Water Body Extraction in the Middle Reaches of the Heilongjiang River Based on Interpretable Machine Learning

To address the challenges of strong spectral mixing, pronounced feature redundancy, and limited generalization capability of traditional methods in complex heterogeneous river water body extraction, this study uses Sentinel-2 Level-2A multispectral imagery obtained from the Copernicus Data Space Ecosystem to investigate water body extraction in the middle reaches of the Heilongjiang River and proposes an optimized Extreme Gradient Boosting (XGBoost) model based on generalized features and balanced sampling, implemented using XGBoost version 2.1.4. The proposed method is compared with the Normalized Difference Water Index (NDWI)-based threshold segmentation method, U-Net deep learning segmentation model and the Random Forest (RF) classifier. The results show that the proposed XGBoost model achieves the best classification performance, with a Precision of 0.9821 and an F1-score of 0.9695 for water body extraction, significantly outperforming the benchmark methods and demonstrating stronger robustness and adaptability. Both ablation experiments and Shapley Additive exPlanations (SHAP)-based interpretability analysis consistently identified the Sentinel-2 red-edge band (B5) as the most influential feature. Its removal resulted in a substantial decline in classification performance, while B5 exhibited the highest SHAP importance. The water-vapour band (B9) and the Automated Water Extraction Index with Shadow (AWEIsh) were the next most influential features, while water indices, band-ratio features, and atmospheric-related bands mainly played auxiliary roles. This study provides an effective technical framework for high-precision water body extraction and river dynamic monitoring in complex fluvial environments.