Application of Remote Sensing Technology in Water Quality Monitoring: From Traditional Approaches to Artificial Intelligence

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.