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• The method combines laboratory analysis, data modelling and deep learning. • HF-DFM quantifies various water quality parameters by hyperspectral reflectance. • The method is effectively applied to monitoring water quality of urban rivers. • Prediction MAPEs range from 8.78%–12.36% for five water quality parameters. [Display omitted] Environmental protection of water resources is of critical importance to daily life of human beings. In recent years, monitoring the variation of water quality using remote sensing techniques has become prevalent. Unmanned aerial vehicle (UAV) based remote sensing techniques have been applied to quantitative retrieval of concentrations of water quality parameters including phosphorus, nitrogen, chemical oxygen demand (COD), biochemical oxygen demand (BOD), and chlorophyll a (Chl-a), successfully and efficiently. In this study, a novel method with deep factorization machine, spatial distribution pattern analysis, and probabilistic analysis engaged, named hybrid feedback deep factorization machine (HF-DFM), has been developed to quantitatively estimate concentrations of water quality parameters based on hyperspectral reflectance data on large scale effectively. Our proposed method is a unified model for quantifying concentrations of water quality parameters with an end to end structure, which integrates UAV based optical remote sensing techniques and deep learning to estimate concentrations of water quality parameters. Furthermore, our proposed model was applied to real-time quantitative monitoring the variation of water quality of Mazhou River, Shenzhen, Guangdong, China. Finally, we evaluate the performance of proposed model on a real-world dataset in terms of root of mean squared error (RMSE), mean absolute percent error (MAPE), and coefficient of determination (R 2). The experimental results show that our proposed model outperforms other state-of-the-art models with respect to RMSE, MAPE, and R 2 , where resulting MAPEs for quantifying all water quality parameters range from 8.78% to 12.36%, and resulting R 2 s range from 0.81 to 0.93. It can serve as a useful tool for decision makers in effectively monitoring water quality of urban rivers. [ABSTRACT FROM AUTHOR]