Estimation of High-Resolution Soil Moisture Using Sentinel-1 and LightGBM over the Miho River Watershed

Soil moisture is a key variable mediating hydrological and energy exchanges between the land surface and atmosphere, and plays an important role in hydrometeorology and disaster management. Although various <i>in-situ</i> observation networks have been established, their spatial representativeness is limited. In this study, high-resolution soil moisture was estimated over the Miho River watershed using Sentinel-1 and the Light Gradient Boosting Model (LightGBM). A time-series analysis between Sentinel-1 C-band backscatter coefficients and soil moisture showed that the vertical-vertical polarized backscatter (σ<sub>VV</sub>) responded more sensitively to soil moisture changes than the vertical-horizontal polarized backscatter (σ<sub>VH</sub>). The statistical evaluation of the LightGBM-based soil moisture estimation yielded a correlation coefficient of 0.722 and a root mean square error of 4.69%. SHapley Additive exPlanation analysis indicated that the day of the year had the highest importance, and σ<sub>VV</sub> showed greater importance than σ<sub>VH</sub>. The spatial distribution of soil moisture revealed high soil moisture (25-40%) in cropland and grassland areas immediately after rainfall, whereas forest areas exhibited more gradual variation owing to canopy interception and shading effects.