Downscaling satellite-derived soil moisture in the Three North region using ensemble machine learning and multiple-source knowledge integration

Abstract. Soil moisture plays a crucial role in hydrological and ecological systems. While remote sensing has advanced large-scale soil moisture monitoring, current satellite products often face spatial resolution limitations. This study presents a reliable framework for downscaling satellite-derived soil moisture, leveraging ensemble machine learning and multiple knowledge sources. Our approach efficiently converges outputs from diverse machine learning algorithms through Bayesian model, harnessing spatiotemporal domains and point-wise data. Covering approximately five million square kilometres in the Three Northern region of China, our model generates 1-km daily soil moisture maps, accurately reflecting soil water content patterns and showing spatial consistency with outputs from two credible numerical models. Validation against in situ measurements from three ground networks confirms the accuracy of the downscaled dataset. Comparative analysis demonstrates the superiority of the Bayesian-based method over four individual machine learning methods. The high-resolution dataset produced proves effective in capturing drought dynamics, particularly extreme drought patterns. The robustness of our framework is further affirmed through uncertainty analysis, employing leave-one-out and progressive sample reduction approaches. In summary, our ensemble machine learning-based framework offers an efficient solution for acquiring accurate and high-resolution soil moisture data across large regions, with implications for water resource management and drought monitoring.