Data from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission have shown promise for the retrieval of soil moisture, and many soil moisture products using CYGNSS data have been developed. In this work, we present a merged product that combines several CYGNSS soil moisture products using a minimum variance estimator (MVE). The MVE identifies an optimal weighted averaging scheme based on the error covariance characteristics of the CYGNSS soil moisture products. The error covariance matrix is computed using two reference datasets: soil moisture data from the Soil Moisture Active Passive (SMAP) radiometer and in situ soil moisture data. The results from each of these provide insights into both the performance of the merged product and the individual input CYGNSS products. Overall, the merged product offers better performance than any individual CYGNSS product while also offering better temporal resolution than SMAP. The results of this work also demonstrate that the use of the MVE is a compelling technique for soil moisture applications.

We reported on the progress of fast hybrid method (FHM) for full- wave simulations of propagation of L-band microwaves in forested environment. For L band, previously we performed full wave simulations of realistic trees initially at 8 meters [1], followed by 13 meters [2]. The progress in this work is at comparisons of the electromagnetic model simulations with SMAPVEX19-22 data: 1) The height of the trees have been extended to 17 meters with the multiple scattering effects of 91 trees in the spatial domain with simulated transmissivity at 0.57 2) the spatial patterns of electric field distribution are simulated with electric field as high as 1.6 that of the incident wave corresponding to 2.56 times the Poynying of the incident waves, and the patterns exhibits gaps and shadows 3) the effects of clustering of trees with gaps show different results from that of uniformly positioned trees and 4) tree structures are varied with examples of trees with two trunks branching out from the main trunk, and the case of tapering trunk radius.

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.

This manuscript explores the use of Global Navigation Satellite System–Reflectometry (GNSS-R) data collected by the Soil Moisture Active Passive (SMAP) mission to estimate antenna view angle offsets. A novel methodology for estimating antenna view angle offsets using GNSS-R data is proposed by comparing the received signal-to-noise ratio (SNR) of the reflected GNSS signal with SMAP’s antenna pattern measured prior to launch. To properly compare them, observation angles are defined with respect to the SMAP position and the specular point position, which are computed from the SMAP telemetry and the Global Positioning System (GPS) ephemeris data using a 1-km resolution digital elevation model (DEM) map. A methodology based on a second-order polynomial fit and a linear fit is proposed to estimate the offset by azimuthal sector and at different times of the year. Results are obtained for a total of six years, showing a consistent dependence on the antenna azimuthal look angle and on the time of the year, with an average antenna offset of 0.2° with a standard deviation of 0.06°, which produces a geolocation error of ~3.6 km. Further analysis proposes a nonlinear model to estimate the antenna offset directly from an azimuthal look angle and the time of the year. Model results show a Pearson correlation coefficient of$R$= 0.85 and$R$= 0.80 for descending and ascending passes, respectively. This model can be used by the SMAP team to further correct the antenna footprint location and pointing angle.

The soil moisture active passive (SMAP) instrument has been used to infer sea surface wind speed from its brightness temperature measurements. To do so, the SMAP wind speed retrieval process requires the removal of sea surface temperature (sst) and sea surface salinity (sss) impact on brightness temperature. Estimating the so-called excess surface emissivity ($\Delta {e}$), that is the sst normalized difference between the measured brightness temperature of the sea surface and the corresponding brightness temperature of a flat surface, is one way of accomplishing such a task. In this article, we investigate whether SMAP$\Delta {e}$contains residual dependencies to sst and sss. To do so, v5.0 SMAP brightness temperature measurements, derived by the Jet Propulsion Laboratory, are used. For any fixed numerical weather prediction model wind speed above 15 m/s, down to a 20% decrease in SMAP$\Delta {e}$is observed as the sst increases from 274 to 304 K. For any fixed wind speed between 8 and 15 m/s, the sst residual dependence is weaker with SMAP$\Delta {e}$exhibiting a 1%–2% decrease as the sst increases. Below 8 m/s, this pattern becomes prevalent again, when the significant wave height (Hs) is greater than 3.5 m. SMAP$\Delta {e}$decreases as much as 50% with increasing sss, most notably below 8 and above 15 m/s when Hs is considered. This analysis has also shown that below 8 m/s and for swell dominant seas, a decrease in either sss or sst results in a decrease in SMAP$\Delta {e}$sensitivity to wind-induced sea surface roughness.

To quantify the forest effects on P-band radar remote sensing, this paper utilizes the fast hybrid method (FHM) combining fast multiple scattering theory (FMST) and a numerical electromagnetic solution. Considering estimation of the domain area, the required number of trees for P-band signal analysis is 121. For the efficient scattering solutions of a large number of trees, the FHM uses the triple FFT applied to the Foldy-Lax equation where two FFTs are applied to a 2-D spatial domain and one FFT to the order of cylindrical waves corresponding to translation addition theorem. This speeds up calculation of a translation addition matrix multiplied by a column vector. The accuracy of FHM is validated by FEKO using 25 trees, showing excellent agreement. Also, FHM provides faster solutions than commercial software. Using dielectric constants of winter and summer conditions, forest effects in P-band signals are validated by calculating transmissivity.

The Soil Moisture Active Passive instrument (SMAP) sea surface wind speed potential dependence to sea surface salinity (sss) and sea surface temperature (sst) is explored. SMAP JPL v5.0 and SMAP REMSS v0.10 are used for this analysis. The SMAP wind speed error (i.e. SMAP-NCEP) is bin averaged per sss and per sst ranges, respectively, and plotted against NCEP wind. This analysis shows that both SMAP wind speed products exhibit clear dependence to both sss and sst: for NCEP winds less than 15 m/s, sst bin averaged error curves for both products show spreads below 1 m/s; for NCEP winds greater than 15 m/s, the maximum spread between the error curves can be greater than 2 m/s. Spreads between sss bin averaged curves can be as high as 5 m/s. This maximum spread is reduced below 1 m/s if data with salinity less than 32 psu is excluded.

We present the Forward Ocean Atmosphere Microwave (FOAM) radiative transfer model, a Python package designed to simulate passive microwave observations of ocean state, and discuss its basic architecture. FOAM is particularly useful for exploring concept architectures for future missions measuring ocean salinity from space. We discuss how FOAM can be used to design missions meeting a range of accuracy and revisit requirements, and we consider an example of ocean salinity retrieval with a wideband radiometer system.

We consider the impacts of wind direction and model function choice on retrieved wind speed statistics using SMAP Version 5 L2 product retrieval algorithms. Attempts to retrieve ocean wind direction using SMAP measurements lead to arti-facts in wind speed statistics below 12 m/s which are absent if the wind direction is taken from ancillary data. Model functions derived from Aquarius matchups achieve biases within 0.5 psu and standard deviations within 2 m/s at speeds greater than 5 m/s, with the better performance from NCEP-based matchups. The SMAP-derived model function obtains bet-ter agreement with the global wind speed distribution of the NCEP ancillary product, but with greater scatter and poorer performance near coastal regions. Further effort is needed to improve SMAP wind retrievals for use in operational applications.

We model the sea foam emissivity at frequencies from 1 to 89 GHz. This model is part of the work done by an international science team to develop a radiative transfer model of reference quality for the ocean surface emissivity from L band to infrared frequencies. A study of the sensitivity to different foam properties (foam layer thickness and upper limit of the foam void fraction) guided the effort to tune the foam emissivity model by frequency and polarization. The results show that the differences between simulated and observed brightness temperatures decrease when using the tuned foam model.

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.

In this paper, the Numerical Maxwell Model of 3D (NMM3D) full-wave simulation is performed over a corn field using a hybrid method to study the vegetation effect on the microwave. The commercial software of FEKO is used to extract T-matrix of single corn in the first step. Then the calculated T-matrix is combined with Wave Multiple Scattering Theory (W-MST) in the second step to consider the multiple scattering among different plants. The hybrid method is validated with HFSS by solving scattering from 2 corns. A corn field of 25 corn is simulated using the hybrid method and the transmission is calculated and compared with those obtained from the classical radiative transfer model.

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.

The Soil Moisture Active Passive (SMAP) mission was launched on 31 st January 2015 in a 6 AM/6 PM sun-synchronous orbit at 685 km altitude to measure soil moisture and free/thaw globally [1]. A radar (active) and a radiometer (passive) are onboard, and they share a single feedhorn and mesh reflector. The antenna pointing was calibrated by the radar and the result is applied to the radiometer. Because the two instruments work at different frequencies, the antenna pointing for the two instruments are slightly different. Calibration of the radiometer antenna pointing is necessary for improving the water-body correction used in the soil moisture retrieval and improving the ocean surface incidence accuracy needed in the retrieval of sea surface salinity (SSS). The calibration activity has been performed and the result will be presented.

Remote sensing of sea surface salinity (SSS) near land is difficult due to land contamination. In this study, we assess SSS retrieved from SMAP (JPL V5 and RSS V4) in coastal region using in situ data collected by saildrones during the North American West Coast Survey. Collocated satellite and saildrone salinity measurements reveal consistent large-scale features: the fresh water (low SSS) related with the Columbia River discharge, and the relatively salty water (high SSS) near Baja California associated with regional upwelling. The standard deviation of the difference (stdD) for collocations with SMAP Level 3 (8 days average) between 40 to 100km from land is 0.51 (0.56) psu for JPL V5 (RSS V4 70km). This is encouraging for the potential application of SMAP SSS in monitoring coastal zone freshwater particularly where exists large freshwater variance. In regions closer to land, stdD for JPL V5 increases to 0.8 (1.4) psu in the zone 20-40km (<20km). RSS V4 delivers 42% less data in 20-40km, and almost no data within 20km. In attempt to reduce the uncertainty of SMAP SSS near land and understand the discrepancy between JPL and RSS products, we investigate saildrone collocations with SMAP Level 2 (direct output from retrieval), in terms of distance to land, and saildrone's simultaneous measurements of sea surface temperature and surface wind speed (both are important ancillary parameters for SSS retrieval). Our analysis identified quite different areas of future improvement for JPL and RSS algorithms.

In this paper, an Analytical Kirchhoff Solution (AKS) and Numerical Kirchhoff approach (NKA) are used to study coherent and incoherent land surface near specular scattering at L and P bands. The AKS model includes both coherent and incoherent waves, and includes the effects of topographic slopes and elevations. The land profile is modelled as a summation of three scales of surface roughness corresponding to “microwave”, “fine topography”, and “coarse topography”, where the microwave roughness and fine topography are treated as random processes while the coarse topography is deterministic. An airborne lidar survey performed over the San Luis Valley, CO is used to obtain surface roughness information for the simulation results of$\mathrm{P}$and L-band scattering. Results using the lidar surface data show that coherent reflection can dominate returns from a 5 km by 5 km area at P band, while incoherent scattering dominates L band returns in the same scenario.

A spaceborne P-band signals of opportunity synthetic aperture radar concept is proposed for the remote sensing of terrestrial snow. We have completed a performance analysis assuming a formation flight of 3 to 5 SmallSats on one orbit plane. The spacing between the SmallSats is chosen so that their ground tracks will be separated by 50 to 100 m to allow the use of interferometric synthetic aperture radar processing technique to obtain a spatial resolution of a few hundred meters. A point system design has been completed to determine the antenna concept and to indicate the dependence of spatial resolution and signal to noise ratio on the number of receivers. The performance for range delay determination was analyzed to assess the impact of various error sources, including instrument receiver noise and ionospheric delay. The dominant error source is the ionospheric delay, which will be corrected using the split-spectrum algorithm. Our overall error budget analysis indicates that an accuracy of about 3 cm for the snow water equivalent in dry snow and 5 cm for the snow depth of wet snow can be achieved.

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.

In this article, we investigate the potential of using single-pass InSAR model-based approaches to retrieve dry snow parameters. Two InSAR scattering models of dry snow are considered: the dense-medium random volume over ground (RVoG) model and the simple variant of the full penetration (FP) model. A quasi-crystalline approximation (QCA)-based extinction analysis confirms the negligible extinction dependence of the InSAR observables at L/C/X-band for fresh dry snow. The FP models the low-frequency (L/C/X-band) InSAR phase as a single constraint of snow depth and density, which can be supplemented by an extra observation (e.g., InSAR coherence orin situdepth/density). The single-pass InSAR models and inversion approaches were validated using X-band InSAR data collected from a tower-based three-frequency (X/Ku-low/Ku-high) fully polarimetric TomoSAR system, where a multi-frequency polarimetric InSAR analysis and ground-to-volume ratio-based snow condition analysis were conducted. We also analyzed the sensitivity and error propagation of the single-pass InSAR phase and coherence in measuring dry snow depth/density. It was found that the X-band HH-pol FP-modeled single-pass InSAR phase along with RVoG-modeled coherence orin situdepth is capable of measuring snow water equivalent (SWE) with a 23–26 mm uncertainty (13–15%) and a 20–26 mm bias (12–15%) for dry snow SWE of 0.2 m, and with an optimal perpendicular baseline on the order of a tenth of the snow depth (0.8 m) at our test site. This single-pass InSAR approach with the FP model is potentially useful and thus needs further investigation for large-scale dry snow retrieval with a wide range of snow conditions using ground-based/airborne/spaceborne low-frequency (L/C/X-band) InSAR observations.

Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission augmented with a physical surface scattering model were analyzed to develop a semiempirical model, which consists of three main modeling components for soil moisture, vegetation, and surface roughness. CYGNSS data collected from March 2017 to March 2020 were collocated with the soil moisture data from the Soil Moisture Active Passive (SMAP) mission and climatology vegetation water content (VWC) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data. The matchup data were binned as a function of soil moisture, VWC, and incidence angle. The CYGNSS data were calibrated using a coherent reflection equation to obtain an effective reflectivity. The response of CYGNSS effective reflectivity to soil moisture changes is consistent with the change of the Fresnel reflectivity based on Mironov’s soil dielectric constant model used by the SMAP and Soil Moisture Ocean Salinity (SMOS) missions for soil moisture retrieval. The CYGNSS effective reflectivity decreases approximately linearly (in dB) with respect to the NDVI-VWC. The estimated values of vegetation attenuation parameter ($b$) agree with values published in the literature and are corroborated with the estimated values of$b$using the SMAP dual-polarized channel algorithm based on land cover types. A CYGNSS surface scattering map has been derived and reveals a mixed contribution of coherent and incoherent scattering effects and the effects of topography. The semiempirical model, leveraging two of the key modeling functions used by microwave radiometry, will pave the way for a synergistic use of reflectometry and radiometry data for multiparameter retrieval and development of consistent soil moisture products.

The Soil Moisture Active/Passive satellite was launched in 2015 to provide global and continuous maps of land surface soil moisture and freeze-thaw using L-Band microwave radiometry. Even though the 1400-1427 MHz frequency used by SMAP is a protected portion of the spectrum, Radio Frequency Interference (RFI) is still observed that can corrupt the radiometer's measurements. Nine distinct algorithms are implemented as part of SMAP's level 1 processing to detect and filter out RFI contributions. However, any remaining undetected RFI are major concern especially at the locations of cal/val sites used for evaluating soil moisture retrieval performance. This paper presents an analysis of the RFI environment at SMAP cal/val site locations and assesses the impact of RFI on soil moisture retrievals at those locations.

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.

To investigate the feasibility of using multi-frequency to further improve the soil moisture retrieval, the recently developed hybrid method is used to perform full-wave simulations of a wheat field at three different frequencies of L-, S-, and C-bands. In the hybrid method, the multiple scattering within a single wheat plant is first captured using the T-matrix based on the full-wave solutions of HFSS. In the second step, the scatterings among different plants are considered using the Foldy-Lax equations of multiple scattering theory (MST). The transmission of microwaves through wheat field at the L-, S- and C-bands are calculated using the hybrid method. Results show that: (1) the transmission obtained from the full-wave simulations is much larger than those computed from the radiative transfer equations (RTE) model, (2) the hybrid method transmission has a weaker frequency dependence than those of the RTE, and (3) the attenuation caused by the vegetation layer would saturate with an increase in frequency.

The Soil Moisture Active Passive (SMAP) mission was launched on 31stJanuary 2015 in a 6 AM/ 6 PM sun-synchronous orbit at 685 km altitude to measure soil moisture and free/thaw globally [1]. The passive instrument of SMAP is a fully polarimetric L-band radiometer (1.4GHz) operating with a bandwidth of 24MHz. The radiometer uses a combination of noise-diodes and Dicke-loads for internal calibration with a design similar to that used by the Aquarius or Jason series radiometers [2], [3]. Pre-launch calibration activities had been performed since 2012 on the engineering model of the radiometer. Post-launch calibration activities have been performed to fine-tune and validate the results from the pre-launch calibration. The major calibration activities and lessons learned in the past 8 years will be described in the following sessions.

The Cyclone Global Navigation Satellite System (CYGNSS) observation received the Global Positioning System (GPS) signals with a revisit time in the range of 2.8 to 7.2 hours and have about a few kilometers of spatial resolution for coherent reflection and ∼25 km for incoherent reflection [1]. The CYGNSS datasets have a great potential to provide the vegetation optical depth (VOD) by combining the soil moisture data from SMAP. In this paper, we developed a physical-based model to retrieve the VOD. The retrieved VOD has been compared with SMAP VOD in both regional and global scale.

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

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 vegetation layer effects play an important role on microwave remote sensing of soil moisture. The classical Radiative Transfer Equation (RTE) and Distorted Born Approximation (DBA) model assume that the position of scatterers in vegetation is statistically homogeneous in 3D space. Such assumptions are incorrect because the scatterers in vegetation are in clusters and also in the form of extended cylinders. In this paper, we develop a new hybrid method that makes the Numerical Maxwell Model of 3D (NMM3D) full-wave simulation possible for vegetation. A geometry setup is introduced to account for the gap effects and vegetation structure. The T-matrix of a single plant composed of multiple cylinders in a cluster is extracted using Huygen's principle and the vector cylindrical wave (VCW) expansions. Foldy-Lax multiple scattering (FL) equations are used to solve for the transmissivity of the vegetation layer. The convergence and accuracy of the hybrid method are verified using Ansys High Frequency Structure Simulator (HFSS). Transmission through wheat is calculated using the hybrid method and compared with those of RTE/DBA.

The SMAP L-band microwave radiometer is in its extended mission of measuring soil moisture and freeze/thaw state globally for quantifying the water and carbon cycles. Instrument behavior has been stable over the past 4 years and 9 months. With the concurrent calibration of the internal calibration parameters and the antenna gain after estimating reflector emissivity, the SMAP radiometer measurements exhibit 0.1 K (rms) stability and nearly zero biases over the averaged global ocean and monthly Cold Sky views. The data (version 4) were released to the public in 2018 for various science activities. Now the radiometer data are under revisit to improve the absolute radiometric calibration and reduce calibration drift. Several approaches are investigated to obtain the optimal solution. In addition, the correction to the radiometer measurement when the SMAP radar transmitter was operational will also be revisited for the next data release. The performances of the calibration revisit and Radio-Frequency Interference (RFI) trends will be presented as well.

Satellite observed sea surface salinity (SSS) reflects the spatial and temporal variability of surface freshwater, which is critical to monitoring the climate change in the Arctic Ocean. Our previous study found that SMAP SSS shows signatures consistent with the inter-annual anomalies of observed sea ice concentration and river discharge, but with large uncertainty (~1 psu) compared with limited in-situ data. One of error sources is the un-corrected sea ice contamination effect. The JPL SMAP algorithm retrieves SSS at each wind-salinity-cell if the matchup sea ice concentration (SIC) is less than 3% with no ice correction implemented yet. Since L-band brightness temperature (TB) of sea ice is much higher than that of seawater, SSS retrieved from TB from a field of view (FOV) mixed with water and ice will result in false fresh signature if the sea ice effect is not accurately accounted for. In this study, we develop an empirical observation-driven sea ice correction algorithm. We characterize the sea ice signature using SMAP TB and ancillary SIC data. The sea ice effect is corrected near ice edge where SIC is under a predetermined threshold for SSS retrieval. We consider the seasonal variation of TB over ice to be likely associated with seasonal change of physical temperature and summer melting pond. The sea ice fraction (ICEF) is calculated by integration of SIC over SMAP FOV weighted by antenna gain patterns. An empirical sea ice correction algorithm is proposed. The impact of sea ice correction is demonstrated by comparing TBs with or without sea ice correction.

Recently, the Signal of Opportunity (SoOp) has been used in monitoring the snow pack from P-band. This technology makes use of existing satellite transmissions and become a cost-effective alternative to existing active technologies. The theoretical principle is based on the phase change of the reflected P-band signal to change in SWE. The P-band radio signals come from geostationary Mobile Use Objective System (MUOS) communication satellites, operating with dual-frequency channels at P-band (360-380 MHz and 240-270 MHz). P-band frequencies have excellent capability in penetration through thick vegetation (a confounding factor in existing SWE retrievals), and will offer unprecedented capability to sense snowpack under forest canopy. This paper provides an end-to-end simulation through OSSEs and support the understanding of physical mechanizes of surface features that contributing to the received signals.

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.

P-band Signals of Opportunity (SoOp) has great potential for remote sensing of root zone soil moisture (RZSM) from space. We have carried out a tower-based experiment with receivers to detect the reflected signals from the communications satellites at the Fraser Experimental Forests (FEFs), Colorado, in 2017. The measured reflectivity data at 260 MHz have a good correlation with in-soil moisture (SM) measurements. Retrieval of SM from the reflectivity data was also performed with results indicating accuracy of about 0.01 bias and 0.02 standard deviation (std) with respect to the average of SM in the upper 10 cm of soil. The experimental data and retrieval analyses lend support to the use of P-band SoOp for the remote sensing of SM. Our data also indicate the limitation of single frequency observations, suggesting the requirement of multiple frequencies to enable RZSM remote sensing because the surface SM plays a critical role on the change of reflectivity even at P-band frequencies.

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.

A new wideband radiometer covering P/L-band was developed at the Jet Propulsion Laboratory for polar ocean salinity and seasonal sea-ice thickness measurements. The instrument was deployed on the US Coast Guard Cutter Healy for an Arctic Ocean research cruise from September 13, 2018 to October 20, 2018. This work shows the first results relating sea ice thickness obtained from the measurements taken with the wideband P/L-band radiometer during the campaign. Results from the Artic cruise campaign were also used to study wideband spectral properties of salinity. In addition to this paper, Salinity and wide-band calibration challenges are presented in two other companion papers.

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).

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

In this paper we discuss some observations of the Soil Moisture Active Passive (SMAP) mission's high-resolution synthetic aperture radar (SAR) for extreme winds and tropical cyclones. We find that the cross-polarized backscatter is far more sensitive to wind speed at extreme winds than the copolarized backscatter and it is essential to observations of extreme winds with L-band SAR. We introduce a cyclone wind speed retrieval algorithm and apply it to the limited SMAP SAR dataset of cyclones. We show that the SMAP SAR instrument is capable of detecting extreme winds up to the category 5 wind speed regime providing unique capabilities as compared to traditional scatterometer with C and Ku-band radars.

The Soil Moisture Active Passive (SMAP) mission was launched January 31st, 2015. It is designed to measure the soil moisture over land using a combined active / passive L-band system. Due to the Aquarius mission, L-band model functions for ocean winds and salinity are already mature and have been directly applied to the SMAP mission. In contrast to Aquarius, the higher resolution and scanning geometry of SMAP allows for wide-swath ocean winds and salinities to be retrieved. In this talk we present the SMAP Sea Surface Salinity (SSS) dataset and algorithm.

The transmission through vegetation/forest canopy is important for remote sensing of soil moisture. The commonly used vegetation models are distorted Born Approximation (DBA) and Radiative Transfer Equation (RTE). We recently developed Numerical Maxwell Model of 3D (NMM3D) full wave simulations of vegetation/forests. The results of NMM3D show much larger transmission than that of RTE/DBA. A much larger transmission of NMM3D means microwave emission from soil can reach the radiometer, which is different from the conclusion for microwave remote sensing of soil moisture based on RTE and DBA. In this paper, we implement NMM3D based on the scattered field formulation of Foldy-Lax multiple scattering equations (FL). The novelty of this method is that the 3D cylindrical vector wave expansions are used in FL. The correctness of the method is verified. We implement the method on parallel computation using a large number of tall cylinders.

Arctic freshwater fluxes make this region key to regulate ocean currents and global climate. Hence, the Arctic Ocean sea surface salinity (SSS) knowledge is crucial to describe some of the processes that govern climate change.Recently, Barcelona Expert Center (BEC) deployed their version 2 of SSS Arctic data retrieved from Soil Moisture and Ocean Salinity mission (SMOS) mission. Nevertheless, in the context of the ESA Arctic+ initiative, BEC has planned to introduce improvements in all the processing levels. Some of the planned improvements include: (i) optimizing the projection grid of level 1, (ii) studying the performance of different dielectric models in the Artic region and (iii) producing an additional level 4 product. The SSS Arctic products from Soil Moisture Active Passive (SMAP) mission could be used to produce a level 4 by merging them with this new version of SSS level 3 produced from SMOS.Big data techniques are applied to produce debiased SSS maps. These techniques will be refined by introducing a new grouping method for the statistical study of the data.The aim of this work is to obtain a more accurate version of the Arctic salinity maps starting at 2011. The new SMOS SSS maps are expected to better capture the Arctic river plumes and thus they will help to better understand the freshwater inflow/outflow in the Arctic Ocean.

A new ultra-wide P/L-band radiometer instrument has been developed at the Jet Propulsion Laboratory for polar ocean salinity and seasonal sea-ice thickness measurements. The Arctic field campaign performed with the instrument deployed on the US Coast Guard Cutter Healy from September 13, 2018 to October 20, 2018. A new calibration strategy is developed for the ultra-wide band instrument to minimize the mismatch related effects. A noise-wave model is built to analyze and validate the instrument behavior for the calibration. The calibration strategy is analyzed using the results from the cruise campaign. Sea-ice thickness and sea surface salinity are presented in two other companion papers.

The SMAP L-band microwave radiometer has completed its 3-year primary mission of measuring soil moisture and freeze/thaw state globally for quantifying the water and carbon cyclces. Instrument behavior is stable over the past 3 years and 9 months. With the concurrent calibration of the internal calibration parameters and the antenna gain after estimating reflector emissivity, the SMAP radiometer measurements exhibit 0.1 K (rms) stability and nearly zero biases over the averaged global ocean and monthly Cold Sky views. The data (version 4) was released to the public in 2018 for various science activities. Now the radiometer is under revisit to improve the absolute radiometric calibration and reduce calibration drift. Several approaches are being used to obtain the optimal solution. In addition, the correction to the impact on the radiometer measurement when the SMAP radar transmitter was on will also be revisited for next data release.

This paper shows results from a proof-of-concept tower experiment that computed phase and reflectivity from a reflected P-band signal. The change in phase of the reflected signal is related to SWE for dry snow and the rate of change of phase is directly correlated to frequency of observation. The effect of vegetation was also evaluated by using measurements from two towers: one with bare soil and one surrounded by small trees with heights of up to 3 meters. SWE has been retrieved from two sites with RMSD of 1.15-1.6 cm for different frequencies and sites. In addition, sensitivity in reflectivity measurement at 260 MHz due to changes in soil moisture was observed in summer 2018 data.

We investigate the variation of sea surface salinity (SSS) retrieved in the Hudson Bay from L-band microwave measurements of SMAP and SMOS for open water seasons 2015-2017. We analyzed the differences between three SSS products (SMAP from JPL and RSS, and SMOS from BEC), and assessed in the context of seasonal and inter-annual variation of the freshwater contents, considering river runoffs, sea ice changes, and surface forcing (P-E). We found SMAP JPL (V4.2) shows reasonable responses to the freshwater inputs which is dominated by the sea ice change particularly early in the melting season. This result demonstrates the potential of currently available L-band missions in monitoring cryospheric changes; also underscore urgent needs of in situ salinity measurements in the region to improve retrieval algorithms.

Traditionally, a snow stratigraphy is characterized through a snow pit study. It only represents a snapshot view of the snow vertical properties and cannot capture the continually evolving snow process. This type of study is also destructive and time-consuming. Recent studies show that by using multi-baseline SAR configuration, the tomographic processing can provide the vertical image of the snowpack and monitor temporal variability. In this study, the full wave solution of the forward model will be used to reconstruct the tomograms and relate snow properties with the images.

Surface roughness and foam are two main components of ocean surface microwave thermal emission. Surface roughness provides scattering element and modifies local incidence angle. Air in foam alters the dielectric property of the surface layer. Bubbles in foam also alter the curvature of foam- water interface and modify emission and scattering properties of the water surface itself. Whitecap coverage Wc is the most accessible oceanographic information to represent surface foam. For emission analysis, it is necessary to establish a function relating to Wc and the effective air fraction Fa interacting with electromagnetic (EM) waves. An empirical relation is established through analyzing several microwave radiometer data sets in high winds covering a wide range of frequency, incidence angle, and vertical and horizontal polarizations. A physical interpretation of the proposed Fa (Wc) relationship is discussed. The relationship is used to quantify several important characteristics of surface foam relevant to microwave emission, including effective air fraction and skin depth as functions of wind speed, microwave frequency, and incidence angle.

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.

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

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).

The Soil Moisture Active Passive (SMAP) mission was launched January 31st, 2015. It is designed to measure the soil moisture over land using a combined active / passive L-band system. Due to the Aquarius mission, L-band model functions for ocean winds and salinity are already mature and may be directly applied to the SMAP mission. In contrast to Aquarius, the higher resolution and scanning geometry of SMAP allows for wide-swath ocean winds and salinities to be retrieved. We have found that the SMAP radiometer displays sensitivity all the way up to the most extreme wind speeds, possibly as high as 70 m/s, far beyond what is capable with typical C and Ku-band ocean wind scatterometers. In previous work we have validated the SMAP high wind speeds against Rapid Scatterometer (RapidScat) and Stepped Frequency Microwave Radiometer (SFMR). In this work we consider the size of the SMAP cyclones and compare them to the Automated Tropical Cyclone Forecasting (ATCF) system B-deck files and we update the SFMR analysis with an additional year of data to strengthen the previous conclusions.

In this paper, we develop a hybrid method combining T matrix of single objects and Fold-Lax multiple scattering theory (FL), for full wave simulations of vegetation/trees. The hybrid method of solving the Maxwell equations consist of off-the-shelf technique for single objects (e.g. HFSS) and newly developed techniques. The newly developed techniques are the three key steps of the hybrid method: (1) extracting the T matrix of each single object, (2) numerical wave transformations and (3) solving the coupled wave interaction equations (i.e. FL) for all the objects. For step (1), we extract the T matrix of a single object by numerical integration with the use of HFSS which is a 3D full-wave electromagnetic field simulation tool. The method of T matrix extraction from HFSS is verified by comparison with the analytical solution of a sphere. The method is applicable to find the T matrix for complicated object where the analytical solution is not available. Then, the wave transformations are performed based on the translation addition theorem. Numerical methods of calculating the transformation coefficients are developed. Finally, the wave interactions among the single objects are accounted for by FL. The results of the hybrid method agree with those of the HFSS brute force method. In comparison, the hybrid method is much more efficient than HFSS for vegetation scattering and applicable to large problems such as full wave simulations of a tree.

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.

The SMAP mission was designed to measure soil moisture globally for quantifying the water and carbon cyclces. The Brightness Temperature (TB) measurement by the SMAP L-band radiometer over ocean is also used for Sea Surface Salinity (SSS) retrieval. Considering the requirement of the uncertainty in the L-band TB measurement for SSS retrieval, the reflected galaxy correction is to be upgraded by using wind speed dependent galactic TB maps (the cosmic microwave background is included) for the radiometer TB data product (version 4) which is expected to be released in the summer 2018. We address the development of the galactic TB maps. In addition, a correction offset to the modeled ocean radiometric observation for horizontal polarization is described.

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

This paper shows results from a proof-of-concept tower experiment that computed phase from a reflected P-band signal. The change in phase of the reflected signal is related to SWE for dry snow and snow depth for wet snow and the rate of change of phase is directly correlated to frequency of observation. The effect of vegetation was also evaluated by using measurements from two towers: one with no vegetation and one surrounded by small trees with heights of up to 3 meters. The phase measurement from the two sites had excellent correlation of 0.99 during the accumulation phase. The correlation between SWE and phase measurement was found to be between 0.95 and 0.98 during the accumulation phase. During the melt phase, negative correlation between 0.68 and 0.80 was found between snow depth and phase measurement.

Sea surface salinity (SSS) plays a critical role in the water cycle. In the Arctic Ocean, SSS responses to river discharge, sea ice melting/freezing and drifting, surface freshwater forcing (precipitation and evaporation), and ocean transport through open straits from/to Pacific and Atlantic oceans. However, in situ SSS data in the Arctic Ocean are very sparse. The L-band microwave radiometer on board of NASA SMAP mission provides salinity measurements since April 2015, at 40 km resolution with global ocean coverage in ~3 days. With improved land/ice correction and RFI detection, SMAP SSS are retrieved in ice-free regions near the river mouth and ice edge. The collocated SMAP SSS and in situ salinity data collected by AXCTD from Ocean Melting Greenland (OMG) 2016 field campaign along Greenland coast show reasonable agreement in revealing the freshening signature, which is not seen in ocean model output. During the open water season (August), SMAP SSS were retrieved in Chukchi Sea (2015), in Prudhoe Bay and East Siberian Sea (2016), and in Kara Sea in both years. The SSS contrast between the two years is consistent with sea ice concentration observations. Variability of SSS in Kara Sea is correlated positively with adjacent river discharges but negatively with the salinity of water transported from Atlantic Ocean.

SnowEx is a multi-year airborne snow campaign led by NASA. The purpose of SnowEx is to figure out how much water is stored in Earth's terrestrial snow-covered regions. As part of the 2017 NASA SnowEx campaign, we deployed a portable triple-frequency (9.6GHz, 13.5GHz and 17.2GHz) and fully polarimetric frequency-modulated continuous-wave (FMCW) radar at Fraser, Colorado. The radar was installed on a 60cmx60cm frame to enable a full reconstruction of the three-dimensional variability per each radar channel. The tomography technique uses the radar echo from the multiple viewing positions and provides a unique access to the vertical structure of the snow layer. With current setup, the range resolution is 30cm. In this paper, we will review the radar design and signalprocessing algorithm - time domain back projection. The generated vertical images show the snow stratigraphy, which is consistent with ground snow pit measurement. The continuous operation demonstrates diurnal thawing and refreezing process. The snow density is retrieved by comparing to the snow free image.

The P-band Signals of Opportunity (SoOp) technique has significant potential for remote sensing of root zone soil moisture from space. We have conducted a proof-of-concept experiment to demonstrate the sensitivity of P-band reflectivity to soil moisture. The reflectivity data has a high correlation (~0.87) with the in situ soil moisture observations. Theoretical forward modeling and retrieval analyses have been carried out to assess the accuracy of using multifrequency SoOp data to retrieve the vertical soil moisture profile. Sensitivity analysis has also been carried out to determine the impact of ancillary data.

The Soil Moisture Active Passive (SMAP) mission was launched on January 31, 2015. It is a combined L-band active/passive system envisioned for the measurement of soil moisture over land. In addition to the soil moisture measurement, the SMAP data readily permit retrievals of ocean surface winds and sea surface salinity. In the previous work, we have found that the SMAP radiometer displays sensitivity to ocean surface wind all the way up to the most extreme wind speeds, possibly as high as 70 m/s, far beyond what is capable with typical$C$- and$Ku$-bands ocean wind scatterometers. In this letter, we use the Rapid Scatterometer and stepped frequency microwave radiometer to further validate these SMAP radiometer-only high-wind speed retrievals. In addition, we consider the size of the retrieved high wind speeds, validating them with the Automated Tropical Cyclone Forecasting system B-deck files.

Radar scattering from ocean surfaces is investigated by 3-D numerical solution of Maxwell's equations [numerical Maxwell's model in 3-D (NMM3D)] using the ocean surface profiles stochastically generated from a 3-D Durden-Vesecky ocean spectrum. The surface integral equations (SIEs) are formulated for dielectric surfaces using Green's functions of the air and the ocean permittivities with the surface tangential electric and magnetic fields as the unknowns. In solving the SIEs using the method of moment, a fast matrix solver of the sparse matrix canonical grid is used in conjunction with Rao-Wilton-Glisson basis functions. The computation has been implemented on a high-performance parallel computing cluster for problems with up to six million surface unknowns. Unlike the two-scale model (TSM) approximation, NMM3D does not require division of the surface spectrum into large- and small-scale ocean waves. The results of backscattering simulations are compared to Aquarius satellite radar measurements for wind speeds of 5, 8, and 10 m/s and for incidence angles of 29°, 39°, and 46°. The results show that NMM3D ocean backscattering solutions at L-band are in good agreement with Aquarius satellite radar data for co-polarized VV, HH, and cross-polarized VH returns as well as for the VV/HH ratio. The azimuthal dependence of L-band backscatter is also assessed. Finally, NMM3D results are compared to TSM solutions and are shown to lie close to Aquarius data in observed VV/HH ratio, and their azimuthal dependencies.

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

Since the beginning of its routine science operation in March 2015, the NASA SMAP observatory has been returning interference-mitigated brightness temperature observations at L-band (1.41 GHz) frequency from space. The resulting data enable frequent global mapping of soil moisture with a retrieval uncertainty below 0.040 m3/m3at a 36 km spatial scale. This paper describes the development and validation of an enhanced version of the current standard soil moisture product. Compared with the standard product that is posted on a 36 km grid, the new enhanced product is posted on a 9 km grid. Derived from the same time-ordered brightness temperature observations that feed the current standard passive soil moisture product, the enhanced passive soil moisture product leverages on the Backus-Gilbert optimal interpolation technique that more fully utilizes the additional information from the original radiometer observations to achieve global mapping of soil moisture with enhanced clarity. The resulting enhanced soil moisture product was assessed using long-term in situ soil moisture observations from core validation sites located in diverse biomes and was found to exhibit an average retrieval uncertainty below 0.040 m3/m3. As of December 2016, the enhanced soil moisture product has been made available to the public from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center.

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.

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

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.

The Soil Moisture Active Passive (SMAP) mission was launched January 31st, 2015. It is designed to measure the soil moisture over land using a combined active / passive L-band system. Due to the Aquarius mission, L-band model functions for ocean winds and salinity are already mature and may be directly applied to the SMAP mission. In contrast to Aquarius, the higher resolution and scanning geometry of SMAP allows for wide-swath ocean winds and salinities to be retrieved. We have found that the SMAP radiometer displays sensitivity all the way up to the most extreme wind speeds, possibly as high as 70 m/s, far beyond what is capable with typical C and Ku-band ocean wind scatterometers. In this work we use the Rapid Scatterometer (RapidScat) and Stepped Frequency Microwave Radiometer (SFMR) to further validate these SMAP radiometer-only high-wind speed retrievals.

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.

In this study, the impact of spatial variability due to the heterogeneity of vegetation in the agricultural region on passive microwave signatures available at various scales are explored using the brightness temperature (TB) observed from ground, air, and space. These observations were conducted during a growing season of corn and soybean in South Fork watershed, Iowa, as part of the NASA-Soil Moisture Active Passive Validation Experiment (SMAPVEX16). Both empirical and physically-based microwave emission models are used to understand the effects of vegetation on TBfor corn and soybean using ground-based TBobservations. The modeled TBwill be upscaled based upon the USDA crop layer map to compare with the TBobserved in the coarse scales.

A new debiased non-Bayesian methodology has demonstrated to be very effective for the retrieval of Sea Surface Salinity (SSS) from brightness temperature (TB) measured by Soil Moisture and Ocean Salinity (SMOS) interferometric radiometer. Applying this methodology it is possible to retrieve SSS values in marginal seas or cold waters where the operational retrieval does not. Another important improvement is the possibility of defining a SMOS-based climatology to characterize spatial biases. Recently, using data from the Soil Moisture Active Passive (SMAP) mission, JPL has started to produce a new 9-km resolution TBproduct. The existence of such product offers the possibility of increasing the spatial resolution and quality of the mentioned SMOS SSS product using fusion techniques. The aim of this work is to produce high resolution SSS maps in marginal seas derived from the fusion of SMAP 9-km TBand SMOS non-Bayesian debiased SSS products.

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

We applied the Numerical Maxwell Model in 3 Dimensions (NMM3D) to radar scattering from ocean surfaces at L band. The formulation is based on the PMCHWT surface integral equation which uses separate Green's functions for air and ocean permittivities. The Sparse Matrix Canonical Grid (SMCG) is used to compute Method of Moments (MoM) solutions in conjunction with Rao-Wilton-Glisson (RWG) basis functions with the surface electric field and surface magnetic field as the unknowns. Surface sizes used are up to 64 wavelengths by 64 wavelengths. Isotropic sea surfaces simulated using the Durden-Vesecky spectrum are studied for wind speeds ranging from 5 m/s to 10 m/s and incidence angles varying from 29° to 46°. Backscattering from NMM3D and composite two-scale model computations are compared with Aquarius satellite scatterometer estimates. Unlike the two-scale model, NMM3D does not impose separation between large scale roughness and small scale roughness elements. Results show that, in spite of using just the isotropic DV spectrum, NMM3D are in measurably better agreement with data than the composite surface model for co-polarized returns as well as the polarization ratio.

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.

Snow water equivalent (SWE) storage is critical parameters of the water cycle and may be important indicators of climate change. Despite their importance in the seasonal and regional terrestrial water cycle, SWE is currently poorly characterized in space and time. We develop a method for observations of these parameters using P-band signals of opportunity (SoOp) concept to measure the SWE. Effect of wet snow on the measurement is analyzed through modeling and it is found that for wet snow, the phase of the SoOp measurement becomes correlated to snow depth while for dry snow, phase is correlated to the Snow Water Equivalent (SWE). In addition, qualitative data analysis from two different site for a proof-of-concept experiment is shown in this paper.

Sea surface salinity (SSS) retrieved from SMAP radiometer measurements is validated with in situ salinity measurements collected from Argo floats, tropical moored buoys and ship-based thermosalinograph (TSG) data. SMAP SSS achieved accuracy of 0.2 PSU on a monthly basis in comparison with Argo gridded data in the tropics and mid-latitudes. In tropical oceans, time series comparison of salinity measured at 1 m by moored buoys indicates that SMAP can track large salinity changes occurred within a month. Synergetic analysis of SMAP, SMOS and Argo data allows us to identify and exclude erroneous jumps or drift in some real-time buoy data from assessment of satellite retrieval. The resulting SMAP-buoy matchup analysis leads to an average standard deviation of 0.22 PSU and correlation coefficient of 0.73 on weekly scale; the average standard deviation reduced to 0.17 PSU and the correlation improved to 0.8 on monthly scale. SMAP L3 daily maps reveals salty water intrusion from the Arabian Sea into the Bay of Bengal during the Indian summer monsoon, consistent with the daily measurements collected from floats deployed during the Bay of Bengal Boundary Layer Experiment (BoBBLE) project field campaign. In the Mediterranean Sea, the spatial pattern of SSS from SMAP is confirmed by the ship-based TSG data.

The objective of this study is to investigate the effect of sea surface temperature (SST) on L-band microwave measurements and its implication on sea surface salinity (SSS) retrieval. Of particular interest is in the cold & fresh water where large SSS retrieval errors exist in comparison with Argo data. We found systematic SST dependence in Aquarius radar backscatter σoand radiometer excess emissivity Δe, with the emissivity of specular surface estimated using collocated HYCOM SSS and NCEP or SSMI/S wind. In the cold water under medium to high wind, σoand Δe show opposite trend on SST: σoreduces while Δe enhances by more than 10% relative to their corresponding values at the reference SST (15°C). The geographical distribution of matchups with SST-1show that data collected under these conditions are coincident with the area where dSSS (=SSS-SArgo) is largely positive. This is consistent with the SST trend observed in σo, which would result in overestimation in roughness if the SST effect not considered. However, the enhanced Δe in cold water is puzzling because it would cause even higher value for SSS retrieval if modeled as roughness. SSS retrieved with SST correction reduces bias but results on error improvement is mixed. We hypothesis there is an unknown defect in the dielectric constant model for cold water and propose an empirical correction for the Aquarius SSS retrieval.

This paper provides a theoretical basis for retrieving snow water equivalent (SWE) and root zone soil moisture (RZSM) by using the coherent reflected signal from the communication satellite at P-band signals of opportunity. Based on theoretical modeling, the wave propagation constant in the snow is proportional to the snow density. It is shown that the phase change of reflected signal from snowpack has a quasi-linear dependence on SWE. The model has been extended to multilayer to accommodate the various vertical snow profiles. In addition, the P-band reflectivity is also sensitive to the change of root zone soil moisture. In the paper, we also shown the reflectivity calculated using coherent wave approach has excellent sensitivity to the change of soil moisture for moderate range of incidence angles. In order to validate the theoretical results, a proof of concept ground-based experiment is conducted at Fraser, CO since 2015.

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

A proof-of-concept experiment was carried out to demonstrate the feasibility of retrieving snow water equivalent (SWE) using P-band signals of opportunity. The fundamental observation is the change in the phase of the reflected waveforms as related to the change in SWE. Through theoretical modeling it was found that the change in SWE was approximately linearly dependent on the change in phase. This was verified by retrieving SWE data collected and processed from a tower-based experiment at Fraser, CO, USA. A linear regression was performed on measured phase and in situ SWE. The correlation was found to be 0.94 and root mean square deviation was found to be 7.5 mm.

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

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

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

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

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

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.

In this talk we introduce the combined active / passive (CAP) data product for the Soil Moisture Active Passive mission. We develop the algorithms for a radiometer-only salinity product, a radar-only vector wind product, and a combined active / passive vector wind and salinity product. We show the radiometer-only salinity product nears the Aquarius salinity accuracy requirements, that the radar-only vector wind product meets the QuikSCAT requirements, and that the combined active / passive salinity and vector wind product has performance better than both.

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

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.

This paper talks about retrieval of Snow Water Equivalent (SWE) using P-band Signals of Opportunity (SoOp). Modeling is done to show that the phase change in the observed signal is primarily due to change in SWE and is independent of snow density, soil moisture, snow grain size. In order to compare theory to experiment, experiment is conducted at Fraser, CO. Some preliminary data analysis from 1 week of data show that the phase changed when SWE changed.

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.

We investigated the use of L-band active and passive microwave data from the Soil Moisture Active Passive (SMAP) observatory for remote sensing of ocean surface winds during hurricanes. We analyzed the dependence of SMAP data on ocean surface wind speed and direction, and found excellent consistency with the geophysical model functions developed for the Aquarius L-band radar/radiometer although the spatial resolutions of SMAP and Aquarius are distinctly different. However the higher resolution data from SMAP allowed us to assess the sensitivity of L-band radiometer/radar signals to hurricane force winds. The matchup analysis with the data from typhoon Nangka confirms the feasibility of extrapolating the Aquarius model functions to very high winds. Therefore we applied the Aquarius model function to the retrieval of ocean winds for hurricanes for two options: 1) radiometer-only and 2) radar-only. Comparison of the SMAP winds with the RapidScat and National Center for Environmental Predictions (NCEP) wind was performed. We also compared the maximum wind speed in the SMAP products with the best track analysis and found a good agreement in general.

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.

In this paper, we introduce the combined active/passive (CAP) data product for the Soil Moisture Active Passive mission. We develop the algorithms for a radiometer-only salinity product, a radar-only vector wind product, and a CAP vector wind and salinity product. We show that the performance of the radiometer-only salinity product nears but is still inferior to the Aquarius salinity accuracy performance when aggregated on a monthly timescale. Then, we show that the radar-only vector wind product has reasonable accuracy away from the nadir track while suffering from inadequate measurement geometry in the middle of the swath. Finally, we demonstrate that the CAP salinity and vector wind performance is superior to individual algorithms and provides wind vectors nearly as good as RapidScat for low-to-moderate winds and possibly superior to traditional scatterometers for wind speeds larger than 12.5 m/s.

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.

The L-band passive microwave data from the Soil Moisture Active Passive (SMAP) observatory are investigated for remote sensing of ocean surface winds during severe storms. The surface winds of Joaquin derived from the real-time analysis of the Center for Advanced Data Assimilation and Predictability Techniques at Penn State support the linear extrapolation of the Aquarius and SMAP geophysical model functions (GMFs) to hurricane force winds. We apply the SMAP and Aquarius GMFs to the retrieval of ocean surface wind vectors from the SMAP radiometer data to take advantage of SMAP's two-look geometry. The SMAP radiometer winds are compared with the winds from other satellites and numerical weather models for validation. The root-mean-square difference (RMSD) with WindSat or Special Sensor Microwave Imager/Sounder is 1.7 m/s below 20-m/s wind speeds. The RMSD with the European Center for Medium-Range Weather Forecasts direction is 18° for wind speeds between 12 and 30 m/s. We find that the correlation is sufficiently high between the maximum wind speeds retrieved by SMAP with a 60-km resolution and the best track peak winds estimated by the National Hurricane Center and the Joint Typhoon Warning Center to allow them to be estimated by SMAP with a correlation coefficient of 0.8 and an underestimation by 8%-18% on average, which is likely due to the effects of spatial averaging. There is also a good agreement with the airborne Stepped-Frequency Radiometer wind speeds with an RMSD of 4.6 m/s for wind speeds in the range of 20-40 m/s.

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

This paper introduces and validates the Aquarius scatterometer-only wind speed algorithm and the combined active passive (CAP) wind speed products. The scatterometer-only algorithm uses the co-polarized radar cross-section to determine the ocean surface wind speed with a maximum-likelihood estimator approach while the CAP algorithm uses both the scatterometer and radiometer channels to achieve a simultaneous ocean vector wind and sea surface salinity retrieval. We discuss complications in the speed retrieval due to the shape of the scatterometer model function at L-band and develop mitigation strategies. We find the performance of the Aquarius scatterometer-only wind speed is better than 1.00 ms-1, with best performance for low wind speeds and increasing noise levels as the wind speed increases. The CAP wind speed product is significantly better than the scatterometer-only due to the inclusion of passive measurements and achieves 0.70 ms-1root-mean-square error.

The CoReH2O mission design is mature; there are two viable technical configurations, and the breadboards of key hardware components, such as the dual-polarized antenna array feeds and high power amplifiers have been built and tested. The baseline retrieval algorithm has been intensively tested using simulated and experimental data. The tests confirm that the threshold performance for SWE products can be met under a wide range of snow conditions. Data from further field campaigns by the airborne SnowSAR acquired Austria, Canada, and Alaska from November 2012 through April 2013 are being analysed to investigate retrieval performance over alpine, glacier and tundra terrains.

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

The L-band passive and active microwave geophysical model functions (GMFs) of ocean surface winds from the Aquarius data are derived. The matchups of Aquarius data with the Special Sensor Microwave Imager (SSM/I) and National Centers for Environmental Prediction (NCEP) winds were performed and were binned as a function of wind speed and direction. The radar HH GMF is in good agreement with the PALSAR GMF. For wind speeds above 10 m·s-1, the L-band ocean backscatter shows positive upwind-crosswind (UC) asymmetry; however, the UC asymmetry becomes negative between about 3 and 8 m·s-1. The negative UC (NUC) asymmetry has not been observed in higher frequency (above C-band) GMFs for ASCAT or QuikSCAT. Unexpectedly, the NUC symmetry also appears in the L-band radiometer data. We find direction dependence in the Aquarius TBV, TBH, and third Stokes data with peak-to-peak modulations increasing from about a few tenths to 2 K in the range of 10-25- m·s-1wind speed. The validity of the GMFs is tested through application to wind and salinity retrieval from Aquarius data using the combined active-passive algorithm. Error assessment using the triple collocation analyses of SSM/I, NCEP, and Aquarius winds indicates that the retrieved Aquarius wind speed accuracy is excellent, with a random error of about 0.75 m·s-1. The wind direction retrievals also appear reasonable and accurate above 10 m·s-1. The results of the error analysis indicate that the uncertainty of the GMFs for the wind speed correction of vertically polarized brightness temperatures is about 0.14 K for wind speed up to 10 m·s-1.

The COld REgions Hydrology High-resolution Observatory (CoReH2O) satellite mission was selected for detailed scientific and technical studies within the Earth Explorer Programme of ESA. The sensor is a dual frequency SAR, operating at 17.2 GHz and 9.6 GHz, VV and VH polarizations The mission will deliver spatially distributed snow and ice observations to improve the representation of the croysphere in hydrological and climate models. Primary parameters are the extent and water equivalent (SWE) of the snow pack and snow accumulation on glaciers. Scientific preparations of the mission include the development and testing of algorithms for retrieval of snow parameters, studies on synergy of CoReH2O-type snow products with passive microwave measurements, the assimilation of satellite snow data in process models, and field experiments. Performance of retrievals for snow extent and SWE was tested with simulated and experimental data, including Ku- and X-band SAR images of the airborne SnowSAR system.

This paper describes an algorithm to simultaneously retrieve ocean surface salinity and wind from combined passive/active L-band microwave observations of sea surfaces. The algorithm takes advantage of the differing response of brightness temperatures and radar backscatter to salinity, wind speed, and direction. The algorithm minimizes the least square error (LSE) measure, signifying the difference between measurements and model functions of brightness temperatures and radar backscatter. Three LSE measures with different measurement combinations are tested. One of the LSE measures uses passive microwave data only with retrieval errors reaching 2 psu for salinity and 2 m/s for wind speed. The second LSE measure uses both passive and active microwave data for vertical and horizontal polarizations. The addition of active microwave data significantly improves the retrieval accuracy by about a factor of five. To mitigate the impact of Faraday rotation on satellite observations, we propose the third LSE measure using measurement combinations invariant under the Faraday rotation. For Aquarius, the expected root-mean-square SSS error will be less than 0.2 psu for low winds and increases to 0.3 psu at 25-m/s wind speed for warm waters, and the accuracy of retrieved wind speed will be high (about 1-2 m/s or lower). Our results suggest that combining passive and active microwave observations will allow retrieval of sea surface salinity along with the wind speed and direction. In particular, the LSE measure invariant under the Faraday rotation will be directly applicable to spaceborne missions, such as the NASA Aquarius and Soil Moisture Active Passive missions.

The COld REgions Hydrology High-resolution Observatory (CoReH2O) satellite mission has been selected for detailed scientific and technical studies within the ESA Living Planet Programme. The mission addresses the need for distributed snow and ice observations to improve the representation of the cryosphere in climate models and the prediction of the water cycle. The sensor is a dual frequency SAR, operating at 17.2 GHz and 9.6 GHz, VV and VH polarizations. This configuration enables the decomposition of the scattering signal for retrieving snow mass (SWE) and other physical properties of snow and ice. A major task for mission preparation is the development and testing of algorithms for SWE retrieval. The baseline algorithm applies a constrained minimization approach, matching forward computed and measured backscatter by iterating for SWE and effective grain size of the snow volume. Experimental campaigns on Kuand X-band backscatter of snow deliver important information for retrieval development and validation.

In February-March 2009, an airborne field campaign was conducted using the Passive Active L- and S-band (PALS) microwave sensor and the Ku-band Polarimetric Scatterometer to collect measurements of brightness temperature and near-surface wind speeds. Flights were conducted over a region of expected high-speed winds in the Atlantic Ocean, for the purposes of algorithm development for sea surface salinity (SSS) retrievals. Wind speeds encountered during the March 2, 2009, flight ranged from 5 to 25 m/s. The Global Positioning System (GPS) delay mapping receiver from the National Aeronautics and Space Administration (NASA) Langley Research Center was also flown to collect GPS signals reflected from the ocean surface and generate postcorrelation power-versus-delay measurements. These data were used to estimate ocean surface roughness. These estimates were found to be strongly correlated with PALS-measured brightness temperature. Initial results suggest that reflected GPS measurements made using small low-power instruments can be used to correct the roughness effects in radiometer brightness temperature measurements to retrieve accurate SSS.

Statistics of radio frequency interference (RFI) observed in the band 1398-1422 MHz during an airborne campaign in the United States are reported for use in analysis and forecasting of L-band RFI for microwave radiometry. The observations were conducted from September to October 2008, and included approximately 92 h of flight time, of which approximately 20 h of “transit” or dedicated RFI observing flights are used in compiling the statistics presented. The observations used include outbound and return flights from Colorado to Maryland, as well as RFI surveys over large cities. The Passive Active L-Band Sensor (PALS) radiometer of NASA Jet Propulsion Laboratory augmented by three dedicated RFI observing systems was used in these observations. The complete system as well as the associated RFI characterization approaches are described, along with the resulting RFI statistical information and examinations of specific RFI sources. The results show that RFI in the protected L-band spectrum is common over North America, although the resulting interference when extrapolated to satellite observations will appear as “low-level” corruption that will be difficult to detect for traditional radiometer systems.

NASA's Soil Moisture Active Passive satellite mission will use both an L-band radiometer and radar to produce global-scale measurements of soil moisture. L-band backscatter is also sensitive to the water content of vegetation. We found that a moderate dew increased the L-band backscatter of a soybean canopy by 1 dB. Dew thus has the potential to add error to satellite observations of soil moisture.

Snow is a critical component of the global water cycle and climate system, and a major source of water supply in many parts of the world. There is a lack of spatially distributed information on the accumulation of snow on land surfaces, glaciers, lake ice, and sea ice. Satellite missions for systematic and global snow observations will be essential to improve the representation of the cryosphere in climate models and to advance the knowledge and prediction of the water cycle variability and changes that depend on snow and ice resources. This paper describes the scientific drivers and technical approach of the proposed Cold Regions Hydrology High-Resolution Observatory (CoReH2O) satellite mission for snow and cold land processes. The sensor is a synthetic aperture radar operating at 17.2 and 9.6 GHz, VV and VH polarizations. The dual-frequency and dual-polarization design enables the decomposition of the scattering signal for retrieving snow mass and other physical properties of snow and ice.

L-band microwave backscatter and brightness temperature of sea surfaces acquired using the Passive/Active L-band Sensor during the High Ocean Wind campaign are reported in terms of their dependence on ocean surface wind speed and direction. We find that the L-band VV, HH, and HV radar backscatter data increase by 6-7 dB from 5 to 25 m/s wind speed at a 45° incidence angle. The data suggest the validity of Phased Array type L-band Synthetic Aperture Radar (PALSAR) HH model function between 5 and 15 m/s wind speeds, but show that the extrapolation of PALSAR model at above 20 m/s wind speeds overpredictsA0anda1coefficients. There is wind direction dependence in the radar backscatter with about 4 dB differences between upwind and crosswind observations at 24 m/s wind speed for VV and HH. The passive brightness temperatures show about a 5-K change forTVand a 7-K change forTHfor a wind speed increasing from 5 to 25 m/s. Circle flight data suggest a wind direction response of about 1-2 K inTVandTHat 14 and 24 m/s wind speeds. The L-band microwave data show excellent linear correlation with the surface wind speed with a correlation better than 0.95. The results support the use of L-band radar data for estimating the wind-driven excess brightness temperature of sea surfaces. The data also support the applications of L-band microwave signals for high-resolution (kilometer scale) observation of ocean surface winds under high wind conditions (10-28 m/s).

Techniques for the retrieval of snow properties from Ku- and X-band radar backscatter measurements were investigated. The work contributes to feasibility studies for the CoReH2O satellite mission of ESA for which a dual frequency SAR, operating at Ku-band (17.2 GHz) and X-band (9.6 GHz), VV and VH polarizations, is proposed. A main parameter to be measured is the snow water equivalent (SWE). For the retrieval of SWE it is necessary to separate the backscatter contributions of the snow volume and the background target and to account for effects of snow grain size. The current version of the SWE retrieval algorithm applies the maximum likelihood approach matching radiative transfer forward computations with measured backscatter data. An application example for SWE retrieval is shown for the Cold Land Processes Experiment (CLPX-II) in Alaska, using Ku-band data of the NASA-JPL PolScat and X-band data of the TerraSAR-X satellite as input.

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

Characteristics of the Ku-band polarimetric scatterometer (POLSCAT) data acquired from five sets of aircraft flights in the winter months of 2006-2008 for the second Cold Land Processes Experiment (CLPX-II) in Colorado are described in this paper. The data showed the response of the Ku-band radar echoes to snowpack changes for various types of background vegetation in the study site in north central Colorado. We observed about 0.15-0.5-dB increases in backscatter for every 1 cm of snow-water-equivalent (SWE) accumulation for areas with short vegetation (sagebrush and pasture). The region with the smaller amount of biomass, signified by the backscatter in November, seemed to have the stronger backscatter response to SWE in decibels. The data also showed the impact of surface hoar growth and freeze/thaw cycles, which created large snow-grain sizes, ice crust layers, and ice lenses and consequently increased the radar signals by a few decibels. The copolarized HH/VV backscatter ratio seems to indicate double-bounce scattering between the ground surface and snow or vegetation. The cross-polarized backscatter [vertical-horizontal (VH)] showed not only the influence of vegetation but also the strong response to snow accumulation. The observed HV/VV ratio suggests the importance of multiple scattering or nonspherical scattering geometry of snow grain in the dense-media radiative transfer scattering model. Comparison of the POLSCAT and QuikSCAT data was made and confirmed the effects of mixed terrain covers in the coarse-resolution QuikSCAT data.

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

In this paper, we model the backscattering coefficients of multi-layer dry snowpacks, based on Dense Media Radiative Transfer theory (DMRT) with the Quasicrystalline Approximation (QCA). The DMRT model accounts for adhesive aggregate effects, which leads to dense media Mie scattering by using a Sticky particle model. The same set of DMRT equations are used for modeling both active and passive remote sensing. The model is validated by using the Cold-Land Processes Field Experiment CLPX ground based polarimetric scatterometry observation at local-scale observation site (LSOS) and airborne polarimetric Ku-band scatterometer (POLSCAT) data at Fool-Creek, Fraser. The snow density profiles are from ground observation and grain sizes are fitting parameters. It shows that the co-polarization simulations are in good agreement with the data, the cross-polarization simulations are around 2 dB lower than ground based observation and 5 dB lower than airborne observation. With the same set of multi-layer snowpack profile, the QCA/DMRT model matched co-polarization backscattering coefficients and all 4 channels of brightness temperature observations simultaneously at LSOS. The cross-polarization simulation can be improved by 3-dimensional numerical solutions of Maxwell equations (NMM3D). Study at Fool-Creek shows that NMM3D/DMRT simulations can match both co-polarization and cross-polarization observations simultaneously.

The COld REgions Hydrology High-resolution Observatory (CoRe-H2O) satellite mission has been selected for scientific and technical studies within the ESA Earth Explorer Programme. The mission addresses the need for spatially detailed snow and ice observations in order to improve the representation of the cryosphere in climate models and to improve the knowledge and prediction of water cycle variability and changes. CoRe-H2O will observe the extent, water equivalent and melting state of the snow cover, accumulation and diagenetic facies of glaciers, and properties of sea ice and lake ice. The sensor is a dual frequency SAR, operating at 17 GHz and 9.6 GHz, VV and VH polarizations. This configuration enables the decomposition of the scattering signal for retrieving physical properties of snow and ice. Scientific preparation activities include experimental field campaigns, improvement of radar backscatter models, and the development of inversion algorithms.

Characteristics of the POLSCAT data acquired from five sets of aircraft flights in the winter months of 2006-2008 for the second Cold Land Processes Experiment (CLPX-II) in Colorado are described in this paper. The data showed the response of the Ku-band radar echoes to snowpack changes for various types of background vegetation in the study site in north central Colorado. We observed about 0.15 to 0.5 dB increases in backscatter for every 1 cm of snow water equivalent (SWE) accumulation for areas with short vegetation. Based on a simplified radiative transfer model, the change detection technique is used to convert the temporal change of radar backscatter into SWE accumulation for dry snow conditions. The resulting SWE accumulation estimates are consistent with the in-situ SWE measurements, with about 2 to 3 cm Root-Mean-Square (RMS) difference for regions with sagebrush or pasture.

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

In this paper, wind-direction signals in passive microwave polarimetry for ocean surfaces under hurricane force winds are presented. We performed analysis of Windsat data for several Atlantic hurricanes from 2003 to 2005. The polarimetric third Stokes parameter observations from the Windsat 10-, 18-, and 37-GHz channels were collocated with the ocean-surface winds from the National Oceanic and Atmospheric Administration Hwind analysis. The collocated data were binned as a function of wind speed and wind direction. The 10-GHz data show clear 4-K peak-to-peak directional signals at 50-60-m/s wind speed after correction for atmospheric attenuation. The signals in the 18- and 37-GHz channels were unclear at above 40-m/s wind speeds, probably caused by the impact of clouds and rain. The data were expanded by sinusoidal series of the relative azimuth angles between the Hwind analysis and observation directions. The coefficients of the sinusoidal series suggest decreasing response to wind direction for increasing wind speed, but the 10-GHz data appear to be fairly constant for up to 50-m/s wind speeds.

Taking into account the needs for improved, spatially detailed observations of snow and ice in climate research, hydrology, and glaciology, the satellite mission COld REgions Hydrology High-resolution Observatory, CoRe-H2O, was proposed to ESA. As payload a co- and cross-polarized Ku-band (17.2 GHz) and X-band (9.6 GHz) SAR was selected, because of its sensitivity to dry snow, thin sea ice, and the metamorphic state of snow, firn and ice on glaciers and ice caps. A cost-effective ScanSAR scheme with parabolic reflectors (each with multiple beams) is proposed fulfilling the requirements for swath width, spatial resolution and radiometry. The mission has been selected by ESA for further scientific and technical studies in the frame of the Earth Explorer Satellite Programme.

Preliminary analyses of the POLSCAT data acquired from the CLPX-II in winter 2006-2007 are described in this paper. The data showed the response of the Ku-band radar echoes to snowpack changes for various types of background vegetation. We observed about 0.4 dB increase in backscatter for every 1 cm SWE accumulation for sage brush and agricultural fields. The data also showed the impact of surface hoar growth and freeze/thaw cycles, which created large snow grain sizes and ice lenses, respectively, and consequently increased the radar signals by a few dBs.

Aquarius is a combined passive/active L-band microwave instrument that is being developed to map the salinity field at the surface of the ocean from space. The data will support studies of the coupling between ocean circulation, global water cycle, and climate. Aquarius is part of the Aquarius/Satelite de Aplicaciones Cientiflcas-D mission, which is a partnership between the U.S. (National Aeronautics and Space Administration) and Argentina (Comision Nacional de Actividades Espaciales). The primary science objective of this mission is to monitor the seasonal and interannual variation of the large-scale features of the surface salinity field in the open ocean with a spatial resolution of 150 km and a retrieval accuracy of 0.2 psu globally on a monthly basis.

The NASA Earth Science System Pathfinder (ESSP) mission Aquarius, will measure global ocean surface salinity with ~120 km spatial resolution every 7-days with an average monthly salinity accuracy of 0.2 psu (parts per thousand) [1]. This requires an L-band low-noise radiometer with the long-term calibration stability of les0.15 K over 7 days. The instrument utilizes a push-broom configuration which makes it impractical to use a traditional warm load and cold plate in front of the feedhorns. Therefore, to achieve the necessary performance Aquarius utilizes a Dicke radiometer with noise injection to perform a warm - hot calibration. The radiometer sequence between antenna, Dicke load, and noise diode has been optimized to maximize antenna observations and therefore minimize NEDT. This is possible due the ability to thermally control the radiometer electronics and front-end components to 0.1degCrms over 7 days.

Aquarius is an L-band microwave instrument being developed to map the surface salinity field of the oceans from space. It is part of the Aquarius/SAC-D mission, a partnership between the USA (NASA) and Argentina (CONAE) with launch scheduled for early in 2009. The primary science objective of this mission is to monitor the seasonal and interannual variation of the large scale features of the surface salinity field in the open ocean with a spatial resolution of 150 km and a retrieval accuracy of 0.2 psu globally on a monthly basis.

A geophysical model function (GMF), relating the directional response of polarimetric brightness temperatures to ocean surface winds, is developed for the WindSat multifrequency polarimetric microwave radiometer. This GMF is derived from the WindSat data and tuned with the aircraft radiometer measurements for very high winds from the Hurricane Ocean Wind Experiment in 1997. The directional signals in the aircraft polarimetric radiometer data are corroborated by coincident Ku-band scatterometer measurements for wind speeds in the range of 20-35 m/s. We applied an iterative retrieval algorithm using the polarimetric brightness temperatures from 18-, 23-, and 37-GHz channels. We find that the root-mean-square direction difference between the Global Data Assimilation System winds and the closest WindSat wind ambiguity is less than 20/spl deg/ for above 7-m/s wind speed. The retrieval analysis supports the consistency of the Windrad05 GMF with the WindSat data.

An experiment using a polarimetric scatterometer (POLSCAT) has been conducted by JPL for ocean surface wind measurement. It shows that sigma0values for HH, VV, HV, and VH have the property of even symmetry with respect to the upwind direction, and correlation coefficients between co- and cross-polarizations have the odd symmetry property. In this paper, the symmetry properties will be further examined using polarimetric analysis to investigate the depolarization effect, the scattering mechanism, and the polarization orientation angle. Theoretical results based on a two scale model are used to verify the derived experiment results. The newly derived symmetry property has the potential to solve the 180deg ambiguity in wind direction, and to enhance the accuracy of wind vector measurements

Active and passive microwave remote techniques have been investigated for the remote sensing of ocean surface wind and salinity. We revised an ocean surface spectrum using the CMOD-5 geophysical model function (GMF) for the European Remote Sensing (ERS) C-band scatterometer and the Ku-band GMF for the NASA SeaWinds scatterometer. The predictions of microwave brightness temperatures from this model agree well with satellite, aircraft and tower-based microwave radiometer data. This suggests that the impact of surface roughness on microwave brightness temperatures and radar scattering coefficients of sea surfaces can be consistently characterized by a roughness spectrum, providing physical basis for using combined active and passive remote sensing techniques for ocean surface wind and salinity remote sensing.

Global mapping of near surface ocean wind vectors is crucial for many oceanographic and atmospheric studies. The US Navy together with the National Polar Orbiting Environmental Satellite System (NPOESS) launched the WindSat with multifrequency polarimetric radiometers in January 2003 to demonstrate the passive polarimetry for large spatial coverage of ocean surface wind vector measurements from space. We derived the geophysical model function (GMF) for Windsat polarimetric brightness temperature measurements using six months of matchup dataset. The Windsat GMF was compared with the aircraft radiometer and radar measurements and the SeaWinds scatterometer winds with good agreement up to about 20 m/s wind speed

L-band radiometer brightness temperature measurements of a saltwater pond were made as a function of salinity and temperature. A precision L-band radiometer with stability better than 0.1 K per day was used for these measurements. The L-band measurements are in good agreement with three dielectric constant models over a temperature range from 8/spl deg/C to 32/spl deg/C and a salinity range from 25-40 psu. Based on this experiment, these dielectric models will provide an excellent basis for the algorithm development and design of the future National Aeronautics and Space Administration Aquarius satellite mission.

The study of snow has become an important area of research in the natural sciences, particularly in hydrology and climatology. This study shows a concept of estimating snow water equivalence under the consideration of a dual frequency L- and Ku-band polarization system.

Ocean salinity measurements using the Passive Active L/S-band instrument flying on the NCAR C-130 aircraft were made in July 2002 near Monterey, CA. When the radiometer data were corrected for the SST using the Klein and Swift salinity model and surface roughness effects using the L-band scatterometer, the retrieved salinity measurements had an RMS difference of 0.2-0.3 psu when compared with the R/V Point Sur ship measured salinity data.

The use of QuikSCAT data for wind retrievls of tropical cyclones is described. The evidence of QuikSCAT /spl sigma//sub 0/ dependence on wind direction for >30 m/s wind speeds is presented. The QuikSCAT /spl sigma//sub 0/s show a peak-to-peak wind direction modulation of /spl sim/1 dB at 35 m/s wind speed, and the amplitude of modulation decreases with increasing wind speed. A correction of the QSCAT1 model function for above 23 m/s wind speed is proposed. We explored two microwave radiative transfer models to correct the effects of rain for wind retrievals. Both radiative transfer models have been used to retrieve the ocean wind vectors from the collocated QuikSCAT and SSM/I rain rate data for several tropical cyclones. The resulting wind speed estimates of these tropical cyclones show improved agreement with the wind fields derived from the best track analysis for up to about 15 mm/h SSM/I rain rate. A comparative analysis of maximum wind speed estimates suggests that other rain parameters likely have to be considered for further improvements.

Sea surface salinity is a key parameter for the study of ocean circulation, global water cycle and hence climate changes. In response to these measurement needs, Aquarius was selected recently for the third NASA Earth System Science Pathfinder (ESSP) Announcement of Opportunity for a planned launch date in September 2008. The characteristics of the Aquarius instrument are provided in this paper.

The development of a compact, lightweight, dual-frequency antenna feed for future soil moisture and sea surface salinity (SSS) missions is described. The design is based on the microstrip stacked-patch array (MSPA) to be used to feed large lightweight deployable rotating mesh antenna for spaceborne L-band (/spl sim/ 1 GHz) passive and active sensing systems. This paper describes the design of a single-element stacked patch element and the 7-element array configuration.

The use of QuikSCAT data for wind retrievals of tropical cyclones is described. The evidence of QuikSCAT /spl sigma//sub 0/ dependence on wind direction for >30-m/s wind speeds is presented. The QuikSCAT /spl sigma//sub 0/s show a peak-to-peak wind direction modulation of /spl sim/1 dB at 35-m/s wind speed, and the amplitude of modulation decreases with increasing wind speed. The decreasing directional sensitivity to wind speed agrees well with the trend of QSCAT1 model function at near 20 m/s. A correction of the QSCAT1 model function for above 23-m/s wind speed is proposed. We explored two microwave radiative transfer models to correct the attenuation and scattering effects of rain for wind retrievals. One is derived from the collocated QuikSCAT and Special Sensor Microwave/Imager (SSM/I) dataset, and the other one is a published parametric model developed for rain radars. These two radiative transfer models account for the effects of volume scattering, scattering from rain-roughened surfaces and rain attenuation. The models suggest that the /spl sigma//sub 0/s of wind-roughened sea surfaces for 40-50-m/s winds are comparable to the /spl sigma//sub 0/s of rain contributions for up to about 10-15 mm/h. Both radiative transfer models have been used to retrieve the ocean wind vectors from the collocated QuikSCAT and SSM/I rain rate data for several tropical cyclones. The resulting wind speed estimates of these tropical cyclones show improved agreement with the wind fields derived from the best track analysis and Holland's model for up to about 15-mm/h SSM/I rain rate. A comparative analysis of maximum wind speed estimates suggests that other rain parameters likely have to be considered for further improvements.

L and S-band radiometer brightness temperature measurements from a salt-water pond were made of as a function of salinity and temperature. The L-band measurements are in excellent agreement with the Klein and Swift salinity model, and will provide accurate predictions for the L-band emission from the ocean for future ocean salinity satellite missions.

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

The accuracy of Ku-band ocean wind scatterometers (i.e., NSCAT and SeaWinds) is impacted to varying degrees by rain. In order to determine how to best flag rain-contaminated wind vector cells and ultimately to calibrate out the effects of rain as much as possible, we must understand the impact of rain on the backscatter measurements that are used to retrieve wind vectors. This study uses collocated SSM/I rain rate measurements, NCEP wind fields, and SeaWinds on QuikSCAT backscatter measurements to empirically fit a simple theoretical model of the effect of rain on /spl sigma//sub 0/, and to check the validity of that model. The chief findings of the study are (1) horizontal polarization measurements are more sensitive to rain than vertical polarization, (2) sensitivity to rain varies dramatically with wind speed, and (3) the additional backscatter due to rain overshadows the rain-related attenuation.

Experimental data are presented to support the development of a new concept for ocean wind velocity measurement (speed and direction) with the polarimetric microwave radar technology. This new concept has strong potential for improving the wind direction accuracy and extending the useful swath width by up to 30% for follow-on NASA spaceborne scatterometer mission to SeaWinds series. The key issue is whether there is a relationship between the polarization state of ocean backscatter and surface wind velocity at NASA scatterometer frequencies (13 GHz). An airborne Ku-band polarimetric scatterometer (POLSCAT) was developed for proof-of-concept measurements. A set of aircraft flights indicated repeatable wind direction signals in the POLSCAT observations of sea surfaces at 9-11 m/s wind speed. The correlation coefficients between co- and cross-polarized radar response of ocean surfaces have a peak-to-peak amplitude of about 0.4 and are shown to have an odd-symmetry with respect to the wind direction, unlike the normalized radar cross sections.

A passive/active WS-band (PALS) microwave aircraft instrument to measure ocean salinity and soil moisture has been built and tested. Because the L-band brightness temperatures associated with salinity changes are expected to be small, it was necessary to build a very sensitive and stable system. This new instrument has dual-frequency, dual polarization radiometer and radar sensors. The antenna is a high beam efficiency conical horn. The PALS instrument was installed on the NCAR C-130 aircraft and soil moisture measurements were made in support of the Southern Great Plains 1999 experiment in Oklahoma from July 8-14, 1999. Data taken before and after a rainstorm showed significant changes in the brightness temperatures, polarization ratios and radar backscatter, as a function of soil moisture. Salinity measurement missions were flown on July 17-19, 1999, southeast of Norfolk, VA, over the Gulf Stream. The measurements indicated a clear and repeatable salinity signal during these three days, which was in good agreement with the Cape Hatteras ship salinity data. Data were also taken in the open ocean and a small decrease of 0.2 K was measured in the brightness temperature, which corresponded to the salinity increase of 0.4 psu measured by the M/V Oleander vessel.

The QuikSCAT radar measurements of several tropical cyclones in 1999 have been studied to develop the geophysical model function (GMF) of Ku-band radar /spl sigma//sub 0/ values (normalized radar cross section) for extreme high wind conditions. To account for the effects of precipitation, the authors analyze the co-located rain rates from the Special Sensor Microwave/Imager (SSM/I) and propose the rain rate as a parameter of the GMF. The analysis indicates the deficiency of the NSCAT2 GMF developed for the NASA scatterometer, which overestimates the ocean /spl sigma//sub 0/ for tropical cyclones and ignores the influence of rain. It is suggested that the QuikSCAT /spl sigma//sub 0/ is sensitive to the wind speed of up to about 40-50 m s/sup -1/. The authors introduce modifications to the NSCAT2 GMF and apply the modified GMF to the QuikSCAT observations of Hurricane Floyd. The QuikSCAT wind estimates for Hurricane Floyd in 1999 was improved with the maximum wind speed reaching above 60 m s/sup -1/. The authors perform an error analysis by comparing the QuikSCAT winds with the analyses fields from the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division (HRD). The reasonable agreement between the improved QuikSCAT winds and the HRD analyses supports the applications of scatterometer wind retrievals for hurricanes.

A set of geophysical error sources for the microwave remote sensing of ocean surface salinity have been examined. The error sources include the sea surface temperature, sea surface roughness, atmospheric gases, ionospheric Faraday rotation, and solar and Galactic emission sources. It is shown that the brightness temperature effects of a few kelvin can be expected for most of these error sources. The key correction requirements for accurate salinity measurements are the knowledge accuracy of 0.5/spl deg/C for the sea surface temperature (SST), 10 mbar for the surface air pressure, 2/spl deg/C for the surface air temperature, 0.20 accuracy for the Faraday rotation, and surface roughness equivalent to 0.3 m s/sup -1/ for the surface wind speed. We suggest the use of several data products for corrections, including the AMSR-type instruments for SST and liquid cloud water, the AMSU-type product for air temperature, the scatterometer products or numerical weather analysis for the air pressure, coincidental radar observations with 0.2 dB precision for surface roughness, and on-board polarimetric radiometer channel for Faraday rotation. The most significant sky radiation is from the Sun. A careful design of the antenna is necessary to minimize the leakage of solar radiation or reflection into the antenna sidelobes. The narrow-band radiation from Galactic hydrogen clouds with a bandwidth of less than 1 MHz is also significant, but can be corrected with a radio sky survey or minimized with a notched (band-rejection) filter centered at 1.421 GHz. The other planetary and Galactic radio sources can also be flagged with a small data loss. We have performed a sampling analysis for a polar-orbiting satellite with 900 km swath width to determine the number of satellite observations over a given surface grid cell during an extended period. Under the assumption that the observations from different satellite passes are independent, it is suggested that an accuracy of 0.1 psu (practical salinity unit) is achievable for global monthly 10 latitude by 10 longitude gridded products.

Microwave radiometry and scatterometry are established techniques for surface remote sensing applications. Some applications, such as measurement of sea surface salinity (SSS), sea surface temperature (SST), and soil moisture, require low frequency observations (/spl sim/6 GHZ and below) for good sensitivity, and sensors with large antennas to achieve adequate spatial resolution. Potentially, benefits can be obtained by observing simultaneously with passive and active channels, at similar frequencies, viewing angles, and spatial resolutions, making use of the complementary information contained in the emissivity and backscattering signatures of land and ocean targets. In this study, the authors investigate a concept for combined passive and active multichannel sensing with high spatial resolution, high measurement sensitivity, and wide swath for frequent global coverage. The system consists of a lightweight, relating, deployable mesh antenna with offset feeds. The system specifications are designed primarily for the measurement of sea surface salinity, since this application drives the precision and calibration requirements and, like soil moisture, is a science measurement for which no spaceborne capability currently exists. Demonstration of a capability for sea surface salinity will enhance the potential of this large antenna concept for other applications such as soil moisture and, by including higher frequencies, high resolution measurements of ocean winds, precipitation, sea-surface temperature, and sea-ice.

A technique based on microwave passive polarimetry for the estimates of ionospheric Faraday rotation for microwave remote sensing of Earth surfaces is described. Under the assumption of azimuth symmetry for the surfaces under investigation, it is possible to estimate the ionospheric Faraday rotation from the third Stokes parameter of microwave radiation. An error analysis shows that the Faraday rotation can be estimated with an accuracy of better than 1/spl deg/ with a space-based L-band system, and the residual correction errors of linearly polarized brightness temperatures can be less than 0.1 K. It is suggested that the estimated Faraday rotation angle can be further utilized to derive the ionospheric total electron content (TEC) with an accuracy of about 1 TECU=10/sup 16/ electrons-m/sup -2/ which will yield 1 mm accuracy for the estimate of an ionospheric differential delay at the Ku-band. Therefore, this technique can potentially provide accurate estimates of ionospheric Faraday rotation, TEC and differential path delay for applications including microwave radiometry and scatterometry of ocean salinity and soil moisture as well as satellite altimetry at sea surface height. A conceptual design applicable to real aperture and aperture synthesis radiometers is described for the measurements of the third Stokes parameter.

The Ku-band dual-polarized backscatter signatures of ocean surfaces are described in this paper with the airborne scatterometer measurements collected in the Hurricane Ocean Wind Experiment in September 1997. The data collected from flights over Hurricane Erika provide a direct evidence that there are wind direction signals in the vertically and horizontally polarized Ku-band backscatter of ocean surfaces under the influence of hurricane force winds. At 46/spl deg/ incidence angle, the vertically polarized backscatter acquired at the upwind direction increases by about 1 dB as the wind speed increases from 22 m/spl middot/s/sup -1/ to 35 m/spl middot/s/sup -1/, while the horizontally polarized backscatter appears to be twice as sensitive with a change of about 2 dB. At 35 m/spl middot/s/sup -1/ wind speeds, the difference between upwind and crosswind observations of vertically polarized backscatter is about 1.5 dB, smaller than the 2 dB difference for the horizontally polarized backscatter. This demonstrates that the horizontal polarization has a greater sensitivity to wind speed and direction than the vertical polarization in the high wind regime. The data also suggest that the upwind and downwind asymmetry of Ku-band backscatter decreases with increasing wind speed and can fall below 0 dB at small incidence angles (<35/spl deg/) for the vertical polarization. A combined interaction of the geometric optics scattering and the short wave modulation by long waves is proposed to interpret this phenomenon and appears to agree with the dependence of the signature on incidence, wind speed, and polarization. The aircraft flight data support the feasibility of dual-polarized Ku-band radar for hurricane ocean wind measurements, although the data do suggest a reduced wind speed and direction sensitivity in the high wind regime. Also, the differing polarization backscatter signatures-suggest the relative contributions of various surface scattering mechanisms. An improved Ku-band GMF is described.

The sensitivities of wind direction signals in passive microwave brightness temperatures of sea surfaces to wind speed, incidence angle, polarization, and frequency are presented in this paper. The experimental data were acquired from a series of aircraft flights from 1993 through 1996 by the Jet Propulsion Laboratory (JPL) using JPL 19 and 37 GHz polarimetric radiometers (WINDRAD). Fourier analysis of the data versus mind direction was carried out and the coefficients of Fourier series are illustrated against the wind speed at 45/spl deg/, 55/spl deg/, and 65/spl deg/ incidence angles. There is a good agreement between the JPL aircraft flight data and Wentz's Special Sensor Microwave/Imager (SSM/I) geophysical model function for the vertically polarized brightness temperatures, but Wentz's SSM/I wind direction model for horizontal polarization shows a significantly stronger upwind and downwind asymmetry than the aircraft flight data. Comparison of the dual-frequency WINDRAD data show's that the wind direction signals are similar at 19 and 37 GHz, although the 37 GHz data have slightly stronger signals than the 19 GHz data. In general, the azimuthal variations of brightness temperatures increase with increasing wind speed from low to moderate winds, then level off and decrease at high minds. The only exception is the U measurements at 65/spl deg/ incidence angle, which have a stronger than expected signal at low winds. An exponential function was proposed to model the sensitivities of wind direction signals to wind speeds. The coefficients of the empirical model are provided in this paper and are useful for the simulation of ocean brightness temperatures and for the development of geophysical retrieval algorithms.

To investigate effects of diurnal thermal cycles on C-band polarimetric backscatter and millimeter-wave emission from sea ice, the authors carried out a winter experiment at the outdoor geophysical research facility (GRF) in the cold regions research and engineering laboratory (CRREL), the ice sheet grew from open sea water to a thickness of 10 cm in 2.5 days, during which they took polarimetric backscatter data with a C-band scatterometer, interlaced with brightness temperature measurements at 90 GHz in conjunction with meteorological and sea ice characterizations. The initial ice growth in the late morning was slow due to high insolation. As the air temperature dropped during the night, the growth rate increased significantly. Air temperature changed drastically from about -12 to -36/spl deg/C between day and night, the diurnal thermal cycle repeated itself the next day and the growth rate varied in the same manner. Ice temperature profiles clearly show the diurnal response in the ice sheet with a lag of 2.5 h behind the time of the maximum short-wave incident solar radiation. The diurnal cycles are also evident in the millimeter-wave brightness temperature data, measured sea ice backscatter revealed substantial diurnal variations up to 6 dB with repeatable cycles in synchronization with the temperature cycles and the brightness temperature modulations, the diurnal cycles in backscatter indicate that the dominant scattering mechanism related to thermodynamic processes in sea ice is reversible, a diurnal backscatter model based on sea ice electrodynamics and thermodynamics explains the observed diurnal signature. This work shows that diurnal effects are important for inversion algorithms to retrieve sea ice geophysical parameters from remote sensing data acquired with a satellite synthetic aperture radar (SAR) or scatterometer on Sun-synchronous orbits.

There has been an increasing interest in the applications of polarimetric microwave radiometers for ocean wind remote sensing. Aircraft and spaceborne radiometers have found a few Kelvins wind direction signals in sea surface brightness temperatures, in addition to their sensitivities to wind speeds. However, it was not clear what physical scattering mechanisms produced the observed brightness dependence on wind direction. To this end, polarimetric microwave emissions from wind-generated sea surfaces are investigated with a polarimetric two-scale scattering model, which relates the directional wind-wave spectrum to passive microwave signatures of sea surfaces. Theoretical azimuthal modulations are found to agree well with experimental observations for all Stokes parameters from near nadir to 65/spl deg/ incidence angles. The upwind and downwind asymmetries of brightness temperatures were interpreted using the hydrodynamic modulation. The contributions of Bragg scattering by short waves, geometric optics scattering by long waves and sea foam are examined. The geometric optics scattering mechanism underestimates the directional signals in the first three Stokes parameters, and predicts no signals in the fourth Stokes parameter (V). In contrast, the Bragg scattering was found to dominate the wind direction signals from the two-scale model and correctly predicted the phase changes of the upwind and crosswind asymmetries in T/sub /spl upsi// and U from middle to high incidence angles. The phase changes predicted by the Bragg scattering theory for radiometric emission from water ripples is corroborated by the numerical Monte Carlo simulation of rough surface scattering. This theoretical interpretation indicates the potential use of polarimetric brightness temperatures for retrieving the directional wave spectrum of short gravity and capillary waves.

This paper describes a classification algorithm using dual-polarized scatterometer measurements to identify the edge of the sea ice cover. The distinct polarization scattering signatures of sea ice and open water are discussed and illustrated with the dual-polarized radar measurements from the Seasat-A scatterometer (SASS). The analysis of SASS data suggests that the ratio of vertical and horizontal polarization backscatter, denoted as the copol ratio, is a useful discriminator of sea ice and open ocean. A simple classification algorithm using the thresholds of the copol ratio and backscatter levels is proposed. The feasibility of this algorithm is demonstrated using the SASS data from the single-sided, dual-polarization mode. The results indicate that the dual-polarized measurements from the NASA scatterometer (NSCAT) can be used to produce routine maps of sea ice edges.

Dual-frequency (19 and 37 GHz), multi-incidence measurements of the Stokes parameters of sea surface microwave emission are reported. A series of aircraft polarimetric radiometer flights were carried out over the National Data Buoy Center (NDBC) moored buoys deployed off the northern California coast in July and August 1994. Measured radiometric temperatures showed a few Kelvin azimuth modulations in all Stokes parameters with respect to the wind direction. Wind directional signals observed in the 37-GHz channel were similar to those in the 19-GHz channel. This indicates that the wind direction signals in sea surface brightness temperatures have a weak frequency dependence in the range of 19-37 GHz. Harmonic coefficients of the wind direction signals were derived from experimental data versus incidence angle. It was found that the first harmonic coefficients, which are caused by the up and downwind asymmetric surface features, had a small increasing trend with the incidence angle. In contrast, the second harmonic coefficients, caused by the up and crosswind asymmetry, showed significant variations in T/sub v/ and U data, with a sign change when the incidence angle increased from 45/spl deg/ to 65/spl deg/. Besides the first three Stokes parameters, the fourth Stokes parameter, V, which had never been measured before for sea surfaces, was measured using our 19-GHz channel. The Stokes parameter V. Has an odd symmetry just like that of the third Stokes parameter U, and increases with increasing incidence angles. In summary, sea surface features created by surface winds are anisotropic in azimuth direction and modulate all Stokes parameters of sea surface microwave brightness temperatures by as large as a few Kelvin in the range of incidence angles from 45/spl deg/ to 65/spl deg/ applicable to spaceborne observations.

Several automatic methods have been developed to classify sea ice types from fully polarimetric synthetic aperture radar (SAR) images, and these techniques are generally grouped into supervised and unsupervised approaches. In previous work, supervised methods have been shown to yield higher accuracy than unsupervised techniques, but suffer from the need for human interaction to determine classes and training regions. In contrast, unsupervised methods determine classes automatically, but generally show limited ability to accurately divide terrain into natural classes. In this paper, a new classification technique is applied to determine sea ice types in polarimetric and multifrequency SAR images, utilizing an unsupervised neural network to provide automatic classification, and employing an iterative algorithm to improve the performance. The learning vector quantization (LVQ) is first applied to the unsupervised classification of SAR images, and the results are compared with those of a conventional technique, the migrating means method. Results show that LVQ outperforms the migrating means method, but performance is still poor. An iterative algorithm is then applied where the SAR image is reclassified using the maximum likelihood (ML) classifier. It is shown that this algorithm converges, and significantly improves classification accuracy. The new algorithm successfully identifies first-year and multiyear sea ice regions in the images at three frequencies. The results show that L- and P-band images have similar characteristics, while the C-band image is substantially different. Classification based on single features is also carried out using LVQ and the iterative ML method. It is found that the fully polarimetric classification provides a higher accuracy than those based on a single feature. The significance of multilook classification is demonstrated by comparing the results obtained using four-look and single-look classifications.>

Presents the first experimental evidence that the polarimetric brightness temperatures of sea surfaces are sensitive to ocean wind direction in the incidence angle range of 30 to 50/spl deg/. The experimental data were collected by a K-band (19.35 GHz) polarimetric wind radiometer (WINDRAD) mounted on the NASA DC-8 aircraft. A set of aircraft radiometer flights was successfully completed in November 1993. The authors performed circle flights over National Data Buoy Center (NDBC) moored buoys deployed off the northern California coast, which provided ocean wind measurements. The results indicate that passive polarimetric radiometry has a strong potential for global ocean wind speed and direction measurements from space.>

A number of methods have been developed to classify ground terrain types from fully polarimetric synthetic aperture radar (SAR) images, and these techniques are often grouped into supervised and unsupervised approaches. Supervised methods have yielded higher accuracy than unsupervised techniques, but suffer from the need for human interaction to determine classes and training regions. In contrast, unsupervised methods determine classes automatically, but generally show limited ability to accurately divide terrain into natural classes. In this paper, a new terrain classification technique is introduced to determine terrain classes in polarimetric SAR images, utilizing unsupervised neural networks to provide automatic classification, and employing an iterative algorithm to improve the performance. Several types of unsupervised neural networks are first applied to the classification of SAR images, and the results are compared to those of more conventional unsupervised methods. Results show that one neural network method-Learning Vector Quantization (LVQ)-outperforms the conventional unsupervised classifiers, but is still inferior to supervised methods. To overcome this poor accuracy, an iterative algorithm is proposed where the SAR image is reclassified using a maximum likelihood (ML) classifier. It is shown that this algorithm converges, and significantly improves classification accuracy.>

A new method for symmetrizing polarimetric scattering matrices is applied to the polarimetric synthetic aperture radar (SAR) images acquired over Mount Shasta. This method symmetrizes the cross-polarized responses in the polarimetric images using a 2*2 matrix derived from the image itself and based on the reciprocal property of natural distributed targets. The covariance parameters of the in-scene trihedral reflectors are presented to demonstrate the effectiveness of this method. The results are also compared with those obtained by the symmetrization technique employed by POLCAL, before and after crosstalk removal. Before crosstalk is removed from the images, there were no significant differences between the results obtained by the POLCAL method and the new method for the covariance parameters of trihedral reflectors. After crosstalk removal, using distributed targets with reflection symmetry, the new symmetrization method outperformed the POLCAL method.>

A branching model is proposed for the remote sensing of vegetation. The frequency and angular responses of a two-scale cylinder cluster are calculated to demonstrate the significance of vegetation architecture. The results indicate that the architecture of vegetation plays an important role in determining the observed coherent effects. A two-scale branching model is implemented for soybean with its internal structure and the resulting clustering effects considered. At the scale of soybean fields, the relative location of soybean plants is described by a pair distribution function. The polarimetric backscattering coefficients are obtained in terms of the scattering properties of soybean plants and the pair distribution function. Theoretical backscattering coefficients evaluated using the hole-correction pair distribution are in good agreement with extensive data from soybean fields. The hole-correction approximation, which prevents two soybean plants from overlapping each other, is more realistic and improves the agreement between the model calculation and experimental data near normal incidence.>