Geographically weighted quantile machine learning for probabilistic soil moisture prediction from spatially resolved remote sensing

This study introduces a geographically weighted (GW) quantile machine learning (GWQML) framework for soil moisture (SM) prediction, integrating spatial kernel functions with quantile-based prediction and uncertainty quantification. The model incorporates satellite radar backscatter, meteorological re-analysis and topographic variables, applied across 15 SM stations and six land use systems at the North Wyke Farm Platform, southwest England, UK. GWQML was implemented using Gaussian and Tricube spatial kernels across a range of kernel bandwidths (500–1500 m). Model performance was evaluated using both in-sample and Leave-One-Land-Use-Out validation schemes, a global quantile machine learning model (QML) without spatial weighting served as benchmark. GWQML achieved R2 values up to 0.85 and prediction interval coverage probabilities up to 0.9, with intermediate kernel bandwidths (750–1250 m) offering the best balance between accuracy and uncertainty calibration. Spatial autocorrelation analysis using Moran’s I revealed a lower residual clustering under GWQML relative to the benchmark model, which suggests improved handling of local spatial variation. To our knowledge, this is the first application of GW kernel functions withing a probabilistic machine learning framework for daily SM modelling. The approach implicitly captures spatially varying relationships while delivering calibrated uncertainty estimates for scalable SM monitoring across heterogenous agricultural landscapes.