Machine Learning-Based Soil Moisture Retrieval from Sentinel-1A Observations over the International Soil Moisture Networks

Soil moisture (SM) is a critical variable in land–atmosphere water and energy exchange, and synthetic aperture radar (SAR) observations offer an effective means for large-scale and fine-resolution SM monitoring. Sentinel-1A, with its all-time and all-weather capability, has become an indispensable data source for SM retrieval, while comprehensive comparisons of machine learning and deep learning methods for regional and global scale SM retrieval remain insufficient. In this study, four widely used machine learning (ML) algorithms, including random forest (RF), eXtreme gradient boosting (XGBoost), convolutional neural network (CNN), and long short-term memory (LSTM), are evaluated for SM retrieval from Sentinel-1A observations across the International Soil Moisture Network (ISMN) at global and regional scales. Multiple-source dynamic parameters, including Sentinel-1A observations, MODIS vegetation parameters, ERA5-Land meteorological and soil variables, are used as inputs, as well as static geospatial parameters. Validation results demonstrate that tree-based ensemble methods (RF and XGBoost) consistently outperform deep learning methods across all scales. Specifically, XGBoost achieves the best performance with satisfactory SM retrieval results. Moreover, XGBoost is insensitive to Sentinel-1A viewing geometry, allowing fusion of multi-orbit observations to improve temporal resolution without accuracy loss. These findings demonstrate the effectiveness of tree-based ML for global/regional SM retrieval from Sentinel-1A. In addition, this study performs a comprehensive evaluation of spatial generalization ability and orbit robustness of different retrieval models under global heterogeneous environments, and proposes a reliable scheme for generating high-spatiotemporal-resolution SM products.