Research progress in surface water quality monitoring based on remote sensing technology

ABSTRACT

Urban surface water is an important freshwater resource, and the surface water environment is increasingly being destroyed. Dynamic monitoring of surface water is of great significance for protecting the ecological environment. Remote sensing technology provides technical support for surface water monitoring, which overcomes the drawbacks of traditional manual sampling. It has been widely applied in surface water monitoring. The paper systematically reviews the research progress of remote sensing technology in surface water monitoring from the aspects of remote sensing data, inversion models and water quality parameters. Advantages and disadvantages of inversion models (analytical methods, empirical methods, semi-empirical methods, machine learning methods and comprehensive methods) are compared and analysed. Furthermore, we summarize the research progress of remote sensing technology in monitoring chlorophyll a (Chl-a), total suspended matter (TSM), coloured dissolved organic matter (CDOM), transparency and non-photosensitive parameters. Although remote sensing technology provides new ideas for surface water monitoring, there are still some problems that need to be solved, such as remote sensing signals being affected by the atmosphere, poor portability of inversion models, low resolution of satellite sensors, and susceptibility to external factors. Therefore, future research should combine multi-source data, conduct in-depth research on the optical characteristics of surface water bodies, optimize inversion methods, construct transferable inversion models, break through temporal and spatial limitations, and promote the rapid development of surface water pollution monitoring and warning.

KEYWORDS:

• Remote sensing
• water quality parameters
• inverse model
• hyperspectral data

Acknowledgements

This study is financially supported by the National High Level Foreign Experts Introduction Project (G2022014116L). We thank the Introduction of Intelligence Projects for Policy Guidance in Yancheng (YCBX2022011) and Scientific Research Project of Yancheng Polytechnic College (ygy2103).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National High Level Foreign Experts Introduction Project [G2022014116L].