Hyperspectral remote sensing is considered an effective tool for monitoring inland water quality. Non-optically active water quality parameters are of great significance to the aquatic environment, although they are rarely used in practical remote sensing applications. This study aims to improve the performance of non-optically active water quality parameter retrieval models by optimizing the wavelength selection and apply to the newly hyperspectral imagery from the Advanced HyperSpectral Imager (AHSI) and Orbita HyperSpectral (OHS) sensors. Focusing on dissolved oxygen, chemical oxygen demand (COD), ammonia nitrogen, and total phosphorus (TP), we propose a hyperspectral dimension reduction method based on the variable importance projection (VIP) and segmented principal component analysis (SPCA) method to determine the sensitive bands of different water quality parameters. A total of 81 in-situ samples of water quality parameters and water spectral reflectance were collected in Shanghai between 2018 and 2019. These were analyzed and used to establish quantitative retrieval models. Furthermore, the principal component regression, partial least squares regression, and back-propagation (BP) network models were compared and partly applied to satellite hyperspectral images. The final results show that models based on VIP-SPCA performed better in the validation set, and the best model was COD estimated by BP (VIP-SPCA) with a coefficient of determination (R²) raised from 0.56 to 0.74. The mean absolute percentage error ranged from 14.23% (COD) to 24.11% (TP). Overall, the AHSI and OHS concentration maps had consistent spatial distributions with monthly monitoring data and reasonable concentration levels. Therefore, the results validate the great potential of hyperspectral remote sensing for inland water quality parameter retrieval using VIP-SPCA.