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Improving the spatial resolution of microwave satellite soil moisture (SM) products is important for various applications. Most of the downscaling methods that fuse optical/thermal and microwave data rely on remotely sensed land surface temperature (LST) or LST-derived SM indexes (SMIs). However, these methods suffer from the problems of “cloud contamination”, “decomposing uncertainty”, and “decoupling effect”. This study presents a new downscaling method, referred to as DSCALE_mod16, without using LST and LST-derived SMIs. This model combines MODIS ET products and a gridded meteorological data set to obtain Land surface Evaporative Efficiency (LEE) as the main downscaling factor. A cosine-square form of downscaling function was adopted to represent the quantitative relationship between LEE and SM. Taking the central part of the United States as the case study area, we downscaled SMAP (Soil Moisture Active and Passive) SM products with an original resolution of 36km to a resolution of 500m. The study period spans more than three years from 2015 to 2018. In situ SM measurements from three sparse networks and three core validation sites (CVS) were used to evaluate the downscaling model. The evaluation results indicate that the downscaled SM values maintain the spatial dynamic range of original SM data while providing more spatial details. Moreover, the moisture mass is conserved during the downscaling process. The downscaled SM values have a good agreement with in situ SM measurements. The unbiased root-mean-square errors (ubRMSEs) of downscaled SM values is 0.035 m3/m3 at Fort Cobb, 0.026 m3/m3 at Little Washita, and 0.055 m3/m3 at South Fork, which are comparable to ubRMSEs of original SM estimates at these three CVS.

The joint use of spaceborne microwave sensor data and Cyclone Global Navigation Satellite System (CYGNSS) data to observe tropical cyclones (TCs) is presented in this paper. The Soil Moisture Active and Passive (SMAP) radiometer was taken as an example of a spaceborne microwave sensor, and its data and the CYGNSS data were fused to fix the center of a TC and to measure the maximum wind speed around the TC inner core. This process included data preprocessing, image fusion, determination of the TC center position, and the estimation of the TC’s intensity. For all of the observed hurricanes, the experimental results demonstrated that the proposed method obtains a more complete structure of the TC and can measure the surface wind speed around the TC inner core at more frequent intervals compared to the case where the SMAP radiometer data or the CYGNSS data are employed alone. Furthermore, when comparing the TC tracks obtained by the proposed method with the best tracks provided by the National Hurricane Center (NHC), we found that the mean absolute error values ranged between 18.4 and 46 km, the standard deviation varied between 15.1 and 28.2 km, and both of these were smaller than the values obtained by only using the CYGNSS data. In addition, when comparing the maximum wind speed around the TC inner core obtained by the proposed method with the best track peak winds estimated by the NHC, we found that the mean absolute error values ranged between 7.7 and 15.7 m/s, the root-mean-square difference values varied between 8.6 and 18 m/s, the correlation coefficients varied between 0.1782 and 0.9877, the bias values varied between −8.5 and 4.5 m/s, and all of these values were smaller in most cases, than those obtained by only using the CYGNSS data.

Global Navigation Satellite Systems-Reflectometry (GNSS-R) has shown unprecedented advantages to sense Soil Moisture Content (SMC) with high spatial and temporal coverage, low cost, and under all-weather conditions. However, implementing an appropriated physical basis to estimate SMC from GNSS-R is still a challenge, while previous solutions were only based on direct comparisons, statistical regressions, or time-series analyses between GNSS-R observables and external SMC products. In this paper, we attempt to retrieve SMC from GNSS-R by estimating the dielectric permittivity from Fresnel reflection coefficients. We employ Cyclone GNSS (CYGNSS) data and effectively account for the effects of bare soil roughness (BSR) and vegetation optical depth by employing ICESat-2 (Ice, Cloud, and land Elevation Satellites 2) and/or SMAP (Soil Moisture Active Passive) products. The tests carried out with ICESat-2 BSR data have shown the high sensitivity in SMC retrieval to high BSR values, due to the high sensitivity of ICESat-2 to land surface microrelief. Our GNSS-R SMC estimates are validated by SMAP SMC products and the results provide an R-square of 0.6, Root Mean Squared Error (RMSE) of 0.05, and a zero p-value, for the 4568 test points evaluated at the eastern region of China during April 2019. The achieved results demonstrate the optimal capability and potential of this new method for converting reflectivity measurements from GNSS-R into Land Surface SMC estimates.

In the last decade, technological advances led to the launch of two satellite missions dedicated to measure the Earth’s surface soil moisture (SSM): the ESA’s Soil Moisture and Ocean Salinity (SMOS) launched in 2009, and the NASA’s Soil Moisture Active Passive (SMAP) launched in 2015. The two satellites have an L-band microwave radiometer on-board to measure the Earth’s surface emission. These measurements (brightness temperatures TB) are then used to generate global maps of SSM every three days with a spatial resolution of about 30–40 km and a target accuracy of 0.04 m3/m3. To meet local applications needs, different approaches have been proposed to spatially disaggregate SMOS and SMAP TB or their SSM products. They rely on synergies between multi-sensor observations and are built upon different physical assumptions. In this study, temporal and spatial characteristics of six operational SSM products derived from SMOS and SMAP are assessed in order to diagnose their distinct features, and the rationale behind them. The study is focused on the Iberian Peninsula and covers the period from April 2015 to December 2017. A temporal inter-comparison analysis is carried out using in situ SSM data from the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) to evaluate the impact of the spatial scale of the different products (1, 3, 9, 25, and 36 km), and their correspondence in terms of temporal dynamics. A spatial analysis is conducted for the whole Iberian Peninsula with emphasis on the added-value that the enhanced resolution products provide based on the microwave-optical (SMOS/ERA5/MODIS) or the active–passive microwave (SMAP/Sentinel-1) sensor fusion. Our results show overall agreement among time series of the products regardless their spatial scale when compared to in situ measurements. Still, higher spatial resolutions would be needed to capture local features such as small irrigated areas that are not dominant at the 1-km pixel scale. The degree to which spatial features are resolved by the enhanced resolution products depend on the multi-sensor synergies employed (at TB or soil moisture level), and on the nature of the fine-scale information used. The largest disparities between these products occur in forested areas, which may be related to the reduced sensitivity of high-resolution active microwave and optical data to soil properties under dense vegetation.

Soil moisture (SM) is crucial for identifying agricultural drought, but due to the limited availability of large scale and high-resolution SM data, drought indices based on hydro-meteorological variables are commonly used. Recent advancements in microwave remote sensing, such as the Soil Moisture Active Passive (SMAP) satellite, provide global daily SM at 9-km spatial resolution making it suitable for agricultural drought monitoring. However, while SMAP offers relatively high spatial resolution, it lacks long-term records (available since April 2015), whereas other long-term satellite products have coarser spatial resolution. So, this study evaluates the potential of short-term satellite SM data, specifically SMAP-SM, for agricultural drought characterization using two SM-based indices: Soil Water Deficit Index (SWDI) and Soil Moisture Deficit Index (SMDI). The Godavari basin is selected as study area, which is not so well gauged for SM data, and the agricultural drought in the basin is assessed from 2016 to 2021. The results revealed that both SWDI and SMDI indices effectively captured inter-annual/seasonal variations, demonstrating their robustness in comparison to precipitation-based indices rainfall anomalies (RA) and Standardized Precipitation Index (SPI). Spatial analysis reveals that western basin consistently experiences drought conditions, while the eastern region remains relatively wet. The drought area ratio (DAR) analysis across agro-ecological zones (AEZs) of basin reveals that SWDI is more sensitive to severe and extreme droughts, whereas SPI is more responsive to mild and moderate droughts. Zone-wise DAR showed SWDI and SMDI identified distinct drought conditions across all AEZs, whereas SPI and RA showed evenly distributed drought levels across AEZs, underscoring their broader, less soil-specific focus. These findings emphasize the potential of short-term satellite-based SM, as well as SM-derived indices in advancing agricultural drought characterization, offering valuable insights for policymakers in developing region-specific mitigation strategies and improving drought preparedness in other poorly gauged river basins.

China is frequently subjected to local and regional drought disasters, and thus, drought monitoring is vital. Drought assessments based on available surface soil moisture (SM) can account for soil water deficit directly. Microwave remote sensing techniques enable the estimation of global SM with a high temporal resolution. At present, the evaluation of Soil Moisture Active Passive (SMAP) SM products is inadequate, and L-band microwave data have not been applied to agricultural drought monitoring throughout China. In this study, first, we provide a pivotal evaluation of the SMAP L3 radiometer-derived SM product using in situ observation data throughout China, to assist in subsequent drought assessment, and then the SMAP-Derived Soil Water Deficit Index (SWDI-SMAP) is compared with the atmospheric water deficit (AWD) and vegetation health index (VHI). It is found that the SMAP can obtain SM with relatively high accuracy and the SWDI-SMAP has a good overall performance on drought monitoring. Relatively good performance of SWDI-SMAP is shown, except in some mountain regions; the SWDI-SMAP generally performs better in the north than in the south for less dry bias, although better performance of SMAP SM based on the R is shown in the south than in the north; differences between the SWDI-SMAP and VHI are mainly shown in areas without vegetation or those containing drought-resistant plants. In summary, the SWDI-SMAP shows great application potential in drought monitoring.

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.