L-band radiometry for retrieving surface soil moisture is challenged by the confounding influence of surface roughness scattering and vegetation attenuation. This study presents a novel retrieval framework that uses GNSS reflectometry (GNSS-R) coherence time metrics from Soil Moisture Active Passive-Reflectometry (SMAP-R) data to characterize global surface roughness and vegetation parameters. By analyzing the decay of signal-to-noise ratio (SNR) across varying coherent integration times, we estimate temporal coherence time (τc) and derive surface roughness (σ) without ancillary topographic data. A polarization mixing parameter (Pmix), derived from the degree of polarization (DoP), is related to the polarization decoupling factor (Q) used in SMAP models. These variables feed into a τ−ω radiative transfer model to retrieve vegetation optical depth (τ) and single-scattering albedo (ω) directly from observations. The resulting global maps of σ,Q,τ, and ω show enhanced spatial structure and biome sensitivity. They can constrain the empirical SMAP static ancillary inputs used now for constraining the inversion of L-band brightness temperatures for soil moisture. To evaluate the impact of the new vegetation parameters, we apply a neural network trained on SMAP data to retrieve soil moisture using both the original and derived VOD inputs. Differences in soil moisture are spatially coherent and physically consistent, with lower values in high-biomass regions and improved retrieval in arid zones. This work demonstrates that GNSS-R coherence metrics can be integrated with L-band radiometry to produce observation-driven land surface parameters. The approach reduces dependence on empirical or static climatological inputs and supports improved soil moisture retrievals from passive microwave missions.

Passive microwave remote sensing can provide direct and frequent measurements for the estimation of surface soil moisture globally. The future Copernicus Imaging Microwave Radiometer (CIMR) mission is projected to operate at five spectral bands, including L- and C-bands, providing a unique capability to observe surface soil moisture at multiple spatial resolutions. In this work, we investigate the potential to improve the coarser resolution of future CIMR L-band (<60 km) using finer resolution C-band (<15 km) by exploiting the overlap of band footprints. We use existing brightness temperature (TB) data from the Soil Moisture Active Passive (SMAP) mission and Advanced Microwave Scanning Radiometer 2 (AMSR2) mission to investigate L- and C-bands multiresolution information content at the global scale and assess the use of C-band information for L-band sharpening with a linear regression model. Comparing the performance of sharpened with true L-band TB, we find global improvements in systematic offset errors and time-varying random errors, especially along coastlines and over diverse vegetation land cover. Results support the conclusion that the C-band can capture information in the spatial enhancement of the L-band, demonstrating the value of future CIMR multifrequency observations to generate soil moisture products at both climatic and meteorological scales.

The Copernicus Imaging Microwave Radiometer (CIMR) is a Copernicus Expansion Mission with an expected launch in 2028+. The satellite will carry a multi-frequency microwave radiometer operating in the L-, C-, X-, Ku-, and Ka-bands. In addition to providing L-band continuity from other missions (e.g., SMOS, SMAP), the CIMR mission brings new soil moisture remote sensing opportunities due to its multi-frequency, multi-resolution, and high temporal revisit characteristics. This study outlines a preliminary version of a soil moisture retrieval approach designed within CIMR preparatory activities. It aims to provide two soil moisture products: the first is based on the inversion of L-band brightness temperature measurements at their native resolution (<60 km). The second product is based on the inversion of enhanced resolution L-band measurements (<15 km) achieved through sharpening the L-band with higher resolution C/X-band measurements. In both cases, the soil moisture retrieval is based on the inversion of the zeroth-order tau-omega radiative transfer model. We present current efforts of algorithm development and performance evaluation based on simulated CIMR L1B data. The results presented here showcase the potential of CIMR to provide soil moisture estimates not only at hydroclimatological scales (<60 km, from L-band) but also at hydrometeorological scales (~10 to 25 km, from L-band, sharpened with C/X bands), meeting the needs of a wide range of science and applications.

This paper presents a refined Active and Passive (AP) algorithm from the Soil Moisture Active Passive (SMAP) mission, highlighting the progressive enhancements made to the passive algorithm over the years. The primary focus centers on the process of disaggregating coarse brightness temperature (TB) directly measured from the radiometer to attain fine-resolution TB, subsequently enabling the retrieval of soil moisture and vegetation optical depth. Throughout the operational phase of the SMAP SAR instrument, approximately 2.5 months of global SAR backscattering data were acquired simultaneously with TB data. With the imminent launch of the NASA-ISRO Synthetic Aperture Radar (NISAR) mission, the availability of continuous L-band SAR data will see a significant boost. The original SMAP SAR data encompassed four polarizations (VV, HH, HV, and VH), which prompted an examination of three disaggregation combinations: 1) The original SMAP AP algorithm, which utilizes HH, VV, and cross-polarization (X-pol) data (averaged from cross-polarizations). 2) Sole reliance on HH and X-pol data, a configuration that aligns with the capabilities of the NISAR mission, offering global coverage. 3) VV and X-pol data, aiming to provide a more comprehensive analysis. Across these three combinations, similar accuracy was observed at the core study sites, affirming the feasibility of utilizing NISAR HH/HV data exclusively for the AP algorithm. Additionally, this paper also demonstrates both the snapshot method and time-series method for parameter determination and engages in the discussion of their respective advantages and disadvantages.

Traditionally, studies on dust rely on polar-orbiting satellites whose limited temporal coverage does not offer a detailed picture of how each dust storm evolves and changes over time. To address this, our study develops a method to identify and track individual dust storms via hourly images from the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on the Eumetsat geostationary orbit satellites. The process involves using the SE-VIRI Dust RGB false color composite to highlight airborne dust in images. We then use the DBSCAN machine learning model to cluster pixels into storms based on their spatial and temporal connectivity. Post-processing enables us to analyze properties such as the storm’s source area and source activity. Through these methods, we gain insights into dust storm sources, emission factors, and seasonal effects, which are key for understanding their impacts on air quality, health, and the environment.

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mgwith help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mgwith help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

The future Copernicus Imaging Microwave Radiometer (CIMR) mission is planned to be launched in the 2027+ time frame. At its present phase, the first version of each Algorithm Theoretical Basis Document (ATBD) must be defined. CIMR will provide observations at L (1.4 GHz), C (6.9 GHz), X (10.65 GHz), Ku (18.7 GHz) and Ka (36.5 GHz) microwave frequencies. These observations will be relevant to develop high resolution land surface products. Here we present a preliminary study with the aim of exploring the future capabilities that the synergy of CIMR frequencies can provide. Focused on the 0th-order Tau-Omega (τ-ω) model, we analysed the influence of soil roughness (H) and scattering albedo (ω) to retrieve soil moisture (SM) and vegetation optical depth (VOD) at L-band and how these parameters can be potentially estimated from higher frequency bands. We evaluated our results over CONUS, concluding that the soil roughness (H) parameter is affecting VOD and ω mainly in non-forested areas: in those areas, the increase of H produces a decrease in VOD. Our maps of ω revealed dependence with land cover type: generally, the lowest ω values were found in forested areas. Instead, our H map yielded patterns that could be mostly associated with topographic effects. Furthermore, by utilizing a depolarization index, TBdep, we discovered that its values were constrained to nearly zero (indicating minimal soil impact) in areas with vegetation, whereas in bare soils, topography had a significant influence on TBdep. We hypothesize that the use of this index could help in finding relationships among the multi-frequency information from CIMR, allowing us to understand the degree of sensitivity of each band to vegetation and topography.

A joint approach for estimating vertically continuous soil moisture profiles by combining P-band SAR polarimetry with soil hydrological modeling is proposed. The approach compares the decomposed soil scattering component from remotely sensed P-band SAR observations of NASA’s Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission with an ensemble of simulated counterparts based on the hydrological model HYDRUS-1D and the soil scattering model multi-layer small perturbation method (SPM). From the best fit between remote sensing and soil modeling, the most probable soil moisture profile can be retrieved. Estimated soil moisture profiles at individual monitoring stations across the U.S. are compared to in situ measurements, as well as the European ReAnalysis (ERA5) land and AirMOSS L4 products. Pearson’s coefficient of determination between estimated and auxiliary products prove the overall feasibility of the proposed method with respective R2of 0.92, 0.95, and 0.87.

This study presents a comparison between satellite-based vegetation optical depth (VOD) from multi-frequency radiometry (X-, C- and L-band), VOD-derived relative water content (RWC) and auxiliary data (e.g., evapotranspiration and soil moisture), which are investigated for their sensitivity to water status of tree canopies under dry and wet conditions for a temperate forest in Thuringia, Central Germany. For this, we estimated RWC directly from VOD normalization assuming no major changes in vegetation biomass or plant structure during the study period (2015-2019).Our results show that RWC seasonalities are aligned for all investigated frequencies showing its maximum in early summer when leaves and twigs of the top and low canopy are particularly wet and photosynthetically active. Investigating drought versus non-drought years, we observed that X-band RWC is the one better capturing drought status by exhibiting low values in the extreme drought year 2018 compared to the wet year 2017 while L-band RWC reflects the ecological memory from the extreme drought conditions in 2018 in year 2019 estimates.

Tracking seasonal dynamics of evapotranspiration (ET) across global biomes and along seasonal time periods using remote sensing is vital for monitoring ecosystem health and indicating early signals of drought. In this study, we assess the potential of adding weather and illumination-independent signals from active and passive microwave remote sensing (SAR backscatter & vegetation optical depth, VOD) to the established set of ET products, like from optical/thermal remote sensing (MODIS, SEVIRI) and reanalysis (ERA-5 land, GLDAS) data.Our study covers a four-year period (2017-2020), including dry (2018 & 2019) and wet (2017) years. The study was conducted over eight ICOS sites across Europe. These sites are predominantly forested with a low biomass dynamic over the observation period.We find that the ET products from in situ Eddy Covariance (EC), MODIS, and GLDAS deviate relatively minor along the seasons (< 1 [mm/day]), but differ between years. Here, the years (2017-2020) indicate a slightly different ET rate between in situ measurements (EC) and derived products (MODIS & GLDAS), which is currently being investigated. The microwave-based indicators (backscatter & VOD) are proxies by their nature and serve as first-order indicators of relative dynamics allowing the identification of seasonal patterns of ET as well as their spatio-temporal anomalies along both dry and wet years.

Soil moisture drydown plays an important role in many hydrometeorological processes such as regulating surface energy budget, evapotranspiration, and infiltration. In this study, we analyzed the spatial and temporal characteristics of global soil moisture drydown using the daily-scale long-term satellite soil moisture product NNSM. We find that the time-series of τSand τLremained stable over the years. The spatial distribution of global τSand τLshows an anti-spatial correlation pattern, implying that strong land-atmosphere interaction in the short and long term occurs in different regions. τS. of NNSM is closer to the observation measurement than SMAP. The results show that NNSM can provide a long-term global reference for global soil moisture memory characterization, and for improving land surface models.

Passive microwave remote sensing is used for mapping soil moisture globally. The upcoming European Copernicus Program’s Conical Imaging Microwave Radiometer (CIMR) mission can operate at five spectral bands, providing observations for understanding earth science at multiple spatial resolutions. This study explores the potential of enhancing spatial resolution by exploiting the overlap of band footprints. We aim to investigate whether spatial information from multi-resolution C-band at finer resolutions can be used to disaggregate L-band at coarser resolutions using existing brightness temperature (TB) data from SMAP and AMSR2 missions. We evaluate global performance between disaggregated TB and true TB retrievals and find improvements globally, especially near water bodies and with increasing heterogeniety. Results support the conclusion that C-band captures useful information for L-band disaggregation, demonstrating a potential to improve the spatial resolution of soil moisture products at L-band within the context of CIMR.

A ground-based distributed sensing network is described in this work that leverages elements of wireless sensor networks (WSN) and uncrewed areal vehicles (UAVs) with software-defined radar payloads. Hardware and software advancements are made towards combining the operations of WSNs and UAVs for dynamic spatiotemporal monitoring of surface to subsurface soil moisture at kilometer scales. The multi-agent and distributed sensing approach demonstrates coordination, collaboration, and parallel operation of discrete assets for optimal soil moisture monitoring. Results from the first field experiment showing this coordinated operation are reported.

The SMAP product provides the soil moisture (SM) computed using three different retrieval algorithms: the single channel H and V algorithms (SCA-H and SCA-V), and the dual-channel algorithm (DCA) which in addition provides the vegetation optical depth (VOD). The roughness model and the roughness parameters play an important role in the determination of the soil moisture and the VOD. In this regard, the SMAP SCA and DCA utilize different approaches to incorporate the effect of roughness. In this work we will summarize those approaches and we will evaluate the effect of the DCA approach on the retrieval of VOD. We will compare the SMAP DCA roughness parameter$h$with topographic parameters such as DEM height, DEM slope, DEM height standard deviation and DEM slope standard deviation.

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

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.

Soil moisture and vegetation optical depth (VOD; related to vegetation water content) retrieved from SMAP and SMOS satellites are widely used for a range of hydrosphere and biosphere applications. However, while soil moisture has been globally well-validated, VOD validation has been sparse. Furthermore, simultaneously retrieval of these parameters results in uncertainties both individually in soil moisture and VOD retrievals as well as in compensation between the parameters. Here, we show global locations where soil moisture and VOD retrievals will have lower uncertainty, based on complementary brightness temperature information content and signal-to-noise ratio metrics. In these same locations, we show that error still propagates more into VOD. However, using VOD regularization algorithms, this error is greatly reduced, especially at sub-weekly timescales where algorithmic error can be most apparent. Despite these regularization approaches that reduce errors, there are yet vast differences in available global regularized retrievals originating from different algorithmic choices.

The Soil Moisture Active Passive (SMAP) satellite has been developed by NASA to make global soil moisture measurements on the Earth's land surface. It can also distinguish frozen from thawed land surfaces. The SMAP satellite was launched on January 31, 2015, and the science data production began on March 31, 2015. It has both L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna. The SMAP radar failed in July 2015, while its radiometer continues nominal operations. In this paper, the approximately two months of SMAP synthetic aperture radar (SAR) data has been revisited and scrubbed. The SAR bad data (aka outlier) are detected and removed by statistically investigating the time series difference between scatterometer and linearly averaged SAR measurements within the SMAP antenna footprint (∼38 km). It is performed orbit by orbit. The outlier or bad orbits were identified when a data point is more than three scaled median absolute deviations (MAD) away from the median. On average only about 10% or less of all SAR orbits (more than 700), in each polarization, are classified as outliers.

In preparation for the NISAR mission soil moisture algorithm, this study performs the validation of the European Centre for Medium-Range Weather Forecast (ECMWF) ERA5-Land volumetric soil water (soil moisture) layer product with in-situ measurements from the Soil Moisture Active Passive (SMAP) core validation sites (CVS) for 2015 – 2021. The ERA5-Land soil water layer was also compared against the SMAP-enhanced radiometer soil moisture product (gridded at 9 km) at a global extent. For comparison between the ERA5-Land and the SMAP soil moisture products, a matching temporal dataset was generated from ERA5-Land based on the acquisition time of SMAP for each 9 km grid. In comparison with the CVS in-situ measurement, the ERA5-Land data exhibits an overall ubRMSE of about ~0.05 m3/m3but has high wet bias over most of the sites except for sites in Australia, Denmark, and Argentina. The global comparison of the ERA5-Land soil moisture with the SMAP 9 km gridded product shows an overall wet bias with a high correlation in the tropical and temperate regions. The lowest bias was observed over the desert region but has poor correlation as it doesn’t have enough soil moisture variability. Poor correlation with high bias and high RMSD observed over dense vegetated and tundra regions is possibly due to the inferior performance of the SMAP soil moisture product, which has not been validated for these areas.

Adaptive and targeted allocation of mobile sensing agents, in the form of unmanned aerial vehicles (UAVs) with software defined radar (UAV-SDRadar) payloads, enable mapping of surface soil moisture in regions wherein situwireless sensor networks (WSNs) undersample soil moisture or upscaling models perform poorly. This work presents an optimization-based UAV path planning methodology that seeks to maximize UAV flight coverage over areas where a complementing WSN yields upscaled soil moisture estimates with high uncertainty. By recursively mapping soil moisture over such areas, the combined UAV and WSN instrumentation can gradually capture the domain’s true mean soil moisture. A series of numerical simulations are presented to demonstrate the algorithm’s basic function while considering real-world and feasible operational scenarios.

Microwave vegetation optical depth (VOD) and soil moisture (SM) can be simultaneously retrieved based on L-band radiometry with polarization information. VOD is indicative of the vegetation water content (VWC) because it captures the extinction of land surface emission. If the connectivity of VOD to VWC is robust, the pair of VWC-SM observations can be viable bases for understanding soil-plant-atmosphere water relations, providing new perspectives on ecosystem science. Simultaneous SM-VOD retrievals are feasible by inverting the τ–ω model with two independent datasets in dual channel algorithms. However, given correlated satellite vertical and horizontal brightness temperatures (TBvand TBh), an ill-posed inverse problem arises where TB errors result in high uncertainties of retrievals. In this study, we apply the Degrees-of-Information (DoI) metric and propose a Signal-to-Noise Ratio (SNR) metric to assess the “retrievability” of VOD given the SMAP TBv-TBhlinear dependence. The application of these metrics allows determining where the VOD retrievals are robust and reliable. This is a necessary step in supporting applications of VOD in ecology and hydrology. Results show that regions with mainly non-woody vegetation have the best potential for VOD retrievals, though regularization is necessary. We then assess VOD time variations from two regularization products that reduce the impact of under-determined inversions: the L3-DCA and the MTDCA, which constrain VOD time dynamics with and without using a priori VOD climatology, respectively. Though they both reduce noise, especially in the VOD retrievals, they result in differences in VOD seasonal amplitude and coupling to SM at high frequencies as we outline here.

The NASA Soil Moisture Active Passive (SMAP) satellite mission aims to produce enhanced resolution surface soil moisture products by combining coincident but multiresolution L-band active and passive microwave measurements. Since the SMAP radar ceased operations early in the mission, Copernicus Sentinel-1 C-band radar observations are used in the combined product. The synergy is built on two basic foundations: first, active and passive signals covary in a known and systematic fashion, and second, measurements are available at multiple resolutions. In this study, we perform numerical simulations and assess global satellite observations to test the first foundation (covariation). Specific focus lies on the role of the vegetation canopy in modulating the active–passive relationship. We use a discrete radiative transfer model to simulate the slope$\beta $and coefficient of determination$R^{2}$of the relationship between active and passive signals, considering three vegetation types for which the model has been extensively assessed in previous experimental studies. We find that a linear relationship between backscatter and emissivity can be established over a range of vegetation conditions. The coupling between active and passive signals decreases with increasing vegetation water content, such that moderate or higher correlations (nonzero slopes) are retained up to 4 kg/m2(6.3 kg/m2) for L-band/L-band and 1.5 kg/m2(2 kg/m2) for the C-band/L-band configuration. We decompose the effects of different soil-vegetation scattering mechanisms, such as double-bounce, and different measurement error levels on the active–passive relationship. Comparisons with satellite data confirm that our simulations capture magnitudes and major trends found across global vegetated land masses.

Incidence angle diversity of space-borne radiometer and radar systems operating at low microwave frequencies needs to be taken into consideration to accurately estimate soil moisture (SM) across spatial scales. In this study, the Single Channel Algorithm (SCA) is first applied to SMOS brightness temperatures at vertical polarization (TBV) to estimateSMat coarse-resolution (25 km) and develop a land cover-specific and incidence angle (32.5°, 42.5° and 52.5°)-adaptive calibration of single scattering albedo (ω) and soil roughness (hs) parameters. These effective parameters are used together with fine-scale multi-angular Sentinel-1 backscatter in a single-pass active-passive downscaling approach to estimateTBVat fine-scale (1 km) for each SMOS incidence angle. TheseTBVare finally inverted to obtain the corresponding high-resolutionSMmaps. Results over the Iberian Peninsula for year 2018 show an increasing trend of ω and a decreasing trend ofhswith SMOS incidence angle, with almost no variability of ω across land cover types. The active-passive covariation parameter is shown to increase with SMOS incidence angle and decrease with Sentinel-1 incidence angle. Coarse and fineTBVmaps from the three SMOS incidence angles show similar distributions (mean differences below 0.38 K). Resulting high-resolutionSMmaps have maximum differences in mean and standard deviation of 0.016 and 0.015 m3/m3, respectively, and compare well within situmeasurements. Our results indicate that model-based microwave approaches to estimateSMcan be adequately adapted to account for the incidence angle diversity of planned missions such as CIMR, ROSE-L and Sentinel-1 next generation.

The attenuation of microwave emissions through the canopy is quantified by the vegetation optical depth (VOD), which is related to the amount of water, the biomass and the structure of vegetation. To provide microwave-derived plant water estimates, one must account for biomass/structure contributions in order to extract the water component from the VOD. This study uses Aquarius scatterometer data to build an L-band global seasonality of vegetation volume fraction (δ), representative of biomass/structure dynamics. The dynamic range of δ is adapted for its application in a gravimetric moisture (Mg) retrieval model. Results show that δ ranging from 0 to 3.35.10-4is needed for modelling physically reasonable Mg values. The global average of δ shows consistent spatial patterns across vegetation distributions, and δ seasonality is coherent with the phenology of the studied vegetation types. These findings enable the separation of information on vegetation water and biomass/structure inherent within VOD.

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.

The NASA Soil Moisture Active Passive (SMAP) mission design includes two instruments that make coincident active and passive measurements in the low frequency microwave L-band range. In the design, the two instruments share the large (6 meter) light-weight mesh rotating reflector and some of the antenna subsystems. The passive radiometer measurements provide measurements that are highly sensitive to surface soil moisture but at coarse resolution. The active radar measurements provide high-resolution measurements but less sensitive to soil moisture variations because of two-way attenuation through the overlying vegetation canopy and more complex rough soil surface scattering. The synergy between the active and passive measurements allows retrieval of surface soil moisture at intermediate scales and with intermediate accuracy. The SMAP radar failed after three months, thus allowing only about three months of active-passive products. The SMAP project switched to using the Copernicus Sentinel 1-A and 1-B C-band SAR measurements for its active-passive product. The disadvantage of the switch-over is the greater vegetation attenuation and more complex rough-surface scattering of C-band when compared to L-band. The advantages are greater resolution of the C-band Synthetic Aperture Radar (SAR). The revisit times are also affected since data from two platforms with different swath widths have to be combined. In this presentation we explore the algorithm issues associated with the switch and compare the products during the period when both the SMAP radar and Sentinel-1 SARs were operating.

A retrieval methodology for forest water potential from ground-based L-band radiometry is proposed. It contains the estimation of the gravimetric and the relative water content of a forest stand and tests in situ- and model-based functions to transform these estimates into forest water potential. The retrieval is based on vegetation optical depth data from a tower-based experiment of the SMAPVEX 19–21 campaign for the period from April to October 2019 at Harvard Forest, MA, USA. In addition, comparison and validation with in situ measurements on leaf and xylem water potential as well as on leaf wetness and complex permittivity are foreseen to understand limitations and potentials of the proposed approach. As a first result the radiometer-based water potential estimates of the forest stand are concurrent in time and similar in value with their in situ (xylem) counterparts from single trees in the radiometer footprint.

The Committee on Radio Frequencies (CORF) is an independent committee of experts convened by the U.S. National Academies of Sciences, Engineering, and Medicine to consider the use of radio frequency spectrum for scientific applications and how such use may be protected amidst rising needs for the use of spectrum for numerous other purposes. This talk will provide an overview of a range of CORF activities with focus on the work of the committee in the past year.

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.

This work presents recent field activities in support of the NASA CYGNSS missions' land applications. Land reflected GNSS signals are known to be sensitive to surface topography, vegetation cover, and soil moisture. To better understand CYGNSS sensitivity to surface conditions, especially freeze-thaw states, two SoilSCAPE wireless in situ network sites were deployed in the San Luis Valley (SLV), CO, in late Oct. 2019. These sites capture similar weather and climatic conditions but have contrasting topography and vegetation cover. Initial analysis of CYGNSS Signal-to-Noise (SNR) observations over SLV indicates the need to fully account for land-scape topography. To this end, a forward wave scattering model that incorporates a Digital Elevation Model (DEM)is currently being developed to help explain the effects of local topography on SNR observations.

With the availability of global satellite remote sensing observations of surface soil moisture, it is now possible to quantify important hydrological fluxes such as evapotranspiration (ET) and drainage. Furthermore, given the current level of accuracy of remotely sensed soil moisture, these fluxes can be estimated without the need for large-scale land surface or climate modeling. In this work, remote sensing data from the NASA SMAP mission, at 36 [km] scale, and gauge-based precipitation data over the US are utilized within an adjoint-state variation estimation method to obtain time-series daily estimates of ET and drainage. The approach uses only SMAP measurements and precipitation. Neither a hydrology or land surface model nor ancillary hydrologic data are used. ET estimates are compared to eddy covariance measurements from the AmeriFlux network and are shown to capture up to 70% of the in-situ measurements' annual variance. Similarly, Drainage estimates are compared to USGS streamflow measurements. On average Drainage under-estimates streamflow by 1-2 [mm day-1] with seasonal correlation (R2) varying between 0.52-0.77. These estimates close the surface water budget with only SMAP measurements and precipitation information.

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

The National Aeronautics Space Administration's (NASA's) Soil Moisture Active Passive (SMAP) mission will be completing it first extension phase in August 2020. The mission has so far completed five full annual cycles of global L-band radiometry. The baseline soil moisture algorithm is based on single-channel (V-polarization) retrieval of surface soil emissivity and the attenuation and scattering effects of the overlying vegetation is accounted for using ancillary data on the canopy cover derived from the seasonal climatology of optical/infrared data. There are however optional algorithms and research science products that use the V- and H-polarization measurements to simultaneously derived both the surface soil dual-polarization measurements are used in several algorithms to derive the surface soil emissivity as well as canopy properties. The vegetation optical depth resulting from the latter derivation is related to depth-average canopy water content and structural characteristics. These science products have been used in a number of global ecology as well as agroecosystems monitoring. In this paper the bases for these products, their strengths and shortcomings as well as example science results are presented.

Terrestrial vegetation dynamics are important for climate variabilities but the understanding of how the vegetation dynamics respond to climate remains limited - not only because the tightly coupled climate-vegetation system makes it tricky to separate water-associated processes (e.g. precipitation and soil moisture) - but the sparse and uneven observations have made it difficult to quantify such links in a larger spatial view. Here, we relate the global vegetation - soil moisture feedbacks to their oscillation characteristics and interpret it in terms of plant-water functional traits from the satellite-based estimates of surface soil moisture (SSM) and normalized difference vegetation index (NDVI). We map the global vegetation - soil moisture oscillation time scales and investigate the spatial distribution across biomes. Our study gives a global quantification on vegetation-soil moisture dynamics, providing references for comparison related to water-and-plant functions with Earth system models.

In this paper we describe the development of new wireless sensor network technologies to coordinate among different ground-based and unmanned aerial vehicle (UAV)-based sensors as “Agents” who, when coordinated, deliver ground-truth at varying temporal and spatial sampling scales for NASA remote sensing science products, as well as for other potential users that may have different application requirements.

Microwave measurements have been previously used to detect melt events due to their sensitivity to the presence of liquid water in snow. Since NASA's SMAP mission offers a valuable set of low frequency radiometer measurements, SMAP measurements have been used as a tool to detect melt events. SMAP's L-band radiometer also covers virtually the entire Greenland ice sheet twice daily. The overpasses center on morning and evening hours as the satellite is on a 6AM/6PM equator-crossing orbit, and the spatial resolution of the instrument is about 40 km. In this paper, the response of L-band measurements to surface melting of the ice sheet from 2015 through 2019 melt seasons is investigated. It is shown that the Greenland ice sheet experienced an unusually strong melt event at the end of July 2019, which extended the melt area across much of dry snow zone of the ice sheet over a period of two days.

A soil moisture retrieval algorithm using L-band radar-only observations is applied to soil moisture active and passive (SMAP) radar observations. This algorithm is based on a nonlinear relationship between L-band backscatter and soil moisture, and any ancillary vegetation or roughness information is not needed. This algorithm is based on three limiting cases and end-members: smooth bare soil, rough bare soil and maximum vegetation covered soil. Those parameters is estimated through a iterative process. Three months global soil moisture is retrieved using SMAP radar observations and this algorithm. The accuracy of soil moisture result is validated by the ground network insitu observations and the SMAP standard radar and radiometer product. The soil moisture has similar spatial pattern with that of SMAP standard product at 9 km resolution. In the future, we can estimate the soil moisture at 3 km with SMAP radar data 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.

We report on recent technology advancements and developments in in situ soil moisture wireless sensor networks (WSN) in support of Earth science microwave remote sensing missions. Specifically, we discuss new hardware features, known as Wakeup-on-Radio (WoR), that enable sensor networks to respond rapidly to short-lived micrometeorological events and record measurements which may typically be lost in-between sampling periods. Additionally, we outline strategies towards WSN autonomy with the goal of enabling an in-situ sensor to "learn" soil moisture dynamics and surrounding ecohydrological processes, to then determine its optimum sampling schedule.

Mapping carbon stocks in the tropics is essential for climate change mitigation. Passive microwave remote sensing allows estimating carbon from deep canopy layers through the Vegetation Optical Depth (VOD) parameter. Although their spatial resolution is coarser than that of optical vegetation indices or airborne Lidar data, microwaves present a higher penetration capacity at low frequencies (L-band) and avoid cloud masking. This work compares the relationships of airborne carbon maps in Central and South America with both (i) SMAP L-band VOD at 9 km gridding and (ii) MODIS Enhanced Vegetation Index (EVI). Models to estimate carbon stocks are built from these two satellite-derived variables. Results show that L-band VOD has a greater capacity to model carbon variability than EVI. The resulting VOD-derived carbon estimates are further presented at a detailed (9 km) spatial scale.

The Soil Moisture Active Passive (SMAP) mission was designed to acquire and combine L-band radar and radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSE of no more than 0.04 m3/m3volumetric accuracy in the top 5 cm for vegetation with water content of less than 5 kg/m2.Currently, a single-channel algorithm that uses the V polarized brightness temperature (SCA-V) is used to retrieve SM satisfying the defined requirements. Even though other alternatives were tested, SCA-V proved to be the best option for the retrieval of SM. In this work, we show that by choosing suitable roughness parameters, the use of two polarizations (H and V), mixed dual-channel algorithm (MDCA), and an additional constraint, regularized DCA (RDCA), not only provides retrieved SM that satisfies the aforementioned requirement but also allows for the retrieval of vegetation optical depth (VOD).

Remote sensing of geophysical parameters often requires parameter estimation from mutually dependent measurements, reducing retrieval robustness when the number of retrieved parameters equals the number of unknowns. The actual number of parameters that can be retrieved from the total information (e.g., degrees of information (DOI)) is reviewed here in the context of current L-band (1.4 GHz) satellite measurements for retrieving soil and vegetation water content. A limitation of DOI metric is noted where measurements at the noise floor (i.e., in vegetated regions) tend to spuriously decrease estimated mutual information. Thus, the signal-to-noise ratio must be considered together with DOI. A proposed retrieval technique that overcomes the mutually-dependent information, called the multi-temporal dual-channel algorithm, is reviewed and its microwave vegetation parameter retrievals are discussed.

The multi-temporal dual-channel algorithm (MT-DCA) is reviewed in its approach in simultaneously estimating soil moisture and vegetation optical depth (VOD) from SMAP brightness temperature (TB) measurements. At a single incidence angle, the two polarized TB measurements from SMAP at a given location do not provide two degrees of information (DOI). Therefore, this approach assumes a constant VOD between SMAP overpasses to increase DOI and stabilize the soil moisture-VOD estimation. Retrieved time-mean VOD covaries spatially with vegetation biomass, but its temporal dynamics have yet to be validated with in-situ measurements. Recent SMAP VOD applications in plant ecology and crop monitoring are discussed. Ultimately, these studies suggest insightful vegetation information is present in both weekly and seasonal SMAP VOD variations. Further study and ground monitoring campaigns will continue to reveal the extent of geophysical information in the VOD signal.

Current satellite soil moisture retrieval algorithms require estimation techniques or a priori information about microwave vegetation properties, specifically the vegetation optical depth and single scattering albedo. Most approaches assume a constant single scattering albedo (ω), a function of canopy architecture and orientation, despite few investigations of this property. Here, dynamic ω is retrieved over cropland and natural landscape pixels with Soil Moisture Active Passive (SMAP) brightness temperature measurements within the multi-temporal dual-channel algorithm using a moving window retrieval approach. A longer moving window length (number of overpasses with ω constant) is recommended to ensure adequate degrees of information for retrieval and to prevent spurious, rapid changes in ω. It was determined that the mean and standard deviation of both soil moisture and vegetation optical depth are reduced with increased ω. ω also interestingly decreased during the vegetation growth phase suggesting it may be an effective parameter absorbing other physics not accounted for in the zeroth-order radiative transfer equation.

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).

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

In this study, we present a research to transfer the merits of SMAP (Soil Moisture Active Passive) to AMSR2 (Advanced Microwave Scanning Radiometer 2) with using machine learning method-artificial neural network. The surface soil moisture (SSM) products of SMAP were set as the reference data, while brightness temperature (TB) of various channels and the microwave vegetation index (MVI) obtained or derived from AMSR2 were input into an Artificial Neural Network (ANN). During training period (2015–2017), the ANN product (NNsm) can reproduce the SMAP SSM accurately, with a correlation coefficient (CC) of 0.74, Root Mean Square Error (RMSE) of 0.033 m3/m3, and Bias of −0.00008 m3/m3. It was found that machine learning method failed to provide reliable SSM over moderate vegetated areas where SMAP works well. With these trained networks, we developed a global soil moisture data set (named as NNsm) using AMSR2 TB from 2012 to 2018. Comparing to the in situ SM observations from all SCAN (Soil Climate Analysis Network) sites (named as SCANsm), NNsm has a good agreement with CC = 0.44, RMSE = 0.113 m3/m3and Bias = 0.030 m3/m3, which is much better than those of the AMSR2 SSM products from JAXA and LPRM.

Active microwave-based retrieval of soil moisture in vegetated areas has uncertainties due to the sensitivity of the signal to both soil (dielectric constant and roughness) and vegetation (dielectric constant and structure) properties. A multi-frequency acquisition system would increase the number of observations that may constrain soil and/or vegetation parameter retrievals. In order to realize this constraint, an understanding of microwaves interaction with the surface and vegetation across frequencies is necessary. Different microwave frequencies have varied interactions with the soil-vegetation medium and increasing penetration into the soil and canopy with the decreasing frequency. In this study, we examine the contributions of different scattering mechanisms to coincident observations from two microwave frequencies (L and P) of airborne synthetic aperture radar instruments. We quantify contributions of surface, vegetation volume, and double-bounce scattering components. Results are analyzed and discussed to guide future multi-frequency retrieval algorithm designs.

Active and passive low-frequency microwave measurements from a number of space- and airborne instruments are used to estimate soil moisture. Each of the sensing approaches has distinct advantages and disadvantages. There is increasing interest in combining active and passive measurements in order to realize the advantages and alleviate the disadvantages. In order to combine active and passive measurements, their covariations with respect to soil moisture need to be known. The covariation is dependent on how the active and passive microwaves interact with vegetation canopy and soil surface. In this paper, we introduce a physics-based model for the covariation of active and passive microwaves over soil surfaces with vegetation cover. The analytical form for a covariation function is derived which depends on the scattering and absorption of microwaves by soil and vegetation with different orientations, structures, and water contents. The main finding is that the covariation function β is related to the roughness and vegetation losses in the two measurements. An increase in soil roughness or in vegetation cover leads to less negative values of β, which is pronounced for dense and moist vegetation. Both the soil and vegetation components introduce a polarization dependence of β that is caused by polarization-induced differences in soil scattering and oriented plant structures. The forward modeled covariations are plotted together with statistically derived covariation estimates from two months of global active and passive L-band observations of the Soil Moisture Active Passive mission. The physically modeled and statistically derived estimates of covariation are comparable in magnitude and scale.

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

Hydrological water balance studies require knowledge of terrestrial water content within an active storage volume. While the advent of microwave remote sensing has enable frequent and global observations of surface soil moisture, by itself, surface moisture content is not directly suitable for use in water balance studies. An appropriate length-scale, Δz, is required to transform the soil moisture state to total water content. This work demonstrates the applicability of SMAP soil moisture in first-order and observation-driven water balance. First, a novel method is presented to estimate the soil water loss function. Then, by resolving the water balance equation and enforcing mass conservation, estimates of the hydrological length scale over the United States is provided. Mean precipitation is the dominant factor on Δz, such that wetter regions with higher mean precipitation have larger lengths scale. The mean soil moisture state and texture weakly influence Δz.

In this study, the τ-ω model framework is used to derive extinction coefficient and canopy penetration depths from multi-frequency SMAP and AMSR2 retrievals of vegetation optical depth together with ICESat LiDAR vegetation heights. The vegetation extinction coefficient serves as an indicator of how strong absorption and scattering processes within the canopy attenuate microwaves at L and C-band. Through inversion of the extinction coefficient, the penetration depth into the canopy can be obtained, which is analyzed on local (Sahel, Illinois) and continental scale (Africa, parts of North America) as well as for a one year time series (04/2015-04/2016). First analyses of the retrieved penetration depth estimates reveal strongest attenuation for densely forested areas, therefore vegetation attenuation should be accounted for when retrieving soil moisture in these areas. For the continents of North America and Africa penetration depths decrease in average with an increase in frequency from L- to C-band. Moreover penetration depth time series were found to match with expected seasonal variations (e.g. vegetation growth period & rainy season) for analyzed local regions.

Vegetation Optical Depth (VOD) at L-band is highly sensitive to the water content and above-ground biomass of vegetation. Hence, it has great potential for monitoring crop phenology and for providing crop yield forecasts. Recently, the Multi-Temporal Dual Channel Algorithm (MT -DCA) has been proposed to retrieve L-band VOD from Soil Moisture Active Passive (SMAP) measurements. In previous research, SMAP VOD has been compared to crop phenology and has been used to derive crop yield estimates. Here, we review and expand these initial research studies. In particular, we quantify the capability of VOD to detect different crop stages, and test different VOD metrics (i.e., maximum, range and integrals of VOD) to provide crop yield estimates in the United States Corn Belt. Results show that VOD captures 50% to 70% of crop changes during growing and maturing phases, and that it explains between 44% (in heterogeneous crop regions) and 74% (in homogenous croplands) of final crop yields.

The Soil Moisture Active Passive (SMAP) mission was designed to acquire and combine L-band radar and radiometer measurements for the estimation of soil moisture with 4% volumetric accuracy away from coastal zones. In regions near the coast or near inland bodies of water, the SMAP footprint contains land and water, resulting in errors in the soil moisture estimation. In this paper, we address the effort to extract the brightness temperature related to the land fraction or water fraction (depending on the center of the footprint location) from the affected SMAP measurements. We evaluate the performance of our algorithm over simulated data. We then show results over real data. The new SMAP upgraded product is expected to be delivered on April 2018.

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).

SMAP and SMOS missions estimate soil moisture using a zeroth-order radiative transfer model, the τ-ω model. Its simplifying assumption of a weakly scattering vegetation medium is insufficient in the presence of woody biomass, greater than 30% of the land surface. Here, a simplified first-order radiative transfer equation for use in retrieval algorithms is proposed. The inclusion of first-order scattering increases sensitivity to soil moisture especially for wet surfaces. The recently developed multi-temporal dual channel algorithm (MT-DCA) is implemented over Africa using both the τ-ω model and the proposed first-order equation with SMAP 36 km gridded brightness temperature measurements as inputs. The algorithm finds large changes in soil moisture mean and standard deviation in areas with woody vegetation and little change elsewhere. This implies that inclusion of first-order scattering can significantly change mean soil moisture retrievals and increase their temporal variability in regions with woody biomass.

Estimating parameters for global climate models via combined active and passive microwave remote sensing data has been a subject of intensive research in recent years. A variety of retrieval algorithms has been proposed for the estimation of soil moisture, vegetation optical depth and other parameters. A novel attenuation-based retrieval approach is proposed here to globally estimate the gravimetric moisture of vegetation (mg) and retrieve information about the amount of water [kg] per amount of wet vegetation [kg]. The parameter mgis particularly interesting for agro-ecosystems, to assess the status of growing vegetation. The key feature of the proposed approach is that it relies on multi-sensor data from three sensor types (microwave radar, microwave radiometer, and lidar) to solve the physics equations and obtain mg-estimates. The comparability of these estimates to literature values as well as to results of a globally applied, retrieval approach of Grant [4], reveal the potential of the developed method.

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.

Combined active-passive remote sensing has the potential for capturing the relative advantage of each sensing approach in geophysical retrievals. One cornerstone of combined active-passive microwave sensing is the modeling of the covariation of active and passive signals, which arise from equivalent sensitivities of both sensor types to changes in geophysical properties. In this research contribution, we propose a physics-based active-passive combination of active and passive microwave observations based on Kirchhoff's law of energy conservation. This allows establishing a physics-based forward model as well as a fully data-driven, single-pass retrieval methodology for active-passive microwave covariation. The forward model and the retrieval approach are adaptable to different sensor characteristics (incidence angle, frequency & polarization). The theoretical (forward model) as well as applied (retrieval method) physics-based covariation framework is tested with SMAP (LL) and SMAP/Sentinel-1 (LC) data to reveal potentials and constraints for active-passive microwave sensing. As a result of the conducted study, a linear functional relationship between active and passive microwave observations (e.g. assumed for the SMAP mission) is confirmed, if higher-order scattering can be omitted.

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.

NASA's Soil Moisture Active-Passive (SMAP) mission aims at disaggregating L-band radiometer (36 km) with L-band radar (1-3 km) observations to obtain an intermediate resolution soil moisture product (1-9 km). Since SMAP's radar stopped operations in July 2015, a substitution with ESA's Sentinel 1 C-band radar is underway. For this purpose, the relationship of L-band radiometer and C-band radar observations needs to be determined, especially considering the frequency dependent influence of vegetation. This study investigates vegetation effects on covariations of backscatter and emissivity, considering the SMAP-Sentinel 1 (C/L-band) and original SMAP (L/L-band) frequency configurations. Covariations are expressed as the linear regression slope β between backscatter and emissivity signatures, which is simulated for corn and coniferous forest stands. Backscatter and emissivity signatures are obtained from Tor Vergata model simulations, whereas random signal disturbances are accounted for by an errors-in-variables model. As a general trend, we find that β tends to zero for increasing VWC, which is mostly explained by a decrease of radar sensitivity to soil moisture. For forest, which shows overall high VWC values (~5-15 kg/m2), we thus find low magnitudes of β in all cases. For corn (~0-7 kg/m2), we find considerable non-zero magnitudes of β for both frequency configurations, whereas the L/L-band case retains high magnitudes of β longer with respect to C/L-band.

This paper evaluates the performance of the passive microwave spectrometer to be launched aboard the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) for retrieving surface precipitation and hydrometeor water paths. The retrieval algorithms employ neural networks trained and evaluated using the physical reference model NCEP/WRF/TBSCAT/F( λ). Results show that TROPICS retrieved surface precipitation rates and hydrometeor water paths agree well with WRF truth. The accuracies of TROPICS retrieved daily, weekly, and monthly surface precipitation amounts are close to those of the Advanced Microwave Sounding Unit (AMSU). The TROPICS constellation will provide useful precipitation retrievals at unprecedented 30-minute temporal resolution.

The Radar Vegetation Index (RVI) is widely applied to indicate vegetation cover. The index includes the backscattering intensities of co- and cross-polarization that do not only contain information coming from vegetation scattering at longer wavelength (L-band), but also from the soil underneath. A forward modelling approach using active and passive microwave-derived parameters to obtain the scattering contribution of the soil is pursued. The idea of this research study is a subtraction of the attenuated soil scattering contribution from the measured backscattering intensities, to provide a clean vegetation-based solution, called improved RVI (RVII). For latter analysis, the vegetation volume is forward modeled to calculate vegetation-only RVI-values without any soil scattering contribution. It reveals that, the pre-factor of the standard RVI leads to values up to 1.2, unfavorable for a normalized index running between zero and one. Hence, improvements for the standard RVI equation are proposed here to obtain a better suited value range and for incorporating soil scattering influences and filtering of regions with dominant soil scattering. Moreover, the improved RVI (RVII) is compared with datasets of vegetation and soil parameters (e.g. vegetation water content) for correlation analysis to find the physical parameters contributing to the index.

In this study the framework of the τ - ω model is used to derive vegetation loss coefficients and canopy penetration depths from SMAP multi-temporal retrievals of vegetation optical depth, single scattering albedo and ICESat lidar vegetation heights. The vegetation loss coefficients serve as a global indicator of how strong absorption and scattering processes attenuate L-band microwave radiation. By inverting the vegetation loss coefficients, penetration depths into the canopy can be obtained, which are displayed for the global forest reservoirs. A simple penetration index is formed combining vegetation heights and penetration depth estimates. The distribution and level of this index reveal that for densely forested areas in the tropics the soil signal is attenuated considerably, and this attenuation must be carefully accounted for in soil moisture retrieval algorithms.

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.

Vegetation Optical Depth (VOD) is related to Vegetation Water Content (VWC). This provides new and highly valuable information for ecological and agricultural studies. In this work, VOD from the Soil Moisture Active-Passive (SMAP) satellite has been retrieved with the new Multi-Temporal Dual-Channel Algorithm (MT-DCA). Then, it has been applied to the study of crop yield trends and crop composition. The increase on VOD (ΔVOD) during crop development has been compared to yield data in two selected regions located in the United States. The first region presents a heterogeneous crop composition and weak ΔVOD-yield relationship (r2=0.21). The second region presents a highly homogenous cover and a strong exponential relationship (r2=0.65) between ΔVOD and yield. A saturation of yield is observed at a certain ΔVOD value. This pattern is probably due to an increasing plant density, which limits the crop yield due to plant physiological stress.

In this paper we summarize the effort to enhance the SMAP radiometer data. The applied technique is based on the Backus-Gilbert theory which is the classical estimation method in microwave radiometry. We show details of our implementation and summarize the assessment of the SMAP L1C_TB_E product.

SMAP project is working on a new and enhanced high-resolution (3km and 1km) soil moisture product. This product will combine SMAP radiometer data and Sentinel-1A and -1B data, and it will use the heritage SMAP active-passive approach. However, modifications in the SMAP active-passive algorithm are done to accommodate the Sentinel-1A and -1B C-band SAR data. Tests of the SMAP and Sentinel active-passive algorithm has been conducted and results show great promise for the high-resolution soil moisture data. The beta version of this product will be released to public in end of the March, 2017. This high-resolution (1 km and 3 km) soil moisture product will be useful for agriculture, flooding, watershed and rangeland management, and ecological and hydrological applications. Specific examples of interest will be shown from the proposed product for the above mention geophysical applications.

SMAP project is working on a new and enhanced high-resolution (3km and 1km) soil moisture product. This product will combine SMAP radiometer data and Sentinel-1A and -1B data, and it will use the heritage SMAP active-passive approach. However, modifications in the SMAP active-passive algorithm are done to accommodate the Sentinel-1A and -1B C-band SAR data. Tests of the SMAP and Sentinel active-passive algorithm has been conducted and results show great promise for the high-resolution soil moisture data. The beta version of this product will be released to public in end of the March, 2017. This high-resolution (1 km and 3 km) soil moisture product will be useful for agriculture, flooding, watershed and rangeland management, and ecological and hydrological applications. Specific examples of interest will be shown from the proposed product for the above mention geophysical applications.

Over land the vegetation canopy affects the microwave brightness temperature by emission, scattering and attenuation of surface soil emission. The questions addressed in this study are: 1) what is the transparency of the vegetation canopy for different biomes around the Globe at the low-frequency L-band?, 2) what is the seasonal amplitude of vegetation microwave optical depth for different biomes?, 3) what is the effective scattering at this frequency for different vegetation types?, 4) what is the impact of imprecise characterization of vegetation microwave properties on retrieval of soil surface conditions? These questions are addressed based on the recently completed one full annual cycle measurements by the NASA Soil Moisture Active Passive (SMAP) measurements.

Vegetation optical depth and scattering albedo are crucial parameters within the widely used τ-ω model for passive microwave remote sensing of vegetation and soil. A multi-sensor data integration approach using ICESat lidar vegetation heights and SMAP radar as well as radiometer data enables a direct retrieval of the two parameters on a physics-derived basis. The crucial step within the retrieval methodology is the calculus of the vegetation scattering coefficient KS, where one exact and three approximated solutions are provided. It is shown that, when using the assumption of a randomly oriented volume, the backscatter measurements of the radar provide a sufficient first order estimate and subsequently lead to effective estimates of vegetation optical depth and scattering albedo acquired with the novel multi-sensor approach.

After failure of the SMAP L-band radar, its substitution by the Sentinel-1A/B C-band instruments for combined active-passive retrieval of soil moisture demands an algorithm update for this dual-frequency (L/C) case. In order to account for the different frequencies and acquisition geometries of the two sensor types, the microwave covariation, being the fundamental building block of the moisture retrieval algorithm, is modeled using a fully physics-based approach. Moreover, a data-driven, single-pass retrieval methodology for dual-frequency microwave covariation is proposed and tested on SMAP and Sentinel-1 data. The retrieval is also physics-based, incidence angle independent and therefore globally applicable without any empirical or statistical calibration.

Decomposition is performed for the 4×4 SMAP radar channel covariance matrix and the correlation between resulting components, surface soil moisture and vegetation is examined. Globally, the first principal component is the most dominant and the correlation coefficients with respect to soil mortise is highest (R2≥ 0.8) in regions with fractional ground cover and sufficient temporal dynamics of soil moisture.

In this study we examined the linear decomposition and relationship between the SMAP observed Polarization Ratio into surface soil moisture and vegetation. Temporal linear regression, per each SMAP pixel, is performed to estimate the decomposition coefficients. Variances (explained variance) in PR is predominantly dominated by dynamics of surface soil moisture and degrades with increasing vegetation amount. Although PR, by itself, is high in arid and semi-arid regions, due to lack of moisture and vegetation dynamics, the explained variance is very small.

Landscape freeze/thaw (FT) state is a key variable in Earth's carbon cycle. NASA's Soil Moisture Active Passive (SMAP) satellite mission, launched in January 2015, provides global retrievals of FT state every two to three days. Validating SMAP FT observations with in-situ observations is difficult due to the substantial scale mismatch between a point estimate and a satellite footprint, inducing “representativeness errors” in the in-situ observations. Triple collocation (TC) is a validation technique that addresses this problem by combining estimates from in-situ, model and spaceborne estimates to obtain error estimates for all three products, without assuming that any product is error-free. Unfortunately, it fails when applied to binary or categorical variables, such as landscape FT state. In this study, we use a new variant of TC - categorical triple collocation (CTC) - that can be applied to binary variables, to validate the SMAP FT product across northern land regions (>45N).

The NASA Soil Moisture Active Passive (SMAP) mission aims to disaggregate L-band microwave brightness temperatures (~40 km2) with finer resolution radar backscatter (1-3 km2) to obtain an intermediate resolution soil moisture product. The disaggregation is based on a linear functional relationship between backscatter and emissivity microwave observations that is captured by a covariation parameter β. Since SMAP's L-Band radar has stopped operations in July 2015, the substitution of Sentinel 1's C-Band radar for an operational soil moisture product is in preparation. However, while multiple studies have provided understanding of active-passive covariation for the L/L-Band case, little is known about the C/L-Band case. We utilize the Tor Vergata discrete backscatter and emission model to simulate growing wheat and corn stands and calculate the covariation parameter β for the L/L-Band and C/L-Band case. The study aims to provide insights into the strength, temporal dynamics and underlying scattering mechanisms of active-passive covariation for different vegetation types and frequency combinations. Our results indicate that for the C/L-Band case, vegetation cover limitations are generally more severe, and different β-dynamics and underlying scattering mechanisms are observed with respect to the L/L-Band case.

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

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 ESA's SMOS and the NASA's SMAP missions, launched in 2009 and 2015, respectively, are the first two missions having on-board L-band microwave sensors, which are very sensitive to the water content in soils and vegetation. Focusing on the vegetation signal at L-band, we have implemented an inversion approach for SMAP that allows deriving vegetation optical depth (VOD, a microwave parameter related to biomass and plant water content) alongside soil moisture, without reliance on ancillary optical information on vegetation. This work aims at using this new observational data to monitor the phenology of crops in major global agro-ecosystems and enhance present agricultural monitoring and prediction capabilities. Core agricultural regions have been selected worldwide covering major crops (corn, soybean, wheat, rice). The complementarity and synergies between the microwave vegetation signal, sensitive to biomass water-uptake dynamics, and optical indices, sensitive to canopy greenness, are explored. Results reveal the value of L-band VOD as an independent ecological indicator for global terrestrial biosphere studies.1

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

The Soil Moisture Active Passive (SMAP) 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.

The synergy of active and passive microwave measurements have attracted increasing attention in recently years. In this study, we investigate the relationship and covariation of the SMAP radar backscatter and radiometer reflectivity as a function of surface roughness and vegetation. Two radar-derived indices, namely the radar vegetation index (RVI) and radar roughness index (RRI) are adopted to account for the contributions from vegetation and surface roughness respectively. The results show RVI distinguishes vegetation density well in sparse to densely vegetated regions, while significantly overestimates the biomass over some dry desert regions due to possible soil volume scattering effects. RRI well captures the negative covariation of active and passive measurements in bare and sparsely vegetated surfaces, while becomes ineffective in densely vegetated areas due to the reduced contribution from soil surfaces.

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.

The NASA Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015 to provide global mapping of high-resolution soil moisture and freeze-thaw state every 2-3 days using an L-band (active) radar and an L-band (passive) radiometer. The Level 2 radiometer-only soil moisture product (L2_SM_P) provides soil moisture estimates posted on a 36-km Earth-fixed grid using brightness temperature observations from descending passes. This paper provides the first comparison of the validated-release L2_SM_P product with soil moisture products provided by the Soil Moisture and Ocean Salinity (SMOS), Aquarius, Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) missions. This comparison was conducted as part of the SMAP calibration and validation efforts. SMAP and SMOS appear most similar among the five soil moisture products considered in this paper, overall exhibiting the smallest unbiased root-mean-square difference and highest correlation. Overall, SMOS tends to be slightly wetter than SMAP, excluding forests where some differences are observed. SMAP and Aquarius can only be compared for a little more than two months; they compare well, especially over low to moderately vegetated areas. SMAP and ASCAT show similar overall trends and spatial patterns with ASCAT providing wetter soil moistures than SMAP over moderate to dense vegetation. SMAP and AMSR2 largely disagree in their soil moisture trends and spatial patterns; AMSR2 exhibits an overall dry bias, while desert areas are observed to be wetter than SMAP.

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

With emphasis on physics-based techniques, a multi-objective optimization approach to combined radar-radiometer soil moisture estimation is presented in this work. Soil moisture estimation is demonstrated via application of this method to SMAP high resolution radar and coarse resolution radiometer data. Comparisons are then made with the SMAP baseline active-passive soil moisture output data product. A strong agreement between the two techniques, especially in capturing spatial distributions of soil moisture is observed.

Microwave-based retrieval of soil moisture in vegetated areas have uncertainties due the sensitivity of the signal to vegetation structure and dielectric constant. In this study, we propose a framework for developing a joint active L-band and active P-band retrieval algorithm to decrease the retrieval uncertainties. The algorithm focuses on the decomposition of soil, vegetation and dihedral components to compare the observations from the two frequencies.

This study presents a quantitative analysis of vegetation and soil parameters retrieved from observations of an airborne P-band SAR instrument across nine different biomes in North America. These measurements are part of the NASA's AirMOSS mission, and data have been collected between 2012 and 2015. We use a three component decomposition algorithm to separate the contribution of surface and vegetation scattering, and subsequently retrieve surface and vegetation parameters. Applying the retrieval algorithm to data across all the campaign sites, we characterize the dynamics of the parameters across different North American biomes and assess their characteristic range.

In this paper we summarize the effort to enhance the resolution of SMAP radiometer data. The SMAP radiometer sampling of the Earth surface provides overlapping measurements along scan and along track. The oversampling combined with the given antenna gain function allows reconstruction of the scene with improved resolution. The applied technique is based on the Backus-Gilbert optimum interpolation theory, which is the classical inversion method in microwave radiometry. The results shown in this paper are based on the simulated SMAP measurements and are applicable to the real SMAP radiometer measurements.

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.

Satellite data provide global observations of many of the earth's system processes and features. These data are valuable for developing scientific products that increase our understanding of how the earth's systems are integrated. The water, energy and carbon cycle exchanges between the land and atmosphere are linked by soil moisture. NASA's Soil Moisture Active Passive (SMAP) mission provides soil moisture and freeze/thaw measurements from space and allows scientiscts to link the water energy and carbon cycles. In order for SMAP data to be best integrated into decision support systems, the mission has engaged with the stakeholder community since 2009 and has attempted to scale the utility of the data to the thematic societal impacts of the satellite product applications. The SMAP Mission, which launched on January 31, 2015, has actively grown an Early Adopter (EA) community as part of it's applications effort and worked with these EAs to demonstrate a scaled thematic impact of SMAP data product in societally relevant decision support applications. The SMAP mission provides global observations of the Earth's surface soil moisture, providing high accuracy, resolution and continuous global coverage. Through the Early Adopters Program, the SMAP Applications Team will spend the next 2 years after launch documenting and evaluating the use of SMAP science products in applications related to weather forecasting, drought, agriculture productivity, floods, human health and national security.

The NASA Soil Moisture Active Passive (SMAP) mission aims at producing high-resolution (9 km) global maps of surface soil moisture based on L-band radar and radiometer measurements. In this study, a physically-based retrieval of the active-passive covariation parameter β from one active-passive (single-pass) SMAP acquisition couple is proposed, circumventing empirical time-series regressions. The key to single-pass retrieval of β is the vegetation correction of the backscatter signal. This can be achieved by use of the measured cross-polarized backscatter signal and parameters appropriately describing the structure of the vegetation volume. These parameters can be derived from the observed Γ-parameters of the SMAP baseline algorithm enabling a fully SMAP data-driven, single-pass estimation of the covariation parameter β without any auxiliary information. Moreover, vegetation structural parameters, indicative of preferential vegetation shape and orientation, are retrieved using the observed Γ-parameters.

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

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 NASA Soil Moisture Active Passive (SMAP) mission aims at producing low (36 km) and high-resolution (9 km) global maps of surface soil moisture based on L-band radiometer and radar/radiometer measurements, respectively. In this research study, results of applying a novel retrieval algorithm, the so-called Multi-Temporal Dual Channel Algorithm (MT-DCA) to the first year of SMAP observations are presented. MT-DCA allows retrieving not only soil moisture, but also vegetation optical depth (VOD) and scattering albedo estimates, from passive microwave measurements alone and without reliance of a priori information. At L-band, VOD is proportional to total vegetation water content and albedo accounts for structural changes. The analysis of these parameters at different temporal and spatial scales will reveal the full potential of L-band microwave for global ecology studies.

In this work, an integration of microwave data coming from different sensors (SMAP, Sentinel-1, AMSR2) has been attempted, in order to obtain an improved estimation of hydrological parameters and in particular of the Soil Moisture (SMC). The failure of radar sensor in SMAP satellite induced to look for other available microwave frequencies, both from active (e.g. Sentinel-1, C band) and passive sensors (e.g. AMSR2, from C to Ka bands).

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

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

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

NASA's Soil Moisture Active Passive (SMAP) mission objective is global mapping of surface volumetric soil moisture at 10-km resolution every two to three days and with accuracy of 0.04 cm3cm-3(one sigma). In order to achieve this resolution and accuracy, the SMAP utilizes L-band radar and L-band radiometer measurements. The instruments share a rotating 6-m mesh reflector antenna that scans across a 1000-km swath in order to meet the required data refresh rate. The Level-2 Active-Passive soil moisture product (L2_SM_AP) at 9 km is retrieved from the disaggregated/downscaled brightness temperature obtained by merging of active and passive L-band observations. The baseline L2_SM_AP algorithm disaggregates the coarse-resolution (~36 km) brightness temperatures of the SMAP L-band radiometer using the high-resolution (~3 km) backscatter data from the SMAP L-band radar with unfocused synthetic aperture processing. The inversion of brightness temperature to estimate surface soil moisture is more mature when compared with inversions of radar backscatter. This is the primary driver of the brightness temperature disaggregation approach to the combined active-passive surface soil moisture product. Furthermore, this approach allows some consistency with the coarse-resolution radiometer-only surface soil moisture product since the disaggregated brightness temperatures sums to the radiometer measurement. The disaggregated brightness temperature contains instrument errors (~0.7 dB for co-pol backscatter and ~1.0 dB for cross-pol backscatter, and ~1.3 K in brightness temperature) inherent in the radar and radiometer. Furthermore, the algorithm has two critical parameters that add uncertainty. Finally, correction of the land brightness temperature (used in the inversion) for water body contributions is a source of uncertainty. In this paper, we introduce analytical expressions for the SMAP downscaled brightness temperature due to all these sources of uncertainty. The expressions allow estimation of uncertainty (in kelvin) for each data granule of the SMAP L2_SM_AP product. Since the uncertainties depend on the given ground conditions, e.g., existing water body fraction and local algorithm parameters that depend on vegetation cover and landscape heterogeneity, it is necessary to evaluate the uncertainty for each data granule. In this paper, we show that the uncertainty expressions closely match Monte Carlo simulations with an overall difference of only ~0.1 K. Whereas Monte Carlo estimates of uncertainty can only be afforded for a nominal case (such as those typically reported in Algorithm Theoretical Basis Documents as uncertainty tables), the analytical expressions allow uncertainty estimates for every data granule. The expressions are now used to provide uncertainty standard deviation of downscaled brightness temperature at 9 km in the SMAP L2_SM_AP product. These standard deviations are useful for the following: 1) guidance on the expected level of error in the estimate brightness temperature due to the downscaling process and 2) observation error in direct radiance data assimilation.

The goal of this study is to assess the performance of the active-passive disaggregation algorithm for the National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission using airborne observations from the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). This algorithm disaggregates the whole domain resolution (around 30 km) radiometer brightness temperature (TB) using the 1.6-km-resolution radar backscatter (σo) observations (both acquired by the aircraft-based Passive Active L- and S-band Sensor), to a medium 6.4-km resolution. The parameters of the disaggregation method are affected by changes in soil and vegetation. Different time windows are studied to assess the best representation of the campaign vegetation growth and senescence processes. The algorithm performance is evaluated by comparing disaggregated and observed TB at the medium resolution. A minimum performance algorithm is also applied where the radar data are withheld. The minimum performance algorithm serves as a benchmark to assess the value of the radar to the SMAP active-passive algorithm.

Field work was conducted in two SMAP 36×36 km grid cells, B and D, located in the west and north of Kuwait, respectively. The in-situ gravimetric sampling field work activity in Grid Cell D indicates a variation of volumetric soil moisture from 0.17 m3m-3in January, 2014 to 0.015 m3m-3in June, 2014. Field work in Grid Cell B indicates a variation from 0.0352 m3m-3in December 2014 to 0.0168 m3m-3in May 2015. Soil roughness was estimated in grid cell D using a pin profilometer and was found to vary from 0.2 to 0.7 RMS with a correlation length ranging from 91 cm to 93 cm. The first weather station was installed in grid cell B in April 2015.

A novel downscaling methodology for intermediate spatial resolution radiometer data is developed in view of forthcoming SMAP mission. It is based on an active and a passive microwave forward model coupled by its ancillary parameters. The combined active/passive model introduced in this work yields backscatter and emission observations relation consistent with Aquarius/SAC-D observations and with what it has been reported in previous papers. Furthermore, merging spatial resolution between radar-derived and radiometer-based brightness temperature (Tb) is performed in a least-square framework. Methodology is tested using a synthetic numerical simulation example which showed that this approach leads to an increase of downscaled Tb accuracy.

The NASA Soil Moisture Active Passive (SMAP) mission is designed to produce high-resolution (9 km) global mapping of surface soil moisture based on L-band radar and radiometer measurements. The multi-scale measurements are combined using time-series of active passive microwave data to retrieve the statistical regression parameters (α, β) from successive overpasses. In this study, we introduce a physically-based forward model as well as data-based retrieval of the β-parameter. The forward model stems from analyses of the scattering and loss terms occurring during bare and vegetated soil scattering/emission and allows a physically-based modelling of the β-parameter. This provides possibilities to analyze the different influences of soil roughness as well as vegetation structure and moisture on the β-parameter under different environmental conditions. In addition, a physically-based retrieval of β from one active-passive SMAP acquisition couple is proposed, circumventing lengthy time-series regressions. The key operation enabling a single-pass retrieval of the β-parameter is the vegetation correction of the backscatter signal. This can be achieved by use of the measured cross-polarized backscatter signal together with an appropriate polarimetric vegetation volume model.

Remote sensing algorithms often invert multiple measurements simultaneously to retrieve a group of geophysical parameters. In order to create a robust retrieval algorithm, it is necessary to ensure that there are more unique measurements than parameters to be retrieved. If this is not the case, the inversion might have multiple solutions and be sensitive to noise. In this letter, we introduce a methodology to calculate the number of (possibly fractional) “degrees of information” in a set of measurements, representing the number of parameters that can be retrieved robustly from that set. Since different measurements may not be mutually independent, the amount of duplicate information is calculated using the information-theoretic concept of total correlation (a generalization of mutual information). The total correlation is sensitive to the full distribution of each measurement and therefore accounts for duplicate information even if multiple measurements are related only partially and nonlinearly. The method is illustrated using several examples, and applications to a variety of sensor types are discussed.

An algorithm for surface soil moisture estimation using L-band radar observations is introduced. The formulation envelops a wide range of land surface conditions based on three limiting cases defined in terms of end-members: smooth bare soil, rough bare soil, and a maximum vegetation covered soil. Parameterizations for these end-members are obtained using forward electromagnetic scattering models. Modulation due to soil surface roughness and overlying vegetation scattering effects between end-members are accounted using the radar vegetation index and the newly introduced radar roughness index. Hence, the retrieval algorithm developed here does not depend on ancillary vegetation or roughness information. The algorithm is tested with ground-based truck-mounted bare soil observations and observations from several airborne field campaigns that represent a wide range of surface conditions.

A soil moisture retrieval algorithm is proposed that takes advantage of the simultaneous radar and radiometer measurements by the forthcoming NASA Soil Moisture Active Passive (SMAP) mission. The algorithm is designed to downscale SMAP L-band brightness temperature measurements at low resolution ( ~ 40 km) to 9-km brightness temperature by using SMAP's L-band synthetic aperture radar (SAR) backscatter measurements at high resolution (1-3 km) in order to estimate soil moisture at 9-km resolution. The SMAP L-band SAR and radiometer instruments are designed to provide coincident observations at constant incidence angle, but at different spatial resolutions, across a wide swath. The algorithm described here takes advantage of the correlation between temporal fluctuations of brightness temperature and backscatter observed when viewing targets simultaneously at the same angle. Surface characteristics that affect the brightness temperature and backscatter measurements influence the signals at different time scales. This feature is applied in an approach in which fine-scale spatial heterogeneity detected by SAR observations is applied on coarser-scale radiometer measurements to produce an intermediate-resolution disaggregated brightness temperature field. These brightness temperatures are then used with established radiometer-based algorithms to retrieve soil moisture at the intermediate resolution. The capability of the overall algorithm is demonstrated using data acquired by the airborne passive and active L-band system from field campaigns and also by simulated global dataset. Results indicate that the algorithm has the potential to retrieve soil moisture at 9-km resolution, with the accuracy required for SMAP, over regions having vegetation up to 5- kg/m2vegetation water content. The results show a reduction in root mean square error of volumetric soil moisture (40% improvement in the statistics) from the minimum performance defined as the soil moisture retrieved using radiometer measurements re-sampled to the intermediate scale.

Radar measurements at P-band are sensitive to profile soil moisture. Associated backscatter measurements depend on the distribution and variation of the soil moisture profile. Existing scattering models account for this variation by approximating the soil moisture profile as consisting of a number of homogeneous layers. Since the inversion of the scattering models during the retrieval process can be based on only a few polarimetric backscatter measurements, the number of obtainable independent layers in the profile representation is limited. The purpose of this paper is to gain insights into the effects of the layering representation on the resulting modeled forward scattering. These insights form the rational basis for the design of retrieval algorithms. The effects of reflections between layers and other sources of error on simulated backscattering coefficients are first illustrated using several case studies. To determine the combined effect of different error sources for realistic soil moisture profiles, ten years of conditions at a grassland in California are studied. Depending on the layering strategy and the polarization, the root-mean-square error (RMSE) of backscattering coefficients due to misrepresenting the profile alone can be up to 2 dB, although errors can be up to 10 dB in particular cases. The error generally decreases as additional layers are added. The HH-polarization is more sensitive to the subsurface than the VV-polarization and has greater errors. Using a profile-dependent layer placement strategy decreases the RMSE of the backscatter simulation by less than 1 dB relative to a strategy with fixed layering.

Simple functions of radar backscatter coefficients have been proposed as indices of soil moisture and vegetation, such as the radar vegetation index, i.e., RVI, and the soil saturation index, i.e., ms. These indices are ratios of noisy and potentially miscalibrated radar measurements and are therefore particularly susceptible to estimation errors. In this study, we consider uncertainty in satellite estimates of RVI and msarising from two radar error sources: noise and miscalibration. We derive expressions for the variance and bias in estimates of RVI and ms due to noise. We also derive expressions for the sensitivity of RVI and msto calibration errors. We use one year (September 1, 2011 to August 31, 2012) of Aquarius scatterometer observations at three polarizations ( σHH, σVV, and σHV) to map predicted error estimates globally, using parameters relevant to the National Aeronautics and Space Administration Soil Moisture Active and Passive satellite mission. We find that RVI is particularly vulnerable to errors in the calibration offset term over lightly vegetated regions, resulting in overestimates of RVI in some arid regions. ms is most sensitive to calibration errors over regions where the dynamic range of the backscatter coefficient is small, including deserts and forests. Noise induces biases in both indices, but they are negligible in both cases; however, it also induces variance, which is large for highly vegetated regions (for RVI) and areas with low dynamic range in backscatter values (for ms). We find that, with appropriate temporal and spatial averaging, noise errors in both indices can be reduced to acceptable levels. Areas sensitive to calibration errors will require masking.

A compact polarimetric (CP) radar system requires fewer measurements than a fully polarimetric (FP) system, thus allowing added flexibility in radar system design. Previous studies have shown the potential of using compact polarimetry for radar remote sensing of soil moisture. This paper extends previous studies by considering a time series data cube retrieval algorithm and measurements in the presence of vegetation. Vegetation information is assumed to be provided by an ancillary data source in the retrieval process. The performance of an algorithm for reconstructing FP information from CP measurements of vegetated soil surfaces is also examined. The results of the study show that only a modest degradation in soil moisture retrieval performance occurs when compact-pol measurements are used in place of full-pol data.

The Soil Moisture Active Passive (SMAP) mission will combine spaceborne L-band radar and radiometer observations to provide improved estimates of Earth's surface geophysical parameters. In this paper we present a new robust radar-only snapshot approach (independent of ancillary data on roughness and vegetation) for mapping near real-time soil moisture at high spatial resolution. Simple formulations are developed based on traditional theories and parameterizations are obtained using electromagnetic scattering basis available in the form of “data cubes”. This new algorithm is tested using Passive and Active L- and S-band (PALS) airborne data and in situ soil moisture observations acquired during different field campaigns, i.e., SGP99, SMEX02, CLASIC07 and SMAPVEX08. The soil moisture retrieval root mean square error observed is in the range of the quality target (0.06 cm3/cm3) set for the SMAP mission.

The knowledge of the soil moisture state in a region plays an important role in hydrology, with particular reference to the flood events prediction. A valid tool for the evaluation of the saturation state at watershed scale is given by remote sensing imagery. In this work temporal sequences of LST images from satellite platform (MSG-SEVIRI and Terra-MODIS) have been used in an assimilation procedure, ACHAB, in order to retrieve estimations of the land surface energy balance components and daily maps of soil moisture saturation index (SMSI). The simulation has been performed over the Italian territory for seven years (2005-2011) with about 5 km of spatial resolution. A climatology of the SMSI maps has been computed and reliability index maps have been provided. This study was realized in the framework of “OPERA - Protezione Civile dalle Alluvioni” ([1]), a project of the Italian Civil Protection aimed to the operational use of satellite data for floods prediction and management.

This paper develops a precipitation retrieval algorithm for the Advanced Technology Microwave Sounder (ATMS) recently launched aboard the U.S. Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. The algorithm is called the ATMS MIT Precipitation retrieval algorithm version 1 (ATMP-1), employs neural network estimators trained and evaluated using the validated global reference physical model NCEP/MM5/TBSCAT/F(λ), and works for snow-free land and seawater with |latitudes|<;50°. Signals were carefully chosen and principal component analysis was used to filter out angle and surface effects, and other noises. Retrievals are useful for surface precipitation rates higher than 1 mm/h at 15-km resolution for both land and sea, as evaluated using MM5. Surface precipitation rates retrieved using ATMP-1 for ATMS aboard Suomi NPP satellite are in good agreement with those retrieved using the AMSU MIT Precipitation retrieval algorithm (AMP) for AMSU aboard NOAA-18 satellite.

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.

A robust and simple algorithm is developed to merge L-band radiometer retrievals and L-band radar observations to obtain high-resolution (9-km) soil-moisture estimates from data of the NASA Soil Moisture Active and Passive (SMAP) mission. The algorithm exploits the established accuracy of coarse-scale radiometer soil-moisture retrievals and blends this with the fine-scale spatial heterogeneity detectable by radar observations to produce a high-resolution optimal soil-moisture estimate at 9 km. The capability of the algorithm is demonstrated by implementing the approach using the airborne Passive and Active L-band System (PALS) instrument data set from Soil Moisture Experiments 2002 (SMEX02) and a four-month synthetic data set in an Observation System Simulation Experiment (OSSE) framework. The results indicate that the algorithm has the potential to obtain better soil-moisture accuracy at a high resolution and show an improvement in root-mean-square error of 0.015-0.02-cm3/cm3volumetric soil moisture over the minimum performance taken to be retrievals based on radiometer measurements resampled to a finer scale. These results are based on PALS data from SMEX02 and a four-month OSSE data set and need to be further confirmed for different hydroclimatic regions using airborne data sets from prelaunch calibration/validation field campaigns of the SMAP mission.

Microwave radiometry soil moisture retrieval methods suffer from uncertainties about the representation of several effects, including dielectric mixing, surface roughness, and vegetation opacity. These uncertainties lead to two major types of error: systematic bias and random errors. The effect of the uncertainties is studied using the Soil Moisture Active Passive Algorithm Testbed, a simulation environment for evaluating error propagation in retrieval algorithms, and two different common retrieval algorithms (single and dual polarizations). The two types of errors are simulated by using different representations for each factor in the forward and retrieval parts. For both algorithms, this approach introduces a spatially variable bias, which is particularly large when using a single-polarization retrieval algorithm. This paper illustrates the emergence of both this bias and the random error due to uncertainty in the representation of vegetation and soil texture effects in retrieval algorithms. The dependence of these two types of error on vegetation and soil texture properties is shown through mapping them over the simulation region. The relative contribution of these errors to the total error is strongly dependent on the simulation conditions and is not necessarily indicative of what may be experienced during actual observations. Uncertainty due to roughness representation causes a lower error than uncertainty in vegetation opacity and dielectric mixing parameterizations in the simulated soil moisture retrieval. Summation and compensation of multiple errors can cause the estimate error to increase with improved radiative transfer knowledge, even after bias removal. The retrieval of soil moisture from microwave measurements depends on several other parameterizations that are also uncertain. This paper is limited to only three parameterizations that are considered to be among the larger contributors to bias.

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.

In this paper, we consider the problem of monitoring soil moisture evolution using a wireless network of in situ sensors. Continuously sampling moisture levels with these sensors incurs high-maintenance and energy consumption costs, which are particularly undesirable for wireless networks. Our main hypothesis is that a sparser set of measurements can meet the monitoring objectives in an energy-efficient manner. The underlying idea is that we can trade off some inaccuracy in estimating soil moisture evolution for a significant reduction in energy consumption. We investigate how to dynamically schedule the sensor measurements so as to balance this tradeoff. Unlike many prior studies on sensor scheduling that make generic assumptions on the statistics of the observed phenomenon, we obtain statistics of soil moisture evolution from a physical model. We formulate the optimal measurement scheduling and estimation problem as a partially observable Markov decision problem (POMDP). We then utilize special features of the problem to approximate the POMDP by a computationally simpler finite-state Markov decision problem (MDP). The result is a scalable, implementable technology that we have tested and validated numerically and in the field.

The 29 papers in this special issue were originally presented at the 2008 International Geoscience and Remote Sensing Symposium (IGARSS'08), held from July 6 to 11 in Boston, MA.

Microwave radiometry will emerge as an important tool for global remote sensing of near-surface soil moisture in the coming decade. In this modeling study, we find that hillslope-scale topography (tens of meters) influences microwave brightness temperatures in a way that produces bias at coarser scales (kilometers). The physics underlying soil moisture remote sensing suggests that the effects of topography on brightness temperature observations are twofold: 1) the spatial distribution of vegetation, moisture, and surface and canopy temperature depends on topography and 2) topography determines the incidence angle and polarization rotation that the observing sensor makes with the local land surface. Here, we incorporate the important correlations between factors that affect emission (e.g., moisture, temperature, and vegetation) and topographic slope and aspect. Inputs to the radiative transfer model are obtained at hillslope scales from a mass-, energy-, and carbon-balance-resolving ecohydrology model. Local incidence and polarization rotation angles are explicitly computed, with knowledge of the local terrain slope and aspect as well as the sky position of the sensor. We investigate both the spatial organization of hillslope-scale brightness temperatures and the sensitivity of spatially aggregated brightness temperatures to satellite sky position. For one computational domain considered, hillslope-scale brightness temperatures vary from approximately 121 to 317 K in the horizontal polarization and from approximately 117 to 320 K in the vertical polarization. Including hillslope-scale heterogeneity in factors effecting emission can change watershed-aggregated brightness temperature by more than 2 K, depending on topographic ruggedness. These findings have implications for soil moisture data assimilation and disaggregation of brightness temperature observations to hillslope scales.

A change detection algorithm has been developed in order to obtain high-resolution soil moisture estimates from future Soil Moisture Active and Passive (SMAP) L-band radar and radiometer observations. The approach combines the relatively noisy 3-km radar backscatter coefficients and the more accurate 36-km radiometer brightness temperature into an optimal 10-km product. In preparation for the SMAP mission, an observation system simulation experiment (OSSE) and field experimental campaigns using the Passive and Active L- and S-band Airborne Sensor (PALS) have been conducted. We use the PALS airborne observations and OSSE data to test the algorithm and develop an error budget table. When applied to four-month OSSE data, the change detection method is shown to perform better than direct inversion of the radiometer brightness temperatures alone, improving the root mean square error by 2% volumetric soil moisture content. The main assumptions of the algorithm are verified using PALS data from the soil moisture experiments held during June-July 2002 (Soil Moisture Experiment 2002) in Iowa. The algorithm error budget is estimated and shown to meet SMAP science requirements.

Temporally and spatially variable rainfall replicates are frequently required in hydrologic applications of ensemble forecasting and data assimilation. Ensemble methods can be expected to work better when the rainfall replicates more closely resemble observed storms. In particular, the replicates should capture the intermittency and variability that are dominant features of rainfall events. In this paper, we present a new probabilistic procedure for generating realistic rainfall replicates that are constrained by (or conditioned on) remote sensing measurements. The procedure uses remotely sensed cloud top temperatures to identify potentially rainy regions. The cloud top temperatures are obtained from visible/infrared instruments in geostationary orbit. A multipoint geostatistical algorithm generates areas of nonzero rain (rain clusters) within each cloudy region. This algorithm relies on statistics derived from ground-based weather radar [National Operational Weather Radar (NOWRAD)] data. A truncated multiplicative cascade generates rain rates within each rain cluster. A computational experiment based on summer 2004 data from the Central U.S. indicates that the rainfall replicates simulated by the procedure are visually and statistically similar to individual NOWRAD images and to a large ensemble of NOWRAD images collected throughout the summer simulation period.

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.

Microwave radiometry is emerging as an important tool for global remote sensing of near-surface soil moisture in the coming decade. In a modeling study, we find that hillslope-scale topography (tens of meters) influences predicted microwave brightness temperatures at significantly coarser scales (kilometers). Through the physics of microwave remote sensing, topography is understood to effect brightness temperature observations in two important ways: (1) modulating the spatial distribution of factors affecting emission like vegetation biomass, moisture, and surface and canopy temperatures, and (2) determining the incidence angle and polarization rotation the observing sensor makes with the local land surface. Local incidence and polarization rotation angles can be explicitly computed knowing local terrain slope and aspect and sky position of the sensor. In an analysis of two synthetic domains, predicted hillslope-scale brightness temperatures within a less rugged landscape that is presented here can vary from approximately 224 to 302 K in the horizontal polarization and from approximately 298 to 320 K in the vertical polarization. Impacts of hillslope-scale heterogeneity in factors effecting emission account for at most approximately 2 K in predicted watershed-scale brightness temperature, while impacts of hillslope-scale topography on observing geometry can account for up to 28 K in predicted watershed-scale brightness temperatures in a topographically rugged area.

We have developed a new concept for a smart sensor web technology for measurements of soil moisture that include spaceborne and in-situ assets. The objective of the technology is to enable a guided/adaptive sampling strategy for the in-situ sensor network to meet the measurement validation objectives of the spaceborne sensors with respect to resolution and accuracy. One potential application is the Soil Moisture Active/Passive (SMAP) mission. The science measurements considered are the surface-to-depth profiles of soil moisture estimated from satellite radars and radiometers, with calibration and validation using in-situ sensors. Installing an in-situ network to sample the field for all ranges of variability is impractical. However, a sparser but smarter network can provide the validation estimates by operating in a guided fashion with guidance from its own sparse measurements. The feedback and control take place in the context of a dynamic data assimilation system subject to energy and accuracy constraints. The overall design of the smart sensor web including the control architecture, assimilation framework, and actuation hardware are presented in this paper. We also present results of initial numerical and laboratory demonstrations of the sensor web concept, which includes a small number of soil moisture.

This paper compares surface precipitation rates retrieved for the United States Great Plains (USGP) during the summer of 2004 using the Advanced Microwave Sounding Unit (AMSU) aboard the United States NOAA-15 and -16 satellites with similar precipitation products produced by AMSR-E aboard the NASA Aqua satellite, SSM/I aboard the United States DMSP F-13, -14, and -15 satellites, TMI aboard the NASA TRMM satellite, and a doppler radar product (NOWRAD) of the Weather Services International Corporation (WSI). AMSU surface precipitation rates were retrieved using neural network algorithms trained with either a cloud-resolving MM5 physical model for 106 global storms (the AMSU/MM5 algorithm), or summer NEXRAD radar data for the USGP (the AMSU/NR algorithm). Observed correlation coefficients between log10(X + 0.01) for NOWRAD surface precipitation rates X (mm/h) at 0.25- degree resolution and those for other sensors were, in declining order, 0.82, 0.79, 0.78, 0.71, and 0.68 for TMI, SSM/I, AMSU/NR, AMSR-E, and AMSU/MM5, respectively. Higher correlation coefficients were obtained when TMI was regarded as truth: 0.86, 0.83, 0.82, 0.80, and 0.78 for SSM/I, AMSU/NR, NOWRAD, AMSU/MM5, and AMSR-E, respectively. Other sensor comparisons include false alarm statistics for all pairs of sensors, rms and mean differences with respect to NOWRAD, precipitation-rate distribution functions, and observed correlations between NOWRAD and AMSU/MM5 precipitation and hydrometeor water path retrievals.

An observing system simulation experiment is developed to test tradeoffs in resolution and accuracy for soil moisture estimation using active and passive L-band remote sensing. Concepts for combined radar and radiometer missions include designs that will provide multiresolution measurements. In this paper, the scientific impacts of instrument performance are analyzed to determine the measurement requirements for the mission concept. The ensemble Kalman smoother (EnKS) is used to merge these multiresolution observations with modeled soil moisture from a land surface model to estimate surface and subsurface soil moisture at 6-km resolution. The model used for assimilation is different from that used to generate "truth." Consequently, this experiment simulates how data assimilation performs in real applications when the model is not a perfect representation of reality. The EnKS is an extension of the ensemble Kalman filter (EnKF) in which observations are used to update states at previous times. Previous work demonstrated that it provides a computationally inexpensive means to improve the results from the EnKF, and that the limited memory in soil moisture can be exploited by employing it as a fixed lag smoother. Here, it is shown that the EnKS can be used in large problems with spatially distributed state vectors and spatially distributed multiresolution observations. The EnKS-based data assimilation framework is used to study the synergy between passive and active observations that have different resolutions and measurement error distributions. The extent to which the design parameters of the EnKS vary depending on the combination of observations assimilated is investigated

The Microwave Observatory of Subcanopy and Subsurface (MOSS) is a mission concept for a spaceborne synthetic aperture radar (SAR) system that provides global observations of soil moisture under substantial vegetation cover (exceeding 20 kg/m2) and at useful depths (1-5 m). The concept was developed and a number of new required technologies were demonstrated through a National Aeronautics and Space Administration Earth Science Technology Office Instrument Incubator Program project. This very high frequency (VHF)/ultrahigh frequency (UHF) polarimetric SAR is designed to provide 7-10-day observations of soil moisture at 1-km resolution. The rapid repeat cycle mandates swath widths in the range of 300-400 km, which must be realized by a 30-m-long antenna. Conventional array implementations would result in a mass of more than 4000 kg, whereas with the technology proposed and demonstrated in this project, the total antenna mass is less than 500 kg. The antenna concept is a dual-stacked patch array feed illuminating a 30-m mesh reflector to synthesize the long apertures and achieve the wide swath. The feed system prototype was fabricated and its performance demonstrated. Other major project components were: (1) system-level SAR and mission design; (2) demonstration of science data and products, using a tower-based VHF/UHF radar; (3) spacecraft and mesh reflector antenna mechanical design; (4) developing mitigation strategies for ionospheric effects; and (5) assessing frequency interference effects. Experimental science data were generated from the tower radar for soil moisture profiling in Arizona and for forest penetration in Oregon. The soil moisture products were demonstrated through an integrated inversion-processing algorithm. This paper summarizes the results from the MOSS project and demonstrates the feasibility of the spaceborne mission.

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.

Triangulated irregular networks (TIN) can form the basis for multiple‐resolution representations in distributed hydrogeomorphic simulations over complex basins. Current methods for deriving TIN meshes depend primarily on surface slope without considering other terrain attributes significant to the watershed response such as the specific basin area. As an alternative, we present a methodology for combining a hydrogeomorphic or landscape index with an unstructured triangulated mesh. Landscape indices provide a concise method for describing steady‐state terrain processes by isolating the dominant physical factors. The mesh‐generation algorithm results in an adaptive discretization that resembles the spatial pattern of the landscape index with a high resolution retained in areas expected to impact the basin response. We compare the proposed algorithm with a slope‐preserving method as a means for initializing the terrain representation in two TIN‐based hydrogeomorphic models. Through three case studies in saturation‐excess runoff, transport‐limited soil erosion and shallow landslide simulation, we assess the distributed model sensitivity to the triangulated terrain algorithm. Model comparisons reveal that the process‐based triangulations focus the distributed simulation in regions anticipated via a steady‐state index to affect the transient watershed response.

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.

A new physically based disaggregation method is developed to improve the spatial resolution of the surface soil moisture extracted from the Soil Moisture and Ocean Salinity (SMOS) data. The approach combines the 40-km resolution SMOS multiangular brightness temperatures and 1-km resolution auxiliary data composed of visible, near-infrared, and thermal infrared remote sensing data and all the surface variables involved in the modeling of land surface-atmosphere interaction available at this scale (soil texture, atmospheric forcing, etc.). The method successively estimates a relative spatial distribution of soil moisture with fine-scale auxiliary data, and normalizes this distribution at SMOS resolution with SMOS data. The main assumption relies on the relationship between the radiometric soil temperature inverted from the thermal infrared and the microwave soil moisture. Based on synthetic data generated with a land surface model, it is shown that the radiometric soil temperature can be used as a tracer of the spatial variability of the 0-5 cm soil moisture. A sensitivity analysis shows that the algorithm remains stable for big uncertainties in auxiliary data and that the uncertainty in SMOS observation seems to be the limiting factor. Finally, a simple application to the SGP97/AVHRR data illustrates the usefulness of the approach.

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 science objectives of the Hydrosphere State Mission (HYDROS) are to provide frequent, global measurements of surface soil moisture and surface freeze/thaw state. In order to adequately measure these geophysical quantities, the key instrument requirements were determined by the HYDROS science team to be: (1) Dual-polarization L-Band radiometer measurements at 40 km resolution, (2) Dual-polarization L-Band radar measurements at 3 km resolution, and (3) A wide swath to insure global three-day refresh time for these measurements (1000 km swath at the selected orbit altitude of 670 km). As an optimal solution to this set of instrument requirements, a relatively large, 6-meter, conically-scanning reflector antenna architecture was selected for the instrument design. The deployable mesh antenna is shared by both the radiometer and radar instruments by using a single L-Band feed.

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.

Microwave sensors are of primary importance in mapping surface states and measuring some significant quantities which affect the hydrological cycle. A space mission aiming at monitoring soil moisture and surface salinity at a global scale is suggested. The mission is based on a combination of polarimetric active and passive microwave sensors.

The performance of a land data assimilation system for surface ground temperature sensing is demonstrated for the U.S. Southern Great Plains 1997 Hydrologic Field Experiment. Adjoint state formulation is used in a variational scheme to minimize the error of surface ground temperature predictions subject to constraints imposed by the system model. It is shown that continuous sampling of observations result in accurate estimation of the components of the surface energy balance and an index of soil moisture. Experiments on the effects of sparse temporal sampling (near the mean of minimum and maximum in the diurnal cycle) on the estimation show that observations at the peak of the diurnal cycle is the most suitable for the land data assimilation system. It is suggested that surface ground temperature within a /spl sim/3 h window centered on this time in the diurnal cycle contains information on the cumulative heating and available energy partitioning at the land surface.

Our ability to accurately describe large-scale variations in soil moisture is severely restricted by process uncertainty and the limited availability of appropriate soil moisture data. Remotely sensed microwave radiobrightness observations can cover large scales but have limited resolution and are only indirectly related to the hydrologic variables of interest. The authors describe a four-dimensional (4D) variational assimilation algorithm that makes best use of available information while accounting for both measurement and model uncertainty. The representer method used is more efficient than a Kalman filter because it avoids explicit propagation of state error covariances. In a synthetic example, which is based on a field experiment, the authors demonstrate estimation performance by examining data residuals. Such tests provide a convenient way to check the statistical assumptions of the approach and to assess its operational feasibility. Internally computed covariances show that the estimation error decreases with increasing soil moisture. An adjoint analysis reveals that trends in model errors in the soil moisture equation can be estimated from daily L-band brightness measurements, whereas model errors in the soil and canopy temperature equations cannot be adequately retrieved from daily data alone. Nonetheless, state estimates obtained from the assimilation algorithm improve significantly on prior model predictions derived without assimilation of radiobrightness data.

The authors estimate the magnitude of the beam-filling error due to soil-type heterogeneity in the determination of sensor-footprint average soil moisture (/spl theta/~/sub f/) retrieved from remote L-band radiometer measurements. Sets of randomly chosen soils are given uniform initial wetness and are subjected to atmospheric drying over 15 days in a numerical model. Results indicate that soil heterogeneity contributes less than 0.7% volumetric soil-moisture error (0.007 m/sub water//sup 3//m/sub soil//sup 3/).

Sequential data assimilation (Kalman filter optimal estimation) techniques are applied to the problem of retrieving near-surface soil moisture and temperature state from periodic terrestrial radiobrightness observations that update soil heat and moisture diffusion models. The retrieval procedure uses a time-explicit numerical model to continuously propagate the soil state profile, its error of estimation, and its interdepth covariances through time. The model's coupled soil moisture and heat fluxes are constrained by micrometeorology boundary conditions drawn from observations or atmospheric modeling. When radiometer data are available, the Kalman filter updates the state profile estimate by weighing the propagated state, error, and covariance estimates against an a priori estimate of radiometric measurement error. The Kalman filter compares predicted and observed radiobrightnesses directly, so no inverse algorithm relating brightness to physical parameters is required. The authors demonstrate Kalman filter model effectiveness using field observations and a simulation study. An observed 1 m soil state profile is recovered over an eight-day period from daily L-band observations following an intentionally poor initial state estimate. In a four-month simulation study, they gauge the longer term behavior of the soil state retrieval and Kalman gain through multiple rain events, soil dry-downs, and updates from radiobrightnesses.

An algorithm is developed to solve the inverse problem for the retrieval of the soil moisture and temperature profiles based on remotely sensed observations of multispectral irradiance. A model of coherent wave radiative transfer and a model of coupled heat and moisture diffusion in porous media are combined in order to estimate the liquid volumetric water content and temperature profiles in a soil column using low-frequency passive microwave and infrared emitted radiation observations and without the use of empirical relations. The central purpose of this mainly theoretical paper is to pose the inverse problem and present the physics-based algorithm as the solution. The algorithm is tested on a basic synthetic example in order to ascertain that the retrieval is feasible. Additional work in the future is necessary and planned in order to test the algorithm with field observations, extend it to include vegetation, and refine it for detail in the specification of heterogeneity in soil types and boundary conditions.>