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Active and passive microwave signals over land co-vary depending on their shared scattering and emission characteristics by soil and vegetation media. Estimates of this covariation can be used beneficially to downscale coarse-resolution brightness temperatures with high-resolution backscatter for enhanced-resolution Earth observations. In this study, a forward model of covariation for vegetated soil is derived by combining two well-established models of active and passive microwave interactions. The covariation model is inverted to obtain a single-pass observation-driven estimation of active-passive microwave covariation (β) based on multi-channel radiometer and Synthetic Aperture Radar (SAR) scenes. A key feature of the estimation approach is that it is applicable to co-located spatial data scenes and does not rely on temporal information. We present applications of the estimation with combinations of SMAP (L-band) radiometry and both SMAP (L-band) and multi-angular Sentinel-1 (C-band) backscatter data. We first show that for the period of available SMAP L-band radiometer and radar data, the estimation approach for β, introduced in this study, yields similar results as the statistical time-series approach originally designed for SMAP. The new single-pass approach also allows estimation of active-passive covariation where the statistical approach cannot be applied because dynamic range of brightness temperature and backscatter are too limited to allow regression. We then apply the developed estimation method to SMAP L-band radiometer and multi-angular Sentinel-1 C-band SAR data. Here, the study quantifies the effects of microwave frequency and look-angles on the covariation by applying the estimation globally and analyzing the results as a function of vegetation cover – a key determinant of the strength of the covariation.

Soil moisture (SM) is a key state variable in understanding the climate system through its control on the land surface energy, water budget partitioning, and the carbon cycle. Monitoring SM at regional scale has become possible thanks to microwave remote sensing. In the past two decades, several satellites were launched carrying on board either radiometer (passive) or radar (active) or both sensors in different frequency bands with various spatial and temporal resolutions. Soil moisture algorithms are in rapid development and their improvements/revisions are ongoing. The latest SM retrieval products and versions of products that have been recently released are not yet, to our knowledge, comprehensively evaluated and inter-compared over different ecoregions and climate conditions. The aim of this paper is to comprehensively evaluate the most recent microwave-based SM retrieval products available from NASA's (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) satellite, ESA's led mission (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) satellite, ASCAT (Advanced Scatterometer) sensor on board the meteorological operational (Metop) platforms Metop-A and Metop-B, and the ESA Climate Change Initiative (CCI) blended long-term SM time series. More specifically, in this study we compared SMAPL3 V4, SMOSL3 V300, SMOSL2 V650, ASCAT H111, and CCI V04.2 and the new SMOS-IC (V105) SM product. This evaluation was achieved using four statistical scores: Pearson correlation (considering both original observations and anomalies), RMSE, unbiased RMSE, and Bias between remotely-sensed SM retrievals and ground-based measurements from >1000 stations from 17 monitoring networks, spread over the globe, disseminated through the International Soil Moisture Network (ISMN). The analysis reveals that the performance of the remotely-sensed SM retrievals generally varies depending on ecoregions, land cover types, climate conditions, and between the monitoring networks. It also reveals that temporal sampling of the data, the frequency of data in time and the spatial coverage, affect the performance metrics. Overall, the performance of SMAP and SMOS-IC products compared slightly better with respect to the ISMN in situ observations than the other remotely-sensed products. ispartof: REMOTE SENSING OF ENVIRONMENT vol:224 pages:289-303 status: published

The NASA Soil Moisture Active Passive (SMAP) mission was launched on January 31st, 2015. The spacecraft was to provide high-resolution (3 km and 9 km) global soil moisture estimates at regular intervals by combining for the first time L-band radiometer and radar observations. On July 7th, 2015, a component of the SMAP radar failed and the radar ceased operation. However, before this occurred the mission was able to collect and process ~2.5 months of the SMAP high-resolution active-passive soil moisture data (L2SMAP) that coincided with the Northern Hemisphere's vegetation green-up and crop growth season. In this study, we evaluate the SMAP high-resolution soil moisture product derived from several alternative algorithms against in situ data from core calibration and validation sites (CVS), and sparse networks. The baseline algorithm had the best comparison statistics against the CVS and sparse networks. The overall unbiased root-mean-square-difference is close to the 0.04 m3/m3 the SMAP mission requirement. A 3 km spatial resolution soil moisture product was also examined. This product had an unbiased root-mean-square-difference of ~0.053 m3/m3. The SMAP L2SMAP product for ~2.5 months is now validated for use in geophysical applications and research and available to the public through the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC). The L2SMAP product is packaged with the geo-coordinates, acquisition times, and all requisite ancillary information. Although limited in duration, SMAP has clearly demonstrated the potential of using a combined L-band radar-radiometer for proving high spatial resolution and accurate global soil moisture.

Over one-third of the global land area undergoes a seasonal transition between predominantly frozen and non-frozen conditions each year, with the land surface freeze/thaw (FT) state a significant control on hydrological and biospheric processes over northern land areas and at high elevations. The NASA Soil Moisture Active Passive (SMAP) mission produced a daily landscape FT product at 3-km spatial resolution derived from ascending and descending orbits of SMAP high-resolution L-band (1.4 GHz) radar measurements. Following the failure of the SMAP radar in July 2015, coarser (36-km) footprint SMAP radiometer inputs were used to develop an alternative daily passive microwave freeze/thaw product. In this study, in situ observations are used to examine differences in the sensitivity of the 3-km radar versus the 36-km radiometer measurements to the landscape freeze/thaw state during the period of overlapping instrument operation. Assessment of the retrievals at high-latitude SMAP core validation sites showed excellent agreement with in situ flags, exceeding the 80% SMAP mission accuracy requirement. Similar performance was found for the radar and radiometer products using both air temperature and soil temperature derived FT reference flags. There was a tendency for SMAP thaw retrievals to lead the surface flags due to the influence of wet snow cover conditions on both the radar and radiometer signal. Comparison with other satellite derived FT products showed those derived from passive measurements (SMAP radiometer; Aquarius radiometer; Advanced Microwave Scanning Radiometer - 2) retrieved less frozen area than the active products (SMAP radar; Aquarius radar).

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission was launched on 31st January 2015, with the aim of providing global soil moisture maps at 9 km spatial resolution by combining L-band radar and radiometer observations. However, after the SMAP radar became inoperable, NASA decided to utilize the Sentinel 1A/1B C-band SAR data in its place. The new version of baseline brightness temperature (Tb) downscaling algorithm for SMAP is tested using L-band airborne data to evaluate the capabilities of the C-band Sentinel-1A SAR relative to L-band radar data in downscaling the SMAP Tb for achieving high resolution brightness temperature. In this study, the downscaling algorithm used L-band airborne Synthetic Aperture Radar (SAR) backscatter (σ) collected from the fifth Soil Moisture Active Passive Experiment (SMAPEx-5) in south-eastern Australia to downscale 36 km L-band SMAP radiometer Tb pixels to 3 km and 9 km. The downscaling results were then compared with the published results using Sentinel-1A C-band backscatter, and evaluated against airborne 1 km resolution L-band passive microwave brightness temperature collected from SMAPEx-5. The results show that for vertical polarization the average Root Mean Square Error (RMSE) of downscaled Tb when compared with reference airborne Tb across 4 days at 9 km resolution were 4.9 K for L-band and 6.0 K for C-band, and increased to 9.3 K for L-band and 9.6 K for C-band at 3 km spatial resolution. Moreover, the correlation coefficient (R) of downscaled and reference Tb across the 4 days was 0.92 for L-band and 0.88 for C-band at 9 km, decreasing to 0.75 for L-band and 0.72 for C-band at 3 km spatial resolution. Accordingly, the RMSE increased and the correlation coefficient decreased when using C-band radar data in place of that at L-band. However, overall there is expected to be only a slight decrease in performance of the downscaling algorithm by using the Sentinel 1A data in place of the SMAP radar.

Soil Moisture Active Passive (SMAP) mission of NASA was launched in January 2015. Currently, SMAP has an L-band radiometer and a defunct L-band radar with a rotating 6-m mesh reflector antenna. On July 7th, 2015, the SMAP radar malfunctioned and became inoperable. Consequently, the production of high-resolution active-passive soil moisture product got hampered, and only ~2.5 months (April 15th, 2015 to July 7th, 2015) of data remain available. Therefore, during the SMAP post-radar phase, many ways were examined to restart the high-resolution soil moisture product generation of the SMAP mission. One of the feasible approaches was to substitute the SMAP radar with other available SAR data. Sentinel-1A/Sentinel-1B SAR data was found most suitable for combining with the SMAP radiometer data because of its nearly similar orbit configuration that allows overlapping of their swaths with a minimal time difference, a key feature/requirement for the SMAP active-passive algorithm. The Sentinel interferometric wide swath (IW) mode acquisition also provides the co-polarized and cross-polarized observations required for the SMAP active-passive algorithm. However, some differences do exist between the SMAP and Sentinel SAR data. They are mainly: 1) Sentinel has a C-band SAR whereas SMAP operates at L-band; 2) Sentinel has multiple incidence angles within its swath, and SMAP has one single incidence angle; and 3) Sentinel 1A/B Interferometric Wide (IW) swath width is ~250 km as compared to SMAP with 1000 km swath width. On any given day, the narrow swath width of the Sentinel observations significantly reduces the overlap spatial coverage between SMAP and Sentinel as compared to the original SMAP radar and radiometer swath coverage. Hence, the temporal resolution (revisit interval) suffers due to narrow overlapped swath width and degrades from 3 days to 12 days. One advantage of using very high-resolution resolution Sentinel-1A/Sentinel-1B data in the SMAP active-passive algorithm is the potential of obtaining the disaggregated brightness temperature and thus soil moisture at a much finer spatial resolution of 3 km and 1 km at global extent. The assessment of high-resolution product at 3 km and 1 km using the soil moisture calibration and validations sites shows reasonable accuracy of ~0.05 m3/m3. The SMAP-Sentinel1 active-passive high-resolution product is now available to the public (new version released in October 2018) through NSIDC (NASA DAAC). The duration of this product is from April 2015 to current date.

Improved maximum cross correlation (MCC) techniques are used to retrieve ocean surface currents from the sequential ocean color imagery provided by multiple newer generations of satellite sensors on hourly scales in the Yellow Sea and the U.S. East and Gulf coasts. The MCC calculation is validated in a series of Bio-Optical Forecasting (BioCast) experiments with predetermined synthetic velocities, and its products are evaluated by examining the errors and biases with respect to the High Frequency Radar (HFRadar) measurements. The root-mean-square (RMS) errors in our best current products derived from the overlap of satellite sensor swath between the VIIRS sequential orbits are less than 0.17 m s − 1 in the evaluation area outside of the Chesapeake Bay. The most accurate current products are those derived from the imagery data of R rs (551), B b (551) and C(551), while the image sequences of B b (551) and Z eu _lee are identified as the most suited products for the retrieval of currents because of their best production capacities of valid velocity vectors. Mechanisms between the advective processes and the dynamic changes of bio-optical properties are discussed regarding the performances of various color products on the retrieval of currents. Similarities of velocity distribution in the retrieved vector arrays are collected across different MCC products derived from ocean color datasets that are of different types and derived from different spectral channels of satellite overpasses. The inter-product similarities themselves can be used to characterize the near-surface advection as well and usually have smaller errors than each of the individual MCC currents. Moreover, efforts are also under way to improve the ocean color derived currents by merging several of the MCC products with similarities to increase the total spatial coverage. This study not only seeks the image-derived products best representing the sea surface current structures in coastal areas, but also exploits how these currents can be improved or optimized to support the ocean forecasts.

We exhibit a technique to detect and estimate precipitation over the global oceans using the radar back scattering coefficient and brightness temperature measurements from Oceansat-II scatterometer along with numerical weather prediction model derived rain sensitive parameters via a neural network (NN) based setup. Rain/no-rain labels are generated by analyzing rainfall observations from Tropical Rainfall Measuring Mission (TRMM) and Advanced Microwave Scanning Radiometer for Earth Observation Satellite (EOS) (AMSR-E) which are concurrent (within a spatiotemporal bin 0.25° × 0.25° latitude–longitude and 900 s) to Oceansat-II overpasses. The rain sensitivity of all the parameters is examined. NN is applied in two stages: (1) rain identification and (2) rain quantification with training samples from five different overlapping geographical regions [I(25°N–25°S), II(15°N–45°N), III(35°N–70°N), IV(15°S–45°S) and V(35°S–70°S)]. Rain identification accuracy is about 93%, 87%, 90%, 79%, and 85%, and no-rain detection accuracy of about 97%, 87%, 86%, 84% and 86% for these regions. The missing rain cases are few compared to the size of no-rain samples and are largely from the low rain regime. The RMS error of instantaneous rain estimation for regions I to V (rain rates varying from > 0 to approximately 45, 25, 25, 45, and 20 mm h− 1) is found to be 1.86, 0.69, 0.47, 0.56, and 0.46 mm h− 1, respectively. The qualitative comparisons of instantaneous, 3-days, monthly and seasonal rain rates from scatterometer and AMSR-E demonstrate a good agreement between them. Probability distribution of monthly rain rates from scatterometer and AMSR-E is also compared, indicating the consistency of scatterometer derived rain with AMSR-E rain in a climatic scale.

Emissivity and backscattering have different sensitivities to soil and vegetation parameters and provide complementary information on the observed scene. For this reason, the main objective of this study was to investigate the potential of the combined use of active and passive microwave signatures to improve the understanding of emission and scattering mechanisms over land. The discrete scattering model developed by the Tor Vergata University, driven by a single set of input parameters, was used to simulate both emission and backscattering. The model accuracy was assessed by comparing modelled emissivity, multiplied by the effective temperature, with C-band AQUA AMSR-E observations and modelled backscattering coefficient with Metop ASCAT observations. The investigation was carried out using datasets collected over the Maqu area, which is located in the north-eastern part of the Tibetan Plateau and is covered by homogeneous grassland with a thin litter layer. In situ soil moisture measurements and MODIS Terra/Aqua 8-days LAI products served as model inputs, whereas the most sensitive other parameters, such as surface roughness and litter and vegetation parameters, were calibrated to obtain the best match between modelled outputs and both ASCAT and AMSR-E 2009 observations. With the calibrated parameters, measured soil moisture and MODIS LAI as input, the model correctly simulated the temporal variations of ASCAT backscattering coefficient, with R2 of 0.9 and rmse of 0.5 dB. The results obtained in the passive case strongly depended on the adopted effective temperature. The best match with satellite observations was found when the surface temperature was obtained from Ka-band AMSR-E data, leading to R2 of 0.8 and rmse of 6 K for H polarisation data and R2 of 0.5 and rmse of 6 K for V polarisation data. The validation carried out using the 2010 data, without any further refinement of the calibrated parameters, showed a slightly worse agreement between modelled and observed signatures. The results showed that the combined use of active and passive microwave signatures contributed to an unambiguous interpretation of emission and scattering processes over the study area, it allowed us to impose constraints to non-available inputs of the Tor Vergata model and it led to correct simulations of the satellite signatures. These findings are important to better understand the potential of the synergistic use of radiometer and radar data, like those to be provided by NASA's SMAP.

SMAP satellite has provided us the first 9-km global soil moisture (SM) product, which is retrieved from the combined L-band radiometer and radar observations with a balance between accuracy and resolution. However, SMAP's radar failed on July 7, 2015, making the continuous production of the 9-km SM impossible, which has considerably affected the application of SMAP in hydrological monitoring. This study was aimed at extending the SMAP 9-km SM by developing a non-local filter based spatio-temporal fusion model (STFM). With the auxiliary of the historical 9-kmand 36-km products, the STFM was used to downscale the daily 36-km product to 9-km. Two-year 9-km product was estimated using STFM in the study. It shows that the estimated product has the detailed information retention of 9-km product and the comparable accuracy with 36-km product, which makes it feasible to improve the application potential of the current SMAP SM products.

We analyze the surface emissivity and radar backscatter measured by the Aquarius L-band radiometer and scatterometer under rainy conditions. The residual signals due to rain are derived from measurements after accounting for roughness due to wind and flat surface emissivity. The wind roughness is accounted for by a geophysical model function (GMF) built using rain-free data. Using more than one year of Aquarius data collocated with SSMI/S, WindSAT rain rate and NCEP wind, our analysis reveals rain rate dependence in radar backscatter and surface emissivity, which become increasingly significant as the wind speed approaches zero and not significant above 15 m s − 1 . The effect of rain on radar backscatter is dominated by raindrop splashing as indicated by the incidence angle dependence and polarization characteristics of the residuals.

The Along-Track Scanning Radiometer 2 (ATSR2) instrument has a dual view capability that allows for stereo height retrievals. Stereo heights were retrieved for selected scenes over the United Kingdom Chilbolton Facility for Atmospheric and Radio Research and the United States Atmospheric Radiation Measurement program Southern Great Plains site from 1997 to 2000. Stereo cloud-top heights obtained with the 11 μm and 1.6 μm channels of ATSR2 were compared with ground-based millimeter-wave cloud radar measurements at both sites. On average ATSR2 11 μm channel cloud-top height retrievals were 350 m higher than those from radar with a standard deviation of 1 km. This difference increased with decreasing cloud-top height. One major problem found in the 11 μm channel cloud-top height retrievals was poor delineation between surface (i.e., clear) and low-altitude cloud pixels, though this tends to lower cloud-top heights rather than raise them. The ATSR2 1.6 μm channel stereo cloud-top heights had large discrepancies compared to radar because of the 1.6 μm channel sensitivity to a lower layer in the case of multilayer clouds or the terrain in the case of optically thin single-layer clouds. For single-layer clouds, the agreement between the ATSR2 1.6 μm channel and the radar cloud-top heights was similar to that between ATSR2 11 μm channel and radar cloud-top heights.

To investigate scattering of Global Positioning System (GPS) signals from terrain and the potential for remotely sensing soil moisture with the L-band GPS bistatic radar concept, a prototype GPS bistatic radar participated in airborne measurements during the Soil Moisture Experiment 2002 (SMEX02). A 12-channel GPS navigation receiver, modified to perform bistatic radar measurements, was mounted on the NCAR C-130 aircraft to make co-located measurements with other instruments. Narrow pulse returns and comparison of the reflected GPS signal-to-noise ratio (SNR) measurements to digital imagery and cover maps indicated that the scattering was most likely quasi-specular, originating from a small footprint on the order of the first Fresnel zone (∼30 m). Temporal changes were observed in the measured signals and were expected to be proportional to varying soil moisture content. To investigate this effect, the bistatic signal measurements were interpolated to a spatial grid to produce daily maps of relative change of surface soil moisture over the study region. The maps of the study region showed a transition from dry surface soil moisture conditions to wet conditions following precipitation events occurring in the middle of the study period. Additionally, the maps showed the scattered power increased in areas with localized rainfall relative to areas without precipitation. Comparing the GPS-reflected SNR measurements with L-band brightness temperatures measured coincidently by the PALS radiometer showed good agreement in the trend measured by the two sensors. The scattered signal measurements were also compared with in situ soil moisture measurements and found to follow the general soil moisture trend as a function of time. These initial results from the first controlled experiment of GPS bistatic radar for soil moisture remote sensing indicate that the technique is sensitive to temporal and spatial variations in soil moisture.

The backscattering and emission measured simultaneously by radar and radiometer show promise for the estimation of surface variables such as near-surface soil moisture and vegetation characteristics. In this paper, the 10.7 GHz Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) channel and 13.8 GHz precipitation radar (PR) observations are simultaneously used for the estimation of the near-surface soil moisture and vegetation properties. The Fresnel model for soil and a simple model for vegetation are used to simulate the passive microwave emission at 10.7 GHz. To determine the PR backscatter signal from a land surface, a theoretical approach is used based on the Geometric Optics Model for simulating bare soil and a semi-empirical water-cloud model for vegetation. The model parameters required in specifying the nature of the soil and vegetation are calibrated on the basis of in situ soil moisture data combined with remotely sensed observations. The calibrated model is subsequently used to retrieve near-surface soil moisture and leaf area index for assumed values of surface roughness and temperature. Algorithm assessment using synthetic passive and active microwave data shows a nonlinearity effect in the system inversion, which results in a varying degree of error statistics in soil wetness and vegetation characteristics retrieval. The technique was applied on TRMM radar/radiometer observations from three consecutive years and evaluated against in situ near-surface (5 cm) soil moisture measurements from the Oklahoma Mesonet showing a consistent performance.

The Special Sensor Microwave Imager (SSM/I) microwave satellite radiometer and its predecessor SMMR are primary sources of information for global sea ice and climate studies. However, comparisons of SSM/I, Landsat, AVHRR, and ERS-1 synthetic aperture radar (SAR) have shown substantial seasonal and regional differences in their estimates of sea ice concentration. To evaluate these differences, we compared SSM/I estimates of sea ice coverage derived with the NASA Team and Bootstrap algorithms to estimates made using RADARSAT, and OKEAN-01 satellite sensor data. The study area included the Barents Sea, Kara Sea, Laptev Sea, and adjacent parts of the Arctic Ocean, during October 1995 through October 1999. Ice concentration estimates from spatially and temporally near-coincident imagery were calculated using independent algorithms for each sensor type. The OKEAN algorithm implemented the satellite's two-channel active (radar) and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter. The RADARSAT algorithm utilized a segmentation approach of the measured radar backscatter, and the SSM/I ice concentrations were derived at National Snow and Ice Data Center (NSIDC) using the NASA Team and Bootstrap algorithms. Seasonal and monthly differences between SSM/I, OKEAN, and RADARSAT ice concentrations were calculated and compared. Overall, total sea ice concentration estimates derived independently from near-coincident RADARSAT, OKEAN-01, and SSM/I satellite imagery demonstrated mean differences of less than 5.5% (S.D.

High-altitude radar and thermal imagery collected by the NASA research aircraft WB57F were used to examine the structural setting and distribution of radiant temperatures of geothermal anomalies in the Pilgrim Springs, Alaska area. Like-polarized radar imagery with perpendicular look directions provides the best structural data for lineament analysis, although more than half the mapped lineaments are easily detectable on conventional aerial photography. Radiometer data and imagery from a thermal scanner were used to evaluate radiant surface temperatures, which ranged from 3 to 17 C. The evening imagery, which utilized density-slicing techniques, detected thermal anomalies associated with geothermal heat sources. The study indicates that high-altitude predawn thermal imagery may be able to locate relatively large areas of hot ground in site-specific studies in the vegetated Alaskan terrain. This imagery will probably not detect gentle lateral gradients.