Asharaf, S.; A. Dobler; Ahrens, B.
Towards the goal to understand the role of land-surface processes over the Indian sub-continent, a series of soil-moisture sensitivity simulations have been performed using a non-hydrostatic regional climate model COSMO-CLM. The experiments were driven by the lateral boundary conditions provided by the ERA-Interim (ECMWF) reanalysis. The simulation results show that the pre-monsoonal soil moisture has a significant influence on the monsoonal precipitation. Both, positive and negative soil-moi...
National Oceanic and Atmospheric Administration, Department of Commerce — The monthly data set consists of a file containing 1/2 degree monthly averaged soil moisture water height equivalents for the globe from 1948 onwards. Values are...
Kerr, Yann; Rodriguez-Frenandez, Nemesio; Al-Yaari, Amen; Parens, Marie; Molero, Beatriz; Mahmoodi, Ali; Mialon, Arnaud; Richaume, Philippe; Bindlish, Rajat; Mecklenburg, Susanne; Wigneron, Jean-Pierre
The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. Subsequently, the satellite has been in operation for over 6 years while the retrieval algorithms from Level 1 to Level 2 underwent significant evolutions as knowledge improved. Other approaches for retrieval at Level 2 over land were also investigated while Level 3 and 4 were initiated. In this présentation these improvements are assessed by inter-comparisons of the current Level 2 (V620) against the previous version (V551) and new products either using neural networks or Level 3. In addition a global evaluation of different SMOS soil moisture (SM) products is performed comparing products with those of model simulations and other satellites (AMSR E/ AMSR2 and ASCAT). Finally, all products were evaluated against in situ measurements of soil moisture (SM). The study demonstrated that the V620 shows a significant improvement (including those at level1 improving level2)) with respect to the earlier version V551. Results also show that neural network based approaches can yield excellent results over areas where other products are poor. Finally, global comparison indicates that SMOS behaves very well when compared to other sensors/approaches and gives consistent results over all surfaces from very dry (African Sahel, Arizona), to wet (tropical rain forests). RFI (Radio Frequency Interference) is still an issue even though detection has been greatly improved while RFI sources in several areas of the world are significantly reduced. When compared to other satellite products, the analysis shows that SMOS achieves its expected goals and is globally consistent over different eco climate regions from low to high latitudes and throughout the seasons.
Schmugge, T.; Oneill, P. E.; Wang, J. R.
During the four years of the AgRISTARS Program, significant progress was made in quantifying the capabilities of microwave sensors for the remote sensing of soil moisture. In this paper, a discussion is provided of the results of numerous field and aircraft experiments, analysis of spacecraft data, and modeling activities which examined the various noise factors such as roughness and vegetation that affect the interpretability of microwave emission measurements. While determining that a 21-cm wavelength radiometer was the best single sensor for soil moisture research, these studies demonstrated that a multisensor approach will provide more accurate soil moisture information for a wider range of naturally occurring conditions.
Before the large scale field experiments described in the call for papers, there were a number of experiments devoted to a single parameter, e.g. soil moisture. In the early 1970's, before the launch of the first microwave radiometer by NASA, there were a number of aircraft experiments to determine utility of these sensors for land observations. For soil moisture, these experiments were conducted in southwestern United States over irrigated agricultural areas which could provide a wide range of moisture conditions on a given day. The radiometers covered the wavelength range from 0.8 to 21 cm. These experiments demonstrated that it is possible to observe soil moisture variations remotely using a microwave radiometer with a sensitivity of about 3 K / unit of soil moisture. The results also showed that the longer wavelengths were better, with a radiometer at the 21 cm wavelength giving the best results. These positive results led to the development of Push Broom Microwave Radiometer (PBMR) and the Electrically Scanned Thinned Array Radiometer (ESTAR) instruments at the 21-cm wavelength. They have been used extensively in the large-scale experiments such as HAPEX-MOBILHY, FIFE, Monsoon90, SMEX, etc. The multi-beam nature of these instruments makes it possible to obtain more extensive coverage and thus to map spatial variations of surface soil moisture. Examples of the early results along with the more recent soil moisture maps will be presented.
Established nematode populations are very persistent in the soil. It is known that they need sufficient soil moisture for movement, feeding and reproduction (fig. 5), and that there are adverse soil moisture conditions which they cannot survive. The influence of soil moisture on survival of nematode
1973-01-01Established nematode populations are very persistent in the soil. It is known that they need sufficient soil moisture for movement, feeding and reproduction (fig. 5), and that there are adverse soil moisture conditions which they cannot survive. The influence of soil moisture on survival
Friis Dela, B.
the building. Consequently, measuring the moisture of the surrounding soil is of great importance for detecting the source of moisture in a building. Up till now, information has been needed to carry out individual calibrations for the different types of gypsum blocks available on the market and to account......For the past 50 years, gypsum blocks have been used to determine soil moisture content. This report describes a method for calibrating gypsum blocks for soil moisture measurements. Moisture conditions inside a building are strongly influenced by the moisture conditions in the soil surrounding...
Friis Dela, B.
For the past 50 years, gypsum blocks have been used to determine soil moisture content. This report describes a method for calibrating gypsum blocks for soil moisture measurements. Moisture conditions inside a building are strongly influenced by the moisture conditions in the soil surrounding...... the building. Consequently, measuring the moisture of the surrounding soil is of great importance for detecting the source of moisture in a building. Up till now, information has been needed to carry out individual calibrations for the different types of gypsum blocks available on the market and to account...
Vilasa, Luis U.; De Jeu, Richard A. M.; Dolman, Han A. J.; Wang, Guojie
This study presents observational evidence for the existence of preferential states in soil moisture content. Recently there has been much debate about the existence, location and explanations for preferential states in soil moisture. A number of studies have provided evidence either in support or against the hypothesis of a positive feedback mechanism between soil moisture and subsequent precipitation in certain regions. Researchers who support the hypothesis that preferential states in soil moisture holds information about land atmosphere feedback base their theory on the impact of soil moisture on the evaporation process. Evaporation recycles moisture to the atmosphere and soil moisture has a direct impact on the supply part of this process but also on the partitioning of the available energy for evaporation. According to this theory, the existence of soil moisture bimodality can be used as an indication of possible land-atmosphere feedbacks, to be compared with model simulations of soil moisture feedbacks. On the other hand, other researchers argue that seasonality in the meteorological conditions in combination with the non-linearity of soil moisture response alone can induce bimodality. In this study we estimate the soil moisture bimodality at a global scale as derived from the recently available 30+ year ESA Climate Change Initative satellite soil moisture dataset. An Expectation-Maximization iterative algorithm is used to find the best Gaussian Mixture Model, pursuing the highest likelihood for soil moisture bimodality. With this approach we mapped the regions where bi-modal probability distribution of soil moisture appears for each month for the period between 1979-2010. These bimodality areas are analyzed and compared to maps of model simulations of soil moisture feedbacks. The areas where more than one preferential state exists compare surprisingly well with the map of land-atmosphere coupling strength from model simulations. This approach might
Gou, S.; Miller, G. R.
Antecedent soil conditions create an ecosystem's "memory" of past rainfall events. Such soil moisture memory effects may be observed over a range of timescales, from daily to yearly, and lead to feedbacks between hydrological and ecosystem processes. In this study, we modeled the soil moisture memory effect on savanna ecosystems in California, Arizona, and Africa, using a system dynamics model created to simulate the ecohydrological processes at the plot-scale. The model was carefully calibrated using soil moisture and evapotranspiration data collected at three study sites. The model was then used to simulate scenarios with various initial soil moisture conditions and antecedent precipitation regimes, in order to study the soil moisture memory effects on the evapotranspiration of understory and overstory species. Based on the model results, soil texture and antecedent precipitation regime impact the redistribution of water within soil layers, potentially causing deeper soil layers to influence the ecosystem for a longer time. Of all the study areas modeled, soil moisture memory of California savanna ecosystem site is replenished and dries out most rapidly. Thus soil moisture memory could not maintain the high rate evapotranspiration for more than a few days without incoming rainfall event. On the contrary, soil moisture memory of Arizona savanna ecosystem site lasts the longest time. The plants with different root depths respond to different memory effects; shallow-rooted species mainly respond to the soil moisture memory in the shallow soil. The growing season of grass is largely depended on the soil moisture memory of the top 25cm soil layer. Grass transpiration is sensitive to the antecedent precipitation events within daily to weekly timescale. Deep-rooted plants have different responses since these species can access to the deeper soil moisture memory with longer time duration Soil moisture memory does not have obvious impacts on the phenology of woody plants
Hollinger, S.E.; Isard, S.A. (Illinois State Water Survey, Champaign, IL (United States) Univ. of Illinois, Urbana, IL (United States))
Ten years of soil moisture measurements (biweekly from March through September and monthly during winter) within the top 1 m of soil at 17 grass-covered sites across Illinois are analyzed to provide a climatology of soil moisture for this important Midwest agricultural region. Soil moisture measurements were obtained with neutron probes that were calibrated for each site. Measurement errors are dependent upon the volumetric water content with errors less than 20 percent when soil moisture is above 0 percent of soil volume. Single point errors in moisture measurements from the top 1 m of soil range from 6 percent to 13 percent when volumetric soil moisture is 30 percent of soil volume. The average depletion in moisture between winter and summer over the 10-year period for the top 2 m of soil in Illinois was 72.3 mm. Three-quarters of this decrease occurred above 0.5 m and only 5 percent occurred between the 1.0-m and 2.0-m depths. The average moisture decrease between winter and summer during a wet year (1985) and a drought year (1988) in the top 2 m of soil was 64 percent and 204 percent of the average for the 10-year period, respectively. Seasonal means in soil moisture averaged for the state show the effects of different seasons and soil types on soil moisture. In the winter and spring a latitudinal gradient exists with the wetter soils in the southern part of the state. During summer and autumn there is a longitudinal gradient with the wetter soils in the eastern half of the state. The longitudinal gradient is closely associated with the depth of loess deposits.
In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land co...
Egido, Alejandro; Caparrini, Marco; Martin, Cristina; Farres, Esteve; Banque, Xavier
The use of GNSS signals as a source of opportunity for remote sensing applications, GNSS-R, has been a research area of interest for more than a decade. One of the possible applications of this technique is soil moisture monitoring. The retrieval of soil moisture with GNSS-R systems is based on the variability of the ground dielectric properties associated to soil moisture. Higher concentrations of water in the soil yield a higher dielectric constant and reflectivity, which incurs in signals that reflect from the Earth surface with higher peak power. Previous investigations have demonstrated the capability of GPS bistatic scatterometers to obtain high enough signal to noise ratios in order to sense small changes in surface reflectivity. Furthermore, these systems present some advantages with respect to others currently used to retrieve soil moisture. Upcoming satellite navigation systems, such as the European Galileo, will represent an excellent source of opportunity for soil moisture remote sensing for vario...
Dekker, L.W.; Ritsema, C.J.
In the Netherlands, water-repellent soils are widespread and they often show irregular moisture patterns, which cause accelerated transport of water and solutes to the groundwater and surface water. Under grass cover, spatial variability in soil moisture content is high owing to fingered flow; in ar
Oct 17, 2011 ... temperature under three soil moisture and two fertilizer levels in solar greenhouse .... temperature is governed by the one-dimensional heat conduction equation in the soil, and the soil temperature varied sinusoidally. We.
Eng-Choon Tan, Adrian; Richards, Sean; Platt, Ian; Woodhead, Ian
In this paper, we presented the development of a proximal soil moisture sensor that measured the soil moisture content of dairy pasture directly from the boom of an irrigator. The proposed sensor was capable of soil moisture measurements at an accuracy of ±5% volumetric moisture content, and at meter scale ground area resolutions. The sensor adopted techniques from the ultra-wideband radar to enable measurements of ground reflection at resolutions that are smaller than the antenna beamwidth of the sensor. An experimental prototype was developed for field measurements. Extensive field measurements using the developed prototype were conducted on grass pasture at different ground conditions to validate the accuracy of the sensor in performing soil moisture measurements.
Wever, N.; Lehning, M.
Soil moisture is an important parameter of the climate system. It constrains evapotranspiration of plants and it functions as a storage of water, giving it an economic value, e.g. for agriculture. Furthermore, soil moisture is an important factor for predicting flood risk. In mountainous areas with a seasonal snow cover, the spatial distribution of snow depth is strongly influencing the spatial variation of soil moisture. To assess potential flooding situations during snow melt and rain on snow events in particular but for any heavy precipitation event in the mountains, it is important to understand the influence of the snow cover on soil status with respect to liquid and solid water. Only if this is known, the reaction of the soil i.e. amount of runoff, storage or melt, on additional water input can be assessed. For an operational assessment of the soil moisture state in the Swiss Alps at 140 measurement sites for snow and avalanche forecasting (IMIS network), the SNOWPACK model has been extended with a soil module, solving the Richards equation for the matrix flow. The modelling is validated with vertical profile measurements of soil moisture at meteorological stations in an Alpine catchment near Davos, Switzerland. It was found that the combination of a physical based snowpack model with a Richards equation solver seems to provide an adequate description of soil moisture fluctuations, especially in near surface layers. Soil moisture fluctuations, both measured and modelled, are strongly reduced when a snow cover is present. The measurements also revealed a strong increase in soil moisture, accompanied by a daily cycle in soil moisture during snow melt, extending down to 120cm depth. When soil properties from literature were assumed for the soil type in the vertical profile, the daily cycle in the model during snow melt was restricted mainly to the top layers, while observations show also a reaction in deeper layers. These observations are consistent with the
Moghaddam, Mahta; Moller, Delwyn; Rodriguez, Ernesto; Rahmat-Samii, Yahya
A two-frequency, polarimetric, spaceborne synthetic-aperture radar (SAR) system has been proposed for measuring the moisture content of soil as a function of depth, even in the presence of overlying vegetation. These measurements are needed because data on soil moisture under vegetation canopies are not available now and are necessary for completing mathematical models of global energy and water balance with major implications for global variations in weather and climate.
Chanzy, A.; Richard, G.; Boizard, H.; Défossez, P.
Many decisions in agriculture are conditional to soil moisture. For instance in wet conditions, farming operations as soil tillage, organic waste spreading or harvesting may lead to degraded results and/or induce soil compaction. The development of a tool that allows the estimation of soil moisture is useful to help farmers to organize their field work in a context where farm size tends to increase as well as the need to optimize the use of expensive equipments. Soil water transfer models simulate soil moisture vertical profile evolution. These models are highly sensitive to site dependant parameters. A method to implement the mechanistic soil water and heat flow model (the TEC model) in a context of limited information (soil texture, climatic data, soil organic carbon) is proposed [Chanzy et al., 2008]. In this method the most sensitive model inputs were considered i.e. soil hydraulic properties, soil moisture profile initialization and the lower boundary conditions. The accuracy was estimated by implementing the method on several experimental cases covering a range of soils. Simulated soil moisture results were compared to soil moisture measurements. The obtained accuracy in surface soil moisture (0-30 cm) was 0.04 m3/m3. When a few soil moisture measurements are available (collected for instance by the farmer using a portable moisture sensor), significant improvement in soil moisture accuracy is obtained by assimilating the results into the model. Two assimilation strategies were compared and led to comparable results: a sequential approach, where the measurement were used to correct the simulated moisture profile when measurements are available and a variational approach which take moisture measurements to invert the TEC model and so retrieve soil hydraulic properties of the surface layer. The assimilation scheme remains however heavy in terms of computing time and so, for operational purposed fast code should be taken to simulate the soil moisture as with the
Strategic ground-based sampling of soil moisture across multiple scales is necessary to validate remotely sensed quantities such as NASA’s Soil Moisture Active Passive (SMAP) product. In the present study, in-situ soil moisture data were collected at two nested scale extents (0.5 km and 3 km) to understand the trend of soil moisture variability across these scales. This ground-based soil moisture sampling was conducted in the 500 km2 Rana watershed situated in eastern India. The study area is characterized as sub-humid, sub-tropical climate with average annual rainfall of about 1456 mm. Three 3x3 km square grids were sampled intensively once a day at 49 locations each, at a spacing of 0.5 km. These intensive sampling locations were selected on the basis of different topography, soil properties and vegetation characteristics. In addition, measurements were also made at 9 locations around each intensive sampling grid at 3 km spacing to cover a 9x9 km square grid. Intensive fine scale soil moisture sampling as well as coarser scale samplings were made using both impedance probes and gravimetric analyses in the study watershed. The ground-based soil moisture samplings were conducted during the day, concurrent with the SMAP descending overpass. Analysis of soil moisture spatial variability in terms of areal mean soil moisture and the statistics of higher-order moments, i.e., the standard deviation, and the coefficient of variation are presented. Results showed that the standard deviation and coefficient of variation of measured soil moisture decreased with extent scale by increasing mean soil moisture. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hearn, John; Eichler, Jeffery; Hare, Christopher; Henley, Michael
The effect of soil moisture on chlorine (Cl(2)) deposition was examined in laboratory chamber experiments at high Cl(2) exposures by measuring the concentration of chloride (Cl(-)) in soil columns. Soil mixtures with varying amounts of clay, sand, and organic matter and with moisture contents up to 20% (w/w) were exposed to ≈3×10(4)ppm Cl(2) vapor. For low water content soils, additional water increased the reaction rate as evidenced by higher Cl(-) concentration at higher soil moisture content. Results also showed that the presence of water restricted transport of Cl(2) into the soil columns and caused lower overall deposition of Cl(2) in the top 0.48-cm layer of soil when water filled ≈60% or more of the void space in the column. Numerical solutions to partial differential equations of Fick's law of diffusion and a simple rate law for Cl(2) reaction corroborated conclusions derived from the data. For the soil mixtures and conditions of these experiments, moisture content that filled 30-50% of the available void space yielded the maximum amount of Cl(2) deposition in the top 0.48cm of soil. Published by Elsevier B.V.
Engman, Edwin T.
The author reviews the development of passive and active microwave techniques for measuring soil moisture with respect to how the data may be used. New science programs such as the EOS, the GEWEX Continental-Scale International Project (GCIP) and STORM, a mesoscale meteorology and hydrology project, will have to account for soil moisture either as a storage in water balance computations or as a state variable in-process modeling. The author discusses future soil moisture needs such as frequency of measurement, accuracy, depth, and spatial resolution, as well as the concomitant model development that must proceed concurrently if the development in microwave technology is to have a major impact in these areas.
LIU Huan-Jun; ZHANG Yuan-Zhi; ZHANG Xin-Le; ZHANG Bai; SONG Kai-Shan; WANG Zong-Ming; TANG Na
Several studies have demonstrated that soil reflectance decreases with increasing soil moisture content,or increases when the soil moisture reaches a certain content;however,there are few analyses on the quantitative relationship between soil reflectance and its moisture,especially in the case of black soils in northeast China.A new moisture adjusting method was developed to obtain soil reflectance with a smaller moisture interval to describe the quantitative relationship between soil reflectance and moisture.For the soil samples with moisture contents ranging from air-dry to saturated,the changes in soil reflectance with soil moisture can be depicted using a cubic equation.Both moisture threshold (MT) and moisture inflexion (MI) of soil reflectance can also be determined by the equation.When the moisture range was smaller than MT,soil reflectance can be simulated with a linear model.However,for samples with different soil organic matter (OM),the parameters of the linear model varied regularly with the OM content.Based on their relationship,the soil moisture can be estimated from soil reflectance in the black soil region.
Chandler, D. G.; Seyfried, M. S.; McNamara, J. P.; Hwang, K.
Soil moisture is an important control on hydrologic function, as it governs flux through the soil and responds to and determines vertical fluxes from and to the atmosphere, groundwater recharge and lateral fluxes through the soil. Most physically based hydrologic models require parameters to represent soil physical properties governing flow and retention of vadose water. The presented analysis compares four methods of objective analysis to determine field capacity, plant extraction limit (or permanent wilting point) and field saturated soil moisture content from decadal records of volumetric water content. These values are found as either data attractors or limits in the VWC records and may vary with interannual moisture availability. Results are compared to values from pedotransfer functions and discussed in terms of historic methods of measurement in soil physics.
Full Text Available In this paper novel miniature metamaterial-based soil moisture sensors are presented. The sensors are based on resonant-type metamaterials and employ split-ring resonators (SRR, spiral resonators and fractal SRRs to achieve small dimensions, high sensitivity, and compatibility with standard planar fabrication technologies. All these features make the proposedsensors suitable for deployment in agriculture for precise mapping of soil humidity.
Passive microwave remote sensing of soil moisture has matured over the past decade as a result of the Advanced Microwave Scanning Radiometer (AMSR) program of JAXA. This program has resulted in improved algorithms that have been supported by rigorous validation. Access to the products and the valida...
Moghaddam, M.; Saatchi, S.; Cuenca, R. H.
The subcanopy soil moisture of a boreal old jack pine forest is estimated using polarimetric L- and P-band AIRSAR data. Model simulations have shown that for this stand, the principal scattering mechanism responsible for radar backscatter is the double-bounce mechanism between the tree trunks and the ground.
US Fish and Wildlife Service, Department of the Interior — This Soil and Moisture Plan for Agassiz NWR provides an overview of the Refuge, a description of soil and moisture problems, and proposed solutions to these...
Dennis P. Lettenmaier
Full Text Available The influence of antecedent soil moisture on North American monsoon system (NAMS precipitation variability was explored using the MM5 mesoscale model coupled with the Variable Infiltration Capacity (VIC land surface model. Sensitivity experiments were performed with extreme wet and dry initial soil moisture conditions for both the 1984 wet monsoon year and the 1989 dry year. The MM5-VIC model reproduced the key features of NAMS in 1984 and 1989 especially over northwestern Mexico. Our modeling results indicate that the land surface has memory of the initial soil wetness prescribed at the onset of the monsoon that persists over most of the region well into the monsoon season (e.g. until August. However, in contrast to the classical thermal contrast concept, where wetter soils lead to cooler surface temperatures, less land-sea thermal contrast, weaker monsoon circulations and less precipitation, the coupled model consistently demonstrated a positive soil moisture – precipitation feedback. Specifically, anomalously wet pre-monsoon soil moisture always lead to enhanced monsoon precipitation, and the reverse was also true. Both the large-scale circulation change and local land-atmospheric interactions in response to pre-monsoon soil moisture anomalies play important roles in the coupled model’s positive soil moisture – monsoon precipitation feedback. However, the former may be sensitive to the strength and location of the thermal anomalies, thus leaving open the possibility of both positive and negative soil moisture – precipitation feedbacks. Furthermore, our use of a regional model with prescribed large-scale circulation at the model boundaries leaves open the possibility that the model behavior may, to some extent, reflect its limited ability to adjust its large-scale circulation to the regional thermal changes.
Aquarius is the first satellite to provide both passive and active L-band observations of the Earth. In addition, the instruments on Satelite de Aplicaciones Cientificas-D (SAC-D) provide complementary information for analysis and retrieval algorithms. Our research focuses on the retrieval of soil m...
Gruhier, Claire; Kerr, Yann; de Rosnay, Patricia; Pellarin, Thierry; Grippa, Manuela
Soil moisture is a crucial variable which influences the land surface processes. Numerous studies shown microwaves at low frequency are particularly performed to access to soil moisture values. SMOS (Soil Moisture and Ocean Salinity), launched the November 2th 2009, is the first space mission dedicated to soil moisture observations. Before SMOS, several soil moisture products were provided, based on active or passive microwaves measurements. Gruhier et al. (2010) analyse five of them over a Sahelian area. The results show that the range of volumetric soil moisture values obtained over Sahel is drastically different depending on the remote sensing approach used to produce soil moisture estimates. Although microwave bands currently available are not optimal, some products are in very good agreement with ground data. The main goal of this study is to introduce the first soil moisture maps from SMOS over West Africa. A first analyse of values over a Sahelian region is investigated. The study area is located in Gourma region in Mali. This site has been instrumented in the context of the AMMA project (African Monsoon Multidisciplinary Analysis) and was specifically designed to address the validation of remotely sensed soil moisture. SMOS soil moisture values was analysed with ground knowledge and placed in the context of previous soil moisture products. The high sensitivity of the L-band used by SMOS should provide very accurate soil moisture values.
O'Neill, P.; Lang, R.; Kurum, M.; Joseph, A.; Jackson, T.; Cosh, M.
Soil moisture is recognized as an important component of the water, energy, and carbon cycles at the interface between the Earth's surface and atmosphere. Current baseline soil moisture retrieval algorithms for microwave space missions have been developed and validated only over grasslands, agricultural crops, and generally light to moderate vegetation. Tree areas have commonly been excluded from operational soil moisture retrieval plans due to the large expected impact of trees on masking the microwave response to the underlying soil moisture. Our understanding of the microwave properties of trees of various sizes and their effect on soil moisture retrieval algorithms at L band is presently limited, although research efforts are ongoing in Europe, the United States, and elsewhere to remedy this situation. As part of this research, a coordinated sequence of field measurements involving the ComRAD (for Combined Radar/Radiometer) active/passive microwave truck instrument system has been undertaken. Jointly developed and operated by NASA Goddard Space Flight Center and George Washington University, ComRAD consists of dual-polarized 1.4 GHz total-power radiometers (LH, LV) and a quad-polarized 1.25 GHz L band radar sharing a single parabolic dish antenna with a novel broadband stacked patch dual-polarized feed, a quad-polarized 4.75 GHz C band radar, and a single channel 10 GHz XHH radar. The instruments are deployed on a mobile truck with an 19-m hydraulic boom and share common control software; real-time calibrated signals, and the capability for automated data collection for unattended operation. Most microwave soil moisture retrieval algorithms developed for use at L band frequencies are based on the tau-omega model, a simplified zero-order radiative transfer approach where scattering is largely ignored and vegetation canopies are generally treated as a bulk attenuating layer. In this approach, vegetation effects are parameterized by tau and omega, the microwave
Owe, M.; VandeGriend, A. A.; deJeu, R.; deVries, J.; Seyhan, E.
Cooperative research in microwave remote sensing between the Hydrological Sciences Branch of the NASA Goddard Space Flight Center and the Earth Sciences Faculty of the Vrije Universiteit Amsterdam began with the Botswana Water and Energy Balance Experiment and has continued through a series of highly successful International Research Programs. The collaboration between these two research institutions has resulted in significant scientific achievements, most notably in the area of satellite-based microwave remote sensing of soil moisture. The Botswana Program was the first joint research initiative between these two institutions, and provided a unique data base which included historical data sets of Scanning Multifrequency Microwave Radiometer (SN4NM) data, climate information, and extensive soil moisture measurements over several large experimental sites in southeast Botswana. These data were the basis for the development of new approaches in physically-based inverse modelling of soil moisture from satellite microwave observations. Among the results from this study were quantitative estimates of vegetation transmission properties at microwave frequencies. A single polarization modelling approach which used horizontally polarized microwave observations combined with monthly composites of Normalized Difference Vegetation Index was developed, and yielded good results. After more precise field experimentation with a ground-based radiometer system, a dual-polarization approach was subsequently developed. This new approach realized significant improvements in soil moisture estimation by satellite. Results from the Botswana study were subsequently applied to a desertification monitoring study for the country of Spain within the framework of the European Community science research programs EFEDA and RESMEDES. A dual frequency approach with only microwave data was used for this application. The Microwave Polarization Difference Index (MPDI) was calculated from 37 GHz data
Emmet-Booth, Jeremy; Forristal, Dermot; Fenton, Owen; Bondi, Giulia; Creamer, Rachel; Holden, Nick
Visual Soil Examination and Evaluation (VSE) techniques offer tools for soil quality assessment. They involve the visual and tactile assessment of soil properties such as aggregate size and shape, porosity, redox morphology, soil colour and smell. An increasing body of research has demonstrated the reliability and utility of VSE techniques. However a number of limitations have been identified, including the potential impact of soil moisture variation during sampling. As part of a national survey of grassland soil quality in Ireland, an evaluation of the impact of soil moisture on two widely used VSE techniques was conducted. The techniques were Visual Evaluation of Soil Structure (VESS) (Guimarães et al., 2011) and Visual Soil Assessment (VSA) (Shepherd, 2009). Both generate summarising numeric scores that indicate soil structural quality, though employ different scoring mechanisms. The former requires the assessment of properties concurrently and the latter separately. Both methods were deployed on 20 sites across Ireland representing a range of soils. Additional samples were taken for soil volumetric water (θ) determination at 5-10 and 10-20 cm depth. No significant correlation was observed between θ 5-10 cm and either VSE technique. However, VESS scores were significantly related to θ 10-20 cm (rs = 0.40, sig = 0.02) while VSA scores were not (rs = -0.33, sig = 0.06). VESS and VSA scores can be grouped into quality classifications (good, moderate and poor). No significant mean difference was observed between θ 5-10 cm or θ 10-20 cm according to quality classification by either method. It was concluded that VESS scores may be affected by soil moisture variation while VSA appear unaffected. The different scoring mechanisms, where the separate assessment and scoring of individual properties employed by VSA, may limit soil moisture effects. However, moisture content appears not to affect overall structural quality classification by either method. References
A new soil moisture dataset from direct gravimetric measurements within the top 50-cm soil layers at 178 soil moisture stations in China covering the period 1981 1998 are used to study the long-term and seasonal trends of soil moisture variations, as well as estimate the temporal and spatial scales of soil moisture for different soil layers. Additional datasets of precipitation and temperature difference between land surface and air (TDSA) are analyzed to gain further insight into the changes of soil moisture. There are increasing trends for the top 10 cm, but decreasing trends for the top 50 cm of soil layers in most regions. Trends in precipitation appear to dominantly influence trends in soil moisture in both cases. Seasonal variation of soil moisture is mainly controlled by precipitation and evaporation, and in some regions can be affected by snow cover in winter. Timescales of soil moisture variation are roughly 1-3 months and increase with soil depth.Further influences of TDSA and precipitation on soil moisture in surface layers, rather than in deeper layers,cause this phenomenon. Seasonal variations of temporal scales for soil moisture are region-dependent and consistent in both layer depths. Spatial scales of soil moisture range from 200-600 km, with topography also having an affect on these. Spatial scales of soil moisture in plains are larger than in mountainous areas. In the former, the spatial scale of soil moisture follows the spatial patterns of precipitation and evaporation,whereas in the latter, the spatial scale is controlled by topography.
Full Text Available Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is influenced by numerous factors such as surface roughness, vegetation cover, and soil texture. Moreover, the relationship between brightness temperature, soil moisture and the factors mentioned above is highly non-linear and ill-posed. Consequently, Artificial Neural Networks (ANNs have been used to retrieve soil moisture from microwave data, but with limited success when dealing with data different to that from the training period. In this study, an ANN is tested for its ability to predict soil moisture at 1 km resolution on different dates following training at the same site for a specific date. A novel approach that utilizes information on the variability of soil moisture, in terms of its mean and standard deviation for a (sub region of spatial dimension up to 40 km, is used to improve the current retrieval accuracy of the ANN method. A comparison between the ANN with and without the use of the variability information showed that this enhancement enables the ANN to achieve an average Root Mean Square Error (RMSE of around 5.1% v/v when using the variability information, as compared to around 7.5% v/v without it. The accuracy of the soil moisture retrieval was further improved by the division of the target site into smaller regions down to 4 km in size, with the spatial variability of soil moisture calculated from within the smaller region used in the ANN. With the combination of an ANN architecture of a single hidden layer of 20 neurons and the dual-polarized brightness temperatures as input, the proposed use of variability and sub-region methodology achieves an average retrieval accuracy of 3.7% v/v. Although this accuracy is not the lowest as comparing to the research in this field, the main contribution is the ability of ANN in
Orr, Barron; Moran, M. Susan; Escobar, Vanessa; Brown, Molly E.
The launch of the NASA Soil Moisture Active Passive (SMAP) mission in 2014 will provide global soil moisture and freeze-thaw measurements at moderate resolution (9 km) with latency as short as 24 hours. The resolution, latency and global coverage of SMAP products will enable new applications in the fields of weather, climate, drought, flood, agricultural production, human health and national security. To prepare for launch, the SMAP mission has engaged more than 25 Early Adopters. Early Adopters are users who have a need for SMAP-like soil moisture or freeze-thaw data, and who agreed to apply their own resources to demonstrate the utility of SMAP data for their particular system or model. In turn, the SMAP mission agreed to provide Early Adopters with simulated SMAP data products and pre-launch calibration and validation data from SMAP field campaigns, modeling, and synergistic studies. The applied research underway by Early Adopters has provided fundamental knowledge of how SMAP data products can be scaled and integrated into users' policy, business and management activities to improve decision-making efforts. This presentation will cover SMAP applications including weather and climate forecasting, vehicle mobility estimation, quantification of greenhouse gas emissions, management of urban potable water supply, and prediction of crop yield. The presentation will end with a discussion of potential international applications with focus on the ESA/CEOS TIGER Initiative entitled "looking for water in Africa", the United Nations (UN) Convention to Combat Desertification (UNCCD) which carries a specific mandate focused on Africa, the UN Framework Convention on Climate Change (UNFCCC) which lists soil moisture as an Essential Climate Variable (ECV), and the UN Food and Agriculture Organization (FAO) which reported a food and nutrition crisis in the Sahel.
Kellogg, K.; Thurman, S.; Edelstein, W.; Spencer, M.; Chen, Gun-Shing; Underwood, M.; Njoku, E.; Goodman, S.; Jai, Benhan
The Soil Moisture Active Passive (SMAP) mission, one of the first-tier missions recommended by the 2007 U.S. National Research Council Committee on Earth Science and Applications from Space, was confirmed in May 2012 by NASA to proceed into Implementation Phase (Phase C) with a planned launch in October 2014. SMAP will produce high-resolution and accurate global maps of soil moisture and its freeze/thaw state using data from a non-imaging synthetic aperture radar and a radiometer, both operating at L-band. Major challenges addressed by the observatory design include: (1) achieving global coverage every 2-3 days with a single observatory; (2) producing both high resolution and high accuracy soil moisture data, including through moderate vegetation; (3) using a mesh reflector antenna for L-band radiometry; (4) minimizing science data loss from terrestrial L-band radio frequency interference; (5) designing fault protection that also minimizes science data loss; (6) adapting planetary heritage avionics to meet SMAP's unique application and data volume needs; (7) ensuring observatory electromagnetic compatibility to avoid degrading science; (8) controlling a large spinning instrument with a small spacecraft; and (9) accommodating launch vehicle selection late in the observatory's development lifecycle.
Vico, Giulia; Porporato, Amilcare
Achieving a sustainable use of water resources, in view of the increased food and biofuel demand and possible climate change, will require optimizing irrigation, a highly nontrivial task given the unpredictability of rainfall and the numerous soil-plant-atmosphere interactions. Here we theoretically analyze two different irrigation schemes, a traditional scheme, consisting of the application of fixed water volumes that bring soil moisture to field capacity, and a microirrigation scheme supplying water continuously in order to avoid plant water stress. These two idealized irrigation schemes are optimal in the sense that they avoid crop water stress while minimizing water losses by percolation and runoff. Furthermore, they cover the two extremes cases of continuous and fully concentrated irrigation. For both irrigation schemes, we obtain exact solutions of the steady state soil moisture probability density function with random timing and amounts of rainfall. We also give analytical expressions for irrigation frequency and volumes under different rainfall regimes, evaporative demands, and soil types. We quantify the excess volumes required by traditional irrigation, mostly lost in runoff and deep infiltration, as a function of climate, soil, and vegetation parameters.
Gruhier, C.; de Rosnay, P.; Richaume, P.; Kerr, Y.; Rudiger, C.; Boulet, G.; Walker, J. P.; Mougin, E.; Ceschia, E.; Calvet, J.
This paper presents an evaluation of AMSR-E (Advanced Microwave Scanning Radiometer for EOS) soil moisture products, based on a comparison with three ground soil moisture networks. The selected ground sites are representative of various climatic, hydrologic and environmental conditions in temperate and semi-arid areas. They are located in the south-west of France, south-east of Australia and the Gourma region of the Sahel. These sites were respectively implemented in the framework of the projects SMOSREX (Surface Monitoring Of Soil Reservoir Experiment), SASMAS/GoREx (Scaling and Assimilation of Soil Moisture and Streamflow in the Goulburn River Experimental catchment) and AMMA (African Monsoon Multidisciplinary Analysis). In all cases, the arrangement of the soil moisture measuring sites was specifically designed to address the validation of remotely sensed soil moisture in the context of the preparation of the SMOS (Soil Moisture and Ocean Salinity) project. For the purpose of this study, 25km AMSR-E products were used, including brightness temperatures at 6.9 and 10.7 GHz, and derived soil moisture. The study is focused on the year 2005. It is based on ground soil moisture network measurements from 4 stations for SMOSREX extended to the SUDOUEST project of CESBIO, 12 stations for GoRex, and 4 stations for AMMA. Temporal and spatial features of soil moisture variability and stability is a critical issue to be addressed for remotely sensed soil moisture validation. While ground measurements provide information on soil moisture dynamics at local scale and high temporal resolution (hourly), satellite measurements are sparser in time (up to several days), but cover a larger region (25km x 25km for AMSR-E). First, a statistical analysis, including mean relative difference and Spearman rank, is conducted for the three soil moisture networks. This method is mainly based on the approach proposed by Cosh et al. (2004) for the purpose of the use of ground networks for
José Paulo Molin; Gustavo Di Chiacchio Faulin
Soil electrical conductivity (ECa) is a soil quality indicator associated to attributes interesting to site-specific soil management such as soil moisture and texture. Soil ECa provides information that helps guide soil management decisions, so we performed spatial evaluation of soil moisture in two experimental fields in two consecutive years and modeled its influence on soil ECa. Soil ECa, moisture and clay content were evaluated by statistical, geostatistical and regression analyses. Semiv...
Reichle, Rolf H.; Liu, Qing; Bindlish, Rajat; Cosh, Michael H.; Crow, Wade T.; deJeu, Richard; DeLannoy, Gabrielle J. M.; Huffman, George J.; Jackson, Thomas J.
The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates from a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Soil moisture skill is measured against in situ observations in the continental United States at 44 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four CalVal watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill (in terms of the anomaly time series correlation coefficient R) of AMSR-E retrievals is R=0.39 versus SCAN and R=0.53 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface moisture skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or soil moisture retrievals increases surface soil moisture skill levels by IDDeltaR=0.06-0.08, and root zone soil moisture skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface soil moisture skill levels by DeltaR=0.13, and root zone soil moisture skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite soil moisture retrievals contribute similar and largely independent amounts of information.
Maier, Thomas; Kamm, Lukas
How can you realize a water saving and demand-driven plant watering device? To achieve this you need a sensor, which precisely detects the soil moisture. Designing such a sensor is the topic of this poster. We approached this subject with comparing several physical properties of water, e.g. the conductivity, permittivity, heat capacity and the soil water potential, which are suitable to detect the soil moisture via an electronic device. For our project we have developed a sensor device, which measures the soil moisture and provides the measured values for a plant watering system via a wireless bluetooth 4.0 network. Different sensor setups have been analyzed and the final sensor is the result of many iterative steps of improvement. In the end we tested the precision of our sensor and compared the results with theoretical values. The sensor is currently being used in the Botanical Garden of the Friedrich-Alexander-University in a long-term test. This will show how good the usability in the real field is. On the basis of these findings a marketable sensor will soon be available. Furthermore a more specific type of this sensor has been designed for the EU:CROPIS Space Project, where tomato plants will grow at different gravitational forces. Due to a very small (15mm x 85mm x 1.5mm) and light (5 gramm) realisation, our sensor has been selected for the space program. Now the scientists can monitor the water content of the substrate of the tomato plants in outer space and water the plants on demand.
New hydrological insights for the region: For three typical soil types and climate zones in Eastern Australia, and for two soil profiles, we have found a significant correlation between the indices and soil moisture droughts detected by Hydrus-1D. The failure rates and false alarm rates for detecting the simulated soil moisture droughts were generally below 50% for both indices and both soil profiles (the Reconnaissance Drought Index at Melbourne was the only exception. However, the complexity of Hydrus-1D and the uncertainty associated with the available, regionalised soil water retention curves encourage using the indices over Hydrus-1D in absence of appropriate soil moisture monitoring data.
Ju Hyoung Lee
Full Text Available Bias correction is a very important pre-processing step in satellite data assimilation analysis, as data assimilation itself cannot circumvent satellite biases. We introduce a retrieval algorithm-specific and spatially heterogeneous Instantaneous Field of View (IFOV bias correction method for Soil Moisture and Ocean Salinity (SMOS soil moisture. To the best of our knowledge, this is the first paper to present the probabilistic presentation of SMOS soil moisture using retrieval ensembles. We illustrate that retrieval ensembles effectively mitigated the overestimation problem of SMOS soil moisture arising from brightness temperature errors over West Africa in a computationally efficient way (ensemble size: 12, no time-integration. In contrast, the existing method of Cumulative Distribution Function (CDF matching considerably increased the SMOS biases, due to the limitations of relying on the imperfect reference data. From the validation at two semi-arid sites, Benin (moderately wet and vegetated area and Niger (dry and sandy bare soils, it was shown that the SMOS errors arising from rain and vegetation attenuation were appropriately corrected by ensemble approaches. In Benin, the Root Mean Square Errors (RMSEs decreased from 0.1248 m3/m3 for CDF matching to 0.0678 m3/m3 for the proposed ensemble approach. In Niger, the RMSEs decreased from 0.14 m3/m3 for CDF matching to 0.045 m3/m3 for the ensemble approach.
Soil moisture spatial patterns have been studied at length in agricultural fields and pasture/rangelands as part of the USDA soil moisture satellite validation program, but recent research has begun to address the distribution of soil beneath a forest canopy. Forests cover a significant portion of ...
Kong, J. A.; Shin, R. T. (Principal Investigator)
Progress in the investigation of problems related to the remote sensing of vegetation and soil moisture is reported. Specific topics addressed include: (1) microwave scattering from periodic surfaces using a rigorous modal technique; (2) combined random rough surface and volume scattering effects; (3) the anisotropic effects of vegetation structures; (4) the application of the strong fluctuation theory to the the study of electromagnetic wave scattering from a layer of random discrete scatterers; and (5) the investigation of the scattering of a plane wave obliquely incident on a half space of densely distributed spherical dielectric scatterers using a quantum mechanical potential approach.
Arya, L. M.; Phinney, D. E. (Principal Investigator)
Soil moisture data acquired to support the development of algorithms for estimating surface soil moisture from remotely sensed backscattering of microwaves from ground surfaces are presented. Aspects of field uniformity and variability of gravimetric soil moisture measurements are discussed. Moisture distribution patterns are illustrated by frequency distributions and contour plots. Standard deviations and coefficients of variation relative to degree of wetness and agronomic features of the fields are examined. Influence of sampling depth on observed moisture content an variability are indicated. For the various sets of measurements, soil moisture values that appear as outliers are flagged. The distribution and legal descriptions of the test fields are included along with examinations of soil types, agronomic features, and sampling plan. Bulk density data for experimental fields are appended, should analyses involving volumetric moisture content be of interest to the users of data in this report.
Mishra, Ashok; Vu, Tue; Veettil, Anoop Valiya; Entekhabi, Dara
Recent launch of space-borne systems to estimate surface soil moisture may expand the capability to map soil moisture deficit and drought with global coverage. In this study, we use Soil Moisture Active Passive (SMAP) soil moisture geophysical retrieval products from passive L-band radiometer to evaluate its applicability to forming agricultural drought indices. Agricultural drought is quantified using the Soil Water Deficit Index (SWDI) based on SMAP and soil properties (field capacity and available water content) information. The soil properties are computed using pedo-transfer function with soil characteristics derived from Harmonized World Soil Database. The SMAP soil moisture product needs to be rescaled to be compatible with the soil parameters derived from the in situ stations. In most locations, the rescaled SMAP information captured the dynamics of in situ soil moisture well and shows the expected lag between accumulations of precipitation and delayed increased in surface soil moisture. However, the SMAP soil moisture itself does not reveal the drought information. Therefore, the SMAP based SWDI (SMAP_SWDI) was computed to improve agriculture drought monitoring by using the latest soil moisture retrieval satellite technology. The formulation of SWDI does not depend on longer data and it will overcome the limited (short) length of SMAP data for agricultural drought studies. The SMAP_SWDI is further compared with in situ Atmospheric Water Deficit (AWD) Index. The comparison shows close agreement between SMAP_SWDI and AWD in drought monitoring over Contiguous United States (CONUS), especially in terms of drought characteristics. The SMAP_SWDI was used to construct drought maps for CONUS and compared with well-known drought indices, such as, AWD, Palmer Z-Index, sc-PDSI and SPEI. Overall the SMAP_SWDI is an effective agricultural drought indicator and it provides continuity and introduces new spatial mapping capability for drought monitoring. As an
Full Text Available The research study strongly focused on creating strong mechanism for measuring and evaluating soil moisture content comparing PR2 capacitance moisture meter and gravimetric approach. PR2 moisture meter shows a better performance accuracy of ± 6%; 0.06 m 3 /m 3 and intercept a0 =1.8; indicating the field is heavy clay. It measures to 1000 mm depth with high precision; while realistic result could not be obtained from gravimetric method at this measuring depth. Therefore, effective soil moisture measuring, monitoring and evaluation can be achieved with PR2 moisture meter.
Pachepsky, Y. A.; Guber, A.; Jacques, D.; Pan, F.; van Genuchten, M.; Cady, R. E.; Nicholson, T. J.
Soil water flow modeling has multiple applications. This modeling is based on simplifications stemming from both conceptual uncertainty and lack of detailed knowledge about parameters. Modern soil moisture sensors can provide detailed information about changes in soil water content in time and with depth. This information can be used for data assimilation in soil water flow modeling. The ensemble Kalman filter appears to be an appropriate method for that. Earlier we demonstrated ensemble simulations of soil water flow by using sets of pedotransfer functions (empirical relationships between soil hydraulic properties and soil basic properties, such as particle size distribution, bulk density, organic carbon content, etc.). The objective of this work was to apply the data assimilation with the ensemble Kalman filter to soil water flow modeling, using soil water content monitoring with TDR probes and an ensemble of soil water flow models parameterized with different pedotransfer functions. Experiments were carried out at the Bekkevoort site, Belgium. Sixty time domain reflectometry (TDR) probes with two rods) were installed along the trench in loamy soil at 12 locations with 50-cm horizontal spacing at five depths (15, 35, 55, 75, and 95 cm). Water content and weather parameters were monitored for one year with 15 min frequency. Soil water flow was simulated using the HYDRUS6 software. Mean daily means of water contents at the observation depths were the measurements used in data assimilation. Eighteen pedotransfer functions for water retention and one for hydraulic conductivity were applied to generate ensembles to evaluate the uncertainty in simulation results, whereas the replicated measurements at each of measurement depths were used to characterize the uncertainty in data. Data assimilation appeared to be very efficient. Even assimilating measurements at a single depth provided substantial improvement in simulations at other observation depths. Results on
Thomas F. McLintock
The trend of soil moisture during the growing season, the alternate wetting from rainfall and drying during clear weather, determines the amount of moisture available for tree growth and also fixes, in part, the environment for root growth. In much of the northern coniferous region both moisture content and root environment are in turn affected by the hummock-and-...
Sadhuram, Y.; Krishnamurthy, L.; Babu, M.T.
Mean atmospheric circulation, moisture budget and net heat exchange were studied during a pre-monsoon period (18th March to 3rd May, 1988), making use of the data collected on board "Akademik Korolev" in the central equatorial and southern Arabian...
Kanniah, Kasturi; Siang, Kang Chuen
Calibration and validation (cal/val) activities on Soil Moisture and Ocean Salinity (SMOS) satellite derived soil moisture products has been conducted worldwide since the data has become available but not over the tropical region . This study focuses on the installation of a soil moisture data collection network over an agricultural site in a tropical region in Peninsular Malaysia, and the validation of SMOS soil moisture products. The in-situ data over one year period was analysed and validation of SMOS Soil Moisture products with these in-situ data was conducted.Bias and root mean square errors (RMSE) were computed between SMOS soil moisture products and the in-situ surface soil moisture collected at the satellite passing time (6 am and 6 pm local time). Due to the known limitations of SMOS soil moisture retrieval over vegetated areas with vegetation water content higher than 5 kgm-2, overestimation of SMOS soil moisture products to in-situ data was noticed in this study. The bias is ranging from 0.064 to 0.119 m3m-3 and the RMSE is from 0.090 to 0.158 m3m-3, when both ascending and descending data were validated. This RMSE was found to be similar to a number of studies conducted previously at different regions. However a wet bias was found during the validation, while previous validation activities at other regions showed dry biases. The result of this study is useful to support the continuous development and improvement of SMOS soil moisture retrieval model, aims to produce soil moisture products with higher accuracy, especially in the tropical region.
Berg, Alexis; Sheffield, Justin; Milly, Paul C.D.
Land aridity has been projected to increase with global warming. Such projections are mostly based on off-line aridity and drought metrics applied to climate model outputs but also are supported by climate-model projections of decreased surface soil moisture. Here we comprehensively analyze soil moisture projections from the Coupled Model Intercomparison Project phase 5, including surface, total, and layer-by-layer soil moisture. We identify a robust vertical gradient of projected mean soil moisture changes, with more negative changes near the surface. Some regions of the northern middle to high latitudes exhibit negative annual surface changes but positive total changes. We interpret this behavior in the context of seasonal changes in the surface water budget. This vertical pattern implies that the extensive drying predicted by off-line drought metrics, while consistent with the projected decline in surface soil moisture, will tend to overestimate (negatively) changes in total soil water availability.
H. Sahraoui and M. Hachicha
Jan 1, 2017 ... produced by the water influence moisture content and corrected ... Previous studies indicated that PXRF analysis was capable of detecting soil trace elements ..... determination of some heavy metals in soil using an x-ray ...
Wang, Di; Shen, Runping; Huang, Xiaolong; Shi, Chunxiang
The gaps in knowledge and existing challenges in precisely describing the land surface process make it critical to represent the massive soil moisture data visually and mine the data for further research.This article introduces a comprehensive soil moisture assimilation data analysis system, which is instructed by tools of C#, IDL, ArcSDE, Visual Studio 2008 and SQL Server 2005. The system provides integrated service, management of efficient graphics visualization and analysis of land surface data assimilation. The system is not only able to improve the efficiency of data assimilation management, but also comprehensively integrate the data processing and analysis tools into GIS development environment. So analyzing the soil moisture assimilation data and accomplishing GIS spatial analysis can be realized in the same system. This system provides basic GIS map functions, massive data process and soil moisture products analysis etc. Besides,it takes full advantage of a spatial data engine called ArcSDE to effeciently manage, retrieve and store all kinds of data. In the system, characteristics of temporal and spatial pattern of soil moiture will be plotted. By analyzing the soil moisture impact factors, it is possible to acquire the correlation coefficients between soil moisture value and its every single impact factor. Daily and monthly comparative analysis of soil moisture products among observations, simulation results and assimilations can be made in this system to display the different trends of these products. Furthermore, soil moisture map production function is realized for business application.
Zhuo, Lu; Han, Dawei
Although many conceptual models are very effective in simulating river runoff, their soil moisture schemes are generally not realistic in comparison with the reality (i.e., getting the right answers for the wrong reasons). This study reveals two significant misrepresentations in those models through a case study using the Xinanjiang model which is representative of many well-known conceptual hydrological models. The first is the setting of the upper limit of its soil moisture at the field capacity, due to the 'holding excess runoff' concept (i.e., runoff begins on repletion of its storage to the field capacity). The second is neglect of capillary rise of water movement. A new scheme is therefore proposed to overcome those two issues. The amended model is as effective as its original form in flow modelling, but represents more logically realistic soil water processes. The purpose of the study is to enable the hydrological model to get the right answers for the right reasons. Therefore, the new model structure has a better capability in potentially assimilating soil moisture observations to enhance its real-time flood forecasting accuracy. The new scheme is evaluated in the Pontiac catchment of the USA through a comparison with satellite observed soil moisture. The correlation between the XAJ and the observed soil moisture is enhanced significantly from 0.64 to 0.70. In addition, a new soil moisture term called SMDS (Soil Moisture Deficit to Saturation) is proposed to complement the conventional SMD (Soil Moisture Deficit).
Pan, Feifei; Nieswiadomy, Michael
Soil moisture in snow-dominated regions has many important applications including evapotranspiration estimation, flood forecasting, water resource and ecosystem services management, weather prediction and climate modeling, and quantification of denudation processes. A simple and robust empirical approach to estimate root-zone soil moisture in snow-dominated regions using a soil moisture diagnostic equation that incorporates snowfall and snowmelt processes is suggested and tested. A five-water-year dataset (10/1/2010-9/30/2015) of daily precipitation, air temperature, snow water equivalent and soil moistures at three depths (i.e., 5 cm, 20 cm, and 50 cm) at each of 12 Snow Telemetry (SNOTEL) sites across Utah (37.583°N-41.883°N, 110.183°W-112.9°W), is applied to test the proposed method. The first three water years are designated as the parameter-estimation period (PEP) and the last two water years are chosen as the model-testing period (MTP). Applying the estimated soil moisture loss function parameters and other empirical parameters in the soil moisture diagnostic equation in the PEP, soil moistures in three soil columns (0-5 cm, 0-20 cm, and 0-50 cm) are estimated in the MTP. The relatively accurate soil moisture estimations compared to the observations at 12 SNOTEL sites (RMSE ⩽ 6.23 (%V/V), average RMSE = 4.28 (%V/V), correlation coefficient ⩾0.75, average correlation coefficient =0.89, the Nash-Sutcliffe efficient coefficient Ec ⩾ 0.24, average Ec = 0.72) indicate that the soil moisture diagnostic equation is capable of accurately estimating soil moisture in snow-dominated regions after the snowfall and snowmelt processes are included in the soil moisture diagnostic equation.
Stacke, Tobias; Hagemann, Stefan
Several modelling studies reported potential impacts of soil moisture anomalies on regional climate. In particular for short prediction periods, perturbations of the soil moisture state may result in significant alteration of surface temperature in the following season. However, it is not clear yet whether or not soil moisture anomalies affect climate also on larger temporal and spatial scales. In an earlier study, we showed that soil moisture anomalies can persist for several seasons in the deeper soil layers of a land surface model. Additionally, those anomalies can influence root zone moisture, in particular during explicitly dry or wet periods. Thus, one prerequisite for predictability, namely the existence of long term memory, is evident for simulated soil moisture and might be exploited to improve climate predictions. The second prerequisite is the sensitivity of the climate system to soil moisture. In order to investigate this sensitivity for decadal simulations, we implemented a soil moisture assimilation scheme into the Max-Planck Institute for Meteorology's Earth System Model (MPI-ESM). The assimilation scheme is based on a simple nudging algorithm and updates the surface soil moisture state once per day. In our experiments, the MPI-ESM is used which includes model components for the interactive simulation of atmosphere, land and ocean. Artificial assimilation data is created from a control simulation to nudge the MPI-ESM towards predominantly dry and wet states. First analyses are focused on the impact of the assimilation on land surface variables and reveal distinct differences in the long-term mean values between wet and dry state simulations. Precipitation, evapotranspiration and runoff are larger in the wet state compared to the dry state, resulting in an increased moisture transport from the land to atmosphere and ocean. Consequently, surface temperatures are lower in the wet state simulations by more than one Kelvin. In terms of spatial pattern
Chandler, David G.; Seyfried, Mark S.; McNamara, James P.; Hwang, Kyotaek
Soil moisture is an important control on hydrologic function, as it governs vertical fluxes from and to the atmosphere, groundwater recharge and lateral fluxes through the soil. Historically, the traditional model parameters of saturation, field capacity and permanent wilting point have been determined by laboratory methods. This approach is challenged by issues of scale, boundary conditions and soil disturbance. We develop and compare four methods to determine values of field saturation, field capacity, plant extraction limit and initiation of plant water stress from long term in-situ monitoring records of TDR-measured volumetric water content (Q). The monitoring sites represent a range of soil textures, soil depths, effective precipitation and plant cover types in a semi-arid climate. The Q records exhibit attractors (high frequency values) that correspond to field capacity and the plant extraction limit at both annual and longer time scales, but the field saturation values vary by year depending on seasonal wetness in the semi-arid setting. The analysis for five sites in two watersheds is supported by comparison to values determined by a common pedotransfer function and measured soil characteristic curves. Frozen soil is identified as a complicating factor for the analysis and users are cautioned to filter data by temperature, especially for near surface soils.
McColl, Kaighin A.; Alemohammad, Seyed Hamed; Akbar, Ruzbeh; Konings, Alexandra G.; Yueh, Simon; Entekhabi, Dara
Surface soil moisture has a direct impact on food security, human health and ecosystem function. It also plays a key role in the climate system, and the development and persistence of extreme weather events such as droughts, floods and heatwaves. However, sparse and uneven observations have made it difficult to quantify the global distribution and dynamics of surface soil moisture. Here we introduce a metric of soil moisture memory and use a full year of global observations from NASA's Soil Moisture Active Passive mission to show that surface soil moisture--a storage believed to make up less than 0.001% of the global freshwater budget by volume, and equivalent to an, on average, 8-mm thin layer of water covering all land surfaces--plays a significant role in the water cycle. Specifically, we find that surface soil moisture retains a median 14% of precipitation falling on land after three days. Furthermore, the retained fraction of the surface soil moisture storage after three days is highest over arid regions, and in regions where drainage to groundwater storage is lowest. We conclude that lower groundwater storage in these regions is due not only to lower precipitation, but also to the complex partitioning of the water cycle by the surface soil moisture storage layer at the land surface.
With the onset of data availability from the ESA Soil Moisture and Ocean Salinity (SMOS) mission (Kerr and Levine, 2008) and the expected 2015 launch of the NASA Soil Moisture Active and Passive (SMAP) mission (Entekhabi et al., 2010), the next five years should see a significant expansion in our ab...
Teuling, A.J.; Uijlenhoet, R.; Troch, P.A.A.
It has recently been suggested that the bimodality in warm season soil moisture observations in Illinois is evidence of a soil moisture-precipitation feedback. Other studies however provide little evidence for a strong feedback in this region. Here we show that seasonality in the meteorological cond
McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R.; Wang, Shugong; Peters-Lidard, Christa D.; Verdin, James P.
To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA), Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture Active Passive (SMAP); however, these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM) over East Africa. Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we find substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies are well correlated (R > 0.5) with modeled soil moisture, and in some regions, NDVI. We use pixel-wise correlation analysis and qualitative comparisons of seasonal maps and time series to show that remotely sensed soil moisture can inform remote drought monitoring that has traditionally relied on rainfall and NDVI in moderately vegetated regions.
Teuling, A.J.; Hupet, F.; Uijlenhoet, R.; Troch, P.A.
We investigate the role of interannual climate variability on spatial soil moisture variability dynamics for a field site in Louvain-la-Neuve, Belgium. Observations were made during 3 years under intermediate (1999), wet (2000), and extremely dry conditions (2003). Soil moisture variability dynamics
José Paulo Molin
Full Text Available Soil electrical conductivity (ECa is a soil quality indicator associated to attributes interesting to site-specific soil management such as soil moisture and texture. Soil ECa provides information that helps guide soil management decisions, so we performed spatial evaluation of soil moisture in two experimental fields in two consecutive years and modeled its influence on soil ECa. Soil ECa, moisture and clay content were evaluated by statistical, geostatistical and regression analyses. Semivariogram models, adjusted for soil moisture, had strong spatial dependence, but the relationship between soil moisture and soil ECa was obtained only in one of the experimental fields, where soil moisture and clay content range was higher. In this same field, coefficients of determinations between soil moisture and clay content were above 0.70. In the second field, the low soil moisture and clay content range explain the absence of a relationship between soil ECa and soil moisture. Data repetition over the years, suggested that ECa is a qualitative indicator in areas with high spatial variability in soil texture.
Goodchild, Martin; Kühn, Karl; Jenkins, Dick
The aim of this work has been to investigate soil-to-atmosphere water transport in potted tomato plants by measuring and processing high-resolution soil moisture data against the environmental driver of vapour pressure deficit (VPD). Whilst many researchers have successfully employed sap flow sensors to determine water uptake by roots and transport through the canopy, the installation of sap flow sensors is non-trivial. This work presents an alternative method that can be integrated with irrigation controllers and data loggers that employ soil moisture feedback which can allow water uptake to be evaluated against environmental drivers such as VPD between irrigation events. In order to investigate water uptake against VPD, soil moisture measurements were taken with a resolution of 2 decimal places - and soil moisture, air temperature and relative humidity measurements were logged every 2 minutes. Data processing of the soil moisture was performed in an Excel spread sheet where changes in water transport were derived from the rate of change of soil moisture using the Slope function over 5 soil moisture readings. Results are presented from a small scale experiment using a GP2-based irrigation controller and data logger. Soil moisture feedback is provided from a single SM300 soil moisture sensor in order to regulate the soil moisture level and to assess the water flow from potted tomato plants between irrigation events. Soil moisture levels were set to avoid drainage water losses. By determining the rate of change in soil moisture between irrigation events, over a 16 day period whilst the tomato plant was in flower, it has been possible to observe very good correlation between soil water uptake and VPD - illustrating the link between plant physiology and environmental conditions. Further data is presented for a second potted tomato plant where the soil moisture level is switched between the level that avoids drainage losses and a significantly lower level. This data
Tadono, Takeo; Qong, Muhtar; Wakabayashi, Hiroyuki; Shimada, Masanobu; Shi, Jiancheng
The goal of this study is to develop an algorithm for estimating the surface soil moisture and surface roughness using polarimetric Synthetic Aperture Radar (SAR) data. In this study, an algorithm was applied to polarimetric airborne SAR data to estimate distributions of surface soil moisture and roughness. To validate the estimated soil moisture, we simultaneously conducted an experiment in October 1999 in Tsukuba Science City, Ibaragi Prefecture of Japan. Surface soil moisture was obtained by the Time- Domain Reflectometry (TDR) method, and the horizontal profiles of the land surface height were measured by a comb- style instrument for calculating the surface roughness parameters in test sites. Because the problem is site- specific and depends upon the measurement accuracy of both the ground truth data, the SAR system including speckle noise, and the effects of vegetation and artificial constructions, such as buildings, houses, roads, and roadside trees, the comparison results did not agree well with measured and inferred soil moisture.
Full Text Available Area-average soil moisture at the sub-kilometer scale is needed but until the advent of the cosmic-ray method (Zreda et al., 2008, it was difficult to measure. This new method is now being implemented routinely in the COsmic-ray Soil Moisture Observing System (or COSMOS. The stationary cosmic-ray soil moisture probe (sometimes called "neutronavka" measures the neutrons that are generated by cosmic rays within air and soil, moderated by mainly hydrogen atoms located primarily in soil water, and emitted to the atmosphere where they mix instantaneously at a scale of hundreds of meters and whose density is inversely correlated with soil moisture. COSMOS has already deployed 53 of the eventual 500 neutronavkas distributed mainly in the USA, each generating a time series of average soil moisture over its hectometer horizontal footprint, with similar networks coming into existence around the world. This paper is written to serve a community need to better understand this novel method and the COSMOS project. We describe the cosmic-ray soil moisture measurement method, the instrument and its calibration, the design, data processing and dissemination used in COSMOS, and give example time series of soil moisture obtained from COSMOS probes.
Full Text Available The newly-developed cosmic-ray method for measuring area-average soil moisture at the hectometer horizontal scale is being implemented in the COsmic-ray Soil Moisture Observing System (or the COSMOS. The stationary cosmic-ray soil moisture probe measures the neutrons that are generated by cosmic rays within air and soil and other materials, moderated by mainly hydrogen atoms located primarily in soil water, and emitted to the atmosphere where they mix instantaneously at a scale of hundreds of meters and whose density is inversely correlated with soil moisture. The COSMOS has already deployed more than 50 of the eventual 500 cosmic-ray probes, distributed mainly in the USA, each generating a time series of average soil moisture over its horizontal footprint, with similar networks coming into existence around the world. This paper is written to serve a community need to better understand this novel method and the COSMOS project. We describe the cosmic-ray soil moisture measurement method, the instrument and its calibration, the design, data processing and dissemination used in the COSMOS project, and give example time series of soil moisture obtained from COSMOS probes.
Fang, Shenghui; Hu, Bo; Lin, Fan
Soil moisture content is one of the most important factors in soil business. The basic of detecting soil moisture content using remote sensing technology is to analyze the relationship between soil moisture content and emissivity. In this paper, based on the analysis of spectrum collection and processing by a portable spectrometer, a set of measure schemes were first established which can accurately measure the reflectivity and emissivity of soil spectrum with different moisture content in near-infrared and thermal infrared bands. Then we selected different bare soil areas as the areas for survey, and studied the relationship of different moisture content and the spectrum curve in the soil both of the same kind and of different kind (like the soil whose structure has been modified caused by the change of organic matter contents or soil particle size). Finally, we emphasized on the quantitative relationship between soil reflectivity & emissivity and soil moisture content using the test data, and establish a model depicting the quantitative relationship above in near-infrared and thermal infrared bands.
Martin, Francisco; Navarro, Victor; Reppucci, Antonio; Mollfulleda, Antonio; Balzter, Heiko; Nicolas-Perea, Virginia; Kissick, Lucy
Soil moisture content (SMC) is an essential parameter from both a scientific and economical point of view. On one hand, it is key for the understanding of hydrological. Secondly, it is a most relevant parameter for agricultural activities and water management. Wide research has been done in this field using different sensors, spanning different parts of the measured electromagnetic spectrum, leading thus several methodologies to estimate soil moisture content. However complying with requirements in terms of accuracy and spatial resolution is still a major challenge. A novel approach based on the measurement of GNSS signals penetrating a soil volume is proposed here. This model relates soil moisture content to the measured soil transmissivity, and attenuation coefficient, which are a function of the soil characteristics (i.e soil moisture content, soit type, soil temperature, etc). A preliminary experiment has been performed to demonstrate the validity of this technique, where the signal received by a GNSS-R L1/E1 RHCP antenna buried at 5, 10, and 15 cm below the surface, was compared to the one received by a GNSS-R L1/E1 RHCP antenna with clear sky visibility. Preliminary results show agreement with theoretical results based on transmissivity and with previous campaigns performed where the soil moisture were collected at two different depths (5 and 15 cm). Details related to the GNSS soil moisture modeling, instrument preparation, measurement campaign, data processing and main results will be presented at the conference.
Shen, Rui; Pennell, Kelly G.; Suuberg, Eric M.
Mathematical models have been widely used in analyzing the effects of various environmental factors in the vapor intrusion process. Soil moisture content is one of the key factors determining the subsurface vapor concentration profile. This manuscript considers the effects of soil moisture profiles on the soil gas vapor concentration away from any surface capping by buildings or pavement. The “open field” soil gas vapor concentration profile is observed to be sensitive to the soil moisture di...
Hales, T.; Ford, C. R.
Climate change will alter the amount, type (i.e., snow vs. rain), and timing of precipitation that controls many hazardous Earth surface processes, including debris flows. Most GCMs agree that as climate warms the frequency of extreme precipitation will increase across the globe. Debris flow events triggered by heavy precipitation will likely also increase. Precipitation also affects the resistance to debris flow initiation by controlling belowground plant hydraulic architecture (e.g. root frequency, diameter distribution, tensile strength). Quantifying the links between precipitation, below ground properties, and the processes that initiate debris flows are therefore critical to understanding future hazard. To explore these links, we conducted a field experiment in the Coweeta Hydrologic Laboratory by excavating 12 soil pits (~1 m3), from two topographies (noses, hollows), and two tree species (Liriodendron tulipifera and Betula lenta). For each species and topography, we collected all biomass from five soil depths and measured soil moisture at 30, 60, and 90cm depth. For each depth we also measured root tensile strength, root cellulose content. Where we collected soil moisture data, we also measured root and soil hydraulic conductivity. Our data show a link between soil moisture content and root biomass distribution; root biomass is more evenly distributed through the soil column in hollows compared to noses. This relationship is consistent with the hypothesis that more consistent soil moisture in hollows allows plant roots to access resources from deeper within the soil column. This physiologic control has a significant effect on root cohesion, with trees on noses (or lower average soil moisture) providing greater root cohesion close to the surface, but considerably less cohesion at depth. Root tensile strength correlated with local daily soil moisture rather than the long term differences represented by noses and hollows. Daily soil moisture affected the amount
Ikonen, Jaakko; Vehviläinen, Juho; Rautiainen, Kimmo; Smolander, Tuomo; Lemmetyinen, Juha; Bircher, Simone; Pulliainen, Jouni
During the last decade there has been considerable development in remote sensing techniques relating to soil moisture retrievals over large areas. Within the framework of the European Space Agency's (ESA) Climate Change Initiative (CCI) a new soil moisture product has been generated, merging different satellite-based surface soil moisture based products. Such remotely sensed data need to be validated by means of in situ observations in different climatic regions. In that context, a comprehensive, distributed network of in situ measurement stations gathering information on soil moisture, as well as soil temperature, has been set up in recent years at the Finnish Meteorological Institute's (FMI) Sodankylä Arctic research station. The network forms a calibration and validation (CAL-VAL) reference site and is used as a tool to evaluate the validity of satellite retrievals of soil properties. In this paper we present the Sodankylä CAL-VAL reference site soil moisture observation network, its instrumentation as well as its areal representativeness over the study area and the region in general as a whole. As an example of data utilization, comparisons of spatially weighted average top-layer soil moisture observations between the years 2012 and 2014 against ESA CCI soil moisture data product estimates are presented and discussed. The comparisons were made against a single ESA CCI data product pixel encapsulating most of the Sodankylä CAL-VAL network sites. Comparisons are made with daily averaged and running weekly averaged soil moisture data as well as through application of an exponential soil moisture filter. The overall achieved correlation between the ESA CCI data product and in situ observations varies considerably (from 0.479 to 0.637) depending on the applied comparison perspective. Similarly, depending on the comparison perspective used, inter-annual correlation comparison results exhibit even more pronounced variation, ranging from 0.166 to 0.840.
Richter, Katja; D'Urso, Guido; Palladino, Mario; Vuolo, Francesco
In the context of agricultural applications, the knowledge of soil moisture availability is an essential aspect for irrigation management. The microwave waveband region (SAR) has been primarily used to estimate soil moisture from Earth Observation (E.O.) data. However, the optical domain (0.4 - 2.5 μm) may as well offer the possibility to get information about soil moisture since an overall decrease of soil reflectance corresponds to increasing surface soil water content. Data from two different experiments (ESA SPARC and AgriSAR) have been exploited aiming at estimating soil moisture from optical E.O. data by using the radiative transfer model PROSAILH. A soil scale factor (α) was introduced into the model and estimated using a LUT inversion technique. Relatively high negative relationships between the α-factor and the measured soil water content (up to R2 = 0.73) could be found for several crop types with low vegetation cover. The results of this study indicate the potential to retrieve surface soil moisture information from optical E.O. data for similar soil types. The method gives the advantage of retrieving simultaneously soil and canopy characteristics from the same E.O. data sources by using a physical method of parameter estimation.
Herold, N.; Kala, J.; Alexander, L. V.
Several regions of Australia are projected to experience an increase in the frequency, intensity and duration of heatwaves (HWs) under future climate change. The large-scale dynamics of HWs are well understood, however, the influence of soil moisture deficits—due for example to drought—remains largely unexplored in the region. Using the standardised precipitation evapotranspiration index, we show that the statistical responses of HW intensity and frequency to soil moisture deficits at the peak of the summer season are asymmetric and occur mostly in the lower and upper tails of the probability distribution, respectively. For aspects of HWs related to intensity, substantially greater increases are experienced at the 10th percentile when antecedent soil moisture is low (mild HWs get hotter). Conversely, HW aspects related to longevity increase much more strongly at the 90th percentile in response to low antecedent soil moisture (long HWs get longer). A corollary to this is that in the eastern and northern parts of the country where HW-soil moisture coupling is evident, high antecedent soil moisture effectively ensures few HW days and low HW temperatures, while low antecedent soil moisture ensures high HW temperatures but not necessarily more HW days.
Sohoulande Djebou, D. C.; Singh, V. P.
The complexity of interactions between the biophysical components of the watershed increases the challenge of understanding water budget. Hence, the perspicacity of the continuum soil-vegetation-atmosphere's functionality still remains crucial for science. This study targeted the Texas Gulf watershed and evaluated the behavior of vegetation covers by coupling precipitation and soil moisture patterns. Growing season's Normalized Differential Vegetation Index NDVI for deciduous forest and grassland were used over a 23 year period as well as precipitation and soil moisture data. The role of time scales on vegetation dynamics analysis was appraised using both entropy rescaling and correlation analysis. This resulted in that soil moisture at 5 cm and 25cm are potentially more efficient to use for vegetation dynamics monitoring at finer time scale compared to precipitation. Albeit soil moisture at 5 cm and 25 cm series are highly correlated (R2>0.64), it appeared that 5 cm soil moisture series can better explain the variability of vegetation growth. A logarithmic transformation of soil moisture and precipitation data increased correlation with NDVI for the different time scales considered. Based on a monthly time scale we came out with a relationship between vegetation index and the couple soil moisture and precipitation [NDVI=a*Log(% soil moisture)+b*Log(Precipitation)+c] with R2>0.25 for each vegetation type. Further, we proposed to assess vegetation green-up using logistic regression model and transinformation entropy using the couple soil moisture and precipitation as independent variables and vegetation growth metrics (NDVI, NDVI ratio, NDVI slope) as the dependent variable. The study is still ongoing and the results will surely contribute to the knowledge in large scale vegetation monitoring. Keywords: Precipitation, soil moisture, vegetation growth, entropy Time scale, Logarithmic transformation and correlation between soil moisture and NDVI, precipitation and
Puri, S.; Stephen, H.; Ahmad, S.
Soil Moisture is an important variable in hydrological cycle. It plays a vital role in agronomy, meteorology, and hydrology. In spite of being an important variable, soil moisture measuring stations are sparse. This is due to high cost involved in the installation of dense network of measuring stations required to map a comprehensive spatio-temporal behavior of soil moisture. Hence, there is a need to develop an alternate method to measure soil moisture. This research relates soil moisture (SM) to backscatter (σ°) obtained from Tropical Rainfall Measuring Mission Precipitation Radar (TRMMPR) and Normalized Difference Vegetation Index (NDVI) obtained from Advanced Very High Resolution Radiometer. SM data is obtained from Soil Climate Analysis Network (SCAN). σ° measurements are normalized at an incidence angle of 10° at which it has the highest sensitivity to SM. An empirical model that relates SM to normalized σ° and NDVI is developed. NDVI takes into account the different vegetation densities. The relationship between model variables is approximated to be linear. The model is applied to data from 1998 to 2008 where 75% of the data is used for calibration and the remaining 25% for validation. Figure 1 shows the comparison of observed and modeled soil moisture for a site with low vegetation. Even though the model underestimates the soil moisture content, it captures the signal well and produces peaks similar to the observed soil moisture. The model performs well with a correlation of 0.71 and root mean square error of 4.0%. The accuracy of the model depends on vegetation density. Table 1 summarizes the model performance for different vegetation densities. The model performance decreases with the increase in vegetation as the leaves in the vegetation canopy attenuate the incident microwaves which reduces the penetration depth and subsequently the sensitivity to soil moisture. This research provides a new insight into the microwave remote sensing of soil
Ross, Morgan A.; Ponce-Campos, Guillermo E.; Barnes, Mallory L.; Hottenstein, John D.; Moran, M. Susan
Soil moisture is commonly used for predictions of plant response and productivity. Climate change is predicted to cause an increase in the frequency and duration of droughts over the next century, which will result in prolonged periods of below-normal soil moisture. This, in turn, is expected to impact regional plant production, erosion and air quality. In fact, the number of consecutive months of soil moisture content below the drought-period mean has recently been linked to regional tree and shrub mortality in the southwest United States. This study investigated the effects of extended periods of below average soil moisture on the response of grassland ANPP to precipitation. Grassland ecosystems were selected for this study because of their ecological sensitivity to precipitation patterns. It has been postulated that the quick ecological response of grasslands to droughts can provide insight to large scale functional responses of regions to predicted climate change. The study sites included 21 grassland biomes throughout arid-to-humid climates in the United States with continuous surface soil moisture records for 2-13 years during the drought period from 2000-2013. Annual net primary production (ANPP) was estimated from the 13-year record of NASA MODIS Enhanced Vegetation Index extracted for each site. Prolonged soil moisture deficit was defined as a period of at least 10 consecutive months during which soil moisture was below the drought-period mean. ANPP was monitored before, during and after prolonged soil moisture deficit to quantify shifts in the functional response of grasslands to precipitation, and in some cases, new species assemblages that included invasive species. Preliminary results indicated that when altered climatic conditions on grasslands led to an increase in the duration of soil water deficit, then the precipitation-to-ANPP relation became non-linear. Non-linearity was associated with extreme grassland dieback and changes in the historic
Kanarek, M.; Cardenas, M.
Moisture dynamics in the critical zone have significant implications for a variety of hydrologic processes, from water availability to plants to infiltration and groundwater recharge rates. These processes are perturbed by events such as wildfires, which may have long-lasting impacts. In September 2011, the most destructive wildfire in Texas history occurred in and around Bastrop State Park, which was significantly affected; thus we take advantage of a rare opportunity to study soil moisture under such burned conditions. A 165 m long transect bridging burned and unburned areas was established within the 'Lost Pines' of the park. Soil moisture and soil temperature were monitored and estimated using a variety of methods, including 2D electrical resistivity imaging (using dipole-dipole and Schlumberger configurations), surface permittivity measurements (ThetaProbe), permittivity-based soil moisture profiling (PR2 profile probes), and installation of thermistors. Field measurements were collected at approximately one-month intervals to study temporal and seasonal effects on soil moisture and temperature in this area. Greater soil moisture and lower resistivity were found near the surface at the heavily burned end of the transect, where trees have been largely killed by the fire and grasses now dominate, and very low near-surface soil moisture and higher resistivity were found at the opposite end, which is still populated by pine trees. These variations can likely be attributed to the vegetative variations between the two ends of the transect, with trees consuming more water at one end and the ground cover of grasses and mosses consuming less water and helping reduce evaporation at the burned end. Higher clay content at the burned end of the transect could also be a factor in greater soil moisture retention there. Given the higher moisture content throughout the soil profile at the heavily burned end of the transect, this could be an indication of greater infiltration
Al-Kayssi, A. W.
Measurements of amplitude temperature and soil moisture content of sandy loam and silty clay loam soils were conducted in Al-Mada'in Research Station south of Baghdad during the period from the 1st of February to the 30th of April, 2004. Exponential regression relations were developed between amplitude temperature and volumetric moisture content for soil depths of 0.5, 3.0, 7.5 and 15cm below surface, which was highly significant (R2>0.96). A good linear regression between measured and predicted soil moisture contents was deduced for each depth (r>0.97). Soil moisture content was successfully predicted from the regression line when amplitude temperature was known.
Stacke, Tobias; Hagemann, Stefan
The state of soil moisture can can have a significant impact on regional climate conditions for short time scales up to several months. However, focusing on seasonal to decadal time scales, it is not clear whether the predictive skill of global a Earth System Model might be enhanced by assimilating soil moisture data or improving the initial soil moisture conditions with respect to observations. As a first attempt to provide answers to this question, we set up an experiment to investigate the life time (memory) of extreme soil moisture states in the coupled land-atmosphere model ECHAM6-JSBACH, which is part of the Max Planck Institute for Meteorology's Earth System Model (MPI-ESM). This experiment consists of an ensemble of 3 years simulations which are initialized with extreme wet and dry soil moisture states for different seasons and years. Instead of using common thresholds like wilting point or critical soil moisture, the extreme states were extracted from a reference simulation to ensure that they are within the range of simulated climate variability. As a prerequisite for this experiment, the soil hydrology in JSBACH was improved by replacing the bucket-type soil hydrology scheme with a multi-layer scheme. This new scheme is a more realistic representation of the soil, including percolation and diffusion fluxes between up to five separate layers, the limitation of bare soil evaporation to the uppermost soil layer and the addition of a long term water storage below the root zone in regions with deep soil. While the hydrological cycle is not strongly affected by this new scheme, it has some impact on the simulated soil moisture memory which is mostly strengthened due to the additional deep layer water storage. Ensemble statistics of the initialization experiment indicate perturbation lengths between just a few days up to several seasons for some regions. In general, the strongest effects are seen for wet initialization during northern winter over cold and humid
Full Text Available This paper's objective is to present generic calibration functions for organic surface layers derived for the soil moisture sensors Decagon ECH2O 5TE and Delta-T ThetaProbe ML2x, using material from northern regions, mainly from the Finish Meteorological Institute's Arctic Research Center in Sodankylä and the study area of the Danish Center for Hydrology HOBE. For the Decagon 5TE sensor such a function is currently not reported in literature. Data were compared with measurements from underlying mineral soils including laboratory and field measurements. Shrinkage and charring during drying were considered. For both sensors all field and lab data showed consistent trends. For mineral layers with low soil organic matter (SOM content the validity of the manufacturer's calibrations was demonstrated. Deviating sensor outputs in organic and mineral horizons were identified: for the Decagon 5TE apparent relative permittivities at a given moisture content decreased for increased SOM content, which was attributed to an increase of bound water in organic materials with large surface areas compared to the studied mineral soils. ThetaProbe measurements from organic horizons showed stronger non-linearity in the sensor response and signal saturation in the high level data. The derived calibration fit functions between sensor response and volumetric water content hold for samples spanning a wide range of humus types with differing SOM characteristics. This strengthens confidence in their validity under various conditions, rendering them highly suitable for large-scale applications in remote sensing and land surface modeling studies. Agreement between independent Decagon 5TE and ThetaProbe time series from an organic surface layer at the Sodankylä site was significantly improved when the here proposed fit functions were used. Decagon 5TE data also well-reflected precipitation events. Thus, Decagon 5TE network data from organic surface layers at the Sodankyl
Bircher, Simone; Andreasen, Mie; Vuollet, Johanna; Vehviläinen, Juho; Rautiainen, Kimmo; Jonard, François; Weihermüller, Lutz; Zakharova, Elena; Wigneron, Jean-Pierre; Kerr, Yann H.
This paper's objective is to present generic calibration functions for organic surface layers derived for the soil moisture sensors Decagon ECH2O 5TE and Delta-T ThetaProbe ML2x, using material from northern regions, mainly from the Finnish Meteorological Institute's Arctic Research Center in Sodankylä and the study area of the Danish Center for Hydrology (HOBE). For the Decagon 5TE sensor such a function is currently not reported in the literature. Data were compared with measurements from underlying mineral soils including laboratory and field measurements. Shrinkage and charring during drying were considered. For both sensors all field and lab data showed consistent trends. For mineral layers with low soil organic matter (SOM) content the validity of the manufacturer's calibrations was demonstrated. Deviating sensor outputs in organic and mineral horizons were identified. For the Decagon 5TE, apparent relative permittivities at a given moisture content decreased for increased SOM content, which was attributed to an increase of bound water in organic materials with large specific surface areas compared to the studied mineral soils. ThetaProbe measurements from organic horizons showed stronger nonlinearity in the sensor response and signal saturation in the high-level data. The derived calibration fit functions between sensor response and volumetric water content hold for samples spanning a wide range of humus types with differing SOM characteristics. This strengthens confidence in their validity under various conditions, rendering them highly suitable for large-scale applications in remote sensing and land surface modeling studies. Agreement between independent Decagon 5TE and ThetaProbe time series from an organic surface layer at the Sodankylä site was significantly improved when the here-proposed fit functions were used. Decagon 5TE data also well-reflected precipitation events. Thus, Decagon 5TE network data from organic surface layers at the Sodankylä and
YANG Yong-hong; ZHANG Jian-guo; ZHANG Jian-hui; LIU Shu-zhen; WANG Cheng-hua; XIAO Qing-hua
It is analyzed that the impacts of vegetation type and soil moisture content on shear strength of unsaturated soil through direct shearing tests for various vegetation types, different soil moisture contents and different-depth unsaturated soil. The results show that the cohesion of unsaturated soil changes greatly, and the friction angle changes a little with soil moisture content. It is also shown that vegetation can improve shear strength of unsaturated soil, which therefore provides a basis that vegetation can reinforce soil and protect slopes.
Hydraulic and electronic analog models are developed for the simulation of moisture flow and accumulation in unsaturated soil. The analog models are compared with numerical models and checked with field observations. Application of soil physical knowledge on a soil technological problem by means of
Fontes, Adan Fimbres
Land surface albedo is the ratio of reflected to incident solar radiation. It is a function of several surface parameters including soil color, moisture, roughness and vegetation cover. A better understanding of albedo and how it changes in relation to variations in these parameters is important in order to help improve our ability to model the effects of land surface modifications on climate. The objectives of this study were (1) To determine empirical relationships between smooth bare soil albedo and soil color, (2) To develop statistical relationships between albedo and ground-based thematic mapper (TM) measurements of spectral reflectances, (3) To determine how increased surface roughness caused by tillage reduces bare soil albedo and (4) To empirically relate albedo with TM data and other physical characteristics of mixed grass/shrubland sites at Walnut Gulch Watershed. Albedos, colors and spectral reflectances were measured by Eppley pyranometer, Chroma Meter CR-200 and a Spectron SE-590, respectively. Measurements were made on two field soils (Gila and Pima) at the Campus Agricultural Center (CAC), Tucson, AZ. Soil surface roughness was measured by a profile meter developed by the USDA/ARS. Additional measurements were made at the Maricopa Agricultural Center (MAC) for statistical model testing. Albedos of the 15 smooth, bare soils (plus silica sand) were determined by linear regression to be highly correlated (r^2 = 0.93, p > 0.01) with color values for both wet and dry soil conditions. Albedos of the same smooth bare soils were also highly correlated (r^2>=q 0.86, p > 0.01) with spectral reflectances. Testing of the linear regression equations relating albedo to soil color and spectral reflectances using the data from MAC showed a high correlation. A general nonlinear relationship given by y = 8.366ln(x) + 37.802 r^2 = 0.71 was determined between percent reduction in albedo (y) and surface roughness index (x) for wet and dry Pima and Gila field soils
Dong, Jianzhi; Steele-Dunne, Susan C.; Ochsner, Tyson E.; van de Giesen, Nick
This study addresses two critical barriers to the use of Passive Distributed Temperature Sensing (DTS) for large-scale, high-resolution monitoring of soil moisture. In recent research, a particle batch smoother (PBS) was developed to assimilate sequences of temperature data at two depths into Hydrus-1D to estimate soil moisture as well as soil thermal and hydraulic properties. However, this approach was limited to bare soil and assumed that the cable depths were perfectly known. In order for Passive DTS to be more broadly applicable as a soil hydrology research and remote sensing soil moisture product validation tool, it must be applicable in vegetated areas. To address this first limitation, the forward model (Hydrus-1D) was improved through the inclusion of a canopy energy balance scheme. Synthetic tests were used to demonstrate that without the canopy energy balance scheme, the PBS estimated soil moisture could be even worse than the open loop case (no assimilation). When the improved Hydrus-1D model was used as the forward model in the PBS, vegetation impacts on the soil heat and water transfer were well accounted for. This led to accurate and robust estimates of soil moisture and soil properties. The second limitation is that, cable depths can be highly uncertain in DTS installations. As Passive DTS uses the downward propagation of heat to extract moisture-related variations in thermal properties, accurate estimates of cable depths are essential. Here synthetic tests were used to demonstrate that observation depths can be jointly estimated with other model states and parameters. The state and parameter results were only slightly poorer than those obtained when the cable depths were perfectly known. Finally, in situ temperature data from four soil profiles with different, but known, soil textures were used to test the proposed approach. Results show good agreement between the observed and estimated soil moisture, hydraulic properties, thermal properties, and
An, Ru; Wang, Hui-Lin; You, Jia-jun; Wang, Ying; Shen, Xiao-ji; Gao, Wei; Wang, Yi-nan; Zhang, Yu; Wang, Zhe; Quaye-Ballardd, Jonathan Arthur; Chen, Yuehong
Soil moisture plays an important role in the water cycle within the surface ecosystem and it is the basic condition for the growth and development of plants. Currently, the spatial resolution of most soil moisture data from remote sensing ranges from ten to several tens of kilometres whilst those observed in situ and simulated for watershed hydrology, ecology, agriculture, weather and drought research are generally less than 1 kilometre. Therefore, the existing coarse resolution remotely sensed soil moisture data needs to be down-scaled. In this paper, a universal soil moisture downscaling model through stepwise regression with moving window suitable for large areas and multi temporal has been established. Datasets comprise land surface, brightness temperature, precipitation, soil and topographic parameters from high resolution data, and active/passive microwave remotely sensed soil moisture data from Essential Climate Variables (ECV_SM) with 25 km spatial resolution were used. With this model, a total of 288 soil moisture maps of 1 km resolution from the first ten-day of January 2003 to the last tenth-day of December 2010 were derived. The in situ observations were used to validate the down-scaled ECV_SM for different land cover and land use types and seasons. In addition, various errors comparative analysis was also carried out for the down-scaled ECV_SM and original one. In general, the down-scaled soil moisture for different land cover and land use types is consistent with the in situ observations. The accuracy is relatively high in autumn and winter. The validation results show that downscaled soil moisture can be improved not only on spatial resolution, but also on estimation accuracy.
ZHANG Qiao; SUN Xiaobing; LI Ya'nan; QIAO Yanli
Besides amplitude, frequency and phase, the polarization is another basic property of the electromagnetic wave. In the remote sensing field, the polarization is mainly applied in active detection systems of radar and iidar. This paper presents the quantitative relationship between soil moisture and polarization signatures in a certain type of soil in a farm. And this relationship is expected to be introduced on agriculture and hydrology ultimately. The experiments were performed both in the laboratory and the field. Soil samples with different moisture contents were measured at three wavebands on visible spectrum,and at several viewing angles in the plane of incidence. The polarization signature was indicated by the multi-band and multi-angle degree of linear polarization (DOLP) in this paper. The soil moisture were divided into five levels according to the properties of DOLP curves, namely, the quasi-quantitative relationship between soil moisture and its polarization signature were established. The percentages of soil moisture of the five levels are:≤10%, 10%-20%, 20%-40%, 40%-56% and >56%,respectively. Although this division for soil moisture is on a rather large scale, it will meet the precision of application agricultural and hydrologic remote sensing.
Full Text Available The possibility of distinguishing different soil moisture levels by electronic nose (e-nose was studied. Ten arable soils of various types were investigated. The measurements were performed for air-dry (AD soils stored for one year, then moistened to field water capacity and finally dried within a period of 180 days. The volatile fingerprints changed during the course of drying. At the end of the drying cycle, the fingerprints were similar to those of the initial AD soils. Principal component analysis (PCA and artificial neural network (ANN analysis showed that e-nose results can be used to distinguish soil moisture. It was also shown that different soils can give different e-nose signals at the same moistures.
Zhang, Yan; Liang, Ai-zhen; Zhang, Xiao-ping; Chen, Sheng-long; Sun, Bing-jie; Liu, Si-yi
In the early stage of an incubation experiment, soil respiration has a sensitive response to different levels of soil moisture. To investigate the effects of soil moisture on soil respiration under different tillage practices, we designed an incubation trial using air-dried soil samples collected from tillage experiment station established on black soils in 2001. The tillage experiment consisted of no-tillage (NT), ridge tillage (RT), and conventional tillage (CT). According to field capacity (water-holding capacity, WHC), we set nine moisture levels including 30%, 60%, 90%, 120%, 150%, 180%, 210%, 240%, 270% WHC. During the 22-day short-term incubation, soil CO₂ emission was measured. In the early stage of incubation, the priming effects occurred under all tillage practices. There were positive correlations between soil respiration and soil moisture. In addition to drought and flood conditions, soil CO₂ fluxes followed the order of NT > RT > CT. We fitted the relationship between soil moisture and soil CO₂ fluxes under different tillage practices. In the range of 30%-270% WHC, soil CO₂ fluxes and soil moisture fitted a quadratic regression equation under NT, and linear regression equations under RT and CT. Under the conditions of 30%-210% WHC of both NT and RT, soil CO₂ fluxes and soil moisture were well fitted by the logarithmic equation with fitting coefficient R² = 0.966 and 0.956, respectively.
Reichle, R.; De Lannoy, G.; Liu, Q.; Ardizzone, J.; Kimball, J.; Koster, R.
The SMAP Level 4 soil moisture (L4_SM) product provides global estimates of surface and root zone soil moisture, along with other land surface variables and their error estimates. These estimates are obtained through assimilation of SMAP brightness temperature observations into the Goddard Earth Observing System (GEOS-5) land surface model. The L4_SM product is provided at 9 km spatial and 3-hourly temporal resolution and with about 2.5 day latency. The soil moisture and temperature estimates in the L4_SM product are validated against in situ observations. The L4_SM product meets the required target uncertainty of 0.04 m(exp. 3)m(exp. -3), measured in terms of unbiased root-mean-square-error, for both surface and root zone soil moisture.
US Fish and Wildlife Service, Department of the Interior — This report is a summary of 18 months of soil and moisture work accomplished on the 7,000 acres of land purchased for Quivira National Wildlife Refuge to date...
Jiankang, Ji; Thomsen, A.; Skriver, Henning
The results of applying data from the Danish polarimetric SAR (EMISAR) to estimate soil moisture for bare fields are presented. Fully calibrated C-band SAR images for hh, vv and cross polarizations have been used in this study. The measured surface roughness data showed that classical roughness...... autocorrelation functions (Gaussian and Exponential) were not able to fit natural surfaces well. A Gauss-Exp hybrid model which agreed better with the measured data has been proposed. Theoretical surface scattering models (POM, IEM), as well as an empirical model for retrieval of soil moisture and surface rms...... height from coand cross-polarized ratio, have been examined, but the results are less satisfactory. As soil moisture response to backscattering coefficient σo is mainly coupled to surface roughness effect for bare fields, a bilinear model coupling volumetric soil moisture mv and surface rms height σ...
National Oceanic and Atmospheric Administration, Department of Commerce — This dataset, DSI-6411 is comprised of soil moisture data and the accompanying information for the agricultural regions of Western Russia (west of ~ 60E) and The...
Baldwin, D.; Manfreda, S.; Keller, K.; Smithwick, E. A. H.
Satellite-based near-surface (0-2 cm) soil moisture estimates have global coverage, but do not capture variations of soil moisture in the root zone (up to 100 cm depth) and may be biased with respect to ground-based soil moisture measurements. Here, we present an ensemble Kalman filter (EnKF) hydrologic data assimilation system that predicts bias in satellite soil moisture data to support the physically based Soil Moisture Analytical Relationship (SMAR) infiltration model, which estimates root zone soil moisture with satellite soil moisture data. The SMAR-EnKF model estimates a regional-scale bias parameter using available in situ data. The regional bias parameter is added to satellite soil moisture retrievals before their use in the SMAR model, and the bias parameter is updated continuously over time with the EnKF algorithm. In this study, the SMAR-EnKF assimilates in situ soil moisture at 43 Soil Climate Analysis Network (SCAN) monitoring locations across the conterminous U.S. Multivariate regression models are developed to estimate SMAR parameters using soil physical properties and the moderate resolution imaging spectroradiometer (MODIS) evapotranspiration data product as covariates. SMAR-EnKF root zone soil moisture predictions are in relatively close agreement with in situ observations when using optimal model parameters, with root mean square errors averaging 0.051 [cm3 cm-3] (standard error, s.e. = 0.005). The average root mean square error associated with a 20-fold cross-validation analysis with permuted SMAR parameter regression models increases moderately (0.082 [cm3 cm-3], s.e. = 0.004). The expected regional-scale satellite correction bias is negative in four out of six ecoregions studied (mean = -0.12 [-], s.e. = 0.002), excluding the Great Plains and Eastern Temperate Forests (0.053 [-], s.e. = 0.001). With its capability of estimating regional-scale satellite bias, the SMAR-EnKF system can predict root zone soil moisture over broad extents and has
Zavodsky, B.; Case, J.; White, K.; Bell, J. R.
Drought detection, analysis, and mitigation has become a key challenge for a diverse set of decision makers, including but not limited to operational weather forecasters, climatologists, agricultural interests, and water resource management. One tool that is heavily used is the United States Drought Monitor (USDM), which is derived from a complex blend of objective data and subjective analysis on a state-by-state basis using a variety of modeled and observed precipitation, soil moisture, hydrologic, and vegetation and crop health data. The NASA Short-term Prediction Research and Transition (SPoRT) Center currently runs a real-time configuration of the Noah land surface model (LSM) within the NASA Land Information System (LIS) framework. The LIS-Noah is run at 3-km resolution for local numerical weather prediction (NWP) and situational awareness applications at select NOAA/National Weather Service (NWS) forecast offices over the Continental U.S. (CONUS). To enhance the practicality of the LIS-Noah output for drought monitoring and assessing flood potential, a 30+-year soil moisture climatology has been developed in an attempt to place near real-time soil moisture values in historical context at county- and/or watershed-scale resolutions. This LIS-Noah soil moisture climatology and accompanying anomalies is intended to complement the current suite of operational products, such as the North American Land Data Assimilation System phase 2 (NLDAS-2), which are generated on a coarser-resolution grid that may not capture localized, yet important soil moisture features. Daily soil moisture histograms are used to identify the real-time soil moisture percentiles at each grid point according to the county or watershed in which the grid point resides. Spatial plots are then produced that map the percentiles as proxies to the different USDM categories. This presentation will highlight recent developments of this gridded, objective soil moisture index, comparison to subjective
A. J. Teuling; Hupet, F.; R. Uijlenhoet; P. A. Troch
We investigate the role of interannual climate variability on spatial soil moisture variability dynamics for a field site in Louvain-la-Neuve, Belgium. Observations were made during 3 years under intermediate (1999), wet (2000), and extremely dry conditions (2003). Soil moisture variability dynamics are simulated with a comprehensive model for the period 1989-2003. The results show that climate variability induces non-uniqueness and two distinct hysteresis modes in the yearly relation between...
Dong, Jianzhi; Steele-Dunne, Susan C.; Ochsner, Tyson E.; van de Giesen, Nick
This study investigates the potential to estimate the vertical profile of soil moisture by assimilating temperature observations at a limited number of depths into a coupled heat and moisture transport model (Hydrus-1D). The method is developed with a view to assimilating temperature data from distributed temperature sensing (DTS) to estimate soil moisture at high resolution over large areas. The correlation between temperature and soil moisture in the shallow soil (top ∼ 50 cm) ensures that soil moisture can be estimated using just soil temperature observations. Synthetic tests across a range of soil textures show that with data assimilation both modeled temperature and the moisture profile are improved considerably compared to the ensemble open loop model simulations. In addition, employing data assimilation provides a means to quantitatively account for different sources of uncertainty. This is particularly relevant in the context of DTS given the influence of spatial variability in soil texture and its impact on estimation error. The data assimilation approach could also be used to determine, the number of temperature observations required and the depths at which they should be made. Results suggest that temperature observed at two depths is typically sufficient to estimate soil moisture using this approach. The root mean square error (RMSE) in soil moisture was reduced by up to 75% in the top 20 cm. Furthermore, this approach solves many of the challenges identified in the application of an inversion approach to estimate soil moisture from DTS.
loam soil did not inﬂ uence the rate of lowering soil moisture content. Contrary, the mole drainage installed in clay soil has effected to increase the rate of lowering soil moisture content. Keywords: Mole drainage, soil moisture content, clay content
Calamita, Giuseppe; Perrone, Angela; Satriani, Antonio; Brocca, Luca; Moramarco, Tommaso
The key role played by soil moisture in both Global Hydrological Cycle and Earth Radiation Budget has been claimed by numerous authors during past decades. The importance of this environmental variable is evident in several natural processes operating in a wide range of spatial and temporal scales. At continental and regional scales soil moisture influences the evapotranspiration process and so acts indirectly on the climate processes; at middle scale is one of the major controls of the infiltration-runoff soil response during rainfall events; at small scales the knowledge of soil moisture evolution is crucial for precision agriculture and the associated site-specific management practices. However, soil moisture exhibits an high temporal and spatial variability and this is even more evident in the vadose zone. Thus, in order to better understand the soil moisture dynamics it is desirable to capture its behavior at different temporal and/or spatial scales. Traditional in situ methods to measure soil moisture like TDR can be very precise and allows an high temporal resolution. Recently, the application in field of geophysical methods for capturing soil moisture spatial and temporal variations has demonstrated to be a promising tool for hydro-geological studies. One of the major advantages relies on the capability to capture the soil moisture variability at larger scales, that is decametric or hectometric scale. In particular, this study is based on the simultaneous application of the electrical resistivity and the TDR methods. We present two study cases that differ from each other by both spatial and temporal resolution. For the first one, simultaneous measurements obtained during four different period of the year and carried out within a test catchment (~60 km2) in Umbria region (central Italy) were analyzed. The second case concerns almost three months of simultaneous measurements carried out in a small test site ( sampling events during 80 days. In both case we
Manfreda, S.; Brocca, L.; Moramarco, T.; Melone, F.; Sheffield, J.; Fiorentino, M.
In recent years, much effort has been given to monitoring of soil moisture from satellite remote sensing. These tools represent an extraordinary source of information for hydrological applications, but they only provide information on near-surface soil moisture. In the present work, we developed a new formulation for the estimation of the soil moisture in the root zone based on the measured value of soil moisture at the surface. The method derives from a simplified form of the soil water balance equation and for this reason all parameters adopted are physically consistent. The formulation provides a closed form of the relationship between the root zone soil moisture and the surface soil moisture with a limited number of parameters, such as: the ratio between the depth of the surface layer and the deeper layer, the water loss coefficient, and the field capacity. The method has been tested using modeled soil moisture obtained from the North American Land Data Assimilation System (NLDAS). The NLDAS is a multi-institution partnership aimed at developing a retrospective data set, using available atmospheric and land surface meteorological observations to compute the land surface hydrological budget. The NLDAS database was extremely useful for the scope of the present research since it provides simulated data over an extended area with different climatic and physical condition and moreover it provides soil moisture data averaged over different depths. In particular, we used values in the top 10 cm and 100 cm layers. One year of simulation was used to test the ability of the developed method to describe soil moisture fluctuation in the 100cm layer over the entire NLDAS domain. The method was adopted by calibrating one of its three parameters and defining the remaining two based on physical characteristics of the site (using the potential evapotranspiration and ratio between the first and the second soil layer depth). In general, the method performed better than
ZHENG Ji-Yong; WANG Li-Mei; SHAO Ming-An; WANG Quan-Jiu; LI Shi-Qing
In order to preliminarily look at rules for soil moisture changes in the bank of the gully and to provide some recommendations for vegetative restoration in gully bank regions in the Loess Plateau, changes of soil moisture with depth and distance to the gully edge and their dynamic changes with time were observed to study the soil water characteristics in the bank of the gully. The results showed that soil water content increased with increasing distance from the gully edge, whereas for the same time period, the closer the distance to the gully wall, the greater the water loss; and that the influential distance of side evaporation decreased as depth increased.
Gaikwad, Pramod; Devendrachari, Mruthyunjayachari Chattanahalli; Thimmappa, Ravikumar; Paswan, Bhuneshwar; Raja Kottaichamy, Alagar; Makri Nimbegondi Kotresh, Harish; Thotiyl, Musthafa Ottakam
Here we report the first potentiometric sensor for soil moisture analysis by bringing in the concept of Galvanic cells wherein the redox energies of Al and conducting polyaniline are exploited to design a battery type sensor. The sensor consists of only simple architectural components, and as such they are inexpensive and lightweight, making it suitable for on-site analysis. The sensing mechanism is proved to be identical to a battery type discharge reaction wherein polyaniline redox energy changes from the conducting to the nonconducting state with a resulting voltage shift in the presence of soil moisture. Unlike the state of the art soil moisture sensors, a signal derived from the proposed moisture sensor is probe size independent, as it is potentiometric in nature and, hence, can be fabricated in any shape or size and can provide a consistent output signal under the strong aberration conditions often encountered in soil moisture analysis. The sensor is regenerable by treating with 1 M HCl and can be used for multiple analysis with little read out hysteresis. Further, a portable sensor is fabricated which can provide warning signals to the end user when the moisture levels in the soil go below critically low levels, thereby functioning as a smart device. As the sensor is inexpensive, portable, and potentiometric, it opens up avenues for developing effective and energy efficient irrigation strategies, understanding the heat and water transfer at the atmosphere-land interface, understanding soil mechanics, forecasting the risk of natural calamities, and so on.
The objectives of the SMAP (Soil Moisture Active Passive) mission include global measurements of soil moisture at three different spatial resolutions. SMAP will provide soil moisture with a 3-day revisit time at an accuracy of 0.04 m3/m3 The 36 km gridded soil moisture product (L2_SM_P) is primar...
Singh, K. P.; Sharma, S. K.
Bistatic outdoor measurements of soil moisture at X-band of microwave frequencies are reported in this paper. The moisture content in the soil greatly affects the dielectric constant and the loss tangent of the composite material and hence the response to microwave signals are also affected. In turn, it has been observed to reflect, in our measurements, on the following derived cognisable parameters, namely, scattering cross-section, and brightness temperature. These parameters are seen to depend, besides moisture content on incidence angle, polarization and surface roughness too. The importance of these outdoor measurements are shown in microwave remote sensing. Limitations of our measurements are also outlined.
Leone, Marco; Principe, Sofia; Consales, Marco; Parente, Roberto; Laudati, Armando; Caliro, Stefano; Cutolo, Antonello; Cusano, Andrea
This work deals with the fabrication, prototyping, and experimental validation of a fiber optic thermo-hygrometer-based soil moisture sensor, useful for rainfall-induced landslide prevention applications. In particular, we recently proposed a new generation of fiber Bragg grating (FBGs)-based soil moisture sensors for irrigation purposes. This device was realized by integrating, inside a customized aluminum protection package, a FBG thermo-hygrometer with a polymer micro-porous membrane. Here, we first verify the limitations, in terms of the volumetric water content (VWC) measuring range, of this first version of the soil moisture sensor for its exploitation in landslide prevention applications. Successively, we present the development, prototyping, and experimental validation of a novel, optimized version of a soil VWC sensor, still based on a FBG thermo-hygrometer, but able to reliably monitor, continuously and in real-time, VWC values up to 37% when buried in the soil.
Full Text Available This work deals with the fabrication, prototyping, and experimental validation of a fiber optic thermo-hygrometer-based soil moisture sensor, useful for rainfall-induced landslide prevention applications. In particular, we recently proposed a new generation of fiber Bragg grating (FBGs-based soil moisture sensors for irrigation purposes. This device was realized by integrating, inside a customized aluminum protection package, a FBG thermo-hygrometer with a polymer micro-porous membrane. Here, we first verify the limitations, in terms of the volumetric water content (VWC measuring range, of this first version of the soil moisture sensor for its exploitation in landslide prevention applications. Successively, we present the development, prototyping, and experimental validation of a novel, optimized version of a soil VWC sensor, still based on a FBG thermo-hygrometer, but able to reliably monitor, continuously and in real-time, VWC values up to 37% when buried in the soil.
Li, M.; Chen, Y.
Direct and in situ measurement of evapotranspiration (ET), such as the eddy covariance (EC) method, is often expensive and complicated, especially over tall canopy. In view of soil water balance, depletion of soil moisture can be attributed to canopy ET when horizontal soil moisture movement is negligible and percolation ceases. This study computed the daily soil moisture depletion at the Lien-Hua-Chih (LHC) station (23°55'52"N, 120°53'39"E, 773 m elevation) from July, 2004 to June, 2007 to estimate daily ET. The station is inside an experimental watershed of a natural evergreen forest and the canopy height is about 17 m. Rainfall days are assumed to be no ET. For those days with high soil moisture content, normally 2 to 3 days after significant rainfall input, ET is estimated by potential ET. Soil moistures were measured by capacitance probes at -10 cm, - 30 cm, -50 cm, -70 cm, and -90 cm. A soil heat flux plate was placed at -5 cm. In the summer of 2006, a 22 m tall observation tower was constructed. Temperature and relative humidity sensors were placed every 5 m from ground surface to 20 m for inner and above canopy measurements. Net radiation and wind speed/directions were also installed. A drainage gauge was installed at -50 cm to collect infiltrated water. Continuous measurements of low response instruments were recorded every 30-minute averaged from 10-minute samplings. A nearby weather station provides daily pan evaporation and precipitation data. Since the response of soil water variations is relatively slow to the fluctuations of atmospheric forcing, only daily ET is estimated from daily soil moisture depletion. The annual average precipitation is 2902 mm and the annual average ET is 700 mm. The seasonal ET patterns of the first two water years are similar. The third year has a higher ET because soil moisture was recharged frequently by rainfall In order to examine the applicability of this approach, an EC system, including a 3-D sonic anemometer (Young
Dijkstra, F. A.; Cheng, W.
While it is well known that soil moisture directly affects microbial activity and soil C decomposition, it is unclear if the presence of plants alters these effects through rhizosphere processes. We studied soil moisture effects on soil C decomposition with and without sunflower and soybean. Plants were grown in two different soil types with soil moisture contents of 45 and 85% of field capacity in a greenhouse experiment. We continuously labeled plants with depleted 13C, which allowed us to separate plant-derived CO2-C from original soil-derived CO2-C in soil respiration measurements. We observed an overall increase in soil-derived CO2-C efflux in the presence of plants (priming effect) in both soils with on average a greater priming effect in the high soil moisture treatment (60% increase in soil-derived CO2-C compared to control) than in the low soil moisture treatment (37% increase). Greater plant biomass in the high soil moisture treatment contributed to greater priming effects, but priming effects remained significantly higher after correcting for plant biomass. Possibly, root exudation of labile C may have increased more than plant biomass and may have become more effective in stimulating microbial decomposition in the higher soil moisture treatment. Our results indicate that changing soil moisture conditions can significantly alter rhizosphere effects on soil C decomposition.
Sandric, I.; Diamandi, A.; Oana, N.; Saizu, D.; Vasile, C.; Lucaschi, B.
The study presents the validation of SMOS soil moisture satellite products for Romania. The validation was performed with in-situ measurements spatially distributed over the country and with in-situ measurements concentrated in on small area. For country level a number of 20 stations from the national meteorological observations network in Romania were selected. These stations have in-situ measurements for soil moisture in the first 5 cm of the soil surface. The stations are more or less distributed in one pixel of SMOS, but it has the advantage that covers almost all the country with a wide range of environmental conditions. Additionally 10 mobile soil moisture measurements stations were acquired and installed. These are spatially concentrated in one SMOS pixel in order to have a more detailed validation against the soil type, soil texture, land surface temperature and vegetation type inside one pixel. The results were compared and analyzed for each day, week, season, soil type, and soil texture and vegetation type. Minimum, maximum, mean and standard deviation were extracted and analyzed for each validation criteria and a hierarchy of those were performed. An upscaling method based on the relations between soil moisture, land surface temperature and vegetation indices was tested and implemented. The study was financed by the Romanian Space Agency within the framework of ASSIMO project http://assimo.meteoromania.ro.
Full Text Available Stemflow of xerophytic shrubs represents a significant component of water replenishment to the soil-root system influencing water utilization of plant roots at the stand scale, especially in water scarce desert ecosystems. In this study, stemflow of Caragana korshinskii was quantified by an aluminum foil collar collection method on re-vegetated sand dunes of the Shapotou restored desert ecosystem in northwestern China. Time domain reflectometry probes were inserted horizontally at 20 different soil profile depths under the C. korshinskii shrub to monitor soil moisture variation at hourly intervals. Results indicated that 2.2 mm precipitation was necessary for the generation of stemflow for C. korshinskii. Stemflow averaged 8% of the gross precipitation and the average funnelling ratio was as high as 90. The soil moisture in the uppermost soil profile was strongly correlated with individual rainfall and the stemflow strengthened this relationship. Therefore, it is favourable for the infiltrated water redistribution in the deeper soil profile of the root zone. Consequently, stemflow contributes significantly to a positive soil moisture balance in the root zone and the replenishment of soil moisture at deeper soil layers. This plays an important role in plant survival and the general ecology of arid desert environments.
E. M. Blyth
Full Text Available Soil moisture heterogeneity has an effect on the rainfall–runoff characteristics of a landscape. The aggregate effect on the mean water balance over an area can be quantified successfully using models such as the PDM (Moore, 1986 and TOPMODEL (Beven and Kirkby, 1979. These rainfall–runoff models have been embedded in the large-scale land surface schemes used in meteorological models. However, there is also a requirement (e.g. model validation to identify the spatial structure of the fine-scale soil moisture heterogeneity that makes up these aggregate models. In some types of landscape, this will be dictated by topography, in others by soil characteristics, or by a combination of both. A method to distribute area-average soil moisture according to the likely effect of local topography is presented and tested. The heterogeneity of the soil moisture is described by the Xinanxiang distribution (Zhao et al., 1980, commonly used to describe the natural spatial heterogeneity of the landscape. This distribution is then mapped onto the terrain using a topographic index to locate the wettest and driest areas. Soil moisture data from the Wye catchment in Wales and from the Pang catchment in Berkshire, England, are used to test the method. It is found that soil moisture data from the Wye catchment follow the topographic index reasonably well, whereas data from the quick-draining, chalky Pang catchment do not. The conclusion that topographic index is a useful indicator only in some landscapes applies equally to using this mapping method and those models that use topographic index directly. Keywords: soil moisture, heterogeneity, topographic index, data
Sadeghi, A. M.; Hancock, G. D.; Waite, W. P.; Scott, H. D.; Rand, J. A.
Laboratory and field experiments were conducted to investigate the ability of microwave remote sensing systems to detect the moisture status of a silt loam soil exhibiting abrupt changes in moisture content near the surface. Laboratory soil profiles were prepared with a discontinuous moisture boundary in the subsurface. Reflectivity measurements of these profiles were made with a bistatic reflectometer operating over the frequency ranges of 1-2 and 4-8 GHz (wavelength ranges of 30-15 and 7.5-3.75 cm, respectively). These measurements exhibited a well-developed coherent interference pattern in good agreement with a simple two-layer reflectivity model. Field measurements of bare soil surfaces were conducted for initially saturated profiles and continued for extended periods of drying. During drying, coherent interference patterns similar to those observed in the laboratory were detected. These appear to be due to steep moisture gradients occurring between drying layers near the surface. The field results were modeled by a five-segment linear moisture profile with one or two steep segments and a multilayer reflectivity program. Agreement between model and field response over the frequency range was used to estimate the depth of drying layers within the soil. These depths were monitored over the second and third drying cycles. Formation of the drying layers under field conditions appears to be influenced by drying time, tillage, and evaporative demand. In any case, it appears that the coherent effects caused by nonuniform moisture profiles may substantially affect the reflectivity of even rough soil surfaces.
Dong, Jianzhi; Steele-Dunne, Susan C.; Ochsner, Tyson E.; Giesen, Nick van de
This study investigates the potential of estimating the soil moisture profile and the soil thermal and hydraulic properties by assimilating soil temperature at shallow depths using a particle batch smoother (PBS) using synthetic tests. Soil hydraulic properties influence the redistribution of soil moisture within the soil profile. Soil moisture, in turn, influences the soil thermal properties and surface energy balance through evaporation, and hence the soil heat transfer. Synthetic experiments were used to test the hypothesis that assimilating soil temperature observations could lead to improved estimates of soil hydraulic properties. We also compared different data assimilation strategies to investigate the added value of jointly estimating soil thermal and hydraulic properties in soil moisture profile estimation. Results show that both soil thermal and hydraulic properties can be estimated using shallow soil temperatures. Jointly updating soil hydraulic properties and soil states yields robust and accurate soil moisture estimates. Further improvement is observed when soil thermal properties were also estimated together with the soil hydraulic properties and soil states. Finally, we show that the inclusion of a tuning factor to prevent rapid fluctuations of parameter estimation, yields improved soil moisture, temperature, and thermal and hydraulic properties.
Full Text Available Land surface models (LSM are widely used as scientific and operational tools to simulate mass and energy fluxes within the soil vegetation atmosphere continuum for numerous applications in meteorology, hydrology or for geobiochemistry studies. A reliable parameterization of these models is important to improve the simulation skills. Soil moisture is a key variable, linking the water and energy fluxes at the land surface. An appropriate parameterisation of soil hydraulic properties is crucial to obtain reliable simulation of soil water content from a LSM scheme. Parameter inversion techniques have been developed for that purpose to infer model parameters from soil moisture measurements at the local scale. On the other hand, remote sensing methods provide a unique opportunity to estimate surface soil moisture content at different spatial scales and with different temporal frequencies and accuracies. The present paper investigates the potential to use surface soil moisture information to infer soil hydraulic characteristics using uncertain observations. Different approaches to retrieve soil characteristics from surface soil moisture observations is evaluated and the impact on the accuracy of the model predictions is quantified. The results indicate that there is in general potential to improve land surface model parameterisations by assimilating surface soil moisture observations. However, a high accuracy in surface soil moisture estimates is required to obtain reliable estimates of soil characteristics.
Ground-reflected Global Positioning System (GPS) signals can be used opportunistically to infer changes in land-surface characteristics surrounding a GPS monument. GPS satellites transmit at L-band, and at microwave frequencies the permittivity of the ground surface changes primarily due to its moisture content. Temporal changes in ground-reflected GPS signals are thus indicative of temporal changes in the moisture content surrounding a GPS antenna. The interference pattern of the direct and reflected GPS signal for a single satellite track is recorded in signal-to-noise ratio (SNR) data. Alternating constructive and destructive interference as the satellite passes over the antenna results in a noisy oscillating wave at low satellite elevation angles, from which the phase, amplitude, and frequency (or reflector height) can be calculated. Here, an electrodynamic model that simulates SNR data is validated against field observations. The model is then used to show that temporal changes in these SNR metrics may be used to estimate changes in surface soil moisture in the top 5 cm of the soil column. Results show that changes in SNR phase are best correlated with changes in soil moisture, with an approximately linear slope. Surface roughness decreases the sensitivity of SNR phase to soil moisture, though the effect is not significant for small roughness values (moisture is to be estimated using phase data. An algorithm is presented that uses modeled relationships between canopy parameters and SNR metrics to remove seasonal vegetation effects from the phase time series, from which soil moisture time series may be estimated. Results indicate that this algorithm can successfully estimate surface soil moisture with an RMSE of 0.05 cm3 cm-3 or lower for many of the antennas that comprise the Plate Boundary Observatory (PBO) network.
Yan, B.; Dickinson, R. E.
Evapotranspiration (ET) is both a moisture flux and an energy flux. It has a substantial impact on climate. Community Land Model Version 4 (CLM4) is a widely used land surface model that simulates moisture, energy and momentum exchange between land and atmosphere. However, ET from CLM4 suffers from relatively low accuracy, especially for ground evaporation. In the parameterization of CLM4, soil texture, by determining soil hydraulic properties, affects the evolution of soil moisture and consequently ET. The three components of ET in climate models can more readily be improved after an evaluation of soil texture dataset's impact on ET simulations. Besides the IGBP-DIS (International Geosphere-Biosphere Programme Data and Information System) dataset used in CLM4, another two US multi-layer soil particle content datasets, Soil Database for the Conterminous United States (CONUS-SOIL) and Global Soil Texture and Derived Water-Holding Capacities (Webb2000), are also used. The latter two show a consistent substantial reduction of both sand and clay contents in Mississippi River Basin. CLM4 is run off line over the US with the three different soil texture datasets (Control Run, CONUS SOIL and Webb2000). Comparisons of simulated soil moisture with NCEP (National Centers for Environmental Prediction) reanalysis data show a higher agreement between CONUS SOIL and NCEP over Mississippi River Basin. Compared with Control Run, soil moisture from the other two runs increases in Western US and decreases in Eastern US, which produces a stronger west-east soil moisture gradient. The response of ET to soil moisture change differs in different climate regimes. In Mississippi River Basin, the change of ET is negligible even if soil moisture increases substantially. On the other hand, in eastern US and US Central Great Plains, ET is very sensitive to soil moisture during the warm seasons, with the change of up to 10 W/m2.
Whitcomb, J.; Clewley, D.; Moghaddam, M.; Akbar, R.; Silva, A. R. D.
There is a large difference in the footprints over which remote sensing instruments, such as the Soil Moisture Active Passive (SMAP) mission, retrieve soil moisture and that of in situ networks. Therefore a method for upscaling in situ measurements is required before they can be used to validate remote sensing instruments. The upscaling problem is made more difficult when measurements are sparse and irregularly spaced within the footprint. To address these needs, we have developed a method for producing upscaled estimates of soil moisture based on a network of in situ soil moisture measurements and airborne P-band SAR data, and utilizing a Random Forests-based regression algorithm. Sites within the SoilSCAPE network, for which the technique was developed, typically contains sensors at ~30 locations, with each location sampled at multiple depths. Measurements are taken at 20 minute intervals and averaged over a selectable time interval, thereby supporting near-real time generation of soil moisture maps. The collected measurements are automatically uploaded to a central database from which they can be accessed for use in the regression algorithm. Our regression-based approach works well with irregularly-spaced sensors by incorporating a set of data layers that correlate well with soil moisture. The layers include thematic land cover, elevation, slope, aspect, flow accumulation, clay fraction, air temperature, precipitation, and P-Band HH, VV, and HV backscatter. Values from these data layers are extracted for each sensor location and applied to train the Random Forests algorithm. The decision trees generated are then applied to estimate soil moisture at a 100 m spacing throughout the network region, after which the evenly-spaced values are averaged to accord with the 3-, 9-, and 36-km SMAP measurement grids. The resulting set of near-real time soil moisture estimates suitable for SMAP calibration and validation is placed online for use by the SMAP Cal/Val team
Shen, Rui; Pennell, Kelly G; Suuberg, Eric M
Mathematical models have been widely used in analyzing the effects of various environmental factors in the vapor intrusion process. Soil moisture content is one of the key factors determining the subsurface vapor concentration profile. This manuscript considers the effects of soil moisture profiles on the soil gas vapor concentration away from any surface capping by buildings or pavement. The "open field" soil gas vapor concentration profile is observed to be sensitive to the soil moisture distribution. The van Genuchten relations can be used for describing the soil moisture retention curve, and give results consistent with the results from a previous experimental study. Other modeling methods that account for soil moisture are evaluated. These modeling results are also compared with the measured subsurface concentration profiles in the U.S. EPA vapor intrusion database.
Tang, X.-L.; Zhou, G.-Y.; Liu, S.-G.; Zhang, D.-Q.; Liu, S.-Z.; Li, J.; Zhou, C.-Y.
The spatial and temporal variations in soil respiration and its relationship with biophysical factors in forests near the Tropic of Cancer remain highly uncertain. To contribute towards an improvement of actual estimates, soil respiration rates, soil temperature, and soil moisture were measured in three successional subtropical forests at the Dinghushan Nature Reserve (DNR) in southern China from March 2003 to February 2005. The overall objective of the present study was to analyze the temporal variations of soil respiration and its biophysical dependence in these forests. The relationships between biophysical factors and soil respiration rates were compared in successional forests to test the hypothesis that these forests responded similarly to biophysical factors. The seasonality of soil respiration coincided with the seasonal climate pattern, with high respiration rates in the hot humid season (April-September) and with low rates in the cool dry season (October-March). Soil respiration measured at these forests showed a clear increasing trend with the progressive succession. Annual mean (?? SD) soil respiration rate in the DNR forests was (9.0 ?? 4.6) Mg CO2-C/hm2per year, ranging from (6.1 ?? 3.2) Mg CO2-C/hm2per year in early successional forests to (10.7 ?? 4.9) Mg CO2-C/hm2 per year in advanced successional forests. Soil respiration was correlated with both soil temperature and moisture. The T/M model, where the two biophysical variables are driving factors, accounted for 74%-82% of soil respiration variation in DNR forests. Temperature sensitivity decreased along progressive succession stages, suggesting that advanced-successional forests have a good ability to adjust to temperature. In contrast, moisture increased with progressive succession processes. This increase is caused, in part, by abundant respirators in advanced-successional forest, where more soil moisture is needed to maintain their activities. ?? 2006 Institute of Botany, Chinese Academy of
Xu-Li Tang; Guo-Yi Zhou; Shu-Guang Liu; De-Qiang Zhang; Shi-Zhong Liu; Jiong Li; Cun-Yu Zhou
The spatial and temporal variations in soil respiration and its relationship with biophysical factors in forests near the Tropic of Cancer remain highly uncertain. To contribute towards an improvement of actual estimates, soil respiration rates, soil temperature, and soil moisture were measured in three successional subtropical forests at the Dinghushan Nature Reserve (DNR) in southern China from March 2003 to February 2005. The overall objective of the present study was to analyze the temporal variations of soil respiration and its biophysical dependence in these forests. The relationships between biophysical factors and soil respiration rates were compared in successional forests to test the hypothesis that these forests responded similarly to biophysical factors. The seasonality of soil respiration coincided with the seasonal climate pattern, with high respiration rates in the hot humid season (April-September) and with low rates in the cool dry season (October-March). Soil respiration measured at these forests showed a clear increasing trend with the progressive succession. Annual mean (± SD) soil respiration rate in the DNR forests was (9.0±4.6) Mg CO2-C/hm2 per year, ranging from (6.1±3.2) Mg CO2-C/hm2 per year in early successional forests to (10.7±4.9) Mg CO2-C/hm2 per year in advanced successional forests. Soil respiration was correlated with both soil temperature and moisture. The T/M model, where the two biophysical variables are driving factors, accounted for 74%-82% of soil respiration variation in DNR forests. Temperature sensitivity decreased along progressive succession stages, suggesting that advanced-successional forests have a good ability to adjust to temperature. In contrast, moisture increased with progressive succession processes. This increase is caused, in part, by abundant respirators in advanced-successional forest, where more soil moisture is needed to maintain their activities.
Bruno, Rogério D.; da Rocha, Humberto R.; de Freitas, Helber C.; Goulden, Michael L.; Miller, Scott D.
We used frequency-domain reflectometry to make continuous, high-resolution measurements for 22 months of the soil moisture to a depth of 10 m in an Amazonian rain forest. We then used these data to determine how soil moisture varies on diel, seasonal and multi-year timescales, and to better understand the quantitative and mechanistic relationships between soil moisture and forest evapotranspiration. The mean annual precipitation at the site was over 1900 mm. The field capacity was approximately 0.53 m3 m-3 and was nearly uniform with soil depth. Soil moisture decreased at all levels during the dry season, with the minimum of 0.38 m3 m-3 at 3 m beneath the surface. The moisture in the upper 1 m showed a strong diel cycle with daytime depletion due to evapotranspiration. The moisture beneath 1 m declined during both day and night due to the combined effects of evapotranspiration, drainage and a nighttime upward movement of water. The depth of active water withdrawal changed markedly over the year. The upper 2 m of soil supplied 56% of the water used for evapotranspiration in the wet season and 28% of the water used in the dry season. The zone of active water withdrawal extended to a depth of at least 10 m. The day-to-day rates of moisture withdrawal from the upper 10 m of soil during rain-free periods agreed well with simultaneous measurements of whole-forest evapotranspiration made by the eddy covariance technique. The forest at the site was well adapted to the normal cycle of wet and dry seasons, and the dry season had only a small effect on the rates of land-atmosphere water vapour exchange.
Srivastava, Prashant K.; Han, Dawei; Rico-Ramirez, Miguel A.; O'Neill, Peggy; Islam, Tanvir; Gupta, Manika
Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale.
Wanders, N.; Karssenberg, D.; Bierkens, M. F. P.; Van Dam, J. C.; De Jong, S. M.
Soil moisture is a key variable in the hydrological cycle and important in hydrological modelling. When assimilating soil moisture into flood forecasting models, the improvement of forecasting skills depends on the ability to accurately estimate the spatial and temporal patterns of soil moisture con
Rahmani, Abdolaziz; Golian, Saeed; Brocca, Luca
Soil moisture (SM) plays a fundamental role for many hydrological applications including water resources, drought analysis, agriculture, and climate variability and extremes. SM is not measured in most parts of Iran and limited measurements do not meet sufficient temporal and spatial resolution. Hence, due to ease of operation, their global coverage and demonstrated accuracy, use of remote sensing SM products is almost the only way for deriving SM information in Iran. In the present research, surface SM (SSM) datasets at six subregions of Iran with different climate conditions were extracted from two satellite-based passive (SMOSL3) and active + passive (ESA CCI SM) microwave observations, and two reanalysis (ERA-Interim and ERA-Interim/Land) products. Time series of averaged monthly mean SSM products and in situ ground precipitation and temperature measurements were derived for each subregion. Results revealed that, generally, all SSM products were in good agreement with each other with correlation coefficients higher than 0.5. The better agreement was found in the Northeast and Southwest region with average correlation values equal to 0.88 and 0.91, respectively. It should be noted that the SSM datasets are characterized by different periods and lengths. Hence, results should be assessed with cautious. Moreover, most SSM products have strong correlations with maximum, minimum and average temperature as well as with total monthly precipitation. Also, trend analysis showed no trend for time series of monthly SSM over all subregions in the two periods 1980-1999 and 2000-2014. The only exceptions were the Southeast subregion for ERA-Interim and Center and Northwest subregions for the ESA CCI SM for which a negative trend was detected for the period 2000-2014. Finally, the Standardized Soil Moisture Index (SSI) calculated from ERA-Interim, ERA-I/Land and ESA CCI SM datasets showed that the Center and Southeast regions suffered from the most severe and longest
Ungar, Stephen G.; Layman, Robert; Campbell, Jeffrey E.; Walsh, John; Mckim, Harlan J.
Daily measurements of the soil dielectric properties at 5 and 10 cm were obtained at five locations throughout the First ISLSCP Field Experiment (FIFE) test site during the 1987 intensive field campaigns (IFCs). An automated vector voltmeter was used to monitor the complex electrical impedance, at 10 MHz, of cylindrical volumes of soil delineated by specially designed soil moisture probes buried at these locations. The objective of this exercise was to test the hypothesis that the soil impedance is sensitive to the moisture content of the soil and that the imaginary part (that is, capacitive reactance) can be used to calculate the volumetric water content of the soil. These measurements were compared with gravimetric samples collected at these locations by the FIFE staff science team.
Hauser, Mathias; Orth, René; Thiery, Wim; Seneviratne, Sonia
Land surface hydrology is an important control of surface weather and climate, especially under extreme dry or wet conditions where it can amplify heat waves or floods, respectively. Prescribing soil moisture in land surface models is a valuable technique to investigate this link between hydrology and climate. It has been used for example to assess the influence of soil moisture on temperature variability, mean and extremes (Seneviratne et al. 2006, 2013, Lorenz et al., 2015). However, perturbing the soil moisture content artificially can lead to a violation of the energy and water balances. Here we present a new method for prescribing soil moisture which ensures water and energy balance closure by using only water from runoff and a reservoir term. If water is available, the method prevents soil moisture decrease below climatological values. Results from simulations with the Community Land Model (CLM) indicate that our new method allows to avoid soil moisture deficits in many regions of the world. We show the influence of the irrigation-supported soil moisture content on mean and extreme temperatures and contrast our findings with that of earlier studies. Additionally, we will assess how long into the 21st century the new method will be able to maintain present-day climatological soil moisture levels for different regions. Lorenz, R., Argüeso, D., Donat, M.G., Pitman, A.J., den Hurk, B.V., Berg, A., Lawrence, D.M., Chéruy, F., Ducharne, A., Hagemann, S. and Meier, A., 2015. Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble. Journal of Geophysical Research: Atmospheres. Seneviratne, S.I., Lüthi, D., Litschi, M. and Schär, C., 2006. Land-atmosphere coupling and climate change in Europe. Nature, 443(7108), pp.205-209. Seneviratne, S.I., Wilhelm, M., Stanelle, T., Hurk, B., Hagemann, S., Berg, A., Cheruy, F., Higgins, M.E., Meier, A., Brovkin, V. and Claussen, M., 2013. Impact of soil moisture
Lellei-Kovács, Eszter; Kovács-Láng, Edit; Botta-Dukát, Zoltán;
Ecosystem carbon exchange is poorly understood in low-productivity, semiarid habitats. Here we studied the controls of soil temperature and moisture on soil respiration in climate change field experiment in a sandy forest-steppe. Soil CO2 efflux was measured monthly from April to November in 2003......–2008 on plots receiving either rain exclusion or nocturnal warming, or serving as ambient control. Based on this dataset, we developed and compared empirical models of temperature and moisture effects on soil respiration. Results suggest that in this semiarid ecosystem the main controlling factor for soil CO2...... efflux is soil temperature, while soil moisture has less, although significant effect on soil respiration. Clear thresholds for moisture effects on temperature sensitivity were identified at 0.6, 4.0 and 7.0vol% by almost each model, which relate well to other known limits for biological activity...
Koster, R. D.; Suarez, M. J.; Tyahla, L.; Houser, Paul (Technical Monitor)
Some studies suggest that the proper initialization of soil moisture in a forecasting model may contribute significantly to the accurate prediction of seasonal precipitation, especially over mid-latitude continents. In order for the initialization to have any impact at all, however, two conditions must be satisfied: (1) the initial soil moisture anomaly must be "remembered" into the forecasted season, and (2) the atmosphere must respond in a predictable way to the soil moisture anomaly. In our previous studies, we identified the key land surface and atmospheric properties needed to satisfy each condition. Here, we tie these studies together with an analysis of an ensemble of seasonal forecasts. Initial soil moisture conditions for the forecasts are established by forcing the land surface model with realistic precipitation prior to the start of the forecast period. As expected, the impacts on forecasted precipitation (relative to an ensemble of runs that do not utilize soil moisture information) tend to be localized over the small fraction of the earth with all of the required land and atmosphere properties.
Igel, Jan; Preetz, Holger
The knowledge of topsoil moisture distribution is an important input for modelling water flow and evapotranspiration which are essential processes in hydrology, meteorology, and agriculture. All these processes involve non-linear effects and thus the small-scale variability of input parameters play an important role. Using smoothed interpolations instead can cause significant biases. Lateral soil moisture distribution can be sensed by different techniques at various scales whereby geophysical methods provide spatial information which closes the gap between point measurements by classical soil scientific methods and measurements on the field or regional scale by remote sensing. Ground-penetrating radar (GPR) can be used to explore soil moisture on the field scale as propagation of electromagnetic waves is correlated to soil water content. By determining the velocity of the ground wave, which is a guided wave travelling along the soil surface, we can sense soil water content. This method has been applied to determine topsoil moisture for several years. We present a new groundwave technique which determines the velocity in between two receiving antennas which enables a higher lateral resolution (approx. 10 cm) compared to classical groundwave technique (half meter and more). We present synthetic data from finite-differences (FD) calculations as well as data from a sandbox experiment carried out under controlled conditions to demonstrate the performance of this method. Further, we carried out field measurements on two sites on a sandy soil which is used as grassland. The measurements were carried out in late summer at dry soil conditions. Soil moisture on the first site shows an isotropic pattern with correlation lengths of approx. 35 cm. We think this natural pattern is governed by rout distribution within the soil and the water uptake of vegetation. On the second site, soil moisture distribution shows a regular stripe pattern. As the land has been used as
Kapilaratne, R. G. C. Jeewantinie; Lu, Minjiao
An effective temperature correction method for dielectric sensors is important to ensure the accuracy of soil water content (SWC) measurements of local to regional-scale soil moisture monitoring networks. These networks are extensively using highly temperature sensitive dielectric sensors due to their low cost, ease of use and less power consumption. Yet there is no general temperature correction method for dielectric sensors, instead sensor or site dependent correction algorithms are employed. Such methods become ineffective at soil moisture monitoring networks with different sensor setups and those that cover diverse climatic conditions and soil types. This study attempted to develop a general temperature correction method for dielectric sensors which can be commonly used regardless of the differences in sensor type, climatic conditions and soil type without rainfall data. In this work an automated general temperature correction method was developed by adopting previously developed temperature correction algorithms using time domain reflectometry (TDR) measurements to ThetaProbe ML2X, Stevens Hydra probe II and Decagon Devices EC-TM sensor measurements. The rainy day effects removal procedure from SWC data was automated by incorporating a statistical inference technique with temperature correction algorithms. The temperature correction method was evaluated using 34 stations from the International Soil Moisture Monitoring Network and another nine stations from a local soil moisture monitoring network in Mongolia. Soil moisture monitoring networks used in this study cover four major climates and six major soil types. Results indicated that the automated temperature correction algorithms developed in this study can eliminate temperature effects from dielectric sensor measurements successfully even without on-site rainfall data. Furthermore, it has been found that actual daily average of SWC has been changed due to temperature effects of dielectric sensors with a
F. E. Moyano; N. Vasilyeva; L. Bouckaert; Cook, F; J. Craine; J. Curiel Yuste; Don, A.; Epron, D.; Formanek, P; A. Franzluebbers; Ilstedt, U; T. Kätterer; Orchard, V.; Reichstein, M.; Rey, A.
Soil moisture is of primary importance for predicting the evolution of soil carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the soil functions used to model the heterotrophic respiration response to moisture have limited empirical support and introduce an uncertainty of at least 4% in global soil carbon stock predictions by 2100. The necessity of improving the rep...
F. E. Moyano; N. Vasilyeva; L. Bouckaert; Cook, F; J. Craine; J. Curiel Yuste; Don, A.; Epron, D.; Formanek, P; A. Franzluebbers; Ilstedt, U; T. Kätterer; Orchard, V.; Reichstein, M.; Rey, A.
Soil moisture is of primary importance for predicting the evolution of soil carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the soil functions used to model the heterotrophic respiration response to moisture have limited empirical support and introduce an uncertainty of at least 4 % in global soil carbon stock predictions by 2100. The necessity of improving t...
Timbal, B.; Power, S.; Colman, R.; Viviand, J.; Lirola, S.
Interannual variations of Australian climate are strongly linked to the El Niño-Southern Oscillation (ENSO) phenomenon. However, the impact of other mechanisms on prediction, such as atmosphere-land surface interactions, has been less frequently investigated. Here, the impact of soil moisture variability on interannual climate variability and predictability is examined using the Bureau of Meteorology Research Centre atmospheric general circulation model. Two sets of experiments are run, each with five different initial conditions. In the first set of experiments, soil moisture is free to vary in response to atmospheric forcing in each experiment according to a set of simple prognostic equations. A potential predictability index is computed as the ratio of the model's internal variability to its external forced variability. This estimates the level of predictability obtained assuming perfect knowledge of future ocean surface temperatures. A second set of five experiments with prescribed soil moisture is performed. A comparison between these two sets of experiments reveals that fluctuations of soil moisture increase the persistence, the variance, and the potential predictability of surface temperature and rainfall. The interrelationship between these two variables is also strongly dependent upon the soil water content. Results are particularly marked over Australia in this model. A novel feature of this study is the focus on the effectiveness of ENSO-based statistical seasonal forecasting over Australia. Forecasting skill is shown to be crucially dependent upon soil moisture variability over the continent. In fact, surface temperature forecasts in this manner are not possible without soil moisture variability. This result suggests that a better representation of land-surface interaction has the potential to increase the skill of seasonal prediction schemes.
Azorin-Molina, César; Cerdà, Artemi; Vicente-Serrano, Sergio M.
Soil moisture plays a key role on the recently abandoned agriculture land where determine the recovery and the erosion rates (Cerdà, 1995), on the soil water repellency degree (Bodí et al., 2011) and on the hydrological cycle (Cerdà, 1999), the plant development (García Fayos et al., 2000) and the seasonality of the geomorphological processes (Cerdà, 2002). Moreover, Soil moisture is a key factor on the semiarid land (Ziadat and Taimeh, 2013), on the productivity of the land (Qadir et al., 2013) and soils treated with amendments (Johnston et al., 2013) and on soil reclamation on drained saline-sodic soils (Ghafoor et al., 2012). In previous study (Azorin-Molina et al., 2013) we investigated the intraannual evolution of soil moisture in soils under different land managements in the Valencia region, Eastern Spain, and concluded that soil moisture recharges are much controlled by few heavy precipitation events; 23 recharge episodes during 2012. Most of the soil moisture recharge events occurred during the autumn season under Back-Door cold front situations. Additionally, sea breeze front episodes brought isolated precipitation and moisture to mountainous areas within summer (Azorin-Molina et al., 2009). We also evidenced that the intraanual evolution of soil moisture changes are positively and significatively correlated (at pMataix-Solera, J., Doerr, S.H. & Cerdà, A. 2011. The wettability of ash from burned vegetation and its relationship to Mediterranean plant species type, burn severity and total organic carbon content. Geoderma, 160, 599-607. 10.1016/j.geoderma.2010.11.009 Cerdà, A. 1995. Soil moisture regime under simulated rainfall in a three years abandoned field in Southeast Spain. Physics and Chemistry of The Earth, 20 (3-4), 271-279. Cerdà, A. 1999. Seasonal and spatial variations in infiltration rates in badland surfaces under Mediterranean climatic conditions. Water Resources Research, 35 (1) 319-328. Cerdà, A. 2002. The effect of season and parent
Chew, C. C.; Small, E. E.; Larson, K. M.
Data from NSF's EarthScope Plate Boundary Observatory (PBO), and similar GPS networks worldwide, can be used to monitor the terrestrial water cycle. GPS satellites transmit L-band microwave signals, which are affected by water at Earth's surface. GPS signals take two paths: (1) the "direct" signal travels from the satellite to the antenna; (2) the "reflected" signal interacts with the Earth's surface before travelling to the antenna. The direct signal is used by geophysicists to measure position of the antenna, while the effects of reflected signals are generally ignored. Recently, our group has developed a technique to retrieve terrestrial water cycle variables from GPS reflections. The sensing footprint is intermediate in scale between in situ and remote sensing observations. Soil moisture, snow depth, and an index of vegetation water content are estimated from data collected at over 400 PBO sites. The products are updated daily and are available online. This presentation provides a description of the soil moisture product. Near-surface soil moisture is estimated at more than 100 sites in the PBO H2O network. At each site, a geodetic-quality GPS antenna records the interference pattern between the direct and ground-reflected GPS signals in signal-to-noise ratio (SNR) interferograms. The ground-reflected GPS signal is altered by changes in the permittivity of the ground surface, which is primarily a function of its water content. Temporal changes in the SNR interferogram, primarily its phase, are indicative of changes in soil moisture. SNR phase data are converted to soil moisture using relationships determined using an electrodynamic model. Soil moisture is not retrieved when there is snow or significant vegetation (> ~1 kg m-2 of vegetation water), as both affect SNR phase. When there is moderate vegetation, a correction is applied to the phase data before conversion to soil moisture. The effect of vegetation on SNR phase and the exact relationship between SNR
Hagemann, Stefan; Stacke, Tobias
Soil moisture-atmosphere feedback effects play an important role in several regions of the globe. For some of these regions, soil moisture memory may contribute significantly to the development of the regional climate. Identifying those regions can help to improve predictability in seasonal to decadal climate forecasts. The present study investigates how different setups of the soil hydrology scheme affect soil moisture memory simulated by the global climate model of the Max Planck Institute for Meteorology (MPI-M), ECHAM6/JSBACH. First, the standard setup applied for the CMIP5 exercise is used, in which soil water is represented by a single soil moisture reservoir. Second, a new five soil layer hydrology scheme is utilized where the previous bucket soil moisture now corresponds to the root zone soil moisture. In the standard setup, transpiration may access the whole soil moisture that is exceeding the wilting point over vegetated areas. However, in the five layer scheme, soil water below the root zone cannot be accessed by transpiration directly, but only be transported upwards into the root zone by diffusion following the Richard's equation. Thus, this below the root zone, which is not present in the standard setup, can act as buffer in the transition between wet and dry periods. A second notable difference between the two setups is the formulation of bare soil evaporation. In the standard setup, it may only occur if the whole soil moisture bucket is almost completely saturated, while in the new setup, it depends only on the saturation of the upper most soil layer. As the latter is much thinner than the root zone (bucket), bare soil evaporation can occur more frequently, especially after rainfall events. For the second setup, two further variants are considered: one where the bare soil evaporation was modified and one where a new parameter dataset of soil water holding capacities was used. Soil moisture memory of the different setups will be analysed from global
Tana Wood; M. Detto; W.L. Silver
Precipitation and temperature are important drivers of soil respiration. The role of moisture and temperature are generally explored at seasonal or inter-annual timescales; however, significant variability also occurs on hourly to daily time-scales. We used small (1.54 m2), throughfall exclusion shelters to evaluate the role soil moisture and temperature as temporal...
Soil characteristics (texture and moisture) are typically assumed to be initially constant when performing simulations with the Regional Atmospheric Modeling System (RAMS). Soil texture is spatially homogeneous and time-independent, while soil moisture is often spatially homogeneous initially, but time-dependent. This report discusses the conversion of a global data set of Food and Agriculture Organization (FAO) soil types to RAMS soil texture and the subsequent modifications required in RAMS to ingest this information. Spatial variations in initial soil moisture obtained from the National Center for Environmental Predictions (NCEP) large-scale models are also introduced. Comparisons involving simulations over the southeastern United States for two different time periods, one during warmer, more humid summer conditions, and one during cooler, dryer winter conditions, reveals differences in surface conditions related to increases or decreases in near-surface atmospheric moisture con tent as a result of different soil properties. Three separate simulation types were considered. The base case assumed spatially homogeneous soil texture and initial soil moisture. The second case assumed variable soil texture and constant initial soil moisture, while the third case allowed for both variable soil texture and initial soil moisture. The simulation domain was further divided into four geographically distinct regions. It is concluded there is a more dramatic impact on thermodynamic variables (surface temperature and dewpoint) than on surface winds, and a more pronounced variability in results during the summer period. While no obvious trends in surface winds or dewpoint temperature were found relative to observations covering all regions and times, improvement in surface temperatures in most regions and time periods was generally seen with the incorporation of variable soil texture and initial soil moisture.
Carroll, Thomas; Knapp, David E. (Editor); Hall, Forrest G. (Editor); Peck, Eugene L.; Smith, David E. (Technical Monitor)
The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-6 team collected several data sets related to the moisture content of soil and overlying humus layers. This data set contains percent soil moisture ground measurements. These data were collected on the ground along the various flight lines flown in the Southern Study Area (SSA) and Northern Study Area (NSA) during 1994 by the gamma ray instrument. The data are available in tabular ASCII files. The HYD-06 ground gravimetric soil moisture data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).
W. B. Anderson
Full Text Available Drought in East Africa is a recurring phenomenon with significant humanitarian impacts. Given the steep climatic gradients, topographic contrasts, general data scarcity, and, in places, political instability that characterize the region, there is a need for spatially distributed, remotely derived monitoring systems to inform national and international drought response. At the same time, the very diversity and data scarcity that necessitate remote monitoring also make it difficult to evaluate the reliability of these systems. Here we apply a suite of remote monitoring techniques to characterize the temporal and spatial evolution of the 2010–2011 Horn of Africa drought. Diverse satellite observations allow for evaluation of meteorological, agricultural, and hydrological aspects of drought, each of which is of interest to different stakeholders. Focusing on soil moisture, we apply triple collocation analysis (TCA to three independent methods for estimating soil moisture anomalies to characterize relative error between products and to provide a basis for objective data merging. The three soil moisture methods evaluated include microwave remote sensing using the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E sensor, thermal remote sensing using the Atmosphere-Land Exchange Inverse (ALEXI surface energy balance algorithm, and physically-based land surface modeling using the Noah land surface model. It was found that the three soil moisture monitoring methods yield similar drought anomaly estimates in areas characterized by extremely low or by moderate vegetation cover, particularly during the below-average 2011 long rainy season. Systematic discrepancies were found, however, in regions of moderately low vegetation cover and high vegetation cover, especially during the failed 2010 short rains. The merged, TCA-weighted soil moisture composite product takes advantage of the relative strengths of each method, as judged by the
W. B. Anderson
Full Text Available Drought in East Africa is a recurring phenomenon with significant humanitarian impacts. Given the steep climatic gradients, topographic contrasts, general data scarcity, and, in places, political instability that characterize the region, there is a need for spatially distributed, remotely derived monitoring systems to inform national and international drought response. At the same time, the very diversity and data scarcity that necessitate remote monitoring also make it difficult to evaluate the reliability of these systems. Here we apply a suite of remote monitoring techniques to characterize the temporal and spatial evolution of the 2010–2011 Horn of Africa drought. Diverse satellite observations allow for evaluation of meteorological, agricultural, and hydrological aspects of drought, each of which is of interest to different stakeholders. Focusing on soil moisture, we apply triple collocation analysis (TCA to three independent methods for estimating soil moisture anomalies to characterize relative error between products and to provide a basis for objective data merging. The three soil moisture methods evaluated include microwave remote sensing using the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E sensor, thermal remote sensing using the Atmosphere-Land Exchange Inverse (ALEXI surface energy balance algorithm, and physically based land surface modeling using the Noah land surface model. It was found that the three soil moisture monitoring methods yield similar drought anomaly estimates in areas characterized by extremely low or by moderate vegetation cover, particularly during the below-average 2011 long rainy season. Systematic discrepancies were found, however, in regions of moderately low vegetation cover and high vegetation cover, especially during the failed 2010 short rains. The merged, TCA-weighted soil moisture composite product takes advantage of the relative strengths of each method, as judged by the
Gibon, François; Pellarin, Thierry; Alhassane, Agali; Traoré, Seydou; Baron, Christian
West Africa is greatly vulnerable, especially in terms of food sustainability. Mainly based on rainfed agriculture, the high variability of the rainy season strongly impacts the crop production driven by the soil water availability in the soil. To monitor this water availability, classical methods are based on daily precipitation measurements. However, the raingauge network suffers from the poor network density in Africa (1/10000km2). Alternatively, real-time satellite-derived precipitations can be used, but they are known to suffer from large uncertainties which produce significant error on crop yield estimations. The present study proposes to use root soil moisture rather than precipitation to evaluate crop yield variations. First, a local analysis of the spatiotemporal impact of water deficit on millet crop production in Niger was done, from in-situ soil moisture measurements (AMMA-CATCH/OZCAR (French Critical Zone exploration network)) and in-situ millet yield survey. Crop yield measurements were obtained for 10 villages located in the Niamey region from 2005 to 2012. The mean production (over 8 years) is 690 kg/ha, and ranges from 381 to 872 kg/ha during this period. Various statistical relationships based on soil moisture estimates were tested, and the most promising one (R>0.9) linked the 30-cm soil moisture anomalies from mid-August to mid-September (grain filling period) to the crop yield anomalies. Based on this local study, it was proposed to derive regional statistical relationships using 30-cm soil moisture maps over West Africa. The selected approach was to use a simple hydrological model, the Antecedent Precipitation Index (API), forced by real-time satellite-based precipitation (CMORPH, PERSIANN, TRMM3B42). To reduce uncertainties related to the quality of real-time rainfall satellite products, SMOS soil moisture measurements were assimilated into the API model through a Particular Filter algorithm. Then, obtained soil moisture anomalies were
Engman, E. T.; Jackson, T. J.; Schmugge, T. J.
A series of six microwave data collection flights for measuring soil moisture were made over a small 7.8 square kilometer watershed in southwestern Minnesota. These flights were made to provide 100 percent coverage of the basin at a 400 m resolution. In addition, three flight lines were flown at preselected areas to provide a sample of data at a higher resolution of 60 m. The low level flights provide considerably more information on soil moisture variability. The results are discussed in terms of reproducibility, spatial variability and temporal variability, and their implications for hydrologic modeling.
Le Vine, D. M.; Griffis, A.; Swift, C. T.; Jackson, T. J.
The measurement of soil moisture from space requires putting relatively large microwave antennas in orbit. Aperture synthesis, an interferometric technique for reducing the antenna aperture needed in space, offers the potential for a practical means of meeting these requirements. An aircraft prototype, electronically steered thinned array L-band radiometer (ESTAR), has been built to develop this concept and to demonstrate its suitability for the measurement of soil moisture. Recent flights over the Walnut Gulch Watershed in Arizona show good agreement with ground truth and with measurements with the Pushbroom Microwave Radiometer (PBMR).
Cook, David R [Argonne National Lab. (ANL), Argonne, IL (United States)
The soil temperature and moisture profile system (STAMP) provides vertical profiles of soil temperature, soil water content (soil-type specific and loam type), plant water availability, soil conductivity, and real dielectric permittivity as a function of depth below the ground surface at half-hourly intervals, and precipitation at one-minute intervals. The profiles are measured directly by in situ probes at all extended facilities of the SGP climate research site. The profiles are derived from measurements of soil energy conductivity. Atmospheric scientists use the data in climate models to determine boundary conditions and to estimate the surface energy flux. The data are also useful to hydrologists, soil scientists, and agricultural scientists for determining the state of the soil. The STAMP system replaced the SWATS system in early 2016.
Shinoda, Masato; Nandintsetseg, Banzragch
Continental climate is established as a result of a complex interplay between the atmosphere and various land-surface systems such as the biosphere, soil, hydrosphere, and cryosphere. These systems function as climate memory, allowing the maintenance of interannual atmospheric anomalies. In this paper, we present new observational evidence of an interseasonal moisture memory mechanism mediated by the land surface that is manifested in the coupled cold and arid climate of Mongolia. Interannual anomalies of soil moisture and vegetation due to rainfall during a given summer are maintained through the freezing winter months to the spring, acting as an initial condition for subsequent summer land-surface and rainfall conditions. Both the soil moisture and vegetation memories were prominent over the eastern part of the Mongolian steppe zone (103-112°E and 46-50°N). That is, the cold-season climate with low evapotranspiration and strong soil freezing acts to prolong the decay time scale of autumn soil moisture anomalies to 8.2 months that is among the longest in the world. The vegetation also has a memory of the similar time scale, likely because the large rootstock of the perennial plants dominant in the Mongolian steppe may remain alive, retain belowground biomass anomalies during the winter, and have an impact on the initial vegetation growth during the spring.
Full Text Available Surface soil moisture content is highly variable in both space and time. Remote sensing can provide an effective methodology for mapping surface moisture content over large areas but ground based measurements are required to test its reliability and to calibrate retrieval algorithms. Recently, we had the opportunity to design and perform an experiment aimed at jointly acquiring measurements of surface soil water content at various locations and remotely sensed hyperspectral data. The area selected for the experiment is located in central Umbria and it extends for 90km2. For the area, detailed lithological and multi-temporal landslide inventory maps were available. We identified eight plots where measurements of soil water content were made using a Time Domain Reflectometer (TDR. The plots range in size from 100m2 to 600m2, and cover a variety of topographic and morphological settings. The TDR measurements were conducted during four days, on 5 April, 15 April, 2 May and 3 May 2004. On 3 May the NERC airborne CASI 2 acquired the hyperspectral data. Preliminary analysis concerning the matching between the landslides and the soil moisture were reported. Statistical and geostatistical analysis investigating the spatial-temporal soil moisture distribution were performed. These results will be compared with the data of surface temperature obtained from the remotely sensed hyperspectral sensor.
Suo, Gai-Di; Xie, Yong-Sheng; Tian, Fei; Chuai, Jun-Feng; Jing, Min-Xiao
For the problem of low water and fertilizer use efficiency caused by nitrate nitrogen lea- ching into deep soil layer and soil desiccation in dryland apple orchard, characteristics of soil moisture were investigated by means of hand tamping in order to find a new approach in improving the water and fertilizer use efficiency in the apple orchard. Two artificial impermeable layers of red clay and dark loessial soil were built in soil, with a thickness of 3 or 5 cm. Results showed that artificial impermeable layers with the two different thicknesses were effective in reducing or blocking water infiltration into soil and had higher seepage controlling efficiency. Seepage controlling efficiency for the red clay impermeable layer was better than that for the dark loessial soil impermeable layer. Among all the treatments, the red clay impermeable layer of 5 cm thickness had the highest bulk density, the lowest initial infiltration rate (0.033 mm · min(-1)) and stable infiltration rate (0.018 mm · min(-1)) among all treatments. After dry-wet alternation in summer and freezing-thawing cycle in winter, its physiochemical properties changed little. Increase in years did not affect stable infiltration rate of soil water. The red clay impermeable layer of 5 cm thickness could effectively increase soil moisture content in upper soil layer which was conducive to raise the water and nutrient use efficiency. The approach could be applied to the apple production of dryland orchard.
In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on soil moisture monitoring byGlobal Navigation Satellite System Reflected signals(GNSS-R) at the Valencia Anchor Station is introduced. L-band microwaves have very good advantages in soil moisture remote sensing, for being unaffected by clouds and the atmosphere, and for the ability to penetrate vegetation. During this experimental campaign, the ESA GNSS-R Oceanpal antenna was installed on the same tower as the ESA ELBARA-II passive microwave radiometer, both measuring instruments having similar field of view. This experiment is fruitfully framed within the ESA - China Programme of Collaboration on GNSS-R. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and two down-looking antennas for receiving LHCP (left-hand circular polarisation) and RHCP (right-hand circular polarisation) reflected signals from the soil surface. We could collect data from the three different antennas through the two channels of Oceanpal and, in addition, calibration could be performed to reduce the impact from the differing channels. Reflectivity was thus measured and soil moisture could be retrieved by the L- MEB (L-band Microwave Emission of the Biosphere) model considering the effect of vegetation optical thickness and soil roughness. By contrasting GNSS-R and ELBARA-II radiometer data, a negative correlation existed between reflectivity measured by GNSS-R and brightness temperature measured by the radiometer. The two parameters represent reflection and absorption of the soil. Soil moisture retrieved by both L-band remote sensing methods shows good agreement. In addition, correspondence with in-situ measurements and rainfall is also good.
Su, Chun-Hsu; Ryu, Dongryeol; Western, Andrew; Wagner, Wolfgang
The use of satellite soil moisture data for scientific and operational hydrologic, meteorological and climatological applications is advancing rapidly due to increasing capability and temporal coverage of current and future missions. However evaluation studies of various existing remotely-sensed soil moisture products from these space-borne microwave sensors, which include AMSR-E (Advanced Microwave Scanning Radiometer) on Aqua satellite, SMOS (Soil Moisture and Ocean Salinity) mission and ASCAT (Advanced Scatterometer) on MetOp-A satellite, found them to be significantly different from in-situ observations, showing large biases and different dynamic ranges and temporal patterns (e.g., Albergel et al., 2012; Su et al., 2012). Moreover they can have different error profiles in terms of bias, variance and correlations and their performance varies with land surface characteristics (Su et al., 2012). These severely impede the effort to use soil moisture retrievals from multiple sensors concurrently in land surface modelling, cross-validation and multi-satellite blending. The issue of systematic errors present in data sets should be addressed prior to renormalisation of the data for blending and data assimilation. Triple collocation estimation technique has successfully yielded realistic error estimates (Scipal et al., 2008), but this method relies on availability of large number of coincident data from multiple independent satellite data sets. In this work, we propose, i) a conceptual framework for distinguishing systematic periodic errors in the form of false spectral resonances from non-systematic errors (stochastic noise) in remotely-sensed soil moisture data in the frequency domain; and ii) the use of digital filters to reduce the variance- and correlation-related errors in satellite data. In this work, we focus on the VUA-NASA (Vrije Universiteit Amsterdam with NASA) AMSR-E, CATDS (Centre National d'Etudes Spatiales, CNES) SMOS and TUWIEN (Vienna University of
Collison, E. J.; Riutta, T.; Slade, E. M.
Changing climatic conditions and habitat fragmentation are predicted to alter the soil moisture conditions of temperate forests. It is not well understood how the soil macrofauna community will respond to changes in soil moisture, and how changes to species diversity and community composition may affect ecosystem functions, such as litter decomposition and soil fluxes. Moreover, few studies have considered the interactions between the abiotic and biotic factors that regulate soil processes. Here we attempt to disentangle the interactive effects of two of the main factors that regulate soil processes at small scales - moisture and macrofauna assemblage composition. The response of assemblages of three common temperate soil invertebrates (Glomeris marginata Villers, Porcellio scaber Latreille and Philoscia muscorum Scopoli) to two contrasting soil moisture levels was examined in a series of laboratory mesocosm experiments. The contribution of the invertebrates to the leaf litter mass loss of two common temperate tree species of contrasting litter quality (easily decomposing Fraxinus excelsior L. and recalcitrant Quercus robur L.) and to soil CO2 fluxes were measured. Both moisture conditions and litter type influenced the functioning of the invertebrate assemblages, which was greater in high moisture conditions compared with low moisture conditions and on good quality vs. recalcitrant litter. In high moisture conditions, all macrofauna assemblages functioned at equal rates, whereas in low moisture conditions there were pronounced differences in litter mass loss among the assemblages. This indicates that species identity and assemblage composition are more important when moisture is limited. We suggest that complementarity between macrofauna species may mitigate the reduced functioning of some species, highlighting the importance of maintaining macrofauna species richness.
Engman, Edwin T.
Many hydrologic processes display a unique signature that is detectable with microwave remote sensing. These signatures are in the form of the spatial and temporal distributions of surface soil moisture and portray the spatial heterogeneity of hydrologic processes and properties that one encounters in drainage basins. The hydrologic processes that may be detected include ground water recharge and discharge zones, storm runoff contributing areas, regions of potential and less than potential ET, and information about the hydrologic properties of soils and heterogeneity of hydrologic parameters. Microwave remote sensing has the potential to detect these signatures within a basin in the form of volumetric soil moisture measurements in the top few cm. These signatures should provide information on how and where to apply soil physical parameters in distributed and lumped parameter models and how to subdivide drainage basins into hydrologically similar sub-basins.
Jiang, Mingliang; Lv, Mouchao; Deng, Zhong; Zhai, Guoliang
In a variety of agricultural activities, such as irrigation scheduling and nutrient management, soil water content is regarded as an essential parameter. Either power supply or long-distance cable is hardly available within field scale. For the necessity of monitoring soil water dynamics at field scale, this study presents a wireless soil moisture sensor based on the impedance transform of the frequency domain. The sensor system is powered by solar energy, and the data can be instantly transmitted by wireless communication. The sensor electrodes are embedded into the bottom of a supporting rod so that the sensor can measure soil water contents at different depths. An optimal design with time executing sequence is considered to reduce the energy consumption. The experimental results showed that the sensor is a promising tool for monitoring moisture in large-scale farmland using solar power and wireless communication.
Jadghuber, Thomas; Hajnsek, Irena; Weiß, Thomas; Papathanassiou, Konstantinos P.
Soil moisture under vegetation cover was estimated by a polarimetric, iterative, generalized, hybrid decomposition and inversion approach at multiple frequencies (X-, C- and L-band). Therefore the algorithm, originally designed for longer wavelength (L-band), was adapted to deal with the short wavelength scattering scenarios of X- and C-band. The Integral Equation Method (IEM) was incorporated together with a pedo-transfer function of Dobson et al. to account for the peculiarities of short wavelength scattering at X- and C-band. DLR's F-SAR system acquired fully polarimetric SAR data in X-, C- and L-band over the Wallerfing test site in Lower Bavaria, Germany in 2014. Simultaneously, soil and vegetation measurements were conducted on different agricultural test fields. The results indicate a spatially continuous inversion of soil moisture in all three frequencies (inversion rates >92%), mainly due to the careful adaption of the vegetation volume removal including a physical constraining of the decomposition algorithm. However, for X- and C-band the inversion results reveal moisture pattern inconsistencies and in some cases an incorrectly high inversion of soil moisture at X-band. The validation with in situ measurements states a stable performance of 2.1- 7.6vol.% at L-band for the entire growing period. At C- and X-band a reliable performance of 3.7-13.4vol.% in RMSE can only be achieved after distinct filtering (X- band) leading to a loss of almost 60% in spatial inversion rate. Hence, a robust inversion for soil moisture estimation under vegetation cover can only be conducted at L-band due to a constant availability of the soil signal in contrast to higher frequencies (X- and C-band).
Full Text Available Synthetic Aperture Radar (SAR imaging system is one of the most effective way for Earth observation. The aim of this study is to present the preliminary results about estimating soil moisture using L-band Synthetic Aperture Radar (SAR data. Full-polarimetric (HH, HV, VV, VH ALOS-2 data, acquired on 22.04.2016 with the incidence angle of 30.4o, were used in the study. Simultaneously with the SAR acquisition, in-situ soil moisture samples over bare agricultural lands were collected and evaluated using gravimetric method. Backscattering coefficients for all polarizations were obtained and linear regression analysis was carried out with in situ moisture measurements. The best correlation coefficient was observed with VV polarization. Cross-polarized backscattering coefficients were not so sensitive to soil moisture content. In the study, it was observed that soil moisture maps can be retrieved with the accuracy about 14% (RMSE.
Baumgardner, M. F., Silva, L. F., Biehl, L. L., and E. R. Stoner, 1985, "The Reflec- tance Properties of Soils," Advances in Agronomy , 38:1-44...retrieval algorithms for the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) on the Aqua satellite. AMSR-E is a passive... Advanced Microwave Scanning Radiometer Soil Moisture Products," IEEE Transactions on Geoscience and Remote Sensing, 48(12):4256- 4272. Jackson, T. J
Carranza, Coleen; van der Ploeg, Martine; Ritsema, Coen
Spatio-temporal patterns of soil moisture have been studied mostly for inputs in land surface models for weather and climate predictions. Remote sensing techniques for estimation of soil moisture have been explored because of the good spatial coverage at different scales. Current available satellite data provide surface soil moisture as microwave systems only measure soil moisture content up to 5cm soil depth. The OWAS1S project will focus on estimation of soil moisture from freely available Sentinel-1 datasets for operational water management in agricultural areas. As part of the project, it is essential to develop spatio-temporal methods to estimate root zone soil moisture from surface soil moisture. This will be used for crop water availability and trafficability in selected agricultural fields in the Netherlands. A network of single capacitance sensors installed per field will provide continuous measurements of soil moisture in the study area. Ground penetrating radar will be used to measure soil moisture variability within a single field for different time periods. During wetter months, optimal conditions for traffic will be assessed using simultaneous soil strength and soil moisture measurements. Towards water deficit periods, focus is on the relation (or the lack thereof) between surface soil moisture and root zone soil moisture to determine the amount of water for crops. Spatio-temporal distribution will determine important physical controls for surface and root zone soil moisture and provide insights for root-zone soil moisture. Existing models for field scale soil-water balance and data assimilation methods (e.g. Kalman filter) will be combined to estimate root zone soil moisture. Furthermore, effects of root development on soil structure and soil hydraulic properties and subsequent effects on trafficability and crop water availability will be investigated. This research project has recently started, therefore we want to present methods and framework of
Sugathan, Neena; Biju, V.; Renuka, G.
Half hourly data of soil moisture content, soil temperature, solar irradiance, and reflectance are measured during April 2010 to March 2011 at a tropical station, viz., Astronomical Observatory, Thiruvananthapuram, Kerala, India (76°59'E longitude and 8°29'N latitude). The monthly, seasonal and seasonal mean diurnal variation of soil moisture content is analyzed in detail and is correlated with the rainfall measured at the same site during the period of study. The large variability in the soil moisture content is attributed to the rainfall during all the seasons and also to the evaporation/movement of water to deeper layers. The relationship of surface albedo on soil moisture content on different time scales are studied and the influence of solar elevation angle and cloud cover are also investigated. Surface albedo is found to fall exponentially with increase in soil moisture content. Soil thermal diffusivity and soil thermal conductivity are also estimated from the subsoil temperature profile. Log normal dependence of thermal diffusivity and power law dependence of thermal conductivity on soil moisture content are confirmed.
Neena Sugathan; V Biju; G Renuka
Half hourly data of soil moisture content, soil temperature, solar irradiance, and reflectance are measured during April 2010 to March 2011 at a tropical station, viz., Astronomical Observatory, Thiruvananthapuram, Kerala, India (76° 59’E longitude and 8°29’N latitude). The monthly, seasonal and seasonal mean diurnal variation of soil moisture content is analyzed in detail and is correlated with the rainfall measured at the same site during the period of study. The large variability in the soil moisture content is attributed to the rainfall during all the seasons and also to the evaporation/movement of water to deeper layers. The relationship of surface albedo on soil moisture content on different time scales are studied and the influence of solar elevation angle and cloud cover are also investigated. Surface albedo is found to fall exponentially with increase in soil moisture content. Soil thermal diffusivity and soil thermal conductivity are also estimated from the subsoil temperature profile. Log normal dependence of thermal diffusivity and power law dependence of thermal conductivity on soil moisture content are confirmed.
Passive microwave remote sensing of soil moisture has matured over the past decade as a result of the Advanced Microwave Scanning Radiometer (AMSR) program of JAXA. This program has resulted in improved algorithms that have been supported by rigorous validation. Access to the products and the valida...
Yueh, Simon H.; Entekhabi, Dara; O'Neill, Peggy; Njoku, Eni; Entin, Jared K.
The Soil Moisture Active Passive (SMAP) observatory was launched January 31, 2015, and its L-band radiometer and radar instruments became operational since mid-April 2015. The SMAP radiometer has been operating flawlessly, but the radar transmitter ceased operation on July 7. This paper provides a status summary of the calibration and validation of the SMAP instruments and the quality assessment of its soil moisture and freeze/thaw products. Since the loss of the radar in July, the SMAP project has been conducting two parallel activities to enhance the resolution of soil moisture products. One of them explores the Backus Gilbert optimum interpolation and de-convolution techniques based on the oversampling characteristics of the SMAP radiometer. The other investigates the disaggregation of the SMAP radiometer data using the European Space Agency's Sentinel-1 C-band synthetic radar data to obtain soil moisture products at about 1 to 3 kilometers resolution. In addition, SMAP's L-band data have found many new applications, including vegetation opacity, ocean surface salinity and hurricane ocean surface wind mapping. Highlights of these new applications will be provided.
In a 19-year twin experiment for the Red-Arkansas river basin we assimilate synthetic surface soil moisture retrievals into the NASA Catchment land surface model. We demonstrate how poorly specified model and observation error parameters affect the quality of the assimilation products. In particul...
Gruber, A.; Su, C.-H.; Zwieback, S.; Crow, W.; Dorigo, W.; Wagner, W.
To date, triple collocation (TC) analysis is one of the most important methods for the global-scale evaluation of remotely sensed soil moisture data sets. In this study we review existing implementations of soil moisture TC analysis as well as investigations of the assumptions underlying the method. Different notations that are used to formulate the TC problem are shown to be mathematically identical. While many studies have investigated issues related to possible violations of the underlying assumptions, only few TC modifications have been proposed to mitigate the impact of these violations. Moreover, assumptions, which are often understood as a limitation that is unique to TC analysis are shown to be common also to other conventional performance metrics. Noteworthy advances in TC analysis have been made in the way error estimates are being presented by moving from the investigation of absolute error variance estimates to the investigation of signal-to-noise ratio (SNR) metrics. Here we review existing error presentations and propose the combined investigation of the SNR (expressed in logarithmic units), the unscaled error variances, and the soil moisture sensitivities of the data sets as an optimal strategy for the evaluation of remotely-sensed soil moisture data sets.
Roshani, E.; Berg, A. A.; Lindsay, J.
Soil Moisture Active Passive satellite (SMAP) is scheduled for launch on Oct 2014. Global efforts are underway for establishment of soil moisture monitoring networks for both the pre- and post-launch validation and calibration of the SMAP products. In 2012 the SMAP Validation Experiment, SMAPVEX12, took place near Carman Manitoba, Canada where nearly 60 fields were sampled continuously over a 6 week period for soil moisture and several other parameters simultaneous to remotely sensed images of the sampling region. The locations of these sampling sites were mainly selected on the basis of accessibility, soil texture, and vegetation cover. Although these criteria are necessary to consider during sampling site selection, they do not guarantee optimal site placement to provide the most efficient representation of the studied area. In this analysis a method for optimization of sampling locations is presented which combines the state-of-art multi-objective optimization engine (non-dominated sorting genetic algorithm, NSGA-II), with the kriging interpolation technique to minimize the number of sampling sites while simultaneously minimizing the differences between the soil moisture map resulted from the kriging interpolation and soil moisture map from radar imaging. The algorithm is implemented in Whitebox Geospatial Analysis Tools, which is a multi-platform open-source GIS. The optimization framework is subject to the following three constraints:. A) sampling sites should be accessible to the crew on the ground, B) the number of sites located in a specific soil texture should be greater than or equal to a minimum value, and finally C) the number of sampling sites with a specific vegetation cover should be greater than or equal to a minimum constraint. The first constraint is implemented into the proposed model to keep the practicality of the approach. The second and third constraints are considered to guarantee that the collected samples from each soil texture categories
C. E. Gabriel
Full Text Available Temperature and moisture are primary environmental drivers of soil organic matter (SOM decomposition, and the development of a better understanding fo their roles in this process through depth in soils is needed. The objective of this research is to independently assess the roles of temperature and moisture in driving heterotrophic soil respiration for shallow and deep soils in a temperate red spruce forest. Minimally disturbed soil cores from shallow (0–25 cm and deep (25–50 cm layers were extracted from a 20 yr old red spruce stand and were then transferred to a climate chamber where they were incubated for 3 months under constant and diurnal temperature regimes. Soils were subjected to different watering treatments representing a full range of water contents. Temperature, moisture, and CO2 surface flux were assessed daily for all soils and continuously on a subset of the microcosms. The results from this study indicate that shallow soils dominate the contribution to surface flux (90% and respond more predictably to moisture than deep soils. An optimum moisture range of 0.15 to 0.60 water-filled pore space was observed for microbial SOM decomposition in shallow cores across which a relatively invariant temperature sensitivity was observed. For soil moisture conditions experienced by most field sites in this region, flux-temperature relationships alone can be used to reasonably estimate heterotrophic respiration, as in this range moisture does not alter flux, with the exception of rewetting events along the lower part of this optimal range. Outside this range, however, soil moisture determines SOM decomposition rates.
Zhang, Fang; Guo, Sheng-Li; Zou, Jun-Liang; Li, Ze; Zhang, Yan-Jun
On the loess plateau, summer fallow season is a hot rainy time with intensive soil microbe activities. To evaluate the response of soil respiration to soil moisture, temperature, and N fertilization during this period is helpful for a deep understanding about the temporal and spatial variability of soil respiration and its impact factors, then a field experiment was conducted in the Changwu State Key Agro-Ecological Experimental Station, Shaanxi, China. The experiment included five N application rates: unfertilized 0 (N0), 45 (N45), 90 (N90), 135(N135), and 180 (N180) kg x hm(-2). The results showed that at the fallow stage, soil respiration rate significantly enhanced from 1.24 to 1.91 micromol x (m2 x s)(-1) and the average of soil respiration during this period [6.20 g x (m2 x d)(-1)] was close to the growing season [6.95 g x (m2 x d)(-1)]. The bivariate model of soil respiration with soil water and soil temperature was better than the single-variable model, but not so well as the three-factor model when explaining the actual changes of soil respiration. Nitrogen fertilization alone accounted for 8% of the variation soil respiration. Unlike the single-variable model, the results could provide crucial information for further research of multiple factors on soil respiration and its simulation.
Full Text Available Robust estimation of soil moisture using microwave remote sensing depends on extensive ground sampling for calibration and validation of the data. Soil surface sealing is a frequent phenomenon in dry environments. It modulates soil moisture close to the soil surface and, thus, has the potential to affect the retrieval of soil moisture from microwave remote sensing and the validation of these data based on ground observations. We addressed this issue using a physically-based modeling approach that accounts explicitly for surface sealing at the hillslope scale. Simulated mean soil moisture at the respective layers corresponding to both the ground validation probe and the radar beam’s typical effective penetration depth were considered. A cyclic pattern was found in which, as compared to an unsealed profile, the seal layer intensifies the bias in validation during rainfall events and substantially reduces it during subsequent drying periods. The analysis of this cyclic pattern showed that, accounting for soil moisture dynamics at the soil surface, the optimal time for soil sampling following a rainfall event is a few hours in the case of an unsealed system and a few days in the case of a sealed one. Surface sealing was found to increase the temporal stability of soil moisture. In both sealed and unsealed systems, the greatest temporal stability was observed at positions with moderate slope inclination. Soil porosity was the best predictor of soil moisture temporal stability, indicating that prior knowledge regarding the soil texture distribution is crucial for the application of remote sensing validation schemes.
Papachristodoulou, C.; Ioannides, K.; Pavlides, S.
Radon is a naturally occurring radioactive gas that is generated in the Earth's crust and is free to migrate through soil and be released to the atmosphere. Due to its unique properties, soil gas radon has been established as a powerful tracer used for a variety of purposes, such as exploring uranium ores, locating geothermal resources and hydrocarbon deposits, mapping geological faults, predicting seismic activity or volcanic eruptions and testing atmospheric transport models. Much attention has also been given to the radiological health hazard posed by increased radon concentrations in the living and working environment. In order to exploit radon profiles for geophysical purposes and also to predict its entry indoors, it is necessary to study its transport through soils. Among other factors, the importance of soil moisture in such studies has been largely highlighted and it is widely accepted that any measurement of radon transport parameters should be accompanied by a measurement of the soil moisture content. In principle, validation of transport models in the field is encountered by a large number of uncontrollable and varying parameters; laboratory methods are therefore preferred, allowing for experiments to be conducted under well-specified and uniform conditions. In this work, a laboratory technique has been applied for studying the effect of soil moisture content on radon diffusion. A vertical diffusion chamber was employed, in which radon was produced from a 226Ra source, was allowed to diffuse through a soil column and was finally monitored using a silicon surface barrier detector. By solving the steady-state radon diffusion equation, diffusion coefficients (D) were determined for soil samples of varying moisture content (m), from null (m=0) to saturation (m=1). For dry soil, a D value of 4.1×10-7 m2s-1 was determined, which increased moderately by a factor of ~3 for soil with low moisture content, i.e. up to m ~0.2. At higher water fractions, a decrease
US Fish and Wildlife Service, Department of the Interior — This is the Ottawa National Wildlife Refuge Soil and Moisture Conservation Plan. The Soil and Moisture Conservation Plan outlines the relationship between refuge...
Christiansen, Jesper; Levy-Booth, David; Barker, Jason; Prescott, Cindy; Grayston, Sue
Soil moisture is a key driver of methane (CH4) fluxes in forest soils, both of the net uptake of atmospheric CH4 and emission from the soil. Climate and land use change will alter spatial patterns of soil moisture as well as temporal variability impacting the net CH4 exchange. The impact on the resultant net CH4 exchange however is linked to the underlying spatial and temporal distribution of the soil microbial communities involved in CH4 cycling as well as the response of the soil microbial community to environmental changes. Significant progress has been made to target specific CH4 consuming and producing soil organisms, which is invaluable in order to understand the microbial regulation of the CH4 cycle in forest soils. However, it is not clear as to which extent soil moisture shapes the structure, function and abundance of CH4 specific microorganisms and how this is linked to observed net CH4 exchange under contrasting soil moisture regimes. Here we report on the results from a research project aiming to understand how the CH4 net exchange is shaped by the interactive effects soil moisture and the spatial distribution CH4 consuming (methanotrophs) and producing (methanogens). We studied the growing season variations of in situ CH4 fluxes, microbial gene abundances of methanotrophs and methanogens, soil hydrology, and nutrient availability in three typical forest types across a soil moisture gradient in a temperate rainforest on the Canadian Pacific coast. Furthermore, we conducted laboratory experiments to determine whether the net CH4 exchange from hydrologically contrasting forest soils responded differently to changes in soil moisture. Lastly, we modelled the microbial mediation of net CH4 exchange along the soil moisture gradient using structural equation modeling. Our study shows that it is possible to link spatial patterns of in situ net exchange of CH4 to microbial abundance of CH4 consuming and producing organisms. We also show that the microbial
Wang, Tiejun; Franz, Trenton E.; Zlotnik, Vitaly A.; You, Jinsheng; Shulski, Martha D.
Due to its complex interactions with various processes and factors, soil moisture exhibits significant spatial variability across different spatial scales. In this study, a modeling approach and field observations were used to examine the soil control on the relationship between mean (θ bar) and standard deviation (σθ) of soil moisture content. For the numerical experiments, a 1-D vadose zone model along with van Genuchten parameters generated by pedotransfer functions was used for simulating soil moisture dynamics under different climate and surface conditions. To force the model, hydrometeorological and physiological data that spanned over three years from five research sites within the continental US were used. The modeling results showed that under bare surface conditions, different forms of the θ bar -σθ relationship as observed in experimental studies were produced. For finer soils, a positive θ bar -σθ relationship gradually changed to an upward convex and a negative one from arid to humid conditions; whereas, a positive relationship existed for coarser soils, regardless of climatic conditions. The maximum σθ for finer soils was larger under semiarid conditions than under arid and humid conditions, while the maximum σθ for coarser soils increased with increasing precipitation. Moreover, vegetation tended to reduce θ bar and σθ, and thus affected the θ bar -σθ relationship. A sensitivity analysis was also conducted to examine the controls of different van Genuchten parameters on the θ bar -σθ relationship under bare surface conditions. It was found that the residual soil moisture content mainly affected σθ under dry conditions, while the saturated soil moisture content and the saturated hydraulic conductivity largely controlled σθ under wet conditions. Importantly, the upward convex θ bar -σθ relationship was mostly caused by the shape factor n that accounts for pore size distribution. Finally, measured soil moisture data from a
Milad Jajarmizadeh; Sobri bin Harun; Shamsuddin Shahid; Shatirah Akib; Mohsen Salarpour
The soil and water assessment tool (SWAT) is a physically based model that is used extensively to simulate hydrologic processes in a wide range of climates around the world. SWAT uses spatial hydrometeorological data to simulate runoff through the computation of a retention curve number. The objective of the present study was to compare the performance of two approaches used for the calculation of curve numbers in SWAT, that is, the Revised Soil Moisture Index (SMI), which is based on previou...
Munoz-Sabater, Joaquín; de Rosnay, Patricia; Albergel, Clément; Isaksen, Lars
Soil moisture is a crucial variable for numerical weather prediction. Accurate, global initialization of soil moisture is obtained through data assimilation systems. However analyses depend largely on the way observations and background errors are defined. In this paper a wide range of short experiments with contrasted specification of the observation error and soil moisture background were conducted. As observations, screen-level variables and brightness temperatures from the Soil Moisture and Ocean Salinity (SMOS) mission were used. The region of interest was North America given the good availability of in-situ observations. The impact of these experiments on soil moisture and the atmospheric layer near the surface were evaluated. The results highlighted the importance of assimilating sensitive observations to soil moisture for air temperature and humidity forecasts. The benefits on the soil water content were more noticeable with increasing the SMOS observation error and with the introduction of soil texture dependency in the soil moisture background error.
V. Sheikh; E. van Loon; R. Hessel; V. Jetten
This study conducts a broad sensitivity analysis, taking into account the influence of initial soil moisture content in two soil layers, layer depths, event properties, and two infiltration models. A distributed hydrology and soil erosion model (LISEM) is used. Using the terrain data from the Catsop
Remote sensing of soil moisture has reached a level of maturity and accuracy for which the retrieved products can be used to improve hydrological and meteorological applications. In this study, the soil moisture product from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) is used...
Lee, Eunhyung; Kim, Sanghyun
Soil moisture is an important factor for understanding hydrological and solute transport processes at the hillslope scale. The selection of representative points for soil moisture measurement has been explored to investigate temporal variation of average soil moisture with minimum costs and maximum stability. The optimal selection of soil moisture monitoring points has been reevaluated to address hillslope hydrological processes and the impacts of seasonal differences. An alternative method to select soil moisture measurement points was developed to adequately represent immediate soil moisture response patterns to sequential rainfall events. To address the seasonal features of rainfall events and their impacts on soil moisture redistribution along the hillslope, field soil moisture data were collected at 49 points for three seasons over periods of 25 days with bi-hourly monitoring intervals. For effective characterization of soil moisture variation, soil moisture datasets were classified using cluster analysis based on Euclidean similarity. Points delineated using the proposed method not only provide better stability of average soil moistures but also adequately represent the response patterns of soil moisture to rainfall events on the hillslope.
The mobile COsmic-ray Soil Moisture Observing System (COSMOS) rover may be useful for validating satellite-based estimates of near surface soil moisture, but the accuracy with which the rover can measure 0-5 cm soil moisture has not been previously determined. Our objectives were to calibrate and va...
Ferraris, Stefano; Canone, Davide; Previati, Maurizio; Brunod, Christian; Ratto, Sara; Cauduro, Marco
There is large mismatch in spatial scale between the climate and meteorological model grid, and the scale of soil and vegetation measurements. Remote sensing data can help to fit the model scale, but they cannot provide rootzone data. In this work some soil moisture datasets are analysed for the sake of providing larger scale estimation of soil moisture and water and energy fluxes. The first dataset refers to a plain site near Torino, where measurements are taken since 1997 (Baudena et al., 2012), and a mountain site close to the town. The second one is a dataset in the mountains of Valle d'Aosta (Brocca et al., 2013), where 4 years of data are available. The use of digital elevation models and vegetation maps is shown in this work. Some soil processes (e.g. Whalley et al., 2012) are usually disregarded, but in this work their possible impact is considered. References L. Brocca, A. Tarpanelli, T. Moramarco, F. Melone, S.M. Ratto, M. Cauduro, S. Ferraris, N. Berni, F. Ponziani, W. Wagner, T. Melzer (2013). Soil Moisture Estimation in Alpine Catchments through Modeling and Satellite Observations VADOSE ZONE JOURNAL, vol. 8-2, p. 1-10, doi:10.2136/vzj2012.0102 M. Baudena, I. Bevilacqua, D. Canone, S. Ferraris, M. Previati, A. Provenzale (2012). Soil water dynamics at a midlatitude test site: Field measurements and box modeling approaches. JOURNAL OF HYDROLOGY, vol. 414-415, p. 329-340, ISSN: 0022-1694, doi: 10.1016/j.jhydrol.2011.11.009 W.R. Whalley, G.P. Matthews, S. Ferraris (2012). The effect of compaction and shear deformation of saturated soil on hydraulic conductivity. SOIL & TILLAGE RESEARCH, vol. 125, p. 23-29, ISSN: 0167-1987
WANG Jian-ming; SHI Jian-cheng; SHAO Yun; LIU Wei
The ERS-1/2 wind scatterometer (WSC) has a low resolution cell of about 50 km but provides a high repetition rate (<4 d) and can make measurements at multiple incidence angles. In order to estimate effective surface reflectivity (related to soil moisture content) over bare soil using WSC data, an original methodology based on the advance integral equation model (AIEM) is presented, which takes advantage of its multiple view angular characteristics. This method includes two steps. First, a simplified two-parameter surface scattering model is calibrated by AIEM simulated-database over a wide parameter space. Second, regression analyses are carried out using the simulated database to build the relation between those parameters of our model at different incident angles from two observations of Mid and Fore beams. From the model simulated database, our technique works quite well in estimating Γ0. The possibility of applying the model to retrieve soil moisture is investigated using a set of data collected from the Intensive Observation Period field campaign in 1998 of the Asian Monsoon Experiment Tibet (GAME-Tibet). The retrieved values obtained for the bare land surface are consistent with ground measurements collected in these areas and the correlation coefficient between retrieved soil moisture and the measured one reaches 0.65.
Zhang, L.; Ji, L.; Wylie, B.K.
The relationships between satellite-derived vegetation indices (VIs) and soil moisture are complicated because of the time lag of the vegetation response to soil moisture. In this study, we used a distributed lag regression model to evaluate the lag responses of VIs to soil moisture for grasslands and shrublands at Soil Climate Analysis Network sites in the central and western United States. We examined the relationships between Moderate Resolution Imaging Spectroradiometer (MODIS)-derived VIs and soil moisture measurements. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) showed significant lag responses to soil moisture. The lag length varies from 8 to 56 days for NDVI and from 16 to 56 days for NDWI. However, the lag response of NDVI and NDWI to soil moisture varied among the sites. Our study suggests that the lag effect needs to be taken into consideration when the VIs are used to estimate soil moisture. ?? 2011 Taylor & Francis.
Full Text Available Validation of satellite-based soil moisture products is necessary to provide users with an assessment of their accuracy and reliability and to ensure quality of information. A key step in the validation process is to upscale point-scale, ground-based soil moisture observations to satellite-scale pixel averages. When soil moisture shows high spatial heterogeneity within pixels, a strategy which captures the spatial characteristics is essential for the upscaling process. In addition, temporal variation in soil moisture must be taken into account when measurement times of ground-based and satellite-based observations are not the same. We applied spatio-temporal regression block kriging (STRBK to upscale in situ soil moisture observations collected as time series at multiple locations to pixel averages. STRBK incorporates auxiliary information such as maps of vegetation and land surface temperature to improve predictions and exploits the spatio-temporal correlation structure of the point-scale soil moisture observations. In addition, STRBK also quantifies the uncertainty associated with the upscaled soil moisture which allows bias detection and significance testing of satellite-based soil moisture products. The approach is illustrated with a real-world application for upscaling in situ soil moisture observations for validating the Polarimetric L-band Multi-beam Radiometer (PLMR retrieved soil moisture product in the Heihe Water Allied Telemetry Experimental Research experiment (HiWATER. The results show that STRBK yields upscaled soil moisture predictions that are sufficiently accurate for validation purposes. Comparison of the upscaled predictions with PLMR soil moisture observations shows that the root-mean-squared error of the PLMR soil moisture product is about 0.03 m3·m−3 and can be used as a high-resolution soil moisture product for watershed-scale soil moisture monitoring.
Baldwin, D. C.; Miller, D. A.; Singha, K.; Davis, K. J.; Smithwick, E. A.
Newly defined relationships between remotely sensed soil moisture and soil hydraulic parameters were used to develop fine-scale (100 m) maps of root-zone soil moisture (RZSM) content at the regional scale on a daily time-step. There are several key outcomes from our research: (1) the first multi-layer regional dataset of soil hydraulic parameters developed from gSSURGO data for hydrologic modeling efforts in the Chequemegon Ecosystem Atmospheric Study (ChEAS) region, (2) the operation and calibration of a new model for estimating soil moisture flow through the root-zone at eddy covariance towers across the U.S. using remotely sensed active and passive soil moisture products, and (3) region-wide maps of estimated root-zone soil moisture content. The project links soil geophysical analytical approaches (pedotransfer functions) to new applications in remote sensing of soil moisture that detect surface moisture (~5 cm depth). We answer two key questions in soil moisture observation and prediction: (1) How do soil hydrologic properties of U.S. soil types quantitatively relate to surface-to-subsurface water loss? And (2) Does incorporation of fine-scale soil hydrologic parameters with remotely sensed soil moisture data provide improved hindcasts of in situ RZSM content? The project meets several critical research needs in estimation of soil moisture from remote sensing. First, soil moisture is known to vary spatially with soil texture and soil hydraulic properties that do not align well with the spatial resolution of current remote sensing products of soil moisture (~ 50 km2). To address this, we leveraged new advances in gridded soil parameter information (gSSURGO) together with existing remotely sensed estimates of surface soil moisture into a newly emerging semi-empirical modeling approach called SMAR (Soil Moisture Analytical Relationship). The SMAR model was calibrated and cross-validated using existing soil moisture data from a portion of AMERIFLUX tower sites and
Moyano, F E; Vasilyeva, N; Bouckaert, L
Soil moisture is of primary importance for predicting the evolution of soil carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the soil functions used to model the heter...... predictions of the response of soil carbon to future climate scenarios will require the integration of soil-dependent moisture-respiration functions coupled with realistic representations of soil water dynamics.......Soil moisture is of primary importance for predicting the evolution of soil carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the soil functions used to model...... the heterotrophic respiration response to moisture have limited empirical support and introduce an uncertainty of at least 4% in global soil carbon stock predictions by 2100. The necessity of improving the representation of this relationship in models has been highlighted in recent studies. Here we present a data...
Greg A. Holt
Full Text Available There is a need for low-cost, high-accuracy measurement of water content in various materials. This study assesses the performance of a new microwave swept frequency domain instrument (SFI that has promise to provide a low-cost, high-accuracy alternative to the traditional and more expensive time domain reflectometry (TDR. The technique obtains permittivity measurements of soils in the frequency domain utilizing a through transmission configuration, transmissometry, which provides a frequency domain transmissometry measurement (FDT. The measurement is comparable to time domain transmissometry (TDT with the added advantage of also being able to separately quantify the real and imaginary portions of the complex permittivity so that the measured bulk permittivity is more accurate that the measurement TDR provides where the apparent permittivity is impacted by the signal loss, which can be significant in heavier soils. The experimental SFI was compared with a high-end 12 GHz TDR/TDT system across a range of soils at varying soil water contents and densities. As propagation delay is the fundamental measurement of interest to the well-established TDR or TDT technique; the first set of tests utilized precision propagation delay lines to test the accuracy of the SFI instrument’s ability to resolve propagation delays across the expected range of delays that a soil probe would present when subjected to the expected range of soil types and soil moisture typical to an agronomic cropping system. The results of the precision-delay line testing suggests the instrument is capable of predicting propagation delays with a RMSE of +/−105 ps across the range of delays ranging from 0 to 12,000 ps with a coefficient of determination of r2 = 0.998. The second phase of tests noted the rich history of TDR for prediction of soil moisture and leveraged this history by utilizing TDT measured with a high-end Hewlett Packard TDR/TDT instrument to directly benchmark the
Zhan, X.; Zheng, W.; Meng, J.; Dong, J.; Ek, M.
Soil moisture is one of the few critical land surface state variables that have long memory to impact the exchanges of water, energy and carbon between the land surface and atmosphere. Accurate information about soil moisture status is thus required for numerical weather, seasonal climate and hydrological forecast as well as for agricultural production forecasts, water management and many other water related economic or social activities. Since the successful launch of ESA's soil moisture ocean salinity (SMOS) mission in November 2009, about 2 years of soil moisture retrievals has been collected. SMOS is believed to be the currently best satellite sensors for soil moisture remote sensing. Therefore, it becomes interesting to examine how the collected SMOS soil moisture data are compared with other satellite-sensed soil moisture retrievals (such as NASA's Advanced Microwave Scanning Radiometer -AMSR-E and EUMETSAT's Advanced Scatterometer - ASCAT)), in situ soil moisture measurements, and how these data sets impact numerical weather prediction models such as the Global Forecast System of NOAA-NCEP. This study implements the Ensemble Kalman filter in GFS to assimilate the AMSR-E, ASCAT and SMOS soil moisture observations after a quantitative assessment of their error rate based on in situ measurements from ground networks around contiguous United States. in situ soil moisture measurements from ground networks (such as USDA Soil Climate Analysis network - SCAN and NOAA's U.S. Climate Reference Network -USCRN) are used to evaluate the GFS soil moisture simulations (analysis). The benefits and uncertainties of assimilating the satellite data products in GFS are examined by comparing the GFS forecasts of surface temperature and rainfall with and without the assimilations. From these examinations, the advantages of SMOS soil moisture data products over other satellite soil moisture data sets will be evaluated. The next step toward operationally assimilating soil moisture
Yueh, Simon; Entekhabi, Dara; O'Neill, Peggy; Entin, Jared
The Soil Moisture Active Passive (SMAP) observatory was launched January 31, 2015, and its L-band radiometer and radar instruments became operational during April 2015. This paper provides a summary of the quality assessment of its baseline soil moisture and freeze/thaw products as well as an overview of new products. The first new product explores the Backus Gilbert optimum interpolation based on the oversampling characteristics of the SMAP radiometer. The second one investigates the disaggregation of the SMAP radiometer data using the European Space Agency's Sentinel-1 C-band synthetic aperture radar (SAR) data to obtain soil moisture products at about 1 to 3 km resolution. In addition, SMAPs L-band data have been found useful for many scientific applications, including depictions of water cycles, vegetation opacity, ocean surface salinity and hurricane ocean surface wind mapping. Highlights of these new applications will be provided.The SMAP soil moisture, freeze/taw state and SSSprovide a synergistic view of water cycle. For example, Fig.7 illustrates the transition of freeze/thaw state, change of soilmoisture near the pole and SSS in the Arctic Ocean fromApril to October in 2015 and 2016. In April, most parts ofAlaska, Canada, and Siberia remained frozen. Melt onsetstarted in May. Alaska, Canada, and a big part of Siberia havebecome thawed at the end of May; some freshwater dischargecould be found near the mouth of Mackenzie in 2016, but notin 2015. The soil moisture appeared to be higher in the Oband Yenisei river basins in Siberia in 2015. As a result,freshwater discharge was more widespread in the Kara Seanear the mouths of both rivers in June 2015 than in 2016. TheNorth America and Siberia have become completely thawedin July. After June, the freshwater discharge from other riversinto the Arctic, indicated by blue, also became visible. Thefreeze-up started in September and the high latitude regionsin North America and Eurasia became frozen. Comparing
Zhang, Donghua; Madsen, Henrik; Ridler, Marc E.; Kidmose, Jacob; Jensen, Karsten H.; Refsgaard, Jens C.
Observed groundwater head and soil moisture profiles are assimilated into an integrated hydrological model. The study uses the ensemble transform Kalman filter (ETKF) data assimilation method with the MIKE SHE hydrological model code. The method was firstly tested on synthetic data in a catchment of less complexity (the Karup catchment in Denmark), and later implemented using data from real observations in a larger and more complex catchment (the Ahlergaarde catchment in Denmark). In the Karup model, several experiments were designed with respect to different observation types, ensemble sizes and localization schemes, to investigate the assimilation performance. The results showed the necessity of using localization, especially when assimilating both groundwater head and soil moisture. The proposed scheme with both distance localization and variable localization was shown to be more robust and provide better results. Using the same assimilation scheme in the Ahlergaarde model, groundwater head and soil moisture were successfully assimilated into the model. The hydrological model with assimilation showed an overall improved performance compared to the model without assimilation.
Koster, R. D.; Walker, G. K.
The time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations, as well as quantifying the inherent predictability of temperature within each ensemble, shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.
Full Text Available The soil and water assessment tool (SWAT is a physically based model that is used extensively to simulate hydrologic processes in a wide range of climates around the world. SWAT uses spatial hydrometeorological data to simulate runoff through the computation of a retention curve number. The objective of the present study was to compare the performance of two approaches used for the calculation of curve numbers in SWAT, that is, the Revised Soil Moisture Index (SMI, which is based on previous meteorological conditions, and the Soil Moisture Condition II (SMCII, which is based on soil features for the prediction of flow. The results showed that the sensitive parameters for the SMI method are land-use and land-cover features. However, for the SMCII method, the soil and the channel are the sensitive parameters. The performances of the SMI and SMCII methods were analyzed using various indices. We concluded that the fair performance of the SMI method in an arid region may be due to the inherent characteristics of the method since it relies mostly on previous meteorological conditions and does not account for the soil features of the catchment.
Song, Kaijun; Zhou, Xiaobing; Fan, Yong
A multilayer soil model is presented for improved estimation of soil moisture content using the first-order small perturbation method (SPM) applied to measurements of radar backscattering coefficient. The total reflection coefficient of the natural bare soil including volume scattering contribution is obtained using the multilayer model. The surface reflection terms in SPM model are replaced by the total reflection coefficient from the multilayer soil surface in estimating soil moisture. The difference between the modified SPM model and the original SPM surface model is that the modified SPM model includes both the surface scattering and the volumetric scattering of the natural bare soil. Both the modified SPM model and the original SPM model are tested in soil moisture retrievals using experimental microwave backscattering coefficient data in the literature. Results show that the mean square errors between the measured data and the values estimated by the modified SPM model from all samples are 5.2%, while errors from the original SPM model are 8.4%. This indicates that the capability of estimating soil moisture by the SPM model is improved when the surface reflection terms are replaced by the total reflection coefficients of multilayer soil model over bare or very sparsely vegetation covered fields.
The soil moisture in Shaanxi Province,a region with complex topography,is simulated using the distributed hydrological model Soil Water Assessment Tool(SWAT).Comparison and contrast of modeled and observed soil moisture show that the SWAT model can reasonably simulate the long-term trend in soil moisture and the spatiotemporal variability of soil moisture in the region.Comparisons to NCEP/NCAR and ERA40 reanalysis of soil moisture show that the trend of variability in soil moisture simulated by SWAT is more consistent with the observed.SWAT model results suggested that high soil moisture in surface soil layers appears in the southern Shaanxi with high vegetation cover,and the Qinling mountainous region with frequent orographic precipitation.In deeper soil layers,high soil moisture appears in the river basins and plains.The regional soil moisture showed a generally decreasing trend on all soil layers from 1951 to 2004,with a stronger and significant decreasing trend in deeper soil layers,especially in the northern parts of the province.
Rodriguez-Fernandez, Nemesio; de Rosnay, Patricia; Albergel, Clement; Aires, Filipe; Prigent, Catherine; Kerr, Yann; Richaume, Philippe; Muñoz-Sabater, Joaquin; Drusch, Matthias
In this study a set of land surface data assimilation (DA) experiments making use of satellite derived soil moisture (SM) are presented. These experiments have two objectives: (1) to test the information content of satellite remote sensing of soil moisture for numerical weather prediction (NWP) models, and (2) to test a simplified assimilation of these data through the use of a Neural Network (NN) retrieval. Advanced Scatterometer (ASCAT) and Soil Moisture and Ocean Salinity (SMOS) data were used. The SMOS soil moisture dataset was obtained specifically for this project training a NN using SMOS brightness temperatures as input and using as reference for the training European Centre for Medium-Range Weather Forecasts (ECMWF) H-TESSEL SM fields. In this way, the SMOS NN SM dataset has a similar climatology to that of the model and it does not present a global bias with respect to the model. The DA experiments are computed using a surface-only Land Data Assimilation System (so-LDAS) based on the HTESSEL land surface model. This system is very computationally efficient and allows to perform long surface assimilation experiments (one whole year, 2012). SMOS NN SM DA experiments are compared to ASCAT SM DA experiments. In both cases, experiments with and without 2 m air temperature and relative humidity DA are discussed using different observation errors for the ASCAT and SMOS datasets. Seasonal, geographical and soil-depth-related differences between the results of those experiments are presented and discussed. The different SM analysed fields are evaluated against a large number of in situ measurements of SM. On average, the SM analysis gives in general similar results to the model open loop with no assimilation even if significant differences can be seen for specific sites with in situ measurements. The sensitivity to observation errors to the SM dataset slightly differs depending on the networks of in situ measurements, however it is relatively low for the tests
An, Yan; Ji, Qiang; Zhao, Shi-xiang; Wang, Xu-dong
Applying biochar to soil has been considered to be one of the important practices in improving soil properties and increasing carbon sequestration. In order to investigate the effects of biochar application on soil aggregates distribution and its organic matter content and soil moisture constant in different size aggregates, various particle-size fractions of soil aggregates were obtained with the dry-screening method. The results showed that, compared to the treatment without biochar (CK), the application of biochar reduced the mass content of 5-8 mm and soil aggregates at 0-10 cm soil horizon, while increased the content of 1-2 mm and 2-5 mm soil aggregates at this horizon, and the content of 1-2 mm aggregates significantly increased along with the rates of biochar application. The mean diameter of soil aggregates was reduced by biochar application at 0-10 cm soil horizon. However, the effect of biochar application on the mean diameter of soil aggregates at 10-20 cm soil horizon was not significant. Compared to CK, biochar application significantly increased soil organic carbon content in aggregates, especially in 1-2 mm aggregates which was increased by > 70% compared to CK. Both the water holding capacity and soil porosity were significantly increased by biochar application. Furthermore, the neutral biochar was more effective than alkaline biochar in increasing soil moisture.
Li, Bonan; Wang, Lixin; Kaseke, Kudzai F.; Li, Lin; Seely, Mary K.
Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months’ continuous daily records of rainfall and soil moisture (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling
Tsegaye, Teferi D.; Inguva, Ramarao; Lang, Roger H.; O'Neill, Peggy E.; Fahsi, Ahmed; Coleman, Tommy L.; Tadesse, Wubishet; Rajbhandari, Narayan B.; Aburemie, Sunnie A.; de Matthaeis, Paolo
The objectives of this study were: to examine the sensitivity of radar backscatter, to estimate soil moisture under a corn plot and to evaluate the effectiveness and sensitivity of a Radiative Transfer Model (RTM), adapted from the earlier work of Njoku and Kong, (1977) in predicting brightness temperature from a grass plot. Microwave radar measurements were collected from plots of different vegetation cover types, vegetation density, and moisture conditions during the Huntsville 1998 field experiment. A large amount of ground data on brightness temperatures, soil moisture, and vegetation characteristics (e.g., biomass, and water content) were collected. The experiments were conducted at Alabama A&M University's, Winfred Thomas Agricultural Research Station, located near Hazel Green, Alabama. Six plots, one 50 X 60 m smooth bare plot, one 50 X 60 m grass plot, and four 30 X 50 m corn plots at full, 2/3, 1/2, and 1/3 densities were used. Radar backscatter data were obtained from a ground based truck mounted radar operating at L, C, and X bands (1.6, 4.75, and 10 GHz) with four linear polarization HH, HV, VV, and VH and two incidence angles (15 and 45 degrees). Soil moisture values were determined using Water Content Reflectometry (WCR). Three types of soil temperature sensors (Infrared Thermometer, Thermistor, and a 4-sensor averaging thermocouple probes) were used. A discrete backscatter approach model and RTM were evaluated. Comparisons between model prediction and experimental observation for HH polarization indicated good agreement for a corn full plot. The direct-reflected scattering coefficient is found to be the most dominant term for both polarization and the backscatter is also highly sensitive to soil moisture. The trends in time variation of brightness temperature are in agreement with the experimental results and the numerical results are within a few percent of the experimental results. The vegetation corrections as estimated by the Jackson and Schmugge
Reichle, Rolf H.; Ardizzone, Joseph V.; Kim, Gi-Kong; Lucchesi, Robert A.; Smith, Edmond B.; Weiss, Barry H.
This is the Product Specification Document (PSD) for Level 4 Surface and Root Zone Soil Moisture (L4_SM) data for the Science Data System (SDS) of the Soil Moisture Active Passive (SMAP) project. The L4_SM data product provides estimates of land surface conditions based on the assimilation of SMAP observations into a customized version of the NASA Goddard Earth Observing System, Version 5 (GEOS-5) land data assimilation system (LDAS). This document applies to any standard L4_SM data product generated by the SMAP Project. The Soil Moisture Active Passive (SMAP) mission will enhance the accuracy and the resolution of space-based measurements of terrestrial soil moisture and freeze-thaw state. SMAP data products will have a noteworthy impact on multiple relevant and current Earth Science endeavors. These include: Understanding of the processes that link the terrestrial water, the energy and the carbon cycles, Estimations of global water and energy fluxes over the land surfaces, Quantification of the net carbon flux in boreal landscapes Forecast skill of both weather and climate, Predictions and monitoring of natural disasters including floods, landslides and droughts, and Predictions of agricultural productivity. To provide these data, the SMAP mission will deploy a satellite observatory in a near polar, sun synchronous orbit. The observatory will house an L-band radiometer that operates at 1.40 GHz and an L-band radar that operates at 1.26 GHz. The instruments will share a rotating reflector antenna with a 6 meter aperture that scans over a 1000 km swath.
Martini, Edoardo; Werban, Ulrike; Zacharias, Steffen; Pohle, Marco; Dietrich, Peter; Wollschläger, Ute
Electromagnetic induction (EMI) methods are widely used for soil mapping, as they allow fast and relatively low-cost surveys of soil apparent electrical conductivity (ECa) at various scales. Soil ECa is well known to be influenced by both the volumetric content and the electrical conductivity (EC) of soil water, as well as by soil temperature and by the volume of the solid particles and their EC. Among other applications, EMI has become widely used to determine soil water content or to study hydrological processes within the field of hydrogeophysics. Although the use of non-invasive EMI for imaging soil spatial properties is very attractive, the dependence of ECa on several properties and states challenges any interpretation with respect to individual soil properties or states such as θ. The major aim of this study was to further investigate the potential of repeated EMI measurements to map soil moisture at the hillslope scale, with particular focus on the temporal variability of the spatial patterns of ECa and soil moisture, respectively, and on the stability of the ECa-soil moisture relationship over time. To this end, we compared time series of EMI measurements with high-resolution soil moisture data for a non-intensively managed hillslope area in the Schäfertal catchment (Central Germany) for which the spatial distribution of soil properties and soil water dynamics were known in detail. Soil water and temperature dynamics were observed in 40 soil profiles at hourly resolution during 14 months using a wireless monitoring network. During this period of time, ECa was mapped on seven occasions using an EM38-DD device. For the investigated site, ECa showed small temporal variations (ranging between 0 and 24 mS/m) whereas the temporal range of soil moisture was very large (from very dry to soil saturation). Furthermore, temporal changes of the spatial pattern of ECa differed from temporal changes of the spatial pattern of soil moisture. The ECa-soil moisture
Claire L. Phillips; Nick Nickerson; David Risk; Zachary E. Kayler; Chris Andersen; Alan Mix; Barbara J. Bond
The carbon isotopic composition (Î´13C) of recently assimilated plant carbon is known to depend on water-stress, caused either by low soil moisture or by low atmospheric humidity. Air humidity has also been shown to correlate with the Î´13C of soil respiration, which suggests indirectly that recently fixed photosynthates...
The response of soil moisture to rainfall events along hillslope transects is an important hydrologic process and a critical component of interactions between soil vegetation and the atmosphere. In this context, the research described in this article addresses the spatial distribution of soil moisture as a function of topography. In order to characterize the temporal variation in soil moisture on a steep mountainous hillside, a transfer function, including a model for noise, was introduced. Soil moisture time series with similar rainfall amounts, but different wetness gradients were measured in the spring and fall. Water flux near the soil moisture sensors was modeled and mathematical expressions were developed to provide a basis for input-output modeling of rainfall and soil moisture using hydrological processes such as infiltration, exfiltration and downslope lateral flow. The characteristics of soil moisture response can be expressed in terms of model structure. A seasonal comparison of models reveals differences in soil moisture response to rainfall, possibly associated with eco-hydrological process and evapotranspiration. Modeling results along the hillslope indicate that the spatial structure of the soil moisture response patterns mainly appears in deeper layers. Similarities between topographic attributes and stochastic model structures are spatially organized. The impact of temporal and spatial discretization scales on parameter expression is addressed in the context of modeling results that link rainfall events and soil moisture.
Piles, Maria; Sánchez, Nilda; Vall-llossera, Mercè; Ballabrera, Joaquim; Martínez, Justino; Martínez-Fernández, José; Camps, Adriano; Font, Jordi
Soil moisture plays an important role in determining the likelihood of droughts and floods that may affect an area. Knowledge of soil moisture distribution as a function of time and space is highly relevant for hydrological, ecological and agricultural applications, especially in water-limited or drought-prone regions. However, measuring soil moisture is challenging because of its high variability; point-scale in-situ measurements are scarce being remote sensing the only practical means to obtain regional- and global-scale soil moisture estimates. The ESA's Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission ever designed to measuring the Earth's surface soil moisture at near daily time scales with levels of accuracy previously not attained. Since its launch in November 2009, significant efforts have been dedicated to validate and fine-tune the retrieval algorithms so that SMOS-derived soil moisture estimates meet the standards required for a wide variety of applications. In this line, the SMOS Barcelona Expert Center (BEC) is distributing daily, monthly, and annual temporal averages of 0.25-deg global soil moisture maps, which have proved useful for assessing drought and water-stress conditions. In addition, a downscaling algorithm has been developed to combine SMOS and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) data into fine-scale (stress conditions. In previous research, SMOS-derived Soil Moisture Anomalies (SSMA), calculated in a ten-day basis, were shown to be in close relationship with well-known drought indices (the Standardized Precipitation Index and the Standardized Precipitation Evapotranspiration Index). In this work, SSMA have been calculated for the period 2010-2013 in representative arid, semi-arid, sub-humid and humid areas across global land biomes. The SSMA reflect the cumulative precipitation anomalies and is known to provide 'memory' in the climate and hydrological system; the water retained in the soil
Gaius R. Shaver; A. E. Giblin; K. J. Nadelhoffer; K. K. Thieler; M. R. Downs; J. A. Laundre; E. B. Rastetter
.... This study describes how soil C loss is related to temperature, moisture and chemical composition of organic matter in Alaskan tundra soils, including soils that were fertilized annually for 8 years prior to the study...
Soil crusting may have significant impacts on infiltration, runoff generation and erosion in agricultural lands or semi-arid and arid soils. The previous investigations on soil crusting were often conducted under simulated rainfall conditions. This study aims to evaluate the effects of soil crusting on soil moisture during inter-storm periods and soil and water losses during storm periods under natural rainfalls. The study site was located in the Loess Plateau of China. Four plots with a uniform slope and size were selected. Soil crusts were kept intact on the two plots throughout the monitoring periods of 1999 and 2000,but were broken after each rain storm event on the other two plots. Soil moisture was measured on all plots with an interval of one week at three depths and total event runoff and sediment discharges were measured in each storm. It was found that no marked difference in soil moisture and runoff exists between the crusted and uncrusted plots. This is because the rapid development of new crusts on the uncrusted plots during the storm events. However, the erosion rate on the uncrusted plots was significantly higher than that on the crusted plots, which was mainly caused by the disturbance of the surface soils on the uncrusted plots. This study questions the effectiveness of a common agricultural practice in the Loess Plateau, hoeing lands after rainfall, in reducing runoff and erosion.
Qiu, Jianxiu; Gao, Quanzhou; Wang, Sheng; Su, Zhenrong
In this study, soil moisture trend during 1996-2010 in China was analyzed based on three soil moisture data sets, namely microwave-based multi-satellite surface soil moisture product released from European Space Agency's Climate Change Initiative (ESA CCI), ERA-Interim/Land reanalysis, and in-situ measurements collected from the nationwide agro-meteorological network. Taking the in-situ soil moisture as reference, it is found that ESA CCI generally captured soil moisture trend more accurately than ERA-Interim/Land did. From the spatial distribution of trend analysis results, it is seen that significant decreasing trend for summer soil moisture in northwestern China and northern Inner Mongolia, as well as the significant increasing trend for autumn soil moisture in northern China were identified by both ESA CCI and ERA-Interim/Land. This is in alignment with results from gauge-based precipitation provided by Institute of Geographic Sciences and Natural Resources Research (IGSNRR) and satellite-based precipitation from Tropical Rainfall Measuring Mission (TRMM). However, disagreements in derived trends between ESA CCI, ERA-Interim/Land and IGSNRR were observed in the southwest and north of China, especially in major irrigation regions, such as the oases in northern Xinjiang and large areas in Sichuan province. Prominent difference between soil moisture and precipitation exhibited in the extensively irrigated Huang-Huai-Hai Plain. The spatial coincidence between significantly wetting areas (identified by ESA CCI) and heavily irrigated areas, as well as the grid-based Student's t-test sampling from various irrigation levels revealed that the observed discrepancy was caused by massive anthropogenic interference in this region. Results indicate that, for regions with great magnitude of human interference, modules considering actual irrigation practice are crucial for successful modeling of soil moisture and capturing the long-term trend. Furthermore, results could
A.SUBHANI; LIAOMIN; 等
The effects of individual and combined additions of urea(100μg N g-1soil) and insecticide (triazophos at field rate,FR) under different moisture levles of air-dried soil(AD),50% of water-holding capacity(WHC),100%,WHC and flooded soil(FS) on some selected soil properties in a paddy field soil were examined in a laboratory incubation study.The results indicated that after 21-day incubation at 25℃ ，the different moisture levels led to significant changes in the parameters studied,Flooding of soil with distilled waer significantly increased the electron transport system(ETS)/dehydrogenase activity and phenol content of the soil compared to the other moisture levels,while protein and phospholipis behaved differently at varied moisture levels with or without the addition of urea and /or triazophos.Increased ETS activity was observed with N addition at higher moisture levels thile insecticide incorporation decreased it at all moisture levels as compared to the control(moisture only).The phenol contents slightly decreasd and increased with N and insecticide applications ,respectively,The soil protein contents were found to be unaffected among all the soil treatents at all moisture levels.The soil protein contents were found to be unaffected among all the soil treatments at all moisture levels.However,among different moisture levels,reduced quantities of proteins were estimated at 50% WHC ,suggesting more N-mineralization.Lower quantities of soil biomass phospholipids,among all treatments,were recored at higher moisture levels(100% WHC and FS) than at the loer levels,An overall slight enhancement in phospholipid contents with N and small reduction with insecticide addition,respectively,was noticed against the untreated soil.The toxictiy of fertilizer and insecticide decreased as the soil moisture contents increased,suggesting rapid degradation of agrochemicals.
J. A. Yeakley
Full Text Available Soil moisture gradients along hillslopes in humid watersheds, although indicated by vegetation gradients and by studies using models, have been difficult to confirm empirically. While soil properties and topographic features are the two general physio-graphic factors controlling soil moisture on hillslopes, studies have shown conflicting results regarding which factor is more important. The relative importance of topographic and soil property controls was examined in an upland forested watershed at the Coweeta Hydrologic Laboratory in the southern Appalachian mountains. Soil moisture was measured along a hillslope transect with a mesic-to-xeric forest vegetation gradient over a period spanning precipitation extremes. The hillslope was transect instrumented with a time domain reflectometry (TDR network at two depths. Soil moisture was measured during a severe autumn drought and subsequent winter precipitation recharge. In the upper soil depth (0-30 cm, moisture gradients persisted throughout the measurement period, and topography exerted dominant control. For the entire root zone (0-90 cm, soil moisture gradients were found only during drought. Control on soil moisture was due to both topography and storage before drought. During and after recharge, variations in soil texture and horizon distribution exerted dominant control on soil moisture content in the root zone (0-90 cm. These results indicate that topographic factors assert more control over hillslope soil moisture during drier periods as drainage progresses, while variations in soil water storage properties are more important during wetter periods. Hillslope soil moisture gradients in southern Appalachian watersheds appear to be restricted to upper soil layers, with deeper hillslope soil moisture gradients occurring only with sufficient drought.
Montaldo, N.; Albertson, J. D.
Meteorological and hydrological forecasting models share soil moisture as a critical boundary condition. Partitioning of received energy at the land surface depends directly on this variable, as does the partitioning of rainfall into its possible routes over and through the soil. In Land Surface Models (LSMs) the temporal dynamic of soil moisture spatial variability is a fundamental issue in large-scale flux predictions. From remote sensing observations soil moisture values are averaged in the horizontal over rather large regions (pixels). The averaging areas will be getting even larger as we move from aircraft mounted sensors to satellite mounting. These data are to be used ultimately to estimate spatial averages of other processes that depend on soil moisture, such as, runoff generation, drainage, evaporation, sensible heat fluxes, crop yield, microbial activity, etc. Consequently, the LSMs have to predict spatial averaged flux over large region from average values of the soil moisture. But soil moisture variances affect flux predictions, which depend nonlinearly on soil moisture, because many of the other processes possess distinct threshold aspects to their nonlinear dependence on soil moisture. Through application of well-developed Reynolds averaging rules from fluid mechanics to the equation of Richards and Darcy-Buckingham, we write a conservation equation for the horizontal variance of soil moisture. And, through closure arguments, we are able to describe the individual terms that produce and destroy spatial variance through time in terms of the mean soil moisture state and other observable system properties such as vegetation and soil properties variability. Finally, we calculate land surface fluxes from second order Taylor expansion, using our soil moisture variance closure model, and the other observable system properties. In this work, we demonstrate significant improvements in land surface large-scale flux predictions using the proposed soil moisture
Montaldo, N.; Albertson, J. D.
Meteorological and hydrological forecasting models share soil moisture as a critical boundary condition. Partitioning of received energy at the land surface depends di- rectly on this variable, as does the partitioning of rainfall into its possible routes over and through the soil. In Land Surface Models (LSMs) the temporal dynamic of soil moisture spatial variability is a fundamental issue in large-scale flux predictions. From remote sensing observations soil moisture values are averaged in the horizontal over rather large regions (pixels). The averaging areas will be getting even larger as we move from aircraft mounted sensors to satellite mounting. These data are to be used ultimately to estimate spatial averages of other processes that depend on soil moisture, such as, runoff generation, drainage, evaporation, sensible heat fluxes, crop yield, mi- crobial activity, etc. Consequently, the LSMs have to predict spatial averaged flux over large region from average values of the soil moisture. But soil moisture variances af- fect flux predictions, which depend nonlinearly on soil moisture, because many of the other processes possess distinct threshold aspects to their nonlinear dependence on soil moisture. Through application of well-developed Reynolds averaging rules from fluid mechanics to the equation of Richards and Darcy-Buckingham, we write a con- servation equation for the horizontal variance of soil moisture. And, through closure arguments, we are able to describe the individual terms that produce and destroy spa- tial variance through time in terms of the mean soil moisture state and other observable system properties such as vegetation and soil properties variability. Finally, we calcu- late land surface fluxes from second order Taylor expansion, using our soil moisture variance closure model, and the other observable system properties. In this work, we demonstrate significant improvements in land surface large-scale flux predictions us- ing the proposed
Chen, Xiang-Bi; Wang, Ai-Hua; Hu, Le-Ning; Huang, Yuan; Li, Yang; He, Xun-Yang; Su, Yi-Rong
Typical paddy and upland soils were collected from a hilly subtropical red-soil region. 14C-labeled dissolved organic carbon (14C-DOC) was extracted from the paddy and upland soils incorporated with 14C-labeled straw after a 30-day (d) incubation period under simulated field conditions. A 100-d incubation experiment (25 degrees C) with the addition of 14C-DOC to paddy and upland soils was conducted to monitor the dynamics of 14C-DOC mineralization under different soil moisture conditions [45%, 60%, 75%, 90%, and 105% of the field water holding capacity (WHC)]. The results showed that after 100 days, 28.7%-61.4% of the labeled DOC in the two types of soils was mineralized to CO2. The mineralization rates of DOC in the paddy soils were significantly higher than in the upland soils under all soil moisture conditions, owing to the less complex composition of DOC in the paddy soils. The aerobic condition was beneficial for DOC mineralization in both soils, and the anaerobic condition was beneficial for DOC accumulation. The biodegradability and the proportion of the labile fraction of the added DOC increased with the increase of soil moisture (45% -90% WHC). Within 100 days, the labile DOC fraction accounted for 80.5%-91.1% (paddy soil) and 66.3%-72.4% (upland soil) of the cumulative mineralization of DOC, implying that the biodegradation rate of DOC was controlled by the percentage of labile DOC fraction.
Mathias, Simon A.; Sorensen, James P. R.; Butler, Adrian P.
Estimating groundwater recharge rates is important for water resource management studies. Modeling approaches to forecast groundwater recharge typically require observed historic data to assist calibration. It is generally not possible to observe groundwater recharge rates directly. Therefore, in the past, much effort has been invested to record soil moisture content (SMC) data, which can be used in a water balance calculation to estimate groundwater recharge. In this context, SMC data is measured at different depths and then typically integrated with respect to depth to obtain a single set of aggregated SMC values, which are used as an estimate of the total water stored within a given soil profile. This article seeks to investigate the value of such aggregated SMC data for conditioning groundwater recharge models in this respect. A simple modeling approach is adopted, which utilizes an emulation of Richards' equation in conjunction with a soil texture pedotransfer function. The only unknown parameters are soil texture. Monte Carlo simulation is performed for four different SMC monitoring sites. The model is used to estimate both aggregated SMC and groundwater recharge. The impact of conditioning the model to the aggregated SMC data is then explored in terms of its ability to reduce the uncertainty associated with recharge estimation. Whilst uncertainty in soil texture can lead to significant uncertainty in groundwater recharge estimation, it is found that aggregated SMC is virtually insensitive to soil texture.
Ouyang, Wei; Xu, Xueting; Hao, Zengchao; Gao, Xiang
In recent years, nitrogen (N) loss from upland fields has become one of the most important sources for agricultural nonpoint source (NPS) pollution. Understanding the relationships between soil hydrological processes and N loss in NPS pollution is vital for controlling the agricultural NPS pollution in upland fields. The objective of this study was to analyze the interaction of N loss with different moisture conditions in the freeze-thaw zone. The semi-distributed hydrologic model Soil and Water Assessment Tool (SWAT) was used in this study to simulate runoff and different forms of N loss, which provided a basis for analyzing characteristics of N loss in the study region. Results showed that the soil moisture content was an important factor affecting N loss in the study region. Different forms of N loss were also analyzed and it was found that N loss occurred primarily in the form of organic-N, which is likely due to the dominant role of erosion-induced pollution. This study provides useful information for preventing NPS pollution within the study region.
Full Text Available Soil compaction causes deleterious effects on physical and mechanical proprieties of agricultural soils. In order to investigate the effect of soil moisture content and tractor wheeling intensity on traffic-induced soil compaction, this study was carried out on a field with clay loam soil. Soil dry bulk density and hydraulic conductivity as well as emergence percentage of corn seedlings and dry mass of the sampled mature plants were considered the dependent variables of the experiment. Independent variables consisted of soil moisture content with five levels (12, 15, 17, 19, and 21%, traffic intensity with three levels (four, two, and zero passes of tractor wheel (tractor model: John Deere 3350 from the entire area of the plot, and soil sampling depth with three levels (0-10, 10-20, and 20-30 cm. According to the results of this study, gradual increase in soil water content generally resulted in an increase in soil bulk density; moreover, increasing the tractor wheeling intensity from 0 to 4 passes increased bulk density by 13%. Furthermore, the driest soil water content had the highest and the wettest soil water content had the lowest emergence percentage of corn seedlings among the treatments; moreover, traffic intensity treatment inversely affected the emergence percentage of corn seedlings and the dry mass of mature plants. To sum up, these results indicate that, for improving water permeability and reducing dry bulk density of the examined clay loam soil, as well as better emergence of corn seedlings and ultimately increasing crop yield, it is recommended to avoid wheeling when soil moisture content is high, reduce the number of machinery wheel passes from the farm as low as possible, and restrict the wheel passes to fixed strips along the field, whenever possible.
Chew, C.; Small, E. E.; Larson, K. M.; Nievinski, F. G.; Zavorotny, V.
GPS-Interferometric Reflectometry (GPS-IR) uses ground-reflected GPS signals to estimate near-surface soil moisture. Data are recorded by high-precision, geodetic-quality GPS antennas/receivers, for example those that comprise NSF's EarthScope Plate Boundary Observatory. The ground reflections used in GPS-IR are representative of a ~1000 m2 area around an antenna. As the dielectric constant of the surface fluctuates, the phase, amplitude, and frequency of signal-to-noise ratio (SNR) data recorded by the GPS unit change. Based on field observations, it has been shown that these characteristics of the SNR data are sensitive to shallow soil moisture. A single-scattering, electrodynamic model was used to simulate SNR output over a range of soil moisture conditions. All simulations were for a 2.4 m tall antenna surrounded by a surface free of roughness or vegetation. The model was run using three different types of soil moisture profiles: constant with depth, monotonic variations with depth, and observed profiles interpolated from field data. For all profiles, amplitude, phase shift, and frequency changes were calculated from simulated SNR data. The three GPS metrics are well correlated with soil moisture content modeled at the soil surface because a majority of the incident microwave energy is reflected at the air-soil interface. When surface soil is dry relative to the underlying soil, GPS metrics are also strongly correlated with soil moisture averaged over the top 5 cm of the soil column. The relationship between GPS metrics and soil moisture averaged over 5 cm is not as strong when surface soil is relatively wet (>35% volumetric soil moisture). Interpolated profiles from field data resulted in a very strong correlation between SNR metrics and soil moisture averaged over the top 5 cm of soil, suggesting that soil moisture estimated from SNR data is useful for various hydrologic applications.
Coopersmith, E. J.; Minsker, B. S.; Sivapalan, M.
Estimating soil moisture typically involves calibrating models to sparse networks of in situ sensors, which introduces considerable error in locations where sensors are not available. We address this issue by calibrating parameters of a parsimonious soil moisture model, which requires only antecedent precipitation information, at gauged locations and then extrapolating these values to ungauged locations via a hydroclimatic classification system. Fifteen sites within the Soil Climate Analysis Network (SCAN) containing multiyear time series data for precipitation and soil moisture are used to calibrate the model. By calibrating at 1 of these 15 sites and validating at another, we observe that the best results are obtained where calibration and validation occur within the same hydroclimatic class. Additionally, soil texture data are tested for their importance in improving predictions between calibration and validation sites. Results have the largest errors when calibration-validation pairs differ hydroclimatically and edaphically, improve when one of these two characteristics are aligned, and are strongest when the calibration and validation sites are hydroclimatically and edaphically similar. These findings indicate considerable promise for improving soil moisture estimation in ungauged locations by considering these similarities.
An, Ru; Zhang, Ling; Wang, Zhe; Quaye-Ballard, Jonathan Arthur; You, Jiajun; Shen, Xiaoji; Gao, Wei; Huang, LiJun; Zhao, Yinghui; Ke, Zunyou
The quality of a newly merged soil moisture product (ECV_SM v0.1) from active and passive microwave sensors has attracted widespread international attention. The performance evaluation of this product will benefit studies on climate, meteorology, agriculture, hydrology, ecology and the environment. In this study, meteorological station data and the Noah soil moisture product were used to validate the ECV_SM product in China. First, some conventional statistical measures, such as correlation coefficients, bias, root mean square difference (RMSD) and mean relative error (MRE), were computed to describe the level of agreement between the meteorological station data and ECV_SM values. The accuracy was moderately high (the correlation was significant at the 0.05 level), although the two datasets differed slightly for various types of land cover. Compared with cropland and urban and built-up areas, the performance of ECV_SM was best in grassland regions. Second, the triple collocation technique was used to assess the random error in the meteorological station data, Noah soil moisture product and ECV_SM product. The mean errors in these three datasets were 0.108, 0.079 and 0.075 m3 m-3, respectively, on July 8, 2010 and 0.099, 0.061 and 0.059 m3 m-3, respectively, on October 8, 2010. Only two days of data were used for the triple collocation test as a representative, but this cannot precisely indicate that the test results on any other day correspond with the test results on these two days. Additionally, a trend analysis of ECV_SM during 2003-2010 was carried out using the Mann-Kendall trend test.
Bindlish, Rajat; Jackson, Thomas; Cosh, Michael; Zhao, Tianjie; O'Neil, Peggy
Aquarius satellite observations over land offer a new resource for measuring soil moisture from space. Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface soil moisture. The soil moisture retrieval algorithm development focused on using only the radiometer data because of the extensive heritage of passive microwave retrieval of soil moisture. The single channel algorithm (SCA) was implemented using the Aquarius observations to estimate surface soil moisture. Aquarius radiometer observations from three beams (after bias/gain modification) along with the National Centers for Environmental Prediction model forecast surface temperatures were then used to retrieve soil moisture. Ancillary data inputs required for using the SCA are vegetation water content, land surface temperature, and several soil and vegetation parameters based on land cover classes. The resulting global spatial patterns of soil moisture were consistent with the precipitation climatology and with soil moisture from other satellite missions (Advanced Microwave Scanning Radiometer for the Earth Observing System and Soil Moisture Ocean Salinity). Initial assessments were performed using in situ observations from the U.S. Department of Agriculture Little Washita and Little River watershed soil moisture networks. Results showed good performance by the algorithm for these land surface conditions for the period of August 2011-June 2013 (rmse = 0.031 m(exp 3)/m(exp 3), Bias = -0.007 m(exp 3)/m(exp 3), and R = 0.855). This radiometer-only soil moisture product will serve as a baseline for continuing research on both active and combined passive-active soil moisture algorithms. The products are routinely available through the National Aeronautics and Space Administration data archive at the National Snow and Ice Data Center.
Orth, René; Seneviratne, Sonia I.
Soil moisture exhibits outstanding memory characteristics and plays a key role within the climate system. Especially through its impacts on the evapotranspiration of soils and plants, it may influence the land energy balance and therefore surface temperature. These attributes make soil moisture an important variable in the context of weather and climate forecasting. In this study we investigate the value of (initial) soil moisture information for sub-seasonal temperature forecasts. For this purpose we employ a simple water balance model to infer soil moisture from streamflow observations in 400 catchments across Europe. Running this model with forecasted atmospheric forcing, we derive soil moisture forecasts, which we then translate into temperature forecasts using simple linear relationships. The resulting temperature forecasts show skill beyond climatology up to 2 weeks in most of the considered catchments. Even if forecasting skills are rather small at longer lead times with significant skill only in some catchments at lead times of 3 and 4 weeks, this soil moisture-based approach shows local improvements compared to the monthly European Centre for Medium Range Weather Forecasting (ECMWF) temperature forecasts at these lead times. For both products (soil moisture-only forecast and ECMWF forecast), we find comparable or better forecast performance in the case of extreme events, especially at long lead times. Even though a product based on soil moisture information alone is not of practical relevance, our results indicate that soil moisture (memory) is a potentially valuable contributor to temperature forecast skill. Investigating the underlying soil moisture of the ECMWF forecasts we find good agreement with the simple model forecasts, especially at longer lead times. Analyzing the drivers of the temperature forecast skills we find that they are mainly controlled by the strengths of (1) the soil moisture-temperature coupling and (2) the soil moisture memory. We
K. C. Kornelsen
Full Text Available This paper introduces and describes the hourly, high-resolution soil moisture dataset continuously recorded by the McMaster Mesonet located in the Hamilton-Halton Watershed in Southern Ontario, Canada. The McMaster Mesonet consists of a network of time domain reflectometer (TDR probes collecting hourly soil moisture data at six depths between 10 cm and 100 cm at nine locations per site, spread across four sites in the 1250 km2 watershed. The sites for the soil moisture arrays are designed to further improve understanding of soil moisture dynamics in a seasonal climate and to capture soil moisture transitions in areas that have different topography, soil and land cover. The McMaster Mesonet soil moisture constitutes a unique database in Canada because of its high spatio-temporal resolution. In order to provide some insight into the dominant processes at the McMaster Mesonet sites, a spatio-temporal and temporal stability analysis were conducted to identify spatio-temporal patterns in the data and to suggest some physical interpretation of soil moisture variability. It was found that the seasonal climate of the Great Lakes Basin causes a transition in soil moisture patterns at seasonal timescales. During winter and early spring months, and at the meadow sites, soil moisture distribution is governed by topographic redistribution, whereas following efflorescence in the spring and summer, soil moisture spatial distribution at the forested site was also controlled by vegetation canopy. Analysis of short-term temporal stability revealed that the relative difference between sites was maintained unless there was significant rainfall (> 20 mm or wet conditions a priori. Following a disturbance in the spatial soil moisture distribution due to wetting, the relative soil moisture pattern re-emerged in 18 to 24 h. Access to the McMaster Mesonet data can be provided by visiting www.hydrology.mcmaster.ca/mesonet.
Calamita, Giuseppe; Perrone, Angela; Brocca, Luca; Straface, Salvatore
The relevant role played by the soil moisture (SM) for global and local natural processes results in an explicit interest for its spatial and temporal estimation in the vadose zone coming from different scientific areas - i.e. eco-hydrology, hydrogeology, atmospheric research, soil and plant sciences, etc... A deeper understanding of natural processes requires the collection of data on a higher number of points at increasingly higher spatial scales in order to validate hydrological numerical simulations. In order to take the best advantage of the Electrical Resistivity (ER) data with their non-invasive and cost-effective properties, sequential Gaussian geostatistical simulations (sGs) can be applied to monitor the SM distribution into the soil by means of a few SM measurements and a densely regular ER grid of monitoring. With this aim, co-located SM measurements using mobile TDR probes (MiniTrase), and ER measurements, obtained by using a four-electrode device coupled with a geo-resistivimeter (Syscal Junior), were collected during two surveys carried out on a 200 × 60 m2 area. Two time surveys were carried out during which Data were collected at a depth of around 20 cm for more than 800 points adopting a regular grid sampling scheme with steps (5 m) varying according to logistic and soil compaction constrains. The results of this study are robust due to the high number of measurements available for either variables which strengthen the confidence in the covariance function estimated. Moreover, the findings obtained using sGs show that it is possible to estimate soil moisture variations in the pedological zone by means of time-lapse electrical resistivity and a few SM measurements.
Time series of soil moisture-related parameters provides important insights in functioning of soil water systems. Analysis of patterns within these time series has been used in several studies. The objective of this work was to compare patterns in observed and simulated soil moisture contents to u...
Jeremy Pinto; John D. Marshall; Kas Dumroese; Anthony S. Davis; Douglas R. Cobos
In many forests of the world, the summer season (temporal element) brings drought conditions causing low soil moisture in the upper soil profile (spatial element) - a potentially large barrier to seedling establishment. We evaluated the relationship between initial seedling root depth, temporal and spatial changes in soil moisture during drought after...
The NASA Soil Moisture Active Passive (SMAP) mission is scheduled for launch in January 2015 and will provide L-band radar and radiometer observations that are sensitive to surface soil moisture (in the top few centimeters of the soil column). For several of the key applications targeted by SMAP, ho...
Lingli WANG; John J.QU
Surface soil moisture is one of the crucial variables in hydrological processes, which influences the exchange of water and energy fluxes at the land surface/ atmosphere interface. Accurate estimate of the spatial and temporal variations of soil moisture is critical for numerous environmental studies. Recent technological advances in satellite remote sensing have shown that soil moisture can be measured by a variety of remote sensing techniques,each with its own strengths and weaknesses. This paper presents a comprehensive review of the progress in remote sensing of soil moisture, with focus on technique approaches for soil moisture estimation from optical,thermal, passive microwave, and active microwave measurements. The physical principles and the status of current retrieval methods are summarized. Limitations existing in current soil moisture estimation algorithms and key issues that have to be addressed in the near future are also discussed.
Panciera, Rocco; Walker, Jeffrey P.; Kalma, Jetse D.
-resolution data from SMOS; and 3) testing its assimilation into land surface models for root zone soil moisture retrieval. This paper describes the NAFE'05 and COSMOS airborne data sets together with the ground data collected in support of both aircraft campaigns. The airborne L-band acquisitions included 40 km x...... was to provide simulated Soil Moisture and Ocean Salinity (SMOS) observations using airborne L-band radiometers supported by soil moisture and other relevant ground data for the following: 1) the development of SMOS soil moisture retrieval algorithms; 2) developing approaches for downscaling the low....... The L-band data were accompanied by airborne thermal infrared and optical measurements. The ground data consisted of continuous soil moisture profile measurements at 18 monitoring sites throughout the 40 km x 40 km study area and extensive spatial near-surface soil moisture measurements concurrent...
ZHAN ZhiMing; QIN QiMing; GHULAN Abduwasit; WANG DongDong
Drought is a complex natural disaster that occurs frequently. Soil moisture has been the main issue in remote monitoring of drought events as the most direct and important variable describing the drought. Spatio-temporal distribution and variation of soil moisture evidently affect surface evapotranspiration, agricultural water demand, etc. In this paper, a new simple method for soil moisture monitoring is developed using near-infrared versus red (NIR-red) spectral reflectance space. First, NIR-red spectral reflectance space is established using atmospheric and geometric corrected ETM+ data, which is manifested by a triangle shape, in which different surface covers have similar spatial distribution rules. Next, the model of soil moisture monitoring by remote sensing (SMMRS) is developed on the basis of the distribution characteristics of soil moisture in the NIR-red spectral reflectance space. Then, the SMMRS model is validated by comparison with field measured soil moisture data at different depths. The results showed that satellite estimated soil moisture by SMMRS is highly accordant with field measured data at 5 cm soil depth and average soil moisture at 0―20 cm soil depths, correlation coefficients are 0.80 and 0.87, respectively. This paper concludes that, being simple and effective, the SMMRS model has great potential to estimate surface moisture conditions.
Drought is a complex natural disaster that occurs frequently. Soil moisture has been the main issue in remote monitoring of drought events as the most direct and important variable describing the drought. Spatio-temporal distribution and variation of soil moisture evidently affect surface evapotranspiration, agricultural water demand, etc. In this paper, a new simple method for soil moisture monitoring is de- veloped using near-infrared versus red (NIR-red) spectral reflectance space. First, NIR-red spectral reflectance space is established using atmospheric and geometric corrected ETM+ data, which is manifested by a triangle shape, in which different surface covers have similar spatial distribution rules. Next, the model of soil moisture monitoring by remote sensing (SMMRS) is developed on the basis of the distribution characteristics of soil moisture in the NIR-red spectral reflectance space. Then, the SMMRS model is validated by comparison with field measured soil moisture data at different depths. The results showed that satellite estimated soil moisture by SMMRS is highly accordant with field measured data at 5 cm soil depth and average soil moisture at 0―20 cm soil depths, correlation coef- ficients are 0.80 and 0.87, respectively. This paper concludes that, being simple and effective, the SMMRS model has great potential to estimate surface moisture conditions.
Full Text Available This study focuses on the seasonal differences in soil moisture patterns considering the impact of meteorological variables (air/ground temperature, precipitation, and the amount of insolation on soil moisture variability over the Korean peninsula between January 2012 and February 2013. We found that soil moisture spatial distributions changed differently with the mean soil moisture content according to the season using statistical metrics (skewness and kurtosis (summer: 1 June to 31 August, winter: 1 November to 31 January. Daily variations in meteorological variables had different relationships with the changes in soil moisture for two seasons. Air and soil temperature changes clearly had negative relationships with the soil moisture change during the summer period while they had positive relationships during the winter period. Temporal stability testing showed that the representative soil moisture sites on a regional scale could be changed with seasonal periods, especially in the Asian monsoon region. In conclusion, these results provide evidence that there are clear differences in soil moisture patterns according to seasonal characteristics. This study might be useful for further researches relating to climate-meteorological effects on soil moisture patterns on a regional scale.
Mohanty, B.; Shin, Y.; Ines, A. M.
Prediction of root zone soil moisture is critical for water resources management. In this study, we explored a non-parametric evolutionary algorithm for estimating root zone soil moisture from a time series of spatially-distributed rainfall across multiple weather locations under two different hydro-climatic regions. A new genetic algorithm-based hidden Markov model (HMMGA) was developed to estimate long-term root zone soil moisture dynamics at different soil depths. Also, we analyzed rainfall occurrence probabilities and dry/wet spell lengths reproduced by this approach. The HMMGA was used to estimate the optimal state sequences (weather states) based on the precipitation history. Historical root zone soil moisture statistics were then determined based on the weather state conditions. To test the new approach, we selected two different soil moisture fields, Oklahoma (130 km x 130 km) and Illinois (300 km x 500 km), during 1995 to 2009 and 1994 to 2010, respectively. We found that the newly developed framework performed well in predicting root zone soil moisture dynamics at both the spatial scales. Also, the reproduced rainfall occurrence probabilities and dry/wet spell lengths matched well with the observations at the spatio-temporal scales. Since the proposed algorithm requires only precipitation and historical soil moisture data from existing, established weather stations, it can serve an attractive alternative for predicting root zone soil moisture in the future using climate change scenarios and root zone soil moisture history.
Coates, Victoria; Pattison, Ian; Sander, Graham
England's rural landscape is dominated by pastoral agriculture, with 40% of land cover classified as either improved or semi-natural grassland according to the Land Cover Map 2007. Since the Second World War the intensification of agriculture has resulted in greater levels of soil compaction, associated with higher stocking densities in fields. Locally compaction has led to loss of soil storage and an increased in levels of ponding in fields. At the catchment scale soil compaction has been hypothesised to contribute to increased flood risk. Previous research (Pattison, 2011) on a 40km2 catchment (Dacre Beck, Lake District, UK) has shown that when soil characteristics are homogeneously parameterised in a hydrological model, downstream peak discharges can be 65% higher for a heavy compacted soil than for a lightly compacted soil. However, at the catchment scale there is likely to be a significant amount of variability in compaction levels within and between fields, due to multiple controlling factors. This research focusses in on one specific type of land use (permanent pasture with cattle grazing) and areas of activity within the field (feeding area, field gate, tree shelter, open field area). The aim was to determine if the soil characteristics and soil compaction levels are homogeneous in the four areas of the field. Also, to determine if these levels stayed the same over the course of the year, or if there were differences at the end of the dry (October) and wet (April) periods. Field experiments were conducted in the River Skell catchment, in Yorkshire, UK, which has an area of 120km2. The dynamic cone penetrometer was used to determine the structural properties of the soil, soil samples were collected to assess the bulk density, organic matter content and permeability in the laboratory and the Hydrosense II was used to determine the soil moisture content in the topsoil. Penetration results show that the tree shelter is the most compacted and the open field area
Hatten, J. A.; Dewey, J.; Roberts, S.; McNeal, K.; Shaman, A.
Inputs of labile organic substrates to soils are commonly associated with elevated soil organic carbon mineralization rates; this process is known as the priming effect. Plant presence and soil conditions (i.e. water regime, nutrient status) are known to be interacting factors governing priming. In this study, we examine the role of differing species, loblolly pine (Pinus taeda L.) and nuttall oak (Quercus texana B.), and moisture regimes (low and high) upon the soil priming effect in a fine textured soil. We explore whether there is depletion of original soil carbon and concurrent replacement through addition of fresh organic matter from the planted tree species. By employing a series of planted and plant-free pots in a greenhouse mesocosm study, we were able to characterize the composition of soil organic matter and its carbon with the use of CuO oxidation products (e.g. lignin, cutin/suberin biomarkers). Carbon was elevated on the low moisture samples relative to all other treatments, and the C:N ratio suggests that newly produced plant carbon replaced original soil carbon. The soil lignin content of the planted treatments was lower than the plant-free treatments suggesting that lignin present in the original soil may have been preferentially degraded by priming and not replaced. We will discuss the utility of CuO oxidation products to explore soil organic carbon dynamics and the implications of understanding the role of species and soil moisture in predicting the response of soil carbon to land use and climate change.
Jarvi, Mickey P; Burton, Andrew J
The response of root respiration to warmer soil can affect ecosystem carbon (C) allocation and the strength of positive feedbacks between climatic warming and soil CO2 efflux. This study sought to determine whether fine-root (respiration in a sugar maple (Acer saccharum Marsh.)-dominated northern hardwood forest would adjust to experimentally warmed soil, reducing C return to the atmosphere at the ecosystem scale to levels lower than that would be expected using an exponential temperature response function. Infrared heating lamps were used to warm the soil (+4 to +5 °C) in a mature sugar maple forest in a fully factorial design, including water additions used to offset the effects of warming-induced dry soil. Fine-root-specific respiration rates, root biomass, root nitrogen (N) concentration, soil temperature and soil moisture were measured from 2009 to 2011, with experimental treatments conducted from late 2010 to 2011. Partial acclimation of fine-root respiration to soil warming occurred, with soil moisture deficit further constraining specific respiration rates in heated plots. Fine-root biomass and N concentration remained unchanged. Over the 2011 growing season, ecosystem root respiration was not significantly greater in warmed soil. This result would not be predicted by models that allow respiration to increase exponentially with temperature and do not directly reduce root respiration in drier soil.
Uebbing, Bernd; Forootan, Ehsan; Kusche, Jürgen; Braakmann-Folgmann, Anne
Soil moisture represents an important component of the terrestrial water cycle that controls., evapotranspiration and vegetation growth. Consequently, knowledge on soil moisture variability is essential to understand the interactions between land and atmosphere. Yet, terrestrial measurements are sparse and their information content is limited due to the large spatial variability of soil moisture. Therefore, over the last two decades, several active and passive radar and satellite missions such as ERS/SCAT, AMSR, SMOS or SMAP have been providing backscatter information that can be used to estimate surface conditions including soil moisture which is proportional to the dielectric constant of the upper (few cm) soil layers . Another source of soil moisture information are satellite radar altimeters, originally designed to measure sea surface height over the oceans. Measurements of Jason-1/2 (Ku- and C-Band) or Envisat (Ku- and S-Band) nadir radar backscatter provide high-resolution along-track information (~ 300m along-track resolution) on backscatter every ~10 days (Jason-1/2) or ~35 days (Envisat). Recent studies found good correlation between backscatter and soil moisture in upper layers, especially in arid and semi-arid regions, indicating the potential of satellite altimetry both to reconstruct and to monitor soil moisture variability. However, measuring soil moisture using altimetry has some drawbacks that include: (1) the noisy behavior of the altimetry-derived backscatter (due to e.g., existence of surface water in the radar foot-print), (2) the strong assumptions for converting altimetry backscatters to the soil moisture storage changes, and (3) the need for interpolating between the tracks. In this study, we suggest a new inversion framework that allows to retrieve soil moisture information from along-track Jason-2 and Envisat satellite altimetry data, and we test this scheme over the Australian arid and semi-arid regions. Our method consists of: (i
Hain, Christopher Ryan
Soil moisture plays a vital role in the partitioning of sensible and latent heat fluxes in the surface energy budget and the lack of a dense spatial and temporal network of ground-based observations provides a challenge to the initialization of the true soil moisture state in numerical weather prediction simulations. The retrieval of soil moisture using observations from both satellite-based thermal-infrared (TIR) and passive microwave (PM) sensors has been developed (Anderson et al., 2007; Hain et al., 2009; Jackson, 1993; Njoku et al., 2003). The ability of the TIR and microwave observations to diagnose soil moisture conditions within different layers of the soil profile provides an opportunity to use each in a synergistic data assimilation approach towards the goal of diagnosing the true soil moisture state from surface to root-zone. TIR and PM retrievals of soil moisture are compared to soil moisture estimates provided by a retrospective Land Information System (LIS) simulation using the NOAH LSM during the time period of 2003--2008. The TIR-based soil moisture product is provided by a retrieval of soil moisture associated with surface flux estimates from the Atmosphere-Land-Exchange-Inversion (ALEXI) model (Anderson et al., 1997; Mecikalski et al., 1999; Hain et al., 2009). The PM soil moisture retrieval is provided by the Vrijie Universiteit Amsterdam (VUA)-NASA surface soil moisture product. The VUA retrieval is based on the findings of Owe et al. (2001; 2008) using the Land Surface Parameter model (LPRM), which uses one dual polarized channel (6.925 or 10.65 GHz) for a dual-retrieval of surface soil moisture and vegetation water content. In addition, retrievals of ALEXI (TIR) and AMSR-E (PM) soil moisture are assimilated within the Land Information System using the NOAH LSM. A series of data assimilation experiments is completed with the following configuration: (a) no assimilation, (b) only ALEXI soil moisture, (c) only AMSR-E soil moisture, and (d) ALEXI
In order to reveal the drought resistance and adaptation of the C4 desert plant Haloxylon ammodendron under artificially controlled soil moisture regimes,representative plants were selected to measure canopy photosynthesis using canopy photosynthetic measurement system.The results showed that appropriate soil moisture significantly enhances the canopy and leaf photosynthetic capacity,and extremely high soil moisture is not conducive to the photosynthesis of H.ammodendron.
Xun Chai; Tingting Zhang; Yun Shao; Huaze Gong; Long Liu; Kaixin Xie
Accurate soil moisture retrieval of a large area in high resolution is significant for plateau pasture. The object of this paper is to investigate the estimation of volumetric soil moisture in vegetated areas of plateau pasture using fully polarimetric C-band RADARSAT-2 SAR (Synthetic Aperture Radar) images. Based on the water cloud model, Chen model, and Dubois model, we proposed two developed algorithms for soil moisture retrieval and validated their performance using experimental data. We ...
Yee, Mei Sun; Walker, Jeffrey P.; Monerris, Alessandra; Rüdiger, Christoph; Jackson, Thomas J.
The high spatio-temporal variability of soil moisture complicates the validation of remotely sensed soil moisture products using in situ monitoring stations. Therefore, a standard methodology for selecting the most representative stations for the purpose of validating satellites and land surface models is essential. Based on temporal stability and geostatistical studies using long-term soil moisture records, intensive ground measurements and airborne soil moisture products, this study investigates the representativeness of soil moisture monitoring stations within the Yanco study area for the validation of NASA's Soil Moisture Active Passive (SMAP) products at 3 km for radar, 9 km for radar-radiometer and 36 km for radiometer pixels. This resulted in the identification of a number of representative stations according to the different scales. Although the temporal stability method was found to be suitable for identifying representative stations, stations based on the mean relative difference (MRD) were not necessarily the most representative of the areal average. Moreover, those identified from standard deviation of the relative difference (SDRD) may be dry-biased. It was also found that in the presence of heterogeneous land use, stations should be weighted based on proportions of agricultural land. Airborne soil moisture products were also shown to provide useful a priori information for identifying representative locations. Finally, recommendations are made regarding the design of future networks for satellite validation, and specifically the most representative stations for the Yanco area.
Luster-Teasley, S; Ubaka-Blackmoore, N; Masten, S J
In this study, pyrene spiked soil (300 ppm) was ozonated at pH levels of 2, 6, and 8 and three moisture contents. It was found that soil pH and moisture content impacted the effectiveness of PAH oxidation in unsaturated soils. In air-dried soils, as pH increased, removal increased, such that pyrene removal efficiencies at pH 6 and pH 8 reached 95-97% at a dose of 2.22 mg O(3)/mg pyrene. Ozonation at 16.2+/-0.45 mg O(3)/ppm pyrene in soil resulted in 81-98% removal of pyrene at all pH levels tested. Saturated soils were tested at dry, 5% or 10% moisture conditions. The removal of pyrene was slower in moisturized soils, with the efficiency decreasing as the moisture content increased. Increasing the pH of the soil having a moisture content of 5% resulted in improved pyrene removals. On the contrary, in the soil having a moisture content of 10%, as the pH increased, pyrene removal decreased. Contaminated PAH soils were stored for 6 months to compare the efficiency of PAH removal in freshly contaminated soil and aged soils. PAH adsorption to soil was found to increase with longer exposure times; thus requiring much higher doses of ozone to effectively oxidize pyrene.
Chen Shanxiong; Yu Song; Liu Zhiguo; Xu Haibin
This study develops a way of analyzing moisture movement in unsaturated expansive soil slope. The basic equations and the integrated finite difference method for moisture movement in unsaturated soils are briefly described, and the calculation code MFUS2 has been developed. The moisture movements in unsaturated expansive soil slopes suffering precipitation were simulated numerically. The simulation results show that expansion or contraction must be taken into account in an analysis model. A simplified equivalent model for calculating rainwater infiltration into expansive soil slopes has been developed. The simplified equivalent model divides the soil slope into two layers according to the extent of weathering of the soil mass at depth. Layer Ⅰ is intensively weathered and moisture can be fully evaporated or rapidly absorbed. The moisture movement parameters take into account the greater soil permeability caused by fissures. Layer Ⅱ is unweathered and the soil is basically undisturbed. The moisture movement parameters of homogeneous soils are applicable. The moisture movements in unsaturated expansive soil slopes suffering precipitation were simulated numerically using the simplified equivalent model. The simulation results show that the moisture movement in the expansive soil slope under rainfall permeation mainly takes place in the extensively weathered layer Ⅰ, which closely simulates the real situation.
Ferraris, Stefano; Canone, Davide; Previati, Maurizio
The vertical variability of soil moisture in the rootzone is a key factor and it is not taken into account in many hydrological models. Therefore it is here proposed a novel approach that is based on the inversion of a semianalytical solution of the equation governing the infiltration and the exfiltration processes. The inversion allows keeping the information contained in the vertical spatial variability. It has been monitored with TDR measurements down to 2 meters depth. Also, the hysteresis and dynamical effects are then taken into account, with water potential measurements, in order to correctly predict the water retention both in infiltration and in drainage/exfiltration transients. References M. Baudena, I. Bevilacqua, D. Canone, S. Ferraris, M. Previati, A. Provenzale (2012). Soil water dynamics at a midlatitude test site: Field measurements and box modeling approaches. JOURNAL OF HYDROLOGY, vol. 414-415, p. 329-340, ISSN: 0022-1694, doi: 10.1016/j.jhydrol.2011.11.009
Smith, J.A.; Chiou, C.T.; Kammer, J.A.; Kile, D.E.
This report presents data on the sorption of trichloroethene (TCE) vapor to vadose-zone soil above a contaminated water-table aquifer at Picatinny Arsenal in Morris County, NJ. To assess the impact of moisture on TCE sorption, batch experiments on the sorption of TCE vapor by the field soil were carried out as a function of relative humidity. The TCE sorption decreases as soil moisture content increases from zero to saturation soil moisture content (the soil moisture content in equilibrium with 100% relative humidity). The moisture content of soil samples collected from the vadose zone was found to be greater than the saturation soil-moisture content, suggesting that adsorption of TCE by the mineral fraction of the vadose-zone soil should be minimal relative to the partition uptake by soil organic matter. Analyses of soil and soil-gas samples collected from the field indicate that the ratio of the concentration of TCE on the vadose-zone soil to its concentration in the soil gas is 1-3 orders of magnitude greater than the ratio predicted by using an assumption of equilibrium conditions. This apparent disequilibrium presumably results from the slow desorption of TCE from the organic matter of the vadose-zone soil relative to the dissipation of TCE vapor from the soil gas.
Srivastava, Prashant K.; Han, Dawei; Rico-Ramirez, Miguel A.; O'Neill, Peggy; Islam, Tanvir; Gupta, Manika; Dai, Qiang
This study explores the performance of soil moisture data from the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim reanalysis datasets using the Weather Research and Forecasting (WRF) mesoscale numerical weather model coupled with the Noah Land surface model for hydrological applications. For evaluating the performance of WRF for soil moisture estimation, three domains are taken into account. The domain with best performance is used for estimating the soil moisture deficit (SMD). Further, several approaches are presented in this study to evaluate the efficiency of WRF simulated soil moisture for SMD estimation and compared against Soil Moisture and Ocean Salinity (SMOS) downscaled and non-downscaled soil moisture. In this study, the first approach is based on the empirical relationship between WRF soil moisture and the SMD on a continuous time series basis, while the second approach is focused on the vegetation cover impact on SMD retrieval, depicted in terms of growing and non-growing seasons. The linear growing and non-growing seasonal model in combination performs well with the NSE = 0.79, RMSE = 0.011 m; Bias = 0.24 m, in comparison to linear model (NSE = 0.70, RMSE = 0.013 m; Bias = 0.01 m). The performance obtained using WRF soil moisture is comparable to SMOS level 2 product but lower than the downscaled SMOS datasets. The results indicate that methodologies could be useful for modelers working in the field of soil moisture information system and SMD estimation at a catchment scale. The study could be useful for ungauged basins that pose a challenge to hydrological modeling due to unavailability of datasets for proper model calibration and validation.
Verhoest, Niko E C; Lievens, Hans; Wagner, Wolfgang; Álvarez-Mozos, Jesús; Moran, M Susan; Mattia, Francesco
Synthetic Aperture Radar has shown its large potential for retrieving soil moisture maps at regional scales. However, since the backscattered signal is determined by several surface characteristics, the retrieval of soil moisture is an ill-posed problem when using single configuration imagery. Unless accurate surface roughness parameter values are available, retrieving soil moisture from radar backscatter usually provides inaccurate estimates. The characterization of soil roughness is not fully understood, and a large range of roughness parameter values can be obtained for the same surface when different measurement methodologies are used. In this paper, a literature review is made that summarizes the problems encountered when parameterizing soil roughness as well as the reported impact of the errors made on the retrieved soil moisture. A number of suggestions were made for resolving issues in roughness parameterization and studying the impact of these roughness problems on the soil moisture retrieval accuracy and scale.
Full Text Available Past studies on soil moisture spatial variability have been mainly conducted in catchment scales where soil moisture is often sampled over a short time period. Because of limited climate and weather conditions, the observed soil moisture often exhibited smaller dynamic ranges which prevented the complete revelation of soil moisture spatial variability as a function of mean soil moisture. In this study, spatial statistics (mean, spatial variability and skewness of in situ soil moisture measurements (from a continuously monitored network across the US, modeled and satellite retrieved soil moisture obtained in a warm season (198 days were examined at large extent scales (>100 km over three different climate regions. The investigation on in situ measurements revealed that their spatial moments strongly depend on climates, with distinct mean, spatial variability and skewness observed in each climate zone. In addition, an upward convex shape, which was revealed in several smaller scale studies, was observed for the relationship between spatial variability of in situ soil moisture and its spatial mean across dry, intermediate, and wet climates. These climate specific features were vaguely or partially observable in modeled and satellite retrieved soil moisture estimates, which is attributed to the fact that these two data sets do not have climate specific and seasonal sensitive mean soil moisture values, in addition to lack of dynamic ranges. From the point measurements to satellite retrievals, soil moisture spatial variability decreased in each climate region. The three data sources all followed the power law in the scale dependency of spatial variability, with coarser resolution data showing stronger scale dependency than finer ones. The main findings from this study are: (1 the statistical distribution of soil moisture depends on spatial mean soil moisture values and thus need to be derived locally within any given area; (2 the boundedness of soil
Lin, Xi-Hao; Chen, Qiu-Bo; Hua, Yuan-Gang; Yang, Li-Fu; Wang, Zhen-Hui
By using soil core sampling method, this paper studied the soil moisture regime of rubber plantations and the fine root biomass of Hevea brasiliensis in immature period (5 a), early yielding period (9 a), and peak yielding period (16 a). With the increasing age of rubber trees, the soil moisture content of rubber plantations increased but the fine root biomass decreased. The soil moisture content at the depth of 0-60 cm in test rubber plantations increased with soil depth, and presented a double-peak pattern over the period of one year. The fine root biomass of rubber trees at different ages had the maximum value in the top 10 cm soil layers and decreased with soil depth, its seasonal variation also showed a double-peak pattern, but the peak values appeared at different time. Soil moisture content and soil depth were the main factors affecting the fine root biomass of H. brasiliensis.
Giraldo, M.A.; Bosch, D.; Madden, M.; Usery, L.; Kvien, Craig
This research addressed the temporal and spatial variation of soil moisture (SM) in a heterogeneous landscape. The research objective was to investigate soil moisture variation in eight homogeneous 30 by 30 m plots, similar to the pixel size of a Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper plus (ETM+) image. The plots were adjacent to eight stations of an in situ soil moisture network operated by the United States Department of Agriculture-Agriculture Research Service USDA-ARS in Tifton, GA. We also studied five adjacent agricultural fields to examine the effect of different landuses/land covers (LULC) (grass, orchard, peanuts, cotton and bare soil) on the temporal and spatial variation of soil moisture. Soil moisture field data were collected on eight occasions throughout 2005 and January 2006 to establish comparisons within and among eight homogeneous plots. Consistently throughout time, analysis of variance (ANOVA) showed high variation in the soil moisture behavior among the plots and high homogeneity in the soil moisture behavior within them. A precipitation analysis for the eight sampling dates throughout the year 2005 showed similar rainfall conditions for the eight study plots. Therefore, soil moisture variation among locations was explained by in situ local conditions. Temporal stability geostatistical analysis showed that soil moisture has high temporal stability within the small plots and that a single point reading can be used to monitor soil moisture status for the plot within a maximum 3% volume/volume (v/v) soil moisture variation. Similarly, t-statistic analysis showed that soil moisture status in the upper soil layer changes within 24 h. We found statistical differences in the soil moisture between the different LULC in the agricultural fields as well as statistical differences between these fields and the adjacent 30 by 30 m plots. From this analysis, it was demonstrated that spatial proximity is not enough to produce similar soil
Li, Bailing; Toll, David; Zhan, Xiwu; Cosgrove, Brian
Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of soil moisture in a footprint area, and can be used to reduce model bias (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce model bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate the actual value of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals to improve the mean of simulated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed.
Full Text Available Land-atmosphere interactions play an important role for hot temperature extremes in Europe. Dry soils may amplify such extremes through feedbacks with evapotranspiration. While previous observational studies generally focused on the relationship between precipitation deficits and the number of hot days, we investigate here the influence of soil moisture (SM on summer monthly maximum temperatures (TXx using water balance model-based SM estimates (driven with observations and temperature observations. Generalized extreme value distributions are fitted to TXx using SM as a covariate. We identify a negative relationship between SM and TXx, whereby a 100 mm decrease in model-based SM is associated with a 1.6 °C increase in TXx in Southern-Central and Southeastern Europe. Dry SM conditions result in a 2–4 °C increase in the 20-year return value of TXx compared to wet conditions in these two regions. In contrast with SM impacts on the number of hot days (NHD, where low and high surface-moisture conditions lead to different variability, we find a mostly linear dependency of the 20-year return value on surface-moisture conditions. We attribute this difference to the non-linear relationship between TXx and NHD that stems from the threshold-based calculation of NHD. Furthermore the employed SM data and the Standardized Precipitation Index (SPI are only weakly correlated in the investigated regions, highlighting the importance of evapotranspiration and runoff for resulting SM. Finally, in a case study for the hot 2003 summer we illustrate that if 2003 spring conditions in Southern-Central Europe had been as dry as in the more recent 2011 event, temperature extremes in summer would have been higher by about 1 °C, further enhancing the already extreme conditions which prevailed in that year.
SONG Dongsheng; ZHAO Kai; GUAN Zhi
Soil moisture is an important factor in global hydrologic circulation and plays a significant role in the research of hydrology, climatology, and agriculture. Microwave remote sensing is less limited by climate and time, and can measure in large scale. With these characteristics, this technique becomes an effective tool to measure soil moisture. Since the 1980s, Chinese researchers have investigated the soil moisture using microwave instruments. The active remote sensors are characteristic of high spatial resolution, thus with launch of a series of satellites, active microwave remote sensing of soil moisture will be emphasized. The passive microwave remote sensing of soil moisture has a long research history, and its retrieval algorithms were developed well, so it is an important tool to retrieve large scale moisture information from satellite data in the future.
Mares, Rachel; Barnard, Holly R.; Mao, Deqiang; Revil, André; Singha, Kamini
The feedbacks among forest transpiration, soil moisture, and subsurface flowpaths are poorly understood. We investigate how soil moisture is affected by daily transpiration using time-lapse electrical resistivity imaging (ERI) on a highly instrumented ponderosa pine and the surrounding soil throughout the growing season. By comparing sap flow measurements to the ERI data, we find that periods of high sap flow within the diel cycle are aligned with decreases in ground electrical conductivity and soil moisture due to drying of the soil during moisture uptake. As sap flow decreases during the night, the ground conductivity increases as the soil moisture is replenished. The mean and variance of the ground conductivity decreases into the summer dry season, indicating drier soil and smaller diel fluctuations in soil moisture as the summer progresses. Sap flow did not significantly decrease through the summer suggesting use of a water source deeper than 60 cm to maintain transpiration during times of shallow soil moisture depletion. ERI captured spatiotemporal variability of soil moisture on daily and seasonal timescales. ERI data on the tree showed a diel cycle of conductivity, interpreted as changes in water content due to transpiration, but changes in sap flow throughout the season could not be interpreted from ERI inversions alone due to daily temperature changes.
Wu, Mousong; Sholze, Marko
We investigated the importance of soil moisture data on assimilation of a terrestrial biosphere model (BETHY) for a long time period from 2010 to 2015. Totally, 101 parameters related to carbon turnover, soil respiration, as well as soil texture were selected for optimization within a carbon cycle data assimilation system (CCDAS). Soil moisture data from Soil Moisture and Ocean Salinity (SMOS) product was derived for 10 sites representing different plant function types (PFTs) as well as different climate zones. Uncertainty of SMOS soil moisture data was also estimated using triple collocation analysis (TCA) method by comparing with ASCAT dataset and BETHY forward simulation results. Assimilation of soil moisture to the system improved soil moisture as well as net primary productivity(NPP) and net ecosystem productivity (NEP) when compared with soil moisture derived from in-situ measurements and fluxnet datasets. Parameter uncertainties were largely reduced relatively to prior values. Using SMOS soil moisture data for assimilation of a terrestrial biosphere model proved to be an efficient approach in reducing uncertainty in ecosystem fluxes simulation. It could be further used in regional an global assimilation work to constrain carbon dioxide concentration simulation by combining with other sources of measurements.
Pärn, Jaan; Mander, Ülo
Human activity has increased the atmospheric concentration of nitrous oxide (N2O), a powerful greenhouse gas and the main stratospheric-ozone depleting agent. Organic soils are considered a minor N2O source but that may be changing due to human activities, particularly drainage and fertilisation for agriculture. Predicting global N2O emission is a challenge owing to high temporal and spatial variability, and in view of the paucity of data from the tropics. We conducted a global soil- and gas-sampling campaign between August 2011 and January 2016, following a standard protocol. We sampled 61 organic-soil sites (>10% soil carbon content in the upper 0.1m at all locations) in 25 regions covering moist tropical, temperate and boreal climates. Of all parameters assessed for the variability in site-mean N2O emission, the logarithm of soil carbon-to-nitrate ratio (log (C/NO3-N)) was the strongest predictor, explaining 68% of the variation in log N2O fluxes. Inclusion of site-mean soil moisture raised the explanatory power of the multiple-regression GAM to R2=0.71. The paraboloid regression surface had a humped shape with large N2O fluxes above 80% soil moisture. Likewise, in an independent test of the model on published data, annual time scales of N2O emission were represented well. The relationship between the mean relative N2O fluxes (scaled to the maximum value in the data set) and soil moisture was best described by a humped log GAM regression with a maximum at 50 to 60% soil moisture (R2=0.66; p= 0.0114). Soil temperature, another factor that has often been used to explain variability in N2O emissions, showed only a weak relationship with both the N2O fluxes measured in our study and published in the analysed papers. We conclude that loss of moisture increases N2O emissions from nitrogen-rich organic soils by two orders of magnitude. Wetland conservation and restoration, and appropriate soil management, are thus essential for climate-change mitigation and protecting
Chen, Yi-yun; Qi, Kun; Liu, Yao-lin; He, Jian-hua; Jiang, Qing-hu
Hyperspectral remote sensing, known as the state-of-the-art technology in the field of remote sensing, can be used to retrieve physical and chemical properties of surface objects based on the interactions between electromagnetic waves and the objects. Soil organic matter (SOM) is one of the most important parameters used in the assessment of soil fertility. Quick estimation of SOM with hyperspectral remote sensing technique can provide essential soil data to support the development of precision agriculture. The presence of external parameters, however, may affect the modeling precision, and further handicap the transfer ability of existing model. With the aim to study the effects of soil moisture on the Vis/NIR estimation of soil organic matter, and the capacity of direct standardization(DS)algorithm in the calibration transfer, 95 soil samples collected in the Jianghan plain were rewetted and air-dried. Reflectance of these samples at 13 moisture levels was measured. Results show that the model calibrated using air-dried samples has the highest prediction accuracy. This model, however, was not suitable for SOM prediction of the rewetted samples. Prediction bias and RPD improved from -8.34-3.32 g x kg(-1) and 0.64-2.04 to 0 and 7.01, when DS algorithm was applied to the spectra of the rewetted samples. DS algorithm has been proven to be effective in removing the effects of soil moisture on the Vis/NIR estimation of SOM, ensuring a transferrable model for SOM prediction with soil samples at different moisture levels.
W. T. Crow
Full Text Available A number of recent studies have focused on enhancing runoff prediction via the assimilation of remotely-sensed surface soil moisture retrievals into a hydrologic model. The majority of these approaches have viewed the problem from purely a state or parameter estimation perspective in which remotely-sensed soil moisture estimates are assimilated to improve the characterization of pre-storm soil moisture conditions in a hydrologic model, and consequently, its simulation of runoff response to subsequent rainfall. However, recent work has demonstrated that soil moisture retrievals can also be used to filter errors present in satellite-based rainfall accumulation products. This result implies that soil moisture retrievals have potential benefit for characterizing both antecedent moisture conditions (required to estimate sub-surface flow intensities and subsequent surface runoff efficiencies and storm-scale rainfall totals (required to estimate the total surface runoff volume. In response, this work presents a new sequential data assimilation system that exploits remotely-sensed surface soil moisture retrievals to simultaneously improve estimates of both pre-storm soil moisture conditions and storm-scale rainfall accumulations. Preliminary testing of the system, via a synthetic twin data assimilation experiment based on the Sacramento hydrologic model and data collected from the Model Parameterization Experiment, suggests that the new approach is more efficient at improving stream flow predictions than data assimilation techniques focusing solely on the constraint of antecedent soil moisture conditions.
The paper describes measurements to quantitatively identify the extent to which moisture affects radon emanation and diffusive transport components of a sandy soil radon concentration gradient obtained in the EPA test chamber. The chamber (2X2X4 m long) was constructed to study t...
The paper describes measurements to quantitatively identify the extent to which moisture affects radon emanation and diffusive transport components of a sandy soil radon concentration gradient obtained in the EPA test chamber. The chamber (2X2X4 m long) was constructed to study t...
Seneviratne, S. I.; Davin, E.; Hirschi, M.; Mueller, B.; Orlowsky, B.; Teuling, A.
Soil moisture is a key variable of the climate system. It constrains plant transpiration and photosynthesis in several regions of the world, with consequent impacts on the water, energy and biogeochemical cycles (e.g. Seneviratne et al. 2010). Moreover it is a storage component for precipitation and radiation anomalies, inducing persistence in the climate system. Finally, it is involved in a number of feedbacks at the local, regional and global scales, and plays a major role in climate-change projections. This presentation will provide an overview on these interactions, based on several recent publications (e.g. Seneviratne et al. 2006, Orlowsky and Seneviratne 2010, Teuling et al. 2010, Hirschi et al. 2011). In particular, it will highlight possible impacts of soil moisture-ecosystem coupling for climate extremes such as heat waves and droughts, and the resulting interconnections between biophysical and biogeochemical feedbacks in the context of climate change. Finally, it will also address recent regional- to global-scale trends in land hydrology and ecosystem functioning, as well as issues and potential avenues for investigating these trends (e.g. Jung et al. 2010, Mueller et al. 2011). References Hirschi, M., S.I. Seneviratne, V. Alexandrov, F. Boberg, C. Boroneant, O.B. Christensen, H. Formayer, B. Orlowsky, and P. Stepanek, 2011: Observational evidence for soil-moisture impact on hot extremes in southeastern Europe. Nature Geoscience, 4, 17-21, doi:10.1038/ngeo1032. Jung, M., et al., 2010: Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467, 951-954. doi:10.1038/nature09396 Mueller, B., S.I. Seneviratne, et al.: Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations, Geophys. Res. Lett., 38, L06402, doi:10.1029/2010GL046230 Orlowsky, B., and S.I. Seneviratne, 2010: Statistical analyses of land-atmosphere feedbacks and their possible pitfalls. J. Climate, 23(14), 3918
Full Text Available We studied the impacts of re-vegetation on soil moisture dynamics and evapotranspiration (ET of five land cover types in the Loess Plateau in northern China. Soil moisture and temperature variations under grass (Andropogon, subshrub (Artemisia scoparia, shrub (Spiraea pubescens, plantation forest (Robinia pseudoacacia, and crop (Zea mays vegetation were continuously monitored during the growing season of 2011. There were more than 10 soil moisture pulses during the period of data collection. Surface soil moisture of all of the land cover types showed an increasing trend in the rainy season. Soil moisture under the corn crop was consistently higher than the other surfaces. Grass and subshrubs showed an intermediate moisture level. Grass had slightly higher readings than those of subshrub most of the time. Shrubs and plantation forests were characterized by lower soil moisture readings, with the shrub levels consistently being slightly higher than those of the forests. Despite the greater post-rainfall loss of moisture under subshrub and grass vegetation than forests and shrubs, subshrub and grass sites exhibit a higher soil moisture content due to their greater soil retention capacity in the dry period. The daily ET trends of the forests and shrub sites were similar and were more stable than those of the other types. Soils under subshrubs acquired and retained soil moisture resources more efficiently than the other cover types, with a competitive advantage in the long term, representing an adaptive vegetation type in the study watershed. The interactions between vegetation and soil moisture dynamics contribute to structure and function of the ecosystems studied.
Stochastic modeling of soil moisture dynamics is crucial to the quantitative understanding of plant responses to water stresses, hydrological control of nutrient cycling processes, water competition among plants, and some other ecological dynamics, and thus has become a hotspot in ecohydrology at present. In this paper, we based on the continuously monitored data of soil moisture during 2002-2005 and daily precipitation date of 1992-2006, and tried to make a probabilistic analysis of soil moisture dynamics at point scale in a grassland of Qilian Mountain by integrating the stochastic model improved by Laio and the Monte Carlo method. The results show that the inter-annual variations for the soil moisture patterns at different depths are very significant, and that the coefficient of variance (CV) of surface soil moisture (20 cm) is almost continually kept at about 0.23 whether in the rich or poor rainy years. Interestingly, it has been found that the maximal CV of soil moisture has not always appeared at the surface layer. Comparison of the analytically derived soil moisture probability density function (PDF) with the statistical distribution of the observed soil moisture data suggests that the stochastic model can reasonably describe and predict the soil moisture dynamics of the grassland in Qilian Mountain at point scale. By extracting the statistical information of the historical precipitation data in 1994-2006, and inputting them into the stochastic model, we analytically derived the long-term soil moisture PDF without considering the inter-annual climate fluctuations, and then numerically derived the one when considering the inter-annual fluctuation effects in combination with a Monte-Carlo procedure. It was found that, though the peak position of the probability density distribution significantly moved towards drought when considering the disturbance forces, and its width was narrowed, accordingly its peak value was increased, no significant bimodality was
Liu, Zhao; Zhou, Yan-Lian; Ju, Wei-Min; Gao, Ping
By using an ensemble Kalman filter (EnKF) to assimilate the observed soil moisture data, the modified boreal ecosystem productivity simulator (BEPS) model was adopted to simulate the dynamics of soil moisture in winter wheat root zones at Xuzhou Agro-meteorological Station, Jiangsu Province of China during the growth seasons in 2000-2004. After the assimilation of observed data, the determination coefficient, root mean square error, and average absolute error of simulated soil moisture were in the ranges of 0.626-0.943, 0.018-0.042, and 0.021-0.041, respectively, with the simulation precision improved significantly, as compared with that before assimilation, indicating the applicability of data assimilation in improving the simulation of soil moisture. The experimental results at single point showed that the errors in the forcing data and observations and the frequency and soil depth of the assimilation of observed data all had obvious effects on the simulated soil moisture.
Hut, Rolf; de Jeu, Richard
Soil moisture is tricky to measure. Currently soil moisture is measured at small footprints using probes and other field devices, or at large footprints using satellites. Promising developments in measuring soil moisture are using fiber optic cables for measurements along a line, or using cosmos rays for field scale measurements. In this demonstration we present a low cost alternative to measure soil moisture at footprints of a few square meters. We use a wifi hotspot and a wifi dongle, both mounted in a cantenna for beam forming. We aim the hotspot on a piece of soil and put the dongle in the path of the reflection. By logging the signal strength of the wifi netwerk, we have a proxy for soil moisture. A first proof of concept is presented.
Wen, J.; Su, Z.
With the consideration of microwave radiation transfer process, physically based simple algorithms have been proposed to estimate land surface soil Fresnel reflectivity or soil moisture and vegetation fractional coverage from ERS wind scatterometer data. The proposed algorithms have been successfull
to 89% for saturated soil, indicating that the polarization method may be viable as a remote sensing system for determining soil moistures. Background on the methods and implications of the results are presented.
Hut, R.; Campbell, C. S.
A low power bluetooth Low Energy (BLE) device is burried 20cm into the soil and a smartphone is placed on top of the soil to test if bluetooth signal strength can be related to soil moisture. The smartphone continuesly records and stores bluetooth signal strength of the device. The soil is artifcially wetted and drained. Results show a relation between BLE signal strength and soil moisture that could be used to measure soil moisture using these off-the-shelf consumer electronics. This opens the possibily to develop sensors that can be buried into the soil, possibly below the plow-line. These sensors can measure local parameters such as electric conductivity, ph, pressure, etc. Readings would be uploaded to a device on the surface using BLE. The signal strength of this BLE would be an (additional) measurement of soil moisture.
Patrignani, A.; Ochsner, T. E.
Although large-scale in situ soil moisture monitoring networks are becoming increasingly valuable research tools, deficiencies of many existing networks include the small spatial support of each station, the low spatial density of stations, and the almost exclusive deployment of stations in grassland vegetation. These grassland soil moisture observations may not adequately represent the real soil moisture patterns in landscapes with intermixed land cover types. The objectives of this study were i) to compare root-zone soil moisture dynamics of two dominant vegetation types across Oklahoma, grassland (observed) and winter wheat cropland (simulated); ii) to relate the soil moisture dynamics of grassland and cropland vegetation using an artificial neural network (ANN) as a transfer function; and iii) to use the resulting ANN to estimate the soil moisture spatial patterns for a landscape of intermixed grassland and wheat cropland. Root-zone soil moisture was represented by plant available water (PAW) in the top 0.8 m of the soil profile. PAW under grassland was calculated from 18 years of soil moisture observations at 78 stations of the Oklahoma Mesonet, whereas PAW under winter wheat was simulated for the same 78 locations using a soil water balance model. Then, we trained an ANN to reproduce the simulated PAW under winter wheat using only seven inputs: day of the year, latitude and longitude, measured PAW under grassland, and percent sand, silt, and clay. The resulting ANN was used, along with grassland soil moisture observations, to estimate the detailed soil moisture pattern for a 9x9 km2 Soil Moisture Active Passive (SMAP) grid cell. The seasonal dynamics of root-zone PAW for grassland and winter wheat were strongly asynchronous, so grassland soil moisture observations rarely reflect cropland soil moisture conditions in the region. The simple ANN approach facilitated efficient and accurate prediction of the simulated PAW under winter wheat, RMSE = 24 mm, using
Full Text Available The present paper proposes a method for the evaluation of soil evaporation, using soil moisture estimations based on radar satellite measurements. We present firstly an approach for the estimation and monitoring of soil moisture in a semi-arid region in North Africa, using ENVISAT ASAR images, over two types of vegetation covers. The first mapping process is dedicated solely to the monitoring of moisture variability related to rainfall events, over areas in the "non-irrigated olive tree" class of land use. The developed approach is based on a simple linear relationship between soil moisture and the backscattered radar signal normalised at a reference incidence angle. The second process is proposed over wheat fields, using an analysis of moisture variability due to both rainfall and irrigation. A semi-empirical model, based on the water-cloud model for vegetation correction, is used to retrieve soil moisture from the radar signal. Moisture mapping is carried out over wheat fields, showing high variability between irrigated and non-irrigated wheat covers. This analysis is based on a large database, including both ENVISAT ASAR and simultaneously acquired ground-truth measurements (moisture, vegetation, roughness, during the 2008–2009 vegetation cycle. Finally, a semi-empirical approach is proposed in order to relate surface moisture to the difference between soil evaporation and the climate demand, as defined by the potential evaporation. Mapping of the soil evaporation is proposed.
J.v.Tyagi; Nuzhat Qazi; S.P.Rai; M.P.Singh
Soil moisture affects various hydrological processes,including evapotranspiration,infiltration,and runoff.Forested areas in the lower western Himalaya in India constitute the headwater catchments for many hill streams and have experienced degradation in forest cover due to grazing,deforestation and other human activities.This change in forest cover is likely to alter the soil moisture regime and,consequently,flow regimes in streams.The effect of change in forest cover on soil moisture regimes of this dry region has not been studied through long term field observations.We monitored soil matric potentials in two small watersheds in the lower western Himalaya of India.The watersheds consisted of homogeneous land covers of moderately dense oak forest and moderately degraded mixed oak forest.Observations were recorded at three sites at three depths in each watershed at fortnightly intervals for a period of three years.The soil moisture contents derived from soil potential measurements were analyzed to understand the spatial,temporal and profile variations under the two structures of forest cover.The analysis revealed large variations in soil moisture storage at different sites and depths and also during different seasons in each watershed.Mean soil moisture storage during monsoon,winter and summer seasons was higher under dense forest than under degraded forest.Highest soil moisture content occurred at shallow soil profiles,decreasing with depth in both watersheds.A high positive correlation was found between tree density and soil moisture content.Mean soil moisture content over the entire study period was higher under dense forest than under degraded forest.This indicated a potential for soil water storage under well managed oak forest.Because soil water storage is vital for sustenance of low flows,attention is needed on the management of oak forests in the Himalayan region.
Synthetic Aperture Radar has shown its large potential for retrieving soil moisture maps at regional scales. However, since the backscattered signal is determined by several surface characteristics, the retrieval of soil moisture is an ill-posed problem when using single configuration imagery. Unles...
Sun, Jian-ying; Li, Min-zan; Zheng, Li-hua; Hu, Yong-guang; Zhang, Xi-jie
The grey-brown alluvial soil in northern China was selected as research object, and the feasibility and possibility of real-time analyzing soil para-fueter with NIR spectroscopic techniques were explored. One hundred fifty samples were collected from a winter wheat farm. NIR absorbance spectra were rapidly measured under their original conditions by a Nicolet Antaris FT-NIR analyzer. Three soil parameters, namely soil moisture, SOM (soil organic matter) and TN (total nitrogen) content, were analyzed. For soil moisture content, a linear regression model was available, using 1920 nm wavelength with correlation coefficient of 0.937, so that the results obtained could be directly used to real-time evaluate soil moisture. SOM content and TN content were estimated with a muviaiple linear regression model, 1870 and 1378 nm wavelengths were selected in the SOM estimate model, and 2262 and 1888 nrameter wavelengths were selected in the TN estimate model. The results showed that soil SOM and TN contents can be evaluated by using NIR absorbance spectra of soil samples.
Soil moisture condition is an important indicator for agricultural drought monitoring. Through the Land Parameter Retrieval Model (LPRM), vegetation optical depth (VOD) as well as surface soil moisture (SM) can be retrieved simultaneously from brightness temperature observations from the Advanced Mi...
Full Text Available The Laoshan forest is the largest forest in Nanjing, and it plays an important role in water resource management in Nanjing. The objectives of this study are to determine if the temperature vegetation dryness index (TVDI is suitable to estimate the soil moisture and if soil moisture is significantly affected by tree species in the Laoshan forest. This paper calculated the spatial distribution of TVDI using LANDSAT-5 TM data. Sixty-two observation points of in situ soil moisture measurements were selected to validate the effectiveness of the TVDI as an index for assessing soil moisture in the Laoshan forest. With the aid of the three different temporal patterns, which are 10 January 2011, 18 May 2011 and 23 September 2011, this paper used the TVDI to investigate the differences of soil moisture under four kinds of mono-species forests and two kinds of mixed forests. The results showed that there is a strong and significant negative correlation between the TVDI and the in situ measured soil moisture (R2 = 0.15–0.8, SE = 0.015–0.041 cm3/cm3. This means that the TVDI can reflect the soil moisture status under different tree species in the Laoshan forest. The soil moisture under these six types of land cover from low to high is listed in the following order: Eucommia ulmoides, Quercus acutissima, broadleaf mixed forest, Cunninghamia lanceolata, coniferous and broadleaf mixed forest and Pinus massoniana.
Predicting the spatial distribution of soil moisture is an important hydrological question. We measured the spatial distribution of surface soil moisture (upper 6 cm) using an Amplitude Domain Reflectometry sensor at the plot scale (2 × 2 m) and small catchment scale (0.84 ha) in...
NASAs Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAPs radiometer-derived standard soil moisture product (L2SMP) provides soil moisture estimates posted on a 36-km fixed Earth grid using brightness temperature observations and ancillary data. A beta quality version of L2SMP was released to the public in October, 2015, Version 3 validated L2SMP soil moisture data were released in May, 2016, and Version 4 L2SMP data were released in December, 2016. Version 4 data are processed using the same soil moisture retrieval algorithms as previous versions, but now include retrieved soil moisture from both the 6 am descending orbits and the 6 pm ascending orbits. Validation of 19 months of the standard L2SMP product was done for both AM and PM retrievals using in situ measurements from global core calval sites. Accuracy of the soil moisture retrievals averaged over the core sites showed that SMAP accuracy requirements are being met.
Predicting the spatial distribution of soil moisture is an important hydrological question. We measured the spatial distribution of surface soil moisture (upper 6 cm) using an Amplitude Domain Reflectometry sensor at the plot scale (2 × 2 m) and small catchment scale (0.84 ha) in...
Daniel M. Bishop
A 1-year study in the Blue Mountains of northeastern Oregon indicates that substantial amounts of soil moisture are consumed during the growing season in lodgepole pine stands. Dual purposes of the study were to estimate the quantities of water that can be stored in basalt-pumice soils typical of the Blue Mountains, and to determine the rate and amount of moisture...
Soil moisture estimation is crucial for agricultural decision-support and a key component of hydrological and climatic research. Unfortunately, quality-controlled soil moisture time series data are uncommon before the most recent decade. However, time series data for precipitation are accessible at ...
Accurate knowledge of antecedent soil moisture and snow depth conditions is often important for obtaining reliable hydrological simulations of stream flow. Data assimilation (DA) methods can be used to integrate remotely-sensed (RS) soil moisture and snow depth retrievals into a hydrology model and...
This article examines the influence of passive microwave based soil moisture and snow depth retrievals towards improving estimates of drought through data assimilation. Passive microwave based soil moisture and snow depth retrievals from a variety of sensors are assimilated separately into the Noah ...
The Soil Moisture Active Passive (SMAP) project has made excellent progress in addressing the requirements and science goals of the primary mission. The primary mission baseline requirement is estimates of global surface soil moisture with an error of no greater than 4% volumetric (one sigma) exclud...
The calibration and validation of soil moisture remote sensing products is complicated due to the logistics of installing a long term soil moisture monitoring network in an active landscape. It is more efficient to locate these stations along agricultural field boundaries, but unfortunately this oft...
Since the beginning of its routine science operation in March 2015, the NASA SMAP observatory has been returning interference-mitigated brightness temperature observations at L-band (1.41 GHz) frequency from space. The resulting data enable frequent global mapping of soil moisture with a retrieval uncertainty below 0.040 cu m/cu m at a 36 km spatial scale. This paper describes the development and validation of an enhanced version of the current standard soil moisture product. Compared with the standard product that is posted on a 36 km grid, the new enhanced product is posted on a 9 km grid. Derived from the same time-ordered brightness temperature observations that feed the current standard passive soil moisture product, the enhanced passive soil moisture product leverages on the Backus-Gilbert optimal interpolation technique that more fully utilizes the additional information from the original radiometer observations to achieve global mapping of soil moisture with enhanced clarity. The resulting enhanced soil moisture product was assessed using long-term in situ soil moisture observations from core validation sites located in diverse biomes and was found to exhibit an average retrieval uncertainty below 0.040 cu m/cu m. As of December 2016, the enhanced soil moisture product has been made available to the public from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center.
Soil moisture controls the exchange of water and energy between the land surface and the atmosphere and exhibits memory that may be useful for climate prediction at monthly time scales. Though spatially distributed observations of soil moisture are increasingly becoming available from remotely sense...
Abstract: Soil moisture is a key geophysical variable in hydrological and meteorological processes. Accurate and current observations of soil moisture over mesoscale to global scales as inputs to hydrological, weather and climate modelling will benefit the predictability and understanding of these p...
Full Text Available Soil moisture is an essential climate variable (ECV of major importance for land–atmosphere interactions and global hydrology. An appropriate representation of soil moisture dynamics in global climate models is therefore important. Recently, a first multidecadal, observation-based soil moisture dataset has become available that provides information on soil moisture dynamics from satellite observations (ECVSM, essential climate variable soil moisture. The present study investigates the potential and limitations of this new dataset for several applications in climate model evaluation. We compare soil moisture data from satellite observations, reanalysis and simulations from a state-of-the-art land surface model and analyze relationships between soil moisture and precipitation anomalies in the different dataset. Other potential applications like model parameter optimization or model initialization are not investigated in the present study. In a detailed regional study, we show that ECVSM is capable to capture well the interannual and intraannual soil moisture and precipitation dynamics in the Sahelian region. Current deficits of the new dataset are critically discussed and summarized at the end of the paper to provide guidance for an appropriate usage of the ECVSM dataset for climate studies.
Tian, Fei; Xie, Yong-Sheng; Suo, Gai-Di; Ding, Ya-Dong
To investigate the controlling effects of dual mulching on soil moisture in an apple orchard on the Weibei rainfed highland, soil moisture in the 0-600 cm soil profile of the apple orchard was measured under four mulching treatments (plastic film plus straw, plastic film and straw mulches, as well as a non-mulching control) , and meanwhile the apple yield and branch growth increment were analyzed statistically. Results showed that the dual mulching treatment had the best effect on soil moisture conservation, and the soil water storage in such a soil profile was 6.7% higher than the control treatment. Long-term dual mulching could effectively alleviate soil desiccation occurring in deep soil layer in the region, and the monthly averaged soil water storage in stable layer (240-600 cm) was 64.22 mm higher than that of the control treatment. Both plastic film plus straw and plastic film mulches were able to reduce the temporal fluctuation of soil moisture in shallow soil (0-60 cm) and enhance the temporal stability of soil moisture in the layer. Compared with the single mulching treatments, the dual mulching treatment could effectively decrease the vertical variation of soil moisture in the profile and improve the stability of the vertical soil moisture distribution. The apple yield under the dual mulching treatment was evidently increased by 48.2%, as compared with the control treatment. All the analyses showed that dual mulching had more advantages in controlling soil moisture and improving apple yield than single mulching.
Niemeyer, Ryan J.; Heinse, Robert; Link, Timothy E.; Seyfried, Mark S.; Klos, P. Zion; Williams, Christopher J.; Nielson, Travis
Woody plant cover has increased 10-fold over the last 140+ years in many parts of the semi-arid western USA. Woody plant cover can alter the timing and amount of plant available moisture in the soil and saprolite. To assess spatiotemporal subsurface moisture dynamics over two water years in a snow-dominated western juniper stand we compared moisture dynamics horizontally across a discontinuous canopy, and vertically in soil and saprolite. We monitored soil moisture at 15 and 60 cm and conducted periodic electromagnetic induction and electrical resistivity tomography surveys aimed at sensing moisture changes within the root zone and saprolite. Timing of soil moisture dry down at 15 cm was very similar between canopy patches and interspace. Conversely, dry down at 60 cm occurred 22 days earlier in the interspace than under canopy patches. After rainfall, interspaces with more shrubs showed greater increases in soil moisture than interspaces with few shrubs. For the few rainfall events that were large enough to increase soil moisture at 60 cm, increases in moisture occurred almost exclusively below the canopy. Soil water holding capacity from 0 to 150 cm was a primary driver of areas that were associated with the greatest change in distributed electrical conductivity - an indicator of changes in soil moisture - across the growing season. Vegetation was also correlated with a greater seasonal change in electrical conductivity at these depths. The seasonal change in resistivity suggested moisture extraction by juniper well into the saprolite, as deep as 12 m below the surface. This change in deep subsurface resistivity primarily occurred below medium and large juniper trees. This study suggests how tree roots are both increasing infiltration below their canopy while also extracting moisture at depths of upwards of 12 m. Information from this study can help improve our understanding of juniper resilience to drought and the hydrologic impacts of semi-arid land cover
Bo, Yaojun; Zhu, Qingke; Zhao, Weijun
Soil moisture is the primary factor limiting plant growth and vegetation rehabilitation in the loess region of northern Shaanxi, China. This 5-year (2008-2012) study investigated methods of selecting appropriate microsites for vegetation restoration based on efficient use of soil moisture; 5-year data were compared with 56 years of precipitation data using standardized precipitation index. In addition, the effects of microtopography on the spatiotemporal variations of soil moisture were analyzed at the Wuqi Ecological Station of Beijing Forestry University. Results showed that average annual precipitation during last 5 years fell by 12.4% during the growing season compared with 1957-2012 data and soil moisture content at depth of 0-160 cm under went dramatic changes and became relatively low in July and August. Soil moisture content varied in different microtopographical units as follows: gullies > gently-sloped terraces > collapsed soils > undisturbed slopes (control) > furrows > escarpments. The vertical distribution of soil moisture content in different microtopographical units showed dramatic changes at depth of 0-40 cm. Soil moisture content of gently-sloped terraces, gullies, collapsed areas, furrows, and undisturbed slopes was highest at depth of 80-160 cm with a level of instability at depth of 40-80 cm. For gently-sloped terraces and gullies, soil moisture content followed the order of 40-80 cm > 0-40 cm; for collapsed areas, furrows, and undisturbed slopes, soil moisture content follows the order of 0-40 cm > 40-80 cm. For escarpments, soil moisture content varied with depth in a different pattern: 0-40 cm > 80-160 cm > 40-80 cm. This study is of theoretical significance and will help guide the sustainable development of ecological restoration and vegetation rehabilitation in the Loess region.
Norouzi, H.; Forbes, A.
In October 2014, the Soil Moisture Active and Passive mission (SMAP) will launch into a near-polar and sun- synchronous orbit. SMAP includes the first 3 KM resolution product, by both radar and radiometer sensors which will transmit useful information concentrating on the global measurements of soil moisture and freeze/thaw cycles. NOAA- CREST (National Oceanic and Atmospheric Administration- Cooperative Remote Sensing Science and Technology) deploys a series of in-situ devices into the soil, and an L-BAND Radiometer close to the site ground at the Cary Institute in Millbrook, NY. The site is important for future validation of SMAP mission. Comparing mathematical and ground based remote sensing of soil moisture is beneficial to ensure the accuracy of the measurements. The focus of this research is to analyze and compare soil moisture from ESA- SMOS (Europe Space Agency- Soil Moisture Ocean Salinity) mission and the Cary Institute's soil moisture measurements within the same time period, and location. In the interest of establishing superb authentication; comparing SMOS and ground measurements will justify the accuracy of the newly launch satellite. Discrepancies can be found between field point measurement and relatively large footprint of SMOS, which affects comparison and validation. Several techniques and statistical methods will provide a more meaningful comparison to analyze soil moisture data. The results of this project will help to provide a useful method to compare the NOAA-CREST soil moisture measurements and SMAP measurements. In conclusion, the SMAP advance technology will provide more accurate feedback for modeling numerical weather and climate models. Keywords: Soil Moisture, Precipitation, CREST-SMART, Cary Institute, In-situ, Remote Sensors Accurate Soil Moisture Data, Millbrook, N.Y., CATDS, Hydrology is the branch of science concerning properties of earth's water especially its movement in relation to land. SMOS MIRAS, SMAP, Sensors (Underground)
Gilewski, Pawei Grzegorz; Kedzior, Mateusz Andrzej; Zawadzki, Jaroslaw
In this paper, authors calculated high resolution volumetric soil moisture (SM) by means of the Sentinel- 1 data for the Kampinos National Park in Poland and verified obtained results.To do so, linear regression coefficients (LRC) between in-situ SM measurements and Sentinel-1 radar backscatter values were calculated. Next, LRC were applied to obtain SM estimates from Sentinel-1 data. Sentinel-1 SM was verified against in-situ measurements and low-resolution SMOS SM estimates using Pearson's linear correlation coefficient. Simple SM retrieval method from radar data used in this study gives better results for meadows and when Sentinel-1 data in VH polarisation are used.Further research should be conducted to prove usefulness of proposed method.
Nakai, Taro; Katul, Gabriel G.; Kotani, Ayumi; Igarashi, Yasunori; Ohta, Takeshi; Suzuki, Masakazu; Kumagai, Tomo'omi
Temporal variability in root zone soil moisture content (w) exhibits a Lorentzian spectrum with memory dictated by a damping term when forced with white-noise precipitation. In the context of regional dimming, radiation and precipitation variability are needed to reproduce w trends prompting interest in how the w memory is altered by radiative forcing. A hierarchy of models that sequentially introduce the spectrum of precipitation, net radiation, and the effect of w on evaporative and drainage losses was used to analyze the spectrum of w at subtropical and temperate forested sites. Reproducing the w spectra at long time scales necessitated simultaneous precipitation and net radiation measurements depending on site conditions. The w memory inferred from observed w spectra was 25-38 days, larger than that determined from maximum wet evapotranspiration and field capacity. The w memory can be reasonably inferred from the Lorentzian spectrum when precipitation and evapotranspiration are in phase.
Full Text Available Salinization and secondary salinization often appear after irrigation with saline water. The Taklimakan Desert Highway Shelterbelt has been irrigated with saline ground water for more than ten years; however, soil salinity in the shelterbelt has not been evaluated. The objective of this study was to analyze the spatial and temporal distribution of soil moisture and salinity in the shelterbelt system. Using a non-uniform grid method, soil samples were collected every two days during one ten-day irrigation cycle in July 2014 and one day in spring, summer, and autumn. The results indicated that soil moisture declined linearly with time during the irrigation cycle. Soil moisture was greatest in the southern and eastern sections of the study area. In contrast to soil moisture, soil electrical conductivity increased from 2 to 6 days after irrigation, and then gradually decreased from 6 to 8 days after irrigation. Soil moisture was the greatest in spring and the least in summer. In contrast, soil salinity increased from spring to autumn. Long time drip-irrigation with saline groundwater increased soil salinity slightly. The soil salt content was closely associated with soil texture. The current soil salt content did not affect plant growth, however, the soil in the shelterbelt should be continuously monitored to prevent salinization in the future.
Martens, B.; Miralles, D.; Lievens, H.; Fernández-Prieto, D.; Verhoest, N. E. C.
Terrestrial evaporation is an essential variable in the climate system that links the water, energy and carbon cycles over land. Despite this crucial importance, it remains one of the most uncertain components of the hydrological cycle, mainly due to known difficulties to model the constraints imposed by land water availability on terrestrial evaporation. The main objective of this study is to assimilate satellite soil moisture observations from the Soil Moisture and Ocean Salinity (SMOS) mission into an existing evaporation model. Our over-arching goal is to find an optimal use of satellite soil moisture that can help to improve our understanding of evaporation at continental scales. To this end, the Global Land Evaporation Amsterdam Model (GLEAM) is used to simulate evaporation fields over continental Australia for the period September 2010-December 2013. SMOS soil moisture observations are assimilated using a Newtonian Nudging algorithm in a series of experiments. Model estimates of surface soil moisture and evaporation are validated against soil moisture probe and eddy-covariance measurements, respectively. Finally, an analogous experiment in which Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture is assimilated (instead of SMOS) allows to perform a relative assessment of the quality of both satellite soil moisture products. Results indicate that the modelled soil moisture from GLEAM can be improved through the assimilation of SMOS soil moisture: the average correlation coefficient between in situ measurements and the modelled soil moisture over the complete sample of stations increased from 0.68 to 0.71 and a statistical significant increase in the correlations is achieved for 17 out of the 25 individual stations. Our results also suggest a higher accuracy of the ascending SMOS data compared to the descending data, and overall higher quality of SMOS compared to AMSR-E retrievals over Australia. On the other hand, the effect of soil moisture data
Tabatabaeenejad, A.; Moghaddam, M.
Soil moisture is of fundamental importance to many hydrological and biological processes. Soil moisture information is vital to understanding the cycling of water, energy, and carbon in the Earth system. Knowledge of soil moisture is critical to agencies concerned with weather and climate, runoff potential and flood control, soil erosion, reservoir management, water quality, agricultural productivity, drought monitoring, and human health. The need to monitor the soil moisture on a global scale has motivated missions such as Soil Moisture Active and Passive (SMAP) . Rough surface scattering models and remote sensing retrieval algorithms are essential in study of the soil moisture, because soil can be represented as a rough surface structure. Effects of soil moisture on the backscattered field have been studied since the 1960s, but soil moisture estimation remains a challenging problem and there is still a need for more accurate and more efficient inversion algorithms. It has been shown that the simulated annealing method is a powerful tool for inversion of the model parameters of rough surface structures . The sensitivity of this method to measurement noise has also been investigated assuming a two-layer structure characterized by the layers dielectric constants, layer thickness, and statistical properties of the rough interfaces . However, since the moisture profile varies with depth, it is sometimes necessary to model the rough surface as a layered structure with a rough interface on top and a stratified structure below where each layer is assumed to have a constant volumetric moisture content. In this work, we discretize the soil structure into several layers of constant moisture content to examine the effect of subsurface profile on the backscattering coefficient. We will show that while the moisture profile could vary in deeper layers, these layers do not affect the scattered electromagnetic field significantly. Therefore, we can use just a few layers
Vogel, M. M.; Orth, R.; Cheruy, F.; Hagemann, S.; Lorenz, R.; Hurk, B. J. J. M.; Seneviratne, S. I.
Regional hot extremes are projected to increase more strongly than global mean temperature, with substantially larger changes than 2°C even if global warming is limited to this level. We investigate the role of soil moisture-temperature feedbacks for this response based on multimodel experiments for the 21st century with either interactive or fixed (late 20th century mean seasonal cycle) soil moisture. We analyze changes in the hottest days in each year in both sets of experiments, relate them to the global mean temperature increase, and investigate processes leading to these changes. We find that soil moisture-temperature feedbacks significantly contribute to the amplified warming of the hottest days compared to that of global mean temperature. This contribution reaches more than 70% in Central Europe and Central North America. Soil moisture trends are more important for this response than short-term soil moisture variability. These results are relevant for reducing uncertainties in regional temperature projections.
Kramarenko, V. V.; Nikitenkov, A. N.; Molokov, V. Yu; Shramok, A. V.; Pozdeeva, G. P.
The problem of rapid drying arises when determining moisture, ash and organic matter content, as well as during many other soil tests. For highly-organic and organo-mineral peat soils the problem of advanced measurement of moisture content is of special importance, since after reweighing the dry sample increase in mass may be observed. The article examines the methods in determining the moisture content in peat and organic soils via microwave radiation, which will greatly speed up the process, simplify the complexity and cost of laboratory tests. The paper presents a detailed review of the methods determining moisture content in soils and characteristics, as well as application scope. The work contains the research results on moisture organic soils: drying in a microwave oven and the current domestic standards.
Martini, Edoardo; Wollschläger, Ute; Kögler, Simon; Behrens, Thorsten; Dietrich, Peter; Reinstorf, Frido; Schmidt, Karsten; Weiler, Markus; Werban, Ulrike; Zacharias, Steffen
Characterizing the spatial patterns of soil moisture is critical for hydrological and meteorological models, as soil moisture is a key variable that controls matter and energy fluxes and soil-vegetation-atmosphere exchange processes. Deriving detailed process understanding at the hillslope scale is not trivial, because of the temporal variability of local soil moisture dynamics. Nevertheless, it remains a challenge to provide adequate information on the temporal variability of soil moisture and its controlling factors. Recent advances in wireless sensor technology allow monitoring of soil moisture dynamics with high temporal resolution at varying scales. In addition, mobile geophysical methods such as electromagnetic induction (EMI) have been widely used for mapping soil water content at the field scale with high spatial resolution, as being related to soil apparent electrical conductivity (ECa). The objective of this study was to characterize the spatial and temporal pattern of soil moisture at the hillslope scale and to infer the controlling hydrological processes, integrating well established and innovative sensing techniques, as well as new statistical methods. We combined soil hydrological and pedological expertise with geophysical measurements and methods from digital soil mapping for designing a wireless soil moisture monitoring network. For a hillslope site within the Schäfertal catchment (Central Germany), soil water dynamics were observed during 14 months, and soil ECa was mapped on seven occasions whithin this period of time using an EM38-DD device. Using the Spearman rank correlation coefficient, we described the temporal persistence of a dry and a wet characteristic state of soil moisture as well as the switching mechanisms, inferring the local properties that control the observed spatial patterns and the hydrological processes driving the transitions. Based on this, we evaluated the use of EMI for mapping the spatial pattern of soil moisture under
Rowlandson, Tracy L.; Berg, Aaron A.; Bullock, Paul R.; Ojo, E. RoTimi; McNairn, Heather; Wiseman, Grant; Cosh, Michael H.
The calibration and validation of remotely sensed soil moisture products relies upon an accurate source of ground truth data. The primary method of providing this ground truth is to conduct intensive field campaigns with manual surface soil moisture sampling measurements, which utilize gravimetric sampling, soil moisture probes, or both, to estimate the volumetric soil water content. Soil moisture probes eliminate the need for labor-intensive gravimetric sampling. To ensure the accuracy of these probes, several studies have determined these probes need various degrees of localized calibration. This study examines six possible calibration techniques using data collected during a field campaign conducted in 2012, with soil moisture samples being collected over 55 fields in southern Manitoba, as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). The use of a general equation, applied to all collected data, resulted in the largest error regardless of whether a linear or third order polynomial relationship was established for the calibration of the soil moisture probes. Calibration equations based on soil texture or vegetation land cover reduced the error; however, the individual calibration equations established for each field in the study had the lowest error of all the calibration techniques. Although average bias was low for all of the calibration techniques, the use of the general equation to calibrate individual fields resulted in high biases for some fields.
S . Prijono
Full Text Available The hedgerow systems are the agroforestry practices suggesting any positive impacts and negative impacts on soil characteristics. This study evaluated the effects of hedgerows on the unsaturated hydraulic conductivity of soil with the Libardi method approach. This study was conducted in North Lampung for 3 months on the hedgerow plots of Peltophorum dassyrachis (P, Gliricidia sepium (G, and without hedgerow plot (K, with four replications. Each plot was watered as much as 150 liters of water until saturated, then the soil surface were covered with the plastic film. Observation of soil moisture content was done to a depth of 70 cm by the 10 cm intervals. Soil moisture content was measured using the Neutron probe that was calibrated to get the value of volumetric water content. Unsaturated hydraulic conductivity of soil was calculated by using the Libardi Equation. Data were tested using the analysis of variance, the least significant different test (LSD, Duncan Multiple Range Test (DMRT, correlation and regression analysis. The results showed that the hedgerow significantly affected the soil moisture content and unsaturated hydraulic conductivity. Soil moisture content on the hedgerow plots was lower than the control plots. The value of unsaturated hydraulic conductivity in the hedgerow plots was higher than the control plots. Different types of hedgerows affected the soil moisture content and unsaturated hydraulic conductivity. The positive correlation was found between the volumetric soil moisture content and the unsaturated hydraulic conductivity of soil.
Liu, Qian; Zhao, Yingshi
Soil moisture can be estimated from point measurements, hydrologic models, and remote sensing. Many researches indicated that the most promising approach for soil moisture is the integration of remote sensing surface soil moisture data and computational modeling. Although many researches were conducted using passive microwave remote sensing data in soil moisture assimilation with coarse spatial resolution, few researches were carried out using active microwave remote sensing observation. This research developed and tested an operational approach of assimilation for soil moisture prediction using active microwave remote sensing data ASAR (Advanced Synthetic Aperture Radar) in Heihe Watershed. The assimilation was based on ensemble Kalman filter (EnKF), a forward radiative transfer model and the Distributed Hydrology Soil Vegetation Model (DHSVM). The forward radiative transfer model, as a semi-empirical backscattering model, was used to eliminate the effect of surface roughness and vegetation cover on the backscatter coefficient. The impact of topography on soil water movement and the vertical and lateral exchange of soil water were considered. We conducted experiments to assimilate active microwave remote sensing data (ASAR) observation into a hydrologic model at two field sites, which had different underlying conditions. The soil moisture ground-truth data were collected through the field Time Domain Reflectometry (TDR) tools, and were used to assess the assimilation method. The temporal evolution of soil moisture measured at point-based monitoring locations were compared with EnKF based model predictions. The results indicated that the estimate of soil moisture was improved through assimilation with ASAR observation and the soil moisture based on data assimilation can be monitored in moderate spatial resolution.
Zhang, H.; Frederiksen, C. S.
Using a version of the Australian Bureau of Meteorology Research Centre (BMRC) atmospheric general circulation model, this study investigates the model's sensitivity to different soil moisture initial conditions in its dynamically extended seasonal forecasts of June-August 1998 climate anomalies, with focus on the south and northeast China regions where severe floods occurred. The authors' primary aim is to understand the model's responses to different soil moisture initial conditions in terms of the physical and dynamical processes involved. Due to a lack of observed global soil moisture data, the efficacy of using soil moisture anomalies derived from the NCEP-NCAR reanalysis is assessed. Results show that by imposing soil moisture percentile anomalies derived from the reanalysis data into the BMRC model initial condition, the regional features of the model's simulation of seasonal precipitation and temperature anomalies are modulated. Further analyses reveal that the impacts of soil moisture conditions on the model's surface temperature forecasts are mainly from localized interactions between land surface and the overlying atmosphere. In contrast, the model's sensitivity in its forecasts of rainfall anomalies is mainly due to the nonlocal impacts of the soil moisture conditions. Over the monsoon-dominated east Asian region, the contribution from local water recycling, through surface evaporation, to the model simulation of precipitation is limited. Rather, it is the horizontal moisture transport by the regional atmospheric circulation that is the dominant factor in controlling the model rainfall. The influence of different soil moisture conditions on the model forecasts of rainfall anomalies is the result of the response of regional circulation to the anomalous soil moisture condition imposed. Results from the BMRC model sensitivity study support similar findings from other model studies that have appeared in recent years and emphasize the importance of improving
Crow, W. T.; Milak, S.; Moghaddam, M.
A critical aspect of the AirMOSS mission is the temporal interpolation of (temporally-discrete) AirMOSS Level 2/3 root-zone soil moisture retrievals into a continuous, hourly root-zone soil moisture product. This is achieved via the assimilation of AirMOSS Level 2/3 root-zone soil moisture retrievals into continuous three-dimensional hydrologic modeling of AirMOSS study sites using the Penn State Integrated Hydrologic (PIHM) model. In this presentation, we will describe the results of a comparison analysis between: 1) hourly PIHM profile soil moisture predictions, 2) AirMOSS Level 2/3 root-zone soil moisture retrievals, and 3) and profile soil moisture observations obtained via ground-based instrumentation at multiple AirMOSS study sites. Since any reasonably-sophisticated integration of remotely-sensed and modeled root-zone soil moisture estimates requires information regarding the objective accuracy of each, the results of this analysis will be used to parameterize a data assimilation approach for integrating discrete AirMOSS Level 2/3 products into a continuous integration of the PIHM model. Based on this integration approach, preliminary AirMOSS Level 4 root-zone soil moisture products will be presented and evaluated. Results will highlight the relative limitations of both the AirMOSS Level 2/3 retrievals and PIHM-based estimates and therefore justify the integrated use of both soil moisture products to create an optimized Level 4 root-zone soil moisture analysis.
Wang Xiaobin,; Cai, D.; Oenema, O.; Perdok, U.D.; Hoogmoed, W.B.
This review discusses the duststorm-related soil erosion and its impact on soil carbon losses and moisture scarcity in northern China. Heavily affected areas show a loss of nutrients, organic carbon and field water capacity of soils. Compared with nondegraded soil, the carbon content in degraded soi
Tomer, S. K.; Al Bitar, A.; Sekhar, M.; Merlin, O.; Bandyopadhyay, S.; Kerr, Y. H.
This study presents an intercomparison of the RADARSAT-2 derived soil moisture, SMOS derived soil moisture and field measured soil moisture in the Berambadi watershed, South India. Seventeen images of RADARSAT-2, SMOS products, and field data collected in the 50 field plots during 2010-2011 were used. The data were collected from field campaigns in the framework of AMBHAS project. A non parametric algorithm was developed based on the CDF transformation to retrieve the soil moisture from RADARSAT-2 backscatter coefficient at a spatial resolution of 100 m based on the measured soil moisture. The developed algorithm to retrieve surface soil moisture from RADARSAT-2 provided a good estimate of the field plot soil moisture with a RMSE of 0.05 cm3 cm-3. The average soil moisture from RADARSAT-2 and field measured soil moisture were compared to SMOS derived soil moisture at the watershed scale. Several averaging strategies were considered to take into account the surface heterogeneity and SMOS antenna patterns. Results were analysed by taking into consideration the soil texture heterogeneity, radio frequency interference effect and climatic effect. SMOS underestimated the soil moisture in compare to both RADARSAT-2 and field averaged soil moisture. A bias correction for the SMOS data is suggested using Clayton copula. SMOS showed a better correlation with the RADARSAT-2 watershed averaged soil moisture than directly averaged field soil moisture, as field campaign covered a smaller region of the watershed than RADARSAT-2 data. This shows the potential synergy between the use of active/passive microwave soil moisture for upscalling/downscalling soil moisture.
Wagner, Wolfgang; Pathe, Carsten; Doubkova, Marcela; Sabel, Daniel; Bartsch, Annett; Hasenauer, Stefan; Blöschl, Günter; Scipal, Klaus; Martínez-Fernández, José; Löw, Alexander
The high spatio-temporal variability of soil moisture is the result of atmosphericforcing and redistribution processes related to terrain, soil, and vegetation characteristics.Despite this high variability, many field studies have shown that in the temporal domainsoil moisture measured at specific locations is correlated to the mean soil moisture contentover an area. Since the measurements taken by Synthetic Aperture Radar (SAR)instruments are very sensitive to soil moisture it is hypothesized that the temporally stablesoil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT AdvancedSynthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located inthe Duero basin, Spain. It is found that a time-invariant linear relationship is well suited forrelating local scale (pixel) and regional scale (50 km) backscatter. The observed linearmodel coefficients can be estimated by considering the scattering properties of the terrainand vegetation and the soil moisture scaling properties. For both linear model coefficients,the relative error between observed and modelled values is less than 5 % and thecoefficient of determination (R²) is 86 %. The results are of relevance for interpreting anddownscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT)and passive (SMOS, AMSR-E) instruments.
Wu, B M; Subbarao, K V
... S. sclerotiorum isolates tested. Carpogenic germination of the two species was compared under a variety of temperature, soil moisture, burial depths, and short periods of high temperature and low soil moisture...
As soil moisture increases, slope stability decreases. Remotely sensed soil moisture data can provide routine updates of slope conditions necessary for landslide predictions. For regional scale landslide investigations, only remote sensing methods have the spatial and temporal resolution required to...
Brown, Molly E.; Escobar, Vanessa; Moran, Susan; Entekhabi, Dara; O'Neill, Peggy; Njoku, Eni G.; Doorn, Brad; Entin, Jared K.
Water in the soil, both its amount (soil moisture) and its state (freeze/thaw), plays a key role in water and energy cycles, in weather and climate, and in the carbon cycle. Additionally, soil moisture touches upon human lives in a number of ways from the ravages of flooding to the needs for monitoring agricultural and hydrologic droughts. Because of their relevance to weather, climate, science, and society, accurate and timely measurements of soil moisture and freeze/thaw state with global coverage are critically important.
Kim, S.; Johnson, J. T.; Moghaddam, M.; Tsang, L.; Colliander, A.
Surface soil moisture of the top 5-cm was estimated at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Radar observations of soil moisture offer the advantage of high spatial resolution, but have been challenging in the past due to the complicating factors of surface roughness and vegetation scattering. In this work, physically-based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of roughness and dynamic vegetation. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-RMSE (root mean square error, ubRMSE) and -0.05 dB (bias). The forward models further allow the mechanisms of radar scattering to be examined to identify the sensitivity of radar scattering to soil moisture. Global patterns of the soil moistures retrieved by the algorithm generally match well with those from other satellite sensors. However biases exist in dry regions, and discrepancies are found in thick vegetation areas. The retrievals are compared with in situ measurements of soil moisture in locations characterized as cropland, grassland, and woody vegetation. Terrain slopes, subpixel heterogeneity, tillage practices, and vegetation growth influence the retrievals, but are largely corrected by the retrieval processes. Soil moisture retrievals agree with the in-situ measurements at 0.052 m3/m3 ubRMSE, -0.015 m3/m3 bias, and a correlation of 0.50. These encouraging retrieval results demonstrate the feasibility of a physically-based time-series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation types. The findings are important for future L-band radar missions with frequent revisits that permit time
M S Roxy; V B Sumithranand; G Renuka
Soil moisture is an important parameter of the earth’s climate system. Regression model for estimation of soil moisture at various depths has been developed using the amount of moisture near the surface layer. The estimated values of soil moisture are tested with the measured moisture values and it is found that the estimations are comparable with the observations. The variation of soil thermal properties with the amount of moisture in isohyperthermic ultisols has been investigated at a tropical site in south Kerala for the year 2008. The soil temperatures at 0.05, 0.10, 0.20, 0.30, and 0.50 m depths and soil moisture at 0.05 and 0.10 m are measured using the hydrometeorological data acquisition system installed at the observational site. For soil water contents ranging between 11 and 42% in the soil layer of depth 0.05–0.10 m, the mean values of the heat capacity, thermal diffusivity, thermal conductivity, and thermal admittance obtained were 2.2466 × 10−6 Jm−3K−1, 0.4238 × 10−6 m2s−1, 0.9658 Wm−1K−1, 2.1517 Jm−2s−1/2K−1, respectively. The magnitudes of the diurnal soil thermal parameters showed strong association with the levels of the water content. The thermal diffusivity was found to increase with the amount of soil moisture, up to about 22% of the volumetric water content, but fell as the water content further increases. Similar patterns of the soil moisture levels were noticeable both for the thermal conductivity and admittance.
Manning, Colin; Widmann, Martin; Bevacqua, Emanuele; Maraun, Douglas; Van Loon, Anne; Vrac, Mathieu
Soil moisture in Europe acts to partition incoming energy into sensible and latent heat fluxes, thereby exerting a large influence on temperature variability. Soil moisture is predominantly controlled by precipitation and evapotranspiration. When these meteorological variables are accumulated over different timescales, their joint multivariate distribution and dependence structure can be used to provide information of soil moisture. We therefore consider soil moisture drought as a compound event of meteorological drought (deficits of precipitation) and heat waves, or more specifically, periods of high Potential Evapotraspiration (PET). We present here a statistical model of soil moisture based on Pair Copula Constructions (PCC) that can describe the dependence amongst soil moisture and its contributing meteorological variables. The model is designed in such a way that it can account for concurrences of meteorological drought and heat waves and describe the dependence between these conditions at a local level. The model is composed of four variables; daily soil moisture (h); a short term and a long term accumulated precipitation variable (Y1 and Y_2) that account for the propagation of meteorological drought to soil moisture drought; and accumulated PET (Y_3), calculated using the Penman Monteith equation, which can represent the effect of a heat wave on soil conditions. Copula are multivariate distribution functions that allow one to model the dependence structure of given variables separately from their marginal behaviour. PCCs then allow in theory for the formulation of a multivariate distribution of any dimension where the multivariate distribution is decomposed into a product of marginal probability density functions and two-dimensional copula, of which some are conditional. We apply PCC here in such a way that allows us to provide estimates of h and their uncertainty through conditioning on the Y in the form h=h|y_1,y_2,y_3 (1) Applying the model to various
Smolander, Tuomo; Lemmetyinen, Juha; Rautiainen, Kimmo; Schwank, Mike; Pulliainen, Jouni
Soil moisture and soil freezing are important for diverse hydrological, biogeochemical, and climatological applications. They affect surface energy balance, surface and subsurface water flow, and exchange rates of carbon with the atmosphere. Soil freezing controls important biogeochemical processes, like photosynthetic activity of plants and microbial activity within soils. Permafrost covers approximately 24% of the land surface in the Northern Hemisphere and seasonal freezing occurs on approximately 51% of the area. The retrieval method presented is based on an inversion technique and applies a semiempirical backscattering model that describes the dependence of radar backscattering of forest as a function of stem volume, soil permittivity, the extinction coefficient of forest canopy, surface roughness, incidence angle, and radar frequency. It gives an estimate of soil permittivity using active microwave measurements. Applying a Bayesian assimilation scheme, it is also possible to use other soil permittivity retrievals to regulate this estimate to combine for example low resolution passive observations with high resolution active observations for a synergistic retrieval. This way the higher variance in the active retrieval can be constricted with the passive retrieval when at the same time the spatial resolution of the product is improved compared to the passive-only retrieval. The retrieved soil permittivity estimate can be used to detect soil freeze/thaw state by considering the soil to be frozen when the estimate is below a threshold value. The permittivity retrieval can also be used to estimate the relative moisture of the soil. The method was tested using SAR (Synthetic Aperture Radar) measurements from ENVISAT ASAR instrument for the years 2010-2012 and from Sentinel-1 satellite for the years 2015-2016 in Sodankylä area in Northern Finland. The synergistic method was tested combining the SAR measurements with a SMOS (Soil Moisture Ocean Salinity) radiometer
Sehgal, Vinit; Sridhar, Venkataramana; Tyagi, Aditya
This study proposes a multi-wavelet Bayesian ensemble of two Land Surface Models (LSMs) using in-situ observations for accurate estimation of soil moisture for Contiguous United States (CONUS). In the absence of a continuous, accurate in-situ soil moisture dataset at high spatial resolution, an ensemble of Noah and Mosaic LSMs is derived by performing a Bayesian Model Averaging (BMA) of several wavelet-based multi-resolution regression models (WR) of the simulated soil moisture from the LSMs and in-situ volumetric soil moisture dataset obtained from the U.S. Climate Reference Network (USCRN) field stations. This provides a proxy to the in-situ soil moisture dataset at 1/8th degree spatial resolution called Hybrid Soil Moisture (HSM) for three soil layers (1-10 cm, 10-40 cm and 40-100 cm) for the CONUS. The derived HSM is used further to study the layer-wise response of soil moisture to drought, highlighting the necessity of the ensemble approach and soil profile perspective for drought analysis. A correlation analysis between HSM, the long-term (PDSI, PHDI, SPI-9, SPI-12 and SPI-24) and the short-term (Palmer Z index, SPI-1 and SPI-6) drought indices is carried out for the nine climate regions of the U.S. indicating a higher sensitivity of soil moisture to drought conditions for the Southern U.S. Furthermore, a layer-wise soil moisture percentile approach is proposed and applied for drought reconstruction in CONUS with a focus on the Southern U.S. for the year 2011.
Kalopesas, Charalampos; Galanis, George; Kalopesa, Eleni; Katsogiannos, Fotis; Kalafatis, Panagiotis; Bilas, George; Patakas, Aggelos; Zalidis, George
Mapping the spatial variation of soil moisture content is a vital parameter for precision agriculture techniques. The aim of this study was to examine the correlation of soil moisture and conductivity (EC) data obtained through scanning techniques with field telemetry data and to spatially separate the field into discrete irrigation management zones. Using the Veris MSP3 model, geo-referenced data for electrical conductivity and organic matter preliminary maps were produced in a pilot kiwifruit field in Chrysoupoli, Kavala. Data from 15 stratified sampling points was used in order to produce the corresponding soil maps. Fusion of the Veris produced maps (OM, pH, ECa) resulted on the delineation of the field into three zones of specific management interest. An appropriate pedotransfer function was used in order to estimate a capacity soil indicator, the saturated volumetric water content (θs) for each zone, while the relationship between ECs and ECa was established for each zone. Validation of the uniformity of the three management zones was achieved by measuring specific electrical conductivity (ECs) along a transect in each zone and corresponding semivariograms for ECs within each zone. Near real-time data produced by a telemetric network consisting of soil moisture and electrical conductivity sensors, were used in order to integrate the temporal component of the specific management zones, enabling the calculation of time specific volumetric water contents on a 10 minute interval, an intensity soil indicator necessary to be incorporated to differentiate spatially the irrigation strategies for each zone. This study emphasizes the benefits yielded by fusing near real time telemetric data with soil scanning data and spatial interpolation techniques, enhancing the precision and validity of the desired results. Furthermore the use of telemetric data in combination with modern database management and geospatial software leads to timely produced operational results
Hottenstein, John D.; Ponce-Campos, Guillermo E.; Moran, M. Susan
Intra-annual precipitation patterns are expected to shift toward more intense storms and longer dry periods due to changes in climate within the next decades. Using MODIS satellite-derived plant growth data from 2000-2012, this study quantified the relationship between extreme precipitation patterns, annual soil moisture, and plant growth at nine grassland sites across the southern United States. Across all sites, total precipitation was strongly linked to surface soil moisture (at 5-cm depth), and in turn, soil moisture was strongly related to MODIS-based estimates of above-ground net primary production (ANPP). In fact, soil moisture was a better predictor of ANPP than was total precipitation. Results showed a fundamental difference in the response to altered precipitation patterns between mesic and semiarid grasslands. Soil moisture in mesic grasslands decreased with an increase of high-intensity storms, and semi-arid grassland soil moisture decreased with longer dry periods. This was explained in relation to general climate patterns in these two precipitation regimes. The soil moisture at mesic sites tends to reside closer to field capacity than soil moisture at semiarid sites. So, for semiarid sites, storm events of any size will impact soil moisture; whereas for mesic sites, high intensity storms result in greater runoff than low intensity storms, and less impact on soil moisture. In this field study, the length of consecutive dry days (CDD) had a significant impact on soil moisture only at semiarid sites. This was attributed to the fact that the variation in length of CDD was naturally low at mesic sites and not variable year-to-year, in contrast to the high variability of CDD at semiarid sites. For semiarid sites, long periods of CDD decreased the mean annual soil moisture regardless of the total precipitation throughout the year. Our decision to use soil moisture measured at 5-cm depth was largely based on the fact that the currently orbiting Soil Moisture
Podest, E.; Das, N. N.
The Soil Moisture Active Passive (SMAP) satellite mission was launched in Jan. 2015 and is currently acquiring global measurements of soil moisture in the top 5 cm of the soil every 3 days. SMAP has partnered with the GLOBE program to engage students from around the world to collect in situ soil moisture and help validate SMAP measurements. The current GLOBE SMAP soil moisture protocol consists in collecting a soil sample, weighing, drying and weighing it again in order to determine the amount of water in the soil. Preparation and soil sample collection can take up to 20 minutes and drying can take up to 3 days. We have hence developed a soil moisture measurement device based on Arduino-like microcontrollers along with off-the-shelf and homemade sensors that are accurate, robust, inexpensive and quick and easy to use so that they can be implemented by the GLOBE community and citizen scientists alike. This talk will discuss building, calibration and validation of the soil moisture measuring device and assessing the quality of the measurements collected. This work was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
Wang, Xinbing; Zhou, Baoyuan; Sun, Xuefang; Yue, Yang; Ma, Wei; Zhao, Ming
The spatial distribution of the root system through the soil profile has an impact on moisture and nutrient uptake by plants, affecting growth and productivity. The spatial distribution of the roots, soil moisture, and fertility are affected by tillage practices. The combination of high soil density and the presence of a soil plow pan typically impede the growth of maize (Zea mays L.).We investigated the spatial distribution coordination of the root system, soil moisture, and N status in response to different soil tillage treatments (NT: no-tillage, RT: rotary-tillage, SS: subsoiling) and the subsequent impact on maize yield, and identify yield-increasing mechanisms and optimal soil tillage management practices. Field experiments were conducted on the Huang-Huai-Hai plain in China during 2011 and 2012. The SS and RT treatments significantly reduced soil bulk density in the top 0-20 cm layer of the soil profile, while SS significantly decreased soil bulk density in the 20-30 cm layer. Soil moisture in the 20-50 cm profile layer was significantly higher for the SS treatment compared to the RT and NT treatment. In the 0-20 cm topsoil layer, the NT treatment had higher soil moisture than the SS and RT treatments. Root length density of the SS treatment was significantly greater than density of the RT and NT treatments, as soil depth increased. Soil moisture was reduced in the soil profile where root concentration was high. SS had greater soil moisture depletion and a more concentration root system than RT and NT in deep soil. Our results suggest that the SS treatment improved the spatial distribution of root density, soil moisture and N states, thereby promoting the absorption of soil moisture and reducing N leaching via the root system in the 20-50 cm layer of the profile. Within the context of the SS treatment, a root architecture densely distributed deep into the soil profile, played a pivotal role in plants' ability to access nutrients and water. An optimal
Korres, W.; Reichenau, T. G.; Schneider, K.
Soil moisture is one of the fundamental variables in hydrology, meteorology and agriculture, influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Numerous studies have shown that in addition to natural factors (rainfall, soil, topography etc.) agricultural management is one of the key drivers for spatio-temporal patterns of soil moisture in agricultural landscapes. Interactions between plant growth, soil hydrology and soil nitrogen transformation processes are modeled by using a dynamically coupled modeling approach. The process-based ecohydrological model components of the integrated decision support system DANUBIA are used to identify the important processes and feedbacks determining soil moisture patterns in agroecosystems. Integrative validation of plant growth and surface soil moisture dynamics serves as a basis for a spatially distributed modeling analysis of surface soil moisture patterns in the northern part of the Rur catchment (1100 sq km), Western Germany. An extensive three year dataset (2007-2009) of surface soil moisture-, plant- (LAI, organ specific biomass and N) and soil- (texture, N, C) measurements was collected. Plant measurements were carried out biweekly for winter wheat, maize, and sugar beet during the growing season. Soil moisture was measured with three FDR soil moisture stations. Meteorological data was measured with an eddy flux station. The results of the model validation showed a very good agreement between the modeled plant parameters (biomass, green LAI) and the measured parameters with values between 0.84 and 0.98 (Willmotts index of agreement). The modeled surface soil moisture (0 - 20 cm) showed also a very favorable agreement with the measurements for winter wheat and sugar beet with an RMSE between 1.68 and 3.45 Vol.-%. For maize, the RMSE was less favorable particularly in the 1.5 months prior to harvest. The modeled soil
Liu, H.; Yu, Z.
The spatiotemporal variability of soil moisture and its affecting factors in a humid area were examined based on the field measuring date in the Tai lake drainage basin, China. 24 sensors near the soil surface and 12 sensors in 2 profiles (6 in each) were set up for collecting hourly soil moisture data with the Frequency Domain Reflectometry (FDR) sensors in 2006. Coefficient of variation (CV) and semi-variogram were calculated to evaluate the temporal variability in different locations and the spatial variability in different periods. The surface soil moisture appears middle or weak variability, and most of the CV values are in the range of 0.13-0.26. Soil characteristics, topography, vegetation, meteorological factors and human activities influenced the soil moisture spatiotemporal variability significantly. The factors appear having different affecting abilities on the spatiotemporal variability, and the domain factors are different in four seasons. Soil characteristics mainly influence the temporal variability in the scale of hill slope. Coarser texture on the upper part of the slope results in a larger variability. Topography and micro-topography affects the spatial variability in all 3 dimensions. The variability is larger at upper locations and chine of the slope. The effect of vegetation on the soil moisture variability is stronger in spring, summer, and autumn than in winter, according to the different growth activities and water demand. The trees on the slope influence the CV values along the slope. Meteorological factors are the forcing factors of the soil water variation. Higher rainfall and evaporation variations produce higher variability in soil moisture while the rainfall has more influence in the summer and the evaporation has more in the fall. The results provide better understanding of soil moisture variation and base for further study on how the soil moisture variation could affect the rainfall runoff partitioning.
Beena K S
Full Text Available Among the diversified use of coir geotextiles, its use as a protective covering to improve crop productivity and to reduce weed problem assumes to be much significant. An experiment has been conducted at Kumbazha, in Pathanamthitta district, Kerala, India to evaluate the different types of coir geotextiles and polythene as soilmulch. The treatments include different mulching materials like natural needled felt, black needled felt, rubberized coir, black polythene and transparent polythene along with a control plot (no mulch. The experiment was laid out in Completely Randomized Design with six replications. The test crops used were bhindi (var. Salkeerthi and pineapple (var. Mauritius. The study reveals that with bhindi crop growth parameters like plant height, leaf number and lateral spread were increased by mulching with rubberized coir and transparent polythene. These two mulches caused early flowering and increased fruit yield. Coir materials as mulch recorded a yield increase ranging from 67 to 196%. Observations also reveal that weeds were not grown in plots mulched with black polythene, transparent polythene and rubberized coir. Rubberized coir as mulch enhancedthe fruit yield in the case of pineapple, which is followed by natural needled felt and transparent polythene. Black polythene resisted weed growth up to 7MAP, whereas rubberized coir and transparent polythene suppressed weeds up to 8MAP. Though the weeds were grown in other treatments the weeds count was significantly lower than that of control plot. Mulching with transparent polythene enhanced the soil temperature whereas rubberized coir lowered soil temperature. More over all mulched treatments had a favourable influence in increasing soil moisture. Observing the biodegradability and eco-friendly nature of coir it could be inferred that rubberized coir can serve as good mulch for bhindi and pineapple with minimum weed problem.
Tuttle, Samuel; Salvucci, Guido
Soil moisture influences fluxes of heat and moisture originating at the land surface, thus altering atmospheric humidity and temperature profiles. However, empirical and modeling studies disagree on how this affects the propensity for precipitation, mainly owing to the difficulty in establishing causality. We use Granger causality to estimate the relationship between soil moisture and occurrence of subsequent precipitation over the contiguous United States using remotely sensed soil moisture and gauge-based precipitation observations. After removing potential confounding effects of daily persistence, and seasonal and interannual variability in precipitation, we find that soil moisture anomalies significantly influence rainfall probabilities over 38% of the area with a median factor of 13%. The feedback is generally positive in the west and negative in the east, suggesting dependence on regional aridity.
Soil moisture plays a key role in runoff generation processes. As a result, the assimilation of soil moisture observations into rainfall-runoff models is increasingly being investigated. Given the scarcity of ground-based in situ measurements, satellite soil moisture observations offer a valuable da...
Stacke, T.; Hagemann, S.
In order to evaluate whether the initialization of soil moisture has the potential to improve the prediction skill of earth system models (ESMs) on seasonal to decadal timescales, an elaborate experiment was conducted. For this task a coupled land-atmosphere model with prescribed ocean was utilized. The experiment design considered soil moisture initialization in different seasons and years and yielded information about the lifetime (memory) of extreme yet realistic soil moisture perturbations. Our analyses were focused on root zone soil moisture (RootSM) as it comprises the part of the soil that directly interacts with the atmosphere via bare-soil evaporation and transpiration. We found that RootSM memory differs not only spatially but also depends on the time of initialization. A long memory of up to 1 year is evident mostly for dry soil moisture regimes after heavy precipitation periods or prior to snow covered conditions. Short memory below 2 weeks prevails in wet soil moisture regimes and prior to distinct precipitation periods or snowmelt. Furthermore, RootSM perturbations affect other land surface states, e.g. soil temperature and leaf carbon content, and even induce anomalies with specific memory in these variables. Especially for deep-layer soil temperature, these anomalies can last for up to several years. As long as RootSM memory is evident, we found that anomalies occur periodically in other land surface states whenever climate conditions allow for interactions between that state and RootSM. Additionally, anomaly recurrence is visible for RootSM itself. This recurrence is related to the thickness of the soil layer below the root zone and can affect RootSM for several years. From our findings we conclude that soil moisture initialization has the potential to improve the predictive skill of climate models on seasonal scales and beyond. However, a sophisticated, multilayered soil hydrology scheme is necessary to allow for the interactions between Root
M. R. Mobasheri
Full Text Available Near-surface soil moisture is one of the crucial variables in hydrological processes, which influences the exchange of water and energy fluxes at the land surface/atmosphere interface. Accurate estimate of the spatial and temporal variations of soil moisture is critical for numerous environmental studies. On the other hand, information of distributed soil moisture at large scale with reasonable spatial and temporal resolution is required for improving climatic and hydrologic modeling and prediction. The advent of hyperspectral imagery has allowed examination of continuous spectra not possible with isolated bands in multispectral imagery. In addition to high spectral resolution for individual band analyses, the contiguous narrow bands show characteristics of related absorption features, such as effects of strong absorptions on the band depths of adjacent absorptions. Our objective in this study was to develop a new spectral angle index to estimate soil moisture based on spectral region (350 and 2500 nm. In this paper, using spectral observations made by ASD Spectroradiometer for predicting soil moisture content, two soil indices were also investigated involving the Perpendicular Drought Index (PDI, NMDI (Normalized Multi-band Drought Index indices. Correlation and regression analysis showed a high relationship between PDI and the soil moisture percent (R2 = 0.9537 and NMDI (R2 = 0.9335. Furthermore, we also simulated these data according to the spectral range of some sensors such as MODIS, ASTER, ALI and ETM+. Indices relevant these sensors have high correlation with soil moisture data. Finally, we proposed a new angle index which shows significant relationship between new angle index and the soil moisture percentages (R2 = 0.9432.angle index relevant bands 3, 4, 5, 6, 7 MODIS also showing high accuracy in estimation of soil moisture (R2 = 0.719.
Unnikrishnan, C. K.; George, John P.; Lodh, Abhishek; Maurya, Devesh Kumar; Mallick, Swapan; Rajagopal, E. N.; Mohandas, Saji
Surface level soil moisture from two gridded datasets over India are evaluated in this study. The first one is the UK Met Office (UKMO) soil moisture analysis produced by a land data assimilation system based on Extended Kalman Filter method (EKF), which make use of satellite observation of Advanced Scatterometer (ASCAT) soil wetness index as well as the screen level meteorological observations. Second dataset is a satellite soil moisture product, produced by National Remote Sensing Centre (NRSC) using passive microwave Advanced Microwave Scanning Radiometer 2 measurements. In-situ observations of soil moisture from India Meteorological Department (IMD) are used for the validation of the gridded soil moisture products. The difference between these datasets over India is minimum in the non-monsoon months and over agricultural regions. It is seen that the NRSC data is slightly drier (0.05%) and UKMO soil moisture analysis is relatively wet during southwest monsoon season. Standard AMSR-2 satellite soil moisture product is used to compare the NRSC and UKMO products. The standard AMSR-2 and UKMO values are closer in monsoon season and AMSR-2 soil moisture is higher than UKMO in all seasons. NRSC and AMSR-2 showed a correlation of 0.83 (significant at 0.01 level). The probability distribution of IMD soil moisture observation peaks at 0.25 m3/m3, NRSC at 0.15 m3/m3, AMSR-2 at 0.25 m3/m3 and UKMO at 0.35 m3/m3 during June-September period. Validation results show UKMO analysis has better correlation with in-situ observations compared to the NRSC and AMSR-2 datasets. The seasonal variation in soil moisture is better represented in UKMO analysis. Underestimation of soil moisture during monsoon season over India in NRSC data suggests the necessity of incorporating the actual vegetation for a better soil moisture retrieval using passive microwave sensors. Both products have good agreement over bare soil, shrubs and grassland compared to needle leaf tree, broad leaf tree and
Bertoldi, Giacomo; Claudia, Notarnicola; Brenner, Johannes; Castelli, Mariapina; Greifeneder, Felix; Niedrist, Georg; Seeber, Julia; Tappeiner, Ulrike
One of the key variables controlling the organization of vegetation and the coevolution of soils and landforms is soil moisture content (SMC). For this reason, understanding the controls on the spatial and temporal patterns of SMC is essential to predict how perturbations in vegetation and climate will affect mountain ecosystem functioning. In this contribution, we focus on the dynamic of surface SMC of water-limited alpine grasslands in the Long Term Ecological Research area Mazia Valley in the European Alps. We analyze the impacts of different land managements (meadows versus pastures) and its relationships with climate and topography. The area has been equipped since 2009 with a network of more than 20 stations, measuring SMC and climatic variables and with two eddy-covariance stations, measuring surface fluxes over meadows and pastures. Monthly biomass production data have been collected and detailed soil and spatial soil moisture surveys are available. Moreover, high spatial resolution SMC maps have been derived from satellites Synthetic Aperture Radar Radar (SAR) images (Sentinel 1 and RADARSAT2 images). Both ground surveys and remote sensing observations show persistent landscape-level patterns. Meadows, in general located in flatter areas, tend to be wetter. This leads to higher vegetation productivity and to the development of soils with higher water holding capacity, thus to a positive feedback on SMC. In contrast, pastures, located on steeper slopes with lower vegetation density and higher soil erosion, tend to be drier, leading to a negative feedback on SMC and soil development. This co-evolution of land cover and SMC leads therefore to persistent spatial patterns. In order to understand quantitatively such linked interactions, a sensitivity analysis has been performed with the GEOtop hydrological model. Results show how both abiotic (mainly slope and elevation) and anthropogenic (irrigation and soil management) factors exert a significant control on
Kumar, Kamal; Arora, M. K.; Hariprasad, K. S.
The aim of this paper is to estimate soil moisture at spatial level by applying geostatistical techniques on the point observations of soil moisture in parts of Solani River catchment in Haridwar district of India. Undisturbed soil samples were collected at 69 locations with soil core sampler at a depth of 0-10 cm from the soil surface. Out of these, discrete soil moisture observations at 49 locations were used to generate a spatial soil moisture distribution map of the region. Two geostatistical techniques, namely, moving average and kriging, were adopted. Root mean square error (RMSE) between observed and estimated soil moisture at remaining 20 locations was determined to assess the accuracy of the estimated soil moisture. Both techniques resulted in low RMSE at small limiting distance, which increased with the increase in the limiting distance. The root mean square error varied from 7.42 to 9.77 in moving average method, while in case of kriging it varied from 7.33 to 9.99 indicating similar performance of the two techniques.
BI Huaxing; LI Xiaoyin; LIU Xin; GUO Mengxia; LI Jun
Soil moisture distribution shows highly variation both spatially and temporally.This study assesses the spatial heterogeneity of soil moisture on a hill-slope scale in the Loess Plateau in West China by using a geostatistical approach.Soil moisture was measured by time-domain reflectometry (TDR) in 313 samples.Two kinds of sampling scales were used (2 × 2 m and 20 × 20m) at two soil layers (0-30 cm and 30-450 cm).The general characteristics of soil moisture were analyzed by a classical statistics method,and the spatial heterogeneity of soil moisture was analyzed using a geostatistical approach.The results showed that the spherical model is the best-fit model to simulate soil moisture on the experimental hill-slope.The parameters of this model indicated that the spatial dependence of soil moisture in the selected hill-slope was moderate.Even the 2 × 2 m sampling scale was too coarse to show the detailed spatial variances of soil moisture in this area.The dependent distance increased from 27.4 m to 494.16 m as the sampling scale became coarse (from 2 ×2 m to 20 × 20 m).A map of soil moisture was generated by using original soil moisture data and interpolated values determined by the Kriging method.The average soil moisture (area weighted) in the different layers of soil was calculated on the basis of this map (10.94% for the 0-30 cm soil layer,11.88% for the 30-60 em soil layer).This average soil moisture is lower than the corresponding average effective soil moisture,which suggests that the soil moisture is not sufficient to support vegetation in this area.
moisture affects crop growth, seed development, root development and agricultural ... benefits, capacitive sensor techniques are applied in precision agriculture . ..... lentil moisture content using dielectric properties. Journal of Agricultural ...
LI Jun; ZHAO ChenYi; ZHU Hong; WANG Feng; WANG LiJuan; KOU SiYong
Spatial variation of soil moisture after snow thawing in South Gurbantunggut was quantitatively studied using ANOVA and geostatistics at various scales. The results show that the soil moisture heterogeneity varies along with spatial scales. At the shrub individual scale, there is a gradient in soil moisture from shrub-canopied area to canopy margin and to the interspaces between shrubs. At the community scale,soil moisture is highly autocorrelated and the semivariogram is fitted as spherical model, with an 89.6% structural variance and a range of 4.02 m. In addition, Kringing map indicates that the soil moisture distribution pattern after snow thawing is highly consistent with the shrub patch pattern. At the typical inter-dune transect scale, soil moisture presents a pattern of high value at inter-dune depression and Iow value at dune, and this variation is fitted as Gaussian model with a structural variance of 95.8% and a range of 66.16 m. The range is comparable with the scale of topography zoning, suggesting that the topography pattern controls the pattern of snowmelt at this scale. The evidence indicates that the heterogeneity of soil moisture at various scales is controlled by various land surface processes after snow thawing. For Gurbantunggut Desert, the spatial heterogeneity of snowmelt at various scales is ecologically valuable, because it promotes the utilization efficiency of the snowmelt for the desert vegetation.
Wei, Lingna; Chen, Xi; Dong, Jianzhi; Gao, Man
Soil moisture plays a significant role in the land surface-atmosphere interactions. Temporal stability was frequently used for estimating areal mean soil moisture using limited number of point measurements. This study investigated the factors that determine soil moisture temporal stability using simulated high spatial resolution soil moisture data at watershed scale. Results show locations under dominate vegetation cover and with low topographic wetness index (TI) values are likely to provide reasonable areal mean soil moisture estimates. We demonstrated that including the information of vegetation cover and TI can effectively reduce the number of the sampling locations that required for determining the representative point. The length of sampling period is also shown to be important in correctly determining the representative point. When 10 sampling points were used, a sampling period of approximately 300 days can provide robust areal mean soil moisture estimates of the entire study period of 9 years. The presented study may be useful for improving our skills in applying the temporal stability method for areal mean soil moisture estimating, and hence remote sensing product validation.
Soman, Vishwas V.; Crosson, William L.; Laymon, Charles; Tsegaye, Teferi
Soil moisture is an important component of analysis in many Earth science disciplines. Soil moisture information can be obtained either by using microwave remote sensing or by using a hydrologic model. In this study, we combined these two approaches to increase the accuracy of profile soil moisture estimation. A hydrologic model was used to analyze the errors in the estimation of soil moisture using the data collected during Huntsville '96 microwave remote sensing experiment in Huntsville, Alabama. Root mean square errors (RMSE) in soil moisture estimation increase by 22% with increase in the model input interval from 6 hr to 12 hr for the grass-covered plot. RMSEs were reduced for given model time step by 20-50% when model soil moisture estimates were updated using remotely-sensed data. This methodology has a potential to be employed in soil moisture estimation using rainfall data collected by a space-borne sensor, such as the Tropical Rainfall Measuring Mission (TRMM) satellite, if remotely-sensed data are available to update the model estimates.
Sorensson, Anna; Berbery, E. Hugo
This work examines the evolution of soil moisture initialization biases and their effects on seasonal forecasts depending on the season and vegetation type for a regional model over the La Plata Basin in South America. WRF/Noah model simulations covering multiple cases during a two-year period are designed to emphasize the conceptual nature of the simulations at the expense of statistical significance of the results. Analysis of the surface climate shows that the seasonal predictive skill is higher when the model is initialized during the wet season and the initial soil moisture differences are small. Large soil moisture biases introduce large surface temperature biases, particularly for Savanna, Grassland and Cropland vegetation covers at any time of the year, thus introducing uncertainty in the surface climate. Regions with Evergreen Broadleaf Forest have roots that extend to the deep layer whose moisture content affects the surface temperature through changes in the partitioning of the surface fluxes. The uncertainties of monthly maximum temperature can reach several degrees during the dry season in cases when: (a) the soil is much wetter in the reanalysis than in the WRF/Noah equilibrium soil moisture, and (b) the memory of the initial value is long due to scarce rainfall and low temperatures. This study suggests that responses of the atmosphere to soil moisture initialization depend on how the initial wet and dry conditions are defined, stressing the need to take into account the characteristics of a particular region and season when defining soil moisture initialization experiments.
Kumar, Sujay V.; Reichle, Rolf H.; Harrison, Kenneth W.; Peters-Lidard, Christa D.; Yatheendradas, Soni; Santanello, Joseph A.
Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.
Full Text Available Observation on the relationship between the depth of soil moisture assessment and turgidity of coffee plant has been carried out at 3 different agroclimates by survey method, i.e. Andungsari experimental station (Andosol soil type, >1.000 m asl. high, and rainfall type of C, Sumberasin experimental station (yellowish-red Mediterranean soil type, 450-500 m asl. high, and rainfall type of C, and Kaliwining experimental station (low humic glei soil type, 45 m asl. high, and rainfall type of D in order to assess the depth of soil moisture through soil profile influencing turgidity of coffee plants at three different agroclimates. The method of assessment is by fitting the relationship between the depth of soil moisture assessment and turgidity of coffee plant and their determination coefficients through the period of dry season up to early rainy season. Plant turgidity is evaluated from its relative water contents of the leaves sampled periodically at the same time as observation of soil moisture content. Plant turgidity is affected by soil moisture condition up to a certain depth which looks to be typical of the agroclimates. At Andungsari experimental station (high land it is necessary to assess soil moisture through the soil profile up to 100 cm deep in order to evaluate water stress of the plants; inversely, at Kaliwining experimental station in order to evaluate water stress of the plants it is just justified from the soil moisture condition of the soil surface layers (0-25 cm. Whereas at Sumberasin experimental station water stress of the plants could be predicted from soil moisture assessment of the surface layer depth or through the deeper layers of the soil profile either. Andungsari-1 and Lini S-795 clones are more resistant to drought than Kartika-2 clone at Andisol soil type with C rainfall type and elevation > 1000 m asl. BP-308 clone showed its response as relatively resistant to drought at yellowish red Mediterranean soil type with C
JunJun Yang; ZhiBin He; WeiJun Zhao; Jun Du; LongFei Chen; Xi Zhu
Soil moisture simulation and prediction in semi-arid regions are important for agricultural production, soil conservation and climate change. However, considerable heterogeneity in the spatial distribution of soil moisture, and poor ability of distributed hydrological models to estimate it, severely impact the use of soil moisture models in research and practical applications. In this study, a newly-developed technique of coupled (WA-ANN) wavelet analysis (WA) and artificial neural network (ANN) was applied for a multi-layer soil moisture simulation in the Pailugou catchment of the Qilian Mountains, Gansu Province, China. Datasets included seven meteorological factors: air and land surface temperatures, relative humidity, global radiation, atmospheric pressure, wind speed, precipitation, and soil water content at 20, 40, 60, 80, 120 and 160 cm. To investigate the effectiveness of WA-ANN, ANN was applied by itself to conduct a comparison. Three main findings of this study were: (1) ANN and WA-ANN provided a statistically reliable and robust prediction of soil moisture in both the root zone and deepest soil layer studied (NSE >0.85, NSE means Nash-Sutcliffe Efficiency coefficient); (2) when input meteorological factors were transformed using maximum signal to noise ratio (SNR) and one-dimensional auto de-noising algorithm (heursure) in WA, the coupling technique improved the performance of ANN especially for soil moisture at 160 cm depth; (3) the results of multi-layer soil moisture prediction indicated that there may be different sources of water at different soil layers, and this can be used as an indicator of the maximum impact depth of meteorological factors on the soil water content at this study site. We conclude that our results show that appropriate simulation methodology can provide optimal simulation with a minimum distortion of the raw-time series; the new method used here is applicable to soil sciences and management applications.
Full Text Available Many physical, chemical and biological processes taking place at the land surface are strongly influenced by the amount of water stored within the upper soil layers. Therefore, many scientific disciplines require soil moisture observations for developing, evaluating and improving their models. One of these disciplines is meteorology where soil moisture is important due to its control on the exchange of heat and water between the soil and the lower atmosphere. Soil moisture observations may thus help to improve the forecasts of air temperature, air humidity and precipitation. However, until recently, soil moisture observations had only been available over a limited number of regional soil moisture networks. This has hampered scientific progress as regards the characterisation of land surface processes not just in meteorology but many other scientific disciplines as well. Fortunately, in recent years, satellite soil moisture data have increasingly become available. One of the freely available global soil moisture data sets is derived from the backscatter measurements acquired by the Advanced Scatterometer (ASCAT that is a C-band active microwave remote sensing instrument flown on board of the Meteorological Operational (METOP satellite series. ASCAT was designed to observe wind speed and direction over the oceans and was initially not foreseen for monitoring soil moisture over land. Yet, as argued in this review paper, the characteristics of the ASCAT instrument, most importantly its wavelength (5.7 cm, its high radiometric accuracy, and its multiple-viewing capabilities make it an attractive sensor for measuring soil moisture. Moreover, given the operational status of ASCAT, and its promising long-term prospects, many geoscientific applications might benefit from using ASCAT soil moisture data. Nonetheless, the ASCAT soil moisture product is relatively complex, requiring a good understanding of its properties before it can be successfully used in
Wanders, N.; Karssenberg, D.; Bierkens, M. F. P.; Van Dam, J. C.; De Jong, S. M.
Soil moisture is a key variable in the hydrological cycle and important in hydrological modelling. When assimilating soil moisture into flood forecasting models, the improvement of forecasting skills depends on the ability to accurately estimate the spatial and temporal patterns of soil moisture content throughout the river basin. Space-borne remote sensing may provide this information with a high temporal and spatial resolution and with a global coverage. Currently three microwave soil moisture products are available: AMSR-E, ASCAT and SMOS. The quality of these satellite-based products is often assessed by comparing them with in-situ observations of soil moisture. This comparison is however hampered by the difference in spatial and temporal support (i.e., resolution, scale), because the spatial resolution of microwave satellites is rather low compared to in-situ field measurements. Thus, the aim of this study is to derive a method to assess the uncertainty of microwave satellite soil moisture products at the correct spatial support. To overcome the difference in support size between in-situ soil moisture observations and remote sensed soil moisture, we used a stochastic, distributed unsaturated zone model (SWAP, van Dam (2000)) that is upscaled to the support of different satellite products. A detailed assessment of the SWAP model uncertainty is included to ensure that the uncertainty in satellite soil moisture is not overestimated due to an underestimation of the model uncertainty. We simulated unsaturated water flow up to a depth of 1.5m with a vertical resolution of 1 to 10 cm and on a horizontal grid of 1 km2 for the period Jan 2010 - Jun 2011. The SWAP model was first calibrated and validated on in-situ data of the REMEDHUS soil moisture network (Spain). Next, to evaluate the satellite products, the model was run for areas in the proximity of 79 meteorological stations in Spain, where model results were aggregated to the correct support of the satellite
Bolten, John D.; Crow, Wade T.; Zhan, Xiwu; Jackson, Thomas J.; Reynolds,Curt
Soil moisture is a fundamental data source used by the United States Department of Agriculture (USDA) International Production Assessment Division (IPAD) to monitor crop growth stage and condition and subsequently, globally forecast agricultural yields. Currently, the USDA IPAD estimates surface and root-zone soil moisture using a two-layer modified Palmer soil moisture model forced by global precipitation and temperature measurements. However, this approach suffers from well-known errors arising from uncertainty in model forcing data and highly simplified model physics. Here we attempt to correct for these errors by designing and applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA modified Palmer soil moisture model. An assessment of soil moisture analysis products produced from this assimilation has been completed for a five-year (2002 to 2007) period over the North American continent between 23degN - 50degN and 128degW - 65degW. In particular, a data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing EnKF soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline Palmer model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.
van Wesemael, Bas; Nocita, Marco
One of the problems for mapping of soil organic carbon (SOC) at large-scale based on visible - near and short wave infrared (VIS-NIR-SWIR) remote sensing techniques is the spatial variation of topsoil moisture when the images are collected. Soil moisture is certainly an aspect causing biased SOC estimations, due to the problems in discriminating reflectance differences due to either variations in organic matter or soil moisture, or their combination. In addition, the difficult validation procedures make the accurate estimation of soil moisture from optical airborne a major challenge. After all, the first millimeters of the soil surface reflect the signal to the airborne sensor and show a large spatial, vertical and temporal variation in soil moisture. Hence, the difficulty of assessing the soil moisture of this thin layer at the same moment of the flight. The creation of a soil moisture proxy, directly retrievable from the hyperspectral data is a priority to improve the large-scale prediction of SOC. This paper aims to verify if the application of the normalized soil moisture index (NSMI) to Airborne Prima Experiment (APEX) hyperspectral images could improve the prediction of SOC. The study area was located in the loam region of Wallonia, Belgium. About 40 samples were collected from bare fields covered by the flight lines, and analyzed in the laboratory. Soil spectra, corresponding to the sample locations, were extracted from the images. Once the NSMI was calculated for the bare fields' pixels, spatial patterns, presumably related to within field soil moisture variations, were revealed. SOC prediction models, built using raw and pre-treated spectra, were generated from either the full dataset (general model), or pixels belonging to one of the two classes of NSMI values (NSMI models). The best result, with a RMSE after validation of 1.24 g C kg-1, was achieved with a NSMI model, compared to the best general model, characterized by a RMSE of 2.11 g C kg-1. These
Shrivastava, Sourabh; Kar, Sarat C.; Sharma, Anu Rani
Variation of soil moisture during active and weak phases of summer monsoon JJAS (June, July, August, and September) is very important for sustenance of the crop and subsequent crop yield. As in situ observations of soil moisture are few or not available, researchers use data derived from remote sensing satellites or global reanalysis. This study documents the intercomparison of soil moisture from remotely sensed and reanalyses during dry spells within monsoon seasons in central India and central Myanmar. Soil moisture data from the European Space Agency (ESA)—Climate Change Initiative (CCI) has been treated as observed data and was compared against soil moisture data from the ECMWF reanalysis-Interim (ERA-I) and the climate forecast system reanalysis (CFSR) for the period of 2002-2011. The ESA soil moisture correlates rather well with observed gridded rainfall. The ESA data indicates that soil moisture increases over India from west to east and from north to south during monsoon season. The ERA-I overestimates the soil moisture over India, while the CFSR soil moisture agrees well with the remotely sensed observation (ESA). Over Myanmar, both the reanalysis overestimate soil moisture values and the ERA-I soil moisture does not show much variability from year to year. Day-to-day variations of soil moisture in central India and central Myanmar during weak monsoon conditions indicate that, because of the rainfall deficiency, the observed (ESA) and the CFSR soil moisture values are reduced up to 0.1 m3/m3 compared to climatological values of more than 0.35 m3/m3. This reduction is not seen in the ERA-I data. Therefore, soil moisture from the CFSR is closer to the ESA observed soil moisture than that from the ERA-I during weak phases of monsoon in the study region.
M S Roxy; V B Sumithranand; G Renuka
Continuous observation data collected over the year 2008 at Astronomical Observatory, Thiruvananthapuram in south Kerala (76° 59′E longitude and 8° 30′N latitude) are used to study the diurnal, monthly and seasonal soil moisture variations. The effect of rainfall on diurnal and seasonal soil moisture is discussed. We have investigated relationships of soil moisture with surface albedo and soil thermal diffusivity. The diurnal variation of surface albedo appears as a U-shaped curve on sunny days. Surface albedo decreases with the increase of solar elevation angle, and it tends to be a constant when solar elevation angle is greater than 40°. So the daily average surface albedo was calculated using the data when solar elevation angle is greater than 40°. The results indicate that the mean daily surface albedo decreases with increases in soil moisture content, showing a typical exponential relation between the surface albedo and the soil moisture. Soil thermal diffusivity increases firstly and then decreases with the increase of soil moisture.
Full Text Available Soil moisture and salinity measurement are the essential factors for crop irrigation as well as to increase the yield. Grapes eminence depends on the water volume contents in soil and soil nutrients. Based on these conditions, we determined water demand for best quality of grapes by wireless sensor network (WSN. Using lot of chemical fertilizers increases soil salinity but reduces soil fertility, soil salinity defines electrical conductivity or salty soil. Precise agriculture systems are integrated with multiple sensors to monitor and control the incident. Integrated WSN is designed and developed to measure soil moisture and salinity. ATmega328 microcontroller, XBee and Soil sensors are integrated across the system. This system is more competent, it can be helpful to automatic irrigation system and soil salinity monitoring.
Osborne, Brooke B.; Baron, Jill S.; Wallenstein, Matthew D.
Climate change is altering the timing and magnitude of biogeochemical fluxes in many highelevation ecosystems. The consequent changes in alpine nitrification rates have the potential to influence ecosystem scale responses. In order to better understand how changing temperature and moisture conditions may influence ammonia oxidizers and nitrification activity, we conducted laboratory incubations on soils collected in a Colorado watershed from three alpine habitats (glacial outwash, talus, and meadow). We found that bacteria, not archaea, dominated all ammonia oxidizer communities. Nitrification increased with moisture in all soils and under all temperature treatments. However, temperature was not correlated with nitrification rates in all soils. Site-specific temperature trends suggest the development of generalist ammonia oxidzer communities in soils with greater in situ temperature fluctuations and specialists in soils with more steady temperature regimes. Rapidly increasing temperatures and changing soil moisture conditions could explain recent observations of increased nitrate production in some alpine soils.
Full Text Available This paper investigates a simplified polarimetric decomposition for soil moisture retrieval over agricultural fields. In order to overcome the coherent superposition of the backscattering contributions from vegetation and underlying soils, a simplification of an existing polarimetric decomposition is proposed in this study. It aims to retrieve the soil moisture by using only the surface scattering component, once the volume scattering contribution is removed. Evaluation of the proposed simplified algorithm is performed using extensive ground measurements of soil and vegetation characteristics and the time series of UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar data collected in the framework of SMAP (Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12. The retrieval process is tested and analyzed in detail for a variety of crops during the phenological stages considered in this study. The results show that the performance of soil moisture retrieval depends on both the crop types and the crop phenological stage. Soybean and pasture fields present the higher inversion rate during the considered phenological stage, while over canola and wheat fields, the soil moisture can be retrieved only partially during the crop developing stage. RMSE of 0.06–0.12 m3/m3 and an inversion rate of 26%–38% are obtained for the soil moisture retrieval based on the simplified polarimetric decomposition.
Moghaddam, M.; Silva, A. R. D.; Akbar, R.; Clewley, D.
The Soil moisture Sensing Controller And oPtimal Estimator (SoilSCAPE) wireless sensor network has been developed to support Calibration and Validation activities (Cal/Val) for large scale soil moisture remote sensing missions (SMAP and AirMOSS). The technology developed here also readily supports small scale hydrological studies by providing sub-kilometer widespread soil moisture observations. An extensive collection of semi-sparse sensor clusters deployed throughout north-central California and southern Arizona provide near real time soil moisture measurements. Such a wireless network architecture, compared to conventional single points measurement profiles, allows for significant and expanded soil moisture sampling. The work presented here aims at discussing and highlighting novel and new technology developments which increase in situ soil moisture measurements' accuracy, reliability, and robustness with reduced data delivery latency. High efficiency and low maintenance custom hardware have been developed and in-field performance has been demonstrated for a period of three years. The SoilSCAPE technology incorporates (a) intelligent sensing to prevent erroneous measurement reporting, (b) on-board short term memory for data redundancy, (c) adaptive scheduling and sampling capabilities to enhance energy efficiency. A rapid streamlined data delivery architecture openly provides distribution of in situ measurements to SMAP and AirMOSS cal/val activities and other interested parties.
Tom-Petersen, Andreas; Hansen, H.C.B.; Nybroe, O.
between total metal content and metal toxicity calls for integrated chemical and biological analysis. The aim of this work was to determine time- and moisture-dependent changes in total water-extractable Cu as well as bioavailable Cu in soil water extracts. Measurements of total water-extractable copper...... to increase with time. The moisture content of the soil was important for Cu retention. Dry soil had higher [Cu](tot) concentrations than humid soil, but the [Cu](bio) to [Cu](tot) ratio was lower in the dry soil. Alternating drying and wetting did not lead to a more rapid Cu retention than observed under...
Full Text Available Row structure causes the anisotropy of microwave brightness temperature (TB of soil surface, and it also can affect soil moisture retrieval accuracy when its influence is ignored in the inversion model. To study the effect of typical row structure on the retrieved soil moisture and evaluate if there is a need to introduce this effect into the inversion model, two ground-based experiments were carried out in 2011. Based on the observed C-band TB, field soil and vegetation parameters, row structure rough surface assumption (Qp model and discrete model, including the effect of row structure, and flat rough surface assumption (Qp model, ignoring the effect of row structure, are used to model microwave TB of soil surface. Then, soil moisture can be retrieved, respectively, by minimizing the difference of the measured and modeled TB. The results show that soil moisture retrieval accuracy based on the row structure rough surface assumption is approximately 0.02 cm3/cm3 better than the flat rough surface assumption for vegetated soil, as well as 0.015 cm3/cm3 better for bare and wet soil. This result indicates that the effect of row structure cannot be ignored for accurately retrieving soil moisture of farmland surface when C-band is used.
Xingming, Zheng; Kai, Zhao; Yangyang, Li; Jianhua, Ren; Yanling, Ding
Row structure causes the anisotropy of microwave brightness temperature (TB) of soil surface, and it also can affect soil moisture retrieval accuracy when its influence is ignored in the inversion model. To study the effect of typical row structure on the retrieved soil moisture and evaluate if there is a need to introduce this effect into the inversion model, two ground-based experiments were carried out in 2011. Based on the observed C-band TB, field soil and vegetation parameters, row structure rough surface assumption (Q p model and discrete model), including the effect of row structure, and flat rough surface assumption (Q p model), ignoring the effect of row structure, are used to model microwave TB of soil surface. Then, soil moisture can be retrieved, respectively, by minimizing the difference of the measured and modeled TB. The results show that soil moisture retrieval accuracy based on the row structure rough surface assumption is approximately 0.02 cm(3)/cm(3) better than the flat rough surface assumption for vegetated soil, as well as 0.015 cm(3)/cm(3) better for bare and wet soil. This result indicates that the effect of row structure cannot be ignored for accurately retrieving soil moisture of farmland surface when C-band is used.
Cumbrera, R.; Tarquis, A. M.; Gascó, G.; Millán, H.
Image analysis could be a useful tool for investigating the spatial patterns of apparent soil moisture at multiple resolutions. The objectives of the present work were (i) to define apparent soil moisture patterns from vertical planes of Vertisol pit images and (ii) to describe the scaling of apparent soil moisture distribution using fractal parameters. Twelve soil pits (0.70 m long x 0.60 m width x 0.30 m depth) were excavated on a bare Mazic Pellic Vertisol. Six of them were excavated in April/2011 and six pits were established in May/2011 after three days of a moderate rainfall event. Digital photographs were taken from each Vertisol pit using a KodakTM digital camera. The mean image size was 1600 x 945 pixels with one physical pixel ≈ 373 μm of the photographed soil pit. Each soil image was analyzed using two prefractal scaling exponents, box counting (capacity) dimension (DBC) and interface fractal dimension (Di), and three prefractal scaling coefficients, the total number of boxes intercepting the foreground pattern at a unit scale (A), fractal lacunarity at the unit scale (Γ1) and Shannon entropy at the unit scale (S1). All prefractal scaling parameters identified significant differences between both sets of spatial patterns. Fractal lacunarity was the best discriminator between apparent soil moisture patterns. Soil image interpretation with prefractal parameters can be incorporated within site-specific agriculture toolbox. While fractal exponents condense information on space filling characteristics of the pattern, prefractal coefficients represent the investigated soil property as seen through a higher resolution microscope. In spite of some computational and practical limitations, image analysis of apparent soil moisture patterns could be used in connection with traditional soil moisture sampling, which always renders punctual estimates. Key words: Image analysis, fractal scaling, apparent soil moisture, Vertisols Acknowledgements This work has been
Soil moisture simulation and prediction in semi-arid regions are important for agricultural production, soil conservation andclimate change. However, considerable heterogeneity in the spatial distribution of soil moisture, and poor ability of distributedhydrological models to estimate it, severely impact the use of soil moisture models in research and practical applications. Inthis study, a newly-developed technique of coupled （WA-ANN） wavelet analysis （WA） and artificial neural network （ANN）was applied for a multi-layer soil moisture simulation in the Pailugou catchment of the Qilian Mountains, Gansu Province,China. Datasets included seven meteorological factors： air and land surface temperatures, relative humidity, global radiation,atmospheric pressure, wind speed, precipitation, and soil water content at 20, 40, 60, 80, 120 and 160 cm. To investigate theeffectiveness of WA-ANN, ANN was applied by itself to conduct a comparison. Three main findings of this study were： （1）ANN and WA-ANN provided a statistically reliable and robust prediction of soil moisture in both the root zone and deepestsoil layer studied （NSE 〉0.85, NSE means Nash-Sutcliffe Efficiency coefficient）; （2） when input meteorological factors weretransformed using maximum signal to noise ratio （SNR） and one-dimensional auto de-noising algorithm （heursure） in WA,the coupling technique improved the performance of ANN especially for soil moisture at 160 cm depth; （3） the results ofmulti-layer soil moisture prediction indicated that there may be different sources of water at different soil layers, and this canbe used as an indicator of the maximum impact depth of meteorological factors on the soil water content at this study site. Weconclude that our results show that appropriate simulation methodology can provide optimal simulation with a minimumdistortion of the raw-time series; the new method used here is applicable to soil sciences and management
Flamme, H. E.; Roberts, D. A.; Miller, D. L.
Soil moisture is a key environmental factor controlling vegetation diversity and productivity, evaporation, transpiration, and rainfall runoff. Despite the contribution of soil moisture to ecological productivity, the hydrologic cycle, and erosion, it is currently not being monitored as accurately or as frequently as other environmental factors. Traditional soil moisture monitoring techniques rely on in situ measurements, which become costly when evaluating areas of unevenly distributed soil characteristics and varying topography. Alternatively, satellite remote sensing, such as passive microwave from SMAP, can provide soil moisture but only at very coarse spatial resolutions. Imagery from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) has the potential to allow better spatial and temporal monitoring of soil moisture. This study established a relationship between plant available water and hyperspectral reflectance via linear regressions of data from 2013-2015 for two grassland field sites: 1) near Santa Barbara, California, at Coal Oil Point Reserve (COPR) and 2) Airstrip station (AIRS) at UC Santa Barbara's Sedgwick Reserve near Santa Ynez, California. Volumetric soil moisture measurements at 10 cm and 20 cm depths were provided by meteorological stations situated in COPR and AIRS while reflectance data were extracted from AVIRIS. We found strong correlations between plant available water and bands centered at wavelengths 704 nm and 831 nm, which we used to create Hyperspectral Soil Moisture Index (HSMI): 0.38((ρ831-ρ704)/(ρ831+ρ704))-0.02. HSMI demonstrated a coefficient of determination (R2) of 0.71 for linear regressions of reflectance versus plant available water with a lag time of 28 days. We applied HSMI to the AIRS and COPR grasslands for 2011 AVIRIS scenes. Plant available water values predicted by HSMI were 0.039 higher at AIRS and 0.048 higher at COPR than the field measurements at the sites. Differences in grass species, soil
Martins Bento, Celia; Yang, Xiaomei; Gort, Gerrit; Xue, Sha; Dam, van Ruud; Zomer, Paul; Mol, Hans G.J.; Ritsema, Coen J.; Geissen, Violette
The dissipation kinetics of glyphosate and its metabolite aminomethylphosphonic acid (AMPA) were studied in loess soil, under biotic and abiotic conditions, as affected by temperature, soil moisture (SM) and light/darkness. Nonsterile and sterile soil samples were spiked with 16 mg kg− 1
Martins Bento, Celia; Yang, Xiaomei; Gort, Gerrit; Xue, Sha; Dam, van Ruud; Zomer, Paul; Mol, Hans G.J.; Ritsema, Coen J.; Geissen, Violette
The dissipation kinetics of glyphosate and its metabolite aminomethylphosphonic acid (AMPA) were studied in loess soil, under biotic and abiotic conditions, as affected by temperature, soil moisture (SM) and light/darkness. Nonsterile and sterile soil samples were spiked with 16 mg kg− 1
Carrao, Hugo; Russo, Simone; Sepulcre, Guadalupe; Barbosa, Paulo
We propose a relatively simple, spatially invariant and probabilistic year-round Standardized Soil Moisture Index (SSMI) that is designed to estimate drought conditions from satellite imagery data. The SSMI is based on soil moisture content alone and is defined as the number of standard deviations that the observed moisture at a given location and timescale deviates from the long- term normal conditions. Specifically, the SSMI is computed by fitting a non-parametric probability distribution function to historical soil moisture records and then trans- forming it into a normal distribution with a mean of zero and standard deviation of one. Negative standard normal values indicate dry conditions and positive values indicate wet conditions. To evaluate the applicability of the SSMI, we fitted empirical and normal cumulative distribution functions (ECDF and nCDF) to 32-years of averaged soil moisture amounts derived from the Essential Climate Variable (ECV) Soil Moisture (SM) dataset, and compared the root-mean-squared errors of residuals. SM climatology was calculated on a 0.25° grid over Latin America at timescales of 1, 3, 6, and 12 months for the long-term period of 1979-2010. Results show that the ECDF fits better the soil moisture data than the nCDF at all timescales and that the negative SSMI values computed with the non-parametric estimator accurately identified the temporal and geographic distribution of major drought events that occurred in the study area.
Full Text Available Observation on the seasonal variations of hydrological condition and turgidity of selected Robusta coffee clones has been carried out in Kaliwining Experimental Station, Indonesian Coffee and Cocoa Research Institute in Jember. The aim was to evaluate the effect of hydrological variation on the coffee plants and the degree of soil moisture effect on plant performance. Experimental site overlays on alluvial plain, + 45 m a.s.l., 8o 15’ South with D rainfall type. Observation was conducted by survey method at the experimental plots of organic fertilizer and nitogen treatments on selected Robusta coffee clones derived from rooted cuttings, i.e. BP 436, BP 42, BP 936 and BP 358. Observation was only conducted at the experimental blocks of organic matter trials of 20 l/tree/year at nitrogen (Urea application of locally recommanded rate during the subsequent years of 1999 to 2001. Parameters observed included plant turgidity and soil moisture content of three different depths, i.e. 0—20, 20—40 and 40—60 cm and the weather. Observation was carried out in five replicates designed as blocks of barn manure treatment and N-fertilizer of recommended rate as basal fertilizer. The results showed that meteorological condition and soil moisture of experimental site through the years have seasonal patterns following the seasonal pattern of rainfall. Compared to other meteorological characteristics, relative humidity dominantly determined evaporation and plant turgidity. Plant turgi-dity was not only determined by soil moisture condition, but also atmospheric demand. When relative humidity (RH was relatively high, plant turgidity was relatively stable although soil moisture of surface layers was very low, and the reversal when soil moisture content was high plant turgidity was controlled by atmospheric demand (relative humidity. With a 3—4 dry month period, relative turgidity of the coffee plants was relatively stable above 82%, except when soil
Macelloni, G.; Paloscia, S.; Santi, E.; Tedesco, M.
Hydrological models require the knowledge of land surface parameters like soil mois- ture and snow properties with a large spatial distribution and high temporal frequency. Whilst conventional methods are unable to satisfy the constraints of space and time estimation of these parameters, the use of remote sensing data represents a real im- provement. In particular the potential of data collected by microwave radiometers at low frequencies to extract soil moisture has been clearly demonstrated in several pa- pers. However, the penetration power into the soil depends on frequency and, whereas L-band is able to estimate the moisture of a relatively thick soil layer, higher frequen- cies are only sensitive to the moisture of soil layer closer to the surface. This remark leads to the hypothesis that multifrequency observations could be able to retrieve a soil moisture profile. In several experiments carried out both on agricultural fields and on samples of soil in a tank, by using the IROE multifrequency microwave radiometers, the effect of moisture and surface roughness on different frequencies was studied. From this experiments the capability of L-band in measuring the moisture of a soil layer of several centimeters, in the order of the wavelength, was confirmed, as well the sensitivity to the moisture of the first centimeters layer at C- and X-bands, and the one of the very first layer of smooth soil at Ka-band. Using an electromagnetic model (Integral Equation Model, IEM) the brightness temperatures as a function of the in- cidence angle were computed at 1.4, 6, 10, and 37 GHz for different soil moisture profiles and different surface roughness. A particular consideration was dedicated to the latter parameter, since, especially at Ka band, surface roughness strongly affects the emission and masks the effect of moisture. Different soil moisture profiles have been tested: increasing and decreasing with depth and also constant for sandy and sandy-loam soils. After this
Yoshifuji, Natsuko; Igarashi, Yasunori; Tanaka, Nobuaki; Tanaka, Katsunori; Sato, Takanori; Tantasirin, Chatchai; Suzuki, Masakazu
To understand the impact of inter-annual climate change on vegetation-atmosphere mass and energy exchanges, it has become necessary to explore changes in leaf-out onset in response to climatic fluctuations. We examined the response of leaf-out and transpiration onset dates to soil moisture in a teak plantation in northern Thailand based on a 12-year leaf area index and sap flow measurements. The date of leaf-out and transpiration onset varied between years by up to 40 days, and depended on the initial date when the relative extractable water in a soil layer of 0-0.6 m (Θ) was greater than 0.2 being consistent with our previous results. Our new finding is that the delay in leaf-out and transpiration onset relative to the initial date when Θ > 0.2 increases linearly as the initial date on which Θ > 0.2 becomes earlier. The delay spans about 20 days in years when Θ > 0.2 occurs in March (the late dry season)-much earlier than usual because of heavy pre-monsoon rainfalls-while there is little delay in years when Θ > 0.2 occurs in May. This delay indicates the influence of additional factors on leaf-out onset, which controls the delay in the response of leaf-out to soil moisture increase. The results increased our knowledge about the pattern and extent of the changes in leaf phenology that occur in response to the inter-annual climate variation in tropical regions, where, in particular, such research is needed.
Jean FranÃƒÂ§ois Desprats
Full Text Available Soil moisture is a key parameter in different environmental applications, suchas hydrology and natural risk assessment. In this paper, surface soil moisture mappingwas carried out over a basin in France using satellite synthetic aperture radar (SARimages acquired in 2006 and 2007 by C-band (5.3 GHz sensors. The comparisonbetween soil moisture estimated from SAR data and in situ measurements shows goodagreement, with a mapping accuracy better than 3%. This result shows that themonitoring of soil moisture from SAR images is possible in operational phase. Moreover,moistures simulated by the operational MÃƒÂ©tÃƒÂ©o-France ISBA soil-vegetation-atmospheretransfer model in the SIM-Safran-ISBA-Modcou chain were compared to radar moistureestimates to validate its pertinence. The difference between ISBA simulations and radarestimates fluctuates between 0.4 and 10% (RMSE. The comparison between ISBA andgravimetric measurements of the 12 March 2007 shows a RMSE of about 6%. Generally,these results are very encouraging. Results show also that the soil moisture estimatedfrom SAR images is not correlated with the textural units defined in the European Soil Geographical Database (SGDBE at 1:1000000 scale. However, dependence was observed between texture maps and ISBA moisture. This dependence is induced by the use of the texture map as an input parameter in the ISBA model. Even if this parameter is very important for soil moisture estimations, radar results shown that the textural map scale at 1:1000000 is not appropriate to differentiate moistures zones.
Kreye, Phillip; Meon, Günter
Complex concepts for the physically correct depiction of dominant processes in the hydrosphere are increasingly at the forefront of hydrological modelling. Many scientific issues in hydrological modelling demand for additional system variables besides a simulation of runoff only, such as groundwater recharge or soil moisture conditions. Models that include soil water simulations are either very simplified or require a high number of parameters. Against this backdrop there is a heightened demand of observations to be used to calibrate the model. A reasonable integration of groundwater data or remote sensing data in calibration procedures as well as the identifiability of physically plausible sets of parameters is subject to research in the field of hydrology. Since this data is often combined with conceptual models, the given interfaces are not suitable for such demands. Furthermore, the application of automated optimisation procedures is generally associated with conceptual models, whose (fast) computing times allow many iterations of the optimisation in an acceptable time frame. One of the main aims of this study is to reduce the discrepancy between scientific and practical applications in the field of hydrological modelling. Therefore, the soil model DYVESOM (DYnamic VEgetation SOil Model) was developed as one of the primary components of the hydrological modelling system PANTA RHEI. DYVESOMs structure provides the required interfaces for the calibrations made at runoff, satellite based soil moisture and groundwater level. The model considers spatial and temporal differentiated feedback of the development of the vegetation on the soil system. In addition, small scale heterogeneities of soil properties (subgrid-variability) are parameterized by variation of van Genuchten parameters depending on distribution functions. Different sets of parameters are operated simultaneously while interacting with each other. The developed soil model is innovative regarding concept
Heerdt, ter Gerard N.J.; Veen, Ciska G.F.; Putten, van der Wim H.; Bakker, Jan P.
Restoration of riparian plant communities on bare soil requires germination of seeds and establishment of seedlings. However, species that are present in the soil seed bank do not always establish in the vegetation. Temperature, moisture conditions and soil type could play a major role in the
Ter Heerdt, Gerard N.J.; Veen, Ciska G.F.; Van der Putten, Wim H.; Bakker, Jan P.
Abstract Restoration of riparian plant communities on bare soil requires germination of seeds and establishment of seedlings. However, species that are present in the soil seed bank do not always establish in the vegetation. Temperature, moisture conditions and soil type could play a major role in
ter Heerdt, Gerard N. J.; Veen, Ciska G.F.; van der Putten, Wim H.; Bakker, Jan P.
Restoration of riparian plant communities on bare soil requires germination of seeds and establishment of seedlings. However, species that are present in the soil seed bank do not always establish in the vegetation. Temperature, moisture conditions and soil type could play a major role in the
Bird, M.I.; Veenendaal, E.M.; Lloyd, J.
We present a study of soil organic carbon (SOC) inventories and d 13C values for 625 soil cores collected from well-drained, coarse-textured soils in eight areas along a 1000 km moisture gradient from Southern Botswana, north into southern Zambia. The spatial distribution of trees and grass in the d
R. J. Ansley; T. W. Boutton; P. W. Jacoby
This study quantified honey mesquite (Prosopis glandulosa) root growth and water use efficiency following chronic soil drought or wetness on a clay loam site in north Texas. Root systems of mature trees were containerized with barriers inserted into the soil. Soil moisture within containers was manipulated with irrigation (Irrigated) or rain...
Flint, A. L.; Flint, L. E.; Dettinger, M. D.
As the changing climate influences precipitation, air temperature, and snowmelt, measurements in the Sierra Nevada are illustrating the contribution of antecedent soil moisture on the timing and volume of springtime runoff. Delays in runoff correspond to low antecedent soil moisture from the preceding fall when snow fell on dry soil. In the Tuolumne River streamgage at the Little Grand Canyon just above Hetch Hetchy reservoir, no delay occurred between runoff and snowmelt in 2007 when the soil was wetter due to a cooler summer and fall rains. However, a 26-day delay in runoff was observed after the onset of snowmelt in 2008 when the soil was drier than the preceding fall at the time of the first snowfall due to a hotter and drier summer. If soils are dry prior to snowfall then the soil moisture is first replenished by springtime snowmelt, which not only delays runoff, but also reduces runoff volume to less than that estimated from snow pack. Typical runoff forecasts rely heavily on snow survey data and snowpack conditions, and the exclusion of soil moisture data could lead to an overestimate of the amount of runoff and compromise reservoir operations. In an average snowfall year, for example, the Kaweah Basin in the southern Sierra Nevada could lose as much as 20 percent of its snow water equivalent and the Merced Basin could lose 12 percent of its snow water equivalent simply to recharge soil moisture. Analyses of measured soil moisture in the Sierra Nevada, corresponding high elevation streamflow records, regional hydrologic modeling, and analysis of future climate projections to define the nature of summer and fall temperature and precipitation, will be used to illustrate the important role antecedent soil moisture plays in the timing and volume of springtime runoff in a changing climate.
Valaee, Morteza; Ayoubi, Shamsollah; Khormali, Farhad; Lu, Sheng Gao; Karimzadeh, Hamid Reza
This study used discriminant analysis to determine the efficacy of magnetic measures for discriminating between four soil moisture regimes in northern Iran. The study area was located on loess deposits and loess-like soils containing similar parent material. Four soil moisture regimes including aridic, xeric, udic, and aquic were selected. A total of 25 soil profiles were drug from each regime and composite soil samples were collected within the moisture control section. A set of magnetic measures including magnetic susceptibility at low (χlf) and high (χhf) frequencies, frequency-dependent magnetic susceptibility (χfd), saturation isothermal remnant magnetization (SIRM), and isothermal remnant magnetization (IRM100 mT, IRM 20 mT) were measured in the laboratory. Dithionite citrate bicarbonate (Fed) and acid oxalate (Feo) contents of all soil samples were also determined. The lowest and highest χlf and χhf were observed in aquic and udic moisture regimes, respectively. A similar trend was obtained for Fed and Fed-Feo. The significant positive correlation between Fed and SIRM (r = 0.60; P < 0.01) suggested the formation of stable single domains (SSD) due to pedogenic processes. The results of discriminant analysis indicated that a combination of magnetic measures could successfully discriminate between the selected moisture regimes in the study area (average accuracy = 80%). It can thus be concluded that magnetic measures could be applied as a powerful indicator for differentiation of soil moisture regimes in the study area.
Seo, D.; Lakhankar, T.; Cosgrove, B.; Khanbilvardi, R.
Climate change and variability increases the probability of frequency, timing, intensity, and duration of flood events. After rainfall, soil moisture is the most important factor dictating flash flooding, since rainfall infiltration and runoff are based on the saturation of the soil. It is difficult to conduct ground-based measurements of soil moisture consistently and regionally. As such, soil moisture is often derived from models and agencies such as the National Oceanic and Atmospheric Administration's National Weather Service (NOAA/NWS) use proxy estimates of soil moisture at the surface in order support operational flood forecasting. In particular, a daily national map of Flash Flood Guidance (FFG) is produced that is based on surface soil moisture deficit and threshold runoff estimates. Flash flood warnings are issued by Weather Forecast Offices (WFOs) and are underpinned by information from the Flash Flood Guidance (FFG) system operated by the River Forecast Centers (RFCs). This study analyzes the accuracy and limitations of the FFG system using reported flash flood cases in 2010 and 2011. The flash flood reports were obtained from the NWS Storm Event database for the Arkansas-Red Basin RFC (ABRFC). The current FFG system at the ABRFC provides gridded flash flood guidance (GFFG) System using the NWS Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) to translate the upper zone soil moisture to estimates of Soil Conservation Service Curve Numbers. Comparison of the GFFG and real-time Multi-sensor Precipitation Estimator derived Quantitative Precipitation Estimate (QPE) for the same duration and location were used to analyze the success of the system. Improved flash flood forecasting requires accurate and high resolution soil surface information. The remote sensing observations of soil moisture can improve the flood forecasting accuracy. The Soil Moisture Active and Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellites are two
Xue, Yan; Wu, Zhi-Jie; Zhang, Li-Li; Gong, Ping; Dong, Xin-Xin; Nie, Yan-Xia
A laboratory incubation test with meadow brown soil was conducted to study the inhibitory effect of 3,4-dimethylpyrazole phosphate (DMPP) on soil nitrification as affected by soil moisture content (40%, 60% and 80% of the maximum field capacity), pH (4, 7 and 10), and organic matter (retained and removal). With the decrease of soil moisture content, the degradation of DMPP in soil tended to slow down, and the oxidation of soil NH4+ was more inhibited. At pH 10, more DMPP was remained in soil, and had the greatest inhibitory effect; at pH 7 and pH 4, the DMPP was lesser remained, with a smaller inhibitory effect. The removal of organic matter prolonged the remaining time of DMPP in soil, and decreased the apparent soil nitrification rate significantly.
Dumedah, Gift; Walker, Jeffrey P.; Chik, Li
Soil moisture information is critically important for water management operations including flood forecasting, drought monitoring, and groundwater recharge estimation. While an accurate and continuous record of soil moisture is required for these applications, the available soil moisture data, in practice, is typically fraught with missing values. There are a wide range of methods available to infilling hydrologic variables, but a thorough inter-comparison between statistical methods and artificial neural networks has not been made. This study examines 5 statistical methods including monthly averages, weighted Pearson correlation coefficient, a method based on temporal stability of soil moisture, and a weighted merging of the three methods, together with a method based on the concept of rough sets. Additionally, 9 artificial neural networks are examined, broadly categorized into feedforward, dynamic, and radial basis networks. These 14 infilling methods were used to estimate missing soil moisture records and subsequently validated against known values for 13 soil moisture monitoring stations for three different soil layer depths in the Yanco region in southeast Australia. The evaluation results show that the top three highest performing methods are the nonlinear autoregressive neural network, rough sets method, and monthly replacement. A high estimation accuracy (root mean square error (RMSE) of about 0.03 m/m) was found in the nonlinear autoregressive network, due to its regression based dynamic network which allows feedback connections through discrete-time estimation. An equally high accuracy (0.05 m/m RMSE) in the rough sets procedure illustrates the important role of temporal persistence of soil moisture, with the capability to account for different soil moisture conditions.
Robinet, Jérémy; von Hebel, Christian; van der Kruk, Jan; Govers, Gerard; Vanderborght, Jan
Soil moisture is a key variable in many natural processes. Therefore, technological and methodological advancements are of primary importance to provide accurate measurements of spatial and temporal variability of soil moisture. In that context, ElectroMagnetic Induction (EMI) instruments are often cited as a hydrogeophysical method with a large potential, through the measurement of the soil apparent electrical conductivity (ECa). To our knowledge, no studies have evaluated the potential of EMI to characterize variability of soil moisture on both agricultural and forested land covers in a (sub-) tropical environment. These differences in land use could be critical as differences in temperature, transpiration and root water uptake can have significant effect, notably on the electrical conductivity of the pore water. In this study, we used an EMI instrument to carry out a first assessment of the impact of deforestation and agriculture on soil moisture in a subtropical region in the south of Brazil. We selected slopes of different topographies (gentle vs. steep) and contrasting land uses (natural forest vs. agriculture) within two nearby catchments. At selected locations on the slopes, we measured simultaneously ECa using EMI and a depth-weighted average of the soil moisture using TDR probes installed within soil pits. We found that the temporal variability of the soil moisture could not be measured accurately with EMI, probably because of important temporal variations of the pore water electrical conductivity and the relatively small temporal variations in soil moisture content. However, we found that its spatial variability could be effectively quantified using a non-linear relationship, for both intra- and inter-slopes variations. Within slopes, the ECa could explained between 67 and 90% of the variability of the soil moisture, while a single non-linear model for all the slopes could explain 55% of the soil moisture variability. We eventually showed that combining
Yan, Zhifeng; Liu, Chongxuan; Todd-Brown, Katherine E.; Liu, Yuanyuan; Bond-Lamberty, Ben; Bailey, Vanessa L.
The relationship between microbial respiration rate and soil moisture content is an important property for understanding and predicting soil organic carbon degradation, CO2 production and emission, and their subsequent effects on climate change. This paper reports a pore-scale modeling study to investigate the response of heterotrophic respiration to moisture conditions in soils and to evaluate various factors that affect this response. X-ray computed tomography was used to derive soil pore structures, which were then used for pore-scale model investigation. The pore-scale results were then averaged to calculate the effective respiration rates as a function of water content in soils. The calculated effective respiration rate first increases and then decreases with increasing soil water content, showing a maximum respiration rate at water saturation degree of 0.75 that is consistent with field and laboratory observations. The relationship between the respiration rate and moisture content is affected by various factors, including pore-scale organic carbon bioavailability, the rate of oxygen delivery, soil pore structure and physical heterogeneity, soil clay content, and microbial drought resistivity. Simulations also illustrates that a larger fraction of CO2 produced from microbial respiration can be accumulated inside soil cores under higher saturation conditions, implying that CO2 flux measured on the top of soil cores may underestimate or overestimate true soil respiration rates under dynamic moisture conditions. Overall, this study provides mechanistic insights into the soil respiration response to the change in moisture conditions, and reveals a complex relationship between heterotrophic microbial respiration rate and moisture content in soils that is affected by various hydrological, geochemical, and biophysical factors.
Full Text Available Recent studies indicate that positive relationships between invasive plants and soil can contribute to further plant invasions. However, it remains unclear whether these relations remain unchanged throughout the growing season. In this study, spatial sequences of field observations along a transect were used to reveal seasonal interactions and spatially covarying relations between one common invasive shrub (Tartarian Honeysuckle, Lonicera tatarica and soil moisture in a tall grassland habitat. Statistical analysis over the transect shows that the contrast between soil moisture in shrub and herbaceous patches vary with season and precipitation. Overall, a negatively covarying relationship between shrub and soil moisture (i.e., drier surface soils at shrub microsites exists during the very early growing period (e.g., May, while in summer a positively covarying phenomenon (i.e., wetter soils under shrubs is usually evident, but could be weakened or vanish during long precipitation-free periods. If there is sufficient rainfall, surface soil moisture and leaf area index (LAI often spatially covary with significant spatial oscillations at an invariant scale (which is governed by the shrub spatial pattern and is about 8 m, but their phase relation in space varies with season, consistent with the seasonal variability of the co-varying phenomena between shrub invasion and soil water content. The findings are important for establishing a more complete picture of how shrub invasion affects soil moisture.
Full Text Available While the synergy between thermal, optical, and passive microwave observations is well known for the estimation of soil moisture and vegetation parameters, the use of remote sensing sources based on the Global Navigation Satellite Systems (GNSS remains unexplored. During an airborne campaign performed in August 2014, over an agricultural area in the Duero basin (Spain, an innovative sensor developed by the Universitat Politècnica de Catalunya-Barcelona Tech based on GNSS Reflectometry (GNSS-R was tested for soil moisture estimation. The objective was to evaluate the combined use of GNSS-R observations with a time-collocated Landsat 8 image for soil moisture retrieval under semi-arid climate conditions. As a ground reference dataset, an intensive field campaign was carried out. The Light Airborne Reflectometer for GNSS-R Observations (LARGO observations, together with optical, infrared, and thermal bands from Landsat 8, were linked through a semi-empirical model to field soil moisture. Different combinations of vegetation and water indices with LARGO subsets were tested and compared to the in situ measurements. Results showed that the joint use of GNSS-R reflectivity, water/vegetation indices and thermal maps from Landsat 8 not only allows capturing soil moisture spatial gradients under very dry soil conditions, but also holds great promise for accurate soil moisture estimation (correlation coefficients greater than 0.5 were obtained from comparison with in situ data.
Nielsen, Klaus B; Brandt, Kristian K; Jacobsen, Anne-Marie; Mortensen, Gerda K; Sørensen, Jan
Moisture affects bioavailability and fate of pollutants in soil, but very little is known about moisture-induced effects on pollutant toxicity. We here report on a modifying effect of moisture on degradation of linear alkylbenzene sulfonates (LASs) and on their toxicity towards ammonia-oxidizing bacteria (AOB) in agricultural soil. In soil spiked with two LAS levels (250 or 1,000 mg/kg) and incubated at four different moisture levels (9-100% of water-holding capacity), degradation was strongly affected by both soil moisture and initial LAS concentration, resulting in degradation half-lives ranging from 13 to more than 160 d. Toxicity towards AOB assessed by a novel Nitrosomonas europaea luxAB-reporter assay was correlated to total LAS concentration, indicating that LAS remained bioavailable over time without accumulation of toxic intermediates. Toxicity towards indigenous AOB increased with increasing soil moisture. The results indicate that dry soil conditions inhibit LAS degradation and provide protection against toxicity within the indigenous AOB, thus allowing for a rapid recovery of this population when LAS degradation is resumed and completed after rewetting. We propose that the protection of microbial populations against toxicity in dry soil may be a general phenomenon caused primarily by limited diffusion and thus a low bioavailability of the toxicant.
Peng, Xiang; Xu, Chi; Zeng, Wenzhi; Wu, JingWei; Huang, JieSheng
Soil salinization is a common desertification process, especially in arid lands. Hyperspectral remote sensing of salinized soil is favored for its advantages of being efficient and inexpensive. However, soil moisture often jointly has a great influence on the soil reflectance spectra under field conditions. It is a challenge to establish a model to eliminate the effect of soil moisture and quantitatively estimate the salinity contents of slightly and moderately salt-affected soil. A controlled laboratory experiment was conducted by way of continuously monitoring changes of soil moisture and salt content, which was mainly focused on the slightly and moderately salt-affected soil. We investigated the external parameter orthogonalization (EPO) method to remove the effect of soil moisture (4 to 36% in weight base) by preprocessing soil spectral reflectance and establishing the partial least squares regression after EPO preprocessing model (EPO-PLS) to predict soil salt content. Through comparing PLS with EPO-PLS model, R2 and ratio of prediction to deviation rose from 0.604 and 1.063, respectively, to 0.874 and 2.865 for validation data. Root mean square error and bias were, respectively, reduced from 1.163 and 0.141 g/100 g to 0.718 and 0.044 g/100 g. The performance of the model after EPO algorithm preprocessing was improved significantly.
Sheikh, V.; Loon, van E.E.; Stroosnijder, L.
The relationship between topography, land use, and topsoil moisture storage is investigated for a small catchment with undulating deep loess hilslopes in the south of the Netherlands. For a period of 10 months, soil moisture profiles have been measured weekly at 15 locations throughout the
Sheikh, V.; Loon, van E.E.; Stroosnijder, L.
The relationship between topography, land use, and topsoil moisture storage is investigated for a small catchment with undulating deep loess hilslopes in the south of the Netherlands. For a period of 10 months, soil moisture profiles have been measured weekly at 15 locations throughout the catchment
Shin, Yongchul; Mohanty, Binayak P.
Soil moisture (SM) at the local scale is required to account for small-scale spatial heterogeneity of land surface because many hydrological processes manifest at scales ranging from cm to km. Although remote sensing (RS) platforms provide large-scale soil moisture dynamics, scale discrepancy between observation scale (e.g., approximately several kilometers) and modeling scale (e.g., few hundred meters) leads to uncertainties in the performance of land surface hydrologic models. To overcome this drawback, we developed a new deterministic downscaling algorithm (DDA) for estimating fine-scale soil moisture with pixel-based RS soil moisture and evapotranspiration (ET) products using a genetic algorithm. This approach was evaluated under various synthetic and field experiments (Little Washita-LW 13 and 21, Oklahoma) conditions including homogeneous and heterogeneous land surface conditions composed of different soil textures and vegetations. Our algorithm is based on determining effective soil hydraulic properties for different subpixels within a RS pixel and estimating the long-term soil moisture dynamics of individual subpixels using the hydrological model with the extracted soil hydraulic parameters. The soil moisture dynamics of subpixels from synthetic experiments matched well with the observations under heterogeneous land surface condition, although uncertainties (Mean Bias Error, MBE: -0.073 to -0.049) exist. Field experiments have typically more variations due to weather conditions, measurement errors, unknown bottom boundary conditions, and scale discrepancy between remote sensing pixel and model grid resolution. However, the soil moisture estimates of individual subpixels (from the airborne Electronically Scanned Thinned Array Radiometer (ESTAR) footprints of 800 m × 800 m) downscaled by this approach matched well (R: 0.724 to -0.914, MBE: -0.203 to -0.169 for the LW 13; R: 0.343-0.865, MBE: -0.165 to -0.122 for the LW 21) with the in situ local scale soil
Bircher, Simone; Richaume, Philippe; Mahmoodi, Ali; Mialon, Arnaud; Fernandez-Moran, Roberto; Wigneron, Jean-Pierre; Demontoux, François; Jonard, François; Weihermüller, Lutz; Andreasen, Mie; Rautiainen, Kimmo; Ikonen, Jaakko; Schwank, Mike; Drusch, Mattias; Kerr, Yann H.
From the passive L-band microwave radiometer onboard the Soil Moisture and Ocean Salinity (SMOS) space mission global surface soil moisture data is retrieved every 2 - 3 days. Thus far, the empirical L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model applied in the SMOS soil moisture retrieval algorithm is exclusively calibrated over test sites in dry and temperate climate zones. Furthermore, the included dielectric mixing model relating soil moisture to relative permittivity accounts only for mineral soils. However, soil moisture monitoring over the higher Northern latitudes is crucial since these regions are especially sensitive to climate change. A considerable positive feedback is expected if thawing of these extremely organic soils supports carbon decomposition and release to the atmosphere. Due to differing structural characteristics and thus varying bound water fractions, the relative permittivity of organic material is lower than that of the most mineral soils at a given water content. This assumption was verified by means of L-band relative permittivity laboratory measurements of organic and mineral substrates from various sites in Denmark, Finland, Scotland and Siberia using a resonant cavity. Based on these data, a simple empirical dielectric model for organic soils was derived and implemented in the SMOS Soil Moisture Level 2 Prototype Processor (SML2PP). Unfortunately, the current SMOS retrieved soil moisture product seems to show unrealistically low values compared to in situ soil moisture data collected from organic surface layers in North America, Europe and the Tibetan Plateau so that the impact of the dielectric model for organic soils cannot really be tested. A simplified SMOS processing scheme yielding higher soil moisture levels has recently been proposed and is presently under investigation. Furthermore, recalibration of the model parameters accounting for vegetation and roughness effects that were thus far only
T. E. Franz
Full Text Available A cosmic-ray soil moisture probe is usually calibrated locally using soil samples collected within its support volume. But such calibration may be difficult or impractical, for example when soil contains stones, in presence of bedrock outcrops, in urban environments, or when the probe is used as a rover. Here we use the neutron transport code MCNPx with observed soil chemistries and pore water distribution to derive a universal calibration function to be used in such environments. Comparisons with independent soil moisture measurements at one cosmic-ray probe site and, separately, at thirty-five sites, show that the universal calibration function explains more than 75% of the total variation within each dataset, permitting accurate isolation of the soil moisture signal from the measured neutron signal.
Willgoose, G. R.; Chen, M.; Cohen, S.; Saco, P. M.; Hancock, G. R.
In humid areas it is generally considered that soil moisture scales spatially according to the wetness index of the landscape. This scaling arises from lateral flow downslope of ground water within the soil zone. However, in semi-arid and drier regions, this lateral flow is small and fluxes are dominated by vertical flows driven by infiltration and evapotranspiration. Thus, in the absence of runon processes, soil moisture at a location is more driven by local factors such as soil and vegetation properties at that location rather than upstream processes draining to that point. The 'apparent' spatial randomness of soil and vegetation properties generally suggests that soil moisture for semi-arid regions is spatially random. In this presentation a new analysis of neutron probe data during summer from the Tarrawarra site near Melbourne, Australia shows persistent spatial organisation of soil moisture over several years. This suggests a link between permanent features of the catchment (e.g. soil properties) and soil moisture distribution, even though the spatial pattern of soil moisture during the 4 summers monitored appears spatially random. This and other data establishes a prima facie case that soil variations drive spatial variation in soil moisture. Accordingly, we used a previously published spatial scaling relationship for soil properties derived using the mARM pedogenesis model to simulate the spatial variation of soil grading. This soil grading distribution was used in the Rosetta pedotransfer model to derive a spatial distribution of soil functional properties (e.g. saturated hydraulic conductivity, porosity). These functional properties were then input into the HYDRUS-1D soil moisture model and soil moisture simulated for 3 years at daily resolution. The HYDRUS model used had previously been calibrated to field observed soil moisture data at our SASMAS field site. The scaling behaviour of soil moisture derived from this modelling will be discussed and
Bartsch, A.; Balzter, H.; George, C.
Forest fires are frequent in the Siberian taiga and are predicted to increase in frequency as a result of increased fire risk under drought conditions caused by climate change. There is, however, some uncertainty as to the extent to which drought influences forest fire frequency. Both, forest fires and drought conditions can be observed with satellite data. Here, we present an analyses of satellite-derived soil moisture anomaly data (ERS-1/2 scatterometer) and burned area maps (AVHRR/ATSR) over central Siberia for the years 1992-2000. Monthly mean soil moisture deviations were compared to the number of fire scars and the burned area. Results show that above average surface soil moisture conditions limit the possible burned area. The magnitude of a negative deviation does not determine the maximum size of by fire affected areas. More than 50% of area is burned under below average surface soil moisture condition in July and 80% in August.
Tawfik, A. B.; Dirmeyer, P.
Interannual changes in how soil moisture can trigger convection are explored within the context of known global-scale oscillations, such as ENSO. Because soil moisture-convection interactions are a local phenomenon that require a sufficiently moist and unstable atmosphere to initiate convection, any systematic changes to water vapor produced by these global circulation changes may manifest in disrupting or promoting the soil moisture-precipitation feedback chain. Using a new framework, the Heated Condensation Framework (HCF; Tawfik and Dirmeyer 2014), local land-atmosphere coupling can be examined by separating the atmospheric background state from the land surface state in terms of convective initiation. The current work explores how the soil moisture-convection relationship changes from year-to-year and during influential El Nino and La Nina events. This is done using several global and regional reanalysis products, as well as observations where available.
Schwingshackl, Clemens; Hirschi, Martin; Seneviratne, Sonia Isabelle
While climate models generally agree on a future global mean temperature increase, the exact rate of change is still uncertain. The uncertainty is even higher for regional temperature trends that can deviate substantially from the projected global temperature increase. Several studies tried to constrain these regional temperature projections. They found that over land areas soil moisture is an important factor that influences the regional response. Due to the limited knowledge of the influence of soil moisture on atmospheric conditions on global scale the constraint remains still weak, though. Here, we use a framework that is based on the dependence of evaporative fraction (i.e. the fraction of net radiation that goes into latent heat flux) on soil moisture to distinguish between different soil moisture regimes (Seneviratne et al., 2010). It allows to estimate the influence of soil moisture on near-surface air temperature in the current climate and in future projections. While in the wet soil moisture regime, atmospheric conditions and related land surface fluxes can be considered as mostly driven by available energy, in the transitional regime - where evaporative fraction and soil moisture are essentially linearly coupled - soil moisture has an impact on turbulent heat fluxes, air humidity and temperature: Decreasing soil moisture and concomitant decreasing evaporative fraction cause increasing sensible heat flux, which might further lead to higher surface air temperatures. We investigate the strength of the single couplings (soil moisture → latent heat flux → sensible heat flux → air temperature) in order to quantify the influence of soil moisture on surface air temperature in the transitional regime. Moreover, we take into account that the coupling strength can change in the course of the year due to seasonal climate variations. The relations between soil moisture, evaporative fraction and near-surface air temperature in re-analysis and observation
National Oceanic and Atmospheric Administration, Department of Commerce — Monthly global soil moisture, runoff, and evaporation data sets produced by the Leaky Bucket model at 0.5? ? 0.5? resolution for the period from 1948 to the present....
US Fish and Wildlife Service, Department of the Interior — Soil and moisture conservation practices were carried out on four Long Island Refuges during fiscal year 1972. This report outlines the reasoning behind the changes...
Jackson, T. J.; O'Neill, P.
A series of 10 aircraft flights was conducted over agricultural fields to evaluate relationships between observed surface soil moisture and soil moisture predicted using passive microwave sensor observations. An a priori approach was used to predict values of surface soil moisture for three types of fields: tilled corn, no-till corn with soybean stubble, and idle fields with corn stubble. Acceptable predictions were obtained for the tilled corn fields, while poor results were obtained for the others. The source of error is suspected to be the density and orientation of the surface stubble layer; however, further research is needed to verify this explanation. Temporal comparisons between observed, microwave predicted, and soil water-simulated moisture values showed similar patterns for tilled well-drained fields. Divergences between the observed and simulated measurements were apparent on poorly drained fields. This result may be of value in locating and mapping hydrologic contributing areas.
Arif, Chusnul; Setiawan, Budi Indra; Doi, Ryoichi
In paddy field, monitoring soil moisture is required for irrigation scheduling and water resource allocation, management and planning. The current study proposes an Artificial Neural Networks (ANN) model to estimate soil moisture in paddy field with limited meteorological data. Dynamic of ANN model was adopted to estimate soil moisture with the inputs of reference evapotranspiration (ETo) and precipitation. ETo was firstly estimated using the maximum, average and minimum values of air temperature as the inputs of model. The models were performed under different weather conditions between the two paddy cultivation periods. Training process of model was carried out using the observation data in the first period, while validation process was conducted based on the observation data in the second period. Dynamic of ANN model estimated soil moisture with R2 values of 0.80 and 0.73 for training and validation processes, respectively, indicated that tight linear correlations between observed and estimated values of s...
Guevara, M.; Vargas, R.
We used a Bayesian regression framework based on Hamiltonian Monte Carlo simulations to identify the main effects of mean annual soil moisture, temperature, evapotranspiration, and precipitation, on long-term soil moisture decline across conterminous United States based on 36 years of remotely sensed available data. We found that mean soil moisture was a positive control of soil moisture decline in areas with long-term high precipitation but low evapotranspiration. Furthermore, mean soil moisture is a negative control on soil moisture decline in areas with long-term low precipitation and low evapotranspiration. In contrast, mean soil moisture had no effect on soil moisture decline in areas with long-term low precipitation and high evapotranspiration. These results highlight the importance of having accurate spatial soil moisture information to better inform earth system models to predict regional to global water balance and climate trends. These results support the current understanding of the basic physical mechanisms governing the coupling of soil moisture with temperature, precipitation, and evapotranspiration, but bring attention to high spatial heterogeneity in the constraints of soil moisture at the continental scale. The response of soil moisture to climate variability is considered to be one of the largest uncertainties for global land surface models, and resolving high spatial resolution of soil moisture is an ongoing challenge. Independent estimates of high spatial resolution of soil moisture could improve parameterizations of land surface models and cross-validate the current functions that mainly relay on precipitation, aerodynamic representation of the latent and sensible heat fluxes, and land surface cover type.
Wang, Yan-Ping; Han, Ming-Yu; Zhang, Lin-Sen; Dang, Yong-Jian; Qu, Jun-Tao
To have an overall understanding on the soil moisture characteristics in the apple orchards of Luochuan County can not only provide theoretical basis for selecting apple orchard sites, choosing the best root-stock combination, and improving the soil water management, but also has reference importance in increasing the productive efficiency of our apple orchards. In this study, a fixed-point continuous monitoring was conducted on the overall soil moisture environment and the variation characteristics of soil moisture in the County apple orchards differed in age class, stand type, and tree type (standard or dwarfed). For the apple orchards in the County, the rhizosphere (0-200 cm) soils of most apple trees were water-deficient, and the deficit in 0-60 cm soil layer was less than that in 60-200 cm layer. During growth season, the water storage in 0-60 cm soil layer had the same variation trend as the rainfall pattern. The relative soil moisture content in most orchards was less than 60% , and seasonal drought was quite severe. The coefficient of variation of soil moisture content decreased with soil depth. With the increasing age of the orchards, soil water storage decreased. At the same planting density, the orchards with dwarfed trees had more water storage in 0-5 m soil layer than the orchards with standard trees. However, when the orchards were planted with dwarfed trees at a higher density, the soil water storage in the orchards with dwarfed trees was lesser than that in the standard orchards. The mature orchards on highland had the highest soil moisture content, followed by the mature orchards on flat land, and on terraced land. Tree density had great effects on the soil moisture content. When the tree density was the same, planting dwarfed trees could decrease the water consumption, and increase the soil moisture content significantly. To decrease the planting density through the removal of trees would be an effective way to maintain the soil water balance of
Hagemann, Stefan; Blome, Tanja; Ekici, Altug; Beer, Christian
Permafrost or perennially frozen ground is an important part of the terrestrial cryosphere; roughly one quarter of Earth's land surface is underlain by permafrost. The currently observed global warming is most pronounced in the Arctic region and is projected to persist during the coming decades due to anthropogenic CO2 input. This warming will certainly have effects on the ecosystems of the vast permafrost areas of the high northern latitudes. The quantification of such effects, however, is still an open question. This is partly due to the complexity of the system, including several feedback mechanisms between land and atmosphere. In this study we contribute to increasing our understanding of such land-atmosphere interactions using an Earth system model (ESM) which includes a representation of cold-region physical soil processes, especially the effects of freezing and thawing of soil water on thermal and hydrological states and processes. The coupled atmosphere-land models of the ESM of the Max Planck Institute for Meteorology, MPI-ESM, have been driven by prescribed observed SST and sea ice in an AMIP2-type setup with and without newly implemented cold-region soil processes. Results show a large improvement in the simulated discharge. On the one hand this is related to an improved snowmelt peak of runoff due to frozen soil in spring. On the other hand a subsequent reduction in soil moisture enables a positive feedback to precipitation over the high latitudes, which reduces the model's wet biases in precipitation and evapotranspiration during the summer. This is noteworthy as soil-moisture-atmosphere feedbacks have previously not been the focus of research on the high latitudes. These results point out the importance of high-latitude physical processes at the land surface for regional climate.
Scipal, K.; Wagner, W.
The lack of global soil moisture observations is one of the most glaring and pressing deficiencies in current research activities of related fields, from climate monitoring and ecological applications to the quantification of biogeophysical fluxes. This has implications for important issues of the international political agenda like managing global water resources, securing food production and studying climate change. Currently it is held that only microwave remote sensing offers the potential to produce reliable global scale soil moisture information economically. Recognising the urgent need for a soil moisture mission several international initiatives are planning satellite missions dedicated to monitor the global hydrological cycle among them two European microwave satellites. ESA is planning to launch the Soil Moisture and Ocean Salinity Mission SMOS, in 2006. SMOS will measure soil moisture over land and ocean salinity over the oceans. The mission rests on a passive microwave sensor (radiometer) operated in L-band which is currently believed to hold the largest potential for soil moisture retrieval. One year before (2005) EUMETSAT will launch the Meteorological Operational satellite METOP which carries the active microwave system Advanced Scatterometer ASCAT on board. ASCAT has been designed to retrieve winds over the oceans but recent research has established its capability to retrieve soil moisture. Although currently it is hold that, using active microwave techniques, the effect of surface roughness dominates that of soil moisture (while the converse is true for radiometers), the ERS scatterometer was successfully used to derive global soil moisture information at a spatial resolution of 50 km with weekly to decadal temporal resolution. The quality of the soil moisture products have been assessed by independent experts in several pilot projects funded by the European Space Agency. There is evidence to believe that both missions will provide a flow of
Mittelbach, Heidi; Hirschi, Martin; Nicolai-Shaw, Nadine; Gruber, Alexander; Dorigo, Wouter; de Jeu, Richard; Parinussa, Robert; Jones, Lucas A.; Wagner, Wolfgang; Seneviratne, Sonia I.
Large-scale and long-term soil moisture observations based on remote sensing are promising data sets to investigate and understand various processes of the climate system including the water and biochemical cycles. Currently, the ESA Climate Change Initiative for soil moisture develops and evaluates a consistent global long-term soil moisture data set, which is based on merging passive and active remotely sensed soil moisture. Within this project an inter-comparison of algorithms for AMSR-E and ASCAT Level 2 products was conducted separately to assess the performance of different retrieval algorithms. Here we present the inter-comparison of AMSR-E Level 2 soil moisture products. These include the public data sets from University of Montana (UMT), Japan Aerospace and Space Exploration Agency (JAXA), VU University of Amsterdam (VUA; two algorithms) and National Aeronautics and Space Administration (NASA). All participating algorithms are applied to the same AMSR-E Level 1 data set. Ascending and descending paths of scaled surface soil moisture are considered and evaluated separately in daily and monthly resolution over the 2007-2011 time period. Absolute values of soil moisture as well as their long-term anomalies (i.e. removing the mean seasonal cycle) and short-term anomalies (i.e. removing a five weeks moving average) are evaluated. The evaluation is based on conventional measures like correlation and unbiased root-mean-square differences as well as on the application of the triple collocation method. As reference data set, surface soil moisture of 75 quality controlled soil moisture sites from the International Soil Moisture Network (ISMN) are used, which cover a wide range of vegetation density and climate conditions. For the application of the triple collocation method, surface soil moisture estimates from the Global Land Data Assimilation System are used as third independent data set. We find that the participating algorithms generally display a better
Srivastava, Prashant K.; Han, Dawei; Islam, Tanvir; Singh, Sudhir Kumar; Gupta, Manika; Gupta, Dileep Kumar; Kumar, Pradeep
Soil moisture is a key variable responsible for water and energy exchanges from land surface to the atmosphere (Srivastava et al., 2014). On the other hand, Soil Moisture Deficit (or SMD) can help regulating the proper use of water at specified time to avoid any agricultural losses (Srivastava et al., 2013b) and could help in preventing natural disasters, e.g. flood and drought (Srivastava et al., 2013a). In this study, evaluation of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and soil moisture from Soil Moisture and Ocean Salinity (SMOS) satellites are attempted for prediction of Soil Moisture Deficit (SMD). Sophisticated algorithm like Adaptive Neuro Fuzzy Inference System (ANFIS) is used for prediction of SMD using the MODIS and SMOS dataset. The benchmark SMD estimated from Probability Distributed Model (PDM) over the Brue catchment, Southwest of England, U.K. is used for all the validation. The performances are assessed in terms of Nash Sutcliffe Efficiency, Root Mean Square Error and the percentage of bias between ANFIS simulated SMD and the benchmark. The performance statistics revealed a good agreement between benchmark and the ANFIS estimated SMD using the MODIS dataset. The assessment of the products with respect to this peculiar evidence is an important step for successful development of hydro-meteorological model and forecasting system. The analysis of the satellite products (viz. SMOS soil moisture and MODIS LST) towards SMD prediction is a crucial step for successful hydrological modelling, agriculture and water resource management, and can provide important assistance in policy and decision making. Keywords: Land Surface Temperature, MODIS, SMOS, Soil Moisture Deficit, Fuzzy Logic System References: Srivastava, P.K., Han, D., Ramirez, M.A., Islam, T., 2013a. Appraisal of SMOS soil moisture at a catchment scale in a temperate maritime climate. Journal of Hydrology 498, 292-304. Srivastava, P.K., Han, D., Rico
Sugimoto, A.; Tei, S.; Ohte, N.; Osaka, K.; Naito, D.; Maximov, T. C.
Interannual variations in soil moisture and vegetation parameters were observed for 9 years in a larch forest near Yakutsk, Russia in Eastern Siberia, to investigate the response of the ecosystem. Soil moisture varied depending on both the amount of summer rainfall in the year and soil moisture at the end of the previous summer carried over as ice. The annual water budget of soil moisture (dQs) from the previous August to the current year primarily corresponds to precipitation, with a deviations caused by runoff (decrease in dQs), limited transpiration and/or upward transport of ice meltwater from the bottom of the active layer (increase in dQs). The source of water for transpiration was inferred from sap water delta18O. Snow meltwater with low delta18O preset in spring was used in early summer (June) every year, while, summer precipitation with high delta18O was transpired in a wet summer and ice meltwater with low delta18O was a major contributor to transpiration during droughts. Tree growth (GBH increment) correlated with soil moisture in August of the same year, and there was no correlation observed with the date of snow thaw. Larch needle delta13C showed negative correlation with soil moisture in the previous August, indicating lowering of stomatal conductance during a drought and carrying over of carbon until the following year. Litter fall production seems to increase with a two-year time lag behind the increase in soil moisture due to carrying over of soil moisture and response of vegetation. Larch needle delta15N (-1.3?n on average) negatively correlated with C/N ratio, possibly caused by water and nutrient availability.
McNally, Amy; Gregory J. Husak,; Molly Brown,; Mark Carroll,; Funk, Christopher C.; Soni Yatheendradas,; Kristi Arsenault,; Christa Peters-Lidard,; Verdin, James
The Soil Moisture Active Passive (SMAP) mission will provide soil moisture data with unprecedented accuracy, resolution, and coverage, enabling models to better track agricultural drought and estimate yields. In turn, this information can be used to shape policy related to food and water from commodity markets to humanitarian relief efforts. New data alone, however, do not translate to improvements in drought and yield forecasts. New tools will be needed to transform SMAP data into agriculturally meaningful products. The objective of this study is to evaluate the possibility and efficiency of replacing the rainfall-derived soil moisture component of a crop water stress index with SMAP data. The approach is demonstrated with 0.1°-resolution, ~10-day microwave soil moisture from the European Space Agency and simulated soil moisture from the Famine Early Warning Systems Network Land Data Assimilation System. Over a West Africa domain, the approach is evaluated by comparing the different soil moisture estimates and their resulting Water Requirement Satisfaction Index values from 2000 to 2010. This study highlights how the ensemble of indices performs during wet versus dry years, over different land-cover types, and the correlation with national-level millet yields. The new approach is a feasible and useful way to quantitatively assess how satellite-derived rainfall and soil moisture track agricultural water deficits. Given the importance of soil moisture in many applications, ranging from agriculture to public health to fire, this study should inspire other modeling communities to reformulate existing tools to take advantage of SMAP data.
Altaf, M. U.
Soil moisture is a crucial component of the hydrologic cycle, significantly influencing runoff, infiltration, recharge, evaporation and transpiration processes. Models characterizing these processes require soil moisture as an input, either directly or indirectly. Better characterization of the spatial variability of soil moisture leads to better predictions from hydrologic/climate models. In-situ measurements have fine resolution, but become impractical in terms of coverage over large extents. Remotely sensed data have excellent spatial coverage extents, but suffer from poorer spatial and temporal resolution. We present here an innovative approach to downscaling coarse resolution soil moisture data by combining data assimilation and physically based modeling. In this approach, we exploit the features of Continuous Data Assimilation (CDA). A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model’s large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (e.g., HYDRUS) are subjected to data assimilation conditioned upon the coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. The large scale features of the model output are constrained to the observations, and as a consequence, the misfit at the fine scale is reduced. The advantage of this approach is that fine resolution soil moisture maps can be generated across large spatial extents, given the coarse resolution data. The data assimilation approach also enables multi-scale data generation which is helpful to match the soil moisture input data to the corresponding modeling scale. Application of this approach is likely in generating fine and intermediate resolution soil
Chew, C. C.; Small, E. E.; Larson, K. M.; Braun, J. J.; Shreve, C. M.
High-precision GPS receivers can be used to estimate fluctuations in near surface soil moisture, snow and vegetation water content. This approach, referred to as GPS-Interferometric Reflectometry (GPS-IR), relates precise changes in the geometry of reflected GPS signals to observe soil moisture and snow while simultaneously using signal attenuation and diffuse scattering to infer changes in vegetative state. Previous remote sensing research has shown that microwave signals (e.g., L-band) are optimal for measuring hydrologic variables, such as soil moisture, and because GPS satellites transmit similar signals, they can be useful for sensing water in the environment. In addition, standard GPS antenna configurations that are used in NSF's Plate Boundary Observatory network yield sensing footprints of ~1000 m2. Given this sensitivity, hundreds of GPS receivers that exist in the U.S. could be used to provide near-real time estimates of soil moisture and vegetation water content for satellite validation, drought monitoring and related studies. A significant obstacle to using L-band (or similar) signals for remote sensing is differentiating the effects of soil moisture and vegetation on the retrieval of hydrologic variables. This same challenge exists when using GPS-IR data. We have established nine research sites with identical GPS and hydrologic infrastructure to study this problem. These sites span a wide range of soil, vegetation, and climate types. In addition to daily GPS and hourly soil moisture data, we have collected weekly vegetation water content samples at all sites. Our data demonstrate that soil moisture fluctuations can be estimated from GPS-IR records when vegetation water content is low (moisture and vegetation signals and quantifying errors in our retrieval algorithm.
Karthikeyan, L.; Kumar, D. Nagesh
A novel approach is proposed that attempts to validate passive microwave soil moisture retrievals using precipitation data (applied over India). It is based on the concept that the expectation of precipitation conditioned on soil moisture follows a sigmoidal convex-concave-shaped curve, the characteristic of which was recently shown to be represented by mutual information estimated between soil moisture and precipitation. On this basis, with an emphasis over distribution-free nonparametric computations, a new measure called Copula-Kernel Density Estimator based Mutual Information (CKDEMI) is introduced. The validation approach is generic in nature and utilizes CKDEMI in tandem with a couple of proposed bootstrap strategies, to check accuracy of any two soil moisture products (here Advanced Microwave Scanning Radiometer-EOS sensor's Vrije Universiteit Amsterdam-NASA (VUAN) and University of Montana (MONT) products) using precipitation (India Meteorological Department) data. The proposed technique yields a "best choice soil moisture product" map which contains locations where any one of the two/none of the two/both the products have produced accurate retrievals. The results indicated that in general, VUA-NASA product has performed well over University of Montana's product for India. The best choice soil moisture map is then integrated with land use land cover and elevation information using a novel probability density function-based procedure to gain insight on conditions under which each of the products has performed well. Finally, the impact of using a different precipitation (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) data set over the best choice soil moisture product map is also analyzed. The proposed methodology assists researchers and practitioners in selecting the appropriate soil moisture product for various assimilation strategies at both basin and continental scales.
Griesfeller, A.; Lahoz, W. A.; Jeu, R. A. M. de; Dorigo, W.; Haugen, L. E.; Svendby, T. M.; Wagner, W.
In this study we evaluate satellite soil moisture products from the advanced SCATterometer (ASCAT) and the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) over Norway using ground-based observations from the Norwegian water resources and energy directorate. The ASCAT data are produced using the change detection approach of Wagner et al. (1999), and the AMSR-E data are produced using the VUA-NASA algorithm (Owe et al., 2001, 2008). Although satellite and ground-based soil moisture data for Norway have been available for several years, hitherto, such an evaluation has not been performed. This is partly because satellite measurements of soil moisture over Norway are complicated owing to the presence of snow, ice, water bodies, orography, rocks, and a very high coastline-to-area ratio. This work extends the European areas over which satellite soil moisture is validated to the Nordic regions. Owing to the challenging conditions for soil moisture measurements over Norway, the work described in this paper provides a stringent test of the capabilities of satellite sensors to measure soil moisture remotely. We show that the satellite and in situ data agree well, with averaged correlation (R) values of 0.72 and 0.68 for ASCAT descending and ascending data vs in situ data, and 0.64 and 0.52 for AMSR-E descending and ascending data vs in situ data for the summer/autumn season (1 June-15 October), over a period of 3 years (2009-2011). This level of agreement indicates that, generally, the ASCAT and AMSR-E soil moisture products over Norway have high quality, and would be useful for various applications, including land surface monitoring, weather forecasting, hydrological modelling, and climate studies. The increasing emphasis on coupled approaches to study the earth system, including the interactions between the land surface and the atmosphere, will benefit from the availability of validated and improved soil moisture satellite datasets, including those
Norouzi, H.; Campo, C.; Temimi, M.; Lakhankar, T.; Khanbilvardi, R.
Soil moisture content is among most important physical parameters in hydrology, climate, and environmental studies. Many microwave-based satellite observations have been utilized to estimate this parameter. The Advanced Microwave Scanning Radiometer 2 (AMSR2) is one of many remotely sensors that collects daily information of land surface soil moisture. However, many factors such as ancillary data and vegetation scattering can affect the signal and the estimation. Therefore, this information needs to be validated against some "ground-truth" observations. NOAA - Cooperative Remote Sensing and Technology (CREST) center at the City University of New York has a site located at Millbrook, NY with several insitu soil moisture probes and an L-Band radiometer similar to Soil Moisture Passive and Active (SMAP) one. This site is among SMAP Cal/Val sites. Soil moisture information was measured at seven different locations from 2012 to 2015. Hydra probes are used to measure six of these locations. This study utilizes the observations from insitu data and the L-Band radiometer close to ground (at 3 meters height) to validate and to compare soil moisture estimates from AMSR2. Analysis of the measurements and AMSR2 indicated a weak correlation with the hydra probes and a moderate correlation with Cosmic-ray Soil Moisture Observing System (COSMOS probes). Several differences including the differences between pixel size and point measurements can cause these discrepancies. Some interpolation techniques are used to expand point measurements from 6 locations to AMSR2 footprint. Finally, the effect of penetration depth in microwave signal and inconsistencies with other ancillary data such as skin temperature is investigated to provide a better understanding in the analysis. The results show that the retrieval algorithm of AMSR2 is appropriate under certain circumstances. This validation algorithm and similar study will be conducted for SMAP mission. Keywords: Remote Sensing, Soil
Ardilouze, Constantin; Batté, Lauriane; Déqué, Michel
Soil moisture is acknowledged as one of the slowly evolving components of the earth system that impact the surface continental climate in summer. An improved initialization of the spring soil moisture in forecast systems generally leads to better near-surface temperature predictive skill at sub-seasonal to seasonal time scales. This has been hypothesized and partly proven over regions usually referred to as hotspots of land-atmosphere coupling, such as South-East Europe for example. Over these transitional regions between wet and arid climate, evapotranspiration is mainly controlled by soil moisture. In order to assess the potential predictability related to this land component, we have carried out a set of idealized global and regional summer re-forecast experiments with soil moisture prescribed daily over the full domain. The skill for summer temperature and precipitation was compared to reference simulations with free running soil moisture. The correlations for near surface temperature anomalies are greatly increased over almost all Europe, including the wettest regions, where soil moisture is usually considered unlimited. Even more unexpected, large parts of Europe including Atlantic regions, Scandinavia or Western Russia show significantly higher correlation for summer precipitation. These results contrast with previous studies on land-atmosphere coupling limiting the potential benefit of soil moisture initialization to the above-mentioned hotspots. Although our experimental framework leads to improved predictive skill for seasonal total precipitation, it is not the case for the dry spell duration, which suggests that prescribed soil moisture influences precipitation intensity rather than frequency. Results on the seasonal prediction of extreme heat events such as those of 2003 and 2010 summers will be discussed in the light of this study.
Micheli, L.; Dodge, C.; Fernandez, D.; Weiss, P. L.; Flint, L. E.; Flint, A. L.; Torregrosa, A.
Summertime coastal fog advects from the ocean and transports water inland in the form of fog droplets to forests and grasslands. The amount of fog water delivered to the soil through fog drip from foliage and other surfaces that have captured and accumulated the droplets is often difficult to quantify due to many challenges including the difficulty of measuring the relatively small variations in soil moisture that accompany fog events. This study details summer season records collected from 4 sites at the Pepperwood Preserve in Santa Rosa, CA. Fog drip volumes were measured using 1 m2 standard fog collectors located at a grassland site for the past three summers. Soil moisture measurements were collected for portions of the three summer seasons from three sites: two oak woodland understory sites and a grassland site on the edge of a forest. One oak woodland site was within 400 m of the standard fog collector grassland site. Leaf wetness sensors (LWS) were co-located at all soil moisture sites. We observe a much higher frequency of wet periods at the grassland site than at the nearby oak woodland site during the summer fog season. One hypothesis is that the oak canopy acts to protect the LWS at the oak woodland site from nocturnal radiative cooling, thereby reducing condensation and dew formation. Another hypothesis is that the oak woodland canopy tends sheltered the understory during light fog events, resulting in edge effects that may tend to reduce fog deposition within the canopy. Leaf and soil moisture measurements both during fog events and during periods without fog but when dew point is reached may provide a more complete picture of non-rain mechanisms of moisture delivery to the foliage and the soil. Investigations are on-going to include corresponding meteorological data (wind speed and direction, relative humidity and temperature) to understand relative contributions to the soil associated with both fog and dew and to better distinguish between fog and
Gu, Y.; Hunt, E.; Wardlow, B.; Basara, J.B.; Brown, J.F.; Verdin, J.P.
The evaluation of the relationship between satellite-derived vegetation indices (normalized difference vegetation index and normalized difference water index) and soil moisture improves our understanding of how these indices respond to soil moisture fluctuations. Soil moisture deficits are ultimately tied to drought stress on plants. The diverse terrain and climate of Oklahoma, the extensive soil moisture network of the Oklahoma Mesonet, and satellite-derived indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) provided an opportunity to study correlations between soil moisture and vegetation indices over the 2002-2006 growing seasons. Results showed that the correlation between both indices and the fractional water index (FWI) was highly dependent on land cover heterogeneity and soil type. Sites surrounded by relatively homogeneous vegetation cover with silt loam soils had the highest correlation between the FWI and both vegetation-related indices (r???0.73), while sites with heterogeneous vegetation cover and loam soils had the lowest correlation (r???0.22). Copyright 2008 by the American Geophysical Union.
Effects of nitrogen fertilizer,soil mosture and temperature and temperature on methane oxidation in paddy soil were investigated under laboratory conditions.Addition of 0.05 g N kg-1 soil as NH4Cl strongly inhibited methane oxidation and addition of the same rate of KCl also inhibited the oxidation but with more slight effect,suggesting that the inhibitory effect was partly caused by increase in osmotic potential in microorganism cell,Not only NH4+ but also NO3- greatly affected methane oxidation.Urea did not affect methane oxidation in paddy soil in the first two days of incubation,but strong inhibitory effect was observed afterwards.Methane was oxidized in the treated soil with an optimum moisture of 280 g kg-1 ,and air-drying inhibited methane oxidation entirely.The optimum temperature of methane oxidation was about 30℃ in paddy soil.while no methane oxidation was observed at 5℃or 50℃。
Thorstensen, A. R.; Nguyen, P.; Hsu, K. L.; Sorooshian, S.
The complexity of a catchment's physical heterogeneities is often addressed through calibration via observed streamflow. As hydrologic models move from lumped to distributed, and Earth observations increase in number and variety, the question is raised as to whether or not such distributed observations can be used to satisfy the possibly heterogenic calibration needs of a catchment. The goal of this study is to examine if calibration of a distributed hydrologic model using soil moisture observations can improve simulated streamflow. The NWS's Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) is used in this study. HL-RDHM uses the Sacramento Heat Transfer with enhanced Evapotranspiration for rainfall-runoff production and can convert conceptual storages to soil layers. This allows for calibration of conceptual parameters based on observed soil moisture profiles. HL-RDHM is calibrated using scalar multipliers of a-priori grids derived from soil surveys, with the premise that heterogeneity of these grids is correct. This assumption is relaxed to study the benefit of distributed calibration. Soil moisture measurements in the Turkey River Basin, which was equipped with 20 in-situ soil moisture sites for the Iowa Flood Studies campaign, were used for calibration of parameters related to soil moisture (i.e. storage and release parameters). The Shuffled Complex Evolution method was used to calibrate pixels collocated with in-situ probes based on soil moisture RMSE at point scale. Methods to allocate calibrated parameter values to remaining pixels include an averaging method, spatial interpolation, and a similarity method. Calibration was done for spring 2013, and validation for 2009 and 2011. Results show that calibration using stream gauges remains the superior method, especially for correlation. This is because calibration based on streamflow can correct peak timing by adjusting routing parameters. Such adjustments using soil moisture cannot be done
Bell, Colin; McIntyre, Nancy; Cox, Stephen; Tissue, David; Zak, John
Global climate change models indicate that storm magnitudes will increase in many areas throughout southwest North America, which could result in up to a 25% increase in seasonal precipitation in the Big Bend region of the Chihuahuan Desert over the next 50 years. Seasonal precipitation is a key limiting factor regulating primary productivity, soil microbial activity, and ecosystem dynamics in arid and semiarid regions. As decomposers, soil microbial communities mediate critical ecosystem processes that ultimately affect the success of all trophic levels, and the activity of these microbial communities is primarily regulated by moisture availability. This research is focused on elucidating soil microbial responses to seasonal and yearly changes in soil moisture, temperature, and selected soil nutrient and edaphic properties in a Sotol Grassland in the Chihuahuan Desert at Big Bend National Park. Soil samples were collected over a 3-year period in March and September (2004-2006) at 0-15 cm soil depth from 12 3 x 3 m community plots. Bacterial and fungal carbon usage (quantified using Biolog 96-well micro-plates) was related to soil moisture patterns (ranging between 3.0 and 14%). In addition to soil moisture, the seasonal and yearly variability of soil bacterial activity was most closely associated with levels of soil organic matter, extractable NH(4)-N, and soil pH. Variability in fungal activity was related to soil temperatures ranging between 13 and 26 degrees C. These findings indicate that changes in soil moisture, coupled with soil temperatures and resource availability, drive the functioning of soil-microbial dynamics in these desert grasslands. Temporal patterns in microbial activity may reflect the differences in the ability of bacteria and fungi to respond to seasonal patterns of moisture and temperature. Bacteria were more able to respond to moisture pulses regardless of temperature, while fungi only responded to moisture pulses during cooler seasons with
Brocca, Luca; Albergel, Clement; Massari, Christian; Ciabatta, Luca; Moramarco, Tommaso; de Rosnay, Patricia
Soil moisture governs the partitioning of mass and energy fluxes between the land surface and the atmosphere and, hence, it represents a key variable for many applications in hydrology and earth science. In recent years, it was demonstrated that soil moisture observations from ground and satellite sensors contain important information useful for improving rainfall estimation. Indeed, soil moisture data have been used for correcting rainfall estimates from state-of-the-art satellite sensors (e.g. Crow et al., 2011), and also for improving flood prediction through a dual data assimilation approach (e.g. Massari et al., 2014; Chen et al., 2014). Brocca et al. (2013; 2014) developed a simple algorithm, called SM2RAIN, which allows estimating rainfall directly from soil moisture data. SM2RAIN has been applied successfully to in situ and satellite observations. Specifically, by using three satellite soil moisture products from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observation) and SMOS (Soil Moisture and Ocean Salinity); it was found that the SM2RAIN-derived rainfall products are as accurate as state-of-the-art products, e.g., the real-time version of the TRMM (Tropical Rainfall Measuring Mission) product. Notwithstanding these promising results, a detailed study investigating the physical basis of the SM2RAIN algorithm, its range of applicability and its limitations on a global scale has still to be carried out. In this study, we carried out a crash test for SM2RAIN algorithm on a global scale by performing a synthetic experiment. Specifically, modelled soil moisture data are obtained from HTESSEL model (Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land) forced by ERA-Interim near-surface meteorology. Afterwards, the modelled soil moisture data are used as input into SM2RAIN algorithm for testing weather or not the resulting rainfall estimates are able to reproduce ERA-Interim rainfall data. Correlation, root
Altaf, M. U.
Soil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model’s large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometer-based, coarse resolution products from remote sensing satellites.
Abbaszadeh, Peyman; Yan, Hongxiang; Moradkhani, Hamid
Understating the soil moisture pattern has significant impact on flood modeling, drought monitoring, and irrigation management. Although satellite retrievals can provide an unprecedented spatial and temporal resolution of soil moisture at a global-scale, their soil moisture products (with a spatial resolution of 25-50 km) are inadequate for regional study, where a resolution of 1-10 km is needed. In this study, a downscaling approach using Genetic Programming (GP), a specialized version of Genetic Algorithm (GA), is proposed to improve the spatial resolution of satellite soil moisture products. The GP approach was applied over a test watershed in United States using the coarse resolution satellite data (25 km) from Advanced Microwave Scanning Radiometer - EOS (AMSR-E) soil moisture products, the fine resolution data (1 km) from Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index, and ground based data including land surface temperature, vegetation and other potential physical variables. The results indicated the great potential of this approach to derive the fine resolution soil moisture information applicable for data assimilation and other regional studies.
Full Text Available This paper describes a comparison between two soil moisture prediction models. One is MORECS (Met Office Rainfall and Evaporation Calculation Scheme, the Met Office soil moisture model that is used by agriculture, flood modellers and weather forecasters to initialise their models. The other is MOSES (Met Office Surface Exchange Scheme, modified with a runoff generation module. The models are made compatible by increasing the vegetation information available to MOSES. Both models were run with standard parameters and were driven using meteorological observations at Wallingford (1995-1997. Detailed soil moisture measurements were available at a grassland site and a woodland site in this area. The comparison between the models and the observed soil moisture indicated that, for the grassland site, MORECS dried out too quickly in the spring and, for the woodland site, was too wet. Overall, the performance of MOSES was superior. The soil moisture predicted by the new, modified MOSES will be included as a product of Nimrod - the 5 km x 5km gridded network of observed meteorological data across the UK. Keywords: Soil moisture, model, observation, field capacity
Blankenship, Clay; Case, Jonathan; Zavodsky, Bradley; Jedlovec, Gary
Soil moisture retrievals from the Soil Moisture and Ocean Salinity (SMOS) instrument are assimilated into the Noah land surface model (LSM) within the NASA Land Information System (LIS). Before assimilation, SMOS retrievals are bias-corrected to match the model climatological distribution using a Cumulative Distribution Function (CDF) matching approach. Data assimilation is done via the Ensemble Kalman Filter. The goal is to improve the representation of soil moisture within the LSM, and ultimately to improve numerical weather forecasts through better land surface initialization. We present a case study showing a large area of irrigation in the lower Mississippi River Valley, in an area with extensive rice agriculture. High soil moisture value in this region are observed by SMOS, but not captured in the forcing data. After assimilation, the model fields reflect the observed geographic patterns of soil moisture. Plans for a modeling experiment and operational use of the data are given. This work helps prepare for the assimilation of Soil Moisture Active/Passive (SMAP) retrievals in the near future.
Hood, R C
The effect of soil temperature and moisture on plant growth and mineralisation of organic residues was investigated using 15N-labelled soybean residues and temperature-controlled tanks in the glasshouse. Treatments were arranged in a factorial design with: three soil temperatures (20, 26 and 30 degrees C), two soil moisture regimes (8% (-800 Kpa) or 12% (-100 Kpa)), soybean residues added (enriched at 1.82 atom % 15N excess) or no residues; and either sown with ryegrass or not sown. Pots were sampled six weeks after planting and 15N-enrichment and delta13C of the plant and soil fractions were determined. Soil inorganic N was also periodically measured. Available inorganic N increased significantly with addition of residues and generally decreased with increasing temperature. Plant dry matter decreased significantly with increase in soil temperature and increased with increasing moisture. Root-to-shoot ratio declined with increased temperature and moisture. Percentage nitrogen derived from residues (%Ndfr) increased linearly with increased temperature and moisture. Delta13C decreased linearly with increasing temperature and decreasing moisture status. There was a significant correlation between transpiration and dry matter production, but there was no correlation between water use efficiency and delta13C. The results suggest that C: N ratio of the root material effects the root turnover and in turn the water supply capacity of the root system.
Full Text Available In paddy field, monitoring soil moisture is required for irrigation scheduling and water resource allocation, management and planning. The current study proposes an Artificial Neural Networks (ANN model to estimate soil moisture in paddy field with limited meteorological data. Dynamic of ANN model was adopted to estimate soil moisture with the inputs of reference evapotranspiration (ETo and precipitation. ETo was firstly estimated using the maximum, average and minimum values of air temperature as the inputs of model. The models were performed under different weather conditions between the two paddy cultivation periods. Training process of model was carried out using the observation data in the first period, while validation process was conducted based on the observation data in the second period. Dynamic of ANN model estimated soil moisture with R2 values of 0.80 and 0.73 for training and validation processes, respectively, indicated that tight linear correlations between observed and estimated values of soil moisture were observed. Thus, the ANN model reliably estimates soil moisture with limited meteorological data.
Peck, Eugene L.
Soil moisture measurements were obtained during the summer of 1987 and 1989 near Manhattan, Kansas, using the National Weather Service (NWS) airborne gamma radiation system. A network of 24 flight lines were established over the research area. Airborne surveys were flown daily during two intensive field campaigns. The data collected was sufficient to modify the NWS standard operational method for estimating soil moisture for the Field Experiment (FIFE) flight lines. The average root mean square error of the soil moisture estimates for shorter FIFE flight lines was found to be 2.5 percent, compared with a reported value of 3.9 percent for NWS flight lines. Techniques were developed to compute soil moisture estimates for portions of the flight lines. Results of comparisons of the airborne gamma radiation soil moisture estimates with those obtained using the NASA Pushbroom Microwave Radiation (PBMR) system and hydrological model are presented. The airborne soil moisture measurements, and real averages computed using all remotely sensed and ground data, have been in support of the research of the many FIFE investigators whose overall goal was the upscale integration of models and the application of satellite remote sensing.