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The NASA's Soil Moisture Active Passive (SMAP) mission conducted a field experiment with its partners over two 40-km agricultural domains in Iowa and Manitoba in the summer of 2016 to address concerns observed in SMAP soil moisture (SM) retrievals over agricultural areas. The experiment featured airborne Passive Active L-band System (PALS) flights over each domain with intensive ground measurements and dense networks of SM monitoring stations. With two intensive observation periods separated in time (May 28–June 20 and July 14–August 16), the flights captured both early-season/low vegetation and later-season/high-vegetation conditions. The comparison of the PALS brightness temperature (TB) measurements to the SMAP TB observed over the sites resulted in root mean square difference (RMSD) of 2.8 K and 4.0 K for vertical and horizontal polarizations, respectively. The subsequent SM analysis rescaled the PALS TB with the SMAP TB to allow equitable comparisons between the SM retrievals from the two instruments. The PALS SM retrieval algorithm used the SM sampled by the ground teams during the overpass days for tuning, and was parameterized by a high-resolution vegetation water content product calibrated using vegetation samples collected during the experiment. The tuning process was not able to find a satisfactory result with a temporally constant set of parameters in the single channel algorithm for the two intensive observation periods of the experiment. This result indicated that the rapid change in the vegetation structure during the growth stages and likely variation in the surface roughness conditions were not compatible with rigid parameterization over the entire period. However, using seasonally variable parameters we found that it was possible to retrieve soil moisture with satisfactory accuracy. Comparative analysis with the SMAP SM product included aggregation of the PALS SM to the SMAP pixel-scale. The RMSD between the PALS SM and the aggregated manual field samples was 0.85 for both sites. The comparison between different in situ sources indicated that the soil moisture network measurements were not the source of the large biases observed for SMAP over the sites reported in earlier studies. Therefore, the results suggested the rapidly growing vegetation and the early-season surface condition changes not captured by the SMAP algorithm caused the SMAP retrieval errors. In addition, the significant deviations of the vegetation water content used by the SMAP product from the calibrated vegetation water content obtained during the experiment compounds the problem.

The Soil Moisture Active and Passive (SMAP) mission, launched by the National Aeronautics and Space Administration (NASA) on 31st January 2015, was designed to provide global soil moisture every 2 to 3 days at 9 km resolution by downscaling SMAP passive microwave observations obtained at 36 km resolution using active microwave observations at 3 km resolution, and then retrieving soil moisture from the resulting 9 km brightness temperature product. This study evaluated the SMAP Active/Passive (AP) downscaling algorithm together with other resolution enhancement techniques. Airborne passive microwave observations acquired at 1 km resolution over the Murrumbidgee River catchment in south-eastern Australia during the fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4/5) were used as reference data. The SMAPEx-4/5 data were collected in May and September 2015, respectively, and aggregated to 9 km for direct comparison with a number of available resolution-enhanced brightness temperature estimates. The results show that the SMAP AP downscaled brightness temperature had a correlation coefficient (R) of 0.84 and Root-Mean-Squared Error (RMSE) of ~10 K, while SMAP Enhanced, Nearest Neighbour, Weighted Average, and the Smoothing Filter-based Modulation (SFIM) brightness temperature estimates had somewhat better performance (RMSEs of ~7 K and an R exceeding 0.9). Although the SFIM had the lowest unbiased RMSE of ~6 K, the effect of cloud cover on Ka-band observations limits data availability.

Global-scale surface soil moisture products are currently available from multiple remote sensing platforms. Footprint-scale assessments of these products are generally restricted to limited number of densely-instrumented validation sites. However, by taking active and passive soil moisture products together with a third independent soil moisture estimates via land surface modeling, triple collocation (TC) can be applied to estimate the correlation metric of satellite soil moisture products (versus an unknown ground truth) over a quasi-global domain. Here, an assessment of Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity (SMOS) and Advanced SCATterometer (ASCAT) surface soil moisture retrievals via TC is presented. Considering the potential violation of TC error assumptions, the impact of active-passive and satellite-model error cross correlations on the TC-derived inter-comparison results is examined at in situ sites using quadruple collocation analysis. In addition, confidence intervals for the TC-estimated correlation metric are constructed from moving-block bootstrap sampling designed to preserve the temporal persistence of the original (unevenly-sampled) soil moisture time-series. This study is the first to apply TC to obtain a robust global-scale cross-assessment of SMAP, SMOS and ASCAT soil moisture retrieval accuracy in terms of anomaly temporal correlation. Our results confirm the overall advantage of SMAP (with a global average anomaly correlation of 0.76) over SMOS (0.66) and ASCAT (0.63) that has been established in several recent regional, ground-based studies. SMAP is also the best-performing product over the majority of applicable land pixels (52%), although SMOS and ASCAT each shows advantage in distinct geographic regions.

Satellite-based passive microwave remote sensing typically involves a scanning antenna that makes measurements at irregularly spaced locations. These locations can change on a day to day basis. Soil moisture products derived from satellite-based passive microwave remote sensing are usually resampled to a fixed Earth grid that facilitates their use in applications. In many cases the grid size is finer than the actual spatial resolution of the observation, and often this difference is not well understood by the user. Here, this issue was examined for the Soil Moisture Active Passive (SMAP) enhanced version of the passive-based soil moisture product, which has a grid size of 9-km and a nominal spatial resolution of 33-km. In situ observations from core validation sites were used to compute comparison metrics. For sites that satisfied the established reliability and scaling criteria, the impact of validating the 9-km grid product with in situ data collected over a 9-km versus a 33-km domain was very small for the sites studied (0.039 m3/m3 unbiased root mean square difference for the 9-km case versus 0.037 m3/m3 for the 33-km case). This result does not mean that the resolution of the product is 9-km but that for the conditions studied here the soil moisture estimated from in situ observations over 9-km is a close approximation of the soil moisture estimated from in situ observations over the 33-km resolution. The implication is that using the enhanced SMAP product at its grid resolution of 9-km should not introduce large errors in most applications.

The NASA SMAP (Soil Moisture Active Passive) mission conducted the SMAP Validation Experiment 2015 (SMAPVEX15) in order to support the calibration and validation activities of SMAP soil moisture data products. The main goals of the experiment were to address issues regarding the spatial disaggregation methodologies for improvement of soil moisture products and validation of the in situ measurement upscaling techniques. To support these objectives high-resolution soil moisture maps were acquired with the airborne PALS (Passive Active L-band Sensor) instrument over an area in southeast Arizona that includes the Walnut Gulch Experimental Watershed (WGEW), and intensive ground sampling was carried out to augment the permanent in situ instrumentation. The objective of the paper was to establish the correspondence and relationship between the highly heterogeneous spatial distribution of soil moisture on the ground and the coarse resolution radiometer-based soil moisture retrievals of SMAP. The high-resolution mapping conducted with PALS provided the required connection between the in situ measurements and SMAP retrievals. The in situ measurements were used to validate the PALS soil moisture acquired at 1-km resolution. Based on the information from a dense network of rain gauges in the study area, the in situ soil moisture measurements did not capture all the precipitation events accurately. That is, the PALS and SMAP soil moisture estimates responded to precipitation events detected by rain gauges, which were in some cases not detected by the in situ soil moisture sensors. It was also concluded that the spatial distribution of the soil moisture resulted from the relatively small spatial extents of the typical convective storms in this region was not completely captured with the in situ stations. After removing those cases (approximately 10% of the observations) the following metrics were obtained: RMSD (root mean square difference) of 0.016 m 3 /m 3 and correlation of 0.83. The PALS soil moisture was also compared to SMAP and in situ soil moisture at the 36-km scale, which is the SMAP grid size for the standard product. PALS and SMAP soil moistures were found to be very similar owing to the close match of the brightness temperature measurements and the use of a common soil moisture retrieval algorithm. Spatial heterogeneity, which was identified using the high-resolution PALS soil moisture and the intensive ground sampling, also contributed to differences between the soil moisture estimates. In general, discrepancies found between the L-band soil moisture estimates and the 5-cm depth in situ measurements require methodologies to mitigate the impact on their interpretations in soil moisture validation and algorithm development. Specifically, the metrics computed for the SMAP radiometer-based soil moisture product over WGEW will include errors resulting from rainfall, particularly during the monsoon season when the spatial distribution of soil moisture is especially heterogeneous.

International audience; The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. The commissioning phase ended in May 2010. Subsequently, the satellite has been in operation for over six years while the retrieval algorithms from Level 1 (L1) to Level 2 (L2) underwent significant evolutions as knowledge improved. Moreover, other approaches for retrieval at L2 over land were investigated while Level 3 (L3) and Level 4 (L4) were initiated. In this paper, these improvements were assessed by inter-comparisons of the current L2 (V620) against the previous version (V551) and new products (using neural networks referred to as SMOS-NN) and L3 (referred to as SMOS-L3). In addition, a global evaluation of different SMOS soil moisture (SM) products (SMOS-L2, SMOS-L3, and SMOS-NN) was performed comparing products with those of model simulations and other satellites. Finally, all products were evaluated against in situ measurements of soil moisture (SM). To achieve such a goal a set of metrics to evaluate different satellite products are suggested.The study demonstrated that the V620 shows a significant improvement (including those at L1 improving L2) with respect to the earlier version V551. Results also show that neural network based approaches can often 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 through the significant reduction of RFI sources in several areas of the world. 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.

Satellite-based passive microwave remote sensing has been shown to be a valuable tool in mapping and monitoring global soil moisture. The Advanced Microwave Scanning Radiometer on the Aqua platform (AMSR-E) has made significant contributions to this application. As the result of agency and individual initiatives, several approaches for the retrieval of soil moisture from AMSR-E have been proposed and implemented. Although the majority of these are based on the same Radiative Transfer Equation, studies have shown that the resulting soil moisture estimates can differ significantly. A primary goal of this investigation is to understand these differences and develop a suitable approach to potentially improve the algorithm currently used by NASA in producing its operational soil moisture product. In order to achieve this goal, the theoretical basis of several alternative soil moisture retrieval algorithms are examined. Analysis has focused on five established approaches: the operational algorithm adopted by NASA, which is referred to as the Normalized Polarization Difference algorithm, the Single Channel Algorithm, the Land Parameter Retrieval Model, the University of Montana soil moisture algorithm, and the HydroAlgo Artificial Neural Network algorithm. Previous comparisons of these algorithms in the literature have typically focused on the retrieved soil moisture products, and employed different metrics and data sets, and have resulted in differing conclusions. In this investigation we attempt to provide a more thorough understanding of the fundamental differences between the algorithms and how these differences affect their performance in terms of range of soil moisture provided. The comparative overview presented in the paper is based on the operating versions of the source codes of the individual algorithms. Analysis has indicated that the differences between algorithms lie in the specific parameterizations and assumptions of each algorithm. The comparative overview of the theoretical basis of the approaches is linked to differences found in the soil moisture retrievals, leading to suggestions for improvements and increased reliability in these algorithms. © 2014.

An important component of satellite-based soil moisture algorithm development and validation is the comparison of coincident remote sensing and in situ observations that are typically provided by intensive field campaigns. The planned NASA Soil Moisture Active Passive (SMAP) mission has unique requirements compared to previous soil moisture satellite programs because both active and passive microwave observations are needed. The primary source of these combined observations has been an aircraft-based SMAP simulator called PALS (Passive and Active L-band System). This paper presents an overview of the field experiment data collected using PALS that spans 10 years. Data from the various campaigns were merged to form a single data set. Analyses showed that the data set contains an extensive range of soil moisture values collected under a variety of conditions and that the quality of both the PALS and ground truth data meets the needs of SMAP algorithm development and validation. The study suggests that the data set should be expanded in order to achieve globally representative land cover diversity and that more observations under dense vegetation conditions and longer time series would be desirable.

Vegetation indices are valuable in many fields of geosciences. Conventional, visible-near infrared, indices are often limited by the effects of atmosphere, background soil conditions, and saturation at high levels of vegetation. In this study, we will establish the theoretical basis for our new passive microwave vegetation indices (MVIs) based on data from the Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite. Through the analysis of numerical simulations by surface emission model, the Advanced Integral Equation Model (AIEM), we found that bare soil surface emissivities at different frequencies can be characterized by a linear function with parameters that are dependent on the pair of frequencies used. This makes it possible to minimize the surface emission signal and maximize the vegetation signal when using multi-frequency radiometer measurements. Using a radiative transfer model (ω–τ model), a linear relationship between the brightness temperatures observed at two adjacent radiometer frequencies can be derived. The intercept and slope of this linear function depend only on vegetation properties and can be used as vegetation indices. These can be derived from the dual-frequency and dual-polarization satellite measurements under assumption that there is no significant impact of the polarization dependence on the vegetation signals. To demonstrate the potential of the new microwave vegetation indices, we compared them with the Normalized Difference of Vegetation Index (NDVI) derived using MODIS the optical sensor at continental and global scales. The major purpose of this paper is to describe the concept and techniques involved in these newly developed MVIs and explore the general relationships between these MVIs and NDVI. In this first investigation, the information content of NDVI and MVIs, both are qualitative indices, was compared by examining its response in global pattern and to seasonal vegetation phenology. The results indicate that the MVIs can provide significant new information since the microwave measurements are sensitive not only to the leafy part of vegetation properties but also to the properties of the overall vegetation canopy when the microwave sensor can “see” through it. In combination with conventional optical sensor derived vegetation indices, they provide a possible complementary dataset for monitoring global short vegetation and seasonal phenology from space.

Satellite soil moisture products, such as those from Advanced Microwave Scanning Radiometer (AMSR), require diverse landscapes for validation. Semi-arid landscapes present a particular challenge to satellite remote sensing validation using traditional techniques because of the high spatial variability and potentially rapid rates of temporal change in moisture conditions. In this study, temporal stability analysis and spatial sampling techniques are used to investigate the representativeness of ground observations at satellite scale soil moisture in a semi-arid watershed for a long study period (March 1, 2002 to September 13, 2005). The watershed utilized, the Walnut Gulch Experimental Watershed, has a dense network of 19 soil moisture sensors, distributed over a 150 km2 study region. In conjunction with this monitoring network, intensive gravimetric soil moisture sampling conducted as part of the Soil Moisture Experiment in 2004 (SMEX04), contributed to the calibration of the network for large-scale estimation during the North American Monsoon System (NAMS). The sensor network is shown to be an excellent estimator of the watershed average with an accuracy of approximately 0.01 m3/m3 soil moisture. However, temporal stability analysis indicated that while much of the network is stable, the soil moisture spatial pattern, as represented by mean relative difference, is not replicated by the network mean relative difference pattern. Rather, the network is composed of statistical samples. Geophysical aspects of the watershed, including topography and soil type are also examined for their influence on the soil moisture variability and stability. Soil type, as characterized by bulk density, clay and sand content, was responsible for nearly 50% of the temporal stability. Topographic effects were less important in defining representativeness and stability.

An unresolved issue in global soil moisture retrieval using passive microwave sensors is the spatial integration of heterogeneous landscape features to the nominal 50 km footprint observed by most low frequency satellite systems. One of the objectives of the Soil Moisture Experiments 2004 (SMEX04) was to address some aspects of this problem, specifically variability introduced by vegetation, topography and convective precipitation. Other goals included supporting the development of soil moisture data sets that would contribute to understanding the role of the land surface in the concurrent North American Monsoon System. SMEX04 was conducted over two regions: Arizona — semi-arid climate with sparse vegetation and moderate topography, and Sonora (Mexico) — moderate vegetation with strong topographic gradients. The Polarimetric Scanning Radiometer (PSR/CX) was flown on a Naval Research Lab P-3B aircraft as part of SMEX04 (10 dates of coverage over Arizona and 11 over Sonora). Radio Frequency Interference (RFI) was observed in both PSR and satellite-based (AMSR-E) observations at 6.92 GHz over Arizona, but no detectable RFI was observed over the Sonora domain. The PSR estimated soil moisture was in agreement with the ground-based estimates of soil moisture over both domains. The estimated error over the Sonora domain (SEE = 0.021 cm3/cm3) was higher than over the Arizona domain (SEE = 0.014 cm3/cm3). These results show the possibility of estimating soil moisture in areas of moderate and heterogeneous vegetation and high topographic variability.

Comparisons to ground-based surface soil moisture estimates are necessary to evaluate the capability of remote sensors to determine soil moisture and its spatiotemporal variability. Soil moisture can be especially variable in regions of complex terrain which exhibit large variations in vegetation, soil properties and hydrologic conditions. The objective of this study is to evaluate the spatiotemporal variability of soil moisture in a mountainous basin in northwestern Mexico. Soil moisture estimates from ground sampling over a topographic transect and high resolution retrievals from the Polarimetric Scanning Radiometer are compared during a two week period in August 2004 as part of the Soil Moisture Experiment 2004. Results indicate that the soil moisture estimates exhibit similar variability with mean water content. Statistical analysis, however, reveals clear differences in soil moisture in the basin, in particular for wet periods and high elevations. Despite these differences, the temporal persistence of soil moisture from the estimates agrees well and indicates locations that capture the basin-averaged conditions. Furthermore, the spatiotemporal soil moisture characteristics from the two products are linked to terrain attributes. As a result, a hypsometric technique is shown to improve comparisons between basin-averaged values derived from ground data and remote sensing, as compared to arithmetic averaging. To our knowledge, this study is the first attempt to evaluate PSR/CX retrievals with respect to ground observations over a region of high topographic and vegetation variability using statistical, time-stability and terrain analysis techniques.

Field experiments (SMEX02) were conducted to evaluate the effects of dense agricultural crop conditions on soil moisture retrieval using passive microwave remote sensing. Aircraft observations were collected using a new version of the Polarimetric Scanning Radiometer (PSR) that provided four C band and four X band frequencies. Observations were also available from the Aqua satellite Advanced Microwave Scanning Radiometer (AMSR-E) at these same frequencies. SMEX02 was conducted over a three-week period during the summer near Ames, Iowa. Corn and soybeans dominate the region. During the study period the corn was approaching its peak water content state and the soybeans were at the mid point of the growth cycle. Aircraft observations are compared to ground observations. Subsequently models are developed to describe the effects of corn and soybeans on soil moisture retrieval. Multiple altitude aircraft brightness temperatures were compared to AMSR-E observations to understand brightness temperature scaling and provide validation. The X-band observations from the two sensors were in reasonable agreement. The AMSR-E C-band observations were contaminated with anthropogenic RFI, which made comparison to the PSR invalid. Aircraft data along with ancillary data were used in a retrieval algorithm to map soil moisture. The PSR estimated soil moisture retrievals on a field-by-field comparison had a standard error of estimate (SEE) of 5.5%. The error reduced when high altitude soil moisture estimates were aggregated to 25 km resolution (same as AMSR-E EASE grid product resolution) (SEE ∼ 2.85%). These soil moisture products provide a validation of the AMSR retrievals. PSR/CX soil moisture images show spatial and temporal patterns consistent with meteorological and soil conditions. The dynamic range of the PSR/CX observations indicates that reasonable soil moisture estimates can be obtained from AMSR, even in areas of high vegetation biomass content (∼ 4–8 kg/m2).

A two-source (soil + vegetation) energy balance model using microwave-derived near-surface soil moisture as a key boundary condition (TSM SM ) and another scheme using thermal-infrared (radiometric) surface temperature (TSM TH ) were applied to remote sensing data collected over a corn and soybean production region in central Iowa during the Soil Moisture Atmosphere Coupling Experiment (SMACEX)/Soil Moisture Experiment of 2002 (SMEX02). The TSM SM was run using fields of near-surface soil moisture from microwave imagery collected by aircraft on six days during the experiment, yielding a root mean square difference (RMSD) between model estimates and tower measurements of net radiation (Rn) and soil heat flux ( G ) of approximately 20 W m − 2 , and 45 W m − 2 for sensible ( H ) and latent heating (LE). Similar results for H and LE were obtained at landscape/regional scales when comparing model output with transect-average aircraft flux measurements. Flux predictions from the TSM SM and TSM TH models were compared for two days when both airborne microwave-derived soil moisture and radiometric surface temperature ( T R ) data from Landsat were available. These two days represented contrasting conditions of moderate crop cover/dry soil surface and dense crop cover/moist soil surface. Surface temperature diagnosed by the TSM SM was also compared directly to the remotely sensed T R fields as an additional means of model validation. The TSM SM performed well under moderate crop cover/dry soil surface conditions, but yielded larger discrepancies with observed heat fluxes and T R under the high crop cover/moist soil surface conditions. Flux predictions from the thermal-based two-source model typically showed biases of opposite sign, suggesting that an average of the flux output from both modeling schemes may improve overall accuracy in flux predictions, in effect incorporating multiple remote-sensing constraints on canopy and soil fluxes.

A sequence of five high-resolution satellite-based land surface temperature (Ts) images over a watershed area in Iowa were analyzed. As a part of the SMEX02 field experiment, these land surface temperature images were extracted from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM) thermal bands. The radiative transfer model MODTRAN 4.1 was used with atmospheric profile data to atmospherically correct the Landsat data. NDVI derived from Landsat visible and near-infrared bands was used to estimate fractional vegetation cover, which in turn was used to estimate emissivity for Landsat thermal bands. The estimated brightness temperature was compared with concurrent tower based measurements. The mean absolute difference (MAD) between the satellite-based brightness temperature estimates and the tower based brightness temperature was 0.98 °C for Landsat 7 and 1.47 °C for Landsat 5, respectively. Based on these images, the land surface temperature spatial variation and its change with scale are addressed. The scaling properties of the surface temperature are important as they have significant implications for changes in land surface flux estimation between higher-resolution Landsat and regional to global sensors such as MODIS.

Watershed scale soil moisture estimates are necessary to validate current remote sensing products, such as those from the Advanced Microwave Scanning Radiometer (AMSR). Unfortunately, remote sensing technology does not currently resolve the land surface at a scale that is easily observed with ground measurements. One approach to validation is to use existing soil moisture measurement networks and scale these point observations up to the resolution of remote sensing footprints. As part of the Soil Moisture Experiment 2002 (SMEX02), one such soil moisture gaging system in the Walnut Creek Watershed, Iowa, provided robust estimates of the soil moisture average for a watershed throughout the summer of 2002. Twelve in situ soil moisture probes were installed across the watershed. These probes recorded soil moisture at a depth of 5 cm from June 29, 2002 to August 19, 2002. The sampling sites were analyzed for temporal and spatial stability by several measures including mean relative difference, Spearman rank, and correlation coefficient analysis. Representative point measurements were used to estimate the watershed scale (∼25 km) soil moisture average and shown to be accurate indicators with low variance and bias of the watershed scale soil moisture distribution. This work establishes the validity of this approach to provide watershed scale soil moisture estimates in this study region for the purposes of satellite validation with estimation errors as small as 3%. Also, the potential sources of error in this type of analysis are explored. This study is a first step in the implementation of large-scale soil moisture validation using existing networks such as the Soil Climate Analysis Network (SCAN) and several Agricultural Research Service watersheds as a basis for calibrating satellite soil moisture products, for networks design, and designing field experiments.

The lack of continuous soil moisture fields at large spatial scales, based on observations, has hampered hydrologists from understanding its role in weather and climate. The most readily available observations from which a surface wetness state could be derived is the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations at 10.65 GHz. This paper describes the first attempt to map daily soil moisture from space over an extended period of time. Methods to adjust for diurnal changes associated with this temporal variability and how to mosaic these orbits are presented. The algorithm for deriving soil moisture and temperature from TMI observations is based on a physical model of microwave emission from a layered soil–vegetation–atmosphere medium. An iterative, least-squares minimization method, which uses dual polarization observations at 10.65 GHz, is employed in the retrieval algorithm. Soil moisture estimates were compared with ground measurements over the U.S. Southern Great Plains (SGP) in Oklahoma and the Little River Watershed, Georgia. The soil moisture experiment in Oklahoma was conducted in July 1999 and Little River in June 2000. During both the experiments, the region was dry at the onset of the experiment, and experienced moderate rainfall during the course of the experiment. The regions experienced a quick dry-down before the end of the experiment. The estimated soil moisture compared well with the ground observations for these experiments (standard error of 2.5%). The TMI-estimated soil moisture during 6–22 July over Southern U.S. was analyzed and found to be consistent with the observed meteorological conditions.

The Integral Equation Model (IEM) was previously used in conjunction with an inversion model to retrieve soil moisture using multifrequency and multipolarization data from Spaceborne Imaging Radar C-band (SIR-C) and X-band Synthetic Aperture Radar (X-SAR). Convergence rates well above 90%, and small RMS errors were attained, for both vegetated and bare soil areas, using radar data collected during Washita 1994. However, the IEM was originally developed to describe the scattering from bare soil surfaces only, and, therefore, vegetation backscatter effects are not explicitly incorporated in the model. In this study, the problem is addressed by introducing a simple, semiempirical, vegetation scattering parameterization to the multifrequency, soil moisture inversion algorithm. The parameterization was formulated in the framework of the water–cloud model and relies on the concept of a land-cover (land-use)-based dimensionless vegetation correlation length to represent the spatial variability of vegetation across the landscape and radar-shadow effects (vegetation layovers). An application of the modified inversion model to the Washita 1994 data lead to a decrease of 32% in the RMSE, while the correlation coefficient between ground-based and SAR-derived soil moisture estimates improved from 0.84 to 0.95.

This study focuses on the development of a consistent methodology for soil-moisture inversion from synthetic aperture radar (SAR) data with the use of the integral equation model (IEM), developed by A. K. Fung and colleagues, without the need to prescribe time-varying land-surface attributes as constraining parameters. Specifically, the dependence of backscatter coefficients obtained from synthetic aperture radar (SAR) on the soil dielectric constant, surface-roughness height, and correlation length was investigated. The IEM was used in conjunction with an inversion model to retrieve soil moisture by using multifrequency and multipolarization data (L-, C-, and X-bands) simultaneously. The results were cross validated with gravimetric observations obtained during the Washita '94 field experiment in the Little Washita Watershed, Oklahoma. The average error in the estimated soil moisture was of the order of 3.4%, which is comparable to that expected due to noise in the SAR data. The retrieval algorithm performed very well for low incidence angles and over bare soil fields, and it deteriorated slightly for vegetated areas and overall for very dry soil conditions. Although the original IEM model was developed for bare soil conditions only, one important result of this study was the fact that the retrieval algorithm performed well for vegetated conditions, as demonstrated by the fact that the convergence ratio varied between 92% (dry conditions) and 98% (wet conditions) of all pixels for all days of the experiment. The sensitivity of soil-moisture estimates to spatial aggregation of remote-sensing data before and after the retrieval also was investigated. The results suggest that there is potential to improve the operational utility of high-resolution SAR data for soil-moisture monitoring by compressing the SAR data (preaggregation) to a spatial resolution at least one order of magnitude above that of measurement.