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High-resolution, large-scale near-surface soil moisture information is critical for many hydrology and climate applications, yet traditional radars and radiometers often fall short of providing information at the required spatial and temporal scales. This study proposes a method for fusing Soil Moisture Active Passive (SMAP) data with spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) measurements from the Cyclone GNSS (CYGNSS) and Spire near-nadir GNSS-R missions, generating soil moisture products at 3- and 9-km resolutions. GNSS-R uses L-band signals that are sensitive to changes in biogeophysical parameters, such as soil moisture. A linear regression-based algorithm retrieves soil moisture from both CYGNSS and Spire data, which, despite showing biases relative to one another, exhibit similar sensitivities to soil moisture variations. The 9-km fused product integrates observed and interpolated GNSS-R estimates to complement daily SMAP 9-km maps, while the 3-km product refines GNSS-R retrievals using available SMAP data. This approach is validated against in situ measurements and the SMAP/Sentinel 3-km product over mainland Australia for 2021. Our findings indicate a median unbiased root-mean-square error (ubRMSE) of 0.049 cm3cm−3 for the 3-km product and 0.054 cm3cm−3 for the 9-km product, both of which are comparable to SMAP's ubRMSE of 0.054 cm3cm−3. The fused products provide daily soil moisture retrievals with accuracy comparable to SMAP while significantly improving temporal resolution. The 3-km product, in particular, captures finer spatial variability, offering a more detailed representation of soil moisture dynamics.

Current research often improves the accuracy of global navigation satellite system-reflectometry soil moisture (SM) inversion by incorporating auxiliary data, which somewhat limits its potential for practical application. To reduce the reliance on auxiliary data, this article presents a cyclone global navigation satellite system SM inversion method based on the time-constrained and spatially explicit artificial intelligence (TCSE-AI) model. The method initially segments data into multiple subsets through time constraints, thus limiting irrelevant factors to a relatively stable state and endowing the data with temporal attributes. Then, it incorporates raster data spatial information, integrating the potential spatiotemporal distribution characteristics of the data into the SM inversion model. Finally, it constructs SM inversion models using machine learning methods. The experimental results indicate that the TCSE-AI SM inversion model based on the XGBoost and random forest model architectures achieved favorable results. Their monthly SM inversion results for 2022 were compared with the soil moisture active passive (SMAP) products, with Pearson's correlation coefficients (R) all greater than 0.91 and root-mean-square errors (RMSEs) less than 0.05 cm3/cm3. Subsequently, this study used the XGBoost method as an example for validation with in situ data and conducted an interannual SM cross-inversion experiment. From January to June 2022, the R between SM inversion results in the study area and in situ SM was 0.788, with an RMSE of 0.063 cm3/cm3. The interannual cross-inversion experimental results, except for cases of missing data over multiple days, indicate that the TCSE-AI model generally achieved the accurate estimates of SM. Compared with SMAP SM, the R was all greater than 0.8, with a maximum RMSE of 0.072 cm3/cm3, and they showed satisfactory consistency with the in situ data.

The estimation of soil moisture (SM) utilizing the data from the Cyclone Global Navigation Satellite System (CYGNSS) has attracted significant interest in recent times. However, CYGNSS’ inherent capability of variable resolution has not been fully exploited, often resulting in a loss of detailed spatial information in the raw data. In this paper, a novel downscaling scheme tailored for CYGNSS data is introduced to yield a “self-adjusting adaptive resolution” SM product, which dynamically varies the resolution of SM estimates based on the available CYGNSS data resolution at different geographic locations. Initially, a direct quantitative relationship is established between the key CYGNSS parameters reflecting SM variations and the reference SM from the Soil Moisture Active Passive (SMAP) mission with a coarse resolution of 36 km. This model is then applied to CYGNSS observations with resolutions down to 3 km to generate high-resolution, self-adjusting SM estimates that better conserve the fine-scale information linked to the original CYGNSS data. Extensive experimental results with error ratio diagrams show that the advanced geographically weighted regression (GWR)-based SM estimation method outperforms other competing estimation models and better retains localized spatial relationships and patterns. This study underscores the potential of CYGNSS as a novel and robust independent data source capable of delivering fine-resolution SM estimations by harnessing its unique multiresolution observational capability.

The soil moisture products derived from the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions have garnered widespread adoption in drought surveillance, meteorological/climatic forecasting, and hydrological investigations. Nevertheless, satellite platforms inherently suffer from coarse spatial resolutions (approximately 43 km), stemming from constraints on antenna dimensions, thereby posing challenges in leveraging their data for agricultural and ecological endeavors that necessitate meter-scale soil moisture maps. This research endeavor innovatively employed a Portable L-band Radiometer (PoLRa), leveraging microstrip patch array antenna technology, to facilitate portable and cost-effective soil moisture monitoring within the context of the Hulunbeier Prairie Experiments in China. Three distinct soil moisture datasets were procured utilizing Scheme I, a PoLRa-based default iteration of the tau-omega semi-empirical model; Scheme II, a brightness temperature forward simulation akin to the CMEM (Community Microwave Emission Model) framework; and Scheme III, a regression-based approach. The findings are: 1. Employing the brightness temperature data as input, the CMEM-inspired schemes achieve soil moisture retrieval with an RMSE (Root Mean Square Error) of approximately 0.06 cm3/cm3, whereas the regression model between retrieved and observed data manifests a linear bias. 2. The CMEM forward simulation scheme outperforms the tau-omega model but necessitates more intricate modules for the retrieval process. 3. Both in terms of linear bias and RMSE, the regression-based scheme exhibits the poorest performance. This study anticipates enhancing the applicability of soil moisture remote sensing devices and methodologies across diverse disciplines, including agriculture, meteorology, and soil science, by advancing the precision and accessibility of soil moisture monitoring at finer scales.

In this article, we present the first demonstration of the FengYun-3E (FY3E) Global Navigation Satellite System Occultation Sounder II-Reflectometry (GNOS-R) payload's capacity to detect near-surface soil freeze/thaw (F/T) states. This study offers an initial analysis of the F/T retrieval algorithm applied to data collected from the Arctic Circle, underscoring the GNOS-R's potential to deliver long-term near-surface soil F/T products. Data for the period extending from the launch day of GNOS-R (Day of Year (DOY) 179, 2021) to DOY 270 in 2022 were analyzed using the surface reflectivity (SR) ratio factor to discriminate F/T variations. Comparisons were made with soil moisture active passive (SMAP) F/T products, serving as an auxiliary analysis. We found a strong consistency between SR ratio factor and SMAP F/T values, with the accuracy of the F/T retrieval algorithm exceeding 60%. These findings corroborate the efficacy of the GNOS-R payload aboard FY3E in monitoring F/T patterns at higher latitudes, specifically, the Arctic Circle. The outcomes of this study will be beneficial for future F/T detection efforts using spaceborne Global Navigation Satellite System-Reflectometry payloads.

In cold regions, the coexistence and interconversion of liquid water and ice in frozen soils have important implications for energy partitioning and surface runoff at the Earth's surface. Passive microwave remote sensing is crucial for the global monitoring of soil moisture (SM). However, current research on SM focuses mainly on unfrozen soil conditions. Limited studies have been conducted on variations in soil liquid water content throughout the freezing season. This article investigated the potential use of brightness temperature observations from the Soil Moisture Active Passive (SMAP) satellite for retrieving all-season liquid SM. The single-channel algorithm and the Zhang-Zhao dielectric model, which was specifically developed for freezing and thawing soils, achieved successful retrieval of liquid SM in both frozen and thawed soils, even when snow cover was present. The results indicate improved spatial coverage (during winter) and consistent spatial patterns in SM compared with the SMAP products. Validation at 17 SM networks suggests that the retrieved all-season liquid SM effectively captures the dynamic characteristics of each region with an average bias of 0.011 m3/m3, an average unbiased root mean square error of 0.056 m3/m3, and an average correlation coefficient of 0.76. The additional retrieval of unfrozen water content during the freezing season would enhance the monitoring and understanding of the hydrological cycle and energy balance in cold regions.

Global Navigation System Reflectometry (GNSS-R) is a microwave remote sensing technology that enables Earth observation by receiving GNSS signals reflected from the Earth's surface. The Cyclone Global Navigation Satellite System (CYGNSS) constellation is a satellite system that uses GNSS-R technology with high temporal resolution and has been a popular data source in soil moisture retrieval in recent years. However, the constant movement of GNSS transmitters and GNSS-R satellites results in potentially chaotic and random observations of the Earth's surface, with many unevenly distributed gaps in the observed data. In this paper, a gap-filling method based on spatial autocorrelation is proposed to interpolate the gaps within these observation datasets, with SM being estimated post-interpolation. The sample set for the model comprises points surrounding the interpolation target, with modeling conducted considering factors of spatial weighting to estimate values at the interpolation target. Different autocorrelation-based gap-filling methods using CYGNSS data can achieve good estimation accuracy, and the data coverage after interpolation is on average 1.8 times greater than before interpolation. The gap-filling method using XGBoost achieves the best performance and offers the highest accuracy in SM estimation, with an average correlation coefficient of 0.8445, and an average RMSE of 0.0457 m3/m3. The gap-filling approach can significantly enhance data coverage and facilitate the filling of daily gaps in CYGNSS data with all maintaining high SM estimation accuracy. The estimation of daily missing values using CYGNSS data can fully exploit the embedded surface features in the data's fine resolution and can provide high-resolution SM retrieval.

Soil moisture (SM) is an important driver for forest ecosystems, creating a need for globally extensive SM information that can only be achieved with satellite-based sensors and/or process-based model. However, the reliability of remotely sensed or modeled SM data in forests is poorly understood due to a lack of suitable validation sites and interference with remote sensing caused by vegetation water content. Here, we examine three multiyear SM products: remotely sensed surface (0–5 cm) SM from combined soil moisture active passive (SMAP) and Sentinel-1 observations (SMAP/Sentinel); the SMAP Level-4 surface (0–5 cm) and root-zone (0–1 m) SM data assimilation product (SMAP-L4); and simulated surface (0–10 cm) and root-zone (0–1 m) SM from the North American land data assimilation system (NLDAS). These estimates were compared with in situ measurements from 39 National Ecological Observatory Network sites throughout the U.S. At 21 unforested sites, the performance of the three products was similar for surface SM, and all three were able to track temporal changes in surface SM. The performance of the three products declined at 18 forested sites; however, while the performance declined modestly for SMAP-L4 and NLDAS, SMAP/Sentinel performance declined so much that it was largely unable to track changes in surface SM. The SMAP-L4 and NLDAS products also reliably captured temporal changes in root-zone SM at both forested and unforested sites. Our findings indicate that both SMAP-L4 and NLDAS can be used to track surface and root-zone SM changes in forests (unbiased root-mean-square deviation: 0.03–0.06 m3 m−3).

Soil moisture (SM) is a critical parameter in understanding the Earth's hydrological cycle and managing water resources. Remote sensing instruments, such as Advanced SCATterometer (ASCAT), can provide valuable long-term SM. However, compatibility issues may arise when integrating ASCAT SM retrieval with another retrieval, such as soil moisture active passive (SMAP), a high-quality microwave radiometer-based SM retrieval. In this study, we propose a novel image-to-image translation approach based on the U-Net architecture to convert ASCAT SM data into the format of SMAP (ASCAT2SMAP). The resulting SM from the ASCAT2SMAP was evaluated using temporally separated SMAP data and independent in-situ SM measurement from the International Soil Moisture Network (ISMN). In the separately divided test periods, ASCAT2SMAP showed good agreement with SMAP with R of 928, ubRMSD of 0.043 $\text{m}^{3}/\text{m}^{3}$, and bias of 0.002 $\text{m}^{3}/\text{m}^{3}$. When evaluating ASCAT2SMAP with ISMN data, it showed a better agreement than ASCAT and more similar metrics with SMAP. Moreover, we found that the ASCAT2SMAP is more robust to a problem of subsurface scattering than the original ASCAT SM. When simulating V-polarized brightness temperature from ASCAT2SMAP SM, it showed good agreement with ubRMSD of 5.602 K and bias of −0.135 K. Our results are expected to provide a valuable perspective preceding to creation of harmonized SM datasets from different sensors, contributing to improved data integration and analysis in the field of geoscience and remote sensing.

The Soil Moisture Active Passive (SMAP) product, which delivers spatially continuous soil moisture (SM) data with reliable accuracy, has been widely used in ecologically and hydrologically related studies. However, the active radar of SMAP failed after operation for approximate three months, leading to the loss of the original 3 km (called A3) and 9 km (called AP9) SMAP SM dataset afterwards. Since merely the 36 km SMAP dataset (called P36 in this research) is available up to now, the relatively coarse spatial resolution is difficult to meet the requirement for monitoring at the regional scale. In this paper, the 36 km SMAP SM dataset in the conterminous United States (CONUS) was downscaled to 3 km after the failure of the active radar. To reduce the great uncertainty in downscaling (i.e., a process from 36 km to 3 km directly), a stepwise strategy was proposed. Specifically, our currently published 9 km SMAP SM dataset (called VIP9, from April 2015 to December 2019) was used as input for downscaling, which was downscaled to 3 km by fusion with auxiliary data (e.g., Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI)) at fine spatial resolution based on area-to-point regression kriging (ATPRK). Furthermore, a random forest (RF)-based correction process was developed to enhance the quality of the downscaled SM dataset by fusing with the ground-based SM data (i.e., in-situ data). As a result, a 16-day composited SM dataset at 3 km spatial resolution was produced from 2015 to 2019, with a mean correlation coefficient (CC) and unbiased root mean square error (ubRMSE) in temporal (spatial) validation of 0.888 (0.912) and 0.021 (0.023), respectively. Experimental results demonstrated that the AP9 dataset (although only available in the three months) is helpful to increase the accuracy of downscaling. Moreover, the difference between satellite- and ground-based SM data can be further reduced through the correction process. The produced 3 km SMAP SM dataset has great potential to extend the defunct A3 data and support related studies. The contributions of the paper are twofold, including the development of the stepwise method for downscaling and the generation of the 3 km SMAP SM dataset in the CONUS during 2015–2019.

The aim of this study was to develop an improved thin sea ice thickness (SIT) retrieval algorithm in the Arctic Ocean from the Soil Moisture Ocean Salinity and Soil Moisture Active Passive L-band radiometer data. This SIT retrieval algorithm was trained using the simulated SIT from the cumulative freezing degree days model during the freeze-up period over five carefully selected regions in the Beaufort, Chukchi, East Siberian, Laptev and Kara seas and utilized the microwave polarization ratio (PR) at incidence angle of 40°. The improvements of the proposed retrieval algorithm include the correction for the sea ice concentration impact, reliable reference SIT data over different representative regions of the Arctic Ocean and the utilization of microwave polarization ratio that is independent of ice temperature. The relationship between the SIT and PR was found to be almost stable across the five selected regions. The SIT retrievals were then compared to other two existing algorithms (i.e., UH_SIT from the University of Hamburg and UB_SIT from the University of Bremen) and validated against independent SIT data obtained from moored upward looking sonars (ULS) and airborne electromagnetic (EM) induction sensors. The results suggest that the proposed algorithm could achieve comparable accuracies to UH_SIT and UB_SIT with root mean square error (RMSE) being about 0.20 m when validating using ULS SIT data and outperformed the UH_SIT and UB_SIT with RMSE being about 0.21 m when validatng using EM SIT data. The proposed algorithm can be used for thin sea ice thickness (<1.0 m) estimation in the Arctic Ocean and requires less auxiliary data in the SIT retrieval procedure which makes its implementation more practical. [ABSTRACT FROM AUTHOR]

Fractional water (FW) correction of satellite microwave brightness temperature (Tb) observations is a prerequisite for accurate soil moisture (SM) mapping over mixed land and water areas. Here, we evaluated the FW impacts on NASA Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) SM retrievals using two water masks including (a) the NASA Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Water Mask version 6 (MOD44W) multi-year (2015–2019) water record and (b) the Ocean Discipline Processing System (ODPS) water mask previously used for SMAP global operational Tb and SM processing. The MOD44W and ODPS data were first compared with the European Commission's Joint Research Centre (JRC) Landsat-based water record. MOD44W showed major improvements in land/water classifications relative to the ODPS, with producer accuracy increasing from 50.02% to 95.02%, and user accuracy from 53.93% to 91.73% for water pixels. For assessing the FW impacts on SM retrievals, the same single channel V-polarization (SCA-V) algorithm was applied to SMAP Tb datasets corrected using ODPS and MOD44W water masks separately. MOD44W showed overall greater FW values (mean increase of 0.006) relative to the ODPS, leading to relatively drier SM retrievals (mean decrease: −0.012 m3/m3). Additional comparisons with globally distributed SM measurements confirmed consistently lower SM retrieval biases (mean decrease 0.04 m3/m3) and higher correlations (mean increase 0.06) of the MOD44W-based results relative to those based on the ODPS. Our results revealed non-negligible SM retrieval uncertainty introduced from the underlying ancillary FW data for areas with substantial water presence (e.g. FW>0.01).

A high spatial and temporal resolution global soil moisture product is essential for understanding hydrologic and meteorological processes and enhancing agricultural applications. Global navigation satellite system (GNSS) signals at L-band frequencies that reflect off the land surface can convey high-resolution land surface information, including surface soil moisture (SM). Cyclone global navigation satellite system (CYGNSS) constellation generates Delay-Doppler Maps (DDMs) that contain important Earth surface information from GNSS reflection measurements. DDMs are affected by soil moisture and other factors such as complex topography, soil texture, and overlying vegetation. Including entire DDM information can help reduce the uncertainty of SM estimation under different conditions along with remotely sensed geophysical data. This work extends our previously developed deep learning (DL) framework to a global scale by utilizing processed DDM measurements (analog power, effective scattering area, and bistatic radar cross-section) and ancillary data (elevation, slope, water percentage, soil properties, and vegetation water content). The DL model is trained and evaluated using the Soil Moisture Active Passive (SMAP) mission's enhanced SM products at 9-km resolution. This study comprehensively evaluates the DL model against publicly available CYGNSS-based SM products at a quasi-global scale. In addition to the typical comparison against in-situ measurements, a robust triple collocation technique is used to evaluate the DL-based SM product and other CYGNSS-derived SM products.

In this paper, we investigated the vertical distribution characteristics of surface soil moisture based on ISMN (International Soil Moisture Network) multilayer in situ data (5, 10, and 20 cm; 2, 4, and 8 in) and performed comparisons between the in situ data and four microwave satellite remote sensing products (SMOS L2, SMOS-IC, SMAP L2, and SMAP L4). The results showed that the mean soil moisture difference between layers can be −0.042~−0.024 (for the centimeter group)/−0.067~−0.044 (for the inch group) m3/m3 in negative terms and 0.020~0.028 (for the centimeter group)/0.036~0.040 (for the inch group) m3/m3 in positive terms. The surface soil moisture was found to have very significant stratification characteristics, and the interlayer difference was close to or beyond the SMOS and SMAP 0.04 m3/m3 nominal retrieval accuracy. Comparisons revealed that the satellite retrievals had a higher correlation with the field measurements of 5 cm/2 in, and SMAP L4 had the smallest difference with the in situ data. The mean difference caused by using 10 cm/4 in and 20 cm/8 in in situ data instead of the 5 cm/2 in data could be about −0.019~−0.018/−0.18~−0.015 m3/m3 and −0.026~−0.023/−0.043~−0.039 m3/m3, respectively, meaning that there would be a potential depth mismatch in the data validation. [ABSTRACT FROM AUTHOR]

Spaceborne L-band data have the potential to monitor flooded and irrigated areas. However, further studies are needed to assess in real cases the impact of flood-irrigated crops on soil moisture and ocean salinity (SMOS) and soil moisture active passive (SMAP) surface soil moisture (SSM) data. This letter demonstrates the ability of SMOS/SMAP SSM retrievals to quantify the fraction of flood-irrigated areas at the seasonal scale and at a 25-km resolution in the Telangana State in southern India. Over irrigated areas, both SMOS level 3 (L3) SSM and SMAP L3 enhanced SSM products present a bimodal annual cycle, with a peak of SSM during the monsoon (wet) season corresponding to rainfall and irrigation, and a peak during the dry season due to irrigation activities solely. The second peak is absent or has a very small amplitude in areas where rice represents a small fraction (typically below 5%–10%). More importantly, the amplitude of the second SSM peak is significantly correlated with the rice cover fraction within $25\times25$ km2 pixels ($R = 0.81$ for SMOS and 0.77 for SMAP), showing its potential to assess crop fraction and hence the water used for irrigation. The SMOS/SMAP L3 SSM peak during the dry period occurs several months before the harvest, constituting an indicator for rice stocks at the end of the season. However, the irrigation signature is absent from the SMAP level 4 SSM product derived from the assimilation of SMAP brightness temperatures (Tbs) in a land surface model, which indicates that the data assimilation scheme is inefficient to restitute irrigation information. [ABSTRACT FROM AUTHOR]

Soil moisture is an essential variable for understanding water and heat exchanges between land and the atmosphere. Presently, L-band remote sensing technology has been widely employed for routine measurement of soil moisture from space. However, the spatial resolution of L-band soil moisture products obtained from microwave radiometers is too low (dozens of kilometres) to meet the needs of practical applications, such as hydrology modeling, weather forecasts, agricultural applications and water resource management. Therefore, this article proposes a new concept to downscale the soil moisture active passive (SMAP) L-band brightness temperature by using advanced microwave scanning radiometer-2 (AMSR2) X-band TB data, including the time-series regression (TSR) algorithm and two-dimensional discrete wavelet transform algorithm. An intercomparison study was conducted over a semiarid area located in the Shandian river basin with the other two algorithms of the Backus–Gilbert (BG) optimal interpolation and natural neighbor interpolation without using the X-band TB data. The results revealed that the BG algorithm outperformed the NNI, 2D-DWT, and TSR algorithms compared with the original 36-km SMAP TB and airborne 1-km TB data. However, the soil moisture retrievals within one 9-km pixel with 8 soil moisture stations showed that the downscaled L-band TB with X-band data are reliable with lower unbiased root-mean-squared errors compared with resolution-enhanced TB without AMSR2 data.

Soil moisture (SM) information can be routinely obtained from high-quality microwave retrievals at a global scale—such as datasets generated by the Soil Moisture Active Passive (SMAP) mission. In this article, using mutual information (MI) theory, we investigate the efficacy of the downscaled SMAP/Sentinel-1 L2 3-km EASE-Grid SM product (SPL2) for the detection of agricultural drought over northwestern China. The SPL2 is generated by merging SMAP enhanced radiometer data with Sentinel-1 radar observations. To evaluate the efficiency of the SPL2 downscaled algorithm, the SMAP Enhanced L3 Radiometer 9-km EASE-Grid SM product (SPL3) is also utilized as a non-downscaled baseline. Over croplands, comparing normalized MI (NMI) values sampled between the NDVI time series and 3-km Sentinel-1 C-band backscatter coefficient (σ) from SPL2 with NMI values between NDVI and SPL3 radiometer brightness temperature (Tb; resampled to 3-km resolution), we find that the Sentinel-1 σ explains more (3-km) NDVI information than the SPL3 Tb, as the NMI between σvh (σvv) and NDVI is 15% (8%), larger than that between SPL3 Tb and NDVI (5%). However, compared to the SPL3 Tb baseline, the information from downscaled SPL2 Tb on NDVI is reduced by approximately 3%, and the SPL2 algorithm extracts only 7% (10%) of the total information available from both enhanced SPL3 Tb and Sentinel-1 σvh (σvv). Overall, the C-band σ signal provides valuable information for vegetation monitoring due to its frequency advantage. However, additional efforts should be focused on SPL2 merging algorithms to improve the value of the downscaled SPL2 product for agricultural applications.

Groundwater is the most significant freshwater source and plays a critical role in the earth's water and energy balance. The lack of groundwater observations with a high spatiotemporal resolution at a global scale hinders our ability to study and model the environment when shallow groundwater has a direct impact on surface soil moisture. This study aims to estimate the spatial and temporal distributions of shallow groundwater-influenced areas at a global scale. We trained an ensemble machine learning algorithm, using outputs from a variably saturated soil moisture flux model, to identify the shallow groundwater occurrence. Model simulations spanned various climate zones and soil types across the globe. The overall accuracy of the algorithm in reproducing the soil moisture flux model results was 95.5%. We applied the algorithm to spaceborne soil moisture observations retrieved by NASA's SMAP satellite and present a global-scale shallow groundwater map derived from the SMAP observations. The derived global distribution of shallow groundwater identifies wetlands, large riparian corridors, and seasonally inundated lowlands. The results showed that 19% of terrestrial land cover had been influenced by shallow groundwater at some point in time during the period of interest (2015–2018). Temporally, shallow groundwater follows an annual cyclic pattern with 2% to 6% of the land surface being influenced globally. This study shows that SMAP observations could be used in estimating shallow groundwater in high spatiotemporal resolution at a global scale, potentially providing invaluable inputs for modeling and environmental monitoring studies.

L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and, hence, flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity and two distributed hydrologic models with measurements from in situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with Moderate Resolution Imaging Spectroradiometer vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model, and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflowpredictions.

Recently, soil moisture (SM) has been estimated using Cyclone Global Navigation Satellite System (CYGNSS) data. Machine learning (ML) algorithms for CYGNSS SM estimation can minimize unpredictable influences and help improve the accuracy of SM retrieval. However, ML-based CYGNSS SM estimation requires ancillary data from other sources, and thus, the uncertainty, internal errors, and even dependence on external parameters of this process may complicate and limit SM estimation. In this article, a simple land type (LT) digitization strategy that incorporates the idea of classification is proposed with feature optimization to achieve an effective and independent SM retrieval without any other auxiliary data. The input features are chosen from the CYGNSS data themselves, and the corresponding labels (digitized stable LTs) are used in the training stage of the SM estimation model. During the fine-tuning stage, several input features (such as the dielectric constant and incident angle) are compared and selected after optimization to achieve better results. Moreover, the CYGNSS data are gridded at 9 × 9 km to validate the enhanced soil moisture active passive mission SM products at a resolution of 9 km. Only three input variables are adopted for the SM learning model, which are directly derived from the CYGNSS data for independently estimating SM at a high spatial resolution. Powerful performance is achieved by extreme gradient boosting based on a LT digitalization strategy, with root-mean-square error (RMSE) and unbiased RMSE (ubRMSE) values of 0.063 cm3/cm3 and a correlation coefficient (R) of 0.71 for the entire dataset. The performances of different ML learning models for various LTs are presented. The mean ubRMSE and RMSE are 0.041 cm3/cm3 and 0.057 cm3/cm3, respectively. The results demonstrate the effectiveness of the proposed LT digitization strategy for retrieving SM from CYGNSS data with various ML methods and the capability of SM estimation using the CYGNSS product as a new independent source.

Accurate knowledge of soil moisture (SM) is crucial in hydrological, micrometeorological, and agricultural applications; however, the SM estimation is particularly challenging in agricultural regions due to high spatial variability and dynamic vegetation conditions. The need for information about SM conditions is even more evident in developing countries with limited monitoring infrastructure. Satellite SM products are a useful tool as a proxy for SM conditions on the ground, but they need to be evaluated for specific regions. In this study, we assess the quality of the soil moisture active passive (SMAP) SM retrievals at 36, 9, and 3 km in an agricultural region in Central Mexico using in situ measurements during the Terrestrial Hydrology Experiments in Mexico 2018 and 2019. In addition, we provide insights into soil and vegetation parameters in the retrieval algorithms compared to those observed in the region. It was found that the SM spatial variability at the SMAP pixel grids was well represented by upscaled in situ SM measurements (SM$_{\text{up}}$) from five monitoring stations using the soil-weighted averaging and the Voronoï diagrams. Overall, the SMAP SM retrievals are highly correlated with SM$_{\text{up}}$ at all scales, but they estimated wetter conditions and the average root-mean-square difference (RMSD) $>$ 0.045 m$^{3}$/m$^{3}$. The lowest RMSD was obtained for the SM product at 36 km, while the highest RMSD was found for the SM product at 3 km. In addition, the single-channel algorithm using H-polarization provided the lowest RMSD for the products at 36 and 9 km. The main sources of uncertainty in the region may arise from the higher clay fraction used in the SMAP retrieval algorithm, by 13% compared to that observed, and a nonrepresentative characterization of land cover heterogeneity for vegetation water content estimation. The incorporation of in situ values into an SM retrieval algorithm resulted in differences $< $0.04 m$^{3}$/m$^{3}$ between SM estimates and in situ SM for the complete growing season. Particularly, the use of in situ information helped in improving SM estimation when optimizing V- and dual-polarization brightness temperature observations.

Mapping the spatial extent of recently identified englacial hydrological features (i.e., ice slabs and perennial firn aquifers) formed by meters-thick water-saturated firn layers over the percolation facies of the Greenland Ice Sheet using L-band microwave radiometry has recently been demonstrated. However, these initial maps are binary, and do not provide a parameter to estimate the spatial variability in the thickness and volumetric fraction of meltwater stored within the firn pore space. Here, we exploit enhanced-resolution vertical-polarization L-band brightness temperature ($T_{V}^{B}$) imagery (2015–2019) generated using observations collected over Greenland by NASA’s Soil Moisture Active Passive (SMAP) satellite and a simple two-layer L-band brightness temperature model. We map water-saturated firn layers via a “firn saturation” parameter, and interpret our results together with ice slab and perennial firn aquifer spatial extents, estimates of snow accumulation simulated via the Regional Atmospheric Climate Model (RACMOp2.3), and airborne radar surveys collected via NASA’s Operation IceBridge (OIB) campaigns. We find that variable firn saturation parameter values are mapped in lower snow accumulation ice slab areas in western, northern, and northeastern Greenland, where firn is colder and water-saturated firn layers seasonally refreeze as solid-ice. Higher firn saturation parameter values are mapped in higher snow accumulation perennial firn aquifer areas in southeastern, southern, and northwestern Greenland, where firn is near the melting point, and meters-thick water-saturated firn layers exist. Our results have implications for identifying expansive englacial reservoirs that store significant volumes of meltwater in locations that are vulnerable to meltwater-induced hydrofracturing and accelerated outlet glacier flow year-round.

National Aeronautics and Space Administration's Cyclone Global Navigation Satellite System (CYGNSS) mission has gained significant attention within the land remote sensing community for estimating soil moisture (SM) by using the Global Navigation System Reflectometry (GNSS-R) technique. CYGNSS constellation generates Delay-Doppler Maps (DDMs), containing important Earth surface information from GNSS reflection measurements. Previous studies considered only designed features from CYGNSS DDM, whereas the whole DDM image is affected by SM, inundation, and vegetation. This paper presents a deep learning (DL) based framework for estimating SM in the Continental United States by leveraging spaceborne GNSS-R DDM observations provided by the CYGNSS constellation along with remotely sensed geophysical data. A data-driven approach utilizing convolutional neural networks (CNNs) is developed to determine complex relationships between the reflected measurements and surface parameters which can provide improved SM estimation. The model is trained jointly with three types of processed DDM images of analog power, effective scattering area, and bistatic radar cross-section with other auxiliary geophysical information such as elevation, soil properties, and vegetation water content (VWC). The model is trained and evaluated using the Soil Moisture Active Passive (SMAP) mission's enhanced SM products at a 9 km resolution with VWC less than 5 kg/m2. The mean unbiased root-mean-square difference between concurrent CYGNSS and SMAP SM retrievals from 2017 to 2020 is 0.0366 m3/m3 with a correlation coefficient of 0.93 over fivefold cross-validation and 0.0333 m3/m3 with a correlation coefficient of 0.94 over year-based cross-validation at spatial resolution of 9 km and temporal resolution similar to CYGNSS mission.

The Dry Chaco in South America is a semi-arid ecoregion prone to dryland salinization. In this region, we investigated coarse-scale surface soil moisture (SM), soil temperature, soil salinity, and vegetation, using L-band microwave brightness temperature (TB) observations and retrievals from the soil moisture ocean salinity (SMOS) and soil moisture active passive satellite missions, Catchment land surface model (CLSM) simulations, and in situ measurements within 26 sampled satellite pixels. Across these 26 sampled pixels, the satellite-based SM outperformed CLSM SM when evaluated against field data, and the forward L-band TB simulations derived from in situ SM and soil temperature performed better than those derived from CLSM estimates when evaluated against SMOS TB observations. The surface salinity for the sampled pixels was on average only 4 mg/g and only locally influenced the TB simulations, when including salinity in the dielectric mixing model of the forward radiative transfer model (RTM) simulations. To explore the potential of retrieving salinity together with other RTM parameters to optimize TB simulations over the entire Dry Chaco, the RTM was inverted using 10 years of multiangular SMOS TB data and constraints of CLSM SM and soil temperature. However, the latter modeled SM was not sufficiently accurate and factors such as open surface water were missing in the background constraints, so that the salinity retrievals effectively represented a bulk correction of the dielectric constant, rather than salinity per se. However, the retrieval of vegetation, scattering albedo, and surface roughness resulted in realistic values.

Accurate high-spatial-resolution soil moisture content (SMC) datasets are crucial for applications, such as erosion modelling, flood forecasting, and agricultural production. Downscaling is an effective way to convert coarse satellite observations to a finer spatial resolution. Two downscaling approaches were proposed to improve the spatial resolution of the soil moisture active passive (SMAP) radiometer SMC. A downscaling method (method 1) based on the triangular feature space concept was developed to express SMAP L3 SMC as a polynomial function of the Global Land Surface Satellite leaf area index, preprocessed synthetic land surface temperature, and microwave polarization difference index. A second downscaling method (method 2) based on the simulated datasets was developed to express high-resolution SMC as a function of coarse-resolution SMC and sentinel-1 synthetic aperture radar observations. Downscaled SMC (1 km) was evaluated by the in situ measurements and compared by the SMAP L2 active and passive SMC product in agroforestry areas, China. The results showed that the two downscaling methods could effectively capture the spatial variability of soil moisture at 1-km spatial scales. The root-mean-square error (RMSE) of downscaled SMC for grass, shrub, and forestland is 0.052–0.055 cm3 cm−3, 0.063–0.069 cm3 cm−3, and 0.067–0.073 cm3 cm−3, respectively. The accuracies of method 1, method 2, and SMAP L2 SMC in the grassland were higher than those in the shrubland and forestland. Overall, the R and RMSE between the downscaled soil moisture from method 1, method 2, and SMAP L2 SMC were 0.613, 0.626, and 0.619 and 0.051 cm3 cm−3, 0.041 cm3 cm−3, and 0.45 cm3 cm−3, respectively. The active and SMAP passive microwave combination method has great potential for soil moisture downscaling in agroforestry areas in China.

Wildfires are a major concern around the globe because of the immediate impact they have on people's lives, local ecosystems, and the environment. Soil moisture is one of the most important factors that influence wildfire occurrences and spread. However, it is also one of the most challenging hydrological variables to measure routinely and accurately. Therefore, soil moisture is significantly underutilized in operational wildfire risk applications. Thus, the aim here is to use a well-established operational soil moisture product to isolate the soil moisture-fire relationship and assess the utility of using soil moisture as a leading indicator of potential fire risk. We evaluated the value of remotely-sensed soil moisture observations from the soil moisture active passive sensor for monitoring and predicting fire risk in Australia and California. We quantified the relationship between observed fire activity and soil moisture conditions and analyzed the soil moisture conditions for two extreme fire events. Our findings show that fire activity is strongly associated with soil moisture anomalies. Lagged correlation analysis demonstrated that a remote-sensing based soil moisture product could predict fire activity with a 1–2 month lead-time. Soil moisture anomalies consistently decreased in the months preceding fire occurrence, often from normal to drier conditions, according to a spatiotemporal analysis of soil moisture in two extreme fire events. Overall, our findings indicate that soil moisture conditions prior to large wildfires can aid in their prediction and operational satellite-based soil moisture products such as the one used here have real value for supporting wildfire susceptibility and impacts.

Passive remote sensing is a crucial technology for climate studies and Earth science. National Aeronautics and Space Administration's soil moisture active passive (SMAP) is a remote sensing observatory that uses passive microwave radiometer measurements to estimate soil moisture and detect the freeze or thaw state. Despite operating in the protected band of the radio spectrum (1400–1427 MHz), the radiometer's measurements are nonetheless tainted by radio frequency interference (RFI). An increasing number of radio frequency transmissions such as those from air surveillance radars, 5G wireless communications, and unmanned aerial vehicles are contributing to RFI through either out-of-band emissions or operating in-band illegally. Physical modeling to detect RFI globally might prove to be challenging as RFI can be generated from single as well as multiple sources and these can be divided as pulsed or continuous wave RFI. In this study, a deep learning (DL) based RFI detection method is proposed with a novel convolutional neural network framework that can detect different types of RFI on a global scale. This is a data-driven approach where the detection framework learns directly from the SMAP data products to make a decision whether a certain footprint is RFI contaminated or not. SMAP's level 1 A data products containing antenna counts of different raw moments along with Stokes parameters are used in this study to produce spectrograms and level 1B data products containing the quality flags are used to dynamically label those spectrograms. This study's robust DL framework provided the highest accuracy with the raw moments of horizontal polarization (99.99%) to detect RFI globally.

Remote sensing of sea surface salinity (SSS) near land is difficult due to land contamination. In this article, we assess SSS retrieved from the soil moisture active passive (SMAP) mission in coastal region. SMAP SSS products from the Jet Propulsion Laboratory (JPL), and from the remote sensing systems (RSS) are collocated with in situ data collected by saildrones during the North American West Coast Survey. Satellite and saildrone salinity measurements reveal consistent large-scale features: the fresh water (low SSS) assocciated 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 for collocations with SMAP Level 3 (eight days average) between 40 and 100 km from land is 0.51 (0.56) psu for JPL V5 (RSS V4 70 km). This is encouraging for the potential application of SMAP SSS in monitoring coastal zone freshwater particularly where there exists large freshwater variance. We analyze the different land correction approaches independently developed at JPL and RSS using SMAP level 2 matchups. We found that JPL's land correction method is more promising in pushing SMAP SSS retrieval towards land. For future improvement, we suggest implementing dynamic land correction versus the current climatology-based static land correction to reduce uncertainty in estimating land contribution. In level 2 to level 3 processing, a more rigorous quality control may help to eliminate outliers and deliver reliable level 3 products without over-smoothing, which is important in resolving coastal processes such as fronts or upwelling.

Building climate data records of soil moisture (SM) requires computing long time series by merging retrievals from sensors on-board different satellites, which implies to perform a bias correction or rescaling on the original time series. Due to their long time span and high temporal frequency, model data could be used as a common reference for the rescaling. However, avoiding model dependence in observational climate data records is needed for some applications. In this article, the possibility of using as reference remote sensing data from one of the $L$-band sensors specifically designed to measure SM is discussed. Advanced Microwave Scanning Radiometer 2 SM time series were rescaled by matching their cumulative distribution functions (CDFs) to those of Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Global Land Data Assimilation System (GLDAS) NOAH model time series. The CDF computation was investigated as a function of the time series length, finding significant differences from four to nine years. Replacing temporal by spatial variance does not allow us to compute better CDFs from short time series. The rescaled time series show a high correlation ($R>0.8$) to the original ones and a low bias with respect to the reference ($< $0.03 m $^{3}\cdot$ m$^{-3}$). The time series rescaled using several SMOS or SMAP datasets were also evaluated against in situ measurements and show performances similar to or slightly better than those rescaled using the model GLDAS. The impact of random errors and gaps of the observational data into the rescaling was evaluated. These results show that it is actually possible to use $L$-band data as reference to rescale time series from other sensors to build long time series of SM.

Soil Moisture Active Passive (SMAP) mission brightness temperature ($T_{b}$) observations are assimilated into NASA's Catchment Land Surface Model using an ensemble Kalman filter to update simulations of surface and root-zone soil moisture. Different time-series components of the $T_{b}$ observations are assimilated, including anomalies, interannual variations, and high-frequency variations. To optimize the weights that the data assimilation (DA) puts on the observations, the ratio between the uncertainties of modeled and observed $T_{b}$ is approximated using modeled and observed soil moisture uncertainties estimated using triple collocation analysis. In a benchmark experiment, $T_{b}$ observations are assimilated using a spatially constant 4-K observation uncertainty, as in the operational SMAP Level-4 algorithm. All the DA experiments exhibit notable skill improvements in most regions. Improvements are largest for the interannual variations in the simulations of both surface and root-zone soil moisture (mean improvements in terms of Pearson correlation (–) are 0.08 and 0.06, respectively). Anomaly simulations improve similarly (0.07), and improvements in the high-frequency variations are only observed for surface soil moisture simulations (0.06). No notable difference in skill—neither improvement nor deterioration—is observed between the experiments that use optimized observation uncertainty parameters and the 4-K benchmark experiment. This may be explained by the presence of large observation operator errors, which are analytically shown to have the potential to render postupdate uncertainty insensitive to inaccuracies in estimates of the Kalman gain. These results have important implications for the design of soil moisture DA systems, in particular for parameterizing model and observation uncertainties.

A thermal hydraulic disaggregation of soil moisture (THySM) algorithm was implemented to downscale NASA's soil moisture active passive (SMAP) enhanced soil moisture (SM) product to 1 km over the continental United States (CONUS). This algorithm was developed by combining thermal inertia theory with a soil hydraulic-based approach that considers fine-scale SM spatial distribution driven by both heat fluxes and hydraulic conductivity in soils. Relative soil wetness values were estimated using land surface temperature and normalized difference vegetation index for the thermal inertia model and using soil properties for the hydraulic model. The relative soil wetness values at 1 km from both models were then combined by using weighting functions whereby the spatial distribution of SM was governed more by thermal fluxes during times of strong heat transport and infiltration during moisture abundant soil conditions. THySM values were evaluated using in situ SM measurements from SMAP Core Validation Sites (CVS), the US Department of Agriculture Soil Climate Analysis Network, and the National Oceanic and Atmospheric Administration Climate Reference Network over CONUS. THySM shows higher accuracy than the SMAP / Sentinel-1 (SPL2SMAP_S) 1 km SM product when compared to in situ measurements. The accuracy of THySM is 0.048 m3/m3 based on unbiased root mean square error (ubRMSE), outperforming SPL2SMAP_S by 0.01–0.02 m3/m3. The ubRMSE of THySM 1 km SM over the SMAP grassland/rangeland-dominated CVS sites is better than 0.04 m3/m3, which meets the SMAP mission SM accuracy requirement applied at 9 and 36 km.

In August 2020, soil moisture active passive (SMAP) released a new version of its soil moisture and vegetation optical depth (VOD) retrieval products. In this article, we review the methodology followed by the SMAP regularized dual-channel retrieval algorithm. We show that the new implementation generates SM retrievals that not only satisfy the SMAP accuracy requirements, but also show a performance comparable to the single-channel algorithm that uses the V polarized brightness temperature. Due to a lack of in situ measurements we cannot evaluate the accuracy of the VOD. In this article, we show analyses with the intention of providing an understanding of the VOD product. We compare the VOD results with those from SMOS. We also study the relation of the SMAP VOD with two vegetation parameters: tree height and biomass.

The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error 3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the European Space Agency Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio–temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

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 $\tau -\omega $ model with two independent datasets in dual-channel algorithms. However, given correlated satellite vertical and horizontal brightness temperatures (TBs; TBv and ${{\mathrm {TB}}}_{h}$), 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 Soil Moisture Active Passive (SMAP) TBv–TBh linear dependence. The application of these metrics allows determining where the VOD retrievals are robust and reliable. This is a necessary step in supporting the applications of VOD in ecology and hydrology. Results show that regions with mainly nonwoody 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 underdetermined inversions: the L3 dual-channel algorithm (L3-DCA) and the multitemporal dual-channel algorithm (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. [ABSTRACT FROM AUTHOR]

The 2020 'Elucidating the role of clouds-circulation coupling in climate-Ocean-Atmosphere' (EUREC4A-OA) and the 'Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign' (ATOMIC) campaigns focused on improving our understanding of the interaction between clouds, convection and circulation and their function in our changing climate. The campaign utilized many data collection technologies, some of which are relatively new. In this study, we used saildrone uncrewed surface vehicles, one of the newer cutting edge technologies available for marine data collection, to validate Level 2 and Level 3 Soil Moisture Active Passive (SMAP) satellite and Hybrid Coordinate Ocean Model (HYCOM) sea surface salinity (SSS) products in the Western Tropical Atlantic. The saildrones observed fine-scale salinity variability not present in the lower-spatial resolution satellite and model products. In regions that lacked significant small-scale salinity variability, the satellite and model salinities performed well. However, SMAP Remote Sensing Systems (RSS) 70 km generally outperformed its counterparts outside of areas with submesoscale SSS variation, whereas RSS 40 km performed better within freshening events such as a fresh tongue. HYCOM failed to detect the fresh tongue. These results will allow researchers to make informed decisions regarding the most ideal product and its drawbacks for their applications in this region and aid in the improvement of mesoscale and submesoscale SSS products, which can lead to the refinement of numerical weather prediction (NWP) and climate models. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key variable in ecology, environment, agriculture, and hydrology. The Soil Moisture Active Passive (SMAP) satellite provides global SM products with reliable accuracy since 2015. However, significant gaps of SMAP SM appeared over Tibetan Plateau. Considering the important role of the Tibetan Plateau in global climate and environment, it is essential to develop methods to infill the gaps to generate seamless SMAP SM data. To address this issue, we proposed two methods, machine learning and geostatistics technique. For the machine learning technique, we train a Random Forest algorithm which aims to match the output of available SMAP L3 SM using a series of input variables such as SMAP brightness temperature (TBH and TBV) in ascending orbits (6:00 PM local time), surface temperature, MODIS NDVI, land cover, DEM, and other auxiliary data. Then, the established RF estimators were applied to the SMAP brightness temperature from descending orbits (6:00 AM local time) to reconstruct complete SM data over the Tibetan Plateau. For the geostatistics technique, the Ordinary kriging was applied to the available SMAP L3 SM pixels to interpolate complete SM data. To cross-validate the performances of the algorithms, we assume certain areas with available SMAP SM values as missing, and then compared the gap-filling results with the actual ones. The cross-validations show that the gap-filling results from two algorithms were highly correlated to the official SMAP SM products with high coefficients of determination (R2RF = 0.97 and R2OK = 0.85) and low RMSE (RMSERF = 0.015 cm3/cm3 and RMSEOK = 0.036 cm3/cm3). Furthermore, the gap-filling SM data present a better correlation with the Soil Moisture and Ocean Salinity SM data (R = 0.55–0.7) than the Global Land Data Assimilation System simulations (R = 0.18–0.62). The reconstructed SM from RF (R = 0.71) and OK (R = 0.55) algorithms are well related to the Maqu network measurements. Thus, the machine learning and geostatistics algorithms have the potential to reproduce the missing SMAP SM products over the Tibetan Plateau.

Soil moisture influences forest health, fire occurrence and extent, and insect and pathogen impacts, creating a need for regular, globally extensive soil moisture measurements that can only be achieved by satellite-based sensors, such as NASA's soil moisture active passive (SMAP). However, SMAP data for forested regions, which account for ∼20% of land cover globally, are flagged as unreliable due to interference from vegetation water content, and forests were underrepresented in previous validation efforts, preventing an assessment of measurement accuracy in these biomes. Here we compare over twelve thousand SMAP soil moisture measurements, representing 88 site-years, to in situ soil moisture measurements from forty National Ecological Observatory Network (NEON) sites throughout the US, half of which are forested. At unforested NEON sites, agreement with SMAP soil moisture (unbiased RMSD: 0.046 m3 m−3) was similar to previous sparse network validations (which include inflation of the metric due to spatial representativeness errors). For the forested sites, SMAP achieved a reasonable level of accuracy (unbiased RMSD: 0.06 m3 m−3 or 0.053 m3 m−3 after accounting for random representativeness errors) indicating SMAP is sensitive to changes in soil moisture in forest ecosystems. Moreover, we identified that both an index of vegetation water content and canopy height were related to mean difference (MD), which incorporates measurement bias and representativeness bias, and suggests a potential approach to improve SMAP algorithm parameterization for forested regions. In addition, expanding the number and extent of soil moisture measurements at forested validation sites would likely further reduce MD by minimizing representativeness errors.

The L-band radiometer onboard the soil moisture active passive (SMAP) mission is used to retrieve sea surface salinity (SSS) over global ocean. In the Arctic seas, one of the major challenges of SSS remote sensing is the presence of sea ice. This paper proposes a data-driven ice correction (IC) algorithm which extracts emission from the water portion of measured brightness temperature (TB) in scenes mixed with water and ice. Emission of the ice portion was removed based on estimation according to the ice fraction (fice) in the satellite footprint and ice signature derived from surrounding pixels. The IC algorithm is applied to SMAP TB data to obtain TB with IC (TBIC), which are used for SSS retrieval using the standard JPL SMAP CAP processing system. We show that the algorithm is most effective near the ice edge, thereby increasing the fice threshold for possible SSS retrieval to 15% from the current 3% without IC. SMAP SSS are validated using in situ salinity collected during NASA's Ocean Melting Greenland (OMG) mission from 2016 to 2020 along the Greenland coast. The number of collocations between OMG and SMAP daily gridded salinity increased by more than 30% with IC. The statistical analysis shows a similar retrieval accuracy with or without IC, with the standard deviation of the difference between OMG and SMAP of 1.41 psu (with IC) and 1.42 psu (without IC). The bias-adjusted SMAP SSS depicts salinity patterns and gradients around Greenland consistent with OMG measurements.

Long-term surface soil moisture (SM) data are increasingly needed in water budget and energy balance analysis of watersheds. The performance of nine remotely sensed SM products from Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture and Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) missions are evaluated based on observations collected from distributed observation networks in the Heihe River Basin (HRB) of China during 2013 to 2017. Results show that the SMAP Level 3 dual channel algorithm SM retrievals reflect the seasonal SM variations well with high temporal correlations of ∼0.7 and high accuracy within 0.04 m3/m3 in terms of unbiased root mean squared error (ubRMSE) over the grassland in the HRB. The SMOS level 3 SM retrievals present increased underestimation and ubRMSE of ∼0.10 m3/m3 as the vegetation increases. The newly published SMOS Institut National de la Recherche Agronomique–Centre d'Etudes Spatiales de la BIOsphère product in version 2 outperforms the SMOS level 3 product with improved temporal correlation coefficient above 0.4 and lower ubRMSE of ∼0.05 m3/m3. AMSR2 Land Parameter Retrieval Algorithm SM products show extremely large overestimation over the vegetated regions in HRB, especially the C-band products. Drastically high underestimation biases are observed in the Japan Aerospace Exploration Agency AMSR2 SM product. Parameter uncertainty analyses indicate that the different parameterization schemes of vegetation optical depth inputs could be one of the main reasons resulting in the systematic overestimation/underestimation biases in the AMSR2/SMOS/SMAP SM retrievals. The findings aim to provide insights into studies on algorithms refinements and data fusions of SM products in HRB.

The capability and synergistic use of multisource satellite observations for flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. Here, surface fractional water cover (FW) retrievals derived from Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) brightness temperatures were used for flood assessment over southeast Africa during the Cyclone Idai event. We then focused on five subcatchments of the Pungwe basin and developed a machine learning based approach with the support of Google Earth Engine for daily (24-h) forecasting of FW and 30-m inundation downscaling and mapping. The Classification and Regression Trees model was selected and trained using retrievals derived from SMAP and Landsat coupled with rainfall forecasts from the NOAA Global Forecast System. Independent validation showed that FW predictions over randomly selected dates are highly correlated (R = 0.87) with the Landsat observations. The forecast results captured the flood temporal dynamics from the Idai event; and the associated 30-m downscaling results showed inundation spatial patterns consistent with independent satellite synthetic aperture radar observations. The data-driven approach provides new capacity for flood monitoring and forecasts leveraging synergistic satellite observations and big data analysis, which is particularly valuable for data sparse regions.

A soil moisture (SM) disaggregation algorithm based on thermal inertia (TI) theory was implemented to downscale the soil moisture active passive (SMAP) enhanced product (SPL2SMP$\_$E) from 9 to 1 km over the continental United States. The algorithm applies land surface temperature and normalized difference vegetation index from moderate resolution imaging spectroradiometer (MODIS) at higher spatial resolution to estimate relative soil wetness within a coarse SMAP grid-this MODIS-derived relative wetness is then used to produce the downscaled SMAP SM. Results from the algorithm were evaluated in terms of their spatio-temporal coverage and accuracy using in situ measurements from SMAP core validation sites (CVS), the U.S. Department of Agriculture Soil Climate Analysis Network (SCAN), and the National Oceanic and Atmospheric Administration Climate Reference Network (CRN). Results were also compared with the baseline SPL2SMP$\_$E and the SMAP/Sentinel-1 (SPL2SMAP$\_$S) 1 km product. Overall, the unbiased root-mean-square error (ubRMSE) of the disaggregated SM at the CVS using the TI approach is approximately 0.04 $\text{m}^3/\text{m}^3$, which is the SMAP mission requirement for the baseline products. The TI approach outperforms the SMAP/Sentinel SL2SMAP$\_$S 1 km product by approximately 0.02 $\text{m}^3/\text{m}^3$. Over the agriculture/crop areas from SCAN and CRN sparse network stations, the TI approach exhibits better ubRMSE compared to SPL2SMP$\_$E and SPL2SMAP$\_$S by about 0.01 and 0.02 $\text{m}^3/\text{m}^3$, indicating its advantage in these areas. However, a drawback of this approach is that there are data gaps due to cloud cover as optical sensors cannot have a clear view of the land surface.

Errors in soil moisture adversely impact the modeling of land–atmosphere water and energy fluxes and, consequently, near-surface atmospheric conditions in atmospheric data assimilation systems (ADAS). To mitigate such errors, a land surface analysis is included in many such systems, although not yet in the currently operational NASA Goddard Earth Observing System (GEOS) ADAS. This article investigates the assimilation of L-band brightness temperature (Tb) observations from the Soil Moisture Active Passive (SMAP) mission in the GEOS weakly coupled land–atmosphere data assimilation system (LADAS) during boreal summer 2017. The SMAP Tb analysis improves the correlation of LADAS surface and root-zone soil moisture versus in situ measurements by ∼0.1–0.26 over that of ADAS estimates; the unbiased root-mean-square error of LADAS soil moisture is reduced by 0.002–0.008 m3/m3 from that of ADAS. Furthermore, the global land average RMSE versus in situ measurements of screen-level air specific humidity (q2m) and daily maximum temperature (T2mmax) is reduced by 0.05 g/kg and 0.04 K, respectively, for LADAS compared to ADAS estimates. Regionally, the RMSE of LADAS q2m and T2mmax is improved by up to 0.4 g/kg and 0.3 K, respectively. Improvement in LADAS specific humidity extends into the lower troposphere (below ∼700 mb), with relative improvements in bias of 15–25%, although LADAS air temperature bias slightly increases relative to that of ADAS. Finally, the root mean square of the LADAS Tb observation-minus-forecast residuals is smaller by up to ∼0.1 K than in a land-only assimilation system, corroborating the positive impact of the Tb analysis on the modeled land–atmosphere coupling.

High spatiotemporal soil moisture (SM) is essential for many meteorological, hydrological, and agricultural applications and studies. Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) provides a promising opportunity for high-resolution SM retrievals. NASA’s Cyclone Global Navigation Satellite System (CYGNSS) is a recent GNSS-R application that offers relatively high spatial and temporal resolution observations from earth’s surface. However, the quasi-random sampling of land surface by the CYGNSS constellation circumvents obtaining fully observed daily SM predictions at high spatial resolutions. Spatial interpolation techniques may fill this gap and provide a fully covered high-resolution daily SM estimation. However, the spatial interpolation errors need to be assessed when applied to the quasi-random 9-km CYGNSS-based SM estimations. In this article, we conduct interpolation error analysis using the Soil Moisture Active Passive (SMAP) Enhanced L3 Radiometer Global Daily 9-km product, sampled at the CYGNSS observation locations. The results indicate that the overall interpolation error (RMSE) was 0.013 m$^3$ m$^{-3}$ over SMAP’s recommended grids. In addition, sparse CYGNSS SM observations are directly interpolated. The achieved results show that interpolated and observed CYGNSS SM values have similar performance metrics when validated with the SMAP 9-km gridded SM product as well as sparse SM networks.

With recent advances in satellite microwave soil moisture estimation, there is a demand for up-to-date validation of satellite soil moisture products. This article presents a sparse network validation over a humid region within the Laurentian Great Lakes basin for five state-of-the-art satellite soil moisture datasets, including the Soil Moisture and Ocean Salinity (SMOS) Level 2 Soil Moisture User Data Product (MIR_SMUDP2) V650, the Soil Moisture Active Passive (SMAP) Enhanced Level 3 Radiometer Soil Moisture (SPL3SMP_E) Version 4, and the European Space Agency Climate Change Initiative (CCI) Soil Moisture v05.2 (containing the Active, Passive, and Combined sets). Unsurprisingly, the five sets of soil moisture products performed differently. With respect to the unbiased root-mean-squared error (ubRMSE), the CCI Combined product performed best (an average ubRMSE of about 0.04 m3 m−3), whereas the CCI Passive had the lowest performance with an average ubRMSE exceeding 0.10 m3 m−3. Overall, in terms of correlation measure, the SMAP and CCI Combined performed better than other products, with the lowest skill from the SMOS product. The SMAP product performed best in the context of the soil moisture anomaly detection, whereas the SMOS and CCI Passive showed the lowest anomaly correlation with the in situ observations. The validation results provide an important guidance for hydrological and meteorological applications involving satellite soil moisture datasets in the study region or other similar areas.