SMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source data

Accurate and continuous monitoring of soil moisture (SM) is crucial for a wide range of applications in agriculture, hydrology, and climate modelling. In this study, we present a novel machine learning (ML) based framework for generating a continuously updated, multilayer global SM dataset: SMRFR (Soil Moisture via Random Forest Regression). Leveraging publicly available reanalysis and remote sensing data, SMRFR provides daily SM estimates at five soil layers (0-5, 5-10, 10-30, 30-50 and 50-100 cm) with a spatial resolution of 9 km, covering the period from 2000 to 2023. Evaluation results demonstrate that SMRFR effectively captures both spatial and temporal SM variability. It also exhibits strong generalization capacity, successfully transferring knowledge across continents and accurately capturing transient and seasonal SM dynamics following rainfall events. SMRFR achieved an unbiased root mean square error of 0.0339 m3/m3 on the validation set. Our novel SM dataset offers a basis and valuable reference for agricultural, hydrological, and ecological research, enabling improved analysis and modelling of SM dynamics at regional to global scales. Due to the whole dataset for the global scale is too big (391.59GB) to deposit at once，we uploaded the data of 2000 to figshare. The whole dataset can be accessed at https://doi.org/10.11888/Terre.tpdc.301526.