From multi-scale observations to integrated applications: a review of remote sensing for inland water quality monitoring

Water resources constitute an indispensable foundation for human survival and sustainable development, and their protection is a core objective of the United Nations Sustainable Development Goals. Conventional water quality monitoring methods are constrained by limitations in spatiotemporal coverage and operational costs, rendering them inadequate for meeting the demands of large-scale, dynamic monitoring. Remote sensing technology, offering distinct advantages in rapid data acquisition, extensive spatial coverage, and repeatable observations, has emerged as a transformative tool for inland water quality monitoring. This review provides a systematic synthesis of the progress in multi-scale remote sensing technologies applied to water quality monitoring, focusing on inversion methods for key parameters and their practical implementation in large lake basins. It proposes a core conceptual framework: inland water quality remote sensing is shifting toward a physics-informed, multi-source intelligent monitoring paradigm. The review elucidates that a mature multi-scale collaborative observation framework spanning “global–regional–local–site” scales is now operational. Medium-resolution sensors (e.g., MODIS) are optimally suited for long-term, large-scale dynamic monitoring of pollution events. High-resolution satellites (e.g., Landsat, Sentinel-2) constitute the primary workhorses for refined monitoring at the watershed and large lake scale. Very-high-resolution satellites and unmanned aerial vehicle platforms deliver unique value for precision investigations in small-scale water bodies. Furthermore, multi-source data fusion has become a pivotal trend in advancing water quality monitoring capabilities. In parameter inversion, machine learning and deep learning algorithms have demonstrably enhanced the accuracy and robustness of retrievals for core optically active constituents, including chlorophyll-a, total suspended solids, colored dissolved organic matter, and turbidity. In response to distinct lacustrine ecological and environmental challenges, such as algal blooms in the North American Great Lakes and eutrophication in large Chinese lakes, differentiated, context-specific multi-scale monitoring strategies have been successfully developed through research and practice. Finally, based on an assessment of current critical research challenges, this review proposes future research directions centered on leveraging multi-source data to serve the goals of integrated watershed management.