A lightweight support vector-based multisensor remote sensing framework for regional soil moisture estimation

Accurate and robust soil moisture estimation remains a major challenge in precision agriculture, particularly under increasing climate change impacts. Reliable assessment of net soil moisture using precipitation and evapotranspiration (ET) derived from multisensor remote sensing is therefore essential. However, most existing approaches rely on single-sensor products or complex data assimilation frameworks, limiting their operational applicability. This study addresses this gap by proposing a simplified interoperable framework, integrating Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) precipitation, Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil Moisture Active Passive (SMAP) soil moisture data. Differences between CHIRPS precipitation and MODIS ET datasets were computed at country, state and subdivision scales and validated against SMAP observations. The correlations between the derived differences and SMAP soil moisture yielded R2 values of 0.66, 0.69, and 0.62, at country, state and subdivision levels, respectively. Furthermore, CHIRPS, MODIS, and SMAP datasets at the subdivision level were used to train a Support Vector Regressor model for the period 2021–2023, at 8-day temporal resolution, with estimations performed for June 2023. The linear kernel SVR achieved superior performance with R2 values of 0.81 and 0.792 during training and testing at the subdivision level. Validation with ground observations gave an R2 of 0.7123, confirming robust sensor interoperability. Overall, the study demonstrates efficient integration of CHIPRS, MODIS, and SMAP datasets.