Exploring the Current Status of Multispectral Data Application in Water Quality Monitoring of Rivers and Lakes

Abstract

Water quality monitoring provides a foundation for managing pollution in the water environment by analyzing pollution sources, concentrations, and trends in water bodies. Traditional water quality monitoring methods are expensive and can be insufficient for the demands of large-scale real-time monitoring; meanwhile, multispectral remote sensing can quickly gather expansive data from water areas and has become widely used in quality of water monitoring. This paper aims to examine the current application of multispectral remote sensing data in monitoring water quality pollution in rivers and lakes. The study reveals that the application of multispectral remote sensing in water quality monitoring has indeed been extensive. Firstly, while multispectral remote sensing data is relatively easy to obtain, its precision is limited; there are challenges regarding low accuracy in water quality parameter inversion models, necessitating its integration with hyperspectral data and drone remote sensing. Secondly, current methods for remote sensing of water quality still depend heavily on a big volume of measured data and face spatiotemporal limitations; thus, it is recommended to optimize modeling techniques to reduce regional constraints and reliance on measured water quality data. Lastly, this research summarizes applications of more readily obtainable multispectral data in monitoring water quality in rivers and lakes for researchers encountering challenges in data acquisition, analyzing both the advantages and shortcomings of the data and methods, and providing recommendations for researchers in selecting remote sensing water quality monitoring data.