A Systematic Review of the Application of Chinese Satellite Observations for Water Quality Remote Sensing

Effective water quality monitoring is essential for understanding and managing aquatic environments under increasing pressures from eutrophication, pollution, and climate change. Although satellite remote sensing provides an efficient means for large-scale and dynamic water quality observation, a systematic review of Chinese Earth observation satellites (CESE) for this purpose is still lackingThis review systematically examines CESE applications in water quality remote sensing. First, it provides a comprehensive overview of China’s multi-type satellite observation framework, encompassing land-resource, meteorological, ocean, and commercial platforms. Subsequently, based on an analysis of 307 publications retrieved from the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) (2000–2024), this review highlights the research trends, hotspots, and major applications of CESE in water quality monitoring. Bibliometric analysis indicates a steady increase in CESE-related publications during 2000–2024, with a rapid surge since 2019. Among the available platforms, the HJ (Huanjing), GF (Gaofen), and HY (Haiyang) are the most widely utilized. Scientifically, chlorophyll-a (Chl-a), total suspended matter (TSM), and eutrophication-related phenomena are the primary research targets. Methodologically, existing studies predominantly employ empirical/semi-empirical, analytical/semi-analytical, spectral index, and machine learning approaches. These methods have successfully supported the retrieval of major parameters and the monitoring of typical phenomena such as algal blooms, red tides, and black and odorous water bodies. Overall, CESE data offer complementary advantages in spatial, spectral, and temporal resolutions, demonstrating strong potential for monitoring inland, coastal, and marine waters. Nevertheless, critical challenges remain regarding atmospheric correction, applications in optically complex waters, model transferability, and data accessibility. Future efforts should prioritize advancing algorithms for complex waters, optimizing aquatic-dedicated sensors, enhancing cloud-based data-sharing platforms, and strengthening multi-source data fusion, thereby improving the scientific and practical value of CESE for water environment monitoring and management.