水质参数遥感反演光谱特征构建与敏感性分析

水质遥感监测是遥感的重要应用方向之一,作为传统水体采样化验的辅助手段,具有快速、大面积、无接触的技术优点.然而,目前内陆河湖水环境监测常用的遥感传感器大多是针对陆地观测或海洋水色观测而设计的,其性能指标的设计和设置并未考虑到内陆水体特性,限制了水质遥感定量监测的应用.针对这一问题,提出了一种基于变异系数和噪声占比指数的...

Spectral Feature Construction and Sensitivity Analysis of Water Quality Parameters Remote Sensing Inversion

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.