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September 12, 2025Open Access

Advancing Water Quality Management: Harnessing the Synergy of Remote Sensing, Process-Based Models, and Machine Learning to Enhance Monitoring and Prediction

PWPeiyan WangChinese Academy of Sciences SZShubin ZouChinese Academy of Sciences JLJie LiBeijing Institute of Technology

Key Points

The integration of remote sensing, process-based models, and machine learning significantly improves water quality monitoring and prediction accuracy.
Combining advanced models with AI techniques extends spatiotemporal coverage and reduces monitoring costs while optimizing analysis.
Challenges remain in data management, model accuracy and efficiency, and real-world implementation that must be addressed for practical applications.
Future research directions aim to strengthen sustainable water management and enhance scientific decision-making amid climate change.

Abstract

Amid the intensifying challenges of climate change and human activities such as shifts in agricultural practices, the pressure on water resources, particularly regarding water quality, has intensified. As a result, improving water quality monitoring and prediction has emerged as an essential strategy to tackle these challenges and ensure the sustainable management of water resources. Traditional water quality monitoring technologies have inherent limitations; however, integrating remote sensing (RS) technologies with modeling approaches has shown significant promise in enhancing water quality monitoring and prediction. This integrated approach significantly improves the accuracy and intelligence of monitoring and prediction, while extending spatiotemporal coverage, lowering monitoring costs, and enabling more comprehensive analysis through optimized monitoring design, multi-source data fusion, and the synergistic coupling of data-driven and process-based models (PBMs). Advanced models, particularly those combining PBMs with AI techniques, further enhance predictive capabilities for water quality. Despite these advances, the application of these integrated methods faces challenges in areas such as data management, monitoring elusive pollutants, model accuracy and efficiency, system integration, and real-world implementation. In response to these challenges, this paper reviews the current status of the integration of RS technology with multi-source data, machine learning (ML), and PBMs for water quality monitoring, modeling, and management, along with practical applications. It offers a thorough analysis of their advantages and challenges, identifies the current research gaps, and outlines future research directions. The goal is to enhance the role of integrated methods in improving water quality in aquatic ecosystems, support sustainable water resource management, and strengthen scientific decision-making in the face of climate change and growing anthropogenic pressures.

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