A review of machine learning applications in inland and nearshore water color remote sensing: from preprocessing to object detection and parameter estimation

A combination of Machine Learning (ML) techniques with satellite remote sensing has brought target objects and water-quality research into a new era. These emerging techniques and unprecedented opportunities have improved the accuracy of satellite data products, facilitated knowledge discovery, expanded the spatial domain of target objects and water-quality research to the globe, and extended the social, economic, and scientific impacts of water-quality research. This review provides the first systematic integration of machine learning (ML) across the full-chain workflow for inland and nearshore watercolor remote sensing, we examine recent progress in the application of ML across the full remote sensing workflow for inland and nearshore water color studies—from image preprocessing (including atmospheric correction, water body extraction, spectral simulation, spatiotemporal fusion and water extent extraction) to object detection and water quality parameter estimation. We also present a curated selection of valuable open-source resources and highlight key challenges—including data scarcity and label bias, limited model interpretability, insufficient uncertainty quantification, and the difficulties of multimodal data integration, long-term, large-scale, and multi-scale analysis, as well as the need for effective spatial relationship modeling—and discuss emerging trends such as multi-temporal data integration, and the incorporation of physical constraints. By providing a comprehensive overview of ML applications in this field, this review serves as a valuable reference for researchers and practitioners seeking to enhance water quality monitoring and assessment. The insights presented here are expected to foster the development of more accurate, scalable, and interpretable ML models, ultimately contributing to improved environmental management and decision-making.

Recommended citation: Shi, J., Li, Z., Liu, G., Fang, C., Wen, Z., Shang, Y., Tao, H., Li, S., Wang, Q., Wang, Z., & Song, K. (2026). A review of machine learning applications in inland and nearshore water color remote sensing: from preprocessing to object detection and parameter estimation. Journal of Hydrology, Volume 677, Part C, Article 135950. https://doi.org/10.1016/j.jhydrol.2026.135950
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