Monitoring Surface Soil Moisture Research Articles

Soil moisture retrieval improvement over agricultural fields by adding entropy–alpha dual-polarimetric decomposition features

Soil moisture is an important parameter that directly impacts crop productivity. Microwave signals are highly sensitive to soil dielectric constant and so they are used to derive soil moisture. The potential of entropy and alpha (H / α) decomposition for moisture estimations at 0- to 5-cm soil depth was assessed. The H / α parameters were extracted from the dual-polarimetric Sentinel-1 dataset. Also, we used the gray level co-occurrence matrix (GLCM) texture parameters extracted from Sentinel-1 and tested them for soil moisture retrieval. The generalized regression neural network (GRNN), neural network (NN), and support vector regression (SVR) algorithms were trained and tested for soil moisture estimation. Multiple input features, including Sentinel-1 intensities, GLCM parameters, and H / α parameters, were used for training these algorithms. For NN, three activation functions of rectified linear unit (ReLU), tanh, and sigmoid and for SVR three kernel functions of radial basis function (RBF), polynomial, and linear kernel were tested. The ReLU outperformed the other two activation functions with root mean squared error (RMSE) of 0.042 m3 m − 3 and coefficient of determination (R2) of 0.72. For the SVR algorithm, the highest accuracies derived from the RBF kernel function with RMSE 0.053 m3 m − 3 and R2 of 0.51. Between all the three machine learning algorithms, the GRNN algorithm outperformed the other two algorithms with RMSE of 0.033 m3 m − 3 and R2 of 0.82. These results demonstrated the high potential of using polarimetric synthetic aperture radar data in combination with the machine learning algorithms for surface soil moisture monitoring.

Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

A temporal polarization ratio algorithm for calibration-free retrieval of soil moisture at L-band

Monitoring of surface soil moisture (SM) through microwave radiometry typically relies on the inversion of a radiative transfer model. Conventional inversion algorithms require proper calibration of surface roughness and radiometric parameters of the overlying canopy, including the scattering albedo and optical depth. However, uncertainty in global characterization of these parameters is one of the main sources of error in satellite SM retrievals. To cope with this uncertainty, this paper presents a new algorithm, called “temporal polarization ratio algorithm” (TPRA), that enables retrieval of SM independent of surface roughness and vegetation parameters. This approach uses the temporal differences of polarized emissivity observations assuming that the surface roughness and vegetation parameters are invariant over a window of time. Unlike the classical dual channel algorithms (DCA), TPRA is not only free of surface calibration but also robust to systematical errors arising from surface soil temperature errors. One caveat is that the algorithm is unable to retrieve SM when surface emissivity does not change appreciably in time. The performance of TPRA is evaluated through several controlled numerical experiments and validated using the Soil Moisture Active Passive (SMAP) satellite retrievals as well as in-situ SM measurements over Australia. The results show improved SM retrievals compared with the DCA and SMAP official products as long as the surface emissivity changes sufficiently over the retrieval time period.

Retrieval of Soil Moisture Content Based on a Modified Hapke Photometric Model: A Novel Method Applied to Laboratory Hyperspectral and Sentinel-2 MSI Data

Soil moisture is the crucial carrier of the global hydrologic cycle and the dynamic energy balance regulation process. Therefore, it is of great significance to monitor surface soil moisture content (SMC) accurately for the study of the natural ecological environment. The Hapke model is the most widely used photometric model in soil remote sensing research, but the development of this model is limited by the lack of valid multi–angular data. The main innovations of this paper have two aspects: (1) A novel soil moisture retrieval approach based on the Hapke (SMR–Hapke) model is derived by exploring the relationship between single scattering albedo (SSA) and SMC on the optical bands from 400 to 2400 nm. The performance of the proposed model was verified on a dataset consisting of four different soil samples, and the experimental results indicated that the inverted soil moisture from SMR–Hapke model coincided with the measurement values, with the R2 being generally more than 0.9 in the solar domain. (2) The SMR–Hapke model has been reduced to a linear form on the SWIR field and a physically-based normalized difference soil moisture index N D S M I H a p k e has been proposed. Based on the laboratory-based hyperspectral data, we compared the performance of N D S M I H a p k e with other traditional soil moisture indices using linear regression analysis, and the results demonstrate that the proposed N D S M I H a p k e had a great potential for estimating SMC with R2 values of 0.88. Finally, high–resolution SMC map was produced by combining the Sentinel–2 MSI data with N D S M I H a p k e . This study provides a novel extended Hapke model for the estimation of surface soil moisture content.

Assessing Drought Conditions in Cloudy Regions Using Reconstructed Land Surface Temperature

Temperature vegetation dryness index (TVDI) in a triangular or trapezoidal feature space can be calculated from the land surface temperature (LST) and normalized difference vegetation index (NDVI), and has been widely applied to regional drought monitoring. However, thermal infrared sensors cannot penetrate clouds to detect surface information of sub-cloud pixels. In cloudy areas, LST data include a large number of cloudy pixels, seriously degrading the spatial and temporal continuity of drought monitoring. In this paper, the Remotely Sensed Daily Land Surface Temperature Reconstruction model (RSDAST) is combined with the LST reconstructed (RLST) by the RSDAST and applied to drought monitoring in a cloudy area. The drought monitoring capability of the reconstructed temperature vegetation drought index (RTVDI) under cloudy conditions is evaluated by comparing the correlation between land surface observations for soil moisture and the TVDI before and after surface temperature reconstruction. Results show that the effective duration and area of the RTVDI in the study area were larger than those of the original TVDI (OTVDI) in 2011. In addition, RLST/NDVI scatter plots cover a wide range of values, with the fitted dry–wet boundaries more representative of real soil moisture conditions. Under continuously cloudy conditions, the OTVDI inverted from the original LST (OLST) loses its drought monitoring capability, whereas RTVDI can completely and accurately reconstruct surface moisture conditions across the entire study area. The correlation between TVDI and soil moisture is stronger for RTVDI (R = −0.45) than that for OTVDI (R = −0.33). In terms of the spatial and temporal distributions, the R value for correlation between RTVDI and soil moisture was higher than that for OTVDI. Hence, in continuously cloudy areas, RTVDI not only expands drought monitoring capability in time and space, but also improves the accuracy of surface soil moisture monitoring and enhances the applicability and reliability of thermal infrared data under extreme conditions.

Surface soil moisture monitoring with near-ground sensors: Performance assessment of a matric potential-based method

This study develops an indirect method for the estimation of surface soil moisture (SM) by means of monitoring in situ soil water potential. Field work was carried out in ten sampling units selected according to coverage, soil, and topographic factors. Relative humidity and soil temperature were hourly measured to assess the soil–water potential through Kelvin's Law for each sampling unit. Water potential was used as input in the soil moisture retention curve for field conditions to assess SM by fitting the Fredlund-Xing model. To verify the performance of our approach, we measured the surface volumetric SM by the gravimetric method for 2,211 soil samples collected in the same survey period. Results show a non-linear relationship between gravimetric SM and the indirect estimation displaying a determination coefficient of 68.4%. The proposed approach is a suitable exploratory alternative to support continuous SM monitoring studies.

Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau

Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements in the central Tibetan Plateau (TP) in 2015. We exploited TERRA moderate resolution imaging spectroradiometer (MODIS) and Sentinel-1A synthetic aperture radar (SAR) observations to estimate SSM through a simplified water-cloud model (sWCM). This model considers the impact of vegetation water content (VWC) to SSM retrieval by integrating the vegetation index (VI), the normalized difference water index (NDWI), or the normalized difference infrared index (NDII). Sentinel-1 SAR C-band backscattering coefficients, incidence angle, and NDWI/NDII were assimilated in the sWCM to monitor SSM. The soil moisture and temperature monitoring network on the central TP (CTP-SMTMN) measures SSM within the study area, and ground measurements were applied to train and validate the model. Via the proposed methods, we estimated the SSM in vegetated area with an R2 of 0.43 and a ubRMSE of 0.06 m3/m3 when integrating the NDWI and with an R2 of 0.45 and a ubRMSE of 0.06 m3/m3 when integrating the NDII.

Evaluating the Operational Application of SMAP for Global Agricultural Drought Monitoring

Over the past two decades, remote sensing has made possible the routine global monitoring of surface soil moisture. Regional agricultural drought monitoring is one of the most logical application areas for such monitoring. However, remote sensing alone provides soil moisture information for only the top few centimeters of the soil profile, while agricultural drought monitoring requires knowledge of the amount of water present in the entire root zone. The assimilation of remotely sensed soil moisture products into continuous soil water balance models provides a way of addressing this shortcoming. Here, we describe the assimilation of NASA's soil moisture active passive (SMAP) surface soil moisture data into the United States Department of Agriculture Foreign Agricultural Service (USDA FAS) Palmer model and assess the impact of SMAP on USDA FAS drought monitoring capabilities. The assimilation of SMAP is specifically designed to enhance the model skill and the USDA FAS drought capabilities by correcting for random errors inherent in its rainfall forcing data. The performance of this SMAP-based assimilation system is evaluated using two approaches. At global scale, the accuracy of the system is assessed by examining the lagged correlation agreement between soil moisture and the normalized difference vegetation index (NDVI). Additional regional-scale evaluation using in situ- based soil moisture estimates is carried out at seven of the SMAP core Cal/Val sites located in the USA. Both types of analysis demonstrate the value of assimilating SMAP into the USDA FAS Palmer model and its potential to enhance operational USDA FAS root-zone soil moisture information.

Soil Moisture Monitoring in Iran by Implementing Satellite Data into the Root-Zone SMAR Model

Monitoring Surface Soil Moisture (SSM) and Root Zone Soil Moisture (RZSM) dynamics at the regional scale is of fundamental importance to many hydrological and ecological studies. This need becomes even more critical in arid and semi-arid regions, where there are a lack of in situ observations. In this regard, satellite-based Soil Moisture (SM) data is promising due to the temporal resolution of acquisitions and the spatial coverage of observations. Satellite-based SM products are only able to estimate moisture from the soil top layer; however, linking SSM with RZSM would provide valuable information on land surface-atmosphere interactions. In the present study, satellite-based SSM data from Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Soil Moisture Active Passive (SMAP) are first compared with the few available SM in situ observations, and are then coupled with the Soil Moisture Analytical Relationship (SMAR) model to estimate RZSM in Iran. The comparison between in situ SM observations and satellite data showed that the SMAP satellite products provide more accurate description of SSM with an average correlation coefficient (R) of 0.55, root-mean-square error (RMSE) of 0.078 m3 m−3 and a Bias of 0.033 m3 m−3. Thereafter, the SMAP satellite products were coupled with SMAR model, providing a description of the RZSM with performances that are strongly influenced by the misalignment between point and pixel processes measured in the preliminary comparison of SSM data.

MODIS remote sensing approaches to monitoring soil moisture in Tibet, China

ABSTRACTSatellite remote sensing from space offers a unique way to monitor surface soil moisture on a large geographical scale. In this work, the general approaches to monitoring surface soil moisture for Tibet area were developed using Moderate Resolution Imaging Spectroradiometer (MODIS) data and ground observations. The result shows that MODIS Vegetation Supply Water Index (VSWI) and band-7 reflectance are significantly correlated with ground measured soil moisture and MODIS band 7 is a simple and more effective method to monitor soil moisture in Tibet with higher spatial resolution at 500 m in comparison with VSWI at 1000 m resolution.

SWCTI: Surface Water Content Temperature Index for Assessment of Surface Soil Moisture Status

The vegetation supply water index (VSWI = NDVI/LST) is an effective metric estimating soil moisture in areas with moderate to dense vegetation cover. However, the normalized difference vegetation index (NDVI) exhibits a long water stress lag and the land surface temperature (LST), sensitive to water stress, does not contribute considerably to surface soil moisture monitoring due to the constraints of the mathematical characteristics of VSWI: LST influences VSWI less when LST value is sufficiently high. This paper mathematically analyzes the characteristics of VSWI and proposes a new operational dryness index (surface water content temperature index, SWCTI) for the assessment of surface soil moisture status. SWCTI uses the surface water content index (SWCI), which provides a more accurate estimation of surface soil moisture than that of NDVI, as the numerator and the modified surface temperature, which has a greater influence on SWCTI than that of LST, as the denominator. The validation work includes comparison of SWCTI with in situ soil moisture and other remote sensing indices. The results show SWCTI demonstrates the highest correlation with in situ soil moisture; the highest correlation R = 0.801 is found between SWCTI and the 0–5 cm soil moisture in a sandy loam. SWCTI is a functional and effective method that has a great potential in surface soil moisture monitoring.

Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets

Two satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d'études spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Données) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40° incidence angle, over the 2013–2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from >400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https://ismn.geo.tuwien.ac.at/) in North of America (PBO_H2O, SCAN, SNOTEL, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE), Bias, Unbiased RMSE (UnbRMSE), and correlation coefficient (R). Results of the comparison of these various products with in situ observations show that the performance of both SMAP products i.e. SMAP_SCA and SMAP_Reg is similar and marginally better to that of the SMOSL3 product particularly over the PBO_H2O, SCAN, and USCRN sites. However, SMOSL3 SM was closer to the in situ observations over the DAHRA and Oznet sites. We found that the correlation between all three datasets and in situ measurements is best (R>0.80) over the Oznet sites and worst (R=0.58) over the SNOTEL sites for SMAP_SCA and over the DAHRA and SMOSMANIA sites (R=0.51 and R=0.45 for SMAP_Reg and SMOSL3, respectively). The Bias values showed that all products are generally dry, except over RSMN, DAHRA, and Oznet (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful to improve the consistency between SMAP and SMOS datasets.

Geolocation of RFI sources with sub-kilometric accuracy from SMOS interferometric data

Abstract The SMOS mission is a European Space Agency project aimed at global monitoring of surface Soil Moisture and Ocean Salinity from radiometric L-band observations. Although the L-band is a protected band, the data collected by SMOS are contaminated by radio frequency interferences (RFI) which degrade the performance of the mission. A precise location of the RFI emitters is required for switching off illegal transmissions or for fixing malfunctioning equipments. This work is concerned with the geolocation of such sources with a sub-kilometric accuracy from SMOS interferometric data themselves. Such a precise location has never been reached by any other published methods using only SMOS measurements.

Roughness and vegetation parameterizations at L-band for soil moisture retrievals over a vineyard field

Abstract The capability of L-band radiometry to monitor surface soil moisture (SM) at global scale has been analyzed in numerous studies, mostly in the framework of the ESA SMOS and NASA SMAP missions. To retrieve SM from L-band radiometric observations, two significant effects have to be accounted for, namely soil roughness and vegetation optical depth. In this study, soil roughness effects on retrieved SM values were evaluated using brightness temperatures acquired by the L-band ELBARA-II radiometer, over a vineyard field at the Valencia Anchor Station (VAS) site during the year 2013. Different combinations of the values of the model parameters used to account for soil roughness effects (HR, QR, NRH and NRV) in the L-MEB model were evaluated. The L-MEB model (L-band Microwave Emission of the Biosphere) is the forward radiative transfer model used in the SMOS soil moisture retrieval algorithm. In this model, HR parameterizes the intensity of roughness effects, QR accounts for polarization effects, and NRH and NRV parameterize the variations of the soil reflectivity as a function of the observation angle, θ, respectively for both H (Horizontal) and V (Vertical) polarizations. These evaluations were made by comparing in-situ measurements of SM (used here as a reference) against SM retrievals derived from tower-based ELBARA-II brightness temperatures mentioned above. The general retrieval approach consists of the inversion of L-MEB. Two specific configurations were tested: the classical 2-Parameter (2-P) retrieval configuration where SM and τNAD (vegetation optical depth at nadir) are retrieved, and a 3-Parameter (3-P) configuration, accounting for the additional effects of the vineyard vegetation structure. Using the 2-P configuration, it was found that setting NRp (p = H or V) equals to − 1 provided the best SM estimations in terms of correlation and unbiased Root Mean Square Error (ubRMSE). The assumption NRV = NRH = − 1 simplifies the L-MEB retrieval, since the two parameters τNAD and HR can then be grouped and retrieved as a single parameter (method here defined as the Simplified Retrieval Method (SRP)). The main advantage of the SRP method is that it is not necessary to calibrate HR before performing the SM retrievals. Using the 3-P configuration, the results improved, with respect to SM retrievals, in terms of correlation and ubRMSE, as the structural characteristics of the vineyards were better accounted for. However, this method still requires the calibration of HR, a disadvantage for operational applications. Finally, it was found that the use of in-situ roughness measurements to calibrate the roughness model parameters did not provide significant improvements in the SM retrievals as compared to the SRP method.

An Additive Mask Correction Approach for Reducing the Systematic Floor Error in Imaging Radiometry by Aperture Synthesis

The Soil Moisture and Ocean Salinity (SMOS) mission is a European Space Agency (ESA) mission aimed at the global monitoring of surface soil moisture and ocean salinity from radiometric L-band observations. This work described in this letter is devoted to the reduction of the systematic error in the reconstruction of brightness temperature maps from SMOS interferometric measurements. Despite the fact that the image reconstruction method currently used was proposed and implemented in the ESA L1 processor for reducing this error, residual offset and ripples still persist. This is particularly penalizing for oceanographic applications with SMOS data. A new approach for reducing this residual error is presented here and illustrated with brightness temperature maps retrieved over the Pacific ocean.

The Computation of the Effective Temperature of the Land Surface Covered with Canopy

—Surface soil moisture is an important parameter in describing the water and energy exchanges at the land surface/atmosphere interface. Passive microwave remote sensors have great potential for monitoring surface soil moisture over land surface. The objective of this study is going to establish a model for estimating the effective temperature of land surface covered with vegetation canopy and to investigate how to compute the microwave radiative brightness temperature of land surface covered with vegetation canopy in considering of the canopy scatter effect.

Improving Soil Moisture Data Retrieval From Airborne L-Band Radiometer Data by Considering Spatially Varying Roughness

This study presents the retrieval of near-surface soil moisture data below crop canopies (winter rye and winter barley) from airborne L-band radiometer observations using a radiative transfer model at very dry soil moisture conditions (<15 Vol.%). Using physically based models, the roughness parameterization plays a crucial role for the description of the surface emissivity. A two-step optimization procedure was performed for choosing an optimal roughness value to minimize the uncertainty of soil moisture estimates. A crop-type specific roughness parameterization within the model did not show satisfactory soil moisture results. Instead, a “pixel”-based (spatially varying) roughness parameter optimization provided significantly improved results, also indicating a strong relationship between the optimal roughness parameter value and the Normalized Difference Vegetation Index (NDVI) derived from imaging spectrometer data. Our results demonstrate the importance of treating surface roughness as spatially variable when retrieving soil moisture information from high spatial resolution L-band brightness temperature data. Furthermore, the results strongly indicate that a combination of passive microwave observations and optical remote sensing data of the vegetation improve the mapping and monitoring of surface soil moisture.

Self-calibrated evaporation-based disaggregation of SMOS soil moisture: An evaluation study at 3 km and 100 m resolution in Catalunya, Spain

A disaggregation algorithm is applied to 40km resolution SMOS (Soil Moisture and Ocean Salinity) surface soil moisture using 1km resolution MODIS (MODerature resolution Imaging Spectroradiometer), 90m resolution ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer), and 60m resolution Landsat-7 data. DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) distributes high-resolution soil moisture around the low-resolution observed mean value using the instantaneous spatial link between optical-derived soil evaporative efficiency (ratio of actual to potential evaporation) and near-surface soil moisture. The objective is three-fold: (i) evaluating DISPATCH at a range of spatial resolutions using readily available multi-sensor thermal data, (ii) deriving a robust calibration procedure solely based on remotely sensed data, and (iii) testing the linear or nonlinear behavior of soil evaporative efficiency. Disaggregated soil moisture is compared with the 0–5cm in situ measurements collected each month from April to October 2011 in a 20km square spanning an irrigated and dry land area in Catalunya, Spain. The target downscaling resolution is set to 3km using MODIS data and to 100m using ASTER and Landsat data. When comparing 40km SMOS, 3km disaggregated and 100m disaggregated data with the in situ measurements aggregated at corresponding resolution, results indicate that DISPATCH improves the spatio-temporal correlation with in situ measurements at both 3km and 100m resolutions. A yearly calibration of DISPATCH is more efficient than a daily calibration. Assuming a linear soil evaporative efficiency model is adequate at kilometric resolution. At 100m resolution, the very high spatial variability in the irrigated area makes the linear approximation poorer. By accounting for non-linearity effects, the slope of the linear regression between disaggregated and in situ measurements is increased from 0.2 to 0.5. Such a multi-sensor remote sensing approach has potential for operational multi-resolution monitoring of surface soil moisture and is likely to help parameterize soil evaporation at integrated spatial scales.

Monitoring surface soil moisture status based on remotely sensed surface temperature and vegetation index information

Surface soil moisture is a key variable for describing water and energy exchanges at the surface/atmosphere interface. The triangle or trapezoid method is appropriate for monitoring the regional surface soil moisture status. In this study, an advanced temperature vegetation dryness index (ATVDI) is used to monitor soil moisture status using an improved surface temperature and a vegetation index space that is formed by the theoretical dry edge determined by the surface energy balance principle and the wet edge extracted from water surface temperature. The ATVDI was validated by in situ soil moisture measurements from the Southern Great Plains (SGP) and is compared with a simple temperature vegetation dryness index (STVDI) estimated from the observed dry edge. The results indicate that the theoretical dry edge forms a trapezoid shape during the period of data coverage whereas the observed dry edge does not. Across all points, the R2 of the ATVDI (0.35) is greater than that for the STVDI (0.28). For almost all of the sites, the R2 between the ATVDI and soil moisture apparently improved. We also examined the spatial variation, and the value range of the ATVDI is closer than the STVDI to the real soil moisture conditions. The temporal variation of the ATVDI is closer to soil moisture than that of the STVDI, and the ATVDI is more sensitive to rainfall. This study demonstrates that a theoretical dry edge, determined by the energy balance principle, sets a standard for different days and that the ATVDI could monitor soil moisture conditions better than the STVDI could.

Potential for High Resolution Systematic Global Surface Soil Moisture Retrieval via Change Detection Using Sentinel-1

The forthcoming two-satellite GMES Sentinel-1 constellation is expected to render systematic surface soil moisture retrieval at 1 km resolution using C-band SAR data possible for the first time from space. Owing to the constellation's foreseen coverage over the Sentinel-1 Land Masses acquisition region-global approximately every six days, nearly daily over Europe and Canada depending on latitude-in the high spatial and radiometric resolution Interferometric Wide Swath (IW) mode, the Sentinel-1 mission shows high potential for global monitoring of surface soil moisture by means of fully automatic retrieval techniques. This paper presents the potential for providing such a service systematically over Land Masses and in near real time using a change detection approach, concluding that such a service is-subject to the mission operating as foreseen-expected to be technically feasible. The work presented in this paper was carried out as a feasibility study within the framework of the ESA-funded GMES Sentinel-1 Soil Moisture Algorithm Development (S1-SMAD) project.