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Launched in January 2015, the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) observatory was designed to provide frequent global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using a radar and a radiometer operating at L-band frequencies. Despite a hardware mishap that rendered the radar inoperable shortly after launch, the radiometer continues to operate nominally, returning more than two years of science data that have helped to improve existing hydrological applications and foster new ones. Beginning in late 2016 the SMAP project launched a suite of new data products with the objective of recovering some high-resolution observation capability loss resulting from the radar malfunction. Among these new data products are the SMAP Enhanced Passive Soil Moisture Product that was released in December 2016, followed by the SMAP/Sentinel-1 Active-Passive Soil Moisture Product in April 2017. This article covers the development and assessment of the SMAP Level 2 Enhanced Passive Soil Moisture Product (L2_SM_P_E). The product distinguishes itself from the current SMAP Level 2 Passive Soil Moisture Product (L2_SM_P) in that the soil moisture retrieval is posted on a 9 km grid instead of a 36 km grid. This is made possible by first applying the Backus-Gilbert optimal interpolation technique to the antenna temperature (T A ) data in the original SMAP Level 1B Brightness Temperature Product to take advantage of the overlapped radiometer footprints on orbit. The resulting interpolated T A data then go through various correction/calibration procedures to become the SMAP Level 1C Enhanced Brightness Temperature Product (L1C_TB_E). The L1C_TB_E product, posted on a 9 km grid, is then used as the primary input to the current operational SMAP baseline soil moisture retrieval algorithm to produce L2_SM_P_E as the final output. Images of the new product reveal enhanced visual features that are not apparent in the standard product. Based on in situ data from core validation sites and sparse networks representing different seasons and biomes all over the world, comparisons between L2_SM_P_E and in situ data were performed for the duration of April 1, 2015–October 30, 2016. It was found that the performance of the enhanced 9 km L2_SM_P_E is equivalent to that of the standard 36 km L2_SM_P, attaining a retrieval uncertainty below 0.040 m 3 /m 3 unbiased root-mean-square error (ubRMSE) and a correlation coefficient above 0.800. This assessment also affirmed that the Single Channel Algorithm using the V-polarized T B channel (SCA-V) delivered the best retrieval performance among the various algorithms implemented for L2_SM_P_E, a result similar to a previous assessment for L2_SM_P. [ABSTRACT FROM AUTHOR]

The monitoring of soil moisture and sea surface salinity over the Earth has been profoundly enhanced during the last thirteen years due to a new generation of satellite sensors. L-band radiometry is currently the only technology providing direct measurements of soil moisture, insensitive to surface roughness and distribution of elements in the soil, and the only technology the only technology for measuring that allows us to measure sea surface salinity from space. The Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite missions resolve global and local variability with a spatial resolution of approximately 43 km, a swath width close to 1000 km, and a sampling time, for each mission, of at least twice every 3 days. These resolutions and samplings can be increased by either merging data from the two sensors, and with complementary information gathered from other passive or active sensors, or with in situ information at higher spatial resolution. Numerous scientific studies based on the use of this new type of measurement have led to a better understanding and constraint of the processes governing the variability of the water cycle, ocean circulation and the Earth's climate. The continuity of measurements, and the increased spatial and radiometric resolution is critical for fulfilling scientific needs. Future L-band radiometry missions currently being planned in Europe (the Copernicus Imaging Microwave Radiometer), and in China (the Ocean Salinity mission) should provide better constraints on auxiliary parameters by combining multiple frequencies, but they will not have improved spatial resolution beyond SMOS and SMAP. The temporal continuity with SMOS and SMAP will likely not be ensured. In parallel, new concepts are being developed to increase spatial resolution of both land and ocean parameters. [ABSTRACT FROM AUTHOR]

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m3/m3 for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve. [ABSTRACT FROM AUTHOR]

An unresolved issue in global soil moisture retrieval using passive microwave sensors is the spatial integration of heterogeneous landscape features to the nominal 50 km footprint observed by most low frequency satellite systems. One of the objectives of the Soil Moisture Experiments 2004 (SMEX04) was to address some aspects of this problem, specifically variability introduced by vegetation, topography and convective precipitation. Other goals included supporting the development of soil moisture data sets that would contribute to understanding the role of the land surface in the concurrent North American Monsoon System. SMEX04 was conducted over two regions: Arizona — semi-arid climate with sparse vegetation and moderate topography, and Sonora (Mexico) — moderate vegetation with strong topographic gradients. The Polarimetric Scanning Radiometer (PSR/CX) was flown on a Naval Research Lab P-3B aircraft as part of SMEX04 (10 dates of coverage over Arizona and 11 over Sonora). Radio Frequency Interference (RFI) was observed in both PSR and satellite-based (AMSR-E) observations at 6.92 GHz over Arizona, but no detectable RFI was observed over the Sonora domain. The PSR estimated soil moisture was in agreement with the ground-based estimates of soil moisture over both domains. The estimated error over the Sonora domain (SEE=0.021 cm3/cm3) was higher than over the Arizona domain (SEE=0.014 cm3/cm3). These results show the possibility of estimating soil moisture in areas of moderate and heterogeneous vegetation and high topographic variability. [Copyright &y& Elsevier]

Abstract The Soil Moisture Active and Passive (SMAP) mission, launched by the National Aeronautics and Space Administration (NASA) on 31st January 2015, was designed to provide global soil moisture every 2 to 3 days at 9 km resolution by downscaling SMAP passive microwave observations obtained at 36 km resolution using active microwave observations at 3 km resolution, and then retrieving soil moisture from the resulting 9 km brightness temperature product. This study evaluated the SMAP Active/Passive (AP) downscaling algorithm together with other resolution enhancement techniques. Airborne passive microwave observations acquired at 1 km resolution over the Murrumbidgee River catchment in south-eastern Australia during the fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4/5) were used as reference data. The SMAPEx-4/5 data were collected in May and September 2015, respectively, and aggregated to 9 km for direct comparison with a number of available resolution-enhanced brightness temperature estimates. The results show that the SMAP AP downscaled brightness temperature had a correlation coefficient (R) of 0.84 and Root-Mean-Squared Error (RMSE) of ~10 K, while SMAP Enhanced, Nearest Neighbour, Weighted Average, and the Smoothing Filter-based Modulation (SFIM) brightness temperature estimates had somewhat better performance (RMSEs of ~7 K and an R exceeding 0.9). Although the SFIM had the lowest unbiased RMSE of ~6 K, the effect of cloud cover on Ka-band observations limits data availability. Highlights • Evaluate SMAP downscaled TB products under Australian land surface conditions. • Use unique airborne observations to validate SMAP AP TB downscaling. • Various SMAP downscaled TB products have been evaluated and intercompared. [ABSTRACT FROM AUTHOR]

The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance. Those sites provide well-calibrated in situ soil moisture measurements within SMAP product grid pixels for diverse conditions and locations. The estimation of the average soil moisture within the SMAP product grid pixels based on in situ measurements is more reliable when location specific calibration of the sensors has been performed and there is adequate replication over the spatial domain, with an up-scaling function based on analysis using independent estimates of the soil moisture distribution. SMAP fulfilled these requirements through a collaborative Cal/Val Partner program. This paper presents the results from 34 candidate core validation sites for the first eleven months of the SMAP mission. As a result of the screening of the sites prior to the availability of SMAP data, out of the 34 candidate sites 18 sites fulfilled all the requirements at one of the resolution scales (at least). The rest of the sites are used as secondary information in algorithm evaluation. The results indicate that the SMAP radiometer-based soil moisture data product meets its expected performance of 0.04 m 3 /m 3 volumetric soil moisture (unbiased root mean square error); the combined radar-radiometer product is close to its expected performance of 0.04 m 3 /m 3 , and the radar-based product meets its target accuracy of 0.06 m 3 /m 3 (the lengths of the combined and radar-based products are truncated to about 10 weeks because of the SMAP radar failure). Upon completing the intensive Cal/Val phase of the mission the SMAP project will continue to enhance the products in the primary and extended geographic domains, in co-operation with the Cal/Val Partners, by continuing the comparisons over the existing core validation sites and inclusion of candidate sites that can address shortcomings. [ABSTRACT FROM AUTHOR]

The Multi‐frequency Scanning Microwave Radiometer (MSMR) aboard the Indian Space Research Organization—Oceansat‐1 platform measured land surface brightness temperature at a C‐band frequency and provided an opportunity for exploring large‐scale soil moisture retrieval during its two‐year period of operation. These data may provide a valuable extension to the Scanning Multichannel Microwave Radiometer (SMMR) and the Advanced Microwave Scanning Radiometer (AMSR) since they covered a portion of the time period between the two missions. This investigation was one of the first to utilize the MSMR data for a land application and, as a result, several data quality issues had to be addressed. These included geolocation accuracy, calibration (particularly over land), erroneous data, and the significance of anthropogenic radio‐frequency interference (RFI). Calibration of the low frequency channels was evaluated using inter‐comparisons between the Tropical Rainfall Measuring Mission/Microwave Imager (TRMM/TMI) and the MSMR brightness temperatures. Biases (TMI TB>MSMR TB) of 3.4 and 3.6 K were observed over land for the MSMR 10.65 GHz horizontal and vertical polarization channels, respectively. These results suggested that additional calibration of the MSMR data was required. Comparisons between the MSMR measured brightness temperature and ground measured volumetric soil moisture collected during the South Great Plain experiment (SGP99) indicated that the lower frequency and horizontal polarization observations had higher sensitivity to soil moisture. Using a previously developed soil emission model, multi‐temporal regional soil moisture distributions were retrieved for the continental United States. Comparisons between the MSMR based soil moisture and ground measured volumetric soil moisture indicated a standard error of estimate of 0.052 m3/m3. [ABSTRACT FROM AUTHOR]

Satellite-based passive microwave remote sensing typically involves a scanning antenna that makes measurements at irregularly spaced locations. These locations can change on a day to day basis. Soil moisture products derived from satellite-based passive microwave remote sensing are usually resampled to a fixed Earth grid that facilitates their use in applications. In many cases the grid size is finer than the actual spatial resolution of the observation, and often this difference is not well understood by the user. Here, this issue was examined for the Soil Moisture Active Passive (SMAP) enhanced version of the passive-based soil moisture product, which has a grid size of 9-km and a nominal spatial resolution of 33-km. In situ observations from core validation sites were used to compute comparison metrics. For sites that satisfied the established reliability and scaling criteria, the impact of validating the 9-km grid product with in situ data collected over a 9-km versus a 33-km domain was very small for the sites studied (0.039 m 3 /m 3 unbiased root mean square difference for the 9-km case versus 0.037 m 3 /m 3 for the 33-km case). This result does not mean that the resolution of the product is 9-km but that for the conditions studied here the soil moisture estimated from in situ observations over 9-km is a close approximation of the soil moisture estimated from in situ observations over the 33-km resolution. The implication is that using the enhanced SMAP product at its grid resolution of 9-km should not introduce large errors in most applications. [ABSTRACT FROM AUTHOR]

The SMOS (Soil Moisture and Ocean Salinity) mission provides surface soil moisture (SM) maps at a mean resolution of ~ 50 km. However, agricultural applications (irrigation, crop monitoring) and some hydrological applications (floods and modeling of small basins) require higher resolution SM information. In order to overcome this spatial mismatch, a disaggregation algorithm called Disaggregation based on Physical And Theoretical scale Change (DISPATCH) combines higher-resolution data from optical/thermal sensors with the SM retrieved from microwave sensors like SMOS, producing higher-resolution SM as the output. A DISPATCH-based processor has been implemented for the whole globe (emerged lands) in the Centre Aval de Traitement des Données SMOS (CATDS), the French data processing center for SMOS Level 3 products. This new CATDS Level-4 Disaggregation processor (C4DIS) generates SM maps at 1 km resolution. This paper provides an overview of the C4DIS architecture, algorithms and output products. Differences with the original DISPATCH prototype are explained and major processing parameters are presented. The C4DIS SM product is compared against L3 and in situ SM data during a one year period over the Murrumbidgee catchment and the Yanco area (Australia), and during a four and a half year period over the Little Washita and the Walnut Gulch watersheds (USA). The four validation areas represent highly contrasting climate regions with different landscape properties. According to this analysis, the C4DIS SM product improves the spatio-temporal correlation with in situ measurements in the semi-arid regions with substantial SM spatial variability mainly driven by precipitation and irrigation. In sub-humid regions like the Little Washita watershed, the performance of the algorithm is poor except for summer, as result of the weak moisture-evaporation coupling. Disaggregated products do not succeed to have and additional benefit in the Walnut Gulch watershed, which is also semi-arid but with well-drained soils that are likely to cancel the spatial contrast needed by DISPATCH. Although further validation studies are still needed to better assess the performance of DISPATCH in a range of surface and atmospheric conditions, the new C4DIS product is expected to provide satisfying results over regions having medium to high SM spatial variability. [ABSTRACT FROM AUTHOR]

The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. The commissioning phase ended in May 2010. Subsequently, the satellite has been in operation for over six years while the retrieval algorithms from Level 1 (L1) to Level 2 (L2) underwent significant evolutions as knowledge improved. Moreover, other approaches for retrieval at L2 over land were investigated while Level 3 (L3) and Level 4 (L4) were initiated. In this paper, these improvements were assessed by inter-comparisons of the current L2 (V620) against the previous version (V551) and new products (using neural networks referred to as SMOS-NN) and L3 (referred to as SMOS-L3). In addition, a global evaluation of different SMOS soil moisture (SM) products (SMOS-L2, SMOS-L3, and SMOS-NN) was performed comparing products with those of model simulations and other satellites. Finally, all products were evaluated against in situ measurements of soil moisture (SM). To achieve such a goal a set of metrics to evaluate different satellite products are suggested. The study demonstrated that the V620 shows a significant improvement (including those at L1 improving L2) with respect to the earlier version V551. Results also show that neural network based approaches can often yield excellent results over areas where other products are poor. Finally, global comparison indicates that SMOS behaves very well when compared to other sensors/approaches and gives consistent results over all surfaces from very dry (African Sahel, Arizona), to wet (tropical rain forests). RFI (Radio Frequency Interference) is still an issue even though detection has been greatly improved through the significant reduction of RFI sources in several areas of the world. When compared to other satellite products, the analysis shows that SMOS achieves its expected goals and is globally consistent over different eco climate regions from low to high latitudes and throughout the seasons. [ABSTRACT FROM AUTHOR]

Abstract: Satellite-based passive microwave remote sensing has been shown to be a valuable tool in mapping and monitoring global soil moisture. The Advanced Microwave Scanning Radiometer on the Aqua platform (AMSR-E) has made significant contributions to this application. As the result of agency and individual initiatives, several approaches for the retrieval of soil moisture from AMSR-E have been proposed and implemented. Although the majority of these are based on the same Radiative Transfer Equation, studies have shown that the resulting soil moisture estimates can differ significantly. A primary goal of this investigation is to understand these differences and develop a suitable approach to potentially improve the algorithm currently used by NASA in producing its operational soil moisture product. In order to achieve this goal, the theoretical basis of several alternative soil moisture retrieval algorithms are examined. Analysis has focused on five established approaches: the operational algorithm adopted by NASA, which is referred to as the Normalized Polarization Difference algorithm, the Single Channel Algorithm, the Land Parameter Retrieval Model, the University of Montana soil moisture algorithm, and the HydroAlgo Artificial Neural Network algorithm. Previous comparisons of these algorithms in the literature have typically focused on the retrieved soil moisture products, and employed different metrics and data sets, and have resulted in differing conclusions. In this investigation we attempt to provide a more thorough understanding of the fundamental differences between the algorithms and how these differences affect their performance in terms of range of soil moisture provided. The comparative overview presented in the paper is based on the operating versions of the source codes of the individual algorithms. Analysis has indicated that the differences between algorithms lie in the specific parameterizations and assumptions of each algorithm. The comparative overview of the theoretical basis of the approaches is linked to differences found in the soil moisture retrievals, leading to suggestions for improvements and increased reliability in these algorithms. [Copyright &y& Elsevier]

Abstract: Vegetation indices are valuable in many fields of geosciences. Conventional, visible-near infrared, indices are often limited by the effects of atmosphere, background soil conditions, and saturation at high levels of vegetation. In this study, we will establish the theoretical basis for our new passive microwave vegetation indices (MVIs) based on data from the Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite. Through the analysis of numerical simulations by surface emission model, the Advanced Integral Equation Model (AIEM), we found that bare soil surface emissivities at different frequencies can be characterized by a linear function with parameters that are dependent on the pair of frequencies used. This makes it possible to minimize the surface emission signal and maximize the vegetation signal when using multi-frequency radiometer measurements. Using a radiative transfer model (ω–τ model), a linear relationship between the brightness temperatures observed at two adjacent radiometer frequencies can be derived. The intercept and slope of this linear function depend only on vegetation properties and can be used as vegetation indices. These can be derived from the dual-frequency and dual-polarization satellite measurements under assumption that there is no significant impact of the polarization dependence on the vegetation signals. To demonstrate the potential of the new microwave vegetation indices, we compared them with the Normalized Difference of Vegetation Index (NDVI) derived using MODIS the optical sensor at continental and global scales. The major purpose of this paper is to describe the concept and techniques involved in these newly developed MVIs and explore the general relationships between these MVIs and NDVI. In this first investigation, the information content of NDVI and MVIs, both are qualitative indices, was compared by examining its response in global pattern and to seasonal vegetation phenology. The results indicate that the MVIs can provide significant new information since the microwave measurements are sensitive not only to the leafy part of vegetation properties but also to the properties of the overall vegetation canopy when the microwave sensor can “see” through it. In combination with conventional optical sensor derived vegetation indices, they provide a possible complementary dataset for monitoring global short vegetation and seasonal phenology from space. [Copyright &y& Elsevier]

Passive microwave remote sensing has been recognized as a potential method for measuring soil moisture. Combined with field observations and hydrological modeling brightness temperatures can be used to infer soil moisture states and fluxes in real time at large scales. However, operationally acquiring reliable soil moisture products from satellite observations has been hindered by three limitations: suitable low-frequency passive radiometric sensors that are sensitive to soil moisture and its changes; a retrieval model (parameterization) that provides operational estimates of soil moisture from top-of-atmosphere (TOA) microwave brightness temperature measurements at continental scales; and suitable, large-scale validation datasets. In this paper, soil moisture is retrieved across the southern United States using measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) X-band (10.65 GHz) radiometer with a land surface microwave emission model (LSMEM) developed by the authors. Surface temperatures required for the retrieval algorithm were obtained from the Variable Infiltration Capacity (VIC) hydrological model using North American Land Data Assimilation System (NLDAS) forcing data. Because of the limited information content on soil moisture in the observed brightness temperatures over regions characterized by heavy vegetation, active precipitation, snow, and frozen ground, quality control flags for the retrieved soil moisture are provided. The resulting retrieved soil moisture database will be available through the NASA Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) at a 1/8° spatial resolution across the southern United States for the 5-yr period of January 1998 through December 2002. Initial comparisons with in situ observations obtained from the Oklahoma Mesonet resulted in seasonal correlation coefficients exceeding 0.7 for half of the time covered by the dataset. The dynamic range of the satellite-derived soil moisture dataset is considerably higher compared to the in situ data. The spatial pattern of the TMI soil moisture product is consistent with the corresponding precipitation fields. [ABSTRACT FROM AUTHOR]