Research Progress on Remote Sensing Monitoring of Lake Water Quality Parameters

Abstract

Lakes are an important component of the hydrosphere and are important freshwater resources. The water environment of lakes has become increasingly polluted, and monitoring the dynamic changes in lake water quality is of great significance for ecological environment protection. Remote sensing can provide technical support for lake water quality monitoring, overcoming the low-cost and poor timeliness characteristics of manual sampling; thus, it has been widely used in lake water quality monitoring. Remote sensing can be used to monitor many indicators, including suspended solids, chlorophyll a, soluble organic matter, dissolved oxygen, transparency, etc. Although remote sensing provides a new method for lake water quality monitoring, there are still some problems in practical application. ① It is difficult to accurately retrieve the component changes in different substances in lake water from the signals received by remote sensing, resulting in the limitation of remote sensing accuracy. Atmospheric correction can eliminate the images reflected by factors such as atmosphere and light. In order to improve the accuracy of water quality monitoring, higher-accuracy atmospheric correction algorithms are needed. However, at present the atmospheric correction algorithm is not mature enough and lacks the portability between sensors. Therefore, atmospheric correction is still a difficult problem of remote sensing retrieval in lake water quality. ② Due to seasonal and spatial constraints, there are differences in surface optical properties of different lakes and biological optical properties, resulting in changes in remote sensing reflectance. There is also a lack of portability of the model established by using limited measured data. ③ The interference of aquatic plants and external forces (wind, fish, etc.) causes the error between the measured data and the model estimation, and the synchronization of the data is difficult to guarantee, which will introduce large errors to the lake water quality model, resulting in a decrease in the reliability of the model. ④ The spatio-temporal scale of measured data and remote sensing data do not match, and it is difficult to capture dynamic and fine changes in water quality. More precise observation of water quality is needed that can capture rapid changes in lakes. However, it is still challenging to obtain real-time measured data that meet the requirements. Therefore, it is necessary to further understand the spectral characteristics of water quality parameters, combining multi-source data and other hydrological models. In the future, a retrieval algorithm with low dependence will be developed, and migrated models will be constructed to break the regional limitations of the model. Additionally, remote sensing promotes the operational development and early warning for the water quality of lakes.