Accurate Monthly Estimation of Shallow Groundwater Depth in Arid Oasis Regions Driven by Multi-Source Data and Machine Learning

Accurate estimation of shallow groundwater depth (SGWD) is crucial for ecological security in arid oases. However, sparse monitoring wells and strong spatiotemporal heterogeneity hinder reliable monthly estimation. Traditional interpolation methods struggle to capture complex environmental drivers in anthropogenically modified regions. To address this issue, multi-source remote sensing data were integrated with machine learning models to estimate SGWD in the Weiku Oasis, Xinjiang, China. Field-measured SGWD data from March to November 2020 were collected. Based on the driving mechanisms of SGWD, six categories of variables were extracted, including optical, radar, topographic, meteorological, soil, and human activity factors. Four modeling strategies were designed by combining these variables with random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) models. The optimal variable-model combination was selected for monthly SGWD mapping. SHAP was employed to interpret monthly feature importance and variable influence mechanisms. The results showed the following: (1) Among the four modeling strategies, Strategy II and Strategy IV achieved the best performance in monthly SGWD estimation, indicating that either single optical remote sensing data or the synergy of optical and radar remote sensing data can effectively characterize the spatiotemporal heterogeneity of SGWD in similar irrigated oasis regions. (2) Under different “variable-model” combinations, the RF model performed best in March, April, May, and October, whereas the SVM model performed best in June, July, August, September, and November, with XGBoost showing the weakest overall performance. Furthermore, the machine learning models outperformed Ordinary Kriging, yielding a mean R2 of 0.790 and a 5.23% MAE reduction compared to −0.325. (3) Monthly SGWD maps revealed shallower depths in central and southeastern farmlands and piedmont alluvial fans, and greater depths in northern and northwestern desert margins due to insufficient groundwater recharge. (4) The dominant controls on SGWD exhibited clear seasonal variation. Salinity indicators, radar-derived wetness, and evapotranspiration prevailed in spring, followed by topographic and thermal–evaporative drivers in summer and by topography, precipitation, and soil properties in autumn. Despite these seasonal shifts, valley depth remained among the top five predictors throughout the nine months. This study provides a scientific method and technical support for dynamic monitoring and refined management of groundwater resources in similar irrigated oasis regions.