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).

Water is a primary element for human life on Earth. Fresh water, which includes rivers, lakes, streams, and ponds, contributes less than one thousandth of a percent of the total water on Earth, but it is critical for the environment and human life. Change in land use and land cover (LULC) is a foremost concern in global environment change. Rapid changes in LULC lead to the degradation of ecosystems and have adverse effects on the environment. There is an urgent need to monitor changes in LULC and evaluate the effects of these changes in order to inform decision makers on how to support sustainable development. This study used Moderate Resolution Imaging Spectroradiometry images to detect and investigate changes in LULC patterns in Gilgit-Baltistan, Pakistan, between 2008 and 2017. Six types of LULC were used to explain the major changes of LULC in the study area. The results showed that there was a reduction of barren lands and an increase of urban areas. It also showed an inconsistent behavior of water bodies during the study. Snow area, which also increased, needs further investigation.