The present study carried out to parameterize the single channel soil moisture active passive (SMAP) passive soil moisture (SM) retrieval algorithm, over Indian conditions. The moderate resolution imaging spectroradiometer (MODIS) data products and soil texture data were used for an improved parameterization of the algorithm. The bias correction was applied to the MODIS leaf area index (LAI) for accurate computation of vegetation optical depth. The necessary vegetation and roughness parameter were calibrated through minimization of the error between model retrieved and ground measured SM. The value of root mean square error (RMSE) for retrieved SM was found as$0.059\,\,m^{3}m^{-3}$with bias and correlation coefficients of$0.036\,\,m^{3}m^{-3}$and 0.724 for ascending overpass, respectively, while a lower value was recorded (RMSE =$0.059\,\,m^{3}m^{-3}$, bias =$0.024\,\,m^{3}m^{-3}$, and correlation coefficients = 0.752) for descending overpass. The same method is also implemented on two other test sites in different regions of India to check the model robustness, which indicates that the current parameterization provides a better estimate of SM over croplands in India. The overall performance of new parameterized model is found as (RMSE = 0.052 and bias = 0.034) for ascending and descending (RMSE = 0.048 and bias = 0.026) satellite overpasses for all the three test sites. Additionally, the intercomparing of various operational SM products SMAP SM (L2_SM_P), Soil Moisture and Ocean Salinity (SMOS) SM (SMOS_L3_SM), and SMOS-IC data products was carried out with the SAC-ISRO PAN India SM network, which showed a significant RMSE, dry and wet biases over all three test sites as compared to the developed improved parameterized algorithm.

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

In 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.

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

An active–passive microwave retrieval algorithm for simultaneous determination of soil surface roughness parameters [vertical root-mean-square (RMS) height (${s}$) and horizontal correlation length (${l}$)] is presented for bare soils. The algorithm is based on active–passive microwave covariation, including the improved Integral Equation Method (I2EM), and is tested with global soil moisture active passive (SMAP) observations. The estimated retrieval results for${s}$and${l}$are overall consistent with values in the literature, indicating the validity of the proposed algorithm. Sensitivity analyses showed that the developed roughness retrieval algorithm is independent of permittivity for${\varepsilon }_{s} > 10$[-]. Furthermore, the physical model basis of this approach (I2EM) allows the application of different autocorrelation functions (ACF), such as Gaussian and exponential ACFs. Global roughness retrieval results confirm bare areas in deserts such as Sahara or Gobi. However, the type of ACF used within roughness parameter estimation is important. Retrieval results for the Gaussian ACF describe a rougher surface than retrieval results for the exponential ACF. No correlations were found between roughness results and the amount of precipitation or the soil texture, which could be due to the coarse spatial resolution of the SMAP data. The extension of this approach to vegetated soils is planned as an add-on study.

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

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 National Aeronautics Space Administration's (NASA`s) Soil Moisture Active Passive (SMAP) mission will be completing its first extension phase in August 2020. The uncertainty of SMAP soil moisture products is≤ 0.04 m3/m3. During the first extension phase, SMAP data have been used to advance our understanding of water, energy and carbon cycles. Significant progress has also been made to transition the use of SMAP data to operational communities. In particular, the United States Air Force (USAF) and United States Department of Agriculture (USDA) Foreign Agriculture Service (FAS) have included SMAP data in their operational forecast systems. The SMAP project has been performing a recalibration of radiometer data using four years of cold sky maneuver data. The recalibrated data and updated soil moisture and freeze/thaw products will be presented during the meeting. The SMAP project is preparing an extension proposal to continue the data acquisition and processing activities for another three years (2021-2023) and also identifying additional activities for 2024-2026. We will describe the activities for the second extension phase, including plans for SMAPVEX20 and `22 field campaigns.

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.

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

The 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.

Soil roughness strongly influences processes like erosion, infiltration, moisture and evaporation of soils as well as growth of agricultural plants. An approach to soil roughness based on active-passive microwave covariation is proposed in order to simultaneously retrieve the vertical RMS height (s) and horizontal correlation length (l) of soil surfaces from simultaneously measured radar and radiometer microwave signatures. The approach is based on a retrieval algorithm for active-passive covariation including the improved Integral Equation Method (I2EM). It is tested with the global active-passive microwave observations of NASA's Soil Moisture Active Passive (SMAP) mission. The developed roughness retrieval algorithm shows independence of permittivity for εs> 10 [-] due to the covariation formalism. Results reveal that s and l can be estimated simultaneously by the proposed approach since surface patterns of nonvegetated areas can be assessed on global scale. In regions with sandy deserts, like the Sahara or the outback in Australia, determined s and l confirm rather smooth to semi-rough surface roughness patterns with most frequent vertical RMS heights smaller 3 cm and corresponding higher horizontal correlation lengths (> 8 cm).

SCoBi-Veg stands for Signals of opportunity Coherent Bistatic scattering model for Vegetated terrains. It simulates polarimetric reflectometry of vegetation canopy over a flat ground using a Monte Carlo scheme. The model is aimed at assessing the value of navigation and communication satellite Signals of Opportunity in a range of frequencies from P- to S-bands for remote sensing of a number of geophysical land parameters such as soil moisture and biomass. A fully polarimetric expression for bistatic scattering from a vegetation canopy is first formulated for a general case and is then specialized to the practical case of ground-based/low-altitude platforms with passive receivers overlooking vegetation using the signals transmitted from large distances. Using analytical wave theory in conjunction with distorted Born approximation, the transmit and receive antenna effects (i.e., polarization crosstalk/mismatch, orientation, and altitude) are explicitly accounted for. The forward model developed here enables the understanding of the effect of different geophysical parameters and system configurations on the coherent and incoherent components of the reflected signatures. It can thus help developing robust inverse algorithm for extraction of soil moisture and biomass. The model is applied to P-band signals of geostationary communication satellites to describe polarimetric reflections from tree canopies as observed from down-looking platforms at various altitudes. The relative contributions of diffuse and specular scattering on total reflected power and reflectivity are quantified for various observing scenarios.

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

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

NASA's Soil Moisture Active Passive (SMAP) Project now has completed its prime-phase (three years) mission and has entered a new five-year extended phase. The global L-band radiometry from SMAP has enabled diverse scientific investigations in water, energy and carbon cycle research, terrestrial ecology and ocean science. These include eliciting the role of soil moisture control on the evaporation regime and vegetation gross primary productivity, observing soil-vegetation continuum water relations, analysis of flood and droughts, climate modeling and weather prediction, detecting ocean high-winds during tropical storms, and observing fresh-water outflow in coastal oceans. This paper highlights the recent enhancements to the SMAP suite of science products (from instrument level-1 to geophysical retrievals level-2 and level-3).

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

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.

A coherent bistatic vegetation scattering model, based on a Monte Carlo simulation, is being developed to simulate polarimetric bi-static reflectometry at VHF/UHF-bands (240-270 MHz). The model is aimed to assess the value of geostationary satellite signals of opportunity to enable estimation of the Earth's biomass and root-zone soil moisture. An expression for bistatic scattering from a vegetation canopy is derived for the practical case of a ground-based/low altitude platforms with passive receivers overlooking vegetation. Using analytical wave theory in conjunction with distorted Born approximation (DBA), the transmit and receive antennas effects (i.e., polarization, orientation, height, etc.) are explicitly accounted for. Both the coherency nature of the model (joint phase and amplitude information) and the explicit account of system parameters (antenna, altitude, polarization, etc) enable one to perform various beamforming techniques to evaluate realistic deployment configurations. In this paper, several test scenarios will be presented and the results will be evaluated for feasibility for future biomass and root-zone soil moisture application using geostationary communication satellite signals of opportunity at low frequencies.

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) observatory was launched January 31, 2015, and its L-band radiometer and radar instruments became operational during April 2015. This paper provides a summary of the quality assessment of its baseline soil moisture and freeze/thaw products as well as an overview of new products. The first new product explores the Backus Gilbert optimum interpolation based on the oversampling characteristics of the SMAP radiometer. The second one investigates the disaggregation of the SMAP radiometer data using the European Space Agency's Sentinel-1 C-band synthetic aperture radar (SAR) data to obtain soil moisture products at about 1 to 3 km resolution. In addition, SMAP's L-band data have been found useful for many scientific applications, including depictions of water cycles, vegetation opacity, ocean surface salinity and hurricane ocean surface wind mapping. Highlights of these new applications will be provided.

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

This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and −0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3ubRMSE, −0.015 m3/m3bias, and a correlation of 0.50, compared toin situmeasurements, thus meeting the accuracy target of 0.06 m3/m3ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation.

The Soil Moisture Active-Passive (SMAP) L-band microwave radiometer is a conical scanning instrument designed to measure soil moisture with 4% volumetric accuracy at 40-km spatial resolution. SMAP is NASA's first Earth Systematic Mission developed in response to its first Earth science decadal survey. Here, the design is reviewed and the results of its first year on orbit are presented. Unique features of the radiometer include a large 6-m rotating reflector, fully polarimetric radiometer receiver with internal calibration, and radio-frequency interference detection and filtering hardware. The radiometer electronics are thermally controlled to achieve good radiometric stability. Analyses of on-orbit results indicate that the electrical and thermal characteristics of the electronics and internal calibration sources are very stable and promote excellent gain stability. Radiometer NEDT1 MHz and 1/f noise rising at longer time scales fully captured by the internal calibration scheme. Results from sky observations and global swath imagery of all four Stokes antenna temperatures indicate that the instrument is operating as expected.

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

This presentation illustrates and discusses the possibility of SMAP-Sentinel combined product for the recovery phase of the SMAP mission post radar failure. Initial assessment and results are preliminary and show great promise.

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

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

The Soil Moisture Active Passive (SMAP) observatory was launched January 31, 2015, and its L-band radiometer and radar instruments became operational during April 2015. The SMAP radiometer has been operating flawlessly, however the radar transmitter ceased operation on July 7. This paper provides a summary of the calibration and validation of the SMAP instruments and the current quality assessment of its soil moisture and freeze/thaw products. Since the loss of the radar, the SMAP project has been conducting two parallel activities to enhance the resolution of its soil moisture products. The first explores the Backus Gilbert optimum interpolation and de-convolution techniques based on the oversampling characteristics of the SMAP radiometer. The second investigates the disaggregation of the SMAP radiometer data using the European Space Agency's Sentinel-1 C-band synthetic aperture radar (SAR) data to obtain soil moisture products at about 1 to 3 km resolution. In addition, SMAP's L-band data have been found useful for many applications, including vegetation opacity, ocean surface salinity and hurricane ocean surface wind mapping. Highlights of these new applications will be provided.

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

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

Aquarius satellite observations over land offer a new resource for measuring soil moisture from space. Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface soil moisture. The soil moisture retrieval algorithm development focused on using only the radiometer data because of the extensive heritage of passive microwave retrieval of soil moisture. The single channel algorithm (SCA) was implemented using the Aquarius observations to estimate surface soil moisture. Aquarius radiometer observations from three beams (after bias/gain modification) along with the National Centers for Environmental Prediction model forecast surface temperatures were then used to retrieve soil moisture. Ancillary data inputs required for using the SCA are vegetation water content, land surface temperature, and several soil and vegetation parameters based on land cover classes. The resulting global spatial patterns of soil moisture were consistent with the precipitation climatology. Initial assessments were performed using in situ observations from the U.S. Department of Agriculture Little Washita and Little River watershed soil moisture networks. Results showed good performance by the algorithm for these land surface conditions for the period of August 2011-June 2013 (rmse = 0.031 m3/m3, Bias = -0.007 m3/m3, and R = 0.855). This radiometer-only soil moisture product will serve as a baseline for continuing research on both active and combined passive-active soil moisture algorithms. The products are routinely available through the National Aeronautics and Space Administration data archive at the National Snow and Ice Data Center.

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

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

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

Once launched in late 2014, NASA's Soil Moisture Active Passive (SMAP) mission will use a combination of a four-channel L-band radiometer and a three-channel L-band radar to provide high resolution global mapping of soil moisture and landscape freeze/thaw state every 2-3 days. These measurements are valuable to improved understanding of the Earth's water, energy, and carbon cycles, and to many applications of societal benefit. In order for soil moisture and freeze/thaw to be retrieved accurately from SMAP microwave data, prelaunch activities are concentrating on developing improved geophysical retrieval algorithms for each of the SMAP baseline products using data from simulations, from existing satellite missions such as SMOS, and from field campaign data, such as the SMAPEx airborne study in Australia discussed in this paper.

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

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

The Soil Moisture Active Passive (SMAP) mission is one of the first-tier satellite missions recommended by the U.S. National Research Council Committee on Earth Science and Applications from Space. The SMAP mission1is under development by NASA and is scheduled for launch late in 2014. The SMAP measurements will allow global and high-resolution mapping of soil moisture and its freeze/thaw state at resolutions from 3-40 km. These measurements will have high value for a wide range of environmental applications that underpin many weather-related decisions including drought and flood guidance, agricultural productivity estimation, weather forecasting, climate predictions, and human health risk. In 2007, NASA was tasked by The National Academies to ensure that "emerging scientific knowledge is actively applied to obtain societal benefits" by broadening community participation and improving means for use of information. SMAP is one of the first missions to come out of this new charge, and its Applications Plan forms the basis for ensuring its commitment to its users. The purpose of this paper is to outline the methods and approaches of the SMAP applications activity, which is designed to increase and sustain the interaction between users and scientists involved in mission development.

The Soil Moisture Active Passive (SMAP) mission is one of the first-tier projects recommended by the U.S. National Research Council Committee on Earth Science and Applications from Space. The SMAP mission is in formulation phase and it is scheduled for launch in 2014. The SMAP mission is designed to produce high-resolution and accurate global mapping of soil moisture and its freeze/thaw state using an instrument architecture that incorporates an L-band (1.26 GHz) radar and an L-band (1.41 GHz) radiometer. The simultaneous radar and radiometer measurements will be combined to derive global soil moisture mapping at 9 [km] resolution with a 2 to 3 days revisit and 0.04 [cm3cm-3] (1 sigma) soil water content accuracy. The radar measurements also allow the binary detection of surface freeze/thaw state. The project science goals address in water, energy and carbon cycle science as well as provide improved capabilities in natural hazards applications.

From a campaign conducted in 2002, hourly L-band H polarized TB(Brightness temperature) measurements are available for five episodes distributed over the corn growth cycle. In this study, fitting the τ-ω model to the TBmeasurements shows that the empirical parameter b, defining the optical depth or canopy opacity (r), and its dependence towards the incidence and azimuth angles both change during the growth cycle. The b found for the early growth stage is about three times larger than expected based on the literature, while near peak biomass and at senescence its value is about half. Moreover, the soil moisture dependence of the roughness and crop row orientation are found to be important uncertainties in the TBsimulations. The latter effect is particularly significant at senescence.

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

Once launched in late 2014/early 2015, the Soil Moisture Active Passive (SMAP) mission will provide high resolution global mapping of soil moisture and landscape freeze/thaw state every 2-3 days. These measurements are valuable to improved understanding of the Earth's water, energy, and carbon cycles, and to applications of societal benefit. In order for soil moisture and freeze/thaw to be retrieved accurately from SMAP microwave data, a variety of global static and dynamic ancillary data are required. The choice of which ancillary datasets to use for SMAP products will be based on a number of factors including availability and ease of use, their inherent error and resulting impact on SMAP retrieval accuracies, and compatibility with similar choices made by ESA's SMOS mission.

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

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

The NASA Soil Moisture Active Passive (SMAP) Mission will provide global observations of soil moisture and freeze/thaw state from space. We outline how priority applications contributed to the SMAP mission measurement requirements and how the SMAP mission plans to foster applications and applied science.

The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey. Its mission design consists of L-band radiometer and radar instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every 2-3 days. The combined active/passive microwave soil moisture product will have a spatial resolution of 10 km and a mean latency of 24 hours. In addition, the SMAP surface observations will be combined with advanced modeling and data assimilation to provide deeper root zone soil moisture and net ecosystem exchange of carbon. SMAP is expected to launch in the late 2014 - early 2015 time frame.

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

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

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

This research presents a methodology to correct backscatter (σo) observations for vegetation effects. The proposed methodology is based on the concept that the ratio between the surface scattering over the total amount of scattering (CTsurface /σsooil) is affected only by the vegetation and can be described as a function of the vegetation water content. The data set used in this study was collected at USDA's Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) experimental site in Beltsville, Maryland (USA) over a corn growth cycle in 2002 and includes C-band (4.75 GHz) HH- and VV-polarized observations acquired at incidence angles of 15, 35 and 55 degrees. During this period the corn crops reached peak biomass of 6.6 kg m−2 and a soil moisture range varying from 0.02 to 0.26 cm3cm−3. The results show that through application of the proposed vegetation correction methodology the soil moisture retrieval accuracy can be improved from 0.033 to 0.032 cm3cm−3, 0.049 to 0.033 cm3cm−3, and 0.079 to 0.047 cm3cm−3 at incidence angles of 15, 35 and 55 degrees, respectively.

A first order scattering model is developed and tested at 1.4 GHz by using microwave brightness temperature data acquired over deciduous tree canopies in Maryland during 2007. Microwave measurements at several incident angles and supporting ground truth data (including size/angle distributions of tree constituents) have been collected over stands of deciduous Paulownia trees under full canopy and leaf-drop conditions. Detailed ground truth data obtained during this experiment have been used to compute the additional radiation due to scattering and emission by the vegetation components. The preliminary model predictions are in good agreement with the data and they give quantitative understanding for the influence of the first order scattering within the canopy on the radiometer brightness temperature. The model results using tree ground truth show that the scattering term is significant for trees and that the tau-omega model needs modification to account for additional scattering contribution. Numerical simulations also indicate that the single scattered radiation increases the canopy brightness temperature considerably. These simulations show that the scattering term has a negligible dependence on soil moisture and is only function of angle and polarization.

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

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

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

This research presents a methodology to correct backscatter (sigmadeg) observations for vegetation effects. The proposed methodology is based on the concept that the ratio between the surface scattering over the total amount of scattering (sigmadegsurface/sigmadegsoil) is affected only by the vegetation and can be described as a function of the vegetation water content. The data set used in this study was collected at USDA's Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) experimental site in Beltsville, Maryland (USA) over a corn growth cycle in 2002 and includes C-band (4.75 GHz) HH- and VV-polarized observations acquired at incidence angles of 15, 35 and 55 degrees. During this period the corn crops reached peak biomass of 6.6 kg m-2and a soil moisture range varying from 0.02 to 0.26 cm3cm-3. The results show that through application of the proposed vegetation correction methodology the soil moisture retrieval accuracy can be improved from 0.033 to 0.032 cm3cm-3, 0.049 to 0.033 cm3cm-3, and 0.079 to 0.047 cm3cm-3at incidence angles of 15, 35 and 55 degrees, respectively.

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

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

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

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

This paper reports on the retrieval of soil moisture from dual-polarized L-band (1.6 GHz) radar observations acquired at view angles of 15$^{\circ}$, 35$^{\circ}$, and 55$^{\circ}$, which were collected during a field campaign covering a corn growth cycle in 2002. The applied soil moisture retrieval algorithm includes a surface roughness and vegetation correction and could potentially be implemented as an operational global soil moisture retrieval algorithm. The surface roughness parameterization is obtained through inversion of the Integral Equation Method (IEM) from dual-polarized (HH and VV) radar observations acquired under nearly bare soil conditions. The vegetation correction is based on the relationship found between the ratio of modeled bare soil scattering contribution and observed backscatter coefficient$(\sigma^{\rm soil}/\sigma^{\rm obs})$and vegetation water content$(W)$. Validation of the retrieval algorithm against ground measurements shows that the top 5-cm soil moisture can be estimated with an accuracy between 0.033 and 0.064$\hbox{cm}^{3}\cdot\hbox{cm}^{-3}$, depending on the view angle and polarization.

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

In 2002 the Hydrosphere State Mission (Hydros) was selected by NASA as the alternate mission for a flight opportunity under its Earth System Science Pathfinder program. The Hydros mission objective was to collect the first global scale measurements of the Earth's soil moisture and land surface freeze/thaw conditions, using a combined L band radiometer and radar system operating at 1.41 and 1.26 GHz, respectively. Although NASA cancelled the Hydros mission in December 2005 due to insufficient funding and its reversion back to alternate mission status, the development of accurate soil moisture retrieval algorithms and associated error analyses begun under the Hydros project are still relevant to SMOS and to other potential future soil moisture missions.

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

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

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

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

Relations between various definitions of albedo for a layer of random discrete scatterers are established. Albedo and attenuation values are obtained for soybeans and corn using a discrete model. Attenuation values are also obtained using an empirical approach. Brightness temperatures, computed by transport theory with these albedo and attenuation values, are compared with the measured brightness temperature values for soybeans and corn

The HYDROS mission objective is to collect global scale measurements of the Earth's soil moisture and land surface freeze/thaw conditions, using a combined L band radiometer and radar system operating at 1.41 and 1.26 GHz, respectively. In order to examine how HYDROS soil moisture retrieval will be performed and how the retrieval accuracy will be impacted by vegetation water content and surface heterogeneity, an observing system simulation experiment (OSSE) was conducted using a modeled geophysical domain in the south-central United States centered on the Arkansas-Red River basin for a one-month period in 1994. Three separate radiometer retrieval algorithms were evaluated: (1) a single-channel algorithm (H polarization), (2) a two-channel iterative algorithm, and (3) a two-channel reflectivity ratio algorithm. Analysis indicates that the HYDROS accuracy goal of 4% volumetric soil moisture can be met anywhere in the test basin except woodland areas. Nonlinear scaling of higher resolution ancillary vegetation data can adversely affect algorithm retrieval accuracies, especially in heavy tree areas on the east side of the basin

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

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

Soil moisture is a critical state variable in land surface hydrology. Large-scale soil moisture mapping based on microwave remote sensing would be valuable in many different practical and theoretical applications, and a real potential exists for new space missions in the near future which well utilize simultaneous active/passive microwave measurements for global soil moisture retrieval. This paper discusses the experiment for the retrieval of soil moisture using radar and radiometric measurements. It was shown that combinations of simultaneous radar and radiometer data can enhance soil moisture retrievals, especially in the presence of dynamic vegetation.

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

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

A soil water and energy budget model coupled with a microwave emission model (MICRO-SWEAT) was used to predict the diurnal courses of soil surface water content and microwave brightness temperatures during a number of drying cycles on soils of contrasting texture that were either cropped or bare. The parameters describing the soil water retention and conductivity characteristics [saturated hydraulic conductivity, air entry potential, bulk density, and the exponent (b) describing the slope of the water release curve] had a strong influence on the modeled bare-soil microwave brightness temperatures. These parameters were varied until the error between the remotely sensed and modeled brightness temperatures was minimized, leading to their predicted values. These predictions agreed with the measured values to within the experimental error. The modeled brightness temperature for a soybean-covered soil was sensitive to some of the vegetation parameters (particularly to the optical depth), in addition to the soil hydraulic properties. Preliminary findings suggest that, given an independent estimate of the vegetation parameters, it may still be possible to estimate the soil hydraulic properties under a moderate vegetation canopy.

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

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

An algorithm based on a fit of the single-scattering integral equation method (IEM) was developed to provide estimation of soil moisture and surface roughness parameter (a combination of rms roughness height and surface power spectrum) from quad-polarized synthetic aperture radar (SAR) measurements. This algorithm was applied to a series of measurements acquired at L-band (1.25 GHz) from both AIRSAR (Airborne Synthetic Aperture Radar operated by the Jet Propulsion Laboratory) and SIR-C (Spaceborne Imaging Radar-C) over a well-managed watershed in southwest Oklahoma. Prior to its application for soil moisture inversion, a good agreement was found between the single-scattering IEM simulations and the L-band measurements of SIR-C and AIRSAR over a wide range of soil moisture and surface roughness conditions. The sensitivity of soil moisture variation to the co-polarized signals were then examined under the consideration of the calibration accuracy of various components of SAR measurements. It was found that the two co-polarized backscattering coefficients and their combinations would provide the best input to the algorithm for estimation of soil moisture and roughness parameter. Application of the inversion algorithm to the co-polarized measurements of both AIRSAR and SIR-C resulted in estimated values of soil moisture and roughness parameter for bare and short-vegetated fields that compared favorably with those sampled on the ground. The root-mean-square (rms) errors of the comparison were found to be 3.4% and 1.9 dB for soil moisture and surface roughness parameter, respectively.

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