Deriving a clear-sky soil moisture index from ECOSTRESS land surface temperature

International audience Agricultural drought threatens food and water security in rapidly growing regions like India and Sub-Saharan Africa, underscoring the importance of remote sensing (RS) for monitoring. However, existing land surface temperature (LST)-based water stress indices often lack sensitivity to soil moisture (SM) deficits in vegetated areas, and high-resolution thermal infrared (TIR) water stress products remain scarce. Additionally, TIR-based indices are rarely validated with ground measurements in Sub-Saharan Africa, limiting their reliability. To address these challenges, we propose a high-resolution (70 m) soil moisture index using ECOSTRESS data, termed Radiative Thermal Inertia (RTI). RTI integrates near real-time noon and midnight ECOSTRESS LSTs with accumulated radiative fluxes, representing the energy required to raise LST by 1 K per unit area. A correction factor (beta) accounts for vegetation cover and relative humidity, enhancing RTI's sensitivity to SM variabilities, especially in vegetated regions. First, we employ an innovative climatology-based LST reconstruction method to fill ECOSTRESS data gaps on missed clear-sky days using VIIRS LSTs, achieving accuracies comparable to official clear-sky retrievals (RMSE = 2.31 K at 13:30, 1.91 K at 01:30). These reconstructed LSTs are subsequently used to calculate RTI across 21 soil moisture in-situ sites in Sub-Saharan Africa and India, demonstrating a strong correlation [r = 0.62 for RTI-beta] with seasonal SM variability compared to other indicators (Keetch-Byram Drought Index, KBDI; Normalized Difference Water Index, NDWI_ rho 1.24; NDWI_ rho 2.13; and Apparent Thermal Inertia, ATI). While the majority of the drought indices tend to saturate at high fractional vegetation cover (FVC), RTI-beta remains stable across a range of vegetation densities. Sensitivity analysis with normalized SM anomalies shows a higher correlation with seasonality-detrended RTI-beta (r = 0.70), marking a significant improvement in vegetated areas over the initial RTI and the Scaled Drought Condition Index (SDCI) in sparsely vegetated regions. Spatial and temporal analyses demonstrate the ability of this ECOSTRESS-based SM index to track drought periods and irrigation events. This study addresses a critical gap in high-resolution spatiotemporal surface water stress mapping for agriculture using thermal remote sensing theory. The findings highlight the RTI's potential for future high-resolution TIR missions, supporting agricultural management and drought early warning systems in Sub-Saharan Africa, India, and beyond.