Soil moisture mapping using machine learning technique (Mongolian Journal of Geography and Geoecology, 2023)
Soil moisture is an essential component in the energy cycle, water resource, hydrological regime, and processes of the land surface. Mapping and monitoring of soil moisture are crucial for the prevention of flood and drought, estimation of evapotranspiration, and water resource management. Using remote sensing to create soil moisture mapping at large scale has become one of the most energy and time-efficient methods in soil study. Thus, we aimed to map the soil moisture for Mongolia based on downscaled Soil Moisture Active Passive (SMAP) data by combining it with the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Temperature (LST) of Moderate Resolution Imaging Spectroradiometer (MODIS) data using the Machine Learning-based Random Forest (RF) approach. The SMAP was positively correlated with NDVI (r=0.72, p