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The problem of environmental water pollution is becoming increasingly important. Inland rivers and lakes form interconnected water networks with fragile water ecosystems, and urban water pollution problems occur frequently. Chemical oxygen demand (COD), dissolved oxygen (DO), total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen (NH3-N) in inland rivers are important indicators to evaluate water health quality. Timely and accurate reflection of dynamic changes to the key indices of urban river health status are of vital practical significance to adjust water treatment policy and ensure the stability of the aquatic environment and people's health. This study used COD, DO, TP, TN and NH3-N as typical water quality parameters for a reservoir in Guangxi Province, China and established a set of standardized processes covering UAV hyperspectral sampling and ground spectral correction, spectral data preprocessing, and modeling. In combination with machine learning and statistical analysis, an inversion method for measuring urban inland water pollution from UAV hyperspectral imaging with different dynamic monitoring parameters was proposed. And we compared the different combinations of preprocessing algorithm-regression algorithm and dimensionality reduction algorithm to get a unified model for quantitative estimation of water quality parameter concentration. We evaluated the performance of the proposed model according to root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination ( R 2 ). The experimental results showed that our model was superior to other algorithms in RMSE, MAE, MAPE, and R 2 . The MAPE of this model ranged from 0.01 to 0.12 and R 2 ranged from 0.84 to 0.98 in all water quality parameters. In general, this study provides an effective tool for decision-makers to investigate the source and physical mechanism of water pollution and establish a graded water quality evaluation model. [ABSTRACT FROM AUTHOR]