmed analysis between 1999 and 2005 using the K-means method. Further, meteorological applications require each land cover type to appear as a partition of fractions of 4 main surface types or tiles (nature, water bodies, sea, urban areas) and, inside the nature tile, fractions of 12 plant functional types (PFTs) representing generic vegetation types - principally broadleaf forest, needleleaf forest, C3 and C4 crops, grassland and bare land - as incorporated by the SVAT model ISBA (Interactions Surface Biosphere Atmosphere) developed at Météo France. This landscape division also forms the cornerstone of a validation exercise. The new ECOCLIMAP-II can be verified with auxiliary land cover products at very fine and coarse resolutions by means of versatile land

Satellite-Based Sunshine Duration for Europe

Directory of Open Access Journals (Sweden)

Bodo Ahrens

2013-06-01

Full Text Available In this study, two different methods were applied to derive daily and monthly sunshine duration based on high-resolution satellite products provided by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT Satellite Application Facility on Climate Monitoring using data from Meteosat Second Generation (MSG SEVIRI (Spinning Enhanced Visible and Infrared Imager. The satellite products were either hourly cloud type or hourly surface incoming direct radiation. The satellite sunshine duration estimates were not found to be significantly different using the native 15-minute temporal resolution of SEVIRI. The satellite-based sunshine duration products give additional spatial information over the European continent compared with equivalent in situ-based products. An evaluation of the satellite sunshine duration by product intercomparison and against station measurements was carried out to determine their accuracy. The satellite data were found to be within ±1 h/day compared to high-quality Baseline Surface Radiation Network or surface synoptic observations (SYNOP station measurements. The satellite-based products differ more over the oceans than over land, mainly because of the treatment of fractional clouds in the cloud type-based sunshine duration product. This paper presents the methods used to derive the satellite sunshine duration products and the performance of the different retrievals. The main benefits and disadvantages compared to station-based products are also discussed.

Global land-surface primary productivity based upon Nimbus-7 37 GHz data

Science.gov (United States)

Choudhury, B. J.

1988-01-01

Accumulation and renewal of organic matter as quantified through net primary productivity (NPP) is considered a very major function of the biosphere, and its estimation is crucial in understanding the carbon cycle. A physically-based model relating NPP to the difference of vertically and horizontally polarized brightness temperatures (Delta T) observed at 37 GHz frequency of the scanning multichannel microwave radiometer on board the Nimbus-7 satellite is used for fitting areally averaged values of NPP and Delta T for five biomes. The land-surface NPP within 80 deg N to 55 deg S is then calculated using the Delta T data and compared with other estimates.

Reconstruction and downscaling of Eastern Mediterranean OSCAR satellite surface current data using DINEOF

Science.gov (United States)

Nikolaidis, Andreas; Stylianou, Stavros; Georgiou, Georgios; Hajimitsis, Diofantos; Gravanis, Elias; Akylas, Evangelos

2015-04-01

During the last decade, Rixen (2005) and Alvera-Azkarate (2010) presented the DINEOF (Data Interpolating Empirical Orthogonal Functions) method, a EOF-based technique to reconstruct missing data in satellite images. The application of DINEOF method, proved to provide relative success in various experimental trials (Wang and Liu, 2013; Nikolaidis et al., 2013;2014), and tends to be an effective and computationally affordable solution, on the problem of data reconstruction, for missing data from geophysical fields, such as chlorophyll-a, sea surface temperatures or salinity and geophysical fields derived from satellite data. Implementation of this method in a GIS system will provide with a more complete, integrated approach, permitting the expansion of the applicability over various aspects. This may be especially useful in studies where various data of different kind, have to be examined. For this purpose, in this study we have implemented and present a GIS toolbox that aims to automate the usage of the algorithm, incorporating the DINEOF codes provided by GHER (GeoHydrodynamics and Environment Research Group of University of Liege) into the ArcGIS®. ArcGIS® is a well known standard on Geographical Information Systems, used over the years for various remote sensing procedures, in sea and land environment alike. A case-study of filling the missing satellite derived current data in the Eastern Mediterranean Sea area, for a monthly period is analyzed, as an example for the effectiveness and simplicity of the usage of this toolbox. The specific study focuses to OSCAR satellite data (http://www.oscar.noaa.gov/) collected by NOAA/NESDIS Operational Surface Current Processing and Data Center, from the respective products of OSCAR Project Office Earth and Space Research organization, that provides free online access to unfiltered (1/3 degree) resolution. All the 5-day mean products data coverage were successfully reconstructed. KEY WORDS: Remote Sensing, Cyprus

Land surface Verification Toolkit (LVT)

Science.gov (United States)

Kumar, Sujay V.

2017-01-01

LVT is a framework developed to provide an automated, consolidated environment for systematic land surface model evaluation Includes support for a range of in-situ, remote-sensing and other model and reanalysis products. Supports the analysis of outputs from various LIS subsystems, including LIS-DA, LIS-OPT, LIS-UE. Note: The Land Information System Verification Toolkit (LVT) is a NASA software tool designed to enable the evaluation, analysis and comparison of outputs generated by the Land Information System (LIS). The LVT software is released under the terms and conditions of the NASA Open Source Agreement (NOSA) Version 1.1 or later. Land Information System Verification Toolkit (LVT) NOSA.

Land cover mapping and change detection in urban watersheds using QuickBird high spatial resolution satellite imagery

Science.gov (United States)

Hester, David Barry

The objective of this research was to develop methods for urban land cover analysis using QuickBird high spatial resolution satellite imagery. Such imagery has emerged as a rich commercially available remote sensing data source and has enjoyed high-profile broadcast news media and Internet applications, but methods of quantitative analysis have not been thoroughly explored. The research described here consists of three studies focused on the use of pan-sharpened 61-cm spatial resolution QuickBird imagery, the spatial resolution of which is the highest of any commercial satellite. In the first study, a per-pixel land cover classification method is developed for use with this imagery. This method utilizes a per-pixel classification approach to generate an accurate six-category high spatial resolution land cover map of a developing suburban area. The primary objective of the second study was to develop an accurate land cover change detection method for use with QuickBird land cover products. This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and meaningful high spatial resolution land cover change analysis. The third study described here is an urban planning application of the high spatial resolution QuickBird-based land cover product developed in the first study. This work both meaningfully connects this exciting new data source to urban watershed management and makes an important empirical contribution to the study of suburban watersheds. Its analysis of residential roads and driveways as well as retail parking lots sheds valuable light on the impact of transportation-related land use on the suburban landscape. Broadly, these studies provide new methods for using state-of-the-art remote sensing data to inform land cover analysis and urban planning. These methods are widely adaptable and produce land cover products that are both meaningful and accurate. As additional high spatial resolution satellites are launched and the

Magnitude and variability of land evaporation and its components at the global scale

NARCIS (Netherlands)

Miralles, D.G.; de Jeu, R.A.M.; Gash, J.H.C.; Holmes, T.R.H.; Dolman, A.J.

2011-01-01

A process-based methodology is applied to estimate land-surface evaporation from multi-satellite information. GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) combines a wide range of remotely-sensed observations to derive daily actual evaporation and its different components. Soil

Improving Land Surface Temperature Retrievals over Mountainous Regions

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Virgílio A. Bento

2017-01-01

Full Text Available Algorithms for Land Surface Temperature (LST retrieval from infrared measurements are usually sensitive to the amount of water vapor present in the atmosphere. The Satellite Application Facilities on Climate Monitoring and Land Surface Analysis (CM SAF and LSA SAF are currently compiling a 25 year LST Climate data record (CDR, which uses water vapor information from ERA-Int reanalysis. However, its relatively coarse spatial resolution may lead to systematic errors in the humidity profiles with implications in LST, particularly over mountainous areas. The present study compares LST estimated with three different retrieval algorithms: a radiative transfer-based physical mono-window (PMW, a statistical mono-window (SMW, and a generalized split-windows (GSW. The algorithms were tested over the Alpine region using ERA-Int reanalysis data and relied on the finer spatial scale Consortium for Small-Scale Modelling (COSMO model data as a reference. Two methods were developed to correct ERA-Int water vapor misestimation: (1 an exponential parametrization of total precipitable water (TPW appropriate for SMW/GSW; and (2 a level reduction method to be used in PMW. When ERA-Int TPW was used, the algorithm missed the right TPW class in 87% of the cases. When the exponential parametrization was used, the missing class rate decreased to 9%, and when the level reduction method was applied, the LST corrections went up to 1.7 K over the study region. Overall, the correction for pixel orography in TPW leads to corrections in LST estimations, which are relevant to ensure that long-term LST records meet climate requirements, particularly over mountainous regions.

ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1-km resolution based on satellite information for use in land surface, meteorological and climate models

Science.gov (United States)

Faroux, S.; Kaptué Tchuenté, A. T.; Roujean, J.-L.; Masson, V.; Martin, E.; Le Moigne, P.

2012-11-01

The overall objective of the present study is to introduce the new ECOCLIMAP-II database for Europe, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, already implemented at global scale. The ECOCLIMAP programme is a dual database at 1-km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modelling (notably leaf area index and albedo). Hence, the aim of this innovative physiography is to enhance the quality of initialisation and impose some surface attributes within the scope of weather forecasting and climate related studies. The strategy for implementing ECOCLIMAP-II is to depart from prevalent land cover products such as CLC2000 (Corine Land Cover) and GLC2000 (Global Land Cover) by splitting existing classes into new classes that possess a better regional character by virtue of the climatic environment (latitude, proximity to the sea, topography). The leaf area index (LAI) from MODIS and NDVI from SPOT/Vegetation yield the two proxy variables that were considered here in order to perform a multi-year trimmed analysis between 1999 and 2005 using the K-means method. Further, meteorological applications require each land cover type to appear as a partition of fractions of 4 main surface types or tiles (nature, water bodies, sea, urban areas) and, inside the nature tile, fractions of 12 Plant Functional Types (PFTs) representing generic vegetation types - principally broadleaf forest, needleleaf forest, C3 and C4 crops, grassland and bare land - as incorporated by the SVAT model ISBA developed at Météo France. This landscape division also forms the cornerstone of a validation exercise. The new ECOCLIMAP-II can be verified with auxiliary land cover products at very fine and coarse resolutions by means of versatile land occupation nomenclatures.

Evaluating Surface Radiation Fluxes Observed From Satellites in the Southeastern Pacific Ocean

Science.gov (United States)

Pinker, R. T.; Zhang, B.; Weller, R. A.; Chen, W.

2018-03-01

This study is focused on evaluation of current satellite and reanalysis estimates of surface radiative fluxes in a climatically important region. It uses unique observations from the STRATUS Ocean Reference Station buoy in a region of persistent marine stratus clouds 1,500 km off northern Chile during 2000-2012. The study shows that current satellite estimates are in better agreement with buoy observations than model outputs at a daily time scale and that satellite data depict well the observed annual cycle in both shortwave and longwave surface radiative fluxes. Also, buoy and satellite estimates do not show any significant trend over the period of overlap or any interannual variability. This verifies the stability and reliability of the satellite data and should make them useful to examine El Niño-Southern Oscillation variability influences on surface radiative fluxes at the STRATUS site for longer periods for which satellite record is available.

A global, 30-m resolution land-surface water body dataset for 2000

Science.gov (United States)

Feng, M.; Sexton, J. O.; Huang, C.; Song, D. X.; Song, X. P.; Channan, S.; Townshend, J. R.

2014-12-01

Inland surface water is essential to terrestrial ecosystems and human civilization. The distribution of surface water in space and its change over time are related to many agricultural, environmental and ecological issues, and are important factors that must be considered in human socioeconomic development. Accurate mapping of surface water is essential for both scientific research and policy-driven applications. Satellite-based remote sensing provides snapshots of Earth's surface and can be used as the main input for water mapping, especially in large areas. Global water areas have been mapped with coarse resolution remotely sensed data (e.g., the Moderate Resolution Imaging Spectroradiometer (MODIS)). However, most inland rivers and water bodies, as well as their changes, are too small to map at such coarse resolutions. Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) imagery has a 30m spatial resolution and provides decades of records (~40 years). Since 2008, the opening of the Landsat archive, coupled with relatively lower costs associated with computing and data storage, has made comprehensive study of the dynamic changes of surface water over large even global areas more feasible. Although Landsat images have been used for regional and even global water mapping, the method can hardly be automated due to the difficulties on distinguishing inland surface water with variant degrees of impurities and mixing of soil background with only Landsat data. The spectral similarities to other land cover types, e.g., shadow and glacier remnants, also cause misidentification. We have developed a probabilistic based automatic approach for mapping inland surface water bodies. Landsat surface reflectance in multiple bands, derived water indices, and data from other sources are integrated to maximize the ability of identifying water without human interference. The approach has been implemented with open-source libraries to facilitate processing large

Use of multispectral satellite imagery and hyperspectral endmember libraries for urban land cover mapping at the metropolitan scale

Science.gov (United States)

Priem, Frederik; Okujeni, Akpona; van der Linden, Sebastian; Canters, Frank

2016-10-01

The value of characteristic reflectance features for mapping urban materials has been demonstrated in many experiments with airborne imaging spectrometry. Analysis of larger areas requires satellite-based multispectral imagery, which typically lacks the spatial and spectral detail of airborne data. Consequently the need arises to develop mapping methods that exploit the complementary strengths of both data sources. In this paper a workflow for sub-pixel quantification of Vegetation-Impervious-Soil urban land cover is presented, using medium resolution multispectral satellite imagery, hyperspectral endmember libraries and Support Vector Regression. A Landsat 8 Operational Land Imager surface reflectance image covering the greater metropolitan area of Brussels is selected for mapping. Two spectral libraries developed for the cities of Brussels and Berlin based on airborne hyperspectral APEX and HyMap data are used. First the combined endmember library is resampled to match the spectral response of the Landsat sensor. The library is then optimized to avoid spectral redundancy and confusion. Subsequently the spectra of the endmember library are synthetically mixed to produce training data for unmixing. Mapping is carried out using Support Vector Regression models trained with spectra selected through stratified sampling of the mixed library. Validation on building block level (mean size = 46.8 Landsat pixels) yields an overall good fit between reference data and estimation with Mean Absolute Errors of 0.06, 0.06 and 0.08 for vegetation, impervious and soil respectively. Findings of this work may contribute to the use of universal spectral libraries for regional scale land cover fraction mapping using regression approaches.

A critical assessment of the JULES land surface model hydrology for humid tropical environments

Science.gov (United States)

Zulkafli, Z.; Buytaert, W.; Onof, C.; Lavado, W.; Guyot, J. L.

2013-03-01

Global land surface models (LSMs) such as the Joint UK Land Environment Simulator (JULES) are originally developed to provide surface boundary conditions for climate models. They are increasingly used for hydrological simulation, for instance to simulate the impacts of land use changes and other perturbations on the water cycle. This study investigates how well such models represent the major hydrological fluxes at the relevant spatial and temporal scales - an important question for reliable model applications in poorly understood, data-scarce environments. The JULES-LSM is implemented in a 360 000 km2 humid tropical mountain basin of the Peruvian Andes-Amazon at 12-km grid resolution, forced with daily satellite and climate reanalysis data. The simulations are evaluated using conventional discharge-based evaluation methods, and by further comparing the magnitude and internal variability of the basin surface fluxes such as evapotranspiration, throughfall, and surface and subsurface runoff of the model with those observed in similar environments elsewhere. We find reasonably positive model efficiencies and high correlations between the simulated and observed streamflows, but high root-mean-square errors affecting the performance in smaller, upper sub-basins. We attribute this to errors in the water balance and JULES-LSM's inability to model baseflow. We also found a tendency to under-represent the high evapotranspiration rates of the region. We conclude that strategies to improve the representation of tropical systems to be (1) addressing errors in the forcing and (2) incorporating local wetland and regional floodplain in the subsurface representation.

Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

Directory of Open Access Journals (Sweden)

Biao Wang

2017-08-01

Full Text Available Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD method—a measure for the spectral distortion of change information—is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation.

Incorporating JULES into NASA's Land Information System (LIS) and Investigations of Land-Atmosphere Coupling

Science.gov (United States)

Santanello, Joseph

2011-01-01

NASA's Land Information System (LIS; lis.gsfc.nasa.gov) is a flexible land surface modeling and data assimilation framework developed over the past decade with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. LIS features a high performance and flexible design, and operates on an ensemble of land surface models for extension over user-specified regional or global domains. The extensible interfaces of LIS allow the incorporation of new domains, land surface models (LSMs), land surface parameters, meteorological inputs, data assimilation and optimization algorithms. In addition, LIS has also been demonstrated for parameter estimation and uncertainty estimation, and has been coupled to the Weather Research and Forecasting (WRF) mesoscale model. A visiting fellowship is currently underway to implement JULES into LIS and to undertake some fundamental science on the feedbacks between the land surface and the atmosphere. An overview of the LIS system, features, and sample results will be presented in an effort to engage the community in the potential advantages of LIS-JULES for a range of applications. Ongoing efforts to develop a framework for diagnosing land-atmosphere coupling will also be presented using the suite of LSM and PBL schemes available in LIS and WRF along with observations from the U. S .. Southern Great Plains. This methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery

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M. C. Anderson

2011-01-01

Full Text Available Thermal infrared (TIR remote sensing of land-surface temperature (LST provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (e.g., air temperature, advection are affecting plant functioning. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil + canopy land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5 to 10-km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites. An overview of this modeling approach is presented, along with strategies for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions on the order of 10 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the US Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the US, Europe, Africa

Preface to the Special Issue on Satellite Altimetry over Land and Coastal Zones: Applications and Challenges

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Cheinway Hwang

2008-01-01

Full Text Available This special issue publishes peer reviewed papers stemming from the International Workshop on Coast and Land applications of satellite altimetry, held 21 -22 July 2006, Beijing, China. This workshop is financially supported by the Chinese Academy of Surveying and Mapping, National Chiao Tung University, Asia GIS and GPS Co., Chung-Hsing Surv. Co., Huanyu Surv. Eng. Cons. Inc., and Real-World Eng. Cons. Inc. Twenty-two papers were submitted to this issue for review, and 16 papers were accepted following an iterative peer-review process. The accepted papers cover subjects on: ICESat coastal altimetry (1, satellite altimetry applications in solid earth sciences (2, hydrology (4, land/coast gravity field modeling (4, and coastal oceanography (5.

Quantifying the Trends in Land Surface Temperature and Surface Urban Heat Island Intensity in Mediterranean Cities in View of Smart Urbanization

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Anastasios Polydoros

2018-02-01

Full Text Available Land Surface Temperature (LST is a key parameter for the estimation of urban fluxes as well as for the assessment of the presence and strength of the surface urban heat island (SUHI. In an urban environment, LST depends on the way the city has been planned and developed over time. To this end, the estimation of LST needs adequate spatial and temporal data at the urban scale, especially with respect to land cover/land use. The present study is divided in two parts: at first, satellite data from MODIS-Terra 8-day product (MOD11A2 were used for the analysis of an eighteen-year time series (2001–2017 of the LST spatial and temporal distribution in five major cities of the Mediterranean during the summer months. LST trends were retrieved and assessed for their statistical significance. Secondly, LST values and trends for each city were examined in relation to land cover characteristics and patterns in order to define the contribution of urban development and planning on LST; this information is important for the drafting of smart urbanization policies and measures. Results revealed (a positive LST trends in the urban areas especially during nighttime ranging from +0.412 °K in Marseille to +0.923 °K in Cairo and (b the SUHI has intensified during the last eighteen years especially during daytime in European Mediterranean cities, such as Rome (+0.332 °K and Barcelona (+0.307 °K.

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 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.

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 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.

Effect of water table dynamics on land surface hydrologic memory

Science.gov (United States)

Lo, Min-Hui; Famiglietti, James S.

2010-11-01

The representation of groundwater dynamics in land surface models has received considerable attention in recent years. Most studies have found that soil moisture increases after adding a groundwater component because of the additional supply of water to the root zone. However, the effect of groundwater on land surface hydrologic memory (persistence) has not been explored thoroughly. In this study we investigate the effect of water table dynamics on National Center for Atmospheric Research Community Land Model hydrologic simulations in terms of land surface hydrologic memory. Unlike soil water or evapotranspiration, results show that land surface hydrologic memory does not always increase after adding a groundwater component. In regions where the water table level is intermediate, land surface hydrologic memory can even decrease, which occurs when soil moisture and capillary rise from groundwater are not in phase with each other. Further, we explore the hypothesis that in addition to atmospheric forcing, groundwater variations may also play an important role in affecting land surface hydrologic memory. Analyses show that feedbacks of groundwater on land surface hydrologic memory can be positive, negative, or neutral, depending on water table dynamics. In regions where the water table is shallow, the damping process of soil moisture variations by groundwater is not significant, and soil moisture variations are mostly controlled by random noise from atmospheric forcing. In contrast, in regions where the water table is very deep, capillary fluxes from groundwater are small, having limited potential to affect soil moisture variations. Therefore, a positive feedback of groundwater to land surface hydrologic memory is observed in a transition zone between deep and shallow water tables, where capillary fluxes act as a buffer by reducing high-frequency soil moisture variations resulting in longer land surface hydrologic memory.

Inclusion of Solar Elevation Angle in Land Surface Albedo Parameterization Over Bare Soil Surface.

Science.gov (United States)

Zheng, Zhiyuan; Wei, Zhigang; Wen, Zhiping; Dong, Wenjie; Li, Zhenchao; Wen, Xiaohang; Zhu, Xian; Ji, Dong; Chen, Chen; Yan, Dongdong

2017-12-01

Land surface albedo is a significant parameter for maintaining a balance in surface energy. It is also an important parameter of bare soil surface albedo for developing land surface process models that accurately reflect diurnal variation characteristics and the mechanism behind the solar spectral radiation albedo on bare soil surfaces and for understanding the relationships between climate factors and spectral radiation albedo. Using a data set of field observations, we conducted experiments to analyze the variation characteristics of land surface solar spectral radiation and the corresponding albedo over a typical Gobi bare soil underlying surface and to investigate the relationships between the land surface solar spectral radiation albedo, solar elevation angle, and soil moisture. Based on both solar elevation angle and soil moisture measurements simultaneously, we propose a new two-factor parameterization scheme for spectral radiation albedo over bare soil underlying surfaces. The results of numerical simulation experiments show that the new parameterization scheme can more accurately depict the diurnal variation characteristics of bare soil surface albedo than the previous schemes. Solar elevation angle is one of the most important factors for parameterizing bare soil surface albedo and must be considered in the parameterization scheme, especially in arid and semiarid areas with low soil moisture content. This study reveals the characteristics and mechanism of the diurnal variation of bare soil surface solar spectral radiation albedo and is helpful in developing land surface process models, weather models, and climate models.

Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites

Science.gov (United States)

Belward, Alan S.; Skøien, Jon O.

2015-05-01

This paper presents a compendium of satellites under civilian and/or commercial control with the potential to gather global land-cover observations. From this we show that a growing number of sovereign states are acquiring capacity for space based land-cover observations and show how geopolitical patterns of ownership are changing. We discuss how the number of satellites flying at any time has progressed as a function of increased launch rates and mission longevity, and how the spatial resolutions of the data they collect has evolved. The first such satellite was launched by the USA in 1972. Since then government and/or private entities in 33 other sovereign states and geopolitical groups have chosen to finance such missions and 197 individual satellites with a global land-cover observing capacity have been successfully launched. Of these 98 were still operating at the end of 2013. Since the 1970s the number of such missions failing within 3 years of launch has dropped from around 60% to less than 20%, the average operational life of a mission has almost tripled, increasing from 3.3 years in the 1970s to 8.6 years (and still lengthening), the average number of satellites launched per-year/per-decade has increased from 2 to 12 and spatial resolution increased from around 80 m to less than 1 m multispectral and less than half a meter for panchromatic; synthetic aperture radar resolution has also fallen, from 25 m in the 1970s to 1 m post 2007. More people in more countries have access to data from global land-cover observing spaceborne missions at a greater range of spatial resolutions than ever before. We provide a compendium of such missions, analyze the changes and shows how innovation, the need for secure data-supply, national pride, falling costs and technological advances may underpin the trends we document.

Physical assessment of coastal vulnerability under enhanced land subsidence in Semarang, Indonesia, using multi-sensor satellite data

Science.gov (United States)

Husnayaen; Rimba, A. Besse; Osawa, Takahiro; Parwata, I. Nyoman Sudi; As-syakur, Abd. Rahman; Kasim, Faizal; Astarini, Ida Ayu

2018-04-01

Research has been conducted in Semarang, Indonesia, to assess coastal vulnerability under enhanced land subsidence using multi-sensor satellite data, including the Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR), Landsat TM, IKONOS, and TOPEX/Poseidon. A coastal vulnerability index (CVI) was constructed to estimate the level of vulnerability of a coastline approximately 48.68 km in length using seven physical variables, namely, land subsidence, relative sea level change, coastal geomorphology, coastal slope, shoreline change, mean tidal range, and significant wave height. A comparison was also performed between a CVI calculated using seven parameters and a CVI using six parameters, the latter of which excludes the land subsidence parameter, to determine the effects of land subsidence during the coastal vulnerability assessment. This study showed that the accuracy of coastal vulnerability was increased 40% by adding the land subsidence factor (i.e., CVI 6 parameters = 53%, CVI 7 parameters = 93%). Moreover, Kappa coefficient indicated very good agreement (0.90) for CVI 7 parameters and fair agreement (0.3) for CVI 6 parameters. The results indicate that the area of very high vulnerability increased by 7% when land subsidence was added. Hence, using the CVI calculation including land subsidence parameters, the very high vulnerability area is determined to be 20% of the total coastline or 9.7 km of the total 48.7 km of coastline. This study proved that land subsidence has significant influence on coastal vulnerability in Semarang.

Remote sensing of land surface phenology

Science.gov (United States)

Meier, G.A.; Brown, Jesslyn F.

2014-01-01

Remote sensing of land-surface phenology is an important method for studying the patterns of plant and animal growth cycles. Phenological events are sensitive to climate variation; therefore phenology data provide important baseline information documenting trends in ecology and detecting the impacts of climate change on multiple scales. The USGS Remote sensing of land surface phenology program produces annually, nine phenology indicator variables at 250 m and 1,000 m resolution for the contiguous U.S. The 12 year archive is available at http://phenology.cr.usgs.gov/index.php.

Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

Directory of Open Access Journals (Sweden)

A. L. Barbu

2014-01-01

Full Text Available The land monitoring service of the European Copernicus programme has developed a set of satellite-based biogeophysical products, including surface soil moisture (SSM and leaf area index (LAI. This study investigates the impact of joint assimilation of remotely sensed SSM derived from Advanced Scatterometer (ASCAT backscatter data and the Copernicus Global Land GEOV1 satellite-based LAI product into the the vegetation growth version of the Interactions between Soil Biosphere Atmosphere (ISBA-A-gs land surface model within the the externalised surface model (SURFEX modelling platform of Météo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function matching technique. A multivariate multi-scale land data assimilation system (LDAS based on the extended Kalman Filter (EKF is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the domain of France at a spatial resolution of 8 km. Each model grid box is divided into a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average. In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008–2011. The EKF is able to extract useful information from the data signal at the grid scale and distribute the root-zone soil moisture and LAI increments throughout the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study of the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil

Multi-Sourced Satellite Observations of Land Cover and Land Use Change in South and Southeast Asia with Challenging Environmental and Socioeconomic Impacts

Science.gov (United States)

Nghiem, S. V.; Small, C.; Jacobson, M. Z.; Brakenridge, G. R.; Balk, D.; Sorichetta, A.; Masetti, M.; Gaughan, A. E.; Stevens, F. R.; Mathews, A.; Frazier, A. E.; Das, N. N.

2017-12-01

An innovative paradigm to observe the rural-urban transformation over the landscape using multi-sourced satellite data is formulated as a time and space continuum, extensively in space across South and Southeast Asia and in time over a decadal scale. Rather than a disparate array of individual cities and their vicinities in separated areas and in a discontinuous collection of points in time, the time-space continuum paradigm enables significant advances in addressing rural-urban change as a continuous gradient across the landscape from the wilderness to rural to urban areas to study challenging environmental and socioeconomic issues. We use satellite data including QuikSCAT scatterometer, SRTM and Sentinel-1 SAR, Landsat, WorldView, MODIS, and SMAP together with environmental and demographic data and modeling products to investigate land cover and land use change in South and Southeast Asia and associated impacts. Utilizing the new observational advances and effectively capitalizing current capabilities, we will present interdisciplinary results on urbanization in three dimensions, flood and drought, wildfire, air and water pollution, urban change, policy effects, population dynamics and vector-borne disease, agricultural assessment, and land degradation and desertification.

Spatio-temporal Root Zone Soil Moisture Estimation for Indo - Gangetic Basin from Satellite Derived (AMSR-2 and SMOS) Surface Soil Moisture

Science.gov (United States)

Sure, A.; Dikshit, O.

2017-12-01

Root zone soil moisture (RZSM) is an important element in hydrology and agriculture. The estimation of RZSM provides insight in selecting the appropriate crops for specific soil conditions (soil type, bulk density, etc.). RZSM governs various vadose zone phenomena and subsequently affects the groundwater processes. With various satellite sensors dedicated to estimating surface soil moisture at different spatial and temporal resolutions, estimation of soil moisture at root zone level for Indo - Gangetic basin which inherits complex heterogeneous environment, is quite challenging. This study aims at estimating RZSM and understand its variation at the level of Indo - Gangetic basin with changing land use/land cover, topography, crop cycles, soil properties, temperature and precipitation patterns using two satellite derived soil moisture datasets operating at distinct frequencies with different principles of acquisition. Two surface soil moisture datasets are derived from AMSR-2 (6.9 GHz - `C' Band) and SMOS (1.4 GHz - `L' band) passive microwave sensors with coarse spatial resolution. The Soil Water Index (SWI), accounting for soil moisture from the surface, is derived by considering a theoretical two-layered water balance model and contributes in ascertaining soil moisture at the vadose zone. This index is evaluated against the widely used modelled soil moisture dataset of GLDAS - NOAH, version 2.1. This research enhances the domain of utilising the modelled soil moisture dataset, wherever the ground dataset is unavailable. The coupling between the surface soil moisture and RZSM is analysed for two years (2015-16), by defining a parameter T, the characteristic time length. The study demonstrates that deriving an optimal value of T for estimating SWI at a certain location is a function of various factors such as land, meteorological, and agricultural characteristics.

Estimating Advective Near-surface Currents from Ocean Color Satellite Images

Science.gov (United States)

2015-01-01

on the SuomiNational Polar-Orbiting Partner- ship (S- NPP ) satellite. The GOCI is the world’s first geostationary orbit satellite sensor over the...radiance Lwn at several wave - lengths. These spectral Lwn channels are used to derive several in- water bio-optical properties (Lee, Carder, & Arnone...the same surface flow, it is the inter-product similarities, instead of the differences, that are more likely to stand for the surface advection. If

Near-surface land disposal

International Nuclear Information System (INIS)

Kittel, J.H.

1989-01-01

The Radioactive Waste Management Handbook provides a comprehensive, systematic treatment of nuclear waste management. Near-Surface Land Disposal, the first volume, is a primary and secondary reference for the technical community. To those unfamiliar with the field, it provides a bridge to a wealth of technical information, presenting the technology associated with the near-surface disposal of low or intermediate level wastes. Coverage ranges from incipient planning to site closure and subsequent monitoring. The book discusses the importance of a systems approach during the design of new disposal facilities so that performance objectives can be achieved; gives an overview of the radioactive wastes cosigned to near-surface disposal; addresses procedures for screening and selecting sites; and emphasizes the importance of characterizing sites and obtaining reliable geologic and hydrologic data. The planning essential to the development of particular sites (land acquisition, access, layout, surface water management, capital costs, etc.) is considered, and site operations (waste receiving, inspection, emplacement, closure, stabilization, etc.) are reviewed. In addition, the book presents concepts for improved confinement of waste, important aspects of establishing a monitoring program at the disposal facility, and corrective actions available after closure to minimize release. Two analytical techniques for evaluating alternative technologies are presented. Nontechnical issues surrounding disposal, including the difficulties of public acceptance are discussed. A glossary of technical terms is included

Mapping Impervious Surface Expansion using Medium-resolution Satellite Image Time Series: A Case Study in the Yangtze River Delta, China

Science.gov (United States)

Gao, Feng; DeColstoun, Eric Brown; Ma, Ronghua; Weng, Qihao; Masek, Jeffrey G.; Chen, Jin; Pan, Yaozhong; Song, Conghe

2012-01-01

Cities have been expanding rapidly worldwide, especially over the past few decades. Mapping the dynamic expansion of impervious surface in both space and time is essential for an improved understanding of the urbanization process, land-cover and land-use change, and their impacts on the environment. Landsat and other medium-resolution satellites provide the necessary spatial details and temporal frequency for mapping impervious surface expansion over the past four decades. Since the US Geological Survey opened the historical record of the Landsat image archive for free access in 2008, the decades-old bottleneck of data limitation has gone. Remote-sensing scientists are now rich with data, and the challenge is how to make best use of this precious resource. In this article, we develop an efficient algorithm to map the continuous expansion of impervious surface using a time series of four decades of medium-resolution satellite images. The algorithm is based on a supervised classification of the time-series image stack using a decision tree. Each imerpervious class represents urbanization starting in a different image. The algorithm also allows us to remove inconsistent training samples because impervious expansion is not reversible during the study period. The objective is to extract a time series of complete and consistent impervious surface maps from a corresponding times series of images collected from multiple sensors, and with a minimal amount of image preprocessing effort. The approach was tested in the lower Yangtze River Delta region, one of the fastest urban growth areas in China. Results from nearly four decades of medium-resolution satellite data from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+) and China-Brazil Earth Resources Satellite (CBERS) show a consistent urbanization process that is consistent with economic development plans and policies. The time-series impervious spatial extent maps derived

High-resolution Continental Scale Land Surface Model incorporating Land-water Management in United States

Science.gov (United States)

Shin, S.; Pokhrel, Y. N.

2016-12-01

Land surface models have been used to assess water resources sustainability under changing Earth environment and increasing human water needs. Overwhelming observational records indicate that human activities have ubiquitous and pertinent effects on the hydrologic cycle; however, they have been crudely represented in large scale land surface models. In this study, we enhance an integrated continental-scale land hydrology model named Leaf-Hydro-Flood to better represent land-water management. The model is implemented at high resolution (5km grids) over the continental US. Surface water and groundwater are withdrawn based on actual practices. Newly added irrigation, water diversion, and dam operation schemes allow better simulations of stream flows, evapotranspiration, and infiltration. Results of various hydrologic fluxes and stores from two sets of simulation (one with and the other without human activities) are compared over a range of river basin and aquifer scales. The improved simulations of land hydrology have potential to build consistent modeling framework for human-water-climate interactions.

Application of Satellite Data for Early Season Assessment of Fallowed Agricultural Lands for Drought Impact Reporting

Science.gov (United States)

Rosevelt, C.; Melton, F. S.; Johnson, L.; Verdin, J. P.; Thenkabail, P. S.; mueller, R.; Zakzeski, A.; Jones, J.

2013-12-01

Rapid assessment of drought impacts can aid water managers in assessing mitigation options, and guide decision making with respect to requests for local water transfers, county drought disaster designations, or state emergency proclamations. Satellite remote sensing offers an efficient way to provide quantitative assessments of drought impacts on agricultural production and land fallowing associated with reductions in water supply. A key advantage of satellite-based assessments is that they can provide a measure of land fallowing that is consistent across both space and time. Here we describe an approach for monthly mapping of land fallowing developed as part of a joint effort by USGS, USDA, and NASA to provide timely assessments of land fallowing during drought events. This effort has used the Central Valley of California as a pilot region for development and testing of an operational approach. To provide quantitative measures of fallowed land from satellite data early in the season, we developed a decision tree algorithm and applied it to timeseries of normalized difference vegetation index (NDVI) data from Landsat TM, ETM+, and MODIS. Our effort has been focused on development of leading indicators of drought impacts in the March - June timeframe based on measures of crop development patterns relative to a reference period with average or above average rainfall. This capability complements ongoing work by USDA to produce and publicly release within-season estimates of fallowed acreage from the USDA Cropland Data Layer. To assess the accuracy of the algorithms, monthly ground validation surveys were conducted along transects across the Central Valley at more than 200 fields per month from March - June, 2013. Here we present the algorithm for mapping fallowed acreage early in the season along with results from the accuracy assessment, and discuss potential applications to other regions.

Spatial and energy distributions of satellite-speed helium atoms reflected from satellite-type surfaces

International Nuclear Information System (INIS)

Liu, S.M.; Rodgers, W.E.; Knuth, E.L.

1977-01-01

Interactions of satellite-speed helium atoms (accelerated in an expansion from an arc-heated supersonic-molecular-beam source) with practical satellite surfaces have been investigated experimentally. The density and energy distributions of the scattered atoms were measured using a detection system developed for this study. This detection system includes (a) a target positioning mechanism, (b) a detector rotating mechanism, and (c) a mass spectrometer and/or a retarding-field energy analyzer. (Auth.)

Constraining the JULES land-surface model for different land-use types using citizen-science generated hydrological data

Science.gov (United States)

Chou, H. K.; Ochoa-Tocachi, B. F.; Buytaert, W.

2017-12-01

Community land surface models such as JULES are increasingly used for hydrological assessment because of their state-of-the-art representation of land-surface processes. However, a major weakness of JULES and other land surface models is the limited number of land surface parameterizations that is available. Therefore, this study explores the use of data from a network of catchments under homogeneous land-use to generate parameter "libraries" to extent the land surface parameterizations of JULES. The network (called iMHEA) is part of a grassroots initiative to characterise the hydrological response of different Andean ecosystems, and collects data on streamflow, precipitation, and several weather variables at a high temporal resolution. The tropical Andes are a useful case study because of the complexity of meteorological and geographical conditions combined with extremely heterogeneous land-use that result in a wide range of hydrological responses. We then calibrated JULES for each land-use represented in the iMHEA dataset. For the individual land-use types, the results show improved simulations of streamflow when using the calibrated parameters with respect to default values. In particular, the partitioning between surface and subsurface flows can be improved. But also, on a regional scale, hydrological modelling was greatly benefitted from constraining parameters using such distributed citizen-science generated streamflow data. This study demonstrates the modelling and prediction on regional hydrology by integrating citizen science and land surface model. In the context of hydrological study, the limitation of data scarcity could be solved indeed by using this framework. Improved predictions of such impacts could be leveraged by catchment managers to guide watershed interventions, to evaluate their effectiveness, and to minimize risks.

Reconstructing satellite images to quantify spatially explicit land surface change caused by fires and succession: A demonstration in the Yukon River Basin of interior Alaska

Science.gov (United States)

Huang, Shengli; Jin, Suming; Dahal, Devendra; Chen, Xuexia; Young, Claudia; Liu, Heping; Liu, Shuguang

2013-01-01

Land surface change caused by fires and succession is confounded by many site-specific factors and requires further study. The objective of this study was to reveal the spatially explicit land surface change by minimizing the confounding factors of weather variability, seasonal offset, topography, land cover, and drainage. In a pilot study of the Yukon River Basin of interior Alaska, we retrieved Normalized Difference Vegetation Index (NDVI), albedo, and land surface temperature (LST) from a postfire Landsat image acquired on August 5th, 2004. With a Landsat reference image acquired on June 26th, 1986, we reconstructed NDVI, albedo, and LST of 1987–2004 fire scars for August 5th, 2004, assuming that these fires had not occurred. The difference between actual postfire and assuming-no-fire scenarios depicted the fires and succession impact. Our results demonstrated the following: (1) NDVI showed an immediate decrease after burning but gradually recovered to prefire levels in the following years, in which burn severity might play an important role during this process; (2) Albedo showed an immediate decrease after burning but then recovered and became higher than prefire levels; and (3) Most fires caused surface warming, but cooler surfaces did exist; time-since-fire affected the prefire and postfire LST difference but no absolute trend could be found. Our approach provided spatially explicit land surface change rather than average condition, enabling a better understanding of fires and succession impact on ecological consequences at the pixel level.

Turbulent flow over an interactive alternating land-water surface

Science.gov (United States)

Van Heerwaarden, C.; Mellado, J. P.

2014-12-01

The alternating land-water surface is a challenging surface to represent accurately in weather and climate models, but it is of great importance for the surface energy balance in polar regions. The complexity of this surface lies in the fact that secondary circulations, which form at the boundary of water and land, interact strongly with the surface energy balance. Due to its large heat capacity, the water temperature adapts slowly to the flow, thus the properties of the atmosphere determine the uptake of energy from the water. In order to study this complex system in a simpler way, retaining only the most essential physics, we have simplified the full surface energy balance including radiation. We have derived a boundary condition that mimics the full balance and can be formulated as a so-called Robin boundary condition: a linear combination of Dirichlet (fixed temperature) and Neumann (fixed temperature gradient) ones. By spatially varying the coefficients, we are able to express land and water using this boundary condition. We have done a series of direct numerical simulations in which we generate artificial land-water patterns from noise created from a Gaussian spectrum centered around a dominant wave number. This method creates realistic random patterns, but we are still in control of the length scales. We show that the system can manifest itself in three regimes: micro-, meso- and macro-scale. In the micro-scale, we find perfect mixing of the near-surface atmosphere that results in identical air properties over water and land. In the meso-scale, secondary circulations alter the heat exchange considerably by advecting air between land and water. In addition, they bring the surface temperature of the land closer to that of the air, thereby modulating the energy loss due to outgoing longwave radiation. In the macro-scale regime, the flow over land and water become independent of each other and only the large scale forcings determine the energy balance.

New Directions in Land Remote Sensing Policy and International Cooperation

Science.gov (United States)

Stryker, Timothy

2010-12-01

Recent changes to land remote sensing satellite data policies in Brazil and the United States have led to the phenomenal growth in the delivery of land imagery to users worldwide. These new policies, which provide free and unrestricted access to land remote sensing data over a standard electronic interface, are expected to provide significant benefits to scientific and operational users, and open up new areas of Earth system science research and environmental monitoring. Freely-available data sets from the China-Brazil Earth Resources Satellites (CBERS), the U.S. Landsat satellites, and other satellite missions provide essential information for land surface monitoring, ecosystems management, disaster mitigation, and climate change research. These missions are making important contributions to the goals and objectives of regional and global terrestrial research and monitoring programs. These programs are in turn providing significant support to the goals and objectives of the United Nations Framework Convention on Climate Change (UN FCCC), the Global Earth Observation System of Systems (GEOSS), and the UN Reduction in Emissions from Deforestation and Degradation (REDD) program. These data policies are well-aligned with the "Data Democracy" initiative undertaken by the international Committee on Earth Observation Satellites (CEOS), through its current Chair, Brazil's National Institute for Space Research (Instituto Nacional de Pesquisas Espaciais, or INPE), and its former chairs, South Africa's Council for Scientific and Industrial Research (CSIR) and Thailand's Geo Informatics and Space Technology Development Agency (GISTDA). Comparable policies for land imaging data are under consideration within Europe and Canada. Collectively, these initiatives have the potential to accelerate and improve international mission collaboration, and greatly enhance the access, use, and application of land surface imagery for environmental monitoring and societal adaption to changing

Recent Enhancements in NOAA's JPSS Land Product Suite and Key Operational Applications

Science.gov (United States)

Csiszar, I. A.; Yu, Y.; Zhan, X.; Vargas, M.; Ek, M. B.; Zheng, W.; Wu, Y.; Smirnova, T. G.; Benjamin, S.; Ahmadov, R.; James, E.; Grell, G. A.

2017-12-01

A suite of operational land products has been produced as part of NOAA's Joint Polar Satellite System (JPSS) program to support a wide range of operational applications in environmental monitoring, prediction, disaster management and mitigation, and decision support. The Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (NPP) and the operational JPSS satellite series forms the basis of six fundamental and multiple additional added-value environmental data records (EDRs). A major recent improvement in the land-based VIIRS EDRs has been the development of global gridded products, providing a format and science content suitable for ingest into NOAA's operational land surface and coupled numerical weather prediction models. VIIRS near-real-time Green Vegetation Fraction is now in the process of testing for full operational use, while land surface temperature and albedo are under testing and evaluation. The operational 750m VIIRS active fire product, including fire radiative power, is used to support emission modeling and air quality applications. Testing the evaluation for operational NOAA implementation of the improved 375m VIIRS active fire product is also underway. Added-value and emerging VIIRS land products include vegetation health, phenology, near-real-time surface type and surface condition change, and other biogeophysical variables. As part of the JPSS program, a global soil moisture data product has also been generated from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor on the GCOM-W1 (Global Change Observation Mission - Water 1) satellite since July 2012. This product is included in the blended NESDIS Soil Moisture Operational Products System, providing soil moisture data as a critical input for land surface modeling.

Daytime urban heat islands from Landsat ETM+ and Corine land cover data: An application to major cities in Greece

Energy Technology Data Exchange (ETDEWEB)

Stathopoulou, Marina; Cartalis, Constantinos [Remote Sensing and Image Processing Laboratory, Division of Applied Physics, Department of Physics, Building PHYS-5, University Campus, 157 84 Athens (Greece)

2007-03-15

Satellite images in the thermal infrared can be used for assessing the thermal urban environment as well as for defining heat islands in urban areas. In this study, the thermal environment of major cities in Greece (Athens, Thessaloniki, Patra, Volos and Heraklion) is examined using satellite images provided by the Landsat Enhanced Thematic Mapper (ETM+) sensor on board Landsat 7 satellite corresponding to the daytime and warm period when the surface urban heat island (SUHI) phenomenon is best observed. The spatial structure of the thermal urban environment is analyzed in each case study and the ''hottest'' surfaces within the urban settings are identified and related to the urban surface characteristics and land use. For the needs of the study, the Corine land cover (CLC) database for Greece is also used, in an effort to define more effectively the link between surface emissivities, land surface temperatures and urban surface characteristics. (author)

Advancements in medium and high resolution Earth observation for land-surface imaging: Evolutions, future trends and contributions to sustainable development

Science.gov (United States)

Ouma, Yashon O.

2016-01-01

Technologies for imaging the surface of the Earth, through satellite based Earth observations (EO) have enormously evolved over the past 50 years. The trends are likely to evolve further as the user community increases and their awareness and demands for EO data also increases. In this review paper, a development trend on EO imaging systems is presented with the objective of deriving the evolving patterns for the EO user community. From the review and analysis of medium-to-high resolution EO-based land-surface sensor missions, it is observed that there is a predictive pattern in the EO evolution trends such that every 10-15 years, more sophisticated EO imaging systems with application specific capabilities are seen to emerge. Such new systems, as determined in this review, are likely to comprise of agile and small payload-mass EO land surface imaging satellites with the ability for high velocity data transmission and huge volumes of spatial, spectral, temporal and radiometric resolution data. This availability of data will magnify the phenomenon of ;Big Data; in Earth observation. Because of the ;Big Data; issue, new computing and processing platforms such as telegeoprocessing and grid-computing are expected to be incorporated in EO data processing and distribution networks. In general, it is observed that the demand for EO is growing exponentially as the application and cost-benefits are being recognized in support of resource management.

Tracking- and Scintillation-Aware Channel Model for GEO Satellite to Land Mobile Terminals at Ku-Band

Directory of Open Access Journals (Sweden)

Ali M. Al-Saegh

2015-01-01

Full Text Available Recent advances in satellite to land mobile terminal services and technologies, which utilize high frequencies with directional antennas, have made the design of an appropriate model for land mobile satellite (LMS channels a necessity. This paper presents LMS channel model at Ku-band with features that enhance accuracy, comprehensiveness, and reliability. The effect of satellite tracking loss at different mobile terminal speeds is considered for directional mobile antenna systems, a reliable tropospheric scintillation model for an LMS scenario at tropical and temperate regions is presented, and finally a new quality indicator module for different modulation and coding schemes is included. The proposed extended LMS channel (ELMSC model is designed based on actual experimental measurements and can be applied to narrow- and wide-band signals at different regions and at different speeds and multichannel states. The proposed model exhibits lower root mean square error (RMSE and significant performance observation compared with the conventional model in terms of the signal fluctuations, fade depth, signal-to-noise ratio (SNR, and quality indicators accompanied for several transmission schemes.

Contrasting self-aggregation over land and ocean surfaces

Science.gov (United States)

Inda Diaz, H. A.; O'Brien, T. A.

2017-12-01

The spontaneous organization of convection into clusters, or self-aggregation, demonstrably changes the nature and statistics of precipitation. While there has been much recent progress in this area, the processes that control self-aggregation are still poorly understood. Most of the work to date has focused on self-aggregation over ocean-like surfaces, but it is particularly pressing to understand what controls convective aggregation over land, since the associated change in precipitation statistics—between non-aggregated and aggregated convection—could have huge impacts on society and infrastructure. Radiative-convective equilibrium (RCE), has been extensively used as an idealized framework to study the tropical atmosphere. Self-aggregation manifests in numerous numerical models of RCE, nevertheless, there is still a lack of understanding in how it relates to convective organization in the observed world. Numerous studies have examined self-aggregation using idealized Cloud Resolving Models (CRMs) and General Circulation Models over the ocean, however very little work has been done on RCE and self-aggregation over land. Idealized models of RCE over ocean have shown that aggregation is sensitive to sea surface temperature (SST), more intense precipitation occurs in aggregated systems, and a variety of feedbacks—such as surface flux, cloud radiative, and upgradient moisture transport— contribute to the maintenance of aggregation, however it is not clear if these results apply over land. Progress in this area could help relate understanding of self-aggregation in idealized simulations to observations. In order to explore the behavior of self-aggregation over land, we use a CRM to simulate idealized RCE over land. In particular, we examine the aggregation of convection and how it compares with aggregation over ocean. Based on previous studies, where a variety of different CRMs exhibit a SST threshold below which self-aggregation does not occur, we hypothesize

Assessment and Enhancement of MERRA Land Surface Hydrology Estimates

Science.gov (United States)

Reichle, Rolf H.; Koster, Randal D.; deLannoy, Gabrielle J. M.; Forman, Barton A.; Liu, Qing; Mahanama, Sarith P. P.; Toure, Ally

2012-01-01

The Modern-Era Retrospective analysis for Research and Applications (MERRA) is a state-ofthe-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and runoff for 1979-present. This study introduces a supplemental and improved set of land surface hydrological fields ("MERRA-Land") generated by re-running a revised version of the land component of the MERRA system. Specifically, the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameter values in the rainfall interception model, changes that effectively correct for known limitations in the MERRA surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ERA-Interim (ERA-I) reanalysis. MERRA-Land and ERA-I root zone soil moisture skills (against in situ observations at 85 US stations) are comparable and significantly greater than that of MERRA. Throughout the northern hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 18 US basins) of MERRA and MERRA-Land is typically higher than that of ERA-I. With a few exceptions, the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using MERRA output for land surface hydrological studies.

Satellite Eye for the Galathea 3 ship expedition: Global tour 2006-2007

DEFF Research Database (Denmark)

Hasager, Charlotte Bay; Badger, Merete; Sørensen, Peter

2007-01-01

Satellite Eye for Galathea 3 (www.satellitecye.dk contains education at the internet for secondary and upper secondary schools and the public. The Galathea 3 ship expedition circumnavigated the globe starting from Denmark 11 August 2006, visiting Greenland, Azores, South Africa, Australia, Solomon...... of the expedition and these classes in particular used the Satellite Eye teaching material. In Google Earth satellite images of many themes are shown. These include sea ice, sea surface temperature, ocean wind, wave height, sea surface level, ozone, clouds and radar images of ocean and land. Also high spatial...

Seasonal dynamics of surface chlorophyll concentration and sea surface temperature, as indicator of hydrological structure of the ocean (by satellite data)

Science.gov (United States)

Shevyrnogov, Anatoly; Vysotskaya, Galina

Continuous monitoring of phytopigment concentrations and sea surface temperature in the ocean by space-borne methods makes possible to estimate ecological condition of biocenoses in critical areas. Unlike land vegetation, hydrological processes largely determine phytoplank-ton dynamics, which may be either recurrent or random. The types of chlorophyll concentration dynamics and sea surface temperature can manifest as zones quasistationary by seasonal dynamics, quasistationary areas (QSA). In the papers of the authors (A. Shevyrnogov, G. Vysotskaya, E. Shevyrnogov, A study of the stationary and the anomalous in the ocean surface chlorophyll distribution by satellite data. International Journal of Remote Sensing, Vol. 25, No.7-8, pp. 1383-1387, April 2004 & A. P. Shevyrnogov, G. S. Vysotskaya, J. I. Gitelson, Quasistationary areas of chlorophyll concentra-tion in the world ocean as observed satellite data Advances in Space Research, Volume 18, Issue 7, Pages 129-132, 1996) existence of zones, which are quasi-stationary with similar seasonal dynamics of chlorophyll concentration at surface layer of ocean, was shown. Results were obtained on the base of processing of time series of satellite images SeaWiFS. It was shown that fronts and frontal zones coincide with dividing lines between quasi-stationary are-as, especially in areas of large oceanic streams. To study the dynamics of the ocean for the period from 1985 through 2012 we used data on the temperature of the surface layer of the ocean and chlorophyll concentration (AVHRR, SeaWiFS and MODIS). Biota of surface oceanic layer is more stable in comparison with quickly changing surface tem-perature. It gives a possibility to circumvent influence of high-frequency component (for exam-ple, a diurnal cycle) in investigation of dynamics of spatial distribution of surface streams. In addition, an analyses of nonstable ocean productivity phenomena, stood out time series of satellite images, showed existence of areas with

Long-range Transport of Asian Dust Storms: A Satellite/Surface Perspective on Societal and Scientific Influence

Science.gov (United States)

2007-01-01

Among the many components contributing to air pollution, airborne mineral dust plays an important role due to its biogeochemical impact on the ecosystem and its radiative forcing effect on the weather/climate system. As much as one-third to half of the global dust emissions, estimated about 800 Tg, are introduced annually into Earth's atmosphere from various deserts in China. Asian dust storm outbreaks are believed to have persisted for hundreds and thousands years over the vast territory of north and northwest China, but not until recent decades that many studies reveal the compelling evidence in recognizing the importance of these eolian dust particles for forming Chinese Loess Plateau and for biogeochemical cycling in the North Pacific Ocean to as far as in the Greenland ice-sheets through long-range transport. The Asian dust and air pollution aerosols can be detected by its colored appearance on current Earth observing satellites and its evolution monitored by satellite and surface network. In this paper, we will demonstrate the capability of a new satellite algorithm, called Deep Blue, to retrieve aerosol properties, particularly but not limited to, over bright-reflecting surfaces such as urban areas and deserts. Recently, many field campaigns were designed and executed to study the compelling variability in spatial and temporal scale of both pollution-derived and naturally occurring aerosols, which often exist in high concentrations over eastern Asia and along the rim of the western Pacific. We will provide an overview of the outbreak of Asian dust storms, near source/sink and their evolution along transport pathway, from space and surface observations. The climatic effects and societal impacts of the Asian dusts will be addressed in depth. (to be presented in the International Workshop on Semi-Arid Land Surface-

Monitoring sea level and sea surface temperature trends from ERS satellites

DEFF Research Database (Denmark)

Andersen, Ole Baltazar; Knudsen, Per; Beckley, B.

2002-01-01

Data from the two ESA satellites ERS-1 and ERS-2 are used in global and regional analysis of sea level and sea surface temperature trends over the last, 7.8 years. T he ERS satellites and in the future the ENVISAT satellite provide unique opportunity for monitoring both changes in sea level and sea...

Land Mobile Satellite Service (LMSS): A conceptual system design and identification of the critical technologies: Part 2: Technical report

Science.gov (United States)

Naderi, F. (Editor)

1982-01-01

A conceptual system design for a satellite-aided land mobile service is described. A geostationary satellite which employs a large (55-m) UHF reflector to communicate with small inexpensive user antennas on mobile vehicles is discussed. It is shown that such a satellite system through multiple beam antennas and frequency reuse can provide thousands of radiotelephone and dispatch channels serving hundreds of thousands of users throughout the U.S.

A Multisensor Approach to Global Retrievals of Land Surface Albedo

Directory of Open Access Journals (Sweden)

Aku Riihelä

2018-05-01

Full Text Available Satellite-based retrievals offer the most cost-effective way to comprehensively map the surface albedo of the Earth, a key variable for understanding the dynamics of radiative energy interactions in the atmosphere-surface system. Surface albedo retrievals have commonly been designed separately for each different spaceborne optical imager. Here, we introduce a novel type of processing framework that combines the data from two polar-orbiting optical imager families, the Advanced Very High-Resolution Radiometer (AVHRR and Moderate Resolution Imaging Spectroradiometer (MODIS. The goal of the paper is to demonstrate that multisensor albedo retrievals can provide a significant reduction in the sampling time required for a robust and comprehensive surface albedo retrieval, without a major degradation in retrieval accuracy, as compared to state-of-the-art single-sensor retrievals. We evaluated the multisensor retrievals against reference in situ albedo measurements and compare them with existing datasets. The results show that global land surface albedo retrievals with a sampling period of 10 days can offer near-complete spatial coverage, with a retrieval bias mostly comparable to existing single sensor datasets, except for bright surfaces (deserts and snow where the retrieval framework shows degraded performance because of atmospheric correction design compromises. A level difference is found between the single sensor datasets and the demonstrator developed here, pointing towards a need for further work in the atmospheric correction, particularly over bright surfaces, and inter-sensor radiance homogenization. The introduced framework is expandable to include other sensors in the future.

Effects of satellite image spatial aggregation and resolution on estimates of forest land area

Science.gov (United States)

M.D. Nelson; R.E. McRoberts; G.R. Holden; M.E. Bauer

2009-01-01

Satellite imagery is being used increasingly in association with national forest inventories (NFIs) to produce maps and enhance estimates of forest attributes. We simulated several image spatial resolutions within sparsely and heavily forested study areas to assess resolution effects on estimates of forest land area, independent of other sensor characteristics. We...

Long Term Validation of Land Surface Temperature Retrieved from MSG/SEVIRI with Continuous in-Situ Measurements in Africa

Directory of Open Access Journals (Sweden)

Frank-M. Göttsche

2016-05-01

Full Text Available Since 2005, the Land Surface Analysis Satellite Application Facility (LSA SAF operationally retrieves Land Surface Temperature (LST for the Spinning Enhanced Visible and Infrared Imager (SEVIRI on board Meteosat Second Generation (MSG. The high temporal resolution of the Meteosat satellites and their long term availability since 1977 make their data highly valuable for climate studies. In order to ensure that the LSA SAF LST product continuously meets its target accuracy of 2 °C, it is validated with in-situ measurements from four dedicated LST validation stations. Three stations are located in highly homogenous areas in Africa (semiarid bush, desert, and Kalahari semi-desert and typically provide thousands of monthly match-ups with LSA SAF LST, which are used to perform seasonally resolved validations. An uncertainty analysis performed for desert station Gobabeb yielded an estimate of total in-situ LST uncertainty of 0.8 ± 0.12 °C. Ignoring rainy seasons, the results for the period 2009–2014 show that LSA SAF LST consistently meets its target accuracy: the highest mean root-mean-square error (RMSE for LSA SAF LST over the African stations was 1.6 °C while mean absolute bias was 0.1 °C. Nighttime and daytime biases were up to 0.7 °C but had opposite signs: when evaluated together, these partially compensated each other.

Dust, Pollution, and Biomass Burning Aerosols in Asian Pacific: A Column Satellite-Surface Perspective

Science.gov (United States)

Tsay, Si-Chee

2004-01-01

Airborne dusts from northern China contribute a significant part of the air quality problem and, to some extent, regional climatic impact in Asia during spring-time. However, with the economical growth in China, increases in the emission of air pollutants generated from industrial and vehicular sources will not only impact the radiation balance, but adverse health effects to humans all year round. In addition, both of these dust and air pollution clouds can transport swiftly across the Pacific reaching North America within a few days, possessing an even larger scale effect. The Asian dust and air pollution aerosols can be detected by its colored appearance on current Earth observing satellites (e.g., MODIS, SeaWiFS, TOMS, etc.) and its evolution monitored by satellites and surface network. Biomass burning has been a regular practice for land clearing and land conversion in many countries, especially those in Africa, South America, and Southeast Asia. However, the unique climatology of Southeast Asia is very different than that of Africa and South America, such that large-scale biomass burning causes smoke to interact extensively with clouds during the peak-burning season of March to April. Significant global sources of greenhouse gases (e.g., CO2, CH4), chemically active gases (e.g., NO, CO, HC, CH3Br), and atmospheric aerosols are produced by biomass burning processes. These gases influence the Earth-atmosphere system, impacting both global climate and tropospheric chemistry. Some aerosols can serve as cloud condensation nuclei, which play an important role in determining cloud lifetime and precipitation, hence, altering the earth's radiation and water budget. Biomass burning also affects the biogeochemical cycling of nitrogen and carbon compounds from the soil to the atmosphere; the hydrological cycle (i.e., run off and evaporation); land surface reflectivity and emissivity; as well as ecosystem biodiversity and stability. Two new initiatives, EAST-AIRE (East

A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land

Directory of Open Access Journals (Sweden)

Feng Gao

2012-10-01

Full Text Available Thermal infrared (TIR imagery is normally acquired at coarser pixel resolution than that of shortwave sensors on the same satellite platform and often the TIR resolution is not suitable for monitoring crop conditions of individual fields or the impacts of land cover changes that are at significantly finer spatial scales. Consequently, thermal sharpening techniques have been developed to sharpen TIR imagery to shortwave band pixel resolutions, which are often fine enough for field-scale applications. A classic thermal sharpening technique, TsHARP, uses a relationship between land surface temperature (LST and Normalized Difference Vegetation Index (NDVI developed empirically at the TIR pixel resolution and applied at the NDVI pixel resolution. However, recent studies show that unique relationships between temperature and NDVI may only exist for a limited class of landscapes, with mostly green vegetation and homogeneous air and soil conditions. To extend application of thermal sharpening to more complex conditions, a new data mining sharpener (DMS technique is developed. The DMS approach builds regression trees between TIR band brightness temperatures and shortwave spectral reflectances based on intrinsic sample characteristics. A comparison of sharpening techniques applied over a rainfed agricultural area in central Iowa, an irrigated agricultural region in the Texas High Plains, and a heterogeneous naturally vegetated landscape in Alaska indicates that the DMS outperformed TsHARP in all cases. The artificial box-like patterns in LST generated by the TsHARP approach are greatly reduced using the DMS scheme, especially for areas containing irrigated crops, water bodies, thin clouds or terrain. While the DMS technique can provide fine resolution TIR imagery, there are limits to the sharpening ratios that can be reasonably implemented. Consequently, sharpening techniques cannot replace actual thermal band imagery at fine resolutions or missions that

Analysis of economic values of land use and land cover changes in crisis territories by satellite data: models of socio-economy and population dynamics in war

Science.gov (United States)

Kostyuchenko, Yuriy V.; Yuschenko, Maxim; Movchan, Dmytro; Kopachevsky, Ivan

2017-10-01

Problem of remote sensing data harnessing for decision making in conflict territories is considered. Approach for analysis of socio-economic and demographic parameters with a limited set of data and deep uncertainty is described. Number of interlinked techniques to estimate a population and economy in crisis territories are proposed. Stochastic method to assessment of population dynamics using multi-source data using remote sensing data is proposed. Adaptive Markov's chain based method to study of land-use changes using satellite data is proposed. Proposed approach is applied to analysis of socio-economic situation in Donbas (East Ukraine) territory of conflict in 2014-2015. Land-use and landcover patterns for different periods were analyzed using the Landsat and MODIS data . The land-use classification scheme includes the following categories: (1) urban or built-up land, (2) barren land, (3) cropland, (4) horticulture farms, (5) livestock farms, (6) forest, and (7) water. It was demonstrated, that during the period 2014-2015 was not detected drastic changes in land-use structure of study area. Heterogeneously distributed decreasing of horticulture farms (4-6%), livestock farms (5-6%), croplands (3-4%), and increasing of barren land (6-7%) have been observed. Way to analyze land-cover productivity variations using satellite data is proposed. Algorithm is based on analysis of time-series of NDVI and NDWI distributions. Drastic changes of crop area and its productivity were detected. Set of indirect indicators, such as night light intensity, is also considered. Using the approach proposed, using the data utilized, the local and regional GDP, local population, and its dynamics are estimated.

Development of land data sets for studies of global climate change

International Nuclear Information System (INIS)

Sadowski, F.G.; Watkins, A.H.

1991-01-01

The U.S. Geological Survey has begun a major initiative to organize, produce, and distribute land data sets that will support the land data requirements of the global change science community. Satellite image data sets, produced from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer sensors, will be developed to provide repetitive, synoptic coverage of regional, continental, and global land areas. These data sets, integrated with related land data and supplemented by coregistered Landsat data sets, will enable scientists to quantify the fundamental land surface attributes that are needed to model land surface processes, to detect and monitor land surface change, and to map land cover. These well-structured, consistent land data sets will form the historical record of land observations prior to the era of the National Aeronautics and Space Administration's Earth Observing System sensors

Land management and land-cover change have impacts of similar magnitude on surface temperature

DEFF Research Database (Denmark)

Luyssaert, Sebastiaan; Jammet, Mathilde; Stoy, Paul C.

2014-01-01

Anthropogenic changes to land cover (LCC) remain common, but continuing land scarcity promotes the widespread intensification of land management changes (LMC) to better satisfy societal demand for food, fibre, fuel and shelter1. The biophysical effects of LCC on surface climate are largely unders...

Mapping Surface Heat Fluxes by Assimilating SMAP Soil Moisture and GOES Land Surface Temperature Data

Science.gov (United States)

Lu, Yang; Steele-Dunne, Susan C.; Farhadi, Leila; van de Giesen, Nick

2017-12-01

Surface heat fluxes play a crucial role in the surface energy and water balance. In situ measurements are costly and difficult, and large-scale flux mapping is hindered by surface heterogeneity. Previous studies have demonstrated that surface heat fluxes can be estimated by assimilating land surface temperature (LST) and soil moisture to determine two key parameters: a neutral bulk heat transfer coefficient (CHN) and an evaporative fraction (EF). Here a methodology is proposed to estimate surface heat fluxes by assimilating Soil Moisture Active Passive (SMAP) soil moisture data and Geostationary Operational Environmental Satellite (GOES) LST data into a dual-source (DS) model using a hybrid particle assimilation strategy. SMAP soil moisture data are assimilated using a particle filter (PF), and GOES LST data are assimilated using an adaptive particle batch smoother (APBS) to account for the large gap in the spatial and temporal resolution. The methodology is implemented in an area in the U.S. Southern Great Plains. Assessment against in situ observations suggests that soil moisture and LST estimates are in better agreement with observations after assimilation. The RMSD for 30 min (daytime) flux estimates is reduced by 6.3% (8.7%) and 31.6% (37%) for H and LE on average. Comparison against a LST-only and a soil moisture-only assimilation case suggests that despite the coarse resolution, assimilating SMAP soil moisture data is not only beneficial but also crucial for successful and robust flux estimation, particularly when the uncertainties in the model estimates are large.

Study on the cloud detection of GOCI by using the simulated surface reflectance from BRDF-model for the land application and meteorological utilization

Science.gov (United States)

Kim, Hye-Won; Yeom, Jong-Min; Woo, Sun-Hee; Chae, Tae-Byeong

2016-04-01

COMS (Communication, Ocean, and Meteorological Satellite) was launched at French Guiana Kourou space center on 27 June 2010. Geostationary Ocean Color Imager (GOCI), which is the first ocean color geostationary satellite in the world for observing the ocean phenomena, is able to obtain the scientific data per an hour from 00UTC to 07UTC. Moreover, the spectral channels of GOCI would enable not only monitoring for the ocean, but for extracting the information of the land surface over the Korean Peninsula, Japan, and Eastern China. Since it is extremely important to utilize GOCI data accurately for the land application, cloud pixels over the surface have to be removed. Unfortunately, infra-red (IR) channels that can easily detect the water vapor with the cloud top temperature, are not included in the GOCI sensor. In this paper, the advanced cloud masking algorithm will be proposed with visible and near-IR (NIR) bands that are within GOCI bands. The main obstacle of cloud masking with GOCI is how to handle the high variable surface reflectance, which is mainly depending on the solar zenith angle. In this study, we use semi-empirical BRDF model to simulate the surface reflectance by using 16 day composite cloudy free image. When estimating the simulated surface reflectance, same geometry for GOCI observation was applied. The simulated surface reflectance is used to discriminate cloud areas especially for the thin cloud and shows more reasonable result than original threshold methods.

Satellite skill in detecting extreme episodes in near-surface air quality

Science.gov (United States)

Ruiz, D. J.; Prather, M. J.

2017-12-01

Ozone (O3) contributes to ambient air pollution, adversely affecting public health, agriculture, and ecosystems. Reliable, long-term, densely distributed surface networks are required to establish the scale, intensity and repeatability of major pollution events (designated here in a climatological sense as air quality extremes, AQX as defined in Schnell's work). Regrettably, such networks are only available for North America (NA) and Europe (EU), which does not include many populated regions where the deaths associated with air pollution exposure are alarmingly high. Directly measuring surface pollutants from space without lidar is extremely difficult. Mapping of daily pollution events requires cross-track nadir scanners and these have limited sensitivity to surface O3 levels. This work examines several years of coincident surface and OMI satellite measurements over NA-EU, in combination with a chemistry-transport model (CTM) hindcast of that period to understand how the large-scale AQX episodes may extend into the free troposphere and thus be more amenable to satellite mapping. We show how extreme NA-EU episodes are measured from OMI and then look for such patterns over other polluted regions of the globe. We gather individual high-quality O3 surface site measurements from these other regions, to check on our satellite detection. Our approach with global satellite detection would avoid issues associated with regional variations in seasonality, chemical regime, data product biases; and it does not require defining a separate absolute threshold for each data product (surface site and satellite). This also enables coherent linking of the extreme events into large-scale pollution episodes whose magnitude evolves over 100's of km for several days. Tools used here include the UC Irvine CTM, which shows that much of the O3 surface variability is lost at heights above 2 km, but AQX local events are readily seen in a 0-3 km column average. The OMI data are taken from X

Land Cover Characterization and Mapping of South America for the Year 2010 Using Landsat 30 m Satellite Data

Directory of Open Access Journals (Sweden)

Chandra Giri

2014-10-01

Full Text Available Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (<5 m satellite data to validate the land cover product. The overall accuracy of the 2010 South American 30-m land cover map is 89% with a Kappa coefficient of 79%. Accuracy of barren areas needs to improve possibly using multi-temporal Landsat data. An update of land cover and change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.

Modelling Angular Dependencies in Land Surface Temperatures From the SEVIRI Instrument onboard the Geostationary Meteosat Second Generation Satellites

DEFF Research Database (Denmark)

Rasmussen, Mads Olander; Pinheiro, AC; Proud, Simon Richard

2010-01-01

on vegetation structure and viewing and illumination geometry. Despite this, these effects are not considered in current operational LST products from neither polar-orbiting nor geostationary satellites. In this paper, we simulate the angular dependence that can be expected when estimating LST with the viewing...... geometry of the geostationary Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager sensor across the African continent and compare it to a normalized view geometry. We use the modified geometric projection model that estimates the scene thermal infrared radiance from a surface covered...

Satellites

International Nuclear Information System (INIS)

Burns, J.A.; Matthews, M.S.

1986-01-01

The present work is based on a conference: Natural Satellites, Colloquium 77 of the IAU, held at Cornell University from July 5 to 9, 1983. Attention is given to the background and origins of satellites, protosatellite swarms, the tectonics of icy satellites, the physical characteristics of satellite surfaces, and the interactions of planetary magnetospheres with icy satellite surfaces. Other topics include the surface composition of natural satellites, the cratering of planetary satellites, the moon, Io, and Europa. Consideration is also given to Ganymede and Callisto, the satellites of Saturn, small satellites, satellites of Uranus and Neptune, and the Pluto-Charon system

Evaluating The Land Use And Land Cover Dynamics In Borena ...

African Journals Online (AJOL)

The integration of satellite remote sensing and GIS was an effective approach for analyzing the direction, rate, and spatial pattern of land use change. Three land use and land cover maps were produced by analyzing remotely sensed images of Landsat satellite imageries at three time points (1972,1985,and 2003) .

Land Surface Model (LSM 1.0) for Ecological, Hydrological, Atmospheric Studies

Data.gov (United States)

National Aeronautics and Space Administration — The NCAR LSM 1.0 is a land surface model developed to examine biogeophysical and biogeochemical land-atmosphere interactions, especially the effects of land surfaces...

The greening of the McGill Paleoclimate Model. Part I: Improved land surface scheme with vegetation dynamics

Energy Technology Data Exchange (ETDEWEB)

Wang, Yi; Mysak, Lawrence A.; Wang, Zhaomin [McGill University, Department of Atmospheric and Oceanic Sciences, Global Environmental and Climate Change Centre (GEC3), Montreal, QC (Canada); Brovkin, Victor [Potsdam Institute for Climate Impact Research (PIK), Potsdam (Germany)

2005-04-01

The formulation of a new land surface scheme (LSS) with vegetation dynamics for coupling to the McGill Paleoclimate Model (MPM) is presented. This LSS has the following notable improvements over the old version: (1) parameterization of deciduous and evergreen trees by using the model's climatology and the output of the dynamic global vegetation model, VECODE (Brovkin et al. in Ecological Modelling 101:251-261 (1997), Global Biogeochemical Cycles 16(4):1139, (2002)); (2) parameterization of tree leaf budburst and leaf drop by using the model's climatology; (3) parameterization of the seasonal cycle of the grass leaf area index; (4) parameterization of the seasonal cycle of tree leaf area index by using the time-dependent growth of the leaves; (5) calculation of land surface albedo by using vegetation-related parameters, snow depth and the model's climatology. The results show considerable improvement of the model's simulation of the present-day climate as compared with that simulated in the original physically-based MPM. In particular, the strong seasonality of terrestrial vegetation and the associated land surface albedo variations are in good agreement with several satellite observations of these quantities. The application of this new version of the MPM (the ''green'' MPM) to Holocene millennial-scale climate changes is described in a companion paper, Part II. (orig.)

The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

Science.gov (United States)

Kim, E.; Tedesco, M.; Reichle, R.; Choudhury, B.; Peters-Lidard C.; Foster, J.; Hall, D.; Riggs, G.

2006-01-01

Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

Analysis and Assessment of Land Use Change in Alexandria, Egypt Using Satellite Images, GIS, and Modelling Techniques

International Nuclear Information System (INIS)

Abdou Azaz, L.K.

2008-01-01

Alexandria is the second largest urban governorate in Egypt and has seen significant urban growth in its modern and contemporary history. This study investigates the urban growth phenomenon in Alexandria, Egypt, using the integration of remote sensing and GIS. The urban physical expansion and change were detected using Landsat satellite images. The satellite images of years 1984 and 1993 were first geo referenced, achieving a very small RMSE that provided high accuracy data for satellite image analysis. Then, the images were classified using a tailored classification scheme with accuracy of 93.82% and 95.27% for 1984 and 1993 images respectively. This high accuracy enabled detecting land use/land cover changes with high confidence using a post-classification comparison method. One of the most important findings here is the loss of cultivated land in favour of urban expansion. If the current loss rates continued, 75% of green lands would be lost by year 2191. These hazardous rates call for an urban growth management policy that can preserve such valuable resources to achieve sustainable urban development. Modelling techniques can help in defining the scenarios of urban growth. In this study, the SLEUTH urban growth model was applied to predict future urban expansion in Alexandria until the year 2055. The application of this model in Alexandria of Egypt with its different environmental characteristics is the first application outside USA and Europe. The results revealed that future urban growth would continue along the edges of the current urban extent. This means that the cultivated lands in the east and the southeast of the city will be decreased. To deal with such crisis, there is a serious need for a comprehensive urban growth management programme that can be based on the best practices in similar situations

Regional seasonal warming anomalies and land-surface feedbacks

Science.gov (United States)

Coffel, E.; Horton, R. M.

2017-12-01

Significant seasonal variations in warming are projected in some regions, especially central Europe, the southeastern U.S., and central South America. Europe in particular may experience up to 2°C more warming during June, July, and August than in the annual mean, enhancing the risk of extreme summertime heat. Previous research has shown that heat waves in Europe and other regions are tied to seasonal soil moisture variations, and that in general land-surface feedbacks have a strong effect on seasonal temperature anomalies. In this study, we show that the seasonal anomalies in warming are also due in part to land-surface feedbacks. We find that in regions with amplified warming during the hot season, surface soil moisture levels generally decline and Bowen ratios increase as a result of a preferential partitioning of incoming energy into sensible vs. latent. The CMIP5 model suite shows significant variability in the strength of land-atmosphere coupling and in projections of future precipitation and soil moisture. Due to the dependence of seasonal warming on land-surface processes, these inter-model variations influence the projected summertime warming amplification and contribute to the uncertainty in projections of future extreme heat.

High spatial resolution satellite observations for validation of MODIS land products: IKONOS observations acquired under the NASA scientific data purchase.

Science.gov (United States)

Jeffrey T. Morisette; Jaime E. Nickeson; Paul Davis; Yujie Wang; Yuhong Tian; Curtis E. Woodcock; Nikolay Shabanov; Matthew Hansen; Warren B. Cohen; Doug R. Oetter; Robert E. Kennedy

2003-01-01

Phase 1I of the Scientific Data Purchase (SDP) has provided NASA investigators access to data from four different satellite and airborne data sources. The Moderate Resolution Imaging Spectrometer (MODIS) land discipline team (MODLAND) sought to utilize these data in support of land product validation activities with a lbcus on tile EOS Land Validation Core Sites. These...

Extratropical Influence of Sea Surface Temperature and Wind on Water Recycling Rate Over Oceans and Coastal Lands

Science.gov (United States)

Hu, Hua; Liu, W. Timothy

1999-01-01

Water vapor and precipitation are two important parameters confining the hydrological cycle in the atmosphere and over the ocean surface. In the extratropical areas, due to variations of midlatitude storm tracks and subtropical jetstreams, water vapor and precipitation have large variability. Recently, a concept of water recycling rate defined previously by Chahine et al. (GEWEX NEWS, August, 1997) has drawn increasing attention. The recycling rate of moisture is calculated as the ratio of precipitation to total precipitable water (its inverse is the water residence time). In this paper, using multi-sensor spacebased measurements we will study the role of sea surface temperature and ocean surface wind in determining the water recycling rate over oceans and coastal lands. Response of water recycling rate in midlatitudes to the El Nino event will also be discussed. Sea surface temperature data are derived from satellite observations from the Advanced Very High Resolution Radiometer (AVHRR) blended with in situ measurements, available for the period 1982-1998. Global sea surface wind observations are obtained from spaceborne scatterometers aboard on the European Remote-Sensing Satellite (ERS1 and 2), available for the period 1991-1998. Global total precipitable water provided by the NASA Water Vapor Project (NVAP) is available for the period 1988-1995. Global monthly mean precipitation provided by the Global Precipitation Climatology Project (GPCP) is available for the period 1987-1998.

Recent trends (2003-2013) of land surface heat fluxes on the southern side of the central Himalayas, Nepal

Science.gov (United States)

Amatya, Pukar Man; Ma, Yaoming; Han, Cunbo; Wang, Binbin; Devkota, Lochan Prasad

2015-12-01

Novice efforts have been made in order to study the regional distribution of land surface heat fluxes on the southern side of the central Himalayas utilizing high-resolution remotely sensed products, but these have been on instantaneous scale. In this study the Surface Energy Balance System model is used to obtain annual averaged maps of the land surface heat fluxes for 11 years (2003-2013) and study their annual trends on the central Himalayan region. The maps were derived at 5 km resolution using monthly input products ranging from satellite derived to Global Land Data Assimilation System meteorological data. It was found that the net radiation flux is increasing as a result of decreasing precipitation (drier environment). The sensible heat flux did not change much except for the northwestern High Himalaya and High Mountains. In northwestern High Himalaya sensible heat flux is decreasing because of decrease in wind speed, ground-air temperature difference, and increase in winter precipitation, whereas in High Mountains it is increasing due to increase in ground-air temperature difference and high rate of deforestation. The latent heat flux has an overall increasing trend with increase more pronounced in the lower regions compared to high elevated regions. It has been reported that precipitation is decreasing with altitude in this region. Therefore, the increasing trend in latent heat flux can be attributed to increase in net radiation flux under persistent forest cover and irrigation land used for agriculture.

Relating Nimbus-7 37 GHz data to global land-surface evaporation, primary productivity and the atmospheric CO2 concentration

Science.gov (United States)

Choudhury, B. J.

1988-01-01

Global observations at 37 GHz by the Nimbus-7 SMMR are related to zonal variations of land surface evaporation and primary productivity, as well as to temporal variations of atmospheric CO2 concentration. The temporal variation of CO2 concentration and the zonal variations of evaporation and primary productivity are shown to be highly correlated with the satellite sensor data. The potential usefulness of the 37-GHz data for global biospheric and climate studies is noted.

Handover aspects for a Low Earth Orbit (LEO) CDMA Land Mobile Satellite (LMS) system

Science.gov (United States)

Carter, P.; Beach, M. A.

1993-01-01

This paper addresses the problem of handoff in a land mobile satellite (LMS) system between adjacent satellites in a low earth orbit (LEO) constellation. In particular, emphasis is placed on the application of soft handoff in a direct sequence code division multiple access (DS-CDMA) LMS system. Soft handoff is explained in terms of terrestrial macroscopic diversity, in which signals transmitted via several independent fading paths are combined to enhance the link quality. This concept is then reconsidered in the context of a LEO LMS system. A two-state Markov channel model is used to simulate the effects of shadowing on the communications path from the mobile to each satellite during handoff. The results of the channel simulation form a platform for discussion regarding soft handoff, highlighting the potential merits of the scheme when applied in a LEO LMS environment.

Cloud Tolerance of Remote-Sensing Technologies to Measure Land Surface Temperature

Science.gov (United States)

Holmes, Thomas R. H.; Hain, Christopher R.; Anderson, Martha C.; Crow, Wade T.

2016-01-01

Conventional methods to estimate land surface temperature (LST) from space rely on the thermal infrared(TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passive microwave(MW) observations. The MW-LST product is informed by six polar-orbiting satellites to create a global record with up to eight observations per day for each 0.25resolution grid box. For days with sufficient observations, a continuous diurnal temperature cycle (DTC) was fitted. The main characteristics of the DTC were scaled to match those of a geostationary TIR-LST product. This paper tests the cloud tolerance of the MW-LST product. In particular, we demonstrate its stable performance with respect to flux tower observation sites (four in Europe and nine in the United States), over a range of cloudiness conditions up to heavily overcast skies. The results show that TIR based LST has slightly better performance than MW-LST for clear-sky observations but suffers an increasing negative bias as cloud cover increases. This negative bias is caused by incomplete masking of cloud-covered areas within the TIR scene that affects many applications of TIR-LST. In contrast, for MW-LST we find no direct impact of clouds on its accuracy and bias. MW-LST can therefore be used to improve TIR cloud screening. Moreover, the ability to provide LST estimates for cloud-covered surfaces can help expand current clear-sky-only satellite retrieval products to all-weather applications.

A global high resolution mean sea surface from multi mission satellite altimetry

DEFF Research Database (Denmark)

Knudsen, Per

1999-01-01

Satellite altimetry from the GEOSAT and the ERS-1 geodetic missions provide altimeter data with a very dense coverage. Hence, the heights of the sea surface may be recovered very detailed. Satellite altimetry from the 35 days repeat cycle mission of the ERS satellites and, especially, from the 10...

Retrieval of Land Surface Temperature over the Heihe River Basin Using HJ-1B Thermal Infrared Data

Directory of Open Access Journals (Sweden)

Xiaoying Ouyang

2014-12-01

Full Text Available The reliable estimation of spatially distributed Land Surface Temperature (LST is useful for monitoring regional land surface heat fluxes. A single-channel method is developed to derive the LST over the Heihe River Basin in China using data from the infrared sensor (IRS onboard the Chinese “Environmental and Disaster Monitoring and Forecasting with a Small Satellite Constellation” (HJ-1B for short for one of the satellites, with ancillary water vapor information from Moderate Resolution Imaging Spectroradiometer (MODIS products (MOD05 and in situ automatic sun tracking photometer CE318 data for the first time. In situ LST data for the period from mid-June to mid-September 2012 were acquired from automatic meteorological stations (AMS that are part of Heihe Watershed Allied Telemetry Experimental Research (HiWATER project. MOD05-based LST and CE318-based LST are compared with in situ measurements at 16 AMS sites with land cover types of vegetable, maize and orchards. The results show that the use of the MOD05 product could achieve a comparable accuracy in LST retrieval with that achieved using the CE318 data. The largest difference between the MOD05-based LST and CE318-based LST is 0.84 K throughout the study period over the Heihe River Basin. The standard deviation (STD, root mean square error (RMSE, and correlation coefficient (R of HJ-1B/IRS vs. the in situ measurements are 2.45 K, 2.78 K, and 0.67, respectively, whereas those for the MODIS 1 km LST product vs. the in situ measurements are 4.07 K, 2.98 K, and 0.79, respectively. The spatial pattern of the HJ-1B/LST over the study area in the Heihe River Basin generally agreed well with the MODIS 1 km LST product and contained more detailed spatial textures.

Land-surface modelling in hydrological perspective

DEFF Research Database (Denmark)

Overgaard, Jesper; Rosbjerg, Dan; Butts, M.B.

2006-01-01

The purpose of this paper is to provide a review of the different types of energy-based land-surface models (LSMs) and discuss some of the new possibilities that will arise when energy-based LSMs are combined with distributed hydrological modelling. We choose to focus on energy-based approaches......, and the difficulties inherent in various evaluation procedures are presented. Finally, the dynamic coupling of hydrological and atmospheric models is explored, and the perspectives of such efforts are discussed......., because in comparison to the traditional potential evapotranspiration models, these approaches allow for a stronger link to remote sensing and atmospheric modelling. New opportunities for evaluation of distributed land-surface models through application of remote sensing are discussed in detail...

Research on Land Surface Thermal-Hydrologic Exchange in Southern China under Future Climate and Land Cover Scenarios

Directory of Open Access Journals (Sweden)

Jianwu Yan

2013-01-01

Full Text Available Climate change inevitably leads to changes in hydrothermal circulation. However, thermal-hydrologic exchanging caused by land cover change has also undergone ineligible changes. Therefore, studying the comprehensive effects of climate and land cover changes on land surface water and heat exchanges enables us to well understand the formation mechanism of regional climate and predict climate change with fewer uncertainties. This study investigated the land surface thermal-hydrologic exchange across southern China for the next 40 years using a land surface model (ecosystem-atmosphere simulation scheme (EASS. Our findings are summarized as follows. (i Spatiotemporal variation patterns of sensible heat flux (H and evapotranspiration (ET under the land cover scenarios (A2a or B2a and climate change scenario (A1B are unanimous. (ii Both H and ET take on a single peak pattern, and the peak occurs in June or July. (iii Based on the regional interannual variability analysis, H displays a downward trend (10% and ET presents an increasing trend (15%. (iv The annual average H and ET would, respectively, increase and decrease by about 10% when woodland converts to the cultivated land. Through this study, we recognize that land surface water and heat exchanges are affected greatly by the future climate change as well as land cover change.

BUILT-UP AREA AND LAND COVER EXTRACTION USING HIGH RESOLUTION PLEIADES SATELLITE IMAGERY FOR MIDRAND, IN GAUTENG PROVINCE, SOUTH AFRICA

Directory of Open Access Journals (Sweden)

E. Fundisi

2017-09-01

Full Text Available Urban areas, particularly in developing countries face immense challenges such as climate change, poverty, lack of resources poor land use management systems, and week environmental management practices. Mitigating against these challenges is often hampered by lack of data on urban expansion, urban footprint and land cover. To support the recently adopted new urban agenda 2030 there is need for the provision of information to support decision making in the urban areas. Earth observation has been identified as a tool to foster sustainable urban planning and smarter cities as recognized by the new urban agenda, because it is a solution to unavailability of data. Accordingly, this study uses high resolution EO data Pleiades satellite imagery to map and document land cover for the rapidly expanding area of Midrand in Johannesburg, South Africa. An unsupervised land cover classification of the Pleiades satellite imagery was carried out using ENVI software, whereas NDVI was derived using ArcGIS software. The land cover had an accuracy of 85% that is highly adequate to document the land cover in Midrand. The results are useful because it provides a highly accurate land cover and NDVI datasets at localised spatial scale that can be used to support land use management strategies within Midrand and the City of Johannesburg South Africa.

Built-Up Area and Land Cover Extraction Using High Resolution Pleiades Satellite Imagery for Midrand, in Gauteng Province, South Africa

Science.gov (United States)

Fundisi, E.; Musakwa, W.

2017-09-01

Urban areas, particularly in developing countries face immense challenges such as climate change, poverty, lack of resources poor land use management systems, and week environmental management practices. Mitigating against these challenges is often hampered by lack of data on urban expansion, urban footprint and land cover. To support the recently adopted new urban agenda 2030 there is need for the provision of information to support decision making in the urban areas. Earth observation has been identified as a tool to foster sustainable urban planning and smarter cities as recognized by the new urban agenda, because it is a solution to unavailability of data. Accordingly, this study uses high resolution EO data Pleiades satellite imagery to map and document land cover for the rapidly expanding area of Midrand in Johannesburg, South Africa. An unsupervised land cover classification of the Pleiades satellite imagery was carried out using ENVI software, whereas NDVI was derived using ArcGIS software. The land cover had an accuracy of 85% that is highly adequate to document the land cover in Midrand. The results are useful because it provides a highly accurate land cover and NDVI datasets at localised spatial scale that can be used to support land use management strategies within Midrand and the City of Johannesburg South Africa.

SAFARI 2000 AVHRR-derived Land Surface Temperature Maps, Africa, 1995-2000

Data.gov (United States)

National Aeronautics and Space Administration — ABSTRACT: Land Surface Temperature (LST) is a key indicator of land surface states, and can provide information on surface-atmosphere heat and mass fluxes,...

The investigation of spatiotemporal variations of land surface temperature based on land use changes using NDVI in southwest of Iran

Science.gov (United States)

Fathizad, Hassan; Tazeh, Mahdi; Kalantari, Saeideh; Shojaei, Saeed

2017-10-01

Land use changes can bring about changes in land surface temperature (LST) which is influenced by climatic conditions and physical characteristics of the land surface. In this study, spatiotemporal variations of land surface temperature have been investigated in the desert area of Dasht-e-Abbas, Ilam, based on a variety of land use changes. The investigated periods for the study include 1990, 2000 and 2010 using Landsat image data. First, in mapping land use we used the Fuzzy ARTMAP Neural Network Classification method followed by determination of the NDVI Index to estimate land surface temperature. The results show an increase in LST in areas where degradation, land use and land cover changes have occurred. In 1990, 2000 and 2010, the average land surface temperature of the Fair Rangelands was 26.72 °C, 30.06 °C and 30.95 °C, respectively. This rangeland has been reduced by about 5%. For poor rangelands, the average LSTs were 26.95, 32.83 and 34.49 Cº, respectively which had a 18% reduction. In 1990, 2000 and 2010, the average land surface temperatures of agricultural lands were 24.31 °C, 27.87 °C and 28.61 °C, respectively which has been an increasing trend. The reason can be attributed to changes in cropping patterns of the study area.

Land Water Storage within the Congo Basin Inferred from GRACE Satellite Gravity Data

Science.gov (United States)

Crowley, John W.; Mitrovica, Jerry X.; Bailey, Richard C.; Tamisiea, Mark E.; Davis, James L.

2006-01-01

GRACE satellite gravity data is used to estimate terrestrial (surface plus ground) water storage within the Congo Basin in Africa for the period of April, 2002 - May, 2006. These estimates exhibit significant seasonal (30 +/- 6 mm of equivalent water thickness) and long-term trends, the latter yielding a total loss of approximately 280 km(exp 3) of water over the 50-month span of data. We also combine GRACE and precipitation data set (CMAP, TRMM) to explore the relative contributions of the source term to the seasonal hydrological balance within the Congo Basin. We find that the seasonal water storage tends to saturate for anomalies greater than 30-44 mm of equivalent water thickness. Furthermore, precipitation contributed roughly three times the peak water storage after anomalously rainy seasons, in early 2003 and 2005, implying an approximately 60-70% loss from runoff and evapotranspiration. Finally, a comparison of residual land water storage (monthly estimates minus best-fitting trends) in the Congo and Amazon Basins shows an anticorrelation, in agreement with the 'see-saw' variability inferred by others from runoff data.

SAFARI 2000 AVHRR-derived Land Surface Temperature Maps, Africa, 1995-2000

Data.gov (United States)

National Aeronautics and Space Administration — Land Surface Temperature (LST) is a key indicator of land surface states, and can provide information on surface-atmosphere heat and mass fluxes, vegetation water...

Assimilation of the ESA CCI Soil Moisture ACTIVE and PASSIVE Product into the SURFEX Land Surface Model using the Ensemble Transform Kalman Filter

Science.gov (United States)

Blyverket, J.; Hamer, P.; Bertino, L.; Lahoz, W. A.

2017-12-01

The European Space Agency Climate Change Initiative for soil moisture (ESA CCI SM) was initiated in 2012 for a period of six years, the objective for this period was to produce the most complete and consistent global soil moisture data record based on both active and passive sensors. The ESA CCI SM products consist of three surface soil moisture datasets: The ACTIVE product and the PASSIVE product were created by fusing scatterometer and radiometer soil moisture data, respectively. The COMBINED product is a blended product based on the former two datasets. In this study we assimilate globally both the ACTIVE and PASSIVE product at a 25 km spatial resolution. The different satellite platforms have different overpass times, an observation is mapped to the hours 00.00, 06.00, 12.00 or 18.00 if it falls within a 3 hour window centred at these times. We use the SURFEX land surface model with the ISBA diffusion scheme for the soil hydrology. For the assimilation routine we apply the Ensemble Transform Kalman Filter (ETKF). The land surface model is driven by perturbed MERRA-2 atmospheric forcing data, which has a temporal resolution of one hour and is mapped to the SURFEX model grid. Bias between the land surface model and the ESA CCI product is removed by cumulative distribution function (CDF) matching. This work is a step towards creating a global root zone soil moisture product from the most comprehensive satellite surface soil moisture product available. As a first step we consider the period from 2010 - 2016. This allows for comparison against other global root zone soil moisture products (SMAP Level 4, which is independent of the ESA CCI SM product).

Offshore Wind Energy: Wind and Sea Surface Temperature from Satellite Observations

DEFF Research Database (Denmark)

Karagali, Ioanna

as the entire atmosphere above. Under conditions of light winds and strong solar insolation, warming of the upper oceanic layer may occur. In this PhD study, remote sensing from satellites is used to obtain information for the near-surface ocean wind and the sea surface temperature over the North Sea......, demonstrate that wind information from SAR is more appropriate when small scale local features are of interest, not resolved by scatterometers. Hourly satellite observations of the sea surface temperature, from a thermal infra-red sensor, are used to identify and quantify the daily variability of the sea...

On the Land-Ocean Contrast of Tropical Convection and Microphysics Statistics Derived from TRMM Satellite Signals and Global Storm-Resolving Models

Science.gov (United States)

Matsui, Toshihisa; Chern, Jiun-Dar; Tao, Wei-Kuo; Lang, Stephen E.; Satoh, Masaki; Hashino, Tempei; Kubota, Takuji

2016-01-01

A 14-year climatology of Tropical Rainfall Measuring Mission (TRMM) collocated multi-sensor signal statistics reveal a distinct land-ocean contrast as well as geographical variability of precipitation type, intensity, and microphysics. Microphysics information inferred from the TRMM precipitation radar and Microwave Imager (TMI) show a large land-ocean contrast for the deep category, suggesting continental convective vigor. Over land, TRMM shows higher echo-top heights and larger maximum echoes, suggesting taller storms and more intense precipitation, as well as larger microwave scattering, suggesting the presence of morelarger frozen convective hydrometeors. This strong land-ocean contrast in deep convection is invariant over seasonal and multi-year time-scales. Consequently, relatively short-term simulations from two global storm-resolving models can be evaluated in terms of their land-ocean statistics using the TRMM Triple-sensor Three-step Evaluation via a satellite simulator. The models evaluated are the NASA Multi-scale Modeling Framework (MMF) and the Non-hydrostatic Icosahedral Cloud Atmospheric Model (NICAM). While both simulations can represent convective land-ocean contrasts in warm precipitation to some extent, near-surface conditions over land are relatively moisture in NICAM than MMF, which appears to be the key driver in the divergent warm precipitation results between the two models. Both the MMF and NICAM produced similar frequencies of large CAPE between land and ocean. The dry MMF boundary layer enhanced microwave scattering signals over land, but only NICAM had an enhanced deep convection frequency over land. Neither model could reproduce a realistic land-ocean contrast in in deep convective precipitation microphysics. A realistic contrast between land and ocean remains an issue in global storm-resolving modeling.

Impacts of land use and land cover on surface and air temperature in urban landscapes

Science.gov (United States)

Crum, S.; Jenerette, D.

2015-12-01

Accelerating urbanization affects regional climate as the result of changing land cover and land use (LCLU). Urban land cover composition may provide valuable insight into relationships among urbanization, air, and land-surface temperature (Ta and LST, respectively). Climate may alter these relationships, where hotter climates experience larger LULC effects. To address these hypotheses we examined links between Ta, LST, LCLU, and vegetation across an urban coastal to desert climate gradient in southern California, USA. Using surface temperature radiometers, continuously measuring LST on standardized asphalt, concrete, and turf grass surfaces across the climate gradient, we found a 7.2°C and 4.6°C temperature decrease from asphalt to vegetated cover in the coast and desert, respectively. There is 131% more temporal variation in asphalt than turf grass surfaces, but 37% less temporal variation in concrete than turf grass. For concrete and turf grass surfaces, temporal variation in temperature increased from coast to desert. Using ground-based thermal imagery, measuring LST for 24 h sequences over citrus orchard and industrial use locations, we found a 14.5°C temperature decrease from industrial to orchard land use types (38.4°C and 23.9°C, respectively). Additionally, industrial land use types have 209% more spatial variation than orchard (CV=0.20 and 0.09, respectively). Using a network of 300 Ta (iButton) sensors mounted in city street trees throughout the region and hyperspectral imagery data we found urban vegetation greenness, measured using the normalized difference vegetation index (NDVI), was negatively correlated to Ta at night across the climate gradient. Contrasting previous findings, the closest coupling between NDVI and Ta is at the coast from 0000 h to 0800 h (highest r2 = 0.6, P urbanized regions of southern California, USA decrease Ta and LST and spatial variation in LST, while built surfaces and land uses have the opposite effect. Furthermore

Satellite-based Calibration of Heat Flux at the Ocean Surface

Science.gov (United States)

Barron, C. N.; Dastugue, J. M.; May, J. C.; Rowley, C. D.; Smith, S. R.; Spence, P. L.; Gremes-Cordero, S.

2016-02-01

Model forecasts of upper ocean heat content and variability on diurnal to daily scales are highly dependent on estimates of heat flux through the air-sea interface. 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. Traditional assimilation of sea surface temperature (SST) observations re-centers ocean models at the start of each forecast cycle. Subsequent evolution depends on estimates of surface heat fluxes and upper-ocean processes over the forecast period. The COFFEE project (Calibration of Ocean Forcing with satellite Flux Estimates) endeavors to correct ocean forecast bias through a responsive error partition among 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 Navy operational global or regional atmospheric forcing. COFFEE addresses satellite-calibration of surface fluxes to estimate surface error covariances and links these to the ocean interior. Experiment cases combine different levels of flux calibration with different assimilation alternatives. The cases may 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 equally applicable to other regions. These approaches within a 3DVAR application are anticipated to be useful for global and larger

Using Land Surface Phenology to Detect Land Use Change in the Northern Great Plains

Science.gov (United States)

Nguyen, L. H.; Henebry, G. M.

2017-12-01

The Northern Great Plains of the US have been undergoing many types of land cover / land use change over the past two decades, including expansion of irrigation, conversion of grassland to cropland, biofuels production, urbanization, and fossil fuel mining. Much of the literature on these changes has relied on post-classification change detection based on a limited number of observations per year. Here we demonstrate an approach to characterize land dynamics through land surface phenology (LSP) by synergistic use of image time series at two scales. Our study areas include regions of interest (ROIs) across the Northern Great Plains located within Landsat path overlap zones to boost the number of valid observations (free of clouds or snow) each year. We first compute accumulated growing degree-days (AGDD) from MODIS 8-day composites of land surface temperature (MOD11A2 and MYD11A2). Using Landsat Collection 1 surface reflectance-derived vegetation indices (NDVI, EVI), we then fit at each pixel a downward convex quadratic model linking the vegetation index to each year's progression of AGDD. This quadratic equation exhibits linearity in a mathematical sense; thus, the fitted models can be linearly mixed and unmixed using a set of LSP endmembers (defined by the fitted parameter coefficients of the quadratic model) that represent "pure" land cover types with distinct seasonal patterns found within the region, such as winter wheat, spring wheat, maize, soybean, sunflower, hay/pasture/grassland, developed/built-up, among others. Information about land cover corresponding to each endmember are provided by the NLCD (National Land Cover Dataset) and CDL (Cropland Data Layer). We use linear unmixing to estimate the likely proportion of each LSP endmember within particular areas stratified by latitude. By tracking the proportions over the 2001-2011 period, we can quantify various types of land transitions in the Northern Great Plains.

Dust, Pollution, and Biomass Burning Aerosols in Asian Pacific: A Column Surface/Satellite Perspective

Science.gov (United States)

Tsay, Si-Chee; Lau, William K. M. (Technical Monitor)

2002-01-01

Many recent field experiments are designed to study the compelling variability in spatial and temporal scale of both pollution-derived and naturally occurring aerosols, which often exist in high concentrations over eastern/southeastern Asia and along the rim of the western Pacific. For example, the phase-I of ACE-Asia was conducted from March-May 2001 in the vicinity of the Gobi desert, East Coast of China, Yellow Sea, Korea, and Japan, along the pathway of Kosa (severe events that blanket East Asia with yellow desert dust, peaked in the Spring season). Asian dust typically originates in desert areas far from polluted urban regions. During transport, dust layers can interact with anthropogenic sulfate and soot aerosols from heavily polluted urban areas. Springtime is also the peak season for biomass burning in southeastern Asia. Added to the complex effects of clouds and natural marine aerosols, dust particles reaching the marine environment can have drastically different properties than those from the source. Thus, understanding the unique temporal and spatial variations of Asian aerosols is of special importance in regional-to-global climate issues such as radiative forcing, the hydrological cycle, and primary biological productivity in the mid-Pacific Ocean. During ACE-Asia we have measured continuously aerosol physical/optical/radiative properties, column precipitable water amount, and surface reflectivity over homogeneous areas from surface. The inclusion of flux measurements permits the determination of aerosol radiative flux in addition to measurements of loading and optical depth. At the time of the Terra/MODIS (Moderate Resolution Imaging Spectroradiometer), SeaWiFS (Sea-viewing Wide Field-of-view Sensor), TOMS (Total Ozone Mapping Spectrometer) and other satellite overpasses, these ground-based observations can provide valuable data to compare with satellite retrievals over land. A column satellite-surface perspective of Asian aerosols will be presented

Monitoring coastal inundation with Synthetic Aperture Radar satellite data

Science.gov (United States)

Suzuoki, Yukihiro; Rangoonwala, Amina; Ramsey, Elijah W.

2011-01-01

Maps representing the presence and absence of surface inundation in the Louisiana coastal zone were created from available satellite scenes acquired by the Japanese Aerospace Exploration Agency's Advanced Land Observing Satellite and by the European Space Agency's Envisat from late 2006 through summer 2009. Detection of aboveground surface flooding relied on the well-documented and distinct signature of decreased backscatter in Synthetic Aperture Radar (SAR), which is indicative of inundated marsh in the Gulf of Mexico. Even though decreases in backscatter were distinctive, the multiplicity of possible interactions between changing flood depths and canopy height yielded complex SAR-based representations of the marshes.

Total Discharge Estimation in the Korean Peninsula Using Multi-Satellite Products

Directory of Open Access Journals (Sweden)

Jae Young Seo

2017-07-01

Full Text Available Estimation of total discharge is necessary to understand the hydrological cycle and to manage water resources efficiently. However, the task is problematic in an area where ground observations are limited. The North Korea region is one example. Here, the total discharge was estimated based on the water balance using multiple satellite products. They are the terrestrial water storage changes (TWSC derived from the Gravity Recovery and Climate Experiment (GRACE, precipitation from the Tropical Rainfall Measuring Mission (TRMM, and evapotranspiration from the Moderate Resolution Imaging Spectroradiometer (MODIS. The satellite-based discharge was compared with land surface model products of the Global Land Data Assimilation System (GLDAS, and a positive relationship between the results was obtained (r = 0.70–0.86; bias = −9.08–16.99 mm/month; RMSE = 36.90–62.56 mm/month; NSE = 0.01–0.62. Among the four land surface models of GLDAS (CLM, Mosaic, Noah, and VIC, CLM corresponded best with the satellite-based discharge, satellite-based discharge has a tendency to slightly overestimate compared to model-based discharge (CLM, Mosaic, Noah, and VIC in the dry season. Also, the total discharge data based on the Precipitation-Runoff Modeling System (PRMS and the in situ discharge for major five river basins in South Korea show comparable seasonality and high correlation with the satellite-based discharge. In spite of the relatively low spatial resolution of GRACE, and loss of information incurred during the process of integrating three different satellite products, the proposed methodology can be a practical tool to estimate the total discharge with reasonable accuracy, especially in a region with scarce hydrologic data.

Monitoring and modeling land-use change in the Pearl River Delta, China, using satellite imagery and socioeconomic data

Science.gov (United States)

Seto, Karen Ching-Yee

Over the last two decades, rapid rates of economic growth in the People's Republic of China have converted large areas of natural ecosystems and agricultural lands to urban uses. The size and rate of these land-use changes may affect local and regional climate, biogeochemistry, and food supply. To assess these impacts, both the amount of land converted and its relation to socioeconomic drivers must be determined. This research combines satellite remote sensing, which is used to monitor land conversion, with socioeconomic data to model the economic and demographic drivers of land-use change in the Pearl River Delta of Southern China. This research modifies existing techniques and develops new methods to assess the type, amount, and timing of land-use change from annual Landsat Thematic Mapper (TM) images from 1988 to 1996. During this period, most of the land-use change is conversion of agricultural land to urban areas. Results indicate that urban areas, increased by over 300% between 1988 and 1996. Field assessments confirm these results and indicate that the land-use change map is highly accurate at 93.5%. To use these data as inputs to statistical models, the year of land conversion derived from satellite imagery must be unbiased. A new method that uses time series techniques identifies the date at which land-use changes occur from a sequential series of TM images. The accuracy and bias of the dates of change identified compare favorably to a more conventional remote sensing change detection technique and may have the additional advantages of reducing efforts required to assemble training data and to correct for atmospheric effects. Data on the quantity of land-use change and the timing of these changes are used in conjunction with socioeconomic data to estimate statistical models that identify and quantify the demographic and economic changes on two types of land conversion: urbanization of agricultural land and urbanization of natural vegetation. Results

Analytical Retrieval of Global Land Surface Emissivity Maps at AMSR-E passive microwave frequencies

Science.gov (United States)

Norouzi, H.; Temimi, M.; Khanbilvardi, R.

2009-12-01

Land emissivity is a crucial boundary condition in Numerical Weather Prediction (NWP) modeling. Land emissivity is also a key indicator of land surface and subsurface properties. The objective of this study, supported by NOAA-NESDIS, is to develop global land emissivity maps using AMSR-E passive microwave measurements along with several ancillary data. The International Satellite Cloud Climatology Project (ISCCP) database has been used to obtain several inputs for the proposed approach such as land surface temperature, cloud mask and atmosphere profile. The Community Radiative Transfer Model (CRTM) has been used to estimate upwelling and downwelling atmospheric contributions. Although it is well known that correction of the atmospheric effect on brightness temperature is required at higher frequencies (over 19 GHz), our preliminary results have shown that a correction at 10.7 GHz is also necessary over specific areas. The proposed approach is based on three main steps. First, all necessary data have been collected and processed. Second, a global cloud free composite of AMSR-E data and corresponding ancillary images is created. Finally, monthly composting of emissivity maps has been performed. AMSR-E frequencies at 6.9, 10.7, 18.7, 36.5 and 89.0 GHz have been used to retrieve the emissivity. Water vapor information obtained from ISCCP (TOVS data) was used to calculate upwelling, downwelling temperatures and atmospheric transmission in order to assess the consistency of those derived from the CRTM model. The frequent land surface temperature (LST) determination (8 times a day) in the ISCCP database has allowed us to assess the diurnal cycle effect on emissivity retrieval. Differences in magnitude and phase between thermal temperature and low frequencies microwave brightness temperature have been noticed. These differences seem to vary in space and time. They also depend on soil texture and thermal inertia. The proposed methodology accounts for these factors and

A factorial assessment of the sensitivity of the BATS land-surface parameterization scheme. [BATS (Biosphere-Atmosphere Transfer Scheme)

Energy Technology Data Exchange (ETDEWEB)

Henderson-Sellers, A. (Macquarie Univ., North Ryde, New South Wales (Australia))

1993-02-01

Land-surface schemes developed for incorporation into global climate models include parameterizations that are not yet fully validated and depend upon the specification of a large (20-50) number of ecological and soil parameters, the values of which are not yet well known. There are two methods of investigating the sensitivity of a land-surface scheme to prescribed values: simple one-at-a-time changes or factorial experiments. Factorial experiments offer information about interactions between parameters and are thus a more powerful tool. Here the results of a suite of factorial experiments are reported. These are designed (i) to illustrate the usefulness of this methodology and (ii) to identify factors important to the performance of complex land-surface schemes. The Biosphere-Atmosphere Transfer Scheme (BATS) is used and its sensitivity is considered (a) to prescribed ecological and soil parameters and (b) to atmospheric forcing used in the off-line tests undertaken. Results indicate that the most important atmospheric forcings are mean monthly temperature and the interaction between mean monthly temperature and total monthly precipitation, although fractional cloudiness and other parameters are also important. The most important ecological parameters are vegetation roughness length, soil porosity, and a factor describing the sensitivity of the stomatal resistance of vegetation to the amount of photosynthetically active solar radiation and, to a lesser extent, soil and vegetation albedos. Two-factor interactions including vegetation roughness length are more important than many of the 23 specified single factors. The results of factorial sensitivity experiments such as these could form the basis for intercomparison of land-surface parameterization schemes and for field experiments and satellite-based observation programs aimed at improving evaluation of important parameters.

Global Land Carbon Uptake from Trait Distributions

Science.gov (United States)

Butler, E. E.; Datta, A.; Flores-Moreno, H.; Fazayeli, F.; Chen, M.; Wythers, K. R.; Banerjee, A.; Atkin, O. K.; Kattge, J.; Reich, P. B.

2016-12-01

Historically, functional diversity in land surface models has been represented through a range of plant functional types (PFTs), each of which has a single value for all of its functional traits. Here we expand the diversity of the land surface by using a distribution of trait values for each PFT. The data for these trait distributions is from a sub-set of the global database of plant traits, TRY, and this analysis uses three leaf traits: mass based nitrogen and phosphorus content and specific leaf area, which influence both photosynthesis and respiration. The data are extrapolated into continuous surfaces through two methodologies. The first, a categorical method, classifies the species observed in TRY into satellite estimates of their plant functional type abundances - analogous to how traits are currently assigned to PFTs in land surface models. Second, a Bayesian spatial method which additionally estimates how the distribution of a trait changes in accord with both climate and soil covariates. These two methods produce distinct patterns of diversity which are incorporated into a land surface model to estimate how the range of trait values affects the global land carbon budget.

Detecting temporal change in land-surface altitude using robotic land-surveying techniques and geographic information system applications at an earthen dam site in Southern Westchester County, New York

Science.gov (United States)

Noll, Michael L.; Chu, Anthony

2017-08-14

In 2005, the U.S. Geological Survey began a cooperative study with New York City Department of Environmental Protection to characterize the local groundwater-flow system and identify potential sources of seeps on the southern embankment at the Hillview Reservoir in southern Westchester County, New York. Monthly site inspections at the reservoir indicated an approximately 90-square-foot depression in the land surface directly upslope from a seep that has episodically flowed since 2007. In July 2008, the U.S. Geological Survey surveyed the topography of land surface in this depression area by collecting high-accuracy (resolution less than 1 inch) measurements. A point of origin was established for the topographic survey by using differentially corrected positional data collected by a global navigation satellite system. Eleven points were surveyed along the edge of the depression area and at arbitrary locations within the depression area by using robotic land-surveying techniques. The points were surveyed again in March 2012 to evaluate temporal changes in land-surface altitude. Survey measurements of the depression area indicated that the land-surface altitude at 8 of the 11 points decreased beyond the accepted measurement uncertainty during the 44 months from July 2008 to March 2012. Two additional control points were established at stable locations along Hillview Avenue, which runs parallel to the embankment. These points were measured during the July 2008 survey and measured again during the March 2012 survey to evaluate the relative accuracy of the altitude measurements. The relative horizontal and vertical (altitude) accuracies of the 11 topographic measurements collected in March 2012 were ±0.098 and ±0.060 feet (ft), respectively. Changes in topography at 8 of the 11 points ranged from 0.09 to 0.63 ft and topography remained constant, or within the measurement uncertainty, for 3 of the 11 points.Two cross sections were constructed through the depression area

Simulation of tsunami effects on sea surface salinity using MODIS satellite data

International Nuclear Information System (INIS)

Ramlan, N E F; Genderen, J van; Hashim, M; Marghany, M

2014-01-01

Remote sensing technology has been recognized as powerful tool for environmental disaster studies. Ocean surface salinity is considered as a major element in the marine environment. In this study, we simulate the 2004 tsunami's impact on a physical ocean parameter using the least square algorithm to retrieve sea surface salinity (SSS) from MODIS satellite data. The accuracy of this work has been examined using the root mean of sea surface salinity retrieved from MODIS satellite data. The study shows a comprehensive relationship between the in situ measurements and least square algorithm with high r 2 of 0.95, and RMS of bias value of ±0.9 psu. In conclusion, the least square algorithm can be used to retrieve SSS from MODIS satellite data during a tsunami event

Surface navigation on Mars with a Navigation Satellite

Science.gov (United States)

Vijayaraghavan, A.; Thurman, Sam W.; Kahn, Robert D.; Hastrup, Rolf C.

Radiometric navigation data from the Deep Space Network (DSN) stations on the earth to transponders and other surface elements such as rovers and landers on Mars, can determine their positions to only within a kilometer in inertial space. The positional error is mostly in the z-component of the surface element parallel to the Martian spin-axis. However, with Doppler and differenced-Doppler data from a Navigation Satellite in orbit around Mars to two or more of such transponders on the planetary surface, their positions can be determined to within 15 meters (or 20 meters for one-way Doppler beacons on Mars) in inertial space. In this case, the transponders (or other vehicles) on Mars need not even be capable of directly communicating to the earth. When the Navigation Satellite data is complemented by radiometric observations from the DSN stations also, directly to the surface elements on Mars, their positions can be determined to within 3 meters in inertial space. The relative positions of such surface elements on Mars (relative to one another) in Mars-fixed coordinates, however, can be determined to within 5 meters from simply range and Doppler data from the DSN stations to the surface elements. These results are obtained from covariance studies assuming X-band data noise levels and data-arcs not exceeding 10 days. They are significant in the planning and deployment of a Mars-based navigation network necessary to support real-time operations during critical phases of manned exploration of Mars.

Shallow to Deep Convection Transition over a Heterogeneous Land Surface Using the Land Model Coupled Large-Eddy Simulation

Science.gov (United States)

Lee, J.; Zhang, Y.; Klein, S. A.

2017-12-01

The triggering of the land breeze, and hence the development of deep convection over heterogeneous land should be understood as a consequence of the complex processes involving various factors from land surface and atmosphere simultaneously. That is a sub-grid scale process that many large-scale models have difficulty incorporating it into the parameterization scheme partly due to lack of our understanding. Thus, it is imperative that we approach the problem using a high-resolution modeling framework. In this study, we use SAM-SLM (Lee and Khairoutdinov, 2015), a large-eddy simulation model coupled to a land model, to explore the cloud effect such as cold pool, the cloud shading and the soil moisture memory on the land breeze structure and the further development of cloud and precipitation over a heterogeneous land surface. The atmospheric large scale forcing and the initial sounding are taken from the new composite case study of the fair-weather, non-precipitating shallow cumuli at ARM SGP (Zhang et al., 2017). We model the land surface as a chess board pattern with alternating leaf area index (LAI). The patch contrast of the LAI is adjusted to encompass the weak to strong heterogeneity amplitude. The surface sensible- and latent heat fluxes are computed according to the given LAI representing the differential surface heating over a heterogeneous land surface. Separate from the surface forcing imposed from the originally modeled surface, the cases that transition into the moist convection can induce another layer of the surface heterogeneity from the 1) radiation shading by clouds, 2) adjusted soil moisture pattern by the rain, 3) spreading cold pool. First, we assess and quantifies the individual cloud effect on the land breeze and the moist convection under the weak wind to simplify the feedback processes. And then, the same set of experiments is repeated under sheared background wind with low level jet, a typical summer time wind pattern at ARM SGP site, to

Significant results from using earth observation satellites for mineral and energy resource exploration

Science.gov (United States)

Carter, William D.

1981-01-01

A large number of Earth-observation satellites orbit our world several times each day, providing new information about the land and sea surfaces and the overlying thin layer of atmosphere that makes our planet unique. Meteorological satellites have had the longest history of experimental use and most are now considered operational. The geologic information collected by the Landsat, Polar Orbiting Geophysical Observatory (POGO), Magsat, Heat Capacity Mapping Mission (HCMM) and Seasat land and ocean observation systems is being thoroughly tested, and some of these systems are now approaching operational use.

Towards scale-independent land-surface flux estimates in Noah-MP

Science.gov (United States)

Thober, Stephan; Mizukami, Naoki; Samaniego, Luis; Attinger, Sabine; Clark, Martyn; Cuntz, Matthias

2017-04-01

Land-surface models use a variety of process representations to calculate terrestrial energy, water and biogeochemical fluxes. These process descriptions are usually derived from point measurements which are, in turn, scaled to much larger resolutions ranging from 1 km in catchment hydrology to 100 km in climate modelling. Both, hydrologic and climate models are nowadays run on different spatial resolutions, using the exactly same land surface representations. A fundamental criterion for the physical consistency of land-surface simulations across scales is that a flux estimated over a given area is independent of the spatial model resolution (i.e., the flux-matching criterion). The Noah-MP land surface model considers only one soil and land cover type per model grid cell without any representation of their subgrid variability, implying a weak flux-matching. A fractional approach simulates the subgrid variability but it requires a higher computational demand than using effective parameters and it is used only for land cover in current land surface schemes. A promising approach to derive scale-independent parameters is the Multiscale Parameter Regionalization (MPR) technique, which consists of two steps: first, it applies transfer functions directly to high-resolution data (such as 100 m soil maps) to derive high-resolution model parameter fields, acknowledging the full subgrid variability. Second, it upscales these high-resolution parameter fields to the model resolution by using appropriate upscaling operators. MPR has shown to improve substantially the scalability of the mesoscale Hydrologic Models mHM (Samaniego et al., 2010 WRR). Here, we apply the MPR technique to the Noah-MP land-surface model for a large sample of basins distributed across the contiguous USA. Specifically, we evaluate the flux-matching criterion for several hydrologic fluxes such as evapotranspiration and drainage at scales ranging from 3 km to 48 km. We investigate the impact of different

Land cover classification of Landsat 8 satellite data based on Fuzzy Logic approach

Science.gov (United States)

Taufik, Afirah; Sakinah Syed Ahmad, Sharifah

2016-06-01

The aim of this paper is to propose a method to classify the land covers of a satellite image based on fuzzy rule-based system approach. The study uses bands in Landsat 8 and other indices, such as Normalized Difference Water Index (NDWI), Normalized difference built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) as input for the fuzzy inference system. The selected three indices represent our main three classes called water, built- up land, and vegetation. The combination of the original multispectral bands and selected indices provide more information about the image. The parameter selection of fuzzy membership is performed by using a supervised method known as ANFIS (Adaptive neuro fuzzy inference system) training. The fuzzy system is tested for the classification on the land cover image that covers Klang Valley area. The results showed that the fuzzy system approach is effective and can be explored and implemented for other areas of Landsat data.

Communicating why land surface heterogeneity matters

Science.gov (United States)

Tague, C.; Burke, W.; Bart, R. R.; Turpin, E.; Wood, T.; Gordon, D.

2017-12-01

As hydrologic scientists, we know that land surface heterogeneity can have nuanced and sometimes dramatic impacts on the water cycle. Land surface characteristics, including the structure and composition of vegetation and soil storage and drainage properties, alter how incoming precipitation is translated into streamflow and evapotranspiration. Land surface heterogeneity can explain why this partitioning of incoming precipitation cannot always be computed by a simple water budget calculation. We also know that land surface characteristics are dynamic - vegetation grows and changes with fire, disease and human actions and these changes will alter the partitioning of water - how much so, however depends itself on other site characteristics - soil water storage and the timing and magnitude of precipitation. This complex impact of space-time dynamics on the water cycle is something we need to effectively communicate to non-experts. For example, we may want to explain why sometimes forest management practices increase water availability but sometimes they don't - or why the impacts of urbanization or fire are location specific. If we do not communicate these dependencies we risk over-simplifying and eroding scientific credibility when observed effects don't match simple generalizations. On the other hand excessive detail can overwhelm and disengage audiences. So how do we help different communities public, private landowners, other scientists, NGOs, governments to better understand the role of space-time heterogeneity. To address this issue, we present some results from ongoing work that looks at the impact of fuel treatment of forest ecohydrology. This work stem from a collaboration between an ecohydrologic modeling team, social-scientists, a visual artist and compute graphics students. We use a coupled model, validated with field measurements, to show why spatial heterogeneity matters for understanding the impact of fuel treatments on the water cycle for the Sierra

Quantifying the Uncertainty in High Spatial and Temporal Resolution Synthetic Land Surface Reflectance at Pixel Level Using Ground-Based Measurements

Science.gov (United States)

Kong, J.; Ryu, Y.

2017-12-01

Algorithms for fusing high temporal frequency and high spatial resolution satellite images are widely used to develop dense time-series land surface observations. While many studies have revealed that the synthesized frequent high spatial resolution images could be successfully applied in vegetation mapping and monitoring, validation and correction of fused images have not been focused than its importance. To evaluate the precision of fused image in pixel level, in-situ reflectance measurements which could account for the pixel-level heterogeneity are necessary. In this study, the synthetic images of land surface reflectance were predicted by the coarse high-frequency images acquired from MODIS and high spatial resolution images from Landsat-8 OLI using the Flexible Spatiotemporal Data Fusion (FSDAF). Ground-based reflectance was measured by JAZ Spectrometer (Ocean Optics, Dunedin, FL, USA) on rice paddy during five main growth stages in Cheorwon-gun, Republic of Korea, where the landscape heterogeneity changes through the growing season. After analyzing the spatial heterogeneity and seasonal variation of land surface reflectance based on the ground measurements, the uncertainties of the fused images were quantified at pixel level. Finally, this relationship was applied to correct the fused reflectance images and build the seasonal time series of rice paddy surface reflectance. This dataset could be significant for rice planting area extraction, phenological stages detection, and variables estimation.

THE USE OF SATELLITE REMOTE SENSING DATA AND GEOGRAPHIC INFORMATION SYSTEMS ON CRITICAL LAND ANALYSIS

Directory of Open Access Journals (Sweden)

Agus Suharyanto

2013-06-01

Full Text Available Critical land classification can be analyzed using combination between Top Soil Thickness - Land erosion method, and BRLT methods. Both methods are needed soil erosion data as one of input data. The soil erosion data can be analyzed using USLE and MUSLE methods. The combination of two critical land analyses methods with input soil erosion data from two analyses methods will be produced four combinations of critical land classification. In this research, four of the critical land classification and two soil erosion classification will be analyzed using GIS. The best method to classify critical land will be investigated in this research. The best classified critical land is the classified critical land data is nearest with the field condition. Percentage of vegetation cover (PVC is one of the most important input data in the critical land classification analysis using BRLKT method. This data have 50% weight. PVC condition is classified into five categories i.e. very good, good, fair, poor, and very poor. Each category have score 5, 4, 3, 2, 1 respectively. To analyze this PVC classification, NDVI generated from satellite remote sensing data is used in this research. From the four methods of land critical classification analyses used in this research, critical land classified using BRLKT method with input soil erosion analyzed using method is produced the critical land classification nearest with the critical land condition in the field.

Achieving scale-independent land-surface flux estimates - Application of the Multiscale Parameter Regionalization (MPR) to the Noah-MP land-surface model across the contiguous USA

Science.gov (United States)

Thober, S.; Mizukami, N.; Samaniego, L. E.; Attinger, S.; Clark, M. P.; Cuntz, M.

2016-12-01

Land-surface models use a variety of process representations to calculate terrestrial energy, water and biogeochemical fluxes. These process descriptions are usually derived from point measurements but are scaled to much larger resolutions in applications that range from about 1 km in catchment hydrology to 100 km in climate modelling. Both, hydrologic and climate models are nowadays run on different spatial resolutions, using the exact same land surface representations. A fundamental criterion for the physical consistency of land-surface simulations across scales is that a flux estimated over a given area is independent of the spatial model resolution (i.e., the flux-matching criterion). The Noah-MP land surface model considers only one soil and land cover type per model grid cell without any representation of subgrid variability, implying a weak flux-matching. A fractional approach simulates subgrid variability but it requires a higher computational demand than using effective parameters and it is used only for land cover in current land surface schemes. A promising approach to derive scale-independent parameters is the Multiscale Parameter Regionalization (MPR) technique, which consists of two steps: first, it applies transfer functions directly to high-resolution data (such as 100 m soil maps) to derive high-resolution model parameter fields, acknowledging the full subgrid variability. Second, it upscales these high-resolution parameter fields to the model resolution by using appropriate upscaling operators. MPR has shown to improve substantially the scalability of hydrologic models. Here, we apply the MPR technique to the Noah-MP land-surface model for a large sample of basins distributed across the contiguous USA. Specifically, we evaluate the flux-matching criterion for several hydrologic fluxes such as evapotranspiration and total runoff at scales ranging from 3 km to 48 km. We also investigate a p-norm scaling operator that goes beyond the current

CRUCIAL: Cryosat-2 Success over Inland Water and Land

DEFF Research Database (Denmark)

Moore, Philip; Berry, Philippa; Balmbra, Robert

2014-01-01

CRUCIAL is an ESA/STSE funded project investigating innovative land and inland water applications from Cryosat-2 with a forward-look component to the future Sentinel-3 mission. The fact that the Earth’s land surface is, in general, a relatively poor reflector of Ku band energy, with the exceptions...... of inland water, salar and ice surfaces has enabled Earth-orbiting satellite radar altimeters to be used for land surface applications including mapping and measurement of river and lake systems. Research with EnviSat Burst Echoes has shown that substantial high frequency information content is present...... of Cryosat-2 altimeter in SAR mode (I8 KHz) offers the opportunity to recover high frequency signals over much of the Earth’s land surface, enhancing the inland water height retrieval capability. Constraining this application is the limited availability of SAR Full Bit Rate (FBR) data from Cryosat-2 over...

Studying the Surfaces of the Icy Galilean Satellites With JIMO

Science.gov (United States)

Prockter, L.; Schenk, P.; Pappalardo, R.

2003-12-01

The Geology subgroup of the Jupiter Icy Moons Orbiter (JIMO) Science Definition Team (SDT) has been working with colleagues within the planetary science community to determine the key outstanding science goals that could be met by the JIMO mission. Geological studies of the Galilean satellites will benefit from the spacecraft's long orbital periods around each satellite, lasting from one to several months. This mission plan allows us to select the optimal viewing conditions to complete global compositional and morphologic mapping at high resolution, and to target geologic features of key scientific interest at very high resolution. Community input to this planning process suggests two major science objectives, along with corresponding measurements proposed to meet them. Objective 1: Determine the origins of surface features and their implications for geological history and evolution. This encompasses investigations of magmatism (intrusion, extrusion, and diapirism), tectonism (isostatic compensation, and styles of faulting, flexure and folding), impact cratering (morphology and distribution), and gradation (erosion and deposition) processes (impact gardening, sputtering, mass wasting and frosts). Suggested measurements to meet this goal include (1) two dimensional global topographic mapping sufficient to discriminate features at a spatial scale of 10 m, and with better than or equal to 1 m relative vertical accuracy, (2) nested images of selected target areas at a range of resolutions down to the submeter pixel scale, (3) global (albedo) mapping at better than or equal to 10 m/pixel, and (4) multispectral global mapping in at least 3 colors at better than or equal to 100 m/pixel, with some subsets at better than 30 m/pixel. Objective 2. Identify and characterize potential landing sites for future missions. A primary component to the success of future landed missions is full characterization of potential sites in terms of their relative age, geological interest, and

Satellite detection of carbon monoxide emission prior to the Gujarat earthquake of 26 January 2001

Energy Technology Data Exchange (ETDEWEB)

Singh, Ramesh P., E-mail: rsingh@chapman.edu [Department of Physics, Computational Science and Engineering, Schmid College of Science, Chapman University, One University Drive, Orange CA 92866 (United States); Senthil Kumar, J.; Zlotnicki, Jacques [Observatoire de Physique du Globe de Clermont-Ferrand, OPGC-CNRS, Campus des Cuzeaux, 24, av. des Landais, 63177 Aubiere Cedex (France); Kafatos, Menas [Department of Physics, Computational Science and Engineering, Schmid College of Science, Chapman University, One University Drive, Orange CA 92866 (United States)

2010-04-15

NOAA AVHRR images have clearly shown anomalous changes in land surface temperature associated with earthquakes in the past two decades. Soon after the Gujarat earthquake of January 26, 2001, an anomalous increase in land surface temperature was inferred from MODIS satellite data a few days prior to the main earthquake event. The cause of such an anomalous change in surface temperature prior to the earthquake is attributed to many probable phenomena, but no definite cause has been identified. In the present study, changes of a complementary nature were found of land surface temperature associated with the emission of CO from the epicentral region. The observed changes on land and atmosphere associated with the Gujarat earthquake of 26 January, 2001, show the existence of strong coupling between land, atmosphere and ionosphere.

Comparison of two perturbation methods to estimate the land surface modeling uncertainty

Science.gov (United States)

Su, H.; Houser, P.; Tian, Y.; Kumar, S.; Geiger, J.; Belvedere, D.

2007-12-01

In land surface modeling, it is almost impossible to simulate the land surface processes without any error because the earth system is highly complex and the physics of the land processes has not yet been understood sufficiently. In most cases, people want to know not only the model output but also the uncertainty in the modeling, to estimate how reliable the modeling is. Ensemble perturbation is an effective way to estimate the uncertainty in land surface modeling, since land surface models are highly nonlinear which makes the analytical approach not applicable in this estimation. The ideal perturbation noise is zero mean Gaussian distribution, however, this requirement can't be satisfied if the perturbed variables in land surface model have physical boundaries because part of the perturbation noises has to be removed to feed the land surface models properly. Two different perturbation methods are employed in our study to investigate their impact on quantifying land surface modeling uncertainty base on the Land Information System (LIS) framework developed by NASA/GSFC land team. One perturbation method is the built-in algorithm named "STATIC" in LIS version 5; the other is a new perturbation algorithm which was recently developed to minimize the overall bias in the perturbation by incorporating additional information from the whole time series for the perturbed variable. The statistical properties of the perturbation noise generated by the two different algorithms are investigated thoroughly by using a large ensemble size on a NASA supercomputer and then the corresponding uncertainty estimates based on the two perturbation methods are compared. Their further impacts on data assimilation are also discussed. Finally, an optimal perturbation method is suggested.

A review on remotely sensed land surface temperature anomaly as an earthquake precursor

Science.gov (United States)

Bhardwaj, Anshuman; Singh, Shaktiman; Sam, Lydia; Joshi, P. K.; Bhardwaj, Akanksha; Martín-Torres, F. Javier; Kumar, Rajesh

2017-12-01

The low predictability of earthquakes and the high uncertainty associated with their forecasts make earthquakes one of the worst natural calamities, capable of causing instant loss of life and property. Here, we discuss the studies reporting the observed anomalies in the satellite-derived Land Surface Temperature (LST) before an earthquake. We compile the conclusions of these studies and evaluate the use of remotely sensed LST anomalies as precursors of earthquakes. The arrival times and the amplitudes of the anomalies vary widely, thus making it difficult to consider them as universal markers to issue earthquake warnings. Based on the randomness in the observations of these precursors, we support employing a global-scale monitoring system to detect statistically robust anomalous geophysical signals prior to earthquakes before considering them as definite precursors.

Detection of heat wave using Kalpana-1 VHRR land surface temperature product over India

Science.gov (United States)

Shah, Dhiraj; Pandya, Mehul R.; Pathak, Vishal N.; Darji, Nikunj P.; Trivedi, Himanshu J.

2016-05-01

Heat Waves can have notable impacts on human mortality, ecosystem, economics and energy supply. The effect of heat wave is much more intense during summer than the other seasons. During the period of April to June, spells of very hot weather occur over certain regions of India and global warming scenario may result in further increases of such temperature anomalies and corresponding heat waves conditions. In this paper, satellite observations have been used to detect the heat wave conditions prevailing over India for the period of May-June 2015. The Kalpana-1 VHRR derived land surface temperature (LST) products have been used in the analysis to detect the heat wave affected regions over India. Results from the analysis shows the detection of heat wave affected pixels over Indian land mass. It can be seen that during the study period the parts of the west India, Indo-gangetic plane, Telangana and part of Vidarbh was under severe heat wave conditions which is also confirmed with Automatic Weather Station (AWS) air temperature observations.

Satellite passive microwave rain measurement techniques for land and ocean

Science.gov (United States)

Spencer, R. W.

1985-01-01

Multiseasonal rainfall was found to be measurable over land with satellite passive microwave data, based upon comparisons between Nimbus 7 Scanning Multichannel Microwave Radiometer (SMME) brightness temperatures (T sub B) and operational WSR-57 radar rain rates. All of the SMMR channels (bipolarized 37, 21, 18, 10.7, and 6.6. GHz T sub B) were compared to radar reflectivities for 25 SMMR passes and 234 radar scans over the U.S. during the spring, summer, and fall of 1979. It was found that the radar rain rates were closely related to the difference between 37 and 21 GHz T sub B. This result is due to the volume scattering effects of precipitation which cause emissivity decreases with frequency, as opposed to emissive surfaces (e.g., water) whose emissivities increase with frequency. Two frequencies also act to reduce the effects of thermometric temperature variations on T sub B to a miminum. During summer and fall, multiple correlation coefficients of 0.80 and 0.75 were obtained. These approach the limit of correlation that can be expected to exist between two very different data sources, especially in light of the errors attributable to manual digitization of PPI photographs of variable quality from various operational weather radar not calibrated for research purposes. During the spring, a significantly lower (0.63) correlation was found. This poorer performance was traced to cases of wet, unvegetated soil being sensed at the lower frequencies through light rain, partly negating the rain scattering signal.

Improving weather predictability by including land-surface model parameter uncertainty

Science.gov (United States)

Orth, Rene; Dutra, Emanuel; Pappenberger, Florian

2016-04-01

The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogenous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model. Focusing on ECMWF's land-surface model HTESSEL we present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. We select 6 poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally we investigate the possibility to construct ensembles from the multiple land surface parameters. In the uncoupled runs we find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. We demonstrate the robustness of our findings by comparing multiple best performing parameter sets and multiple randomly chosen parameter sets. We find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings. Finally, we construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system. Orth, R., E. Dutra, and F. Pappenberger, 2016: Improving weather predictability by

Assimilation of SMOS (and SMAP) Retrieved Soil Moisture into the Land Information System

Science.gov (United States)

Blankenship, Clay; Zavodsky, Bradley; Case, Jonathan; Stano, Geoffrey

2016-01-01

Goal: Accurate, high-resolution (approx.3 km) soil moisture in near-real time. Situational awareness (drought assessment, flood and fire threat). Local modeling applications (to improve sfc-PBL exchanges) Method: Assimilate satellite soil moisture retrievals into a land surface model. Combines high-resolution geophysical model data with latest satellite observations.

Comparing soil moisture memory in satellite observations and models

Science.gov (United States)

Stacke, Tobias; Hagemann, Stefan; Loew, Alexander

2013-04-01

A major obstacle to a correct parametrization of soil processes in large scale global land surface models is the lack of long term soil moisture observations for large parts of the globe. Currently, a compilation of soil moisture data derived from a range of satellites is released by the ESA Climate Change Initiative (ECV_SM). Comprising the period from 1978 until 2010, it provides the opportunity to compute climatological relevant statistics on a quasi-global scale and to compare these to the output of climate models. Our study is focused on the investigation of soil moisture memory in satellite observations and models. As a proxy for memory we compute the autocorrelation length (ACL) of the available satellite data and the uppermost soil layer of the models. Additional to the ECV_SM data, AMSR-E soil moisture is used as observational estimate. Simulated soil moisture fields are taken from ERA-Interim reanalysis and generated with the land surface model JSBACH, which was driven with quasi-observational meteorological forcing data. The satellite data show ACLs between one week and one month for the greater part of the land surface while the models simulate a longer memory of up to two months. Some pattern are similar in models and observations, e.g. a longer memory in the Sahel Zone and the Arabian Peninsula, but the models are not able to reproduce regions with a very short ACL of just a few days. If the long term seasonality is subtracted from the data the memory is strongly shortened, indicating the importance of seasonal variations for the memory in most regions. Furthermore, we analyze the change of soil moisture memory in the different soil layers of the models to investigate to which extent the surface soil moisture includes information about the whole soil column. A first analysis reveals that the ACL is increasing for deeper layers. However, its increase is stronger in the soil moisture anomaly than in its absolute values and the first even exceeds the

Sentinel-3 For Land Applications

Science.gov (United States)

Goryl, Philippe; Gobron, Nadine; Mecklenburg, Susanne; Donlon, Craig; Bouvet, Marc; Buongiorno, Alessandra; Wilson, Hilary

2016-07-01

The Copernicus Programme, being Europe's Earth Observation and Monitoring Programme led by the European Union, aims to provide, on a sustainable basis, reliable and timely services related to environmental and security issues. The Sentinel-3 mission forms part of the Copernicus Space Component. Its main objectives, building on the heritage and experience of the European Space Agency's (ESA) ERS and ENVISAT missions, are to measure sea-surface topography, sea- and land-surface temperature and ocean- and land-surface colour in support of ocean forecasting systems, and for environmental and climate monitoring. The series of Sentinel-3 satellites will ensure global, frequent and near-real time ocean, ice and land monitoring, with the provision of observation data in a routine, long-term (up to 20 years of operations) and continuous fashion, with a consistent quality and a high level of reliability and availability. The launch of Sentinel-3 was successful last February 2016. The Sentinel-3 missions are jointly operated by ESA and EUMETSAT. ESA will be responsible for the operations, maintenance and evolution of the Sentinel-3 ground segment on land related products and EUMETSAT on the marine products and the satellite monitoring and control. All facilities supporting the Sentinel-3 operations are in place. The Sentinel-3 ground segment systematically acquires, processes and distributes a set of pre-defined core data products to the users. For a detailed description of the core data products please see https://earth.esa.int/web/sentinel/missions/sentinel-3/data-products. On request from the European Commission, ESA and EUMETSAT are presently assessing the possibility to include further core data products, in particular on aerosol optical depth, fire monitoring and synergistic products over land. This paper will provide an update on the status of the mission operations after the initial months in orbit and provide in particular an overview on the status of the Sentinel-3

Monitoring the variability of sea level and surface circulation with satellite altimetry

NARCIS (Netherlands)

Volkov, Denis L. "Jr"

2004-01-01

Variability in the ocean plays an important role in determining global weather and climate conditions. The advent of satellite altimetry has significantly facilitated the study of the variability of sea level and surface circulation. Satellites provide high-quality regular and nearly global

Sensitivity of Satellite-Based Skin Temperature to Different Surface Emissivity and NWP Reanalysis Sources Demonstrated Using a Single-Channel, Viewing-Angle-Corrected Retrieval Algorithm

Science.gov (United States)

Scarino, B. R.; Minnis, P.; Yost, C. R.; Chee, T.; Palikonda, R.

2015-12-01

Single-channel algorithms for satellite thermal-infrared- (TIR-) derived land and sea surface skin temperature (LST and SST) are advantageous in that they can be easily applied to a variety of satellite sensors. They can also accommodate decade-spanning instrument series, particularly for periods when split-window capabilities are not available. However, the benefit of one unified retrieval methodology for all sensors comes at the cost of critical sensitivity to surface emissivity (ɛs) and atmospheric transmittance estimation. It has been demonstrated that as little as 0.01 variance in ɛs can amount to more than a 0.5-K adjustment in retrieved LST values. Atmospheric transmittance requires calculations that employ vertical profiles of temperature and humidity from numerical weather prediction (NWP) models. Selection of a given NWP model can significantly affect LST and SST agreement relative to their respective validation sources. Thus, it is necessary to understand the accuracies of the retrievals for various NWP models to ensure the best LST/SST retrievals. The sensitivities of the single-channel retrievals to surface emittance and NWP profiles are investigated using NASA Langley historic land and ocean clear-sky skin temperature (Ts) values derived from high-resolution 11-μm TIR brightness temperature measured from geostationary satellites (GEOSat) and Advanced Very High Resolution Radiometers (AVHRR). It is shown that mean GEOSat-derived, anisotropy-corrected LST can vary by up to ±0.8 K depending on whether CERES or MODIS ɛs sources are used. Furthermore, the use of either NOAA Global Forecast System (GFS) or NASA Goddard Modern-Era Retrospective Analysis for Research and Applications (MERRA) for the radiative transfer model initial atmospheric state can account for more than 0.5-K variation in mean Ts. The results are compared to measurements from the Surface Radiation Budget Network (SURFRAD), an Atmospheric Radiation Measurement (ARM) Program ground

Land Surface Microwave Emissivities Derived from AMSR-E and MODIS Measurements with Advanced Quality Control

Science.gov (United States)

Moncet, Jean-Luc; Liang, Pan; Galantowicz, John F.; Lipton, Alan E.; Uymin, Gennady; Prigent, Catherine; Grassotti, Christopher

2011-01-01

A microwave emissivity database has been developed with data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and with ancillary land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same Aqua spacecraft. The primary intended application of the database is to provide surface emissivity constraints in atmospheric and surface property retrieval or assimilation. An additional application is to serve as a dynamic indicator of land surface properties relevant to climate change monitoring. The precision of the emissivity data is estimated to be significantly better than in prior databases from other sensors due to the precise collocation with high-quality MODIS LST data and due to the quality control features of our data analysis system. The accuracy of the emissivities in deserts and semi-arid regions is enhanced by applying, in those regions, a version of the emissivity retrieval algorithm that accounts for the penetration of microwave radiation through dry soil with diurnally varying vertical temperature gradients. These results suggest that this penetration effect is more widespread and more significant to interpretation of passive microwave measurements than had been previously established. Emissivity coverage in areas where persistent cloudiness interferes with the availability of MODIS LST data is achieved using a classification-based method to spread emissivity data from less-cloudy areas that have similar microwave surface properties. Evaluations and analyses of the emissivity products over homogeneous snow-free areas are presented, including application to retrieval of soil temperature profiles. Spatial inhomogeneities are the largest in the vicinity of large water bodies due to the large water/land emissivity contrast and give rise to large apparent temporal variability in the retrieved emissivities when satellite footprint locations vary over time. This issue will be dealt with in the future by

Satellite information for wind energy applications

Energy Technology Data Exchange (ETDEWEB)

Nielsen, M.; Astrup, P.; Bay Hasager, C.

2004-11-01

An introduction to satellite information relevant for wind energy applications is given. It includes digital elevation model (DEM) data based on satellite observations. The Shuttle Radar Topography Mission (SRTM) is useful for regional scale wind resource studies. Comparison results from complex terrain in Spain and flat terrain in Denmark are found to be acceptable for both sites. Also land cover type information can be retrieved from satellite observations. Land cover type maps have to be combined with roughness data from field observation or literature values. Land cover type maps constitute an aid to map larger regions within shorter time. Field site observations of obstacles and hedges are still necessary. The raster-based map information from DEM and land cover maps can be converted for use in WASP. For offshore locations it is possible to estimate the wind resources based on ocean surface wind data from several types of satellite observations. The RWT software allows an optimal calculation of SAR wind resource statistics. A tab-file with SAR-based observed wind climate (OWC) data can be obtained for 10 m above sea level and used in WASP. RWT uses a footprint averaging technique to obtain data as similar as possible to mast observations. Maximum-likelihood fitting is used to calculate the Weibull A and k parameters from the constrained data set. Satellite SAR wind maps cover the coastal zone from 3 km and offshore with very detailed information of 400 m by 400 m grid resolution. Spatial trends in mean wind, energy density, Weibull A and k and uncertainty values are provided for the area of interest. Satellite scatterometer wind observations have a spatial resolution of 25 km by 25 km. These data typically represent a site further offshore, and the tab-file statistics should be used in WASP combined with topography and roughness information to assess the coastal wind power potential. Scatterometer wind data are observed {approx} twice per day, whereas SAR only

Location of irrigated land classified from satellite imagery - High Plains Area, nominal date 1992

Science.gov (United States)

Qi, Sharon L.; Konduris, Alexandria; Litke, David W.; Dupree, Jean

2002-01-01

Satellite imagery from the Landsat Thematic Mapper (nominal date 1992) was used to classify and map the location of irrigated land overlying the High Plains aquifer. The High Plains aquifer underlies 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The U.S. Geological Survey is conducting a water-quality study of the High Plains aquifer as part of the National Water-Quality Assessment Program. To help interpret data and select sites for the study, it is helpful to know the location of irrigated land within the study area. To date, the only information available for the entire area is 20 years old. To update the data on irrigated land, 40 summer and 40 spring images (nominal date 1992) were acquired from the National Land Cover Data set and processed using a band-ratio method (Landsat Thematic Mapper band 4 divided by band 3) to enhance the vegetation signatures. The study area was divided into nine subregions with similar environmental characteristics, and a band-ratio threshold was selected from imagery in each subregion that differentiated the cutoff between irrigated and nonirrigated land. The classified images for each subregion were mosaicked to produce an irrigated-land map for the study area. The total amount of irrigated land classified from the 1992 imagery was 13.1 million acres, or about 12 percent of the total land in the High Plains. This estimate is approximately 1.5 percent greater than the amount of irrigated land reported in the 1992 Census of Agriculture (12.8 millions acres).

Monitoring land use/land cover changes using CORINE land cover data: a case study of Silivri coastal zone in Metropolitan Istanbul.

Science.gov (United States)

Yilmaz, Rüya

2010-06-01

The objective of the present study was to assess changes in land use/land cover patterns in the coastal town of Silivri, a part of greater Istanbul administratively. In the assessment, remotely sensed data, in the form of satellite images, and geographic information systems were used. Types of land use/land cover were designated as the percentage of the total area studied. Results calculated from the satellite data for land cover classification were compared successfully with the database Coordination of Information on the Environment (CORINE). This served as a reference to appraise the reliability of the study presented here. The CORINE Program was established by the European Commission to create a harmonized Geographical Information System on the state of the environment in the European Community. Unplanned urbanization is causing land use changes mainly in developing countries such as Turkey. This situation in Turkey is frequently observed in the city of Istanbul. There are only a few studies of land use-land cover changes which provide an integrated assessment of the biophysical and societal causes and consequences of environmental degradation in Istanbul. The research area comprised greater Silivri Town which is situated by the coast of Marmara Sea, and it is located approximately 60 km west of Istanbul. The city of Istanbul is one of the largest metropolises in Europe with ca. 15 million inhabitants. Additionally, greater Silivri is located near the terminal point of the state highway connecting Istanbul with Europe. Measuring of changes occurring in land use would help control future planning of settlements; hence, it is of importance for the Greater Silivri and Silivri Town. Following our evaluations, coastal zone of Silivri was classified into the land use groups of artificial surfaces agricultural areas and forests and seminatural areas with 47.1%, 12.66%, and 22.62%, respectively.

GLORI (GLObal navigation satellite system Reflectometry Instrument): A New Airborne GNSS-R receiver for land surface applications

Science.gov (United States)

Motte, Erwan; Zribi, Mehrez; Fanise, Pascal

2015-04-01

GLORI (GLObal navigation satellite system Reflectometry Instrument) is a new receiver dedicated to the airborne measurement of surface parameters such as soil moisture and biomass above ground and sea state (wave height and direction) above oceans. The instrument is based on the PARIS concept [Martin-Neira, 1993] using both the direct and surface-reflected L-band signals from the GPS constellation as a multistatic radar source. The receiver is based on one up-looking and one down-looking dual polarization hemispherical active antennas feeding a low-cost 4-channel SDR direct down-conversion receiver tuned to the GPS L1 frequency. The raw measurements are sampled at 16.368MHz and stored as 2-bit, IQ binary files. In post-processing, GPS acquisition and tracking are performed on the direct up-looking signal while the down-looking signal is processed blindly using tracking parameters from the direct signal. The obtained direct and reflected code-correlation waveforms are the basic observables for geophysical parameters inversion. The instrument was designed to be installed aboard the ATR42 experimental aircraft from the French SAFIRE fleet as a permanent payload. The long term goal of the project is to provide real-time continuous surface information for every flight performed. The aircraft records attitude information through its Inertial Measurement Unit and a commercial GPS receiver records additional information such as estimated doppler and code phase, receiver location, satellites azimuth and elevation. A series of test flights were performed over both the Toulouse and Gulf of Lion (Mediterranean Sea) regions during the period 17-21 Nov 2014 together with the KuROS radar [Hauser et al., 2014]. Using processing methods from the literature [Egido et al., 2014], preliminary results demonstrate the instrument sensitivity to both ground and ocean surface parameters estimation. A dedicated scientific flight campaign is planned at the end of second quarter 2015 with

Regional geology mapping using satellite-based remote sensing approach in Northern Victoria Land, Antarctica

Science.gov (United States)

Pour, Amin Beiranvand; Park, Yongcheol; Park, Tae-Yoon S.; Hong, Jong Kuk; Hashim, Mazlan; Woo, Jusun; Ayoobi, Iman

2018-06-01

Satellite remote sensing imagery is especially useful for geological investigations in Antarctica because of its remoteness and extreme environmental conditions that constrain direct geological survey. The highest percentage of exposed rocks and soils in Antarctica occurs in Northern Victoria Land (NVL). Exposed Rocks in NVL were part of the paleo-Pacific margin of East Gondwana during the Paleozoic time. This investigation provides a satellite-based remote sensing approach for regional geological mapping in the NVL, Antarctica. Landsat-8 and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) datasets were used to extract lithological-structural and mineralogical information. Several spectral-band ratio indices were developed using Landsat-8 and ASTER bands and proposed for Antarctic environments to map spectral signatures of snow/ice, iron oxide/hydroxide minerals, Al-OH-bearing and Fe, Mg-OH and CO3 mineral zones, and quartz-rich felsic and mafic-to-ultramafic lithological units. The spectral-band ratio indices were tested and implemented to Level 1 terrain-corrected (L1T) products of Landsat-8 and ASTER datasets covering the NVL. The surface distribution of the mineral assemblages was mapped using the spectral-band ratio indices and verified by geological expeditions and laboratory analysis. Resultant image maps derived from spectral-band ratio indices that developed in this study are fairly accurate and correspond well with existing geological maps of the NVL. The spectral-band ratio indices developed in this study are especially useful for geological investigations in inaccessible locations and poorly exposed lithological units in Antarctica environments.

An Improved Single-Channel Method to Retrieve Land Surface Temperature from the Landsat-8 Thermal Band

Directory of Open Access Journals (Sweden)

Jordi Cristóbal

2018-03-01

Full Text Available Land surface temperature (LST is one of the sources of input data for modeling land surface processes. The Landsat satellite series is the only operational mission with more than 30 years of archived thermal infrared imagery from which we can retrieve LST. Unfortunately, stray light artifacts were observed in Landsat-8 TIRS data, mostly affecting Band 11, currently making the split-window technique impractical for retrieving surface temperature without requiring atmospheric data. In this study, a single-channel methodology to retrieve surface temperature from Landsat TM and ETM+ was improved to retrieve LST from Landsat-8 TIRS Band 10 using near-surface air temperature (Ta and integrated atmospheric column water vapor (w as input data. This improved methodology was parameterized and successfully evaluated with simulated data from a global and robust radiosonde database and validated with in situ data from four flux tower sites under different types of vegetation and snow cover in 44 Landsat-8 scenes. Evaluation results using simulated data showed that the inclusion of Ta together with w within a single-channel scheme improves LST retrieval, yielding lower errors and less bias than models based only on w. The new proposed LST retrieval model, developed with both w and Ta, yielded overall errors on the order of 1 K and a bias of −0.5 K validated against in situ data, providing a better performance than other models parameterized using w and Ta or only w models that yielded higher error and bias.

Object-based Dimensionality Reduction in Land Surface Phenology Classification

Directory of Open Access Journals (Sweden)

Brian E. Bunker

2016-11-01

Full Text Available Unsupervised classification or clustering of multi-decadal land surface phenology provides a spatio-temporal synopsis of natural and agricultural vegetation response to environmental variability and anthropogenic activities. Notwithstanding the detailed temporal information available in calibrated bi-monthly normalized difference vegetation index (NDVI and comparable time series, typical pre-classification workflows average a pixel’s bi-monthly index within the larger multi-decadal time series. While this process is one practical way to reduce the dimensionality of time series with many hundreds of image epochs, it effectively dampens temporal variation from both intra and inter-annual observations related to land surface phenology. Through a novel application of object-based segmentation aimed at spatial (not temporal dimensionality reduction, all 294 image epochs from a Moderate Resolution Imaging Spectroradiometer (MODIS bi-monthly NDVI time series covering the northern Fertile Crescent were retained (in homogenous landscape units as unsupervised classification inputs. Given the inherent challenges of in situ or manual image interpretation of land surface phenology classes, a cluster validation approach based on transformed divergence enabled comparison between traditional and novel techniques. Improved intra-annual contrast was clearly manifest in rain-fed agriculture and inter-annual trajectories showed increased cluster cohesion, reducing the overall number of classes identified in the Fertile Crescent study area from 24 to 10. Given careful segmentation parameters, this spatial dimensionality reduction technique augments the value of unsupervised learning to generate homogeneous land surface phenology units. By combining recent scalable computational approaches to image segmentation, future work can pursue new global land surface phenology products based on the high temporal resolution signatures of vegetation index time series.

estimation of land surface temperature of kaduna metropolis, nigeria

African Journals Online (AJOL)

Zaharaddeen et. al

Land surface temperature can provide noteworthy information about the surface ... modelling the surface energy balance (Kalma, et al., 2008; ... Landsat, in addition some of the Landsat data have cloud cover and ..... The Impact Of Urban.

Dust Aerosols at the Source Region During ACE-ASIA: A Surface/Satellite Perspective

Science.gov (United States)

Tsay, Si-Chee; Lau, William K. M. (Technical Monitor)

2001-01-01

ACE (Aerosol Characterization Experiment)-Asia is designed to study the compelling variability in spatial and temporal scale of both pollution-derived and naturally occurring aerosols, which often exist in high concentrations over eastern Asia and along the rim of the western Pacific. The phase-I of ACE-Asia was conducted from March-May 2001 in the vicinity of the Gobi desert, East Coast of China, Yellow Sea, Korea, and Japan, along the pathway of Kosa (severe events that blanket East Asia with yellow desert dust, peaked in the Spring season). Asian dust typically originates in desert areas far from polluted urban regions. During transport, dust layers can interact with anthropogenic sulfate and soot aerosols from heavily polluted urban areas. Added to the complex effects of clouds and natural marine aerosols, dust particles reaching the marine environment can have drastically different properties than those from the source. Thus, understanding the unique temporal and spatial variations of Asian dust is of special importance in regional-to-global climate issues such as radiative forcing, the hydrological cycle, and primary biological productivity in the mid-Pacific Ocean. During ACE-Asia we have measured continuously aerosol physical/optical/radiative properties, column precipitable water amount, and surface reflectivity over homogeneous areas from surface. The inclusion of flux measurements permits the determination of dust aerosol radiative flux in addition to measurements of loading and optical thickness. At the time of the Terra/MODIS, SeaWiFS, TOMS and other satellite overpasses, these ground-based observations can provide valuable data to compare with satellite retrievals over land. Preliminary results will be presented and discussed their implications in regional climatic effects.

IMPLEMENTATION OF AERONAUTICAL LOCAL SATELLITE AUGMENTATION SYSTEM

Directory of Open Access Journals (Sweden)

Stojce Ilcev

2011-03-01

Full Text Available Abstract. This paper introduces development and implementation of new Local Satellite AugmentationSystem as an integration component of the Regional Satellite Augmentation System (RSAS employingcurrent and new Satellite Communications, Navigation and Surveillance (CNS for improvement of the AirTraffic Control (ATC and Air Traffic Management (ATM and for enhancement safety systems includingtransport security and control of flights in all stages, airport approaching, landing, departures and allmovements over airport surface areas. The current first generation of the Global Navigation Satellite SystemGNSS-1 applications are represented by fundamental military solutions for Position, Velocity and Time ofthe satellite navigation and determination systems such as the US GPS and Russian GLONASS (Former-USSR requirements, respectively. The establishment of Aeronautical CNS is also discussed as a part ofGlobal Satellite Augmentation Systems of GPS and GLONASS systems integrated with existing and futureRSAS and LSAS in airports areas. Specific influence and factors related to the Comparison of the Currentand New Aeronautical CNS System including the Integration of RSAS and GNSS solutions are discussedand packet of facts is determined to maximize the new satellite Automatic Dependent Surveillance System(ADSS and Special Effects of the RSAS Networks. The possible future integration of RSAS and GNSS andthe common proposal of the satellite Surface Movement Guidance and Control are presented in thechangeless ways as of importance for future enfacements of ATC and ATM for any hypothetical airportinfrastructure.Keywords: ADSS, ATC, ATM, CNS, GSAS, LRAS, RSAS, SMGC, Special Effects of RSAS.

Land Surface Process and Air Quality Research and Applications at MSFC

Science.gov (United States)

Quattrochi, Dale; Khan, Maudood

2007-01-01

This viewgraph presentation provides an overview of land surface process and air quality research at MSFC including atmospheric modeling and ongoing research whose objective is to undertake a comprehensive spatiotemporal analysis of the effects of accurate land surface characterization on atmospheric modeling results, and public health applications. Land use maps as well as 10 meter air temperature, surface wind, PBL mean difference heights, NOx, ozone, and O3+NO2 plots as well as spatial growth model outputs are included. Emissions and general air quality modeling are also discussed.

Simulated impacts of land cover change on summer climate in the Tibetan Plateau

International Nuclear Information System (INIS)

Li Qian; Xue Yongkang

2010-01-01

The Tibetan Plateau (TP) is a key region of land-atmosphere interactions with severe eco-environment degradation. This study uses an atmospheric general circulation model, NCEP GCM/SSiB, to present the major TP summer climate features for six selected ENSO years and preliminarily assess the possible impact of land cover change on the summer circulation over the TP. Compared to Reanalysis II data, the GCM using satellite derived vegetation properties generally reproduces the main 6-year-mean TP summer circulation features despite some discrepancies in intensity and geographic locations of some climate features. Two existing vegetation maps with very different land cover conditions over the TP, one with bare ground and one with vegetation cover, derived from satellite derived data, are tested and produce clearer climate signals due to land cover change. It shows that land cover change from vegetated land to bare ground decreases the radiation absorbed by the surface and results in weaker surface thermal effects, which lead to lower atmospheric temperature, as well as weaker vertical ascending motion, low-layer cyclonic, upper level anticyclonic, and summer monsoon circulation. These changes in circulation cause a decrease in the precipitation in the southeastern TP.

Seasonal evaluation of the land surface scheme HTESSEL against remote sensing derived energy fluxes of the Transdanubian region in Hungary

Directory of Open Access Journals (Sweden)

E. L. Wipfler

2011-04-01

Full Text Available The skill of the land surface model HTESSEL is assessed to reproduce evaporation in response to land surface characteristics and atmospheric forcing, both being spatially variable. Evaporation estimates for the 2005 growing season are inferred from satellite observations of the Western part of Hungary and compared to model outcomes. Atmospheric forcings are obtained from a hindcast run with the Regional Climate Model RACMO2. Although HTESSEL slightly underpredicts the seasonal evaporative fraction as compared to satellite estimates, the mean, 10th and 90th percentile of this variable are of the same magnitude as the satellite observations. The initial water as stored in the soil and snow layer does not have a significant effect on the statistical properties of the evaporative fraction. However, the spatial distribution of the initial soil and snow water significantly affects the spatial distribution of the calculated evaporative fraction and the models ability to reproduce evaporation correctly in low precipitation areas in the considered region. HTESSEL performs weaker in dryer areas. In Western Hungary these areas are situated in the Danube valley, which is partly covered by irrigated cropland and which also may be affected by shallow groundwater. Incorporating (lateral groundwater flow and irrigation, processes that are not included now, may improve HTESSELs ability to predict evaporation correctly. Evaluation of the model skills using other test areas and larger evaluation periods is needed to confirm the results.

Based on earlier sensitivity analysis, the effect of a number of modifications to HTESSEL has been assessed. A more physically based reduction function for dry soils has been introduced, the soil depth is made variable and the effect of swallow groundwater included. However, the combined modification does not lead to a significantly improved performance of HTESSEL.

Land Mobile Satellite Service (LMSS): A conceptual system design and identification of the critical technologies. Part 1: Executive summary

Science.gov (United States)

Naderi, F. (Editor)

1982-01-01

A system design for a satellite aided land mobile service is described. The advanced system is based on a geostationary satellite which employs a large UHF reflector to communicate with small user antennas on mobile vehicles. It is shown that the system through multiple beam antennas and frequency reuse provides for radiotelephone and dispatch channels. It is concluded that the system is technologically feasible to provide service to rural and remote regions.

Impact of Optimized Land Surface Parameters on the Land-Atmosphere Coupling in WRF Simulations of Dry and Wet Extremes

Science.gov (United States)

Kumar, S.; Santanello, J. A.; Peters-Lidard, C. D.; Harrison, K.

2011-12-01

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty module in NASA's Land Information System (LIS-OPT), whereby parameter sets are calibrated in the Noah land surface model and classified according to the land cover and soil type mapping of the observations and the full domain. The impact of the calibrated parameters on the a) spinup of land surface states used as initial conditions, and b) heat and moisture fluxes of the coupled (LIS-WRF) simulations are then assessed in terms of ambient weather, PBL budgets, and precipitation along with L-A coupling diagnostics. In addition, the sensitivity of this approach to the period of calibration (dry, wet, normal) is investigated. Finally, tradeoffs of computational tractability and scientific validity (e.g.,. relating to the representation of the spatial dependence of parameters) and the feasibility of calibrating to multiple observational datasets are also discussed.

Assessing the influence of groundwater and land surface scheme in the modelling of land surface-atmosphere feedbacks over the FIFE area in Kansas, USA

DEFF Research Database (Denmark)

Larsen, Morten Andreas Dahl; Højmark Rasmussen, Søren; Drews, Martin

2016-01-01

The land surface-atmosphere interaction is described differently in large scale surface schemes of regional climate models and small scale spatially distributed hydrological models. In particular, the hydrological models include the influence of shallow groundwater on evapotranspiration during dry...... by HIRHAM simulated precipitation. The last two simulations include iv) a standard HIRHAM simulation, and v) a fully coupled HIRHAM-MIKE SHE simulation locally replacing the land surface scheme by MIKE SHE for the FIFE area, while HIRHAM in standard configuration is used for the remaining model area...

Analysis of Multipath Mitigation Techniques with Land Mobile Satellite Channel Model

Directory of Open Access Journals (Sweden)

M. Z. H. Bhuiyan J. Zhang

2012-12-01

Full Text Available Multipath is undesirable for Global Navigation Satellite System (GNSS receivers, since the reception of multipath can create a significant distortion to the shape of the correlation function leading to an error in the receivers’ position estimate. Many multipath mitigation techniques exist in the literature to deal with the multipath propagation problem in the context of GNSS. The multipath studies in the literature are often based on optimistic assumptions, for example, assuming a static two-path channel or a fading channel with a Rayleigh or a Nakagami distribution. But, in reality, there are a lot of channel modeling issues, for example, satellite-to-user geometry, variable number of paths, variable path delays and gains, Non Line-Of-Sight (NLOS path condition, receiver movements, etc. that are kept out of consideration when analyzing the performance of these techniques. Therefore, this is of utmost importance to analyze the performance of different multipath mitigation techniques in some realistic measurement-based channel models, for example, the Land Multipath is undesirable for Global Navigation Satellite System (GNSS receivers, since the reception of multipath can create a significant distortion to the shape of the correlation function leading to an error in the receivers’ position estimate. Many multipath mitigation techniques exist in the literature to deal with the multipath propagation problem in the context of GNSS. The multipath studies in the literature are often based on optimistic assumptions, for example, assuming a static two-path channel or a fading channel with a Rayleigh or a Nakagami distribution. But, in reality, there are a lot of channel modeling issues, for example, satellite-to-user geometry, variable number of paths, variable path delays and gains, Non Line-Of-Sight (NLOS path condition, receiver movements, etc. that are kept out of consideration when analyzing the performance of these techniques. Therefore, this

Land surface temperature downscaling using random forest regression: primary result and sensitivity analysis

Science.gov (United States)

Pan, Xin; Cao, Chen; Yang, Yingbao; Li, Xiaolong; Shan, Liangliang; Zhu, Xi

2018-04-01

The land surface temperature (LST) derived from thermal infrared satellite images is a meaningful variable in many remote sensing applications. However, at present, the spatial resolution of the satellite thermal infrared remote sensing sensor is coarser, which cannot meet the needs. In this study, LST image was downscaled by a random forest model between LST and multiple predictors in an arid region with an oasis-desert ecotone. The proposed downscaling approach was evaluated using LST derived from the MODIS LST product of Zhangye City in Heihe Basin. The primary result of LST downscaling has been shown that the distribution of downscaled LST matched with that of the ecosystem of oasis and desert. By the way of sensitivity analysis, the most sensitive factors to LST downscaling were modified normalized difference water index (MNDWI)/normalized multi-band drought index (NMDI), soil adjusted vegetation index (SAVI)/ shortwave infrared reflectance (SWIR)/normalized difference vegetation index (NDVI), normalized difference building index (NDBI)/SAVI and SWIR/NDBI/MNDWI/NDWI for the region of water, vegetation, building and desert, with LST variation (at most) of 0.20/-0.22 K, 0.92/0.62/0.46 K, 0.28/-0.29 K and 3.87/-1.53/-0.64/-0.25 K in the situation of +/-0.02 predictor perturbances, respectively.

Improve observation-based ground-level ozone spatial distribution by compositing satellite and surface observations: A simulation experiment

Science.gov (United States)

Zhang, Yuzhong; Wang, Yuhang; Crawford, James; Cheng, Ye; Li, Jianfeng

2018-05-01

Obtaining the full spatial coverage of daily surface ozone fields is challenging because of the sparsity of the surface monitoring network and the difficulty in direct satellite retrievals of surface ozone. We propose an indirect satellite retrieval framework to utilize the information from satellite-measured column densities of tropospheric NO2 and CH2O, which are sensitive to the lower troposphere, to derive surface ozone fields. The method is applicable to upcoming geostationary satellites with high-quality NO2 and CH2O measurements. To prove the concept, we conduct a simulation experiment using a 3-D chemical transport model for July 2011 over the eastern US. The results show that a second order regression using both NO2 and CH2O column densities can be an effective predictor for daily maximum 8-h average ozone. Furthermore, this indirect retrieval approach is shown to be complementary to spatial interpolation of surface observations, especially in regions where the surface sites are sparse. Combining column observations of NO2 and CH2O with surface site measurements leads to an improved representation of surface ozone over simple kriging, increasing the R2 value from 0.53 to 0.64 at a surface site distance of 252 km. The improvements are even more significant with larger surface site distances. The simulation experiment suggests that the indirect satellite retrieval technique can potentially be a useful tool to derive the full spatial coverage of daily surface ozone fields if satellite observation uncertainty is moderate.

Climatic Changes Effects On Spectral Vegetation Indices For Forested Areas Analysis From Satellite Data

International Nuclear Information System (INIS)

Zoran, M.; Stefan, S.

2007-01-01

Climate-induced changes at the land surface may in turn feed back on the climate itself through changes in soil moisture, vegetation, radiative characteristics, and surface-atmosphere exchanges of water vapor. Thresholding based on biophysical variables derived from time trajectories of satellite data is a new approach to classifying forest land cover via remote . sensing .The input data are composite values of the Normalized Difference Vegetation Index (NDVI). Classification accuracies are function of the class, comparison method and season of the year. The aim of the paper is forest biomass assessment and land-cover changes analysis due to climatic effects

Cloud-enabled large-scale land surface model simulations with the NASA Land Information System

Science.gov (United States)

Duffy, D.; Vaughan, G.; Clark, M. P.; Peters-Lidard, C. D.; Nijssen, B.; Nearing, G. S.; Rheingrover, S.; Kumar, S.; Geiger, J. V.

2017-12-01

Developed by the Hydrological Sciences Laboratory at NASA Goddard Space Flight Center (GSFC), the Land Information System (LIS) is a high-performance software framework for terrestrial hydrology modeling and data assimilation. LIS provides the ability to integrate satellite and ground-based observational products and advanced modeling algorithms to extract land surface states and fluxes. Through a partnership with the National Center for Atmospheric Research (NCAR) and the University of Washington, the LIS model is currently being extended to include the Structure for Unifying Multiple Modeling Alternatives (SUMMA). With the addition of SUMMA in LIS, meaningful simulations containing a large multi-model ensemble will be enabled and can provide advanced probabilistic continental-domain modeling capabilities at spatial scales relevant for water managers. The resulting LIS/SUMMA application framework is difficult for non-experts to install due to the large amount of dependencies on specific versions of operating systems, libraries, and compilers. This has created a significant barrier to entry for domain scientists that are interested in using the software on their own systems or in the cloud. In addition, the requirement to support multiple run time environments across the LIS community has created a significant burden on the NASA team. To overcome these challenges, LIS/SUMMA has been deployed using Linux containers, which allows for an entire software package along with all dependences to be installed within a working runtime environment, and Kubernetes, which orchestrates the deployment of a cluster of containers. Within a cloud environment, users can now easily create a cluster of virtual machines and run large-scale LIS/SUMMA simulations. Installations that have taken weeks and months can now be performed in minutes of time. This presentation will discuss the steps required to create a cloud-enabled large-scale simulation, present examples of its use, and

Reforesting unused surface mined lands by replanting with native trees

Science.gov (United States)

Patrick N. Angel; James A. Burger; Carl E. Zipper; Scott Eggerud

2012-01-01

More than 600,000 ha (1.5 million ac) of mostly forested land in the Appalachian region were surface mined for coal under the Surface Mining Control and Reclamation Act. Today, these lands are largely unmanaged and covered with persistent herbaceous species, such as fescue (Festuca spp.) and sericea lespedeza (Lespedeza cuneata [Dum. Cours.] G. Don,) and a mix of...

Application of spectrometer cropscan MSR 16R and Landsat imagery for identification the spectral characteristics of land cover

Science.gov (United States)

Tampubolon, Togi; Abdullah, Khiruddin bin; San, Lim Hwee

2013-09-01

The spectral characteristics of land cover are basic references in classifying satellite image for geophysics analysis. It can be obtained from the measurements using spectrometer and satellite image processing. The aims of this study to investigate the spectral characteristics of land cover based on the results of measurement using Spectrometer Cropscan MSR 16R and Landsat satellite imagery. The area of study in this research is in Medan, (Deli Serdang, North Sumatera) Indonesia. The scope of this study is the basic survey from the measurements of spectral land cover which is covered several type of land such as a cultivated and managed terrestrial areas, natural and semi-natural, cultivated aquatic or regularly flooded areas, natural and semi-natural aquatic, artificial surfaces and associated areas, bare areas, artificial waterbodies and natural waterbodies. The measurement and verification were conducted using a spectrometer provided their spectral characteristics and Landsat imagery, respectively. The results of the spectral characteristics of land cover shows that each type of land cover have a unique characteristic. The correlation of spectral land cover based on spectrometer Cropscan MSR 16R and Landsat satellite image are above 90 %. However, the land cover of artificial waterbodiese have a correlation under 40 %. That is because the measurement of spectrometer Cropscan MSR 16R and acquisition of Landsat satellite imagery has a time different.

Land-ocean contrast on electrical characteristics of lightning discharge derived from satellite optical measurements

Science.gov (United States)

Adachi, T.; Said, R.; Cummer, S. A.; Li, J.; Takahashi, Y.; Hsu, R.; Su, H.; Chen, A. B.; Mende, S. B.; Frey, H. U.

2010-12-01

Comparative studies on the electrical properties of oceanic and continental lightning are crucial to elucidate air discharge processes occurring under different conditions. Past studies however have primarily focused on continental lightning because of the limited coverage of ground-based instruments. Recent satellite measurements by FORMOSAT-2/ISUAL provided a new way to survey the global characteristics of lightning and transient luminous events regardless of land and ocean. In this study, we analyze ISUAL/spectrophotometer data to clarify the electrical properties of lightning on a global level. Based on the results obtained by Cummer et al. [2006] and Adachi et al. [2009], the OI-777.4nm emission intensity is used to infer lightning electrical parameters. Results show a clear land-ocean contrast on the parameters of lightning discharge: in oceanic lightning, peak luminosity is 60 % higher and the time scale of return stroke is 30 % shorter. These results suggest higher peak current in oceanic lightning, which is consistent with the fact that elves, EMP-driven phenomena, also tend to occur over the ocean [Chen et al., 2008]. Further analysis of lightning events occurring around the Caribbean Sea shows that the transition-line of lightning electrical properties is precisely located along the coastline. We suggest that the differences in these electrical properties may be due to the boundary conditions (conductivity, surface terrain, etc). In this talk, based on the calibration with NLDN and Duke magnetometer data, current moment change and charge moment change will be globally evaluated using a complete set of the ISUAL-observed lightning events.

Merged Land and Ocean Surface Temperature, Version 3.5

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The historical Merged Land-Ocean Surface Temperature Analysis (MLOST) is derived from two independent analyses, an Extended Reconstructed Sea Surface Temperature...

Atmospheric sensitivity to land surface changes: comparing the impact of albedo, roughness, and evaporative resistance on near-surface air temperature using an idealized land model.

Science.gov (United States)

Lague, M. M.; Swann, A. L. S.; Bonan, G. B.

2017-12-01

Past studies have demonstrated how changes in vegetation can impact the atmosphere; however, it is often difficult to identify the exact physical pathway through which vegetation changes drive an atmospheric response. Surface properties (such as vegetation color, or height) control surface energy fluxes, which feed back on the atmosphere on both local and global scales by modifying temperatures, cloud cover, and energy gradients. Understanding how land surface properties influence energy fluxes is crucial for improving our understanding of how vegetation change - past, present, and future - impacts the atmosphere, global climate, and people. We explore the sensitivity of the atmosphere to perturbations of three land surface properties - albedo, roughness, and evaporative resistance - using an idealized land model coupled to an Earth System Model. We derive a relationship telling us how large a change in each surface property is required to drive a local 0.1 K change in 2m air temperature. Using this idealized framework, we are able to separate the influence on the atmosphere of each individual surface property. We demonstrate that the impact of each surface property on the atmosphere is spatially variable - that is, a similar change in vegetation can have different climate impacts if made in different locations. This analysis not only improves our understanding of how the land system can influence climate, but also provides us with a set of theoretical limits on the potential climate impact of arbitrary vegetation change (natural or anthropogenic).

Land surface and climate parameters and malaria features in Vietnam

Science.gov (United States)

Liou, Y. A.; Anh, N. K.

2017-12-01

Land surface parameters may affect local microclimate, which in turn alters the development of mosquito habitats and transmission risks (soil-vegetation-atmosphere-vector borne diseases). Forest malaria is a chromic issue in Southeast Asian countries, in particular, such as Vietnam (in 1991, approximate 2 million cases and 4,646 deaths were reported (https://sites.path.org)). Vietnam has lowlands, sub-tropical high humidity, and dense forests, resulting in wide-scale distribution and high biting rate of mosquitos in Vietnam, becoming a challenging and out of control scenario, especially in Vietnamese Central Highland region. It is known that Vietnam's economy mainly relies on agriculture and malaria is commonly associated with poverty. There is a strong demand to investigate the relationship between land surface parameters (land cover, soil moisture, land surface temperature, etc.) and climatic variables (precipitation, humidity, evapotranspiration, etc.) in association with malaria distribution. GIS and remote sensing have been proven their powerful potentials in supporting environmental and health studies. The objective of this study aims to analyze physical attributes of land surface and climate parameters and their links with malaria features. The outcomes are expected to illustrate how remotely sensed data has been utilized in geohealth applications, surveillance, and health risk mapping. In addition, a platform with promising possibilities of allowing disease early-warning systems with citizen participation will be proposed.

A One-Source Approach for Estimating Land Surface Heat Fluxes Using Remotely Sensed Land Surface Temperature

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Yongmin Yang

2017-01-01

Full Text Available The partitioning of available energy between sensible heat and latent heat is important for precise water resources planning and management in the context of global climate change. Land surface temperature (LST is a key variable in energy balance process and remotely sensed LST is widely used for estimating surface heat fluxes at regional scale. However, the inequality between LST and aerodynamic surface temperature (Taero poses a great challenge for regional heat fluxes estimation in one-source energy balance models. To address this issue, we proposed a One-Source Model for Land (OSML to estimate regional surface heat fluxes without requirements for empirical extra resistance, roughness parameterization and wind velocity. The proposed OSML employs both conceptual VFC/LST trapezoid model and the electrical analog formula of sensible heat flux (H to analytically estimate the radiometric-convective resistance (rae via a quartic equation. To evaluate the performance of OSML, the model was applied to the Soil Moisture-Atmosphere Coupling Experiment (SMACEX in United States and the Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE in China, using remotely sensed retrievals as auxiliary data sets at regional scale. Validated against tower-based surface fluxes observations, the root mean square deviation (RMSD of H and latent heat flux (LE from OSML are 34.5 W/m2 and 46.5 W/m2 at SMACEX site and 50.1 W/m2 and 67.0 W/m2 at MUSOEXE site. The performance of OSML is very comparable to other published studies. In addition, the proposed OSML model demonstrates similar skills of predicting surface heat fluxes in comparison to SEBS (Surface Energy Balance System. Since OSML does not require specification of aerodynamic surface characteristics, roughness parameterization and meteorological conditions with high spatial variation such as wind speed, this proposed method shows high potential for routinely acquisition of latent heat flux estimation

Citizen science land cover classification based on ground and satellite imagery: Case study Day River in Vietnam

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Nguyen, Son Tung; Minkman, Ellen; Rutten, Martine

2016-04-01

Citizen science is being increasingly used in the context of environmental research, thus there are needs to evaluate cognitive ability of humans in classifying environmental features. With the focus on land cover, this study explores the extent to which citizen science can be applied in sensing and measuring the environment that contribute to the creation and validation of land cover data. The Day Basin in Vietnam was selected to be the study area. Different methods to examine humans' ability to classify land cover were implemented using different information sources: ground based photos - satellite images - field observation and investigation. Most of the participants were solicited from local people and/or volunteers. Results show that across methods and sources of information, there are similar patterns of agreement and disagreement on land cover classes among participants. Understanding these patterns is critical to create a solid basis for implementing human sensors in earth observation. Keywords: Land cover, classification, citizen science, Landsat 8

Benchmarking sensitivity of biophysical processes to leaf area changes in land surface models

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Forzieri, Giovanni; Duveiller, Gregory; Georgievski, Goran; Li, Wei; Robestson, Eddy; Kautz, Markus; Lawrence, Peter; Ciais, Philippe; Pongratz, Julia; Sitch, Stephen; Wiltshire, Andy; Arneth, Almut; Cescatti, Alessandro

2017-04-01

Land surface models (LSM) are widely applied as supporting tools for policy-relevant assessment of climate change and its impact on terrestrial ecosystems, yet knowledge of their performance skills in representing the sensitivity of biophysical processes to changes in vegetation density is still limited. This is particularly relevant in light of the substantial impacts on regional climate associated with the changes in leaf area index (LAI) following the observed global greening. Benchmarking LSMs on the sensitivity of the simulated processes to vegetation density is essential to reduce their uncertainty and improve the representation of these effects. Here we present a novel benchmark system to assess model capacity in reproducing land surface-atmosphere energy exchanges modulated by vegetation density. Through a collaborative effort of different modeling groups, a consistent set of land surface energy fluxes and LAI dynamics has been generated from multiple LSMs, including JSBACH, JULES, ORCHIDEE, CLM4.5 and LPJ-GUESS. Relationships of interannual variations of modeled surface fluxes to LAI changes have been analyzed at global scale across different climatological gradients and compared with satellite-based products. A set of scoring metrics has been used to assess the overall model performances and a detailed analysis in the climate space has been provided to diagnose possible model errors associated to background conditions. Results have enabled us to identify model-specific strengths and deficiencies. An overall best performing model does not emerge from the analyses. However, the comparison with other models that work better under certain metrics and conditions indicates that improvements are expected to be potentially achievable. A general amplification of the biophysical processes mediated by vegetation is found across the different land surface schemes. Grasslands are characterized by an underestimated year-to-year variability of LAI in cold climates

Surface drainage in leveled land: Implication of slope

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Antoniony S. Winkler

Full Text Available ABSTRACT In the lowlands of Rio Grande do Sul, land leveling is mostly carried out with no slope for the purpose of rice production. In this environment, soils with a low hydraulic conductivity are predominant owing to the presence of a practically impermeable B-horizon near the surface. Land leveling leads to soil accommodation resulting in the formation of depressions where water accumulates after heavy rainfalls, subsequently leading to problems with crops implanted in succession to rice, such as soybeans. The objective of this research was to quantify the areas and volumes of water accumulation in soil as a function of the slope of land leveling. Five typical leveled lowland areas were studied as a part of this research. The original areas presented slopes of 0, 0.20, 0.25, 0.28 and 0.40%, which were used to generate new digital elevation models with slopes between 0 and 0.5%. These newly generated digital models were used to map the depressions with surface water storage. In conclusion, land leveling with slopes higher than 0.1% is recommended to minimize problems with superficial water storage in rice fields.

Towards a public, standardized, diagnostic benchmarking system for land surface models

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G. Abramowitz

2012-06-01

Full Text Available This work examines different conceptions of land surface model benchmarking and the importance of internationally standardized evaluation experiments that specify data sets, variables, metrics and model resolutions. It additionally demonstrates how essential the definition of a priori expectations of model performance can be, based on the complexity of a model and the amount of information being provided to it, and gives an example of how these expectations might be quantified. Finally, the Protocol for the Analysis of Land Surface models (PALS is introduced – a free, online land surface model benchmarking application that is structured to meet both of these goals.

Operational Satellite-based Surface Oil Analyses (Invited)

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Streett, D.; Warren, C.

2010-12-01

During the Deepwater Horizon spill, NOAA imagery analysts in the Satellite Analysis Branch (SAB) issued more than 300 near-real-time satellite-based oil spill analyses. These analyses were used by the oil spill response community for planning, issuing surface oil trajectories and tasking assets (e.g., oil containment booms, skimmers, overflights). SAB analysts used both Synthetic Aperture Radar (SAR) and high resolution visible/near IR multispectral satellite imagery as well as a variety of ancillary datasets. Satellite imagery used included ENVISAT ASAR (ESA), TerraSAR-X (DLR), Cosmo-Skymed (ASI), ALOS (JAXA), Radarsat (MDA), ENVISAT MERIS (ESA), SPOT (SPOT Image Corp.), Aster (NASA), MODIS (NASA), and AVHRR (NOAA). Ancillary datasets included ocean current information, wind information, location of natural oil seeps and a variety of in situ oil observations. The analyses were available as jpegs, pdfs, shapefiles and through Google, KML files and also available on a variety of websites including Geoplatform and ERMA. From the very first analysis issued just 5 hours after the rig sank through the final analysis issued in August, the complete archive is still publicly available on the NOAA/NESDIS website http://www.ssd.noaa.gov/PS/MPS/deepwater.html SAB personnel also served as the Deepwater Horizon International Disaster Charter Project Manager (at the official request of the USGS). The Project Manager’s primary responsibility was to acquire and oversee the processing and dissemination of satellite data generously donated by numerous private companies and nations in support of the oil spill response including some of the imagery described above. SAB has begun to address a number of goals that will improve our routine oil spill response as well as help assure that we are ready for the next spill of national significance. We hope to (1) secure a steady, abundant and timely stream of suitable satellite imagery even in the absence of large-scale emergencies such as

Rapid prototyping of soil moisture estimates using the NASA Land Information System

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Anantharaj, V.; Mostovoy, G.; Li, B.; Peters-Lidard, C.; Houser, P.; Moorhead, R.; Kumar, S.

2007-12-01

The Land Information System (LIS), developed at the NASA Goddard Space Flight Center, is a functional Land Data Assimilation System (LDAS) that incorporates a suite of land models in an interoperable computational framework. LIS has been integrated into a computational Rapid Prototyping Capabilities (RPC) infrastructure. LIS consists of a core, a number of community land models, data servers, and visualization systems - integrated in a high-performance computing environment. The land surface models (LSM) in LIS incorporate surface and atmospheric parameters of temperature, snow/water, vegetation, albedo, soil conditions, topography, and radiation. Many of these parameters are available from in-situ observations, numerical model analysis, and from NASA, NOAA, and other remote sensing satellite platforms at various spatial and temporal resolutions. The computational resources, available to LIS via the RPC infrastructure, support e- Science experiments involving the global modeling of land-atmosphere studies at 1km spatial resolutions as well as regional studies at finer resolutions. The Noah Land Surface Model, available with-in the LIS is being used to rapidly prototype soil moisture estimates in order to evaluate the viability of other science applications for decision making purposes. For example, LIS has been used to further extend the utility of the USDA Soil Climate Analysis Network of in-situ soil moisture observations. In addition, LIS also supports data assimilation capabilities that are used to assimilate remotely sensed soil moisture retrievals from the AMSR-E instrument onboard the Aqua satellite. The rapid prototyping of soil moisture estimates using LIS and their applications will be illustrated during the presentation.

Selection of soil hydraulic properties in a land surface model using remotely-sensed soil moisture and surface temperature

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Shellito, P. J.; Small, E. E.; Gutmann, E. D.

2013-12-01

Synoptic-scale weather is heavily influenced by latent and sensible heating from the land surface. The partitioning of available energy between these two fluxes as well as the distribution of moisture throughout the soil column is controlled by a unique set of soil hydraulic properties (SHPs) at every location. Weather prediction systems, which use coupled land surface and atmospheric models in their forecasts, must therefore be parameterized with estimates of SHPs. Currently, land surface models (LSMs) obtain SHP values by assuming a correlation exists between SHPs and the soil type, which the USDA maps in 12 classes. This method is spurious because texture is only one control of many that affects SHPs. Alternatively, SHPs can be obtained by calibrating them within the framework of an LSM. Because remotely-sensed data have the potential for continent-wide application, there is a critical need to understand their specific role in calibration efforts and the extent to which such calibrated SHPs can improve model simulations. This study focuses on SHP calibration with soil moisture content (SMC) and land surface temperature (Ts), data that are available from the SMOS and MODIS satellite missions, respectively. The scientific goals of this study are: (1) What is the model performance tradeoff between weighting SMC and Ts differently during the calibration process? (2) What can the tradeoff between calibration using in-situ and remotely-sensed SMC reveal about SHP scaling? (3) How are these relationships influenced by climatic regime and vegetation type? (4) To what extent can calibrated SHPs improve model performance over that of texture-based SHPs? Model calibrations are carried out within the framework of the Noah LSM using the Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm in five different climatic regimes. At each site, a five-dimensional parameter space of SHPs is searched to find the location that minimizes the difference between observed and

The Morphology, Dynamics and Potential Hotspots of Land Surface Temperature at a Local Scale in Urban Areas

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Jiong Wang

2015-12-01

Full Text Available Current characterization of the Urban Heat Island (UHI remains insufficient to support the effective mitigation and adaptation of increasing temperatures in urban areas. Planning and design strategies are restricted to the investigation of temperature anomalies at a city scale. By focusing on Land Surface Temperature of Wuhan, China, this research examines the temperature variations locally where mitigation and adaptation would be more feasible. It shows how local temperature anomalies can be identified morphologically. Technically, the MODerate-resolution Imaging Spectroradiometer satellite image products are used. They are first considered as noisy observations of the latent temperature patterns. The continuous latent patterns of the temperature are then recovered from these discrete observations by using the non-parametric Multi-Task Gaussian Process Modeling. The Multi-Scale Shape Index is then applied in the area of focus to extract the local morphological features. A triplet of shape, curvedness and temperature is formed as the criteria to extract local heat islands. The behavior of the local heat islands can thus be quantified morphologically. The places with critical deformations are identified as hotpots. The hotspots with certain yearly behavior are further associated with land surface composition to determine effective mitigation and adaptation strategies. This research can assist in the temperature and planning field on two levels: (1 the local land surface temperature patterns are characterized by decomposing the variations into fundamental deformation modes to allow a process-based understanding of the dynamics; and (2 the characterization at local scale conforms to planning and design conventions where mitigation and adaptation strategies are supposed to be more practical. The weaknesses and limitations of the study are addressed in the closing section.