<b>SMRFR: </b><b>A global long-term multilayer soil moisture dataset (2000-2023)</b><b> generated using machine learning</b>

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, and we introduced SMRFR (e.g., Soil Moisture from Random Forest Regression). Utilizing publicly available reanalysis and remote sensing data for generation, SMRFR provides daily SM estimates at five soil layers (0-5, 5-10, 10-30, 30-50 and 50-100 cm) with a fine spatial resolution of 9-km over the period 2000 to 2023. The evaluation results demonstrate SMRFR's potential to capture the spatial and temporal variability of SM. SMRFR exhibits robust performance in transferring knowledge across continents in capturing transient and seasonal SM dynamics after rainfall events, with medium ubRMSE of 0.0339 m³/m³. Our novel SM dataset offers basis and scientific reference for agricultural, hydrological, and ecological studies. 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.