This study presents an investigation that involves comparing L-band Vegetation Optical Depth (L-VOD) obtained from Global Navigation Satellite System Transmissometry (GNSS-T) against metrics derived from airborne Light Detection and Ranging (LiDAR) data. Both data were collected during the SMAPVEX 2022 campaign in the temperate forests of the northeastern United States, covering Massachusetts and New York. From the LiDAR data, various parameters related to tree characteristics can be extracted, such as tree height, crown diameter and shape, vegetation area density, and woody volume. In this investigation, we initially computed LiDAR point cloud density as a proxy measure of vegetation structure for a given receiver position and the satellite's field of view, comparing it with L-VOD estimates at different positions within the studied forest. Our primary findings reveal a notable correlation between point density and L-VOD, despite the inherent errors in L-VOD estimates and the fact that the number of points may not be the optimal descriptor of the canopy architecture. In this paper, we will explore aforementioned LiDAR derived metrics against the GNSS-T L-VOD estimates to provide insights into the impact of canopy architecture on the L-VOD estimates, determining the specific vegetation layers that influence the measurement.

This article explores the potential of P-band and L-band radiometry for estimating soil moisture and temperature profiles. A total of 3977 hourly in situ soil data were collected at depths (d) of 5–60 cm in Beltsville, MD, USA. Using the data, a coherent model was used to generate synthetic brightness temperatures at an incidence angle of 40° for frequencies of 0.8, 0.9, 1.1, and 1.4 GHz. These synthetic brightness temperatures facilitated the estimation of soil moisture ($m_{v}$) and temperature (T) profiles, which were modeled as quadratic functions with three coefficients. The inversion problem was formulated as a least-squares problem and optimized by the adaptive simulated annealing (ASA) algorithm. Regression analysis highlighted the sensitivity of the soil moisture and temperature function coefficients on the brightness temperature at 1.4 GHz and the differential between 1.4 and 0.8 GHz ($R^{2}=0.62-0.97$), yielding the best-fit models to describe the relationships. The application of the ASA algorithm incorporating the best-fit models to constrain the search spaces showed the RMSE of soil moisture${m_{v}}\leq 0.04~\rm cm^{3}/cm^{3}$and soil temperature${T}\leq 1.35~^{\circ }\text {C}$for depths$d\leq 20~ \rm cm$, and${m_{v}}\leq 0.10~\rm cm^{3}/cm^{3}$and${T}\leq 1.60~^{\circ }\text {C}$for$d\leq 60 ~\rm cm$. A streamlined inversion method was also investigated which used the best-fit models as the retrieval formula and solely relied on the V-pol data. The streamlined inversion method achieved comparable retrieval accuracy but reducing the runtime from 770 to 0.54 s for one inversion. This approach simplifies the data collection process by eliminating the need for H-pol data, potentially broadening its flexibility and applicability.

L-band FMCW radar is implemented for monitoring forest dynamics. It took short-term and long-term measurements with an internal calibration system that guarantees stability and precision. The radar data is compared to in-situ measurement, which infers causal relationships between radar backscatter signal and forest physiology index such as tree dielectric. This paper explains the relationship between radar signals and environmental components such as precipitation based on the measurement. The radar demonstrates some interesting observations, for example, trees’ diurnal activity and freeze-thaw process.

Evaluation of soil moisture information should consider when key crop development stages occur and, ultimately, when farmers must make decisions based on soil moisture status. Therefore, we assessed the performance of three microwave satellites (SMAP, SMOS, and METOP/ASCAT) and three reanalysis models (MERRA-2, NARR, and WEPP) in the U.S. Corn Belt in the context of agricultural management. Thermal time and crop progress reports from the USDA-NASS defined critical transition periods of crop growth and management decisions for the validation process. Contrary to calendar timelines like annual segments, these key events separate the year into five irregular segments. Measurements at the South Fork Core Validation Site from 2016 to 2020 show that the two passive microwave satellites are dry compared to in-situ observations, but the active satellite and reanalysis model products were almost always wetter. Overall, most products perform the best during the pre-planting and post-harvest segments when no crops are present and worst during the active management phase when crops are starting to grow.

Two intense observation periods (IOPs) are included in the Soil Moisture Active Passive (SMAP) Validation Experiment (SMAPVEX) 2022 in the temperate forests of the northeastern US (Massachusetts and New York). Because a sizable portion of the U.S. and the world have non-uniform forest cover at the SMAP resolution scale, the IOPs aim to test the SMAP retrieval in both fully wooded and partially forested instances. Destructive sampling is often used to assess the opacity of the forest canopy, which is intrusive and labor-intensive in forest characterization. To measure vegetative opacity directly utilizing widely accessible Global Navigation Satellite System (GNSS) signals, we have instead developed a GNSS Transmissometry (GNSS-T) approach from a mobile platform (such as a helmet wearable and quadruped ground robot). The created system gathers two simultaneous GNSS readings, one in the unobstructed open sky area and the other under the forest canopy. The difference between the two can yield information on forest transmissivity (water content). That can be used to test the SMAP retrieval methods over wooded areas. In this study, we have processed SMAPVEX’s IOP-1 GNSS-T data at selected sites, including GPS, GLONASS, Beidou and Galileo satellites, and generated forest transmissivity and vegetation optical depth (VOD) heatmaps averaged to different angular bins at both SMAPVEX’22 locations.

The European Space Agency (ESA) launched the SMOS (Soil Moisture Ocean Salinity) mission in 2009; currently, multiple global soil moisture (SM) products are based on the measurements of its L-band (1.4 GHz) radiometer. We compared four SMOS products with each other: Level 2, Level 3, IC (INRA-CESBIO), and Near Real Time products. The comparisons focused on core validation sites (CVS), whose spatial representativeness errors allow the estimation of the SM product performance for bias-insensitive metrics (unbiased root mean square error (ubRMSE) and correlation (R), and anomaly R) with negligible uncertainty and for bias-sensitive metrics (mean difference (MD) and root mean square difference or RMSD) with acceptable uncertainty. When the products were compared with CVS independently, the results showed that the ubRMSE, R, and anomaly R of the IC product were better than those of the other products, while the MD was larger. However, the differences between the performances were smaller when the products were assessed using only the data points when each product had a valid retrieval. This indicates that the algorithms have similar performance and that data screening and quality flagging of the retrievals markedly affects the performance. The NASA Soil Moisture Active Passive (SMAP) mission produces a similar SM product as SMOS using an L-band radiometer. The closeness of the ubRMSE, R, and anomaly R performance of the IC product and the SMAP product (0.039 m3/m3vs. 0.041 m3/m3, 0.80 vs. 0.81, and 0.75 vs. 0.75) demonstrate that the SMOS and SMAP radiometers can achieve similar SM sensitivity.

The retrieval of soil moisture (SM) under forest canopy has long been an important goal for low frequency remote sensing. The NASA Soil Moisture Active Passive (SMAP) mission is engaged at three separate experiment sites to improve its SM retrieval algorithm in forested areas. Two of the sites are located in the deciduous forest region in Massachusetts and New York, US and one is located in southern boreal forest zone in Saskatchewan, Canada. Each site has a SM measurement network of 20-25 stations spread out over an area of about 30 km, which covers the SMAP radiometer footprint. In 2022, intensive observations will be carried out at each site which involve deployments of an airborne instrument, which is similar to the SMAP instrument, and intensive manual measurements of SM, surface and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. Here we show some early results using the networks and SMAP measurements to analyze the sensitivity of the SMAP L-band measurements to SM changes in forested area and the impact of the vegetation to the signal. The results suggest an upper limit for vegetation attenuation accounting for surface roughness effect and relate that to the values used in the current SMAP SM products.

The capability of high-resolution L-band Synthetic Aperture Radar (SAR) to retrieve field-scale$(< 30\mathrm{m})$soil moisture has been investigated over corn and soybean crop fields. The time-series retrieval algorithm inverts the look-up-table (LUT) representation of physics-based forward scattering model under the assumption of temporally invariant roughness condition. In order to improve the retrieval accuracy of both surface roughness and soil moisture, an enhancement in sensitivity of forward scattering model has been implemented through a linear scaling of LUT. The retrieval algorithm has been applied to the time-series UAVSAR data acquired during AMPM campaign over northern Arkansas in USA. The unbiased RMSE between the forward modeled and observed backscattering coefficients$(\sigma^{0})$are 1.34 dB (HH), 1.83 dB (VV) for corn and 3.77 dB (HH), 3.53 dB (VV) for soybean respectively. The validation of retrieved soil moisture using multi-pol (HH & VV) inputs with in situ measurements shows an unbiased RMSE (correlation) of 0.061$\mathrm{m}^{3}/\mathrm{m}^{3}\ (0.71)$and 0.081$\mathrm{m}^{3}/\mathrm{m}^{3}\ (0.47)$for corn and soybean crop fields respectively.

This paper demonstrates the potential use of P and L band passive measurements to determine root zone soil moisture (SM) and soil temperature (ST). SM and ST data have been taken as a function of depth during the NASA GSFC PLEX 19 experiment in the summer of 2019 at Beltsville, MD, USA. Using these data, a coherent model has been used to compute H and V brightness temperatures at frequencies of 0.8 and 1.4 GHz with an observation angle of 35 degrees. These synthetic brightness data are then used to estimate the SM and ST profiles which are represented by linear polynomials. The inversion problem is formulated as a least square problem that is solved by a global optimization method known as the Adaptive Simulated Annealing (ASA) method. Four inversion examples having different SM and ST profiles are presented. Selected results show that the standard deviation between the retrieved and measured data is less than 0.077$\text{cm}^{3}/\text{cm}^{3}$for SM, and 2.245 °C for ST.

In croplands, the L-band terrestrial brightness temperature is a function of not only soil moisture and vegetation, but also time-varying soil surface roughness. Soil surface roughness changes in response to human activities, such as the planting of crops, soil tillage, and rainfall. We use in situ data from the South Fork SMAP Core Validation Site in the US Corn Belt to determine the magnitude and polarization dependence of the soil surface roughness signal at the satellite scale. We find that when crops are not present, soil surface roughness retrievals are larger than anticipated, and are effectively independent of polarization except for their largest values.

Satellite L-band vegetation optical depth (L-VOD) contains new information about terrestrial ecosystems. However, it has not been evaluated against the geophysical variable that it represents, plant water, the mass of liquid water contained within vegetation tissue per ground area. We quantitatively assess the seasonal variation of three L-VOD products at the South Fork Core Validation Site in the Corn Belt state of Iowa where L-VOD is directly proportional to crop plant water. We use three satellite-scale crop plant water estimates:in situmeasurements; a normalized difference water index (NDWI) calibrated within situmeasurements; and a crop model. We find that overall the L-VOD satellite products are 0.02–0.09 Np (0.4–${1.7} \,\,\text {kg} \cdot \text {m}^{-2}$) lower than the three estimates. We show that overestimation of L-VOD can be attributed to dynamic soil surface roughness, and hypothesize that crop plant water observations will require the incorporation of this effect into retrieval algorithms.

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will be augmented with two intensive observation periods (IOP). The first IOP is planned for April 2022 and the other one for July 2022. The IOPs will entail a deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

The Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup has been established to coordinate the development of standardized validation across the satellite-derived products from different platforms, sensors, and algorithms with reference measurements from the in situ networks. Soil moisture exhibits a high variability in space that challenges the in situ validation. One of the main drivers for this variability is the characteristic heterogeneity in the soil texture. By the machine learning methods using the soil profile measurements and the remotely sensed predictors, spatially continuous maps of basic soil properties such as soil texture and bulk density are available. Those can be used to estimate soil moisture variability within a satellite product grid cell, here exemplarily shown for the Soil Moisture Active Passive (SMAP) 36-km product. The soil moisture standard deviation is described as a function of the mean soil moisture, whereby the approach needs the mean and standard deviation of the hydraulic parameters as input. The resulting global data set helps identifying the number of in situ stations necessary to validate the coarse soil moisture products. For most SMAP grid cells, three to four stations are adequate to estimate the mean soil moisture for validation; however, also regions were identified where 80 stations are necessary.

The FengYun-3 (FY-3) series satellite is the second generation of Chinese polar-orbiting meteorological satellite missions. The FY-3D satellite was launched on November 2017 and has been providing valuable data for meteorological applications, including brightness temperature ( TB) data from the MicroWave Radiation Imager (MWRI). In this study, we developed a global soil moisture retrieval algorithm, based on the radiative transfer equation (RTE) for using the FY-3D MWRI TBto continue the soil moisture record from FY-3 satellites. We adopted a new empirical model to compute vegetation water content (VWC) based on the leaf area index (LAI) and canopy height ( H) for vegetation effects correction. The Qpmodel, which addresses the soil surface roughness effects using dual-polarization information, is then used for soil moisture retrieval. Validation of the FY-3D soil moisture was conducted with the in-situ data and the validation results showed encouraging accuracy over a variety of landcovers, with bias and unbiased root-mean-squared difference (ubRMSE) at or below the level of 0.06 m3·m-3. Monthly averaged soil moisture products generated from FY-3D could represent the seasonal changes in soil moisture and show reasonable spatial distribution of soil moisture at a global scale.

Prediction of large-scale water-related natural disasters such as droughts, floods, wildfires, landslides, and dust outbreaks can benefit from the high spatial resolution soil moisture (SM) data of satellite and modeled products because antecedent SM conditions in the topsoil layer govern the partitioning of precipitation into infiltration and runoff. SM data retrieved from Soil Moisture Active Passive (SMAP) have proved to be an effective method of monitoring SM content at different spatial resolutions: 1) radiometer-based product gridded at 36 km; 2) radiometer-only enhanced posting product gridded at 9 km; and 3) SMAP/Sentinel-1A/B products at 3 and 1 km. In this article, we focused on 9-, 3-, and 1-km SM products: three products were validated against in situ data using conventional and triple collocation analysis (TCA) statistics and were then merged with a Noah-Multiparameterization version-3.6 (NoahMP36) land surface model (LSM). An exponential filter and a cumulative density function (CDF) were applied for further evaluation of the three SM products, and the maximize-R method was applied to combine SMAP and NoahMP36 SM data. CDF-matched 9-, 3-, and 1-km SMAP SM data showed reliable performance: R and ubRMSD values of the CDF-matched SMAP products were 0.658, 0.626, and 0.570 and 0.049, 0.053, and 0.055 m3/m3, respectively. When SMAP and NoahMP36 were combined, the R-values for the 9-, 3-, and 1-km SMAP SM data were greatly improved: R-values were 0.825, 0.804, and 0.795, and ubRMSDs were 0.034, 0.036, and 0.037 m3/m3, respectively. These results indicate the potential uses of SMAP/Sentinel data for improving regional-scale SM estimates and for creating further applications of LSMs with improved accuracy.

The retrieval of soil moisture under forest canopy has long been an important goal for low frequency remote sensing. The NASA mission started a dedicated field experiment in May 2019 by deploying two temporary soil moisture networks in northeast US that cover two separate SMAP pixels with variable degree of forest cover. The measurements will run through 2021 and they will be augmented with two intensive observation periods (IOP). The first IOP will be conducted in April 2021, and a second one in July 2021. The IOPs will see deployment of the airborne PALS (Passive Active L-band sensor) instrument, which is similar to the SMAP instrument, and intensive manual measurements of soil moisture and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. The early results have shown that the SMAP measurement signal at L-band is sensitive to soil moisture changes observed on the ground.

The soil moisture active passive (SMAP) mission was designed to acquire L-band radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSD of not more than 0.04 m3/m3volumetric accuracy in the top 5 cm for vegetation with a water content of less than 5 kg/m2. Single-channel algorithm (SCA) and dual-channel algorithm (DCA) are implemented for the processing of SMAP radiometer data. The SCA using the vertically polarized brightness temperature (SCA-V) has been providing satisfactory SM retrievals. However, the DCA using prelaunch design and algorithm parameters for vertical and horizontal polarization data has a marginal performance. In this article, we show that with the updates of the roughness parameter h and the polarization mixing parameters Q, a modified DCA (MDCA) can achieve improved accuracy over DCA; it also allows for the retrieval of vegetation optical depth (VOD or τ). The retrieval performance of MDCA is assessed and compared with SCA-V and DCA using four years (April 1, 2015 to March 31, 2019) of in situ data from core validation sites (CVSs) and sparse networks. The assessment shows that SCA-V still outperforms all the implemented algorithms.

Surface roughness parameterization plays an important role in passive microwave soil moisture (SM) retrieval. This article proposes a new formulation for estimating surface roughness. The proposed model incorporates the field-scale (micro) roughness as well as topographic (macro) roughness. The performance of the model is evaluated by inverting the traditional tau-omega model for retrieving SM. The study focuses on the passive active L-band system (PALS) radiometer data collected as a part of two Soil Moisture Active Passive Validation Experiment (SMAPVEX), i.e., SMAPVEX12 (humid Manitoba, Canada) and SMAPVEX15 (semiarid Arizona, USA) with highly different microroughness and macroroughness. The measured surface roughness is observed to increase exponentially with clay fraction (CF). This behavior is minimized with increase in leaf area index (LAI). In the absence of vegetation, the contribution of topography toward surface roughness increases. A higher surface roughness value is estimated for SMAPVEX12, which positively correlate with LAI and CF and negatively correlate with wetness conditions. On the other hand, due to the high topographic variability in SMAPVEX15 region, the contribution of topography (surface curvature) toward total surface roughness is significant. Also, consistently dry SM resulted in high microroughness for SMAPVEX15. Nevertheless, a total surface roughness estimated for SMAPVEX15 region is less than for SMAPVEX12. The surface roughness formulation presented in this study can be extrapolated to any spatial resolution.

Modeling L-band backscatter from a corn canopy continues to be a challenge due to the complex dynamics in both plant phenology and the underlying soil. An experiment has been conducted to better understand the relationship between L-band backscatter and canopy parameters such as soil moisture, vegetation water content, dew, and periodic rows. The experiment consists of field measurements that take into account plant phenology and are concurrent with L-band backscatter returns from a corn canopy over a full growing season. The field measurements of the corn plants' constituents highlight modeling complexities, such as an inhomogeneity in the dielectric constant of the stalk and cobs. A simple method to replace the stalk and cob with a homogeneous dielectric constant is validated. Using the field measurements in a scattering model developed at George Washington University (GW), both coherent and incoherent backscatter are computed. The results show coherent effects contributing to enhanced backscatter by up to 2.7 dB for both HH-pol and VV-pol. The coherent model and the detailed measurements, especially, the dielectric constant of the stalks, resulted in good agreement with the measurements. These measurements have an average root mean square difference (RMSD) with the results from the coherent model of around 1 dB for both HH-pol and VV-pol over the entire growing season. The incoherent mode does not perform as well.

The Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band synthetic aperture radar (SAR) was flown more than 1200 h from August 2012 to September 2015, covering regions of 2500 km2spread over nine major biomes in North America. The flights, as a part of the NASA AirMOSS Earth Venture Suborbital 1 (EVS-1) mission, collected radar data used to map root-zone soil moisture (RZSM) at 3-arcsec resolution. We previously reported the baseline retrieval algorithm and demonstrated its performance for a semiarid shrubland (Walnut Gulch, AZ, USA); we represented the RZSM profile as a continuous quadratic function and solved a radar scattering nonlinear optimization problem to obtain the unknown polynomial coefficients. In this article, we expand the retrievals to other AirMOSS sites that, in addition to the semiarid shrubland, include grassland and crops (MOISST, OK, USA), woody savanna (Tonzi Ranch, CA, USA), temperate conifer forest (Metolius, OR, USA), and boreal forest (Saskatchewan, Canada). Due to a wide range of land covers, soil types, and soil moisture regimes, we parameterize the forward model and constrain the inverse algorithm for each site separately. We present the full set of retrievals for these sites, validating the results against in situ observations. Error sources and strategies to minimize their effects are discussed. The concept of sensing depth is introduced. We find that the retrieval errors are smallest for the top 25 cm of soil with a root-mean-square error (RMSE) of less than 0.05 m3/m3. The RMSE remains around 0.06 m3/m3even for depths reaching 45 cm, which is the typical sensing depth for the sites considered. These AirMOSS RZSM products (known as Level-2/3 RZSM, or L2/3-RZSM, products) are the first of their kind in that it is the first time RZSM has been retrieved directly from remote sensing observation.

The NASA SMAP (Soil Moisture Active Passive) mission provides a global coverage of soil moisture measurements based on its L-band microwave radiometer every 2-3 days at about 40 km resolution. The soil moisture retrieval algorithms model the brightness temperature as a function of soil moisture, surface conditions and vegetation. External data sources inform the algorithms about the surface conditions and vegetation, which enable the retrieval of soil moisture. The inversion process contains uncertainties related to radiometer measurements, forward model assumptions and ancillary data sources. This study focuses on the uncertainties that depend on the seasonal evolution of the surface conditions and vegetation. The study compares the SMAP and core validation site (CVS) soil moisture values over a period of four years to extract the evolution of performance metrics over time. The analysis showed that most CVS that include managed agriculture exhibit significant time-dependent seasonal bias. This bias was linked to seasonal temperature cycle, which is a proxy to several features that can cause seasonally dependent errors in the SMAP product.

Cloudy conditions impede and reduce the utility of optical imagery. With the launch of Sentinel-1A and B, the ongoing availability of RADARSAT-2 imagery, and the expected launch of the RADARSAT Constellation Mission (RCM), dense time series of C-band Synthetic Aperture Radar (SAR) data will now be readily available. For crop classification and mapping, SAR imagery has yet to be used to its full potential and has generally been combined with optical imagery. The JECAM SAR Inter-Comparison Experiment is a multi-year, multi-partner project that aims to compare global methods for SAR-based crop monitoring and inventory. Sets of dense time-series SAR imagery which include RADARSAT-2 and Sentinel-1 data were prepared for this experiment. AAFC's operational Decision Tree (DT) and newly implemented Random Forest (RF) classification methodologies were applied to these SAR only data-stacks, and to optimized, traditional data-stacks of optical/SAR combinations. This paper outlines the results of these dense time-series classifications and how these results were affected by changing numbers of agriculture classes, numbers of available SAR imagery and numbers of training and validation data points for individual crop types. In general, for the dense time-series SAR stacks, overall accuracies of greater than 85%, a typical operational goal, were obtained for 6 of 12 sites. These results have important operational implications for particularly cloudy regions where the availability of optical imagery is limited.

SMAP soil moisture retrieval currently relies on a relatively smooth parameterization of soil surface roughness in croplands. However, in agricultural regions like the U. S. Corn Belt where tillage is common, roughness varies according to farm management practices, increasing due to harvest and tillage and decreasing from field cultivation and rainfall. We approximate roughness at the South Fork core validation site during 2016 by re-arranging the SMAP Single Channel Algorithm to retrieve HR from observed brightness temperature when in situ observations of soil moisture and ancillary data are provided. The result is a temporally dynamic HR, rougher than the SMAP default parameterization, with a slight sensitivity to soil moisture. This analysis is believed to be the first to measure a temporally dynamic HR for croplands at the satellite-scale. We hypothesize that SMAP performance will improve in the U. S. Corn Belt when the refined HR is utilized during soil moisture retrieval.

In order to calibrate and validate the SMAP soil moisture products, networks of ground-based soil moisture sensors have been deployed. Measurements collected from the networks must be upscaled to the radiometer footprint scale (30-40 km) for comparison with the SMAP radiometer-based retrievals. The upscaling is typically performed as a weighted average of individual sensor measurements within the SMAP grid. Since different weighting schemes have been found to result in different upscaled soil moisture estimates, an independent method of assessing soil moisture estimation biases is needed. We therefore present a method for calculating estimation biases at each SMAP Core Validation Site (CVS). The estimation was enabled by networks of enhanced soil moisture sampling that were deployed at four CVSs for a limited time. Based on Random Forests, our method offers a straightforward, systematic, and unified approach to bias estimation across a variety of sites. The method was applied to estimate biases at the four SMAP CVSs.

As part of the development of a long-term soil moisture record, we inter-calibrate several low microwave radiometric sensors (SMOS, SMAP, AMSR-E, AMSR2). We use a radiometric simulator over open ocean as a common reference for sensors operating at various frequencies (L-, C- and X-band) and incidence angles. The simulator includes a radiative transfer model for the Earth's emission and the sensors characteristics. It produces antenna temperatures that can be used to account for differences in the incidence and azimuth angle, timing, and frequency. The double difference method is used to inter-calibrate the various sensors.

In the past the effect of soil roughness was often considered secondary within the determination of soil moisture from remote sensing data. Several studies showed that accurate determination of soil roughness leads to an improved estimation of soil moisture. Two standard parameters in microwave sensing to describe the surface roughness are the standard deviation of the surface height variation s and the surface correlation length l with its corresponding autocorrelation function (ACF). Both parameters (s, l) affect the emissivity measured by radiometers as well as the backscattering observed by radars. In this study, we develop a physics-based approach to retrieve s and l by combining both microwave signals based on active-passive microwave covariation. To test the approach, containing a forward model and a retrieval algorithm, we used active/passive microwave data measured with the ComRAD truck-based SMAP simulator at L-band. Results and validations with corresponding field measurements on ground show that s and l can be estimated when using this approach. The physics-based retrieval algorithm works robustly for two investigated test fields having an RMS-Error of 0.68 cm and 0.69 cm between the microwave-based and field-measured s-values, and of 3.13 cm and 3.04 cm for l-values. Validation of the results reveals that the influence of the ACF, needed within the retrieval, is distinct.

Soil moisture plays a significant role in disciplines such as hydrology, meteorology and agriculture, and passive microwave remote sensing has become a widely used technique for global soil moisture estimation over the past three decades. Several satellite missions carrying radiometers have been launched over the past years. Among them are Japan Aerospace Exploration Agency's (JAXA's) Advanced Microwave Scanning Radiometer-EOS (AMSR-E) launched on NASA's Aqua satellite, European Space Agency's (ESA's) Soil Moisture and Ocean Salinity (SMOS) mission, JAXA's Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite, and NASA's Soil Moisture Active Passive (SMAP) mission. Based on the availability of these four missions, there is an opportunity to develop a consistent inter-calibrated longterm soil moisture data record. This study focuses on the parametrization of the tau-omega model for soil moisture retrieval at L-, C- and X-band using brightness temperature observations from the four missions and in-situ soil moisture and soil temperature data from the SMAP core validation sites across various land cover types. The ancillary data sets used in the SMAP baseline algorithm are used for the retrievals at different frequencies. Simultaneous calibrations of the vegetation parameter b and roughness parameter h at both horizontal and vertical polarizations are performed. A set of model parameters to successfully retrieve soil moisture at different validation sites at L-, C- and X -band are presented. A preliminary comparison of SMAP and AMSR2 soil moisture retrievals against in situ observations at the Yanco (Australia) and TxSON (U.S.) sites showed the best accuracy and correlation at L-band (RMSD=0.03-0.05 m3/m3; R=0.92-0.94). The C-/X-band performance was not as satisfactory as L-band (RMSD=0.08-0.17 m3/m3; R=0.24-0.79). This also indicates that retrieval at higher frequencies can be very challenging when dense vegetation is present.

Soil moisture content is an essential climate variable that is operationally delivered at low resolution (e.g. 36-9 km) by earth observation missions, such as ESA/SMOS, NASA/SMAP and EUMETSAT/ASCAT. However numerous land applications would benefit from the availability of soil moisture maps at higher resolution. For this reason, there is a large research effort to develop soil moisture products at higher resolution using, for instance, data acquired by the new ESA's Sentinel missions. The objective of this study is twofold. First, it presents the validation status of a pre-operational soil moisture product derived from Sentinel-1 at 1 km resolution. Second, it assesses the possibility of integrating Sentinel-2 data and additional ancillary information, such as parcel borders and high resolution soil texture maps, in order to obtain soil moisture maps at “field scale” resolution, i.e. ~0.1 km. Case studies concerning agricultural sites located in Europe are presented.

The SMOS (Soil Moisture and Ocean Salinity) mission, together with other passive microwave based missions (AMSR, SMAP), provides soil moisture estimates at resolutions ranging from 30 to 55 km. These estimates are validated by direct comparison to in situ measurements that typically measure over an area of a few centimeters. There exist a spatial scale mismatch between the satellite (large support) and the in situ measurements (point support), which contributes to the differences observed. Their magnitude depends on the spatial representativeness of the in situ measurements, which varies in time and with the selected location. This communication will show how the spatial scale mismatch evolves through timescales. It is characterized by using modeled, in situ and satellite soil moisture time series. Timescales, from 0.5 to 128 days, are obtained using wavelet transforms and the spatial representativeness is assessed with a new approach that uses wavelet-based correlations (WCor).

Utilizing Synthetic Aperture Radar (SAR) sensors for crop inventory and condition monitoring offers many advantages, particularly the ability to collect data under cloudy conditions. The JECAM SAR Inter-Comparison Experiment is a multi-year, multi-partner project that aims to compare global methods for SAR crop monitoring and inventory, and Leaf Area Index (LAI) and biomass retrieval. Data sets of SAR imagery including RADARSAT-2 and Sentinel-1 are being prepared for this experiment and it is important to develop best practices to ensure consistency across the data sets. This paper outlines the speckle filter testing results based upon changing filter types and window sizes in comparison with changing field size. In general, it was found that the adaptive Touzi filter resulted in the highest overall classification accuracies for all field sizes. It was also found that there may be importance to speckle filter window size in relation to agriculture field size for other filter types.

The Advanced Microwave Scanning Radiometer 2 (AMSR2) is part of the Global Change Observation Mission-Water (GCOM-W) mission. AMSR2 fills the void left by the loss of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) after almost 10 years. Both missions provide brightness temperature observations that are used to retrieve soil moisture. Merging AMSR-E and AMSR2 will help build a consistent long-term dataset. Before tackling the integration of AMSR-E and AMSR2 it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites. Three products that rely on different algorithms were evaluated; the JAXA Soil Moisture Algorithm (JAXA), the Land Parameter Retrieval Model (LPRM), and the Single Channel Algorithm (SCA). Results indicate that overall the SCA has the best performance based upon the metrics considered.

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 m3/m3at 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 m3/m3. 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.

NASA's SMAP (Soil Moisture Active Passive) calibration and validation program revealed that the soil moisture products are experiencing difficulties in meeting the mission requirements in certain agricultural areas. Therefore, the mission organized airborne field experiments at two core validation sites to investigate these anomalies. The SMAP Validation Experiment 2016 included airborne observations with the PALS (Passive Active L-band Sensor) instrument and intensive ground sampling. The goal of the PALS measurements are to investigate the soil moisture retrieval algorithm formulation and parameterization under the varying (spatially and temporally) conditions of the agricultural domains and to obtain high resolution soil moisture maps within the SMAP pixels. In this paper the soil moisture retrieval using the PALS brightness temperature measurement in SMAPVEX16 is discussed in relation to in situ and SMAP soil moisture.

There are a variety of soil moisture station designs and networks deployed throughout the world, each with varying applications and uses. For the purpose of satellite validation of soil moisture products, a dense network of soil moisture networks are required with soil moisture sensors at the near surface (~5 cm or less) to correspond to the satellite footprints and signals. The USDA-Agricultural Research Service operates a collection of soil moisture networks as a part of the Long Term Agro-ecosystem Research (LTAR) network to this end. These networks have been used to validate products from AMSR-E, SMOS, Aquarius, and SMAP. A review of these results and a synopsis of successful scaling strategies are discussed.

Soil moisture at high spatial resolution is required for various land processes related studies. However, currently the resolution of passive microwave retrieved soil moisture is low. To solve this problem, a soil moisture disaggregation algorithm based on thermal inertia relationship between daily temperature change and average soil moisture modulated by vegetation conditions has been formulated. This algorithm was applied to the SMAP (Soil Moisture Active/Passive) to produce the 1 km downscaled soil moisture over the SMAPVEX15 (SMAP Validation Experiment 2015). The disaggregated soil moisture has been compared to in situ observations and the results of this approach are very encouraging.

Soil moisture is of great importance to disciplines such as agriculture, hydrology and meteorology. Over the past three decades, passive microwave remote sensing has been demonstrated as a promising tool for global soil moisture estimation and several missions have been launched over the past years. This study focuses on the parametrization of the tau-omega model at L-, C- and X-band for the Yanco site in New South Wales, Australia, and compares the resulting forward-simulated brightness temperatures with two missions: NASA's Soil Moisture Active Passive (SMAP) mission and JAXA's Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W mission. Preliminary comparison of SMAP and AMSR2 brightness temperatures and forward-simulated brightness temperatures at the Yanco site showed a generally good agreement and higher correlation for the vertical polarization. This is consistent with other studies analyzing the SMAP soil moisture products. Simultaneous calibration of the vegetation parameter b and roughness parameter h was also performed for the L-, C- and X-band data sets, respectively, at both horizontal and vertical polarizations.

Microwave back and forward scattering from a layer of vegetation continues to have important applications in remotely sensing soil moisture and vegetation biomass. Satellite radars detect backscattered returns while GNSS receivers can measure forward scatter. The scattering cross sections can be computed by the Distorted Born Approximation (DBA) and by 1st order transport theory. The first is a field based method while the second is based on conservation of energy. These two theories give the same results except for the presence of enhancement terms in the DBA theory. The DBA results will be derived by a simple voltage based pencil beam approach. The role of enhancement will be explored in both the backscatter and forward scatter cases. Results for realistic vegetation canopies such as agriculture crops at L-band frequencies will be used to demonstrate where enhancement effects are important; both like and cross polarization calculations. The effect of the antenna beamwidth will be presented.

The systematic retrieval of near surface soil moisture (SSM) fields at high resolution (e.g., 0.1-1.0 km) is a challenging task that requires the exploitation of new retrieval algorithms and SAR data with advanced observational capabilities (in terms of spatial/temporal resolution, radiometric accuracy, very large swath, long-term continuity and rapid data dissemination). The launch of the Sentinel-1 (S-1) constellation provides these capabilities and calls for the development and validation of pre-operational SSM products at high resolution. The objective of this paper is to present and initially assess a SSM retrieval algorithm developed in view of S-1 data exploitation. The activity is supported by a large scientific community engaged in fostering a more effective interaction between researchers working in the field of high and low resolution SSM retrieval.

The SMOS, and SMAP retrievals are not only influenced by soil moisture, vegetation but also highly sensitive to surface roughness. The current retrieval algorithms use roughness models and parameterization developed at field scale to estimate soil moisture at satellite footprint. These models ignored large scale roughness features due to differences in elevation, concavity etc., observed within the satellite footprint. In this study, it is hypothesized that the satellite observed surface roughness is contributed by micro-roughness (small-scale roughness) and macro-roughness (large-scale roughness), incorporating the scattering features observed at field and satellite scale. The traditional algorithm is used to retrieve soil moisture for SMAPVEX12 (Soil Moisture Active Passive Validation Experiment, 2012), and SMAPVEX15 the field experiments.

NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 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. SMAP's 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 cal/val sites. Accuracy of the soil moisture retrievals averaged over the core sites showed that SMAP accuracy requirements are being met.

The Soil Moisture Ocean Salinity (SMOS) soil moisture retrievals are too dry and noisy when compared to the South Fork of the Iowa River (SFIR), a heavily agricultural watershed with a USDA-ARS in situ soil moisture network. After testing for invalid retrievals, errors in auxiliary datasets, and a non-representative parameterization of scattering in the canopy, soil surface roughness changes were identified as the next potential source of the dry bias. Soil surface roughness increases the amount of radiation emitted from the surface; if the SMOS processor does not “know” that the soil is rough, the increased brightness temperature is interpreted as a drier soil. When the processor does account for a rougher soil surface, the previous dry bias between SMOS and the SFIR will reduce. This ability to zero the bias comes at a high cost: SMOS sensitivity to the SFIR soil moisture is halved between the moderately-rough and rough scenarios.

With the loss of the L-band radar, the NASA SMAP satellite lost the capability to directly provide high resolution global soil moisture data products after July 7th, 2015. However, the SMAP L-band radiometer has been successfully and continuously providing high quality coarse resolution observations with the best RFI mitigation since April 2015. These coarse resolution soil moisture observations could be downscaled to finer resolution using finer scale observations of soil moisture sensitive quantities from existing satellite sensors. In the past decade, several algorithms have been introduced to downscale passive microwave soil moisture observations. Most of these methods exploit the soil moisture information from optical sensing of land surface temperature and vegetation dynamics while others use active microwave (radar) observations. In this study, alternative algorithms are intercompared in order to find out the most reliable algorithm that could be implemented for routine or operational product generation. In this paper, coarse scale satellite data are from NASA SMAP radiometer and fine scale satellite data are backscatter from SMAP radar, land surface temperature (LST) and vegetation index from NOAA GOES, and AMSR2 Ka band observations for the warm seasons in 2015 and 2016. Results from three downscaling algorithms were analyzed. They were the NASA SMAP Active-Passive product algorithm, a simple LST regression algorithm, and a regression tree algorithm. Four sets of in situ soil moisture measurement data were collected and processed from Millbrook, NY, Walnut Gulch, AZ, Tibetan Plateau, China, and Yanco, Australia, respectively. Preliminary results of this inter-comparison study are reported.

A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithm's performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3/cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.

This paper evaluates the retrieval of soil moisture in the top 5-cm layer 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. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, 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 static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and −0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3ubRMSE, −0.015 m3/m3bias, and a correlation of 0.50, compared toin situmeasurements, thus meeting the accuracy target of 0.06 m3/m3ubRMSE. The successful retrieval demonstrates 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.

Many previous studies have shown the sensitivity of radar backscatter to surface soil moisture content, particularly at L-band. Moreover, the estimation of soil moisture from radar for bare soil surfaces is well-documented, but estimation underneath a vegetation canopy remains unsolved. Vegetation significantly increases the complexity of modeling the electromagnetic scattering in the observed scene, and can even obstruct the contributions from the underlying soil surface. Existing approaches to estimating soil moisture under vegetation using radar typically rely on a forward model to describe the backscattered signal and often require that the vegetation characteristics of the observed scene be provided by an ancillary data source. However, such information may not be reliable or available during the radar overpass of the observed scene (e.g., due to cloud coverage if derived from an optical sensor). Thus, the approach described herein is an extension of a change-detection method for soil moisture estimation, which does not require ancillary vegetation information, nor does it make use of a complicated forward scattering model. Novel modifications to the original algorithm include extension to multiple polarizations and a new technique for bounding the radar-derived soil moisture product using radiometer-based soil moisture estimates. Soil moisture estimates are generated using data from the Soil Moisture Active/Passive (SMAP) satellite-borne radar and radiometer data, and are compared with up-scaled data from a selection ofin situnetworks used in SMAP validation activities. These results show that the new algorithm can consistently achieve rms errors less than 0.07 m3/m3over a variety land cover types.

GCOM-W AMSR2 provides continuity following AMSR-E and the opportunity to generate a global long-term satellite soil moisture data record from the same instrument type. Various soil moisture products are being developed using AMSR observations. The JAXA soil moisture along with the Single Channel Algorithm (SCA) product were evaluated using in situ observations from different geographical domains. Both the JAXA and SCA soil moisture estimates capture the overall climatological features and the overall spatial structure of the two products is similar. The JAXA soil moisture product shows a lower dynamic range in the retrieved soil moisture. The SCA performs well over low and moderately vegetated areas. This study focuses on the development of the AMSR2 soil moisture product. Validation results using in situ observations from diverse climate and land cover conditions will be presented.

Field experiments have played a critical role in the development and implementation of satellite soil moisture missions. A review of key experiments is presented that includes tower-, aircraft, and satellite-focused efforts conducted over four decades that have supported two dedicated satellite missions; Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active passive (SMAP).

Surface soil moisture was retrieved globally by systematically correcting for the effects of vegetation and soil surface roughness. The retrieval is enabled by employing physical-models of radar forward scattering for individual vegetation types to account for vegetation scattering and absorption, and by constraining the surface roughness effect using time-series observations. The L-band SMAP multi-polarized (HH/VV/HV) σ° data acquired globally every three days were used from mid-April to early July, 2015. Assessment was conducted over 13 rigorously-chosen core validation sites covering a wide range of biomass types, biomass amount, and soil conditions. The soil moisture retrieval reached an accuracy of 0.06 m3/m3RMSE, a bias of 0.003 m3/m3, and a correlation of 0.56. The successful retrieval demonstrates that the physically-based retrieval method is capable of characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation on a global scale.

This paper reports on a recent field campaign that aims to collect time-series multi-frequency microwave data over winter wheat during the entire growth cycle. The data are being collected to characterize vegetation dynamics and to quantify its effects on soil moisture retrievals. A C-band radar was recently incorporated within the existing L-band radar/radiometer system called ComRAD (SMAP's ground based simulator) and an additional VHF receiver is being constructed as well. With C-band's ability to sense vegetation details and VHF's root-zone soil moisture within ComRAD's footprint, we will have an opportunity to test our `discrete scatterer' vegetation models and parameters at various surface conditions. The purpose of this investigation is to determine optical depth and effective scattering albedo of vegetation of a given type (i.e. winter wheat) at various stages of growth that are needed to refine soil moisture retrieval algorithms for the SMAP mission.

NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days using an L-band (active) radar and an L-band (passive) radiometer. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAP's radiometer-derived soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36 km fixed Earth grid using brightness temperature observations from descending (6 am) passes and ancillary data. A beta quality version of L2_SM_P was released to the public in September, 2015, with the fully validated L2_SM_P soil moisture data expected to be released in May, 2016. Additional improvements (including optimization of retrieval algorithm parameters and upscaling approaches) and methodology expansions (including increasing the number of core sites, model-based intercomparisons, and results from several intensive field campaigns) are anticipated in moving from accuracy assessment of the beta quality data to an evaluation of the fully validated L2_SM_P data product.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m3/m3unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.

Vegetation water content (VWC) is an important component of microwave soil moisture retrieval algorithms. This paper aims to estimate VWC using L band active and passive radar/radiometer datasets obtained from a NASA ground-based Soil Moisture Active Passive (SMAP) simulator known as ComRAD (Combined Radar/Radiometer). Several approaches to derive vegetation information from radar and radiometer data such as HH, HV, VV, Microwave Polarization Difference Index (MPDI), HH/VV ratio, HV/(HH+VV), HV/(HH+HV+VV) and Radar Vegetation Index (RVI) are tested for VWC estimation through a generalized linear model (GLM). The overall analysis indicates that HV radar backscattering could be used for VWC content estimation with highest performance followed by HH, VV, MPDI, RVI, and other ratios.

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. 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 m3/m3, Bias = -0.007 m3/m3, 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.

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need for long-duration combined active and passive L-band microwave observations. In response to this need, a joint Canada-U.S. field experiment (SMAPVEX12) was conducted in Manitoba (Canada) over a six-week period in 2012. Several times per week, NASA flew two aircraft carrying instruments that could simulate the observations the SMAP satellite would provide. Ground crews collected soil moisture data, crop measurements, and biomass samples in support of this campaign. The objective of SMAPVEX12 was to support the development, enhancement, and testing of SMAP soil moisture retrieval algorithms. This paper details the airborne and field data collection as well as data calibration and analysis. Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach. Passive active L-band sensor (PALS) antenna temperatures and reflectivity, as well as backscatter, closely follow dry down and wetting events observed during SMAPVEX12. The SMAPVEX12 experiment was highly successful in achieving its objectives and provides a unique and valuable data set that will advance algorithm development.

NASA's Soil Moisture Active Passive (SMAP) mission is scheduled for launch in November 2014. In the prelaunch time frame, the SMAP team has focused on improving retrieval algorithms for the various SMAP baseline data products. The SMAP passive-only soil moisture product depends on accurate parameterization of the tau-omega model to achieve the required accuracy in soil moisture retrieval. During a field experiment (APEX12) conducted in the summer of 2012 under dry conditions in Maryland, the ComRAD truck-based SMAP simulator collected active/passive microwave time series data at the SMAP incident angle of 40° over corn and soybeans throughout the crop growth cycle. A similar experiment was conducted only over corn in 2002 under normal moist conditions. Data from these two experiments will be analyzed and compared to evaluate how changes in vegetation conditions throughout the growing season in both a drought and normal year can affect parameterizations in the tau-omega model for more accurate soil moisture retrieval.

Once launched in late 2014, NASA's Soil Moisture Active Passive (SMAP) mission will use a combination of a four-channel L-band radiometer and a three-channel L-band radar to provide high resolution global mapping of soil moisture and landscape freeze/thaw state every 2-3 days. These measurements are valuable to improved understanding of the Earth's water, energy, and carbon cycles, and to many applications of societal benefit. In order for soil moisture to be retrieved accurately from SMAP microwave data, prelaunch activities are concentrating on developing improved geophysical retrieval algorithms for each of the SMAP baseline products. The ComRAD truck-based SMAP simulator collected active/passive microwave time series data at the SMAP incident angle of 40° over corn and soybeans during 2012 for use in refining SMAP retrieval algorithms.

The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada, from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean Salinity (SMOS) mission validation and the prelaunch assessment of the proposed Soil Moisture Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (leaf area index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Moreover, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km × 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets, and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data (prototype 346). The radio frequency interference observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of the SMOS soil moisture product (prototype 307) matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates from the National Snow and Ice Data Center more closely reflected soil moisture measurements.

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m3/m3for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve.

This study reports on the utilization of microwave modeling, together with ground truth, and L-band (1.4-GHz) brightness temperatures to investigate the passive microwave characteristics of a conifer forest floor. The microwave data were acquired over a natural Virginia Pine forest in Maryland by a ground-based microwave active/passive instrument system in 2008/2009. Ground measurements of the tree biophysical parameters and forest floor characteristics were obtained during the field campaign. The test site consisted of medium-sized evergreen conifers with an average height of 12 m and average diameters at breast height of 12.6 cm. The site is a typical pine forest site in that there is a surface layer of loose debris/needles and an organic transition layer above the mineral soil. In an effort to characterize and model the impact of the surface litter layer, an experiment was conducted on a day with wet soil conditions, which involved removal of the surface litter layer from one half of the test site while keeping the other half undisturbed. The observations showed detectable decrease in emissivity for both polarizations after the surface litter layer was removed. A first-order radiative transfer model of the forest stands including the multilayer nature of the forest floor in conjunction with the ground truth data are used to compute forest emission. The model calculations reproduced the major features of the experimental data over the entire duration, which included the effects of surface litter and ground moisture content on overall emission. Both theory and experimental results confirm that the litter layer increases the observed canopy brightness temperature and obscure the soil emission.

SMOS observations provide an opportunity to develop a testbed for the evaluation of different SMAP algorithm options. The use of real-world global observations will help in the development and selection of different land surface parameters and ancillary observations needed for the soil moisture algorithms. In this study, SMOS observations were used with one soil moisture retrieval algorithm and the results were evaluated using in situ soil moisture measurements. The SMOS soil moisture product, which exploits multiple incidence angle observations, compares well with the ground-based observations (RMSE 0.043 m3/m3(ascending) and 0.047 m3/m3(descending)). The alternative SMAP compatible algorithm also performed well (RMSE 0.040 m3/m3(ascending) and 0.043 m3/m3(descending)). Although preliminary, these initial results are encouraging for the potential of SMAP to meet its required soil moisture accuracy.

Soil moisture products provided by the Soil Moisture and Ocean Salinity (SMOS) satellite were evaluated using in situ observations. The sites are located in different regions of the U.S. and provide replicate sampling of surface soil moisture at the SMOS footprint scale. Data from a sparse network were also considered. Soil moisture products from the Advanced Microwave Scanning Radiometer were also used for validation. Results based upon a preliminary version of the retrieval algorithm indicate promising performance. It is anticipated that the accuracy and reliability of the retrievals will improve as validation information is evaluated.

This paper investigates tree scattering effects at L-band by using a first-order radiative transfer (RT) model and truck-based measurements of brightness temperature over natural conifer stands to assess the performance of the τ - ω (tau-omega) model, a zero-order RT solution, over forest canopies. The tau-omega model accounts for vegetation effects in terms of "effective" vegetation parameters (single-scattering albedo and vegetation opacity) which represent the canopy as a whole. This approach inherently ignores multiple-scattering effects and it thus has a limited validity depending on the level of scattering within the canopy. The fact that the scattering from large forest components such as branches and trunks is significant at L-band requires that retrieved vegetation parameters be evaluated (compared) with their theoretical definitions to provide better understanding of these parameters in the soil moisture (SM) retrievals over moderately to densely vegetated landscapes. In this paper, the tau-omega model is fitted to a first-order RT model with an "effective" albedo assuming that "effective" vegetation optical depth is same as the "theoretical" opacity [1]. The "effective" albedo is found to be less than half of the "theoretical" one, which is generally around 0.5-0.6 for tree canopies at L-band. The "effective" albedo differs from the albedo of a single forest canopy element and becomes a global parameter which depends on all the processes taking place within the canopy including multiple-scattering and ground reflection.

In this study, a first-order radiative transfer (RT) model is developed to more accurately account for vegetation canopy scattering by modifying the basic τ-ω model (the zero-order RT solution). In order to optimally utilize microwave radiometric data in soil moisture (SM) retrievals over vegetated landscapes, a quantitative understanding of the relationship between scattering mechanisms within vegetation canopies and the microwave brightness temperature is desirable. The first-order RT model is used to investigate this relationship and to perform a physical analysis of the scattered and emitted radiation from vegetated terrain. This model is based on an iterative solution (successive orders of scattering) of the RT equations up to the first order. This formulation adds a new scattering term to the τ-ω model. The additional term represents emission by particles (vegetation components) in the vegetation layer and emission by the ground that is scattered once by particles in the layer. The model is tested against 1.4-GHz brightness temperature measurements acquired over deciduous trees by a truck-mounted microwave instrument system called ComRAD in 2007. The model predictions are in good agreement with the data, and they give quantitative understanding for the influence of first-order scattering within the canopy on the brightness temperature. The model results show that the scattering term is significant for trees and modifications are necessary to the τ-ω model when applied to dense vegetation. Numerical simulations also indicate that the scattering term has a negligible dependence on SM and is mainly a function of the incidence angle and polarization of the microwave observation.

In order to validation soil moisture products from remote sensing platforms, an accurate and verified ground dataset is necessary. However, the diversity of in situ soil moisture networks makes the aggregation of available data difficult because of the difference in quality and accuracy. A testbed was initiated as part of the Soil Moisture Active Passive satellite mission to intercompare the current and emerging in situ soil moisture technologies. The testbed is located in Marena, Oklahoma, USA, in a large grass pasture. Four base stations were installed in May 2010 with more sensors to be added as they are manufactured. Included in the testbed are the new COSMOS soil moisture instruments and GPS receivers which are emerging technologies measuring soil moisture on a large scale. Regular soil moisture and vegetation sampling will be conducted to check the accuracy and representativeness of the sensors.

This paper discusses the results from a series of field experiments using ground-based L-band microwave active/passive sensors. Three independent approaches are applied to the microwave data to determine vegetation opacity of coniferous trees. First, a zero-order radiative transfer model is fitted to multi-angular microwave emissivity data in a least-square sense to provide “effective” vegetation optical depth. Second, a ratio between radar backscatter measurements with a corner reflector under trees and in an open area is calculated to obtain “measured” tree propagation characteristics. Finally, the “theoretical” propagation constant is determined by forward scattering theorem using detailed measurements of size/angle distributions and dielectric constants of the tree constituents (trunk, branches, and needles). The results indicate that “effective” values underestimate attenuation values compared to both “theoretical” and “measured” values.

A recently developed data assimilation technique offers the potential to greatly expand the geographic domain over which remotely sensed surface soil moisture retrievals can be evaluated by effectively substituting (relatively plentiful) rain-gauge observations for (less commonly available) ground-based soil moisture measurements. The technique is based on calculating the Pearson correlation coefficient (Rvalue) between rainfall errors and Kalman filter analysis increments realized during the assimilation of a remotely sensed soil moisture product into the antecedent precipitation index (API). Here, the existing Rvalueapproach is modified by reformulating it to run on an anomaly basis where long-term seasonal trends are explicitly removed and by calculating API analysis increments using a Rauch-Tung-Striebel smoother instead of a Kalman filter. This reformulated approach is then applied to a number of Advanced Microwave Scanning Radiometer soil moisture products acquired within three heavily instrumented watershed sites in the southern U.S. Rvalue-based evaluations of soil moisture products within these sites are verified based on comparisons with available ground-based soil moisture measurements. Results demonstrate that, without access to ground-based soil moisture measurements, the Rvaluemethodology can accurately mimic anomaly correlation coefficients calculated between remotely sensed soil moisture products and soil moisture observations obtained from dense ground-based networks. Sensitivity results also indicate that the predictive skill of the Rvaluemetric is enhanced by both proposed modifications to its methodology. Finally, Rvaluecalculations are expanded to a quasi-global (50? S-50? N) domain using rainfall measurements derived from the Tropical Rainfall Measurement Mission Precipitation Analysis. Spatial patterns in calculated Rvaluefields are compared to regions of strong land-atmosphere coupling and used to refine expectations concerning the global distribution of land areas in which remotely sensed surface soil moisture retrievals will contribute to atmospheric forecasting applications.

Validation is an important and particularly challenging task for remote sensing of soil moisture. A key issue in the validation of soil moisture products is the disparity in spatial scales between satellite and in situ observations. Conventional measurements of soil moisture are made at a point, whereas satellite sensors provide an integrated area/volume value for a much larger spatial extent. In this paper, four soil moisture networks were developed and used as part of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) validation program. Each network is located in a different climatic region of the U.S., and provides estimates of the average soil moisture over highly instrumented experimental watersheds and surrounding areas that approximate the size of the AMSR-E footprint. Soil moisture measurements have been made at these validation sites on a continuous basis since 2002, which provided a seven-year period of record for this analysis. The National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) standard soil moisture products were compared to the network observations, along with two alternative soil moisture products developed using the single-channel algorithm (SCA) and the land parameter retrieval model (LPRM). The metric used for validation is the root-mean-square error (rmse) of the soil moisture estimate as compared to the in situ data. The mission requirement for accuracy defined by the space agencies is 0.06 m3/m3. The statistical results indicate that each algorithm performs differently at each site. Neither the NASA nor the JAXA standard products provide reliable estimates for all the conditions represented by the four watershed sites. The JAXA algorithm performs better than the NASA algorithm under light-vegetation conditions, but the NASA algorithm is more reliable for moderate vegetation. However, both algorithms have a moderate to large bias in all cases. The SCA had the lowest overall rmse with a small bias. The LPRM had a very large overestimation bias and retrieval errors. When site-specific corrections were applied, all algorithms had approximately the same error level and correlation. These results clearly show that there is much room for improvement in the algorithms currently in use by JAXA and NASA. They also illustrate the potential pitfalls in using the products without a careful evaluation.

During 2007-2009 field experiments have been conducted using the ComRAD microwave truck instrument system with a goal of optimizing microwave soil moisture retrieval algorithms for small to medium deciduous and coniferous trees. A joint effort of NASA / GSFC and George Washington University, ComRAD consists of a quad-polarized 1.25 GHz radar and a dual-polarized 1.4 GHz radiometer sharing the same antenna. In the current study, ComRAD microwave data and ground truth measurements of soil moisture, temperature, soil texture, and vegetation water content and geometry statistics have been used to assess whether the zero-order tau-omega model can be employed successfully to retrieve soil moisture under tree canopies using effective values for tau (the vegetation opacity) and omega (the single scattering albedo). In addition, the tau-omega model has been modified to include a first-order scattering term, which will be discussed in a companion paper.

An important research direction in advancing higher spatial resolution and better accuracy in soil moisture remote sensing is the integration of active and passive microwave observations. In an effort to address this objective, an airborne instrument, the passive/active L-band sensor (PALS), was flown over two watersheds as part of the cloud and land surface interaction campaign (CLASIC) conducted in Oklahoma in 2007. Eleven flights were conducted over each watershed during the field campaign. Extensive ground observations (soil moisture, soil temperature, and vegetation) were made concurrent with the PALS measurements. Extremely wet conditions were encountered. As expected from previous research, the radiometer-based retrievals were better than the radar retrievals. The standard error of estimates (SEEs) of the retrieved soil moisture using only the PALS radiometer data were 0.048 m3/m3for Fort Cobb (FC) and 0.067 m3/m3for the Little Washita (LW) watershed. These errors were higher than typically observed, which is likely the result of the unusually high soil moisture and standing water conditions. The radar-only-based retrieval SEEs were 0.092 m3/m3for FC and 0.079 m3/ m3for LW. Radar retrievals in the FC domain were particularly poor due to the high vegetation water content of the agricultural fields. These results indicate the potential for estimating soil moisture for low-vegetation water content domains from radar observations using a simple vegetation model. Results also showed the compatibility between passive and active microwave observations and the potential for combining the two approaches.

In the radiometric sensing of soil moisture through a forest canopy, knowledge of canopy attenuation is required. Active sensors have the potential of providing this information since the backscatter signals are more sensitive to forest structure. In this paper, a new radar technique is presented for estimating canopy attenuation. The technique employs details found in a transient solution where the canopy (volume-scattering) and the tree-ground (double-interaction) effects appear at different times in the return signal. The influence that these effects have on the expected time-domain response of a forest stand is characterized through numerical simulations. A coherent forest scattering model, based on a Monte Carlo simulation, is developed to calculate the transient response from distributed scatterers over a rough surface. The forest transient-response model for linear copolarized cases is validated with the microwave deciduous tree data acquired by the Combined Radar/Radiometer (ComRAD) system. The attenuation algorithm is applicable when the forest height is sufficient to separate the components of the radar backscatter transient response. The frequency correlation functions of double-interaction and volume-scattering returns are normalized after being separated in the time domain. This ratio simply provides a physically based system of equations with reduced parameterizations for the forest canopy. Finally, the technique is used with ComRAD L-band stepped-frequency data to evaluate its performance under various physical conditions.

An important issue in advancing higher spatial resolution and better accuracy in soil moisture remote sensing is the integration of active and passive observations. In an effort to address these questions an airborne passive/active L-band system (PALS) was flown as part of CLASIC in Oklahoma over the Little Washita (rangeland and winter wheat) and Fort Cobb watersheds (irrigated agriculture and winter wheat). A total of 11 flight days were flown during the field campaign over each watershed. Extensive ground observations (soil moisture, soil temperature, vegetation) were made concurrent with the PALS observations. These flights were complemented by the acquisition of ALOS PALSAR data. Inter-comparison of radar observations indicated comparative calibration and possibly linear scaling. Extremely wet conditions were encountered during the field experiment. Initial results show the potential of combining passive and active PALS observations. Over the sampling sites PALS radiometer estimated soil moisture was in closer agreement over the Fort Cobb (SEE=0.048 m3/m3) than over the Little Washita watershed (SEE=0.067 m3/m3).

Validation is an important and particularly challenging task for passive microwave remote sensing of soil moisture from Earth orbit. As part of the Advance Microwave Scanning Radiometer-E (AMSR-E) validation programs networks of dedicated validation sites were developed. Measurements have been made on a continuous basis since 2002. The NASA and JAXA standard soil moisture products were compared to the network observations, along with an alternative single channel algorithm. The results indicate that each algorithm has different performance statistics that depend upon the site. Results clearly show that there is much room for improvement in the algorithms adopted by JAXA and NASA. They also illustrate the potential pitfalls in using the products without caution.

Truck-based measurements of brightness temperature at L- band over small deciduous stands located in Maryland were made in 2006 and 2007. Ground truth data related to forest stands and the ground were also collected. The deciduous trees were modeled by the Distorted Born Approximation (DBA) in conjunction with Peak's principle. The ground was modeled as a half space with surface roughness. The model has been used to investigate the sensitivity of L-band radiometers to soil moisture under forest stands. It was observed that it is possible to see through the forest to sense the underlying soil moisture and to see the seasonal changes.

During 2007 a field experiment was conducted with a goal of optimizing microwave soil moisture retrieval algorithms for small to medium deciduous trees. After initial field checkout in Fall 2006, the ComRAD microwave truck instrument system was deployed to a test site with several stands of deciduous paulownia trees. A joint effort of NASA/GSFC and George Washington University, ComRAD consists of a quad-polarized 1.25 GHz radar and a dual-polarized 1.4 GHz radiometer sharing the same antenna. ComRAD can function as a ground-based instrument simulator for L band space missions such as SMOS, SMAP, and Aquarius. In the current study, ComRAD acquired data from April to November 2007 to monitor the seasonal difference in microwave response to soil moisture under deciduous trees. To conclude the three-year planned field measurement effort, ComRAD will deploy to a natural coniferous pine tree site in 2008.

Validation of satellite-based soil moisture algorithms and products is particularly challenging due to the disparity of scales of the two observation methods. Validation programs for the Advanced Microwave Scanning Radiometer-E (AMSR-E) instrument on the Aqua satellite is currently ongoing. As part of the AMSR-E validation activities several networks of ground based in-situ soil moisture sensors were established in research watersheds. These networks provide estimates of the average soil moisture over the watersheds and surrounding areas that approximate the size of the AMSR-E passive microwave footprint. Four watersheds in different vegetation/climate regions of the U.S. were selected. All instrumentation was installed prior to the launch of AMSR-E in 2002. There are now over five years of observations available. Quality control of the data has included short term field experiments at some of the watersheds to verify calibration and scaling. The National Aeronautics and Space Administration (NASA) and Japanese Aerospace Exploration Agency (JAXA) soil moisture products were compared to the network observations, along with an alternative algorithm. The results indicate that each algorithm has different performance statistics that depend upon the site. A positive outcome of the analysis is that it appears that the algorithms have the potential to perform within acceptable error bounds. Preliminary results indicate the single channel algorithm performs very well in all four watersheds. These results are not final because the products of both agencies are undergoing revisions. The issues addressed here are common to both current and future satellite missions.

The NASA/GSFC and George Washington University network analyzer-based multifrequency truck- mounted radar system has recently been upgraded with the addition of a dual-polarized 1.4 GHz total power radiometer. The system, now called ComRAD for Combined Radar/Radiometer, can function as a ground-based instrument simulator for L band space missions such as Hydros, Aquarius, and SMOS. In late summer 2006 ComRAD was deployed to the field to begin a series of coordinated active/passive L band measurements over small stands of deciduous and coniferous trees in order to improve our understanding of the microwave properties of trees and their effect on soil moisture retrieval algorithms. This paper describes the preliminary measurements obtained at the start of a three-year planned field measurement effort.

Satellite soil moisture products, such as those derived from the advanced microwave scanning radiometer - EOS (AMSR-E), require calibration and validation in diverse landscape and land cover conditions. Semi-arid regions present a particular challenge because of the high spatial variability and temporal change in surface moisture conditions. This study will address this problem by using data from a soil moisture observing network in semi-arid watershed and temporal stability (persistence) analysis to quantify its' ability to represent the larger (satellite) scale soil moisture average. The watershed utilized, the Walnut Gulch Experimental Watershed (WGEW), has a dense soil moisture sensor network (SMSN) of 19 soil moisture sensors installed at 5 cm depth, distributed over a 150 km2 study region. A study period of 3.5 years was available for this study. In conjunction with this monitoring network, intensive gravimetric soil moisture sampling was conducted as part of the Soil Moisture Experiment in 2004 SMEX04 to the calibrate the network for large-scale. Temporal stability analysis considers how each individual sensor relates to the overall average of the watershed. Stable sensors (and collectively stable networks) are useful for the long-term study of satellite remote sensing products because of their proven reliability and accuracy. The results demonstrate that the WGEW SMSN is an accurate and stable estimator of the watershed average. The root mean square error (RMSE) of the network to the average from the high density SMEX04 sampling is less than 0.01 m3/m3. Future studies should focus specifically on how best to obtain more reliable satellite soil moisture estimates using this soil moisture network.

Validation is a challenging task for passive microwave remote sensing of soil moisture from Earth orbit. The key issue is spatial scale; conventional measurements of soil moisture are made at a point, whereas satellite sensors provide an integrated area/volume value for a much larger spatial extent. A robust validation program should include as many types of comparisons as possible and must attempt to provide actual spatially representative ground based soil moisture. A ground based validation program also requires a wide range of conditions, long temporal coverage and continuous observations. As part of the AMSR-E validation activity an augmented network of dedicated validation sites at actively monitored watersheds has been developed. These provide estimates of the average soil moisture over watersheds and surrounding areas that approximate the size of the AMSR-E footprint. This is done on a continuous basis, partially in real time. A public database of the watershed data for all sites is being developed and made available. To implement this network, additional surface soil moisture and temperature sensors (0-5 cm depth) were installed at and around existing instrument locations in four watersheds located in different climate regions of the U.S. Through short term and extended field campaigns the calibration of these instruments has been established. Methods for scaling from the point measurements to the integrated watershed/footprint average have also been developed as part of the validation effort. These efforts will be of value in alternate algorithm comparisons and will benefit future missions including SMOS.

The Soil Moisture Experiments in 2005 (SMEX05) and Polarimetry Land experiments (POLEX) were conducted jointly by the US Naval Research Laboratory, the USDA ARS Hydrology and Remote Sensing Laboratory and other cooperators in Ames, Iowa between 13 June and 3 July 2005. Leveraging upon proven methodology and existing facilities and experience from the preceding SMEX02 experiment, SMEX05/POLEX was designed to address algorithm development and validation issues related to current and future soil moisture sensor systems, including enhancement of the Aqua AMSR-E and WindSat soil moisture validation. It also encompasses three unique elements in its scientific objectives: (1) Exploration of unique polarimetric information from satellite sensors such as WindSat and CMIS for soil moisture with supporting NRL aircraft instrumentation. (2) Exploration of diurnal effects associated with soil, vegetation and atmosphere at the 6 am/6 pm observing times of WindSat, CMIS, Hydros, and SMOS. (3) Statistics and mitigation of RFI for CMIS risk reduction. In this paper, we present an overview of the experiment and some preliminary data analysis.

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 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 topography and convective precipitation. Other goals included understanding the role of the land surface in the North American Monsoon System. Data were collected during the month of August 2004 at three scales; ground based point measurements, aircraft passive microwave mapping, and satellite observations using AMSR-E and other sensors. 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 (11 dates of coverage over Arizona and 10 over Sonora). General meteorological conditions, the PSR/CX data sets and selected comparisons of this data to AMSR-E are presented.

Laboratory and field methods for determining soil hydraulic properties are time consuming and expensive. An alternative approach is to use pedotransfer functions which predict various soil hydraulic properties based on more readily available physical properties. Pedotransfer functions have been developed that operate with various levels of information. Greater available information yields more reliable estimates of any particular hydraulic property. Because some of the physical properties being used in pedotransfer functions are not available on a regional scale, there is a need to develop pedotransfer functions for use with regional soil databases. A system is described that uses soil texture classes and remotely sensed soil moisture in the dry state to estimate soil hydraulic properties at a 800 m pixel scale.

A mid-infrared Normalized Difference Water Index (NDWI) is proposed for the estimation of vegetation water content (VWC). As part of a large-scale hydrology experiment (SMEX02) an extensive VWC data set was collected for corn and soybeans over a portion of the growth cycle. Landsat Thematic Mapper (TM) data for several dates were used to develop two indices, the well-known NDVI and the NDWI, for each ground sampling site. Since multiple dates and satellites were used, atmospheric correction and cross calibration was performed on each TM image. Two atmospheric correction models, 6S and MODTRAN, and an empirical method, produced comparable results. Comparisons of VWC and the indices showed that the NDVI saturated at a lower VWC than did the NDWI. This indicates that the NDWI is a better index for use in estimating the VWC over a full crop growing cycle. Relationships were developed between the VWC and NDWI for corn and soybeans. The error with this approach was approximately 0.5 kg/m/sup 2/ for corn and 0.2 kg/m/sup 2/ for soybeans.

The electronically scanned thin array radiometer (ESTAR) was utilized for soil moisture mapping during the Southern Great Plains Experiment (SGP99). A retrieval algorithm was applied to obtain soil moisture from passive microwave measurements at 1.4 GHz. The algorithm was verified using ground data collected during SGP99. The results indicate a good correlation between observed and predicted soil moisture values and are consistent with results obtained from the same instrument in previous experiments. The present results demonstrate the validity of the retrieval algorithm for moderately to extremely dry soils. The ESTAR measurements along with ancillary data were used to create soil moisture maps of the entire region.