A novel framework for multi-layer soil moisture estimation with high spatio-temporal resolution based on data fusion and automated machine learning

High spatiotemporal resolution monitoring of multi-layer soil moisture (SM) is crucial for optimizing agricultural water management and precision irrigation strategy. However, achieving high temporal resolution at a 30 m spatial scale remains challenging given the confine of current satellite sensors. To overcome this, we developed an innovative framework synergizing multi-source remote sensing data, reanalysis data, auxiliary information (topography and soil texture), and ground-based SM observation. Initially, we generated seamless 30 m resolution metrics, including the normalized difference vegetation index (NDVI), land surface temperature (LST), and surface albedo, by employing the modified neighborhood similar pixel interpolator (MNSPI) in conjunction with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). These variables, combined with reanalysis data, auxiliary data, and ground-based SM observations, were input into an Automated Machine Learning (AutoML) workflow to estimate SM at 0–20, 20–40, and 40–60 cm soil layers. Validation conducted in the People's Victory Canal irrigation area revealed depth-dependent prediction accuracy, with Pearson correlation coefficient (R) values of 0.806, 0.772, and 0.680, root mean square errors (RMSEs) of 0.038, 0.047, and 0.054 cm³/cm³, and relative root mean square errors (RRMSEs) of 16.170 %, 20.346 %, and 22.689 % for the 0–20, 20–40, and 40–60 cm soil layers, respectively. This framework shows significant potential for enhancing water resources management at the field scale by providing accurate, high-resolution SM estimates across multiple depths.