High-resolution soil moisture (SM) information is essential for regional to global hydrological and agricultural applications. The Soil Moisture Active Passive (SMAP) offers daily global composites of SM at coarse-resolution 9 and 36 km, with data gaps limiting its local application to depict SM distribution in detail. To overcome the aforementioned problem, a downscaling and gap-filling novel approach was adopted, using random forest (RF) and artificial neural network (ANN) algorithms to downscale SMAP SM data, using land-surface variables from moderate-resolution imaging spectroradiometer (MODIS) onboard Aqua and Terra satellites from the years 2018 to 2019. Firstly, four combinations (RF+Aqua, RF+Terra, ANN+Aqua, and ANN+Terra) were developed. Each combination downscaled SMAP SM at a high resolution (1 km). These combinations were evaluated by using error matrices and in situ SM at different scales in the ShanDian River (SDR) Basin. The combination RF+Terra showed a better performance, with a low averaged unbiased root mean square error (ubRMSE) of 0.034 ${\mathrm{m}}^3$/${\mathrm{m}}^3$ and high averaged correlation (R) of 0.54 against the small-, medium-, and large-scale in situ SM. Secondly, the impact of various land covers was examined by using downscaled SMAP and in situ SM. Vegetation attenuation makes woodland more error-prone and less correlated than grassland and farmland. Finally, the RF+Terra and ANN+Terra combinations were selected for their higher accuracy in gap filling of downscaled SMAP SM. The gap-filled downscaled SMAP SM results were compared spatially with China Land Data Assimilation System (CLDAS) SM and in situ SM. The RF+Terra combination outcomes were more humid than ANN+Terra combination results in the SDR basin. Overall, the RF+Terra combination gap-filled data showed high R (0.40) and less ubRMSE (0.064 ${\mathrm{m}}^3$/${\mathrm{m}}^3$) against in situ SM, which was close to CLDAS SM. This study showed that the proposed RF- and ANN-based downscaling methods have a potential to improve the spatial resolution and gap-filling of SMAP SM at a high resolution (1 km).

The Soil Moisture Active Passive (SMAP) mission with high-precision soil moisture (SM) retrieval products provides global daily composites of SM at 3, 9, and 36 km earth grids measured by L-band active and passive microwave sensors. The capability of passive microwave remote sensing has been recognized for the estimation of SM variations. The purpose of this work was to establish an interaction between the highly variable SM spatial distribution on the ground and the SMAP’s coarse resolution radiometer-based SM retrievals. In this work, SMAP Level 3 (L3) and Level 4 (L4) SM products are validated with in situ datasets observed from the different locations of the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin over the period of January 2018 to December 2019. The values of the unbiased root mean square error (ubRMSE) for L3 (SPL3SMP_E) SM retrievals are close to the standard SMAP mission SM accuracy requirement of 0.04 ${\mathrm{m}}^3/{\mathrm{m}}^3$ at the 9-km scale, with an averaged ubRMSE value of 0.041 ${\mathrm{m}}^3/{\mathrm{m}}^3$ (0.050 ${\mathrm{m}}^3/{\mathrm{m}}^3$) for descending (ascending) SM with the correlation (R) values of 0.62 (0.42) against the sparse network sites. The L4 (SPL4SMGP) Surface and Root-zone SM (RZSM) estimates show less error (ubRMSE < 0.04) and high correlation (R > 0.60) values, and are consistent with the previous SMAP-based SM estimations. The SMAP L4 SM products (SPL4SMGP) performed well compared to the L3 SM retrieval products (SPL3SMP_E). In vegetated land, the variability and compatibility of the SMAP SM estimates with the evaluation metrics for both products (L3 and L4) showed a good performance in the grassland, then in the farmland, and worst in the woodlands. Finally, SMAP algorithm parameters sensitivity analysis of the satellite products was conducted to produce time-series and highly precise SM datasets in China.

Groundwater is a precious natural resource that is vital to various aspects of life. Punjab is experiencing groundwater stress due to urbanization and population growth, leading to overuse and reduced aquifer recharge. Sustainable groundwater supplies can only be created through better management and artificial recharge techniques. This study uses multi-influencing factor, literature-based, and combined techniques to identify and characterize groundwater-managed aquifer recharge potential sites (GWMARPSs) in Punjab. There are limitations to the previous work in this field, and these factors have not been used to estimate GWRPSs in the study area. The study uses GIS and RS techniques to overlay twelve geo-informative layers, with rainfall being the most significant factor. High-quality data and observations from the field are incorporated into the model. The study classifies the GWMARPSs into five categories, with Punjab having 0.34%, 13.29%, 60.68%, 25.26%, and 0.43% of the least, poorly, moderately, well-, and highly suitable sites. Punjab’s southern regions are least suitable for recharge, while some areas in eastern and northern Punjab are well-suited for recharge. Alluvial plains, valleys, low-lying areas, and areas with volcanic landforms are classified as least to poorly suitable zones. Model predictions are validated using piezometric level data and ROC and exhibit good performance (AUC, 0.74). This study could serve as a baseline for future groundwater research.