Bonnet, Wessel JCho, Moses AAtyosi, YonwabaMajozi, Nobuhle P2026-08-312026-08-3120260010-36241532-2416https://doi.org/10.1080/00103624.2026.2707639http://hdl.handle.net/10204/14920Soil moisture is the main water source for plants, affecting everything from germination to nutrient absorption. The physics-informed brightness-shape-moisture (BSM) radiative transfer model can be used in the inverse direction for accurate soil moisture estimation from high-resolution spectra. However, this accuracy is not maintained once spectra are resampled to fewer-band multispectral resolutions. By coupling a simulated BSM look-up table, resampled to Sentinel-2 response spectra, with gradient boosting regression, the authors developed a transferable method capable of estimating soil moisture from Sentinel-2 imagery, achieving high accuracy on simulated data (R 2 Department of Geography, = 0.96) and moderate accuracy on real-world data (R 2 = 0.51), while avoiding the poorer generalization typically associated with empirical, field- calibrated models. This approach shows strong potential for operational, long-term soil moisture monitoring across Southern Africa.FulltextenSoil moistureBrightness-shape-moistureBSMSentinel-2 response spectraPlant analysisArticleN/A