Water quality remote sensing monitoring is one of the important application directions of remote sensing. As an auxiliary mean of traditional water sampling and testing, remote sensing has the advantages of rapid, large-area and contactless. However, most remote sensing sensors commonly used in inland water monitoring are designed for land observation or ocean watercolor observation. The design and setting of sensor performance indicators do not consider the characteristics of inland water, limiting the application of water quality remote sensing monitoring. This study proposes a method for constructing the spectral characteristics of water quality parameter based on variation coefficient and noise ratio index, and study the influence of the spectral resolution, signal-to-noise ratio (SNR) and radiation resolution on typical water quality parameters inversion models through the spectral simulation experiments. Firstly, aimed at three main water quality parameters in Shanghai, we construct the spectral characteristics of dissolved oxygen(DO), total phosphorus(TP) and ammonia nitrogen (NH3-N) respectively and establish remote sensing inversion models. Then, we carry out the spectral simulation experiment and calculate the sensitivity (S) and water quality sensitive differential index (CI) for sensitivity analysis. Finally, we evaluate the spectral resolution’s influence, SNR and radiation resolution on water quality parameters inversion models from two aspects of accuracy and stability. The results show that this method can effectively determine the bands of water quality parameters inversion models. Spectral resolution has little effect on the contrast-type inversion models, while SNR and radiation resolution greatly influence the models. With the increase of SNR and radiation resolution, the water quality inversion models’ accuracy and stability are improved to some extent. According to Comprehensive sensitivity analysis of sensors parameters, when the SNR is better than 56 dB, the radiation resolution is not less than 9 bit, and the spectral resolution is appropriate. It can be better applied to inland water quality remote sensing monitoring. This research can provide a reference for the development of sensors for inland water quality monitoring and provide technical support for water resources supervision departments to carry out remote sensing monitoring of water quality, which is conducive to accelerating the construction of an intelligent monitoring system for the water environment.