Application of remote sensing technology in water quality monitoring: From traditional approaches to artificial intelligence.

Autor: Sun Y; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China., Wang D; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China., Li L; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China. Electronic address: lilei@tongji.edu.cn., Ning R; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China., Yu S; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China., Gao N; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China.
Jazyk: angličtina
Zdroj: Water research [Water Res] 2024 Dec 01; Vol. 267, pp. 122546. Date of Electronic Publication: 2024 Sep 29.
DOI: 10.1016/j.watres.2024.122546
Abstrakt: Quantitative estimation is a key and challenging issue in water quality monitoring. Remote sensing technology has increasingly demonstrated its potential to address these challenges. Remote sensing imagery, combined with retrieval algorithms such as empirical band ratio methods, analytical bio-optical models, and semi-empirical three-band models, enables efficient, large-scale, real-time acquisition of water quality distribution characteristics, overcoming the limitations of traditional monitoring methods. Furthermore, artificial intelligence (AI), with its powerful autonomous learning capabilities and ability to solve complex problems, can deal with the nonlinear relationships between different spectral bands' apparent optical properties and various water quality parameter concentrations. This review provides a comprehensive overview of remote sensing applications in retrieving concentrations of nine water quality parameters, ranging from traditional methods to AI-based approaches. These parameters include chlorophyll-a (Chl-a), phycocyanin (PC), total suspended matter (TSM), colored dissolved organic matter (CDOM) and five non-optically active constituents (NOACs). Finally, it discusses five major issues that need further research in the application of remote sensing technology and AI in water quality monitoring. This review aims to provide researchers and relevant management departments with a potential roadmap and information support for innovative exploration in automated and intelligent water quality remote sensing monitoring.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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