Industrial and mining cities are affected by activities of these industries that damage the water environment to various degrees and make water pollution a particular problem. At present, field sampling with a grid pattern and indoor laboratory analysis are mainly used for routine water quality monitoring. However, the environment is complex and variable, and spatial differences are considerable. Therefore, the survey sites render limited representativeness, low overall accuracy, and poor efficiency, making it difficult to realize dynamic monitoring regionally. In this study, we took Daye City under Huangshi City, Hubei Province, as the research area. Daye is an important mining city that thrives on mining. Unmanned aerial vehicle (UAV) hyperspectral imaging, ground spectral measurements, and water body sampling were carried out simultaneously. As a result, 49-band hyperspectral imaging data and water body spectra with a spectral resolution of 1 nm were obtained. The imaging data have a wavelength range of 505～890 nm, a spectral resolution of 778 nm, and a spatial resolution of 30 cm. After outlier removal, spectral calibration and radiometric correction were performed on the hyperspectral imaging data and spectral measurements and a comparative analysis was carried out between the spectral data of various water bodies located in the study area in terms of their absorption/reflectance spectra and the morphological features of their spectral curves. We subsequently extracted 25 spectral features from these hyperspectral images and measurement spectra, and these were classified under the following categories: morphological features of reflectance spectra, morphological features of continuum-removed spectra, morphological features of third-derivative spectra, and 4-value spectral encoding. The Pearson’s correlation coefficient was used to analyze the correlation between the water quality parameters and the spectral features of the water specimens and to select the water quality parameters and spectral features that were significantly correlated with each other. On this basis, a multivariate linear inversion model was constructed for the water quality parameters using the following model variables, which were selected via stepwise regression analysis: the maximum reflectance and its corresponding wavelength, symmetry, and spectral code Ⅲ, and the maximum and minimum third derivative values. F-tests and t-tests were then performed on this model. After the tests, our inversion model was used to obtain the water quality parameters of typical water bodies, such as tailings ponds, rivers, and lakes, from the hyperspectral imaging data from the study area. We have thus succeeded in achieving the rapid acquisition of water quality information in a “point-to-surface” manner. The results of this study indicate that our model has high inversion accuracies for water quality parameters such as pH, hardness (Ca2++Mg2+), potassium-to-chloride ratio (K+/Cl-), and magnesium-to-alkalinity ratio ［Mg2+/(HCO3-+CO2-3)］. Between these parameters, pH has the lowest coefficient of determination (R2) of 0669, whereas the magnesium-to-alkalinity ratio has the highest R2 of 0895. The relative root mean square errors (RRMSE) were generally lower than 28%. In contrast, the inversion accuracy of our model for total dissolved solids (TDS) was relatively low, and its R2 and RRMSE were 0463 and 36762%, respectively. This study proposes a hyperspectral remote-sensing quantitative inversion method of water quality parameters based on spectral curve patterns. The method achieves hyperspectral quantitative inversion of water quality parameters such as pH, hardness and magnesium-to-alkalinity ratio, and it provides a new technique for dynamic monitoring of the regional water environment.