Monitoring farmland soil moisture status in semi-arid regions using multi-source remote sensing fusion data and machine learning algorithms
This study demonstrates that integrating optical vegetation indices and radar backscatter features within an XGBoost machine learning framework effectively monitors the spatiotemporal dynamics of soil moisture across various depths and growth stages in semi-arid spring maize fields, revealing the complementary roles of these data sources and the superior performance of the combined approach.