Development of gridded root-zone soil moisture product for India, 1981-2024

Accurate and long-term soil moisture data is vital for drought monitoring and agricultural planning in monsoon-dependent and irrigation-intensive regions such as India. Satellite missions like SMAP and SMOS have improved global monitoring but remain limited by short records, shallow sensing depths, and reduced accuracy under dense vegetation and irrigation. To address these gaps, we reconstructed a 0.05° daily root-zone soil moisture (RZSM, 100 cm) dataset for India covering 1981–2024. The dataset was developed using a hybrid approach that combines simulations from the calibrated H08 land surface model with SMAP RZSM through Random Forest regression. Predictors included H08-derived soil moisture and evapotranspiration, precipitation, and temperature, trained against SMAP observations for 2016–2024. Cross-validation demonstrates strong agreement with SMAP, achieving R² and NSE values above 0.90 and an RMSE of less than 0.03 m³/m³ across most regions. Comparison with available in-situ measurement yields an RMSE of 0.04 m³/m³ and a correlation coefficient of 0.94. Independent validation with Solar-Induced Chlorophyll Fluorescence further confirmed consistency with vegetation activity during drought years (2002, 2009). This high-resolution, long-term dataset provides a robust resource for analysing drought variability, calibrating hydrological models, and assessing agricultural risks in India.