监测饮用水水库的总氮（TN）对于防止富营养化至关重要，但在复杂的内陆水体中，遥感反演往往面临精度有限和决策过程不透明的问题。本研究提出了一种可解释框架，耦合了创新特征工程、集成学习算法（XGBoost、CatBoost、LightGBM）、模拟与观测指数距离（DISO）指标以及SHapley Additive exPlanations（SHAP）。基于4270个Landsat 8–9波段组合，特征优化实现了超过99%的冗余度降低。利用新丰江水库2020–2024年的实测数据进行验证，LightGBM表现出最优性能（R² = 0.72，RMSE = 0.25 mg/L，BIAS = −0.02，DISO = 0.35）。SHAP分析揭示(B4 − B3)/(B3 − B6)和(B2 + B1)/(B1 + B4)为最具影响力的特征（SHAP值 > 0.1），证明了其与藻类生物量和水体光学特性相关的生物物理合理性。时空映射显示TN浓度总体较低（>90%符合地表水II–III类标准），但上游存在持续热点区域，需采取针对性缓解措施。通过量化特征贡献，该可解释框架提高了反演精度和方法透明度，为水库水质管理和可持续生态治理提供了实用工具。

Monitoring total nitrogen (TN) in drinking water reservoirs is vital for preventing eutrophication, yet remote sensing retrievals often struggle with limited accuracy and opaque decision processes in complex inland waters. This study presents an interpretable framework coupling innovative feature engineering, ensemble learning (XGBoost, CatBoost, LightGBM), the Distance between Indices of Simulation and Observation (DISO) metric, and SHapley Additive exPlanations (SHAP). From 4270 Landsat 8–9 band combinations, feature optimization achieved >99% redundancy reduction. Validated with in-situ data (2020–2024) from the Xinfengjiang Reservoir, LightGBM achieved superior performance (R² = 0.72, RMSE = 0.25 mg/L, BIAS = −0.02 and DISO = 0.35). SHAP analysis revealed (B4 − B3)/(B3 − B6) and (B2 + B1)/(B1 + B4) as the most influential features (SHAP value > 0.1), demonstrating biophysical plausibility linked to algal biomass and water optical properties. Spatiotemporal mapping showed predominantly low TN concentrations (>90% Class II–III surface water standard) alongside persistent upstream hotspots requiring targeted mitigation. By quantifying feature contributions, this interpretable framework enhances retrieval accuracy and methodological transparency, offering actionable tools for reservoir water quality management and sustainable ecological governance.