Spatial Domain Dependence Evolution of Input Parameter Importance in Soil Moisture Retrieval Under the XGBoost and SHAP Framework
Abstract
Soil moisture (SM) is a core state variable of terrestrial hydrological and land–atmosphere interactions. Machine learning-based downscaling and retrieval frameworks that integrate multi-source remote sensing and auxiliary datasets have become mainstream approaches for generating high-spatial-resolution SM products. However, the spatial domain dependence evolution law governing the relative importance of these multiple predictors remains insufficiently quantified. This study constructs an integrated XGBoost and SHAP interpretability framework to reveal how the contribution and driving mechanisms of predictors shift across spatial domains. We compiled global in-situ SM observations from 24 international soil moisture network (ISMN) monitoring networks spanning 2017–2024. Predictors were classified into five categories: Sentinel-1A radar backscatter, vegetation indices, ERA5-Land meteorological forcing, topographic geospatial variables, and static soil texture attributes. Two modeling approaches were adopted: independent local network models representing the regional domains and a unified composite model representing the global domain. Model performance metrics demonstrate that the global composite model yields robust generalization with minimal overfitting, while individual regional models exhibit domain-specific retrieval differences due to varying land surface conditions. Pearson correlation analyses confirm that physical covariances remain nearly consistent across different domains, whereas cross-category correlations vary with spatial domain and local landscape backgrounds. SHAP-based feature importance quantification reveals clear domain-dependent differences among dominant predictors. This work quantitatively verifies the spatial domain dependence of parameter importance in machine learning-based SM retrieval, providing guidance for domain-adaptive predictor selection and interpretable high-resolution SM modeling under diverse land surface conditions.