We report a study of radio frequency interference (RFI) in the GPS L1 band (1575.42 ± 2 MHz) using spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data. An examination of the “noise” levels reported by both Spire, Inc. and CYGNSS (Cyclone Global Navigation Satellite System) standard Level 1 products is first presented that shows clear evidence of RFI contributions. Data from 498 acquisitions in CYGNSS’s special raw I/F (Intermediate Frequency) mode is then used to examine RFI source properties in greater detail. Both kurtosis and cross-frequency algorithms are applied with the raw I/F data to detect the presence of RFI. The results suggest that up to 25% of the acquisitions considered contain RFI, with both “narrow” and “wideband” sources of varying amplitudes observed. The results provide insights into RFI source properties in the GPS L1 band as well as methods by which the RFI can be detected.

The development of soil moisture retrieval algorithms for the upcoming NASA/ISRO SAR mission motivates investigations into soil surface scattering effects and their influence on soil moisture retrievals. To better understand the conditions that impact retrieval performance, an investigation is performed into the second-order solution small slope approximation (SSA2) for rough surface backscattering. Scattering amplitudes are calculated for varying soil moisture and surface roughness conditions and compared to the first-order solution. The results show the ability of the second-order solution to model cross-pol scattering and help pinpoint physical conditions that may influence soil moisture retrievals. Overall, the results suggest that the first-order SSA solution should be applicable under the physical conditions where NISAR soil moisture retrievals are expected to be performed.

The use of a “time-series ratio” soil moisture retrieval approach is under consideration for the upcoming NISAR mission’s soil moisture product. As such, it is of interest to characterize the algorithm’s anticipated error budget as part of pre-launch activities. This paper develops an error model to estimate retrieval errors for the proposed algorithm. The model accounts for error contributions from speckle and thermal noise as well as uncertainties that arise as part of the retrieval process. Spatial and temporal behaviors of the retrieval errors are determined using a SMAP-based soil moisture climatology. The results show that soil moisture retrieval errors from the time-series ratio method meet the 0.06 m3/m3unbiased root mean square error (URMSE) performance metric established for the NISAR soil moisture product.

In this paper we provide an update on the development status and performance estimates for the proposed Global L-band active/passive Observatory for Water cycle Studies (GLOWS) L-band active/passive SMAP continuity mission. GLOWS is currently funded by the NASA Instrument Incubator Program.

Land-reflected signals from a geosynchronous communication satellite broadcasting in P-band (367.5 MHz) were captured in low Earth orbit using a simple dipole antenna. A delay-Doppler map (DDM) was generated through autocorrelation. Estimates of the specular point delay were obtained from the lag of the second peak in the DDM with a bias of 239.4 m and a standard deviation of 44 m (12 m over a frozen lake) with respect to a predicted orbit model. Relative magnitudes of the first and second DDM peaks fell within the range of values predicted using dielectric models for the frozen ground and lake. Lastly, retrievals of surface reflection coefficient were generated using a range of realistic values for the transmitter link budgetG/T, these also fell within the range of possible values for the antenna gain pattern. Given the lack of calibration and the large uncertainties in the receiver orbit and attitude, this agreement is sufficient to conclude a successful demonstration of the fundamental principle of single-antenna reflectometry in P-band. P-band reflectometry may offer a new approach to remote sensing of sub-canopy and root-zone soil moisture.

The National Aeronautics and Space Administration (NASA) - Indian Space Research Organization (ISRO) Synthetic Aperture Radar (NISAR) mission plan to launch a SAR operating at L- and S-band with a 12-day repeat frequency. A global soil moisture product at 200 m spatial resolution derived from 200 m NISAR radar measurements is currently under development. Although several retrieval algorithms are being investigated, this paper focuses on a “time series ratio” retrieval approach. In order to understand and assess the performance of this algorithm, an error model has been developed and is reported in this paper. The model is applied to examine errors as a function of the instrument characteristics and for a given location. Initial progress in including vegetation effects and in predicting errors as a function of spatial location is also described.

Science and application results appearing in peer-reviewed journal papers in 2021 are highlighted in this paper. With over six years of science data acquisition, science data products of the NASA Soil Moisture Active Passive (SMAP) satellite project are now being applied in diverse subdisciplines in Earth System science. In 2021, there were close to two-hundred papers appearing in peer-reviewed disciplinary journals. In this paper we highlight a few of the research and applications findings that were reported in the calendar year.

SigNals Of Opportunity: P-band Investigation (SNOOPI) will be the first in-space validation of P-band (240–380 MHz) SoOp techniques and a prototype science instrument. These techniques have the potential to enable remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE). SNOOPI technology validation goals will be met by targeting observations within 9 km of the SMAP calibration/validation sites in the continental United States. A second priority is collection of continuous phase data over snow-covered regions. These goals are evaluated under constraints of a limited data budget and mission lifetime, with a launch readiness in August 2022. This presentation will review the instrument science plans aimed at achieving the validation objectives defined for the mission. Mission planning and data processing approaches are described.

L- and S-band observations from NASA's Passive/Active L/S band (PALS) sensor from the SMEX02 campaign were used to estimate soil moisture. The retrieval process is based on the “alpha approximation” method. This method utilizes a time-series of normalized radar backscatter measurements as well as ancillary information to estimate soil moisture over the Walnut Creek watershed. The resulting retrieved soil moistures are compared to in-situ soil moisture measurements at multiple test sites within the watershed. The calculations show reasonable results for both L- and S-band and provide further insight into the use of L- and S-bands for the upcoming NASA/ISRO mission.

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

SMOS and SMAP radiometers have demonstrated the ability to monitor soil moisture and sea surface salinity and continue to provide high quality radiometric measurements to this day in extended mission operations. It is important to maintain data continuity for these science measurements. The proposed instrument concept (Global L-band active/passive Observatory for Water cycle Studies - GLOWS) will enable low-cost L-band data continuity (that includes both L-band radar and radiometer measurements). The objective of this project is to develop key instrument technology to enable L-band observations using an Earth Venture class satellite. Specifically, a new deployable reflectarray lens antenna is being developed that will enable a smaller EELV Secondary Payload Adapter (ESPA) Grande-class satellite mission to continue the L-band observations at SMAP and SMOS resolution and accuracy at substantially lower cost, size, and weight.

The NASA ISRO Synthetic Aperture Radar (NISAR) mission is currently under development and will provide global L-band radar observations that will be helpful for various soil moisture applications. The final NISAR soil moisture product will have 200m spatial resolution with 12-day exact revisit time. A time-series ratio algorithm was implemented using NISAR simulated UAVSAR data collected during the SMAPVEX12 field experiment. In this paper, the performance of the time series ratio algorithm was assessed using in situ observations. Performance of the soil moisture retrieval algorithm was also assessed for dual polarization and quad-polarization observations modes.

Following the launch of multiple global navigation satellite system (GNSS) reflectometry (GNSS-R) missions, the Signals of Opportunity (SoOp) method has proven to be a powerful tool for geophysical parameter retrieval for land applications such as soil moisture. Having demonstrated the feasibility of the SoOp techniques at P- and S-band, the development of SoOp measurements beyond the GNSS frequency regime is highly anticipated. The SoOp Coherent Bistatic (SCoBi) model and simulator, developed in 2017 and open-sourced in 2018, has been made available to provide multifrequency, fully polarimetric SoOp simulations for ground-based applications through the joint use of analytical wave theory and distorted Borne approximation to evaluate land contributions from multilayer dielectric profiles composed of soil moisture, vegetation, and surface roughness effects. This paper describes the advancement of SCoBi from a ground-and airborne-based model to a spaceborne model. This extension allows for fully polarimetric, complex delay-Doppler map (DDM) simulations through evaluation of the coherent superposition of electric fields emerging from a grid of oriented facets. The model generates a grid of facets by determining the geometry of contributing elements from digital elevation models, with each element providing its contribution under a flat-earth assumption. This module will enable the analysis of fully polarimetric scattering from frequencies available across the ultra-high frequency (UHF) regime.

In August 2020, SMAP released a new version of its soil moisture (SM) and vegetation optical depth (VOD) products. In this work, we review the methodology followed by the SMAP regularized dual-channel (DCA) retrieval algorithm. We show that the new implementation generated SM retrievals that not only satisfy the SMAP accuracy requirements but also show a performance comparable to the baseline single-channel algorithm that uses the V polarized brightness temperature (SCA-V). Due to a lack of in situ measurements we cannot evaluate the accuracy of the VOD, but in this work, we will show analysis with the intention of providing an understanding of the VOD product.

SigNals Of Opportunity: P-band Investigation (SNOOPI) will be the first on-orbit demonstration of remote sensing using Signals of Opportunity (SoOp) in P-band (240–380 MHz). P-band is needed to penetrate through dense vegetation and into the root zone. The longer wavelength of P-band also increases the unwrapping interval for phase observations. These observations hold the potential for spaceborne remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE), two variables identified as priorities in the 2017–2027 Decadal Survey for Earth Science and Applications from Space. SNOOPI will provide in-space validation of both the P-band SoOp technique and a science instrument prototype. SNOOPI technology validation goals will be met by targeting observations within 9 km of the SMAP calibration/validation sites in the continental United States. A secondary priority is collection of continuous phase data over snow-covered regions. These goals are evaluated under constraints of a limited data budget and mission lifetime, with a launch readiness in early 2022. Updates on the development of measurement models and mission planning to support SNOOPI are provided. A ground-based station will be deployed to monitor the noncooperative sources, in order to reduce risk due to uncertainty in knowledge of the broadcast power, spectrum shape, and orbital position.

S-band observations from NASA's Passive/Active L/S Band (PALS) radar from the SMEX02 campaign were used to estimate soil moisture. The “alpha” method is applied for this process, in which a time series of ratios of normalized radar cross section values at successive measurements is used to infer the corresponding soil moisture time series given ancillary information on the minimum and maximum soil moisture values expected over the time series. Results are examined as a function of the polarization and crop type. The results show reasonable retrieval performance, indicating the potential of using S-band observations from the future NASA/ISRO SAR (NISAR) mission.

L-band observations have proven useful for estimating soil moisture and ocean salinity variables to study the land surface and ocean. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission was the first (2009-present) spaceborne L-band radiometer. This was followed by two L-band missions flown by the National Aeronautics and Space Administration (NASA) to measuresea surface salinity (Aquarius 2011–2015) and soil moisture (SMAP 2015-present). It is critical to continue the time series of L-band observations that these missions have begun. To address this need we propose a new low-cost instrument concept known as the Global L-band active/passive Observatory for Water cycle Studies (GLOWS) that will include an L- band radiometer and radar to provide data continuity. The new mission concept includes a deployable reflectarray lens antenna with a compact feed that can be flown on an Earth Venture class satellite in a EELV Secondary Payload Adapter (ESPA) Grande-class mission. GLOWS will continue the science observations of SMAP and SMOS at the same resolution and accuracy at substantially lower cost, size, and weight.

The NASA ISRO Synthetic Aperture Radar (NISAR) mission is currently under development and is scheduled for launch in 2022. The NISAR mission will provide global data sets of Earth land surface dynamics that are critical for multiple Earth Science disciplines including observations of ecosystem carbon and water cycles. Global L-band radar observations at high spatial resolution will be helpful for soil moisture applications. One of the goals of the NISAR mission is to provide a global soil moisture product at 200 m resolution with a global revisit frequency of 6 days. A time-series ratio algorithm was implemented using NISAR simulated SMAPVEX12 UAVSAR data, which is an L-band airborne radar backscatter measurement. For a NISAR-like configuration, backscatter at incidence angles from 30 to 50 degrees was considered in this study. The initial retrieval statistics following comparisons with in-situ ground truth show correlation coefficients (R) to be about 0.81, and the unbiased RMSE to be about 0.06 m3/m3. Results from dual co-polarization and/or cross-polarization modes were evaluated and considered for performance improvement.

Timely, frequent, and complete cropland soil moisture information acquired throughout the growing season is critical for agricultural policy, production, food security, and food prices. The NASA Soil Moisture Active and Passive (SMAP) mission provides a reliable data source for cropland soil moisture assessment. This paper presents Crop-CASMA - a web GIS application tool for cropland soil moisture monitoring and assessment based on SMAP data. This interactive Web service-based GIS application tool enables CONUS SMAP derived soil moisture data visualization, dissemination, and analytics. In this paper, we describe the Crop-CASMA application system architecture, the application implementation, and the data it serves. In addition, we also present a few snapshots of the Crop-CASMA data for cropland soil moisture monitoring. The release of Crop-CASMA greatly enhances the user experience and facilitates using soil moisture data products for crop condition monitoring and decision support.

The National Aeronautics and Space Administration (NASA) - Indian Space Research Organization (ISRO) Synthetic Aperture Radar (NISAR) is being developed in order to provide highly spatial resolution L- and S-band backscatter observations of the Earth's surface. Remotely sensed soil moisture is one of NISAR applications of interest. This paper reports an initial analysis of the possibility of using NISAR modes to retrieve soil moisture based on two different methods: a snapshot method and the time series technique. Preliminary results of the forward modeling of the backscattered signal over land and the soil moisture retrievals are presented for bare surfaces.

SigNals of Opportunity: P-band Investigation (SNOOPI) will be an in-space technology demonstration of reflectometry using 240-380 MHz communications transmissions. SNOOPI will both demonstrate essential techniques for root-zone soil moisture (RZSM) and snow water equivalent (SWE) remote sensing as well as provide in-space validation of prototype instrument technology. This paper presents results from studies conducted to define key parameters of the SNOOPI mission, including orbital coverage, signal processing, and the estimated power from the non-cooperative sources.

After almost 10 years in operation (SMOS- Aquarius - SMAP) the very high potential of L band radiometry is clearly demonstrated. Several applications are already operational (assimilation at ECMWF, for hurricanes, for sea ice etc.) so it is crucial to maintain such measurements. To do so while satisfying the current missions specifications is also of prime importance. Degrading spatial resolution is thus a significant step back which will impact science and applications). These missions are now getting older and the goal of the study presented in this paper is to assess which planned mission could fulfill the requirements to ensure data continuity. For this purpose, an extensive users' requirements study was performed in 2018-2019 assessing what would be required in the near future as well as when L band radiometry was absolutely necessary to satisfy the requirements. From the gathered results a cluster analysis was performed and the only.

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

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 potentiality of Signals of Opportunity (SoOp) over land can be investigated by advanced forward and inverse modeling and simulation tools to provide viable measurements for Earth science data products over land. This research investigates various inversion techniques that can leverage SoOp sources for land-based Earth science measurements by applying them to simulated soil moisture profiles over bare- and vegetated- soils. Forward modeling is accomplished using Mississippi State University’s Signals of Opportunity Coherent Bistatic Scattering Model (SCoBi), a new, open-source electromagnetic scattering model that can determine coherent received signals at a receiving antenna through application of Maxwell’s equations at discrete scattering soil layer boundaries in conjunction with the distorted Born approximation to describe vegetation propagation and scattering. The results of the forward model are used in various inverse methods to investigate the potentiality of using multiple SoOp sources for Soil Moisture Profile (SMP) retrieval. Multiple SMPs are analyzed by SCoBi to determine the sensitivity of soil moisture variation to SoOp transmitter characteristics such as polarization and elevation angle. Simultaneously, SoOp measurements conducted at Purdue University’s Agronomy Center for Research and Education (ACRE) are used to determine the impact that changes in both physical SMPs and vegetation canopies have on the scattered SoOp. The characteristics of the scattering surfaces, vegetation, and SMPs at the ACRE facility are modeled within SCoBi to observe patterns and relationships captured in reflectivity measurements that are caused by vegetation growth periods as well as rain and drought effects manifested by changing SMPs.

In this paper, we explore a time series approach to using the tau-omega (τ-ω) model to retrieve vegetation water content (kg/m2) with minimal use of ancillary data. Analytically, this approach calls for nonlinear optimization in two steps. First, multiple days of co-located brightness temperature observations are used to retrieve the effective vegetation opacity, which incorporates the combined radiometric and polarization effects of surface roughness and vegetation opacity. The resulting effective vegetation opacity is then used to retrieve vegetation water content to within a gain factor α and an offset factor β. By using a climatological vegetation water content ancillary database as the one adopted in the development of the SMAP standard and enhanced soil moisture products, α and β can be determined globally using the annual minimum and annual maximum of vegetation water content. The resulting values of α and β can then be used to reconstruct the retrieved vegetation water content. Formulation, assumptions, and limitations of this approach are presented alongside the preliminary global retrieval of vegetation water content using one year (2016) of SMAP brightness temperature observations.

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.

SigNals of Opportunity: P-band Investigation (SNOOPI) will be the first on-orbit demonstration of remote sensing using Signals of Opportunity (SoOp) in P-band (240-380 MHz). P-band SoOp has the potential for spaceborne remote sensing of root-zone soil moisture (RZSM) and snow water equivalent (SWE), two variables identified as priorities in the 2017-2027 Decadal Survey for Earth Science and Applications from Space. P-band is needed to penetrate through dense vegetation and into the root zone. SNOOPI will provide inspace validation of both the technique of P-band SoOp and a science instrument prototype. This is a necessary risk-reduction step on the path to a science mission, which will verify important assumptions about reflected signal coherence, robustness to the RFI environment, and our ability to capture and process the reflected signal from orbit. SoOp observations will be used to estimate the complex reflection coefficient over various land surface conditions. These will be used to verify models and show that P-band SoOp can meet working requirements for future RZSM and SWE missions. The SNOOPI instrument design builds upon the heritage of a low noise front end (LNFE), developed from an airborne demonstrator, and a digital back end (DBE) evolved from the Cion, TriG and Blackjack GPS receivers. Success with SNOOPI will retire the critical risks associated with a P-band SoOp satellite instrument and exit at TRL-7. Not only would this instrument enable direct measurements of RZSM and SWE which are not presently possible, it's size, weight, power and cost (SWaP-C) would also be orders of magnitude smaller than comparable monostatic radars due to the re-utilization of existing, powerful, anthropogenic signals.

This letter presents the results evaluating retrievals of liquid water content (θliq) performed with a zero-order radiative transfer (τ-ω) model under frozen and thawed soil conditions from Soil Moisture Active Passive (SMAP) and ELBARA-III brightness temperature (TBp) measurements collected over a Tibetan meadow ecosystem. A good agreement is found between time series of the SMAP and ELBARA-III measured TBpresulting in a Pearson product-moment coefficient (R) larger than 0.87. Differences noted between the two data sets can be associated with discrepancies in θliqmeasured in the specific footprints, whereby the SMAP measurements are best explained by the in situ θliq. Furthermore, the in situ θliqhas a better agreement with the horizontally polarized SMAP and ELBARA-III measurements (THB ) in the cold season, whereas the vertically polarized measurements (TVB ) are1111better correlated with θliqin the warm season. With the implementation of new vegetation and surface roughness parameterizations for the τ-ω model, the dynamics of in situ θliqis better reproduced by corresponding retrievals for both frozen and thawed soil conditions, leading to the reduction in the unbiased root-mean-square error (ubRMSE) by more than 31% in comparison with these retrievals using SMAP default parameterizations. Notably, the single-channel algorithm configured with the new parameterizations using SMAP TVB measured during the ascending overpass provides the best θliqretrievals with a ubRMSE of 0.035 m3·m-3that is well within the SMAP mission requirements.

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.

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 (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.

Root zone soil moisture (RZSM) is an essential variable in meteorology, hydrology, and agriculture. A penetration depth sufficient to sense RZSM requires frequencies below about 500 MHz (I- and P-band). Active or passive microwave sensing in these bands presents substantial technical challenges due to antenna size, radio frequency interference (RFI) and competition for spectrum. Bistatic radar using Signal of Opportunity (SoOp) (e.g. digital satellite transmitters) offers an alternative approach, through reutilizing powerful signals already occupying bands allocated for communications. Airborne experiments using 240-270 MHz sources were conducted in October 2016, followed by a campaign using 360-380 MHz from a fixed tower location in an agricultural research site during the 2017 growing season. A new campaign that will also include I-band (137 MHz) is presently being installed in advance of the 2018 season. This paper will summarize activities to support the reduction of data from these campaigns and development of soil moisture profile retrievals.

After almost 9 years in orbit L band satellite radiometry has demonstrated its impacts and values for a wide range of science and applications. In some cases it has demonstrated its uniqueness for assessing key environmental variables and in many others its high impact.

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

Microwave remote sensing measurements at L-band (~1.2-1.6 GHz) of geophysical parameters such as soil moisture will need to be at higher spatial resolution than current systems (SMOS/SMAP/ Aquarius) in order to meet the requirements of land surface, ocean, and numerical weather prediction models in the near future, which will operate at ~9-15 km global grids and 1-3 km regional grids in the next few years. In order to make progress toward these needed spatial resolutions, advancements in technology are necessary which would lead to improved effective (i.e. equivalent) antenna size. An architecture trade study was conducted to quantitatively define the value and limits of different microwave technology paths, and to select the most appropriate path to achieve the high spatial resolution required by science in the future without sacrificing performance, accuracy, and global coverage.

The SMAP microwave radiometer will see its third anniversary of operations on March 31, 2018. Instrument behavior is stable over 33 months of operation to date. The physical temperature of the internal calibration sources varies 0.5°C. The bias current of the noise source drifted by less than 0.1%. The avalanche breakdown voltage of the noise diode shows 0.01% seasonal variation. The average NEDT of the radiometer has maintained a stable 1-K value over the period. This stable behavior of the hardware is critical for the consistent calibration. The reflector emissivity was re-estimated using on-orbit data. Use of the new value nearly eliminates bias caused by solar eclipse during the southern hemisphere winter. The radiometer data were recalibrated using, as earlier, global ocean and cold sky views with additional ocean and land views at nadir incidence. The Version 4 recalibrated data exhibit 0.1-K RMS stability over average global ocean and monthly cold-sky views.

The Soil Moisture Active Passive (SMAP) satellite mission launched in January 2015 provides worldwide soil moisture (SM) monitoring based on L-band brightness temperature (TBp) measurements at horizontal (TBH) and vertical (TBV) polarizations. This paper presents a performance assessment of SMAP soil emission model and SM retrieval algorithms for a Tibetan desert ecosystem. It is found that the SMAP emission model largely underestimates the SMAP measured THB(≈ 15 K), and the TBVis underestimated during dry-down episodes. A cold bias is noted for the SMAP effective temperature due to underestimation of soil temperature, leading to the TBpunderestimation (>5 K). The remaining TBHunderestimation is found to be related to the surface roughness parameterization that underestimates its effect on modulating the TBpmeasurements. Further, the topography and uncertainty of soil information are found to have minor impacts on the TBpsimulations. The SMAP baseline SM products produced by single-channel algorithm (SCA) using the TBVmeasurements capture the measured SM dynamics well, while an underestimation is noted for the dry-down periods because of TBVunderestimation. The products based on the SCA with TBHmeasurements underestimate the SM due to underestimation of TBH, and the dual-channel algorithm overestimates the SM. After implementing a new surface roughness parameterization and improving the soil temperature and texture information, the deficiencies noted above in TBpsimulation and SM retrieval are greatly resolved. This indicates that the SMAP SM retrievals can be enhanced by improving both surface roughness and adopted soil temperature and texture information for Tibetan desert ecosystem.

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.

NASA's Soil Moisture Active Passive (SMAP) mission launched on January 31, 2015 into a sun-synchronous 6 am/6 pm orbit with an objective to produce global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days. The SMAP radiometer began acquiring routine science data on March 31, 2015 and continues to operate nominally. SMAP's radiometer-derived standard soil moisture product (L2SMP) provides soil moisture estimates posted on a 36-km fixed Earth grid using brightness temperature observations and ancillary data. A beta quality version of L2SMP was released to the public in October, 2015, Version 3 validated L2SMP soil moisture data were released in May, 2016, and Version 4 L2SMP data were released in December, 2016. Version 4 data are processed using the same soil moisture retrieval algorithms as previous versions, but now include retrieved soil moisture from both the 6 am descending orbits and the 6 pm ascending orbits. Validation of 19 months of the standard L2SMP product was done for both AM and PM retrievals using in situ measurements from global core cal/val sites. Accuracy of the soil moisture retrievals averaged over the core sites showed that SMAP accuracy requirements are being met.

The Soil Moisture Active Passive (SMAP) mission was launched on 31stJanuary 2015 in a 6 AM/6 PM sun-synchronous orbit at 685 km altitude to measure soil moisture and free/thaw globally [1]. The passive instrument of SMAP is a fully polarimetric L-band radiometer (1.4GHz) operating with a bandwidth of 24MHz. The radiometer uses a combination of noise-diodes and Dicke-loads for internal calibration with a design similar to that used by the Aquarius or Jason series radiometers [3]. The SMAP digital backend back-end enables implementation of advanced Radio Frequency Interference (RFI) detection and mitigation algorithms for corrupted L-band measurements [6]. The radiometer uncalibrated raw counts are converted to Level 1B antenna temperatures and brightness temperature (TB) values [2]. These TB values are used with other ancillary data to retrieve soil-moisture products on a 40km global grid. The error requirement for the SMAP radiometer is 1.3K and calibration drift is less than 0.4 K/month to measure soil-moisture with volumetric fraction uncertainty of less than 0.04 m3/m3.

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.

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.

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).

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).

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 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.

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.

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 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.

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.

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

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

Vegetation water content (VWC) is a useful parameter in agriculture, forestry and hydrology studies. It is particularly valuable in accounting for vegetation effects in retrieving soil moisture using microwave radiometers. Microwave vegetation indices (MVIs) reflect information of the whole vegetation canopy. They may provide a mean for estimating VWC. In this study, a methodology for retrieving VWC using MVIs is presented. Coefficients of the relationship were found to be dependent only on a vegetation structure parameter. The methodology was tested with brightness temperature observations at C and X bands collected over a growing season of cotton. It was found that results compared well with field observations of VWC measured during the early growing season. The methodology should be useful for vegetation monitoring and soil moisture retrieval over low vegetated areas.

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.

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.

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

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

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.

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.

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.

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.

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.

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.

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

An unresolved issue in global soil moisture retrieval using passive microwave sensors is the spatial integration of heterogeneous landscape features to the nominal 50 km footprint observed by most satellite systems. One of the objectives of the Soil Moisture Experiments 2004 (SMEX04) was to address some aspects of this problem, specifically variability introduced by topography and convective precipitation. Other goals included understanding the role of the land surface in the North American Monsoon System. Data were collected during the month of August 2004 at three scales; ground based point measurements, aircraft passive microwave mapping, and satellite observations using AMSR-E and other sensors. SMEX04 was conducted over two regions: Arizona - semi-arid climate with sparse vegetation and moderate topography, and Sonora (Mexico) - moderate vegetation with strong topographic gradients. The Polarimetric Scanning Radiometer (PSR/CX) was flown on a Naval Research Lab P-3B aircraft as part of SMEX04 (11 dates of coverage over Arizona and 10 over Sonora). General meteorological conditions, the PSR/CX data sets and selected comparisons of this data to AMSR-E are presented.

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

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.

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.

The representation of subpixel variability in soil moisture estimates from passive microwave data was investigated through sensitivity analysis and by comparison against the spatial structure of soil moisture fields derived from radar data. This work shows that the subpixel variability not represented in brightness temperature fields is directly associated with the spatial organization of soil hydraulic properties and the spatial distribution of vegetation. The significant implication of this result is that the physical connection between soil moisture estimates at the pixel scale and local values within the pixel weakens strongly as the sensor resolution decreases. Subsequently, the application of scaling and fractal interpolation principles to downscale passive microwave data to the spatial resolution of radar data was investigated as a means to recover spatial structure. In particular, ESTAR soil moisture data was successfully downscaled from 200 to 40 m using only one radar frequency (e.g., L-band). This application suggests that the combined use of active and passive single-band microwave remote-sensing of soil moisture is a viable approach to improve the spatial resolution of soil moisture remote-sensing.

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.

During the 1997 Southern Great Plains Hydrology Experiment (SGP97), passive microwave observations using the L-band electronically scanned thinned array radiometer (ESTAR) were used to extend surface soil moisture retrieval algorithms to coarser resolutions and larger regions with more diverse conditions. This near-surface soil moisture product (W) at 800 m pixel resolution together with land use and fractional vegetation cover (f/sub c/) estimated from normalized difference vegetation index (NDVI) was used for computing spatially distributed sensible (H) and latent (LE) heat fluxes over the SGP97 domain (an area /spl sim/40/spl times/260 km) using a remote sensing model (called the two-source energy Balance-soil moisture, TSEB/sub SM/, model). With regional maps of W and the heat fluxes, spatial correlations were computed to evaluate the influence of W on H and LE. For the whole SGP97 domain and full range in f/sub c/, correlations (R) between W and LE varied from 0.4 to 0.6 (R/spl sim/0.5 on average), while correlations between W and H varied from -0.3 to -0.7 (R/spl sim/-0.6 on average). The W-LE and W-H correlations were dramatically higher when variability due to f/sub c/ was considered by using NDVI as a surrogate for f/sub c/ and computing R between heat fluxes and corresponding W values under similar fractional vegetation cover conditions. The results showed a steady decline in correlation with increasing NDVI or f/sub c/. Typically, |R|/spl gsim/0.9 for data sorted by NDVI having values /spl lsim/0.5 or f/sub c//spl lsim/0.5, while |R|/spl lsim/0.5 for the data sorted under high canopy cover where NDVI/spl gsim/0.6 or f/sub c//spl gsim/0.7.