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Satellite remote sensing offers a cost-effective solution for the continuous monitoring of black and odorous water bodies (BOWs). However, limitations in spatial and spectral resolution hinder the quantitative inversion of water quality parameters and the precise assessment of risk levels using satellite data alone. To address this challenge, this study proposes a synergistic approach combining satellite and Unmanned Aerial Vehicle (UAV) remote sensing to rapidly identify potentially polluted water bodies and quantitatively assess their risk levels. First, a Black and Odorous Water Index (MBOWI) was constructed based on reflectance characteristics in the visible to near-infrared bands to screen for potential black and odorous water bodies using satellite imagery. Subsequently, high-resolution multispectral UAV imagery, integrated with in situ sampling data, was employed to develop machine learning models for inverting key water quality parameters, including Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Total Phosphorus (TP) and Ammonia Nitrogen (NH3-N). Comparative analysis of Polynomial Regression (PR), Random Forest (RF), and Simulated Annealing-optimized Support Vector Regression (SA-SVR) revealed that RF and SA-SVR exhibited superior performance in inverting four non-optically active water quality parameters due to their robust nonlinear fitting capabilities, with the mean Adjusted Coefficient of Determination (Radj2) ranging from 0.57 to 0.69. Water quality classification based on the single-factor worst-case method achieved an overall accuracy of 0.70 across validation samples. Notably, for Class V (heavily polluted) water bodies, both classification accuracy and recall rate reached 0.89, demonstrating the model’s high precision in identifying high-risk waters. Finally, the proposed framework was applied to northern Zhejiang Province to assess seven potential black and odorous water bodies, successfully identifying four as high-risk and one as low-risk. This study validates satellite and UAV synergistic remote sensing for the hierarchical risk management of black and odorous water bodies.