China’s 1 km Daily Surface Soil Moisture Fusion Dataset (2000–2024) based on Explainable Machine Learning.

High-resolution soil moisture (SM) data are critical for drought monitoring and flood forecasting. This study establishes an interpretable machine learning (ML)-based framework for SM data fusion and generates a daily-scale, 1 km resolution, surface SM (0–10 cm) dataset over China (2000–2024). Four state-of-the-art ML models—Random Forest, XGBoost, LightGBM, and CatBoost are trained based on the in situ SM data from 2,371 automatic observation stations across China. To optimize model performance, we implemented Recursive Feature Elimination (RFE) feature selection and automated hyperparameter tuning via the Optuna framework. Furthermore, SHapley Additive exPlanations (SHAP) were employed to elucidate the interpretability of ML models. The key findings of this study are as follows: (1) The fusion model primarily enhances SM estimation, exhibiting lower root mean square error than CLDAS (China Meteorological Administration Land Data Assimilation System) SM despite marginally weaker daily temporal correlation; (2) RFE eliminated 57% of features while preserving predictive accuracy; (3) SHAP analysis revealed high-accuracy SM inputs as the most influential predictors, followed by static (terrain and soil properties) and meteorological variables. The SM fusion method developed in this study is transferable to multi-source satellite SM fusion and downscaling. The dataset is publicly available at https://doi.org/10.11888/Terre.tpdc.302923.