Understanding the vertical distribution of soil moisture is crucial for making informed decisions in various applications, ranging from precision agriculture to hydrological modeling. Four machine learning algorithms, including random forest, extreme gradient boosting, deep learning, and support vector regression were employed to estimate the soil moisture profile from collected tower-based L-band and P-band brightness temperature observations in Victoria, Australia. The results showed that random forest outperformed the other algorithms, with root mean square error (RMSE) values of 0.03, 0.04, and 0.06 m3/m3for depths of 0-5 cm, 0-30 cm, and 0-60 cm, respectively

As an emerging technique, P-band (0.3-1 GHz) may improve soil moisture remote sensing compared to L-band (1.4 GHz) SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, because of its greater moisture retrieval depth resulting from its longer wavelength. Consequently, a number of tower-based experiments were undertaken in Victoria, Australia, to understand and quantify potential improvements. The study reported here has extended the evaluation of the tau-omega model to a scenario with a dense corn canopy whose vegetation water content reached ~20 kg/m2, and compared the soil moisture retrieval performance at P- and L-band. Based on the locally calibrated parameters, the results from both the SCA (Single Channel Algorithm) and DCA (Dual Channel Algorithm) approaches presented a clear reduction in vegetation impact at P-band compared to L-band. While the root-mean-square error (RMSE) for P-band did not achieve the 0.04-m3/m3target accuracy of SMOS and SMAP, i.e., 0.054 m3/m3for the SCA and 0.074 m3/m3for the DCA, this performance can be regarded as acceptable considering the extremely high vegetation water content. In comparison, the RMSEs at L-band were larger than 0.1 m3/m3for both the SCA and the DCA approaches. Additionally, DCA performed better in correlation coefficient and unbiased RMSE, while SCA performed better in RMSE at P-band due to the larger bias when using DCA. Moreover, the calibrated vegetation parameters at P-band were found to apply to broader conditions than those at L-band, likely due to the reduced vegetation impact.

Currently, near-surface soil moisture at a global scale is being provided using National Aeronautics and Space Administration's (NASA's) Soil Moisture Active Passive (SMAP) and European Space Agency's (ESA's) Soil Moisture and Ocean Salinity (SMOS) satellites, both of which utilize L-band (1.4 GHz; 21 cm wavelength ) passive microwave remote sensing techniques. However, a fundamental limitation of this technology is that the water content can only be measured for approximately the top 5-cm layer of soil moisture, and only over low-to-moderate vegetation covered areas in order to meet the 0.04 m3/m3target accuracy, limiting its applicability. Consequently, a longer wavelength radiometer is being explored as a potential solution for measuring soil moisture in a deeper surface layer of soil and under denser vegetation. It is expected that P-band ( wavelength of 40 cm and frequency of 750 MHz) could potentially provide soil moisture information for the top ~10-cm layer of soil, being one-tenth to one-quarter of the wavelength. In addition, P-band is expected to have higher soil moisture retrieval accuracy due to its reduced sensitivity to vegetation water content and surface roughness. To demonstrate the potential of P-band passive microwave soil moisture remote sensing, a short-term airborne field experiment was conducted over a center pivot irrigated farm at Cressy in Tasmania, Australia, in January 2017. First results showing a comparison of airborne P-band brightness temperature observations against airborne L-band brightness temperature observations and ground soil moisture measurements are presented. The P-band brightness temperature was found to have a similar but stronger response to soil moisture compared to L-band.

The fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4 and -5) were conducted at the beginning of the SMAP operational phase, May and September 2015, to: 1) evaluate the SMAP microwave observations and derived soil moisture (SM) products and 2) intercompare with the Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions over the Murrumbidgee River Catchment in the southeast of Australia. Airborne radar and radiometer observations at the same microwave frequencies as SMAP were collected over SMAP footprints/grids concurrent with its overpass. In addition, intensive ground sampling of SM, vegetation water content, and surface roughness was carried out, primarily for validation of airborne SM retrieval over six ~ 3 km × 3 km focus areas. In this study, the SMAPEx-4 and -5 data sets were used as independent reference for extensively evaluating the brightness temperature and SM products of SMAP, and intercompared with SMOS and Aquarius under a wide range of SM and vegetation conditions. Importantly, this is the only extensive airborne field campaign that collected data while the SMAP radar was still operational. The SMAP radar, radiometer, and derived SM showed a high agreement with the SMAPEx-4 and -5 data set, with a root-mean-squared error (RMSE) of ~3 K for radiometer brightness temperature, and an RMSE of ~ 0.05 m3 for the radiometer-only SM product. The SMAP radar backscatter had an RMSE of 3.4 dB, while the retrieved SM had an RMSE of 0.11 m3/m3 when compared with the SMAPEx-4 data set.

Soil Moisture is an important geophysical variable that needs reliable quantification for applications in hydrology, meteorology and agriculture. L-band radiometry has proved to be one of the best methods in soil moisture estimation using microwave signals. However, they provide measurements that correspond to a shallow depth of 5 cm and are also affected by the presence of overlaying vegetation and roughness. In contrast, P-band radiometry is expected to provide moisture information on a deeper layer of soil. Moreover, these lower frequency measurements are expected to be less affected by soil roughness and vegetation contributions. Consequently, this pilot study uses the Polarimetric P-band Multibeam Radiometer (PPMR) at 740 MHz to evaluate the response of the P-band radiometer over a realistic range of surface conditions at the field scale. A preliminary framework of P-band Microwave Emission of the Biosphere (P-MEB) has been developed as a forward model that simulates brightness temperature from soil moisture and other ancillary data collected from the field. This paper presents the model for the bare soil condition observed during June 2018 to August 2018. The results show that H-polarised PPMR data has better correlation to the soil moisture over a depth of 10 cm than the V-polarized PPMR data. A model is under improvement by incorporating a more suitable effective temperature formulation.

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.

Soil Moisture Active Passive (SMAP) mission and the Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2-3 days. SMAP brightness temperature observations were compared with SMOS observations at 40° incidence angle. The brightness temperatures from the two missions are close to each other but SMAP observations show a warmer TB bias (about 0.64 K: V pol and 1.14 K: H pol) as compared to SMOS. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between their soil moisture products. The reprocessed constant-angle SMOS brightness temperatures (SMOS-SMAP) were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product has an increased global revisit frequency (1 day) and period of record that is unattainable by either one of the satellites alone. The SMOS-SMAP soil moisture retrievals compared with in situ observations show a retrieval accuracy of less than 0.04 m3/m3. Results from the development and validation of the integrated soil moisture product will be presented.

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.

The SMAP (Soil Moisture Active/Passive) satellite provides global soil moisture (SM) estimates that can be used for scientific research and applications (such as the hydrological cycle, agriculture, ecology, and land atmosphere interactions). Currently, SMAP provides the enhanced radiometer-only SM product (L2SMP) at 9 km grid resolution. However, this spatial resolution is still not enough to satisfy the needs of some studies that require a finer spatial resolution SM product, particularly in agricultural and watershed applications. This study applied a downscaling algorithm to the SMAP 9 km SM product to produce a 1 km resolution over the CONUS (Contiguous United States). The downscaling algorithm is based on the relationship between temperature change and SM modulated by Normalized Difference Vegetation Index (NDVI) of a given time period. This relationship was modeled using variables derived from NLDAS (North America Land Data Assimilation System) and NASA's LTDR (Land Long Term Data Record) between 1981 - 2018. The algorithm was implemented uses the 1 km MODIS Aqua LST (Land Surface Temperature) product. The downscaled SMAP 1 km SM was validated using in situ SM measurements from the ISMN (International Soil Moisture Network). The validation metrics show an improved overall accuracy of the downscaled SM.

The Soil Moisture Active Passive (SMAP) mission currently provides three surface soil moisture products based solely on instrument measurements. The three soil moisture products are: 1) the radiometer-only 36 km gridded; 2) a radiometer-only enhanced product gridded at 9 km; and 3) a high-resolution (3 km) SMAP-Sentinel active–passive product. It is important to validate these released SMAP soil moisture products over various land covers and hydroclimatic domains before they are routinely used in scientific research and applications. This paper evaluates SMAP-based soil moisture products for typical Indian conditions of extreme seasonal variability that leads to changes from very wet to dry soil, especially for the paddy dominated region. The assessment metrics indicate that the enhanced passive-only soil moisture product meets the SMAP accuracy requirement of 0.04 m3/m3during the nongrowing season (NGS) with unbiased root-mean-square error (ubRMSE) values ranging between 0.025 and 0.036 m3/m3. However, this product underperformed during the paddy growing season (GS) with ubRMSE values ranging between 0.063 and 0.097 m3/m3. In addition, the SMAP-Sentinel active–passive soil moisture product shows satisfactory performance during the NGS (ubRMSE, 0.017–0.051 m3/m3), but during the GS, ubRMSE ranged between 0.089 and 0.104 m3/m3. Use of the vegetation water content climatology and low clay fraction in SMAP baseline algorithm (auxiliary database) that mismatched with the actual values may be the possible source of errors and biases in the SMAP soil moisture products. The reported study provides guidelines for the application of enhanced SMAP soil moisture products in India, especially for the tropical region, and provides information that can be used to improve the retrieval algorithm.

Soil Moisture Active Passive (SMAP) mission and the Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2-3 days. SMAP brightness temperature observations were compared with SMOS observations at 40° incidence angle. The brightness temperatures from the two missions are not consistent. SMAP observations show a warmer TB bias (about 1.27 K: V pol and 0.62 K: H pol) as compared to SMOS. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between their soil moisture products. The reprocessed constant-angle SMOS brightness temperatures were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product has an increased global revisit frequency (1 day) and period of record that is unattainable by either one of the satellites alone. Results from the development and validation of the integrated soil moisture product will be presented.

Soil moisture measurement using L-band radiometry is now widely accepted as the state-of-art remote sensing approach, and has been adopted by both the SMOS and SMAP soil moisture dedicated satellite missions. However, it suffers from the shallow depth of its soil moisture measurement, and the confounding effects of vegetation and soil roughness on soil moisture retrieval. P-band, which is a longer wavelength measurement, provides the potential to retrieve deeper soil moisture information, and to do so more accurately due to reduced soil roughness and vegetation effects. This paper presents some pioneering work on the use of P-band for soil moisture retrieval. The Polarimetric P-band Multibeam Radiometer (PPMR) used in this research operates at 740 MHz / wavelength of 40 cm. It is used together with the Polarimetric L-band Multibeam Radiometer (PLMR) which operates at 1.4 GHz / wavelength of 21 cm. The PPMR and PLMR are mounted onto a 10m high tower in an agricultural farm located at Cora Lynn, Victoria. This paper outlines the initial set up for the study and the experimental plan for understanding PPMR's performance, along with some initial data.

In passive microwave remote sensing of soil moisture, the tau-omega (τ-ω) model has often been used to provide soil moisture estimates at a spatial scale representative of the satellite footprint dimensions. For modeling simplicity, model parameters such as the single scattering albedo (ω) and vegetation opacity (τ) that go into the geophysical inversion process are often assumed to be independent of polarizations. Although this absence of polarization dependence can often be justified in special cases as in low-frequency remote sensing or under dense vegetation conditions, it is not a robust assumption in general. Additional model parameterization errors arising from this assumption are possible, leading to degradation in soil moisture estimation accuracy. In this paper, we propose a time series approach to try to resolve the polarization dependence of several τ-ω model parameters as well as the temperature bias arising from the ancillary temperature data. The Version 4 of the Soil Moisture Active Passive (SMAP) Level 1B brightness temperature time series observations were used to illustrate the mechanics of this approach, with an emphasis on the comparison between resulting satellite retrieval and in situ data collected at several core validation sites. It was found that this time series approach resulted in significant reduction of dry bias exhibited in the current SMAP passive soil moisture data products, while retaining the same performance in other metrics of the current baseline passive soil moisture retrieval algorithm.

SMAP project released a new enhanced high-resolution (3km) soil moisture active-passive product. This product is obtained by combining the SMAP radiometer data and the Sentinel-IA and -IB Synthetic Aperture Radar (SAR) data. The approach used for this product draws heavily from the heritage SMAP active-passive algorithm. Modifications in the SMAP active-passive algorithm are done to accommodate the Copernicus Program's Sentinel-IA and -IB multi-angular C-band SAR data. Assessment of the SMAP and Sentinel active-passive algorithm has been conducted and results show feasibility of estimating surface soil moisture at high-resolution in regions with low vegetation density . The beta version of this product is released to public on Nov 1st, 2017. This high resolution (3 km) soil moisture product is useful for agriculture, flood mapping, watershed/rangeland management, and ecological/hydrological applications.

SMAP (Soil Moisture Active/Passive) and SMOS (Soil Moisture Ocean Salinity) provide soil moisture observations that can be used for studying the global hydrological cycle, agriculture, ecology, and land atmosphere interactions. SMAP provides soil moisture at two grid scales; 36 km (which is close to its native radiometer spatial resolution) and an enhanced grid resolution of 9 km. However, these scales are not compatible with some agricultural and watershed applications that require a higher spatial resolution. This study applied a downscaling algorithm to the SMAP Level-2 radiometer 36 km product and improved the grid resolution to 1 km over the CONUS (Contiguous United States). The downscaling algorithm is built on the thermal inertial relationship between daily temperature change and averaged soil moisture modulated by Normalized Difference Vegetation Index (NDVI). The average soil moisture and thermal inertia model functions were developed by using data from NLDAS (North America Land Data Assimilation System) and LTDR (Land Long Term Data Record) for 1981 - 2016. The algorithm is applied with the 1 km MODIS Aqua LST product and the downscaled SMAP 1 km soil moisture was validated by in situ soil moisture measurements from the ISMN (International Soil Moisture Network). The validation variables show improved accuracy of the downscaled soil moisture.

L-band airborne synthetic aperture radar observations were made over California shrublands to better understand the effects of soil and vegetation parameters on backscattering coefficient (σ0) for the period of 2011 to 2015. HH was always greater than VV, suggesting the importance of double-bounce scattering by the woody parts. However, the geometric and dielectric properties of the woody parts did not vary significantly over time. Instead the changes in vegetation water content (VWC) were observed to occur primarily in thin leaves that may not meaningfully influence absorption and scattering. Accordingly, unlike in the past literature, the VWC input of the plant to the model was formulated as a function of plant's dielectric property (water fraction) while the plant geometry remains static in time. A physically-based model for single scattering by discrete elements of plants successfully simulated the magnitude of the temporal variations in HH, VV, and HH/VV with a difference of less than 0.9 dB. The modeling results offer an explanation of why soil moisture correlated highly with σ0, which is that the dominant mechanisms for HH and VV are double-bounce scattering by trunk, and soil surface scattering, respectively.

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.

A fundamental limitation of current soil moisture remote sensing technology is that can only provide moisture information on the top 5 cm layer of soil at most, being one-tenth to one-quarter of the wavelength (21 cm at L-band; 1.4 GHz) using the current SMAP and SMOS soil moisture dedicated missions of NASA and ESA. Consequently, we have developed an airborne passive microwave sensing capability at P-band to develop a new state-of-the-art satellite concept that will provide soil moisture data for the top 10 cm layer of soil using radiometer observations at P-band (40 cm; 750 MHz). Not only would P-band provide soil moisture information on a soil layer thickness that more closely relates to that affecting crop and pasture growth, but it is expected to produce greater spatial coverage with improved accuracy to that from L-band. This is because P-band should be less affected by surface roughness conditions and have a reduced attenuation by the overlaying vegetation. This paper describes a series of small airborne field experiments at P-band, and presents some early results of P-band passive microwave observations in comparison with L-band and K-band passive microwave from initial trial flights.

Soil Moisture Active Passive (SMAP) mission and the ESA Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2-3 days. SMAP brightness temperature observations were compared with SMOS observations at 40° incidence angle. The brightness temperatures from the two missions are not consistent and have a bias of about 2.7K over land with respect to each other. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between the soil moisture products. The reprocessed constant-angle SMOS brightness temperatures were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product will have an increased global revisit frequency (1 day) and period of record that would be unattainable by either one of the satellites alone. Results from the development and validation of the integrated product will be presented.

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.

In this study, a data-fusion algorithm is developed for estimation of high-resolution brightness temperatures (TB) at 1km from Soil Moisture Active Passive (SMAP) fine-grid TBproduct at 9km. It uses image segmentation to spatio-temporally cluster the study region based on meteorological and land cover similarity, followed by a support vector machine based regression that computes the value of the high-resolution TBat all pixels. High resolution remote sensing products such as land surface temperature, normalized difference vegetation index, enhanced vegetation index, precipitation, soil texture, and land-cover were used for disaggregation. The algorithm was implemented in Iowa, United States, from May to September 2016, and compared with the field observations of TBfrom Microwave Water and Energy Balance Experiment conducted as a part of the Soil Moisture Active Passive Validation Experiment (SMAPVEX16-MicroWEX). Additionally, they were also compared with the Sentinel downscaled SMAP TBat 1km. High resolution soil moisture is subsequently derived from high resolution TBusing inverse models.

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.

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.

Soil moisture impacts exchanges of water, energy and carbon fluxes between the land surface and the atmosphere. Passive microwave remote sensing at L-band can capture spatial and temporal patterns of soil moisture in the landscape. Both ESA and NASA have launched L-band radiometers, in the form of the SMOS and SMAP satellites respectively, to monitor soil moisture globally, every 3-day at about 40 km resolution. However, their coarse scale restricts the range of applications. While SMAP included an L-band radar to downscale the radiometer soil moisture to 9 km, the radar failed after 3 months and this initial approach is not applicable to developing a consistent long term soil moisture product across the two missions anymore. Existing optical-, radiometer-, and oversampling-based downscaling methods could be an alternative to the radar-based approach for delivering such data. Nevertheless, retrieval of a consistent high resolution soil moisture product remains a challenge, and there has been no comprehensive intercomparison of the alternate approaches. This research undertakes an assessment of the different downscaling approaches using the SMAPEx-4 field campaign data.

The Soil Moisture Active Passive (SMAP) mission provides a global surface soil moisture (SM) product at 36-km resolution from its L-band radiometer. While the coarse resolution is satisfactory to many applications, there are also a lot of applications which would benefit from a higher resolution SM product. The SMAP radiometer-based SM product was downscaled to 1 km using Moderate Resolution Imaging Spectroradiometer (MODIS) data and validated against airborne data from the Passive Active L-band System instrument. The downscaling approach uses MODIS land surface temperature and normalized difference vegetation index to construct soil evaporative efficiency, which is used to downscale the SMAP SM. The algorithm was applied to one SMAP pixel during the SMAP Validation Experiment 2015 (SMAPVEX15) in a semiarid study area for validation of the approach. SMAPVEX15 offers a unique data set for testing SM downscaling algorithms. The results indicated reasonable skill (root-mean-square difference of 0.053 m3/m3for 1-km resolution and 0.037 m3/m3for 3-km resolution) in resolving high-resolution SM features within the coarse-scale pixel. The success benefits from the fact that the surface temperature in this region is controlled by soil evaporation, the topographical variation within the chosen pixel area is relatively moderate, and the vegetation density is relatively low over most parts of the pixel. The analysis showed that the combination of the SMAP and MODIS data under these conditions can result in a high-resolution SM product with an accuracy suitable for many applications.

The NASA Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015 to provide 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 Level 2 radiometer-only soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36-km Earth-fixed grid using brightness temperature observations from descending passes. This paper provides the first comparison of the validated-release L2_SM_P product with soil moisture products provided by the Soil Moisture and Ocean Salinity (SMOS), Aquarius, Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) missions. This comparison was conducted as part of the SMAP calibration and validation efforts. SMAP and SMOS appear most similar among the five soil moisture products considered in this paper, overall exhibiting the smallest unbiased root-mean-square difference and highest correlation. Overall, SMOS tends to be slightly wetter than SMAP, excluding forests where some differences are observed. SMAP and Aquarius can only be compared for a little more than two months; they compare well, especially over low to moderately vegetated areas. SMAP and ASCAT show similar overall trends and spatial patterns with ASCAT providing wetter soil moistures than SMAP over moderate to dense vegetation. SMAP and AMSR2 largely disagree in their soil moisture trends and spatial patterns; AMSR2 exhibits an overall dry bias, while desert areas are observed to be wetter than SMAP.

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.

The Soil Moisture Active/Passive (SMAP) microwave radiometer is a fully polarimetric L-band radiometer flown on the SMAP satellite in a 6 a.m./6 p.m. sun-synchronous orbit at 685-km altitude. Since April 2015, the radiometer has been under calibration and validation to assess the quality of the radiometer L1B data product. Calibration methods, including the SMAP L1B TA2TB [from antenna temperature (TA) to the Earth's surface brightness temperature (TB)] algorithm and TA forward models, are outlined, and validation approaches for calibration stability/quality are described in this paper, including future work. Results show that the current radiometer L1B data product (version 3) satisfies its requirements (uncertainty <;1.3 K and calibration drift <;0.4 K/months, and geolocation uncertainty <;4 km) although there are biases in TA over cold sky and in TB comparing with the Soil Moisture and Ocean Salinity TB v620 data products.

In this study, we used a multilinear regression approach to retrieve surface soil moisture from NASA's Soil Moisture Active Passive (SMAP) satellite data to create a global dataset of surface soil moisture which is consistent with ESA's Soil Moisture and Ocean Salinity (SMOS) satellite retrieved surface soil moisture. This was achieved by calibrating coefficients of the regression model using SMOS soil moisture and horizontal and vertical brightness temperatures (TB), over the 2013 — 2014 period. Next, this model was applied to recent SMAP TB data from 31/03/2015–08/09/2015. The retrieved surface soil moisture from SMAP (referred here to as SMAP-reg) was compared to the operational SMAP L3 surface soil moisture retrieved using the single channel algorithm. Both exhibit comparable temporal dynamics with a good agreement of correlation (correlation coefficient R mostly > 0.8) between the SMAP-reg and the operational SMAP L3 surface soil moisture products.

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.

Verifying the calibration of the SMAP radiometer over land observations is an important mission requirement. Inter-comparison of L-band brightness temperature observations from different satellites (SMAP, SMOS and Aquarius) is a useful tool for radiometer calibration. Brightness temperatures observations made at the same frequency, polarization, incidence angle and coincident in time and location should be consistent with each other. SMAP brightness temperature observations were compared with SMOS observations at 40o incidence angle. The observations from the two satellites were found to be consistent with each other over the entire dynamic range (both ocean and land). The RMSD between the two missions was less than 3 K. The two observations exhibit a strong linear relationship and the observed bias was less than 0.5 K for both polarizations. This bias is within the required target accuracy requirement of the SMAP radiometer (requirement of 1.3 K).

The distorted Born approximation (DBA) combined with the numerical solutions of Maxwell equations (NMM3D) has been used for the radar backscattering model for NASA's Soil Moisture Active Passive (SMAP) mission. The models for vegetated surfaces such as wheat, grass, soybean and corn have been validated with the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) data. In this paper we report progress on development of a consistent model for combined active and passive microwave remote sensing of vegetated surfaces by using the same approach to obtain backscatter and emissivity. The active model DBA/NMM3D is extended to calculate bistatic scattering for each of the three scattering mechanisms: volume, double bounce and surface scattering. Then emissivity is obtained by integration of the bistatic scattering. An advantage of this combined active and passive model is that the same physical parameters of vegetation and soil surfaces are used in both the active model and the passive model. The β parameter that relates backscattering to emissivity is also derived for various vegetated surfaces.

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.

In this paper we discuss the steps taken for the calibration and validation of the Soil Moisture Active Passive (SMAP) L-band radiometer. We discuss the use of multiple vicarious sources such as the global ocean mean and celestial cold-sky emissions along with various spacecraft maneuvers to calibrate out gain, offset, antenna pattern of the radiometer. We present initial validation comparison of SMAP brightness temperatures with other L-band missions.

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 L-band (1 - 2 GHz) microwave remote sensing has been widely acknowledged as the most promising method to monitor regional to global soil moisture. Consequently, the Soil Moisture Active Passive (SMAP) satellite applied this technique to provide global soil moisture every 2 to 3 days. To verify the performance of SMAP, the fourth and fifth campaign of SMAP Experiments (SMAPEx-4 & -5) were carried out at the beginning of the SMAP operational phase in the Murrumbidgee River catchment, southeast Australia. The airborne radar and radiometer observations together with ground sampling on soil moisture, vegetation water content, and surface roughness were collected in coincidence with SMAP overpasses. The SMAPEx-4 & -5 data sets will benefit to SMAP post-launch calibration and validation under Australian land surface conditions.

The goal of this study is to assess the performance of the active-passive algorithm for the NASA Soil Moisture Active Passive mission (SMAP) using airborne and ground observations from a field campaign. The SMAP active-passive algorithm disaggregates the coarse-resolution radiometer brightness temperature (TB) using high-resolution radar backscatter (σo) observations. The colocated TB and σoacquired by the aircraft-based Passive Active Land S-band sensor during the SMAP Validation Experiment 2012 (SMAPVEX12) are used to evaluate this algorithm. The estimation of its parameters is affected by changes in vegetation during the campaign. Key features of the campaign were the wide range of vegetation growth and soil moisture conditions during the experiment period. The algorithm performance is evaluated by comparing retrieved soil moisture from the disaggregated brightness temperatures to in situ soil moisture measurements. A minimum performance algorithm is also applied, where the radar data are withheld. The minimum performance algorithm serves as a benchmark to asses the value of the radar to the SMAP active-passive algorithm. The temporal correlation between ground samples and the SMAP active-passive algorithm is improved by 21% relative to minimum performance. The unbiased root-mean-square error is decreased by 15% overall.

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.

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.

In this letter, it is shown that spaceborne observations made by the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite agreed closely with the Passive Active L-band System (PALS) brightness temperature acquisitions during the Soil Moisture Active Passive (SMAP) Validation Experiment 2012. The difference between the SMOS and PALS measurements was less than 5 K and 6 K for vertical and horizontal polarizations, respectively, over the relatively homogeneous agricultural areas. These values are less than the SMOS subpixel variability determined from the PALS measurement. This result demonstrated that the measurements obtained in the experiment are scalable to spaceborne brightness temperature observations, are representative of the expected SMAP observations, and will be of value in the development of soil moisture algorithms for spaceborne missions.

Despite the continuing efforts to improve existing soil moisture retrieval algorithms, the ability to estimate soil moisture from passive microwave observations is still hampered by problems in accurately modeling the observed microwave signal. This paper focuses on the estimation of effective surface roughness parameters of the L-band Microwave Emission from the Biosphere (L-MEB) model in order to improve soil moisture retrievals from passive microwave observations. Data from the SMAP Validation Experiment 2012 conducted in Canada are used to develop and validate a simple model for the estimation of effective roughness parameters. Results show that the L-MEB roughness parameters can be empirically related to the observed brightness temperatures and the leaf area index of the vegetation. These results indicate that the roughness parameters are compensating for both roughness and vegetation effects. It is also shown, using a leave-one-out cross validation, that the model is able to accurately estimate the roughness parameters necessary for the inversion of the L-MEB model. In order to demonstrate the usefulness of the roughness parameterization, the performance of the model is compared to more traditional roughness formulations. Results indicate that the soil moisture retrieval error can be reduced to 0.054 m3/m3if the roughness formulation proposed in this study is implemented in the soil moisture retrieval algorithm.

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.

Validation is important for any satellite-based remote sensing products. In this paper, in situ soil moisture observations from 38 stations over the 1 °×1 ° domain from the central Tibetan Plateau Soil Moisture/Temperature Monitoring Network (CTP-SMTMN) and 152 stations from the Soil Climate Analysis Network (SCAN) over continental U.S. are used to determine the reliability of Aquarius level-2 soil moisture products. Evaluation of the time series in CTP-SMTMN shows good performances of the products to capture surface soil moisture annual cycle with the correlation coefficient of 0.767 and RMSD of 0.078m3m-3. The evaluation results in SCAN suggest that the average correlation is 0.58 and 71.83% sites have correlation larger than 0.5 but differences are observed over many other sites and need to be addressed. The evaluation results also show that the retrieval results performed better for descending orbits (6 AM overpass time).

The radar vegetation index (RVI) has low sensitivity to changes in environmental conditions and has the potential as a tool to monitor vegetation growth. In this letter, we expand on previous research by investigating the radar response over a wheat canopy. RVI was computed using observations made with a ground-based multifrequency polarimetric scatterometer system over an entire wheat growth cycle. We analyzed the temporal variations of backscattering coefficients for L-, C-, and X-bands; RVI; vegetation water content (VWC); and fresh weight. We found that the L-band RVI was highly correlated with both VWC (r = 0.98) and fresh weight (r = 0.98). Based upon these analyses, linear equations were developed for estimation of VWC (root-mean-square error (RMSE = 0.126 kg m-2)) and fresh weight (RMSE = 0.12 kg m-2). In addition, the results of the wheat study were combined with previous investigations with other crops (e.g., rice and soybean). We found that a single linear relationship between L-band RVI and VWC can be used for all crop types (RMSE = 0.47 kg m-2). These results clearly demonstrate the potential of RVI as a robust method for characterizing vegetation canopies. VWC is a key input requirement for retrieving soil moisture from microwave remote sensing observations. The results of this investigation will be useful for the Soil Moisture Active and Passive mission (2014), which is designed to measure global soil moisture.

As part of the Soil Moisture and Ocean Salinity (SMOS) validation process, a comparison of the skills of three satellites [SMOS, Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) or Advanced Microwave Scanning Radiometer, and Advanced Scatterometer (ASCAT)], and one-model European Centre for Medium Range Weather Forecasting (ECMWF) soil moisture products is conducted over four watersheds located in the U.S. The four products compared in for 2010 over four soil moisture networks were used for the calibration of AMSR-E. The results indicate that SMOS retrievals are closest to the ground measurements with a low average root mean square error of 0.061 m3·m-3for the morning overpass and 0.067 m3·m-3for the afternoon overpass, which represents an improvement by a factor of 2-3 compared with the other products. The ECMWF product has good correlation coefficients (around 0.78) but has a constant bias of 0.1-0.2 m3·m-3over the four networks. The land parameter retrieval model AMSR-E product gives reasonable results in terms of correlation (around 0.73) but has a variable seasonal bias over the year. The ASCAT soil moisture index is found to be very noisy and unstable.

Overlapping soil moisture time series derived from two satellite microwave radiometers (the Soil Moisture and Ocean Salinity (SMOS) and the Advanced Microwave Scanning Radiometer-Earth Observing System) are used to generate a soil moisture time series from 2003 to 2010. Two statistical methodologies for generating long homogeneous time series of soil moisture are considered. Generated soil moisture time series using only morning satellite overpasses are compared to ground measurements from four watersheds in the U.S. with different climatologies. The two methods, cumulative density function (CDF) matching and copulas, are based on the same statistical theory, but the first makes the assumption that the two data sets are ordered the same way, which is not needed by the second. Both methods are calibrated in 2010, and the calibrated parameters are applied to the soil moisture data from 2003 to 2009. Results from these two methods compare well with ground measurements. However, CDF matching improves the correlation, whereas copulas improve the root-mean-square error.

NASA's Soil Moisture Active Passive (SMAP) mission will carry the first combined spaceborne L-band radiometer and Synthetic Aperture Radar (SAR) system with the objective of mapping near-surface soil moisture and freeze/thaw state globally every 2-3 days. SMAP will provide three soil moisture products: i) high-resolution from radar (~3 km), ii) low-resolution from radiometer (~36 km), and iii) intermediate-resolution from the fusion of radar and radiometer (~9 km). The Soil Moisture Active Passive Experiments (SMAPEx) are a series of three airborne field experiments designed to provide prototype SMAP data for the development and validation of soil moisture retrieval algorithms applicable to the SMAP mission. This paper describes the SMAPEx sampling strategy and presents an overview of the data collected during the three experiments: SMAPEx-1 (July 5-10, 2010), SMAPEx-2 (December 4-8, 2010) and SMAPEx-3 (September 5-23, 2011). The SMAPEx experiments were conducted in a semi-arid agricultural and grazing area located in southeastern Australia, timed so as to acquire data over a seasonal cycle at various stages of the crop growth. Airborne L-band brightness temperature (~1 km) and radar backscatter (~10 m) observations were collected over an area the size of a single SMAP footprint (38 km × 36 km at 35° latitude) with a 2-3 days revisit time, providing SMAP-like data for testing of radiometer-only, radar-only and combined radiometer-radar soil moisture retrieval and downscaling algorithms. Airborne observations were supported by continuous monitoring of near-surface (0-5 cm) soil moisture along with intensive ground monitoring of soil moisture, soil temperature, vegetation biomass and structure, and surface roughness.

In a recent paper, Leroux compared three satellite soil moisture data sets (SMOS, AMSR-E, and ASCAT) and ECMWF forecast soil moisture data to in situ measurements over four watersheds located in the United States. Their conclusions stated that SMOS soil moisture retrievals represent “an improvement [in RMSE] by a factor of 2-3 compared with the other products” and that the ASCAT soil moisture data are “very noisy and unstable.” In this clarification, the analysis of Leroux is repeated using a newer version of the ASCAT data and additional metrics are provided. It is shown that the ASCAT retrievals are skillful, although they show some unexpected behavior during summer for two of the watersheds. It is also noted that the improvement of SMOS by a factor of 2-3 mentioned by Leroux is driven by differences in bias and only applies relative to AMSR-E and the ECWMF data in the now obsolete version investigated by Leroux et al.

In this study, we computed the Radar Vegetation Index (RVI) using observations made with a ground based multi-frequency polarimetric scatterometer system over an entire wheat growth period. The temporal variations of the backscattering coefficients for L-, C-, and X-band, RVI, Vegetation water content (VWC), and fresh weight were analyzed. We found that the L-band RVI was very sensitive to VWC and fresh weight. Based on the correlation analysis between RVI and these growth parameters, we developed equations for estimation VWC and fresh weight.

Aquarius is a combined passive/active L-band microwave instrument developed to map the ocean surface salinity field from space [1]. The primary science objective of this mission is to monitor the seasonal and interannual variation of the large scale features of the surface salinity field in the open ocean with a spatial resolution of 150 km and a retrieval accuracy of 0.2 psu globally on a monthly basis. The measurement principle is based on the response of the L-band (1.413 GHz) sea surface brightness temperatures to sea surface salinity.

The Soil Moisture Ocean Salinity (SMOS) mission has been providing L-band multi-angular brightness temperature observations at a global scale since its launch in November 2009 and has performed well in the retrieval of soil moisture. The multiple incidence angle observations are not obtained at fixed values and the resolution and accuracy change with the grid locations over SMOS snapshot images. Radio frequency interference issues and aliasing at lower look angles increases the uncertainty of observations and thereby affects the soil moisture retrieval that utilizes observations at specific angles. In this study, we propose a processing chain that uses a mixed objective function based on SMOS L1c data products to refine the characteristics of multi-angular observations. The approach was validated using simulations from a radiative transfer model and analyzed over three external targets: Amazon rainforest, Sahara desert, and Antarctic ice. These results could provide insights for selecting and utilizing external targets as part of the upcoming Soil Moisture Active Passive (SMAP) mission.

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.

The National Aeronautics and Space Administration's (NASA) proposed Soil Moisture Active Passive (SMAP) satellite mission ( ~ 2014) will include a radar system that will provide L-band multi-polarization backscatter at a constant incidence angle of 40°. During the pre-launch phase of the project, there is a need for observations that will support the radar-based soil moisture algorithm development and validation. A valuable resource for providing these observations is the NASA Jet Propulsion Laboratory Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). However, SMAP will observe at a constant incidence angle of 40°, and UAVSAR collects data over a wide range of incidence angles (25°-60°). In this investigation, a technique was developed and tested for normalizing UAVSAR data to a constant incidence angle. The approach is based on a histogram matching procedure. The data used to develop and demonstrate this approach were collected as part of the Canadian Soil Moisture Experiment 2010 (CanEx-SM10). Land cover in the region included agriculture and forest. Evaluation was made possible by the acquisition of numerous overlapping UAVSAR flight lines that provided multiple incidence angle observations of the same locations. Actual observations at a 40°incidence angle were compared to the normalized data to assess performance of the normalization technique. An optimum technique should be able to reduce the systematic error (Bias) to 0 dB and to lower the total root mean square error (RMSE) computed after correction to the level of the initial residual error (RMSEres) present in the data set. The normalization approach developed here achieved both of these. Bias caused by the incidence angle variability was minimized to ~ 0 dB, whereas the residual error caused by instrument related random errors and amplitude fluctuations due to ground variability was reduced to approximately 3 dB for agricultural areas and 2.6 dB for forests; these values were consistent with the initial RMSEresestimated using the un-corrected data. The residual error can be reduced further by aggregating the radar observations to a coarser grid spacing. The technique adequately adjusted the backscatter over the full swath width irrespective of the original incidence angle, polarization, and ground conditions (vegetation cover and soil moisture). In addition to providing a basis for fully exploiting UAVSAR (or similar aircraft systems) for SMAP algorithm development and validation, the technique could also be adapted to satellite radar systems. This normalization approach will also be beneficial in terms of reducing the number of flight lines required to cover a study area, which would eventually result in more cost-effective soil moisture field campaigns.

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 and freeze/thaw 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 using data from simulations, from existing satellite missions such as SMOS, and from field campaign data, such as the SMAPEx airborne study in Australia discussed in this paper.

Vegetation water content (VWC) is an important biophysical parameter and has a significant role in the retrieval of soil moisture using microwave remote sensing. Here, the radar vegetation index (RVI) was evaluated for estimating VWC. Analysis utilized a data set obtained by a ground-based multifrequency polarimetric scatterometer system, with a single incidence angle of 40°, during an entire growth period of rice and soybean. Temporal variations of the backscattering coefficients for the L-, C-, and X-bands, RVI, VWC, leaf area index, and normalized difference vegetation index were analyzed. The L-band RVI was found to be correlated to the different vegetation indices. Prediction equations for the estimation of VWC from the RVI were developed. The results indicated that it was possible to estimate VWC with an accuracy of 0.21 kg·m-2using L-band RVI observations. These results demonstrate that valuable new information can be extracted from current and future radar satellite systems on the vegetation condition of two globally important crop types. The results are directly applicable to systems such as the proposed NASA Soil Moisture Active Passive satellite.

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 Active Passive (SMAP), a proposed mission in support of the Earth Science Decadal Survey, conducted afield campaign in June 2010 to support algorithm development. As part of the experiment in situ soil moisture measurements were made over a two week period in which multiple UAVSAR flights were conducted. Repeat-pass polarimetric-interferometric data generated from these flights were analyzed to see if phase changes could be correlated with soil moisture changes. Also, we compared the data to that predicted by simple surface scattering models and showed moderate agreement with the Oh model [4].

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.

WindSat has provided an opportunity to investigate the first spaceborne passive fully polarimetric observations of the Earth's surface. In this paper, Arctic sea ice was investigated. The passive polarimetric data are provided in the form of the modified Stokes vector consisting of four parameters. The first two components of the modified Stokes vector are the vertically and horizontally polarized brightness temperatures, which have been continuously measured by various radiometers over the last three decades. The third and fourth Stokes components provide in formation on the degree of polarization of the emission. In this paper, three types of analysis are carried out: spatial (maps considering different azimuth angle intervals), temporal (time series of daily averaged Stokes components over a small selected azimuth angle range), and azimuthal (variations w.r.t. the azimuth angle over selected study areas). Analysis has shown the highest brightness temperature variations for the 37-GHz third Stokes component (>; 2 K) during summer. The next highest signals were observed for the 10.7-GHz third and fourth Stokes components (>; 1 K) during summer as well. The 37-GHz fourth Stokes component exhibited the least variability (>; 1 K). Spikes of up to 2 K were identified in the time series of the 37-GHz third Stokes component during mid-January 2004 (winter) over first-year ice regions. The near-surface air temperature of the European Center for Medium-Range Weather Forecasts model data and the Special Sensor Microwave/Imager National Aeronautics and Space Administration Team ice concentrations revealed that, during these events, the surface temperatures reached near melting levels and the retrieved ice concentrations were reduced to about 80%. Moreover, these observations also showed clear evidence of first harmonic azimuthal dependence. Geophysical parameters, such as temperature and ice leads, are likely to be the causes. The larger signals which occurred during summer were identified as being related to the ice surface temperatures being near melting.

The 19 papers in this special issue were originally presented at MicroRad 2010, held in Washington, DC from March 1 to 4, 2010.

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.

Retrieval of soil moisture content from microwave sensors also returns an estimate of vegetation water content. Remotely sensed indices from optical sensors can be used to estimate canopy water content. For corn and soybean in central Iowa, there are allometric relationships between canopy water content and vegetation water content. The Normalized Difference Infrared Index from MODIS was used to estimate vegetation water content. We compared independent estimates of vegetation water content from WindSat and MODIS over central Iowa from 2003 to 2005. There was a strong linear relationship between the MODIS and WindSat estimates, but the WindSat estimates were about two times higher. These results suggest that soil moisture retrievals from microwave sensors may be more accurate with estimates of vegetation water content from optical 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.

In this study, we develop a combined active/passive technique to estimate surface soil moisture with the focus on the short vegetated surfaces. We first simulated a database for both active and passive signals under SMAP's sensor configurations using the radiative transfer model with a wide range of conditions for surface soil moisture, roughness and vegetation properties that we considered as the random orientated disks and cylinders. Using this database, we developed 1) the techniques to estimate surface backscattering and emission components and 2) the technique to estimate soil moisture with the estimated surface backscattering and emission components. We will demonstrate these techniques with the model simulated data and its validation with the airborne PALS image data from the soil moisture SGP'99 and SMEX'02 experiments.

The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey. SMAP will make global measurements of the soil moisture present at the Earth's land surface and will distinguish frozen from thawed land surfaces. Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere. The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers. Soil moisture measurements are also directly applicable to flood assessment and drought monitoring. SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes. The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP is scheduled for launch in the 2014-2015 time frame.

This special issue covers recent and future Earth observing satellites including the purpose of each mission, technology of the measurements, modeling of the physics and data, image and signal processing.

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.

A physically based six-channel land algorithm is developed to simultaneously retrieve global soil moisture (SM), vegetation water content (VWC), and land surface temperature. The algorithm is based on maximum-likelihood estimation and uses dual-polarization WindSat passive microwave data at 10, 18.7, and 37 GHz. The global retrievals are validated at multispatial and multitemporal scales against SM climatologies,in situnetwork data, precipitation patterns, and Advanced Very High Resolution Radiometer (AVHRR) vegetation data.In situSM observations from the U.S., France, and Mongolia for diverse land/vegetation cover were used to validate the results. The performance of the estimated volumetric SM was within the requirements for most science and operational applications (standard error of 0.04 m3/m3, bias of 0.004 m3/m3, and correlation coefficient of 0.89). The retrieved SM and VWC distributions are very consistent with global climatology and mesoscale precipitation patterns. The comparisons between the WindSat vegetation retrievals and the AVHRR Green Vegetation Fraction data also reveal the consistency of these two independent data sets in terms of spatial and temporal variations.

WindSat fully polarimetric passive microwave observations, expressed in the form of the Stokes vector, were analyzed over the Antarctic ice sheet. The vertically and horizontally polarized brightness temperatures (first two Stokes components) from WindSat are shown to be consistent with previous studies. Azimuthal modulations in the third and fourth Stokes components were analyzed and related to surface topography, roughness, and snow morphology. A second harmonic sine function of the azimuth angle was used to estimate the orientation angle of snow features, such as topographic slope and sastrugi. The results show good agreement with the orientations derived in a previous study using scatterometer data at similar frequencies. Seasonal variability in the third and fourth Stokes components is discussed. A consistent pattern of response emerged for 10.7 GHz. Under winter conditions, the large contribution of multiple volume scattering causes a high and regionally varying 10.7-GHz fourth Stokes signal. Under summer conditions, surface scattering dominates and results in a high 10.7-GHz third Stokes signal. The third and fourth Stokes observations at 37 GHz were found to correspond to the smaller penetration depth at this higher frequency, resulting in a low difference between the amplitudes of summer and winter. The study demonstrates the potential of the spaceborne fully polarimetric passive microwave radiometers in monitoring the thermal and morphological properties of large ice sheets.

Surface soil moisture was retrieved from the L-band radiometer data collected in semiarid regions during the Soil Moisture Experiment in 2004. The 2-D synthetic aperture radiometer (2D-STAR) was flown over regional-scale study sites located in AZ, USA, and Sonora, Mexico (SO). The study sites are characterized by a range of topographic relief with a land cover that varies from bare soil to grass and scrubland and includes areas with high rock fraction near the soil surface. The 2D-STAR retrieval of soil moisture was in good agreement with the ground-based estimates of surface soil moisture in both AZ (raise = 0.012 m3m-3) and SO (rmse = 0.011 m3m-3). The 2D-STAR also showed a good performance in the Walnut Gulch Experimental Watershed (rmse = 0.014 m3m-3) where the surface soil featured high rock fraction was as high as 60%. Comparison of the results with the Polarimetric Scanning Radiometer at the Cand X-band data indicates the superior soil moisture retrieval performance of the L-band data over the regions with high rock fraction and moderate vegetation density.

The U.S. Department of Agriculture (USDA) International Production Assessment Division (IPAD) is responsible for providing monthly global crop estimates that heavily influence global commodity market access. These estimates are derived from a merging of many data sources including satellite and ground observations, and more than 20 years of climatology and crop behavior data over key agricultural areas. The goal of IPAD is to provide timely and accurate estimates of global crop conditions for use in up-to-date commodity intelligence reports. A crucial requirement of these global crop yield forecasts is the regional characterization of surface and sub-surface soil moisture. However, due to the spatial heterogeneity and dynamic nature of precipitation events and soil wetness, accurate estimation of regional land surface-atmosphere interactions based sparse ground measurements is difficult. Temporal resolution is particularly important for predicting adequate surface wetting and drying between precipitation events and is closely integrated with CADRE. We attempt to improve upon the existing system by applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA soil moisture model. The improved temporal resolution and spatial coverage of the satellite-based EOS Advanced Microwave Scanning Radiometer (AMSR-E) is envisaged to provide a better characterization of root zone soil moisture at the regional scale and enable more accurate crop monitoring in key agricultural areas This work aims at evaluating the utility of merging satellite-retrieved soil moisture estimates with the IPAD two-layer soil moisture model used within the DBMS. We present a quantitative analysis of the assimilated soil moisture product over West Africa (9?N-20?N; 20?W-20?E). This region contains many key agricultural areas and has a high agro-meteorological gradient from desert and semi-arid vegetation in the North, to grassland, trees and crops in the South, thus providing an ideal location for evaluating the assimilated soil moisture product over multiple land cover types and conditions. A data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing assimilated soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.

Vegetation biomass is an important parameter in the carbon cycle study. In this paper, a new technique to estimate aboveground vegetation wet biomass based on the Microwave Vegetation Indices (MVIs), which are computed through the observed brightness temperature of AMSR-E/Aqua under two adjacent frequencies, has been developed. The MVIs can provide significant new information compared with the conventional optical vegetation indices since the microwave measurements are sensitive not only to the leafy part of vegetation properties but also to the properties of the overall vegetation canopy where the microwave sensor can ¿see¿ through. We know that the absorption effect of vegetation canopy is mostly controlled by the total wet biomass. In this technique, we first retrieve the single scattering albedo and the optical thickness based on model simulations under AMSR-E configuration. Then, the estimated above two properties are used to derive the absorption fraction of vegetation. Finally, it can be related to the aboveground vegetation wet biomass.

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.

This study demonstrates a new algorithm development for estimating bare surface soil moisture using dual-polarization L-band backscattering measurements. Through our analyses on the numerically simulated surface backscattering database by Advanced Integral Equation Model (AIEM) with a wide range of soil moisture and surface roughness conditions, we found that the relative difference of the overall surface roughness parameters at the different co-polarizations can be well estimated through a roughness index. This new finding leads to an algorithm on estimation of bare surface soil moisture. We will demonstrate the theory and techniques of this algorithm through the AIEM simulated database and validate it with two field ground scatterometer experimental data. The results indicate that bare surface soil moisture can be estimated quite well with only co-polarized backscattering signals. It provides a solid support for Soil Moisture Active and Passive mission (SMAP).

Accurate information concerning antecedent soil moisture conditions is a key source of hydrologic forecasting skill for regional-scale flooding events occurring over time scales of days to weeks. Remotely sensed surface soil moisture observations are a viable source of such information and can potentially improve flood peak timing and magnitude forecasting in such events. C- and X-band brightness temperature data from the Advanced Microwave Scanning Radiometer (AMSR-E) aboard NASA's Aqua satellite are used here to demonstrate the potential for improving streamflow forecasts by using remotely sensed surface soil moisture during a flooding event in northeastern Australia (Queensland) during January-February 2004. An analysis of AMSR-E brightness temperature imagery reveals a clear anomaly of low AMSR-E brightness temperatures (i.e., high soil moisture conditions) over the affected areas in the four- to five-day period preceding peak streamflow conditions. Land surface conditions are a remotely detectable precursor to subsequent downstream flooding. Use of a simple adaptive model demonstrates that AMSR-E passive microwave observations can add skill to streamflow forecasts during the event.

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.

The spatial and temporal invariance of Soil Moisture and Ocean Salinity (SMOS) forward model parameters for soil moisture retrieval was assessed at 1-km resolution on a diurnal basis with data from the National Airborne Field Experiment 2006. The approach used was to apply the SMOS default parameters uniformly over 27 1-km validation pixels, retrieve soil moisture from the airborne observations, and then to interpret the differences between airborne and ground estimates in terms of land use, parameter variability, and sensing depth. For pastures (17 pixels) and nonirrigated crops (5 pixels), the root mean square error (rmse) was 0.03 volumetric (vol./vol.) soil moisture with a bias of 0.004 vol./vol. For pixels dominated by irrigated crops (5 pixels), the rmse was 0.10 vol./vol., and the bias was -0.09 vol./vol. The correlation coefficient between bias in irrigated areas and the 1-km field soil moisture variability was found to be 0.73, which suggests either 1) an increase of the soil dielectric roughness (up to about one) associated with small-scale heterogeneity of soil moisture or/and 2) a difference in sensing depth between an L-band radiometer and thein situmeasurements, combined with a strong vertical gradient of soil moisture in the top 6 cm of the soil.

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).

Global estimates of soil moisture are a large component of crop yield fluctuations provided by the US department of agriculture (USDA) Production estimation and crop assessment division (PECAD). The current system utilized by PECAD estimates soil moisture from a 2-layer water balance model based on daily precipitation and temperature data. However, many regions of the globe lack climate observations at the temporal and spatial resolutions required by PECAD. This study integrates NASA's soil moisture remote sensing product provided by the EOS advanced microwave scanning radiometer (AMSR-E) into the U.S. Department of agriculture crop assessment and data retrieval (CADRE) decision support system. An Ensemble Kalman Filter (EnKF) has been designed to optimally merge the AMSR-E observations with the PECAD soil moisture outputs when available. A methodology of system design and an evaluation of the system performance over the Conterminous United States (CONUS) is presented.

The Soil Moisture Active/Passive (SMAP) mission will deliver global views of soil moisture content and its freeze/thaw state that are critical terrestrial water cycle state variables. Polarized measurements obtained with a shared antenna L-band radar and radiometer system will allow accurate estimation of soil moisture at hydrometeorological scale (10 km) and hydroclimatological scale (40 km) resolutions. The sensors will share a feed and a deployable light-weight mesh reflector that will make conical scans of the Earth surface at a constant look angle. The wide-swath (1000 km) measurements will allow global mapping of soil moisture and its freeze/thaw state with 2-3 days revisit. Freeze/thaw in boreal latitudes will be mapped using the radar at 3 km resolution with 1-2 days revisit. The synergy of active and passive measurements enables global soil moisture mapping with unprecedented resolution, sensitivity, area coverage, and revisit. This paper outlines the science objectives of the SMAP mission and provides an overview of the measurement approach and data products.

The Soil Moisture Experiments in 2004 and 2005 were conducted to validate algorithms for soil moisture retrievals. One of the key parameters for determination of soil moisture from microwave sensors is the vegetation water content of canopy and stems. We tested if canopy water content could be determined from reflectances in the shortwave-infrared and if the amount of canopy water content was related to the total vegetation water content by allometric equations. The normalized difference infrared index (NDII) was linearly related to canopy water content for all plants up to an equivalent water thickness of 1.0 mm. The biggest factor affecting the estimation of canopy water content was the soil background reflectance. For corn and soybean canopy equivalent water thickness were linearly related to total vegetation water content. However, there may be a separate allometric equation required for each vegetation type.

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.

The combination of radiometer and radar observations is a very promising technique for spatial disaggregation of soil moisture. The enhanced QuikSCAT sigma-0 product (2.225 km) offers a possibility for overcoming the temporal and spatial limitations of the available radar systems. The current study investigates QuikSCAT sensitivity to soil moisture and its capability to accurately monitor and capture change in soil moisture. The research was undertaken for the National Airborne Field Experiment area located in the Murrumbidgee catchment, SE Australia. Validation of the temporal change detection analysis was undertaken using an airborne soil moisture product derived from the Polarimetric L-band Multibeam Radiometer (PLMR). The main propose of the PLMR use was to assess accuracy in terms of spatial patterns distribution. The results reveal expected temporal variability and adequate response of the active sensor to change in meteorological conditions. The presence of irrigation and standing water (rice fields) in the region challenges the spatial agreement throughout the study area.

The radiation of the 3rdand 4thStokes components emitted by Arctic sea ice and observed by the spaceborne fully polarimetric radiometer WindSat is investigated. Two types of analysis are carried out, spatial (maps of different quadrants of azimuth look angles) and temporal (time series of daily averages over small selected ranges of azimuth angles). The 3rdStokes component at 37 GHz has shown the highest signal during early summer (Gt2 K). The next highest signals were observed at the 10.7 GHz 3rdand 4thStokes components during the summer months (Gt1 K). The 37 GHz 4thStokes component has shown the least variability (Lt1 K). The 10.7 GHz 4thStokes component has higher signal at the ice edge, confirmed from the sea ice concentration maps derived from Advanced Microwave Scanning Radiometer (AMSR-E) data, and similar to signals observed over global coastlines. From the comparison with near surface air temperature of the European Center for Medium-Range Weather Forecasts (ECMWF) model data and Scanning Multichannel Microwave/Imager (SSM/I) NASA Team ice concentrations it was concluded that the microphysical processes during early melting in the topmost sea ice layers are responsible for the high 37 GHz 3rdStokes signal.

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.

Soil moisture retrievals made using data from the airborne L-band microwave radiometer, 2D-STAR, over a wide range of land cover types are presented. The 2D-STAR was flown over six regional-scale sites during Soil Moisture Experiments in 2003 and 2004. Four sites located in Alabama, Georgia, Arizona, and Sonora were selected for this work. Land cover types included bare soil, bare soil with gravelly surface, shrub, crop field, and forest. Topographic conditions varied from flat or gently rolling plains to high-relief hilly or mountainous area. Results indicate fairly good soil moisture retrieval performance of the 2D-STAR over the various land cover types and moisture conditions (overall RSME=0.22 m3/m3). The 2D-STAR also showed improved soil moisture retrieval over a C- and X-band microwave instrument (PSR-C/X) for densely vegetated areas and gravelly soil surfaces.

The purpose of this study is to evaluate whether the NASA/JPL dual frequency airborne system, Passive Active L-band and S-band (PALS), can provide a reliable soil moisture measurements so that they can be integrated to provide soil moisture data at the scales of the spaceborne coarse resolutions. Through evaluations of the AIEM simulated the random rough surface emissivities with a wide range of soil moisture and roughness conditions at both L-band and S-band, it was found that the bare surface emission signals at above two frequencies are essentially close to identical regardless of the surface soil moisture and roughness properties when the effect of soil properties (temperature and moisture) in the vertical profile on emission signals is minor. This makes it possible to further estimate the vegetation components in the omega-tau model at each frequency and polarization and to carry out the corrections for the vegetation effects without assumption on polarization dependence of the vegetation effects. Thus, the surface soil moisture can be inferred by the estimated surface emission signals. We will show the evaluation and validation of this technique with the airborne PALS measurements obtained during SMEX'02 soil moisture experiment with the intensive ground soil moisture measurements.

Effectively monitoring vegetation water is essential to improve our understanding of agriculture and hydrology. Vegetation water content is often estimated using vegetation indices derived from optical satellite sensors. In this study, we introduced the new microwave vegetation indices (MVIs) and derived the new MVIs using observation from the Advanced Microwave Scanning Radiometer (AMSR-E). To demonstrate the potential of the proposed MVIs, we compared them with vegetation water content which were obtained using ground based observations of vegetation water content and reflectance from a MultiSpectral Radiometer (MSR) during the National Airborne Field Experiment 2006 (NAFE'06), as well as the coincident Landsat 5 TM data. The estimated vegetation water content were averaged and compared with daily MVIs derived from AMSR-E on EASE-GRID pixels. The results of comparison between the MVIs and crop water content on EASE-GRID pixels demonstrated that the MVIs could be used to monitor the vegetation water content, which suggests additional all weather information and a potential linkage of the two data sources. In combination with vegetation indices derived from conventional optical sensor, MVIs provide a possible complementary dataset for monitoring global vegetation from space.

This article describes the upgraded PALS instrument and the characteristics of data acquired from the Cloud Land Atmospheric Interaction Campaign (CLASIC) 2007. The data acquired over lake passes were used to remove the radiometer calibration bias. The calibrated radiometer data showed significant consistency with the L-band land emission model for soil surfaces published in the literature. We observed significant temporal (days) changes of a few dB in the radar data. The change of radar backscatter appeared to correlate well with the change of in situ soil moisture or the soil moisture data derived from the PALS dual-polarized brightness temperatures. The radar vegetation index also correlated well with the vegetation opacity estimated from the radiometer data. The preliminary analyses suggest complementary information contained in the surface emissivity and backscatter signatures for the retrieval of soil moisture and vegetation water content.

Retrieving soil moisture from spaceborne microwave radiometer observations often requires ancillary parameters such as surface vegetation opacity or vegetation water content (VWC). The conventional approach for deriving representative footprint- scale values of these parameters is to simply average the corresponding parameters of high-resolution pixels over the entire radiometer footprint. However, previous work has shown that simple averaging of ancillary parameter values may result in biased soil moisture retrievals from spaceborne radiometers. This letter uses a synthetic observing-system simulation experiment framework to theoretically demonstrate that ancillary VWC or canopy-opacity parameter values obtained with an alternative aggregation rule may significantly reduce the magnitude of soil moisture retrieval errors for highly heterogeneous vegetated areas.

The soil moisture is one of the key hydrological parameters that control the water and energy flows between the atmosphere and land surface, the partitioning of rainfall into infiltration, runoff and evapotranspiration, the photosynthetic activities of vegetation, and the respiration of soil microorganisms. Conventional methods of measuring soil moisture are time-consuming and the data are typically point-based estimates. This study presents the results of spatial and temporal variation analyses of Aqua/AMSR-E brightness temperature data from the Brazilian tropical savanna.

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 effect of ice on polarimetric 4thStokes component observations is investigated using WindSat data over Antarctica. The difference in the magnitude of the signal observed during (July 2003) and summer (February 2004) months are investigated using a second harmonic sine function of the azimuth look angle. The seasonal variations are further investigated by a time series of the 4,th Stokes component for a location in Wilkes Land in east Antarctica and compared with a time series observed by the ERS scatterometer (ESCAT) from a previous work. The paper discusses the potential of a polarimetric radiometer in providing information about scattering and thermal properties of a snow ice pack.

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.

Microwave radiometry at low frequencies (L-band, ~ 1.4 GHz) has been known as an optimal solution for remote- sensing of soil moisture. However, the antenna size required to achieve an appropriate resolution from space has limited the development of spaceborne L-band radiometers. This problem can be addressed by interferometric technology called aperture synthesis. The Soil Moisture and Ocean Salinity (SMOS) mission will apply this technique to monitor global-scale surface parameters in the near future. The first airborne experiment using an aircraft prototype of this approach, the Two-Dimensional Synthetic Aperture Radiometer (2D-STAR), was performed in the Soil Moisture Experiment in 2003 (SMEX03). The L-band brightness temperature data acquired in Alabama by the ID- STAR was compared with ground-based measurements of soil moisture and with C-band data collected by the Polarimetric Scanning Radiometer (PSR). Our results demonstrate a good response of the 2D-STAR brightness temperature to changes in surface wetness, both in agricultural and forest lands. The behavior of the horizontally polarized brightness temperature data with increasing view-angle over the forest area was noticeably different than over bare soil. The results from the comparison of 2D-STAR and PSR indicate a better response of the 2D-STAR to the surface wetness under both wet and dry conditions. Our results have important implications for the performance of the future SMOS mission.

Major uncertainties in deriving vegetation indices from satellite measurements are the effects of atmosphere and background soil conditions. Through numerical simulations by surface emission model - Advanced Integral Equation Model (AIEM), we found that bare surface emissivities at different frequencies can be well characterized by a linear function with parameters that are dependent on the pair of frequencies to be used. This makes it possible to minimize the surface emission signal and maximize the vegetation signal when using multifrequency radiometer measurements. Using the radiative transfer model (ω-τ model), a linear relationship between the brightness temperatures observed at two adjacent radiometer frequencies can be derived. It can be shown that the microwave vegetation index derived by the intercept and slope of this linear function depends only on vegetation properties and can be derived from the dual-frequency and dual-polarization measurements. We will demonstrate the theoretical basis of this new microwave vegetation index and show comparisons of the microwave derived vegetation index with the optical sensor derived NDVI measurements.

WindSat provides an opportunity to explore the passive microwave polarimetric signatures of land surfaces. In order to accommodate the large sensor footprint, large homogeneous regions with unique features were used. These included forest, rangeland, desert, and agricultural conditions. WindSat observations at horizontal and vertical polarizations over land surfaces were found to be well calibrated and consistent with other passive microwave sensors. Isotropic regions (e.g., Amazon rainforest) had no polarimetric response at all azimuth angles. Results showed that land surfaces with aligned features (topography or row structured vegetation) produced systematic variations in the third and fourth Stokes parameters. These responses were found to be in good agreement with previous sea surface studies. Analysis of the temporal trends of the variation in polarimetric measurements for a specific azimuth angle could be attributed to the crop growth cycle in the agricultural region. Further analyses will seek to isolate specific features that could be used in applications such as soil moisture retrieval.

Initial results are presented for the new synthetic aperture radiometer, 2D-STAR, which is a dual-polarized L-band radiometer that employs aperture synthesis in two dimensions. This airborne instrument is the natural evolution of the Electronically Scanned Thinned Array Radiometer, which employs aperture synthesis only in the across-track dimension, and represents a further step in the development of aperture synthesis for remote sensing applications. 2D-STAR was successfully tested in June 2003 and, then, participated in the SMEX03 and SMEX04 soil moisture experiments. A description of the instrument and initial results in the form of first images and a preliminary comparison with changes in soil moisture during SMEX03 are presented here.

Soil moisture mapping using Synthetic Aperture Radar (SAR) can be achieved at high spatial resolution but has not been rigorously demonstrated over vegetated areas.. Radar measurements are sensitive to the structure and dielectric constant of the target. Of some concern are the crop type and row direction which are known to influence the backscatter observations. In this work, L- and P-band SAR images are used to map crop type and row orientation. The VV-polarized L-band σ o values are used to identify the crop type and the ratio of P-band VV- polarized over VH-polarized σ o measurements are used to determine the row orientation. Validation results show classification accuracies of 86% for the crop type classification and an accuracy of 94% accuracy for the detection of the row orientation. A vegetation correction approach derived from this analysis was used in combination with a semi-empirical surface scattering algorithm to estimate the soil moisture content using high resolution aircraft SAR data. Three different retrieval methods were employed to estimate spatially distributed soil moisture; 1) the Dubois model without vegetation correction, 2) the Water Cloud model using vegetation water content, 3) the Water Cloud model using vegetation water content and row direction information. These three methodologies were applied to airborne SAR) data collected during the soil moisture experiments in 2002 (SMEX02). For corn and soybean fields with an east-west row direction a standard error of estimate of approximately 0.075 cm3cm-3 was obtained and for north-south oriented crop rows accuracies up to 0.043 cm3cm-3 were achieved.

Soil moisture is estimated by the U. S. Department of Agriculture (USDA) Production Estimates and Crop Assessment Division (PECAD) by utilizing a modified two-layer Palmer water balance model derived from temperature and precipitation observations. It is envisaged that these soil moisture estimates can be improved by integrating passive microwave data which has greater temporal frequency and covers larger spatial domains than available in the past. By integrating direct observations from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the current PECAD soil moisture model, more accurate soil moisture and correspondingly crop yield estimates may be possible. This paper presents a methodology for soil moisture data assimilation using a simple bias correction and 1D Ensemble Kalman Filter data assimilation algorithm. An outline of the technical approach is presented.

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.

Vegetation water content (VWC) is important for accurate retrievals of soil moisture using microwave sensors and may be important for determining water stress and forest fire potential. The MODerate resolution Imaging Spectroradiometer (MODIS) and future operational sensors have bands in the shortwave infrared region which can be used for monitoring VWC. The Soil Moisture Experiments 2004 (SMEX04) were conducted during the summer-monsoon season in Arizona, USA, and Sonora, Mexico, as part of the North American Monsoon Experiment. Plots from different vegetation types were sampled for leaf area index and leaf equivalent water thickness. Landsat 5 Thematic Mapper (TM) and MODIS imagery were acquired for three dates, before, during and after the SMEX04 experiment. The Normalized Difference Infrared Index [NDII = (R850- R1650)/(R850+ R1650)] was linearly related to canopy equivalent water thickness for the Landsat 5 TM data. The TM-estimated canopy equivalent water thickness were aggregated and linearly related to canopy equivalent water thickness from MODIS, showing that MODIS and future sensors would be useful in estimating vegetation water content.

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.

This study demonstrates the impacts of land water, dry snow cover, and terrain at sub-pixel scale on soil estimation through numerical simulations. We will demonstrate the quantity of the above different factors contributing to the errors on the soil moisture estimation.

Retrieving soil moisture from space-borne microwave radiometer observations often requires ancillary parameters such as surface vegetation opacity and roughness. The conventional approach for deriving representative values of these parameters is usually to simply average the corresponding parameters of high resolution pixels over the whole microwave sensor footprint. This paper uses a microwave emission model (MEM) to demonstrate that the aggregation rules significantly impact the results and if chosen correctly can reduce the soil moisture retrieval errors from the single-channel retrieval and the MEM inversion algorithms.

The soil moisture experiments held during June-July 2002 (SMEX02) at Iowa demonstrated the potential of the L-band radiometer (PALS) in estimation of near surface soil moisture under dense vegetation canopy conditions. The L-band radar was also shown to be sensitive to near surface soil moisture. However, the spatial resolution of a typical satellite L-band radiometer is of the order of tens of kilometers, which is not sufficient to serve the full range of science needs for land surface hydrology and weather modeling applications. Disaggregation schemes for deriving subpixel estimates of soil moisture from radiometer data using higher resolution radar observations may provide the means for making available global soil moisture observations at a much finer scale. This paper presents a simple approach for estimation of change in soil moisture at a higher (radar) spatial resolution by combining L-band copolarized radar backscattering coefficients and L-band radiometric brightness temperatures. Sensitivity of AIRSAR L-band copolarized channels has been demonstrated by comparison with in situ soil moisture measurements as well as PALS brightness temperatures. The change estimation algorithm has been applied to coincident PALS and AIRSAR datasets acquired during the SMEX02 campaign. Using AIRSAR data aggregated to a 100-m resolution, PALS radiometer estimates of soil moisture change at a 400-m resolution have been disaggregated to 100-m resolution. The effect of surface roughness variability on the change estimation algorithm has been explained using integral equation model (IEM) simulations. A simulation experiment using synthetic data has been performed to analyze the performance of the algorithm over a region undergoing gradual wetting and dry down.

A physically based bare-surface soil moisture inversion technique for application with passive microwave satellite measurements, including the Advanced Microwave-Scanning Radiometer-Earth Observing System, Special Sensor Microwave/Imager, Scanning Multichannel Microwave Radiometer, and Tropical Rainfall Measuring Mission Microwave Imager, was developed in this paper. The inversion technique is based on the concept of a simple parameterized surface emission model, the Qpmodel, which was developed using advanced integral equation model simulations of microwave emission. Through evaluation of the relationship between roughness parameters Qpat different polarizations, it was found that they could be described by a linear function. Using this relationship and the surface emissivities measured from two polarizations, the effect of the surface roughness is cancelled out. In other words, this approach consisted in adding different weights on the v and h polarization measurements so as to minimize the surface roughness effects. This method leads to a dual-polarization inversion technique for the estimation of the surface dielectric properties directly from the emissivity measurements. For validation, we compared the soil moisture estimates, derived from ground radiometer measurements at C- to Ka-band obtained from the Institute National de Recherches Agronomiques' field experimental data in 1993 and the Beltsville Agricultural Research Center's field experimental data at C- and X-band obtained in 1979-1982, with the field in situ soil moisture measurements. The accuracies [root-mean-square error (rmse)] are higher than 4% for the available experimental data at the incidence angles of 50deg and 60deg. The newly developed inversion technique should be very useful in monitoring global soil moisture properties using the currently available satellite instruments that commonly have incidence angles between 50deg and 55deg

Abstract : An important issue in the development of a dedicated space borne soil moisture sensor has been concern over the reliability of soil moisture retrievals in densely vegetated areas and the global extent over which retrievals will be possible. Errors in retrieved soil moisture can originate from a variety of sources within the measurement and retrieval process. In addition to instrument error, three key contributors to retrieval error are the masking of the soil microwave signal by vegetation, the interplay between nonlinear retrieval physics and the relatively poor spatial resolution of space borne sensors, and retrieval parameter uncertainty. Quantification of these errors requires the realistic specification of land surface soil moisture heterogeneity and spatial vegetation patterns. Since detailed soil moisture patterns are currently difficult to obtain from direct observations, an attractive alternative is the application of an observing system simulation experiment (OSSE) in which simulated land surface states are propagated through the sensor measurement and retrieval process to investigate and constrain expected levels of retrieval error. This manuscript describes results from an OSSE designed out to simulate the impact of land surface heterogeneity, instrument error, and retrieval parameter uncertainty on radiometer-only soil moisture products derived from the NASA ESSP Hydrosphere State (Hydros) mission.

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.

Abstract – This study demonstrates a technique of estimating soil moisture using the combined passive/active L-band microwave measurements. It shows 1) evaluation of the small albedo assumption for using dual polarization passive measurements, 2) development of a synthesized technique to estimate soil moisture, and 3) evaluation with ground soil moisture measurements from the SMEX02 experiment data. I.

Based on 1-km land surface model geophysical predictions within the United States Southern Great Plains (Red-Arkansas River basin), an observing system simulation experiment (OSSE) is carried out to assess the impact of land surface heterogeneity, instrument error, and parameter uncertainty on soil moisture products derived from the National Aeronautics and Space Administration Hydrosphere State (Hydros) mission. Simulated retrieved soil moisture products are created using three distinct retrieval algorithms based on the characteristics of passive microwave measurements expected from Hydros. The accuracy of retrieval products is evaluated through comparisons with benchmark soil moisture fields obtained from direct aggregation of the original simulated soil moisture fields. The analysis provides a quantitative description of how land surface heterogeneity, instrument error, and inversion parameter uncertainty impacts propagate through the measurement and retrieval process to degrade the accuracy of Hydros soil moisture products. Results demonstrate that the discrete set of error sources captured by the OSSE induce root mean squared errors of between 2.0% and 4.5% volumetric in soil moisture retrievals within the basin. Algorithm robustness is also evaluated for the case of artificially enhanced vegetation water content (W) values within the basin. For large W(>3 kg/spl middot/m/sup -2/), a distinct positive bias, attributable to the impact of sub- footprint-scale landcover heterogeneity, is identified in soil moisture retrievals. Prospects for the removal of this bias via a correction strategy for inland water and/or the implementation of an alternative aggregation strategy for surface vegetation and roughness parameters are discussed.

Soil Moisture Experiment 2003 (SMEX03) was the second in a series of field campaigns using the National Oceanic and Atmospheric Administration Polarimetric Scanning Radiometer (PSR/CX) designed to validate brightness temperature (T/sub B/) data and soil moisture retrieval algorithms for the Advanced Microwave Scanning Radiometer (AMSR-E) for the Earth Observing System on the Aqua satellite. Objectives related to the PSR/CX during SMEX03 included: calibration and validation of AMSR-E T/sub B/ observations over different climate/vegetation regions of the U.S. [Alabama (AL), Georgia (GA), Oklahoma (OK)], identification of possible areas of radio-frequency interference (RFI), comparison of X-band observations from Tropical Rainfall Measurement Mission Microwave Imager (TMI), AMSR-E, and PSR/CX, and exploring the potential of soil moisture retrieval algorithms using C- and X-band imagery in diverse landscapes. In the current investigation, more than 100 flightlines of PSR/CX data were extensively processed to produce gridded T/sub B/ products for the four study regions. Due to the lack of significant rainfall in OK, generally dry soil moisture conditions were observed. Observations obtained over AL include a wide range of soil moisture and vegetation conditions. Results from the AL site clearly showed a lack of sensitivity to rainfall/soil moisture under forest canopy cover. Quantitative comparisons made with the TMI validated that both the PSR/CX and AMSR-E X-band channels were well calibrated. Spectral analyses indicated that the PSR/CX observations at C-band also are reasonable. As expected, there were varying degrees of RFI in the AMSR-E C-band data for the study sites that will prevent further soil moisture analysis using these data. X-band comparisons of the PSR/CX high-resolution and AMSR-E and TMI low-resolution data indicated a linear scaling for the range of conditions studied in SMEX03. These results will form the basis for further soil moisture investigations.

The potential of using satellite based microwave observations of soil moisture to improve flood predictability was explored during a specific major flood event. Predictability is a key contribution to forecasting skill for regional-scale flooding events occurring over time scales of days to weeks and remote sensing observations could add skill to predictions of flood peak timing and magnitude. Data from the Advanced Microwave Sensing Radiometer (AMSR-E) was used to demonstrate the potential of remotely sensed soil moisture in flood forecasting applications. The current study demonstrates the potential of these observations to predicting the floods in the northeastern Australia (Queensland) during January-February 2004. There is a clear signal expressed by low brightness temperatures (i.e., highest soil moistures) over the affected areas preceding the peak streamflow conditions. That is, the inundated land surface conditions displayed are a detectable precursor to subsequent downstream flooding. The use of remotely sensed passive microwave observations improves the forecasting skill for regional scale flooding

An automated soil moisture network for continuous measurement of soil moisture in the top 30 cm of the soil over an 8000 km2region has been established. The network consists of 32 stations encompassing a diversity of soil types. The measurements are being used to improve drought, flood, and agronomic production forecasts. In addition, the data are being used to examine the accuracy of remotely sensed measurements of soil moisture and the degree to which they represent natural variability across the landscape. The data were used to evaluate soil moisture conditions during the SMEX03 experiment. The data are being used to support testing of AMSR, AMSR-E, PSR, and synthetic aperture radar (SAR) observations. Gravimetric samples were collected for the period from June 23, 2003 to July 2, 2003 for comparison to both the in situ network and the remotely sensed data. During the experiment, daily in situ soil moisture measurements were taken and plant and soil samples collected for oven drying and determination of moisture content. The automated network provided continuous in situ soil moisture measurements throughout the coverage area. A wide variation in soil moisture was observed both over the time period and from site to site

Soil Moisture Experiments 2003 (SMEX03) was the second in a series of field campaigns using the NOAA Polarimetric Scanning Radiometer (PSR/CX) designed to validate brightness temperature data and soil moisture retrieval algorithms for the Advanced Microwave Scanning Radiometer on the Aqua satellite. Data from the TRMM Microwave Imager were also used for X-band comparisons. The study was conducted in different climate/vegetation regions of the US (Alabama, Georgia, Oklahoma). In the current investigation, more than one hundred flightlines of PSR/CX data were extensively processed to produce gridded brightness temperature products for the four study regions. Variations associated with soil moisture were not as large as hoped for due to the lack of significant rainfall in Oklahoma. Observations obtained over Alabama include a wide range of soil moisture and vegetation conditions. Comparisons were made between the PSR and AMSR for all sites

A two-source (soil + vegetation) energy balance model using microwave-derived near-surface soil moisture has been successfully applied in areas of relatively low vegetation cover. The utility of this approach in areas with high vegetation cover is explored and compared with a two-source scheme using thermal infrared data. The investigation used data collected over areas of high corn and soybean cover in central Iowa during the Soil Moisture Experiment in 2002 (SMEX02) and the Soil Moisture Atmosphere Coupling Experiment (SMACEX). Maps of near-surface soil moisture data were obtained from the Polarimetric Scanning Radiometer (PSR) observations, which provided 800 m resolution for the regional area. Fractional vegetation cover and leaf area index were estimated from the Landsat data, which also provided surface temperature. These data, along with local meteorological data, provided inputs for a two-source model. The model output using microwave data were compared with tower-based flux measurements in the watershed area. The model computed reliable estimates of net radiation and soil heat flux, yielding a root-mean-square-difference (RMSD) of around 20 Wm/sup -2/. However, the model generally underestimated latent heat flux, LE, and overestimated sensible heat flux, H resulting in RMSD values of /spl sim/70 Wm/sup -2/ for LE and 45 Wm/sup -2/ for H respectively. The larger discrepancies in heat fluxes are due in part to the mismatch in model output resolution (800 m) versus source area contributing to the tower fluxes (/spl sim/100m). The comparison between the model output of surface temperature and Landsat indicates that the temperature estimation is reasonable agreement with a RMSD 1.4/spl deg/C. A comparison of fluxes mapped over the regional area using the microwave-based and thermal-infrared based model is made and factors contributing to the differences between model outputs are discussed.

A two-source(soil+vegetation) energy balance model using radiometric surface temperature as a key boundary condition was applied to remote sensing data collected under a range of crop cover and soil moisture conditions during the Soil Moisture Atmosphere Coupling Experiment(SMACEX). Two formulations of the heat exchange, one allowing interaction between the soil and vegetation(series version) and another neglecting such interaction (parallel version) were evaluated. Comparison of local model output with tower-based flux observations indicated that both the parallel and series resistance formulations produced similar estimates with root-mean-square-differences(RMSD) values ranging from approximately 20 to 50 W m/sup -2/ for net radiation and latent heat fluxes, respectively. Although both the series and parallel versions gave similar results, the parallel resistance formulation was more sensitive to model parameter specification, particularly in accounting for vegetation clumping via row crop planting effects on flux partitioning.

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.

This study presents an overview of the field design and satellite data analysis strategy of the Brazilian SMEX03 (Soil Moisture Experiment in 2003) campaign. The goal of the SMEX03 Brazil is to validate existing algorithms to retrieve soil moisture from Aqua/AMSR-E data under tropical savanna vegetation cover. The test site corresponded to the Barreiras region, located in the western part of the Bahia State (12/spl deg/05' south latitude; 45/spl deg/00' west longitude). The Barreiras study site offers low to intermediate vegetation cover (near the border of semiarid vegetation called Caatinga), gentle topography and large fields of annual crops (mainly soybean and maize) and cultivated pastures. Field surface soil moisture, soil temperature and leaf water content were acquired during the wet season of December, 2003 over a set of 12 sampling locations. Three other field campaigns are scheduled for 2004 (June, end of wet season; August, dry season; and November, beginning of the wet season). Remote sensing data analysis includes the processing of all AMSR-E brightness temperature images of 2004 with maximum elevation higher than 80/spl deg/ (3-4 scenes per month).

Through analyses of the model simulated database, we developed a technique to estimate surface soil moisture under HYDROS radar sensor (L-band multipolarizations and 40deg incidence) configuration. This technique includes two steps. First, it decomposes the total backscattering signals into two components - the surface scattering components (the bare surface backscattering signals attenuated by the overlaying vegetation layer) and the sum of the direct volume scattering components and surface-volume interaction components at different polarizations. From the model simulated data-base, our decomposition technique works quit well in estimation of the surface scattering components with RMSEs of 0.12, 0.25, and 0.55 dB for VV, HH, and VH polarizations, respectively. Then, we use the decomposed surface backscattering signals to estimate the soil moisture and the combined surface roughness and vegetation attenuation correction factors with all three polarizations

The Hydrosphere State Mission (Hydros) is a pathfinder mission in the National Aeronautics and Space Administration (NASA) Earth System Science Pathfinder Program (ESSP). The objective of the mission is to provide exploratory global measurements of the earth's soil moisture at 10-km resolution with two- to three-days revisit and land-surface freeze/thaw conditions at 3-km resolution with one- to two-days revisit. The mission builds on the heritage of ground-based and airborne passive and active low-frequency microwave measurements that have demonstrated and validated the effectiveness of the measurements and associated algorithms for estimating the amount and phase (frozen or thawed) of surface soil moisture. The mission data will enable advances in weather and climate prediction and in mapping processes that link the water, energy, and carbon cycles. The Hydros instrument is a combined radar and radiometer system operating at 1.26 GHz (with VV, HH, and HV polarizations) and 1.41 GHz (with H, V, and U polarizations), respectively. The radar and the radiometer share the aperture of a 6-m antenna with a look-angle of 39/spl deg/ with respect to nadir. The lightweight deployable mesh antenna is rotated at 14.6 rpm to provide a constant look-angle scan across a swath width of 1000 km. The wide swath provides global coverage that meet the revisit requirements. The radiometer measurements allow retrieval of soil moisture in diverse (nonforested) landscapes with a resolution of 40 km. The radar measurements allow the retrieval of soil moisture at relatively high resolution (3 km). The mission includes combined radar/radiometer data products that will use the synergy of the two sensors to deliver enhanced-quality 10-km resolution soil moisture estimates. In this paper, the science requirements and their traceability to the instrument design are outlined. A review of the underlying measurement physics and key instrument performance parameters are also presented.

This study demonstrates the capability of estimating soil moisture using repeat-pass L-band radiometer. It shows (1) evaluation of the effects of the surface roughness and vegetation in the repeat-pass measurements and (2) development of a technique to estimate soil moisture.

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.

Field experiments were conducted to evaluate the effects of dense agricultural crop conditions on soil moisture retrieval using passive microwave remote sensing. Aircraft observations were collected using a new version of the Polarimetric Scanning Radiometer (PSR) that provided C band and X band channels. Observations were also available from the Aqua satellite Advanced Microwave Scanning Radiometer (AMSR-E) at the same frequencies. Soil Moisture Experiments 2002 (SMEX02) was conducted over a three-week period during the summer near Ames, Iowa, an area which is dominated by corn and soybeans. Aircraft data were processed and channels selected to minimize radiofrequency interference. A preliminary comparison of he aircraft (PSR) and satellite (AMSR-E) 10.7 GHz data showed comparable brightness temperature values. Sensitivity of brightness temperature to soil moisture was observed for nearly all the ground validation sites, even under dense corn canopies. Similar sensitivities were observed for C and X band channels. These results illustrate the potential to develop soil moisture retrieval techniques for wide range of agricultural conditions using AMSR-E frequencies.

Soil moisture is an important variable controlling biogeochemical cycles, heat exchange and infiltration rates at land/atmosphere boundary. The microwave portion of the electromagnetic spectrum have been used to monitor the moisture content of soils due to the sensitivity of microwave brightness temperatures to land surface variables. In this paper, the simulation of the C-band brightness temperatures are carried out for the SMEX02 (Soil Moisture Experiments 2002) regions in Ames, Iowa for the time period between June 25 to July 31, 2002. The simulated brightness temperatures have been compared with the corresponding observations using the Advanced Microwave Scanning Radiometer (AMSR).

The southern Great Plains region of the US has been a focus area for experimental remote sensing of surface soil moisture since the 1970's. Intercomparison of soil moisture retrieval using both experimental data and operational data is carried out during the SGP99 remote sensing campaign in July 1999. Passive microwave measurements obtained from the airborne ESTAR instruments at L-band and TRMM microwave imager (TMI) measurements at X-band were processed to retrieve surface soil moisture during SGP99 and compared to field measurements. TMI retrieved soil moisture for June-September 1999 were compared with operational soil moisture sensors in the Oklahoma (OK) Mesonet system. To mimic operational products, the correction for vegetation and surface roughness in the TMI retrievals are based on average literature values, and surface temperatures estimated from a land surface hydrologic model forced with operational products.

In July 2002, the AIRSAR system flew several data acquisition flights during the SMEX'02 field experiment in Iowa. The test site was chosen specifically because of the varying vegetation cover to allow more quantitative testing of the radar inversion algorithms under these conditions. Field conditions were generally favorable for this experiment, and data were acquired during an initial dry period, followed by a wet period after significant rain, and again followed by a drier period. This paper discusses in detail the characteristics of the AIRSAR data acquired, and provides an initial quantitative assessment of the accuracy of the radar algorithm under these vegetated conditions.

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.

The Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System (EOS) Aqua satellite was launched on May 4, 2002. The AMSR-E instrument provides a potentially improved soil moisture sensing capability over previous spaceborne radiometers such as the Scanning Multichannel Microwave Radiometer and Special Sensor Microwave/Imager due to its combination of low frequency and higher spatial resolution (approximately 60 km at 6.9 GHz). The AMSR-E soil moisture retrieval approach and its implementation are described in this paper. A postlaunch validation program is in progress that will provide evaluations of the retrieved soil moisture and enable improved hydrologic applications of the data. Key aspects of the validation program include assessments of the effects on retrieved soil moisture of variability in vegetation water content, surface temperature, and spatial heterogeneity. Examples of AMSR-E brightness temperature observations over land are shown from the first few months of instrument operation, indicating general features of global vegetation and soil moisture variability. The AMSR-E sensor calibration and extent of radio frequency interference are currently being assessed, to be followed by quantitative assessments of the soil moisture retrievals.

This study demonstrates the capability of estimating the relative soil moisture change using repeat-pass L-band radiometer. It shows 1) evaluation of the effects of the surface roughness and vegetation in the repeat-pass measurements, 2) development of a technique to estimate the relative soil moisture change, and 3) validation with the ground soil moisture measurements from SGP99 experiment.

In preparing for future L band soil moisture satellite missions, investigators have employed ground, aircraft and satellite sensors. Of the satellite sensors, there has been only one instrument that provides any heritage at L band, the Skylab S-194 instrument that operated in the 1970s. Data from theses missions have been analyzed and reported in a few applications, however, these studies utilized either the linear regression approach or the Antecedent Precipitation Index (API). We explore the use of products from climate model reanalysis projects as ancillary data to exploit the S-194 data for a broad range of soil moisture conditions by employing a radiative transfer approach. The spatial resolution and the accuracy of reanalysis outputs are major limitations to this approach.

Soil moisture is an important hydrologic variable in both natural and agricultural ecosystems. Recent experiments, based on remote sensing data, provided observations of soil moisture distributions at a regional scale. One of the instruments is the Electronically Scanned Thinned Array Radiometer (ESTAR). Deployed on the aircraft, it measures brightness temperature (with the spatial resolution about 400 m) that can be converted into estimates of volumetric soil moisture. The soil moisture retrieval algorithm requires additional data layers (like soil physical temperature, land cover, and soil texture) that come from different sources and have to be combined together in a uniform GIS. The objective of this paper is to report results of the GIS use to map soil surface moisture from passive microwave measurements and to quantify spatial structure of soil moisture distributions. The data were collected across a 10 000 km 2 area in Oklahoma during June and July 1997. The precipitation data and ground measurements from the test sites were also incorporated into GIS. The GIS data layers enable soil moisture distribution analysis with geostatistical tools. Semi-variograms of soil moisture were nested and showed the presence of two scales. Rainfall was the dominant factor influencing soil moisture distribution at the regional scale, whereas soil texture was important at the local scale. Information about soil moisture, obtained with remote sensing techniques over space and time, and organized in a GIS with complementary environmental data layers, can be used in landscape pattern analysis, landscape models, land use assessment and management.

The Advanced Microwave Scanning Radiometer (AMSR) holds promise for retrieving soil moisture in regions with low levels of vegetation. Algorithms for this purpose have been proposed, but none have been rigorously evaluated due to a lack of datasets. Accordingly, the Southern Great Plains 1999 Experiment (SGP99) was designed to provide C-band datasets for AMSR algorithm development and validation. Ground observations of soil moisture and related variables were collected in conjunction with aircraft measurements using a C-band radiometer similar to the AMSR sensor (6.92 GHz), the Polarimetric Scanning Radiometer with its C-band scanhead (PSR/C). The study region has been the focus of several previous remote sensing field experiments and contains vegetation conditions compatible with the expected capabilities of C-band for soil moisture retrieval. Flights were conducted under a wide range of soil moisture conditions, thus providing a robust dataset for validation. A significant issue found in data processing was the removal of anthropogenic radio-frequency interference. Several approaches to estimating the parameters of a single-channel soil moisture retrieval algorithm were used. PSR/C soil moisture images show spatial and temporal patterns consistent with meteorological and soil conditions, and the dynamic range of the PSR/C observations indicates that the AMSR instrument can provide useful soil moisture information.

Data were acquired by the Passive and Active L- and S-band airborne sensor (PALS) during the 1999 Southern Great Plains (SGP99) experiment in Oklahoma to study remote sensing of soil moisture in vegetated terrain using low-frequency microwave radiometer and radar measurements. The PALS instrument measures radiometric brightness temperature and radar backscatter at L- and S-band frequencies with multiple polarizations and approximately equal spatial resolutions. The data acquired during SGP99 provide information on the sensitivities of multichannel low-frequency passive and active measurements to soil moisture for vegetation conditions including bare, pasture, and crop surface cover with field-averaged vegetation water contents mainly in the 0-2.5 kg m/sup -2/ range. Precipitation occurring during the experiment provided an opportunity to observe wetting and drying surface conditions. Good correlations with soil moisture were observed in the radiometric channels. The 1.41-GHz horizontal-polarization channel showed the greatest sensitivity to soil moisture over the range of vegetation observed. For the fields sampled, a radiometric soil moisture retrieval accuracy of 2.3% volumetric was obtained. The radar channels showed significant correlation with soil moisture for some individual fields, with greatest sensitivity at 1.26-GHz vertical copolarized channel. However, variability in vegetation cover degraded the radar correlations for the combined field data. Images generated from data collected on a sequence of flight lines over the watershed region showed similar patterns of soil moisture change in the radiometer and radar responses. This indicates that under vegetated conditions for which soil moisture estimates may not be feasible using current radar algorithms, the radar measurements nevertheless show a response to soil moisture change, and they can provide useful information on the spatial and temporal variability of soil moisture. An illustration of the change detection approach is given.

Soil moisture is an important parameter for hydrological and climatic investigations. Future satellite missions with L-band passive microwave radiometers will significantly increase the capability of monitoring Earth's soil moisture globally. Understanding the effects of surface roughness on microwave emission and developing quantitative bare-surface soil moisture retrieval algorithms is one of the essential components in many applications of geophysical properties in the complex Earth terrain by microwave remote sensing. We explore the use of the integral equation model (IEM) for modeling microwave emission. This model was validated using a three-dimensional Monte Carlo model. The results indicate that the IEM model can be used to simulate the surface emission quite well for a wide range of surface roughness conditions with high confidence. Several important characteristics of the effects of surface roughness on radiometer emission signals at L-band 1.4 GHz that have not been adequately addressed in the current semiempirical surface effective reflectivity models are demonstrated by using IEM-simulated data. Using an IEM-simulated database for a wide range of surface soil moisture and roughness properties, we developed a parameterized surface effective reflectivity model with three typically used correlation functions and an inversion model that puts different weights on the polarization measurements to minimize surface roughness effects and to estimate the surface dielectric properties directly from dual-polarization measurements. The inversion technique was validated with four years (1979-1982) of ground microwave radiometer experiment data over several bare-surface test sites at Beltsville, Maryland. The accuracies in random-mean-square error are within or about 3% for incidence angles from 20/spl deg/ to 50/spl deg/.

Passive microwave Earth observing systems provide coarse resolution data. Heterogeneity in physical characteristics will typically be present within footprints, especially over land. How this affects the development and validation of methods of retrieving soil moisture has not been verified. In this study, aircraft-based 1.4 GHz microwave radiometer data were collected sit several altitudes over test sites where soil moisture was measured concurrently. The use of multiple flightlines at lower altitudes allowed the direct comparison of different spatial resolutions using independent samples over the same ground location. Results showed that the brightness temperature data from 1.4 GHz sensor in this study region provides the same mean values for an area regardless of the spatial resolution of the original data. The relationship between brightness temperature and soil moisture was similar at different resolutions. These results suggest that soil moisture retrieval methods developed using high resolution data can be extrapolated to satellite scales.

Satellite data collected by the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) and the special sensor microwave/imager (SSM/I) were compared to soil moisture observations as part of the Southern Great Plains (SGP) 1999 Experiment. SGP99 was conducted to address significant gaps in the knowledge base on the microwave remote sensing of soil moisture. Satellite, aircraft and ground based data collection were conducted between July 8, 1999 and July 20, 1999, during which an excellent sequence of meteorological conditions occurred. Cross calibration of the SSM/I data to the same TMI channels showed nearly identical brightness temperatures, 19 GHz SSM/I data and soil moisture relationships were similar to those observed in previous experiments in this region. Comparison studies of the SSM/I and TMI channels revealed that only sampling areas with adequate spatial domains should be used for soil moisture validation. Analyses of the TMI 10 GHz data provide new information on potential improvements that this channel can provide for soil moisture estimation. Soil moisture maps of the region were derived for dates of coverage.

ASSIVE microwave remote sensing of soil moisture has been a focus of research for several decades. Only in recent years has this work begun to address the key issues related to implementing this approach as part of large scale and global applications such as climate analysis and prediction. The papers collected in this Special Issue on Large Scale Passive Microwave Remote Sensing of Soil Moisture address one of three important aspects that will eventually contribute to operational studies. These are • theoretical and experimental investigations to develop and refine robust retrieval algorithms; • demonstration of retrieval techniques over regional scales using aircraft and satellite observations; • integration of remotely sensed soil moisture measurements in hydrologic applications. One additional paper is included that describes a planned L-band satellite mission. The impending launches of the ad

A ground-based experiment in passive microwave remote sensing of soil moisture was conducted in Huntsville, AL, from July 1-14, 1996. The goal of the experiment was to evaluate the overall performance of an empirically-based retrieval algorithm at S-band and L-band under a different set of conditions and to characterize the site-specific accuracy inherent within the technique. With high temporal frequency observations at S-band and L-band, the authors were able to observe large scale moisture changes following irrigation and rainfall events, as well as diurnal behavior of surface moisture among three plots, one bare, one covered with short grass and another covered with alfalfa. The L-band emitting depth was determined to be on the order of 0-3 or 0-5 cm below 0.30 cm/sup 3//cm/sup 3/ with an indication that it is less at higher moisture values. The S-band emitting depth was not readily distinguishable from L-band. The uncertainty in remotely sensed soil moisture observations due to surface heterogeneity and temporal variability in variables and parameters was characterized by imposing random errors on the most sensitive variables and parameters and computing the confidence limits on the observations. Discrepancies between remotely sensed and gravimetric soil moisture estimates appear to be larger than those expected from errors in variable and parameter estimation. This would suggest that a vegetation correction procedure based on more dynamic modeling may be required to improve the accuracy of remotely sensed soil moisture.

During the Southern Great Plains experiment (SGP99), the electronically scanned thinned array radiometer (ESTAR) mapped L-band brightness temperature over a swath about 50-km wide and 300 km long, extending west from Oklahoma City, OK, to El Reno, OK, and north from the Little Washita River watershed to the Kansas border. ESTAR flew on the NASA P-3B Orion aircraft at an altitude of 7.6 km, and maps were made on seven days between July 8-20, 1999. The brightness temperature maps reflect the patterns of soil moisture expected from rainfall and are consistent with values of soil moisture observed at the research sites within the SGP99 study area and with previous measurements in this area. The data add to the resources for hydrologic modeling in this area and are further validation of the technology represented by ESTAR as a potential path to a future mission to map soil moisture globally from space.

Surface soil moisture retrieval algorithms based on passive microwave observations, developed and verified at high spatial resolution, were evaluated in a regional scale experiment. Using previous investigations as a base, the Southern Great Plains Hydrology Experiment (SGP97) was designed and conducted to extend the algorithm to coarser resolutions, larger regions with more diverse conditions, and longer time periods. The L-band electronically scanned thinned array radiometer (ESTAR) was used for daily mapping of surface soil moisture over an area greater than 10000 km/sup 2/ for a one month period. Results show that the soil moisture retrieval algorithm performed the same as in previous investigations, demonstrating consistency of both the retrieval and the instrument. Error levels were on the order of 3% for area Integrated averages of sites used for validation. This result showed that for the coarser resolution used that the theory and techniques employed in the algorithm apply at this scale. Spatial patterns observed in the Little Washita Watershed in previous investigations were also observed. These results showed that soil texture dominated the spatial pattern at this scale. However, the regional soil moisture patterns were a reflection of the spatially variable rainfall and soil texture patterns were not as obvious.

Experiments were conducted using truck-based microwave radiometers operating at 1.41- (L-band) and 2.65-GHz (S-band) horizontal polarization to observe small plots during and following sprinkler irrigation. These experiments were conducted on a sandy loam soil in 1993 and a silt loam in 1995. Sandy loam soils typically have higher infiltration capabilities than clays, and in the authors' studies, they were not able to exceed this with the irrigation system. The observed brightness temperature (T/sub B/) quickly reached a nominally constant value during irrigation. When the irrigation was stopped, the T/sub B/ began to increase as drainage took place. Contributing depth-related differences were observed for L- and S-band as expected. The irrigation rates in 1995 with the silt loam soil exceeded the saturated conductivity of the soil. During irrigation, the T/sub B/ values exhibited a phenomenon that had not been previously observed and identified and is associated with coherent interference. The Land S-band exhibited similar patterns but were not identical due to contributing depth. These results suggested the existence of a sharp dielectric boundary (wet over dry soil) that was increasing in depth with time. The temporal description of the wetting front boundary was used with a coherent radiative transfer model to predict T/sub B/ for L- and S-band.

Surface roughness has a significant effect on the relationship between radar backscatter and soil moisture. In order to use existing radar satellite data for soil moisture, roughness effects must be corrected. A technique is presented that utilizes the data bases from soil erosion studies and soil moisture remote sensing investigations to provide first order estimates of the roughness parameters.

Microwave radiometers operating at low frequencies are sensitive to surface soil moisture changes. Few studies have been conducted that have involved multifrequency observations at frequencies low enough to measure a significant soil depth and not be attenuated by the vegetation cover. Another unexplored aspect of microwave observations at low frequencies has been the impact of diurnal variations of the soil moisture and temperature on brightness temperature. In this investigation, observations were made using a dual frequency radiometer (1.4 and 2.65 GHz) over bare soil and corn for extended periods in 1994. Comparisons of emissivity and volumetric soil moisture at four depths for bare soils showed that there was a clear correspondence between the 1 cm soil moisture and the 2.65-GHz emissivity and between the 3-5 cm soil moisture and the 1.4-GHz emissivity, which confirms previous studies. Observations during drying and rainfall demonstrate that new and unique information for hydrologic and energy balance studies can be extracted from these data.

ESTAR represents a new technology being developed for passive microwave remote sensing of the environment from space. The instrument employs an interferometric technique called aperture synthesis in which the coherent product from pairs of antennas is measured as a function of pair spacing. Substantial reductions in the antenna aperture needed for a given spatial resolution can be achieved with this technique. As a result, aperture synthesis could lead to practical passive microwave remote sensing instruments in space to measure parameters such as soil moisture and ocean salinity which require observations at long wavelengths and, therefore, large antennas. ESTAR is an L-band, aircraft built as part of research to develop this technique ESTAR is a hybrid real-and-synthetic aperture radiometer which employs stick antennas to achieve resolution along track and uses aperture synthesis to achieve resolution across track. Experiments to validate the instrument's ability to measure soil moisture have recently been conducted at the USDA watersheds at Walnut Gulch in Arizona and the Little Washita River in Oklahoma. The results of both experiments indicate that a valid image reconstruction and calibration have been obtained for this remote sensing technique.>

MACHYDR0'90 was an experiment conducted in Pennsylvania in 1990 to study the synergistic use of remote sensors in multitemporal hydrologic studies. As part of this mission the pushbroom microwave radiometer was flown and used to produce brightness temperature maps. Verification studies and vegetation algorithms for mixed land cover areas are described.>

The application of an airborne electronically steered thinned array L-band radiometer (ESTAR) for soil moisture mapping was investigated over the semiarid rangeland Walnut Gulch Watershed in southeastern Arizona. During the experiment, antecedent rainfall and evaporation were very different and resulted in a wide range of soil moisture conditions. The high spatial variability of rainfall events within this region resulted in moisture conditions with distinct spatial patterns. Analysis showed a correlation between the decrease in brightness temperature after a rainfall and the amount of rain. The sensor's performance was verified using two approaches. First, the microwave data were used to predict soil moisture, and the predictions were compared to ground observations of soil moisture. A second verification used an extensive data set collected the previous year at the same site with a conventional L-band push broom microwave radiometer (PBMR). Both tests showed that the ESTAR is capable of providing soil moisture with the same level of accuracy as existing systems.>

The effects of the rock fraction were investigated through a combination of laboratory dielectric measurements and field observation of emissivity. A series of field measurements were conducted which included soils with (35% by volume) and without rocks. Analysis focused on the use of a 21-cm wavelength, although some field observations at 6 cm were also made. For the rock samples, the average values of the dielectric constant were 4.7 and 0.07 for the real and imaginary parts, respectively. The effects of rock fraction are not significant in estimating the sample soil moisture when 21-cm data are used, for the rock fraction examined. Data collected at 6 cm clearly showed that the presence of rocks will make this and shorter wavelengths useless as soil moisture sensors.>

A layer of vegetation over the soil surface absorbs some of the radiation emitted from the soil and emits at its own temperature. This results in a reduction of the information in the microwave radiation about the soil surface. To study this problem further the authors use the model of F.T. Ulaby and M.A. El-Rayes (1987) for the dielectric constant of vegetation to estimate the absorption loss and optical depth, tau , of plant canopies for frequencies between 1 and 40 GHz. The authors treated tau as the product of a vegetation parameter b and vegetation water content, VW. They compared both the linear and square root (refractive) mixing models with the observed data in terms of the b parameter. These data were obtained from published reports on the values of tau and VW for crops ranging from prairie grass to corn and soybeans. The data fit the curve for the refractive model quite well. For the refractive model the value of b was independent of VW, while for the linear model there was some dependence on VW. For both models b is roughly proportional to the frequency.>