Crop Dynamic Root-Depth-Matched High-Resolution Multilayer Soil Moisture Dataset Generated by In Situ-Remote Sensing Data Fusion

Frequent agricultural droughts pose a serious threat to crop production. Soil moisture (SM), as a key monitoring indicator, is essential for developing effective agricultural drought indices. Most existing soil-moisture-based agricultural drought indices overlook variations in crops' water requirements across developmental stages and root-zone depths, instead focusing on a single soil moisture layer (surface or root zone). A primary challenge for field-scale agricultural drought monitoring is the lack of high-spatial-resolution, multi-layer soil moisture datasets. To address this gap, we propose a data-fusion framework that integrates remote-sensing products and pointwise in-situ observations to generate daily, 30 m resolution, multi-layer soil moisture across the root zone. The newly developed multi-layer soil moisture dataset demonstrated excellent performance, with an average Pearson correlation coefficient (PCC) of 0.989 and root mean square error (RMSE) of 0.034 m³ m-3. We also tested spatio-temporal generalization and confirmed that the fusion approach robustly captures field-scale soil moisture when downscaling from 9 km to 30 m. Using this multi-layer dataset, we developed the Dynamic Root-Depth-Matched Agricultural Drought Index (DRDI), which dynamically adapts to crop root-depth distributions across phenological stages. Spatio-temporal analysis using DRDI revealed drought conditions across areas and seasons. Compared with existing indices, DRDI better reflected drought impacts on crop yield and provided more accurate field-scale agricultural drought assessments. Through data sensitivity analysis, DRDI demonstrated resilience to input perturbations. This work has important implications for Geo-AI–based generation of high-quality soil moisture data. More importantly, the proposed drought index offers new perspectives for the geoscience community and agricultural drought applications.