CHEN Chunyu, SHI Tianyu, LI Zhe. Advances in remote sensing of water quality in inland waters[J]. Yangtze River, 2025, 56(8): 41-48. DOI: 10.16232/j.cnki.1001-4179.2025.08.006
    Citation: CHEN Chunyu, SHI Tianyu, LI Zhe. Advances in remote sensing of water quality in inland waters[J]. Yangtze River, 2025, 56(8): 41-48. DOI: 10.16232/j.cnki.1001-4179.2025.08.006

    Advances in remote sensing of water quality in inland waters

    • Remote sensing technology enables efficient and low-cost monitoring on inland water quality parameters at large scales, playing a crucial role in river-lake management and water resource protection.Using bibliometric methods, this study analyzes 2, 796 relevant publications from the Web of Science Core Collection (SCI-Expanded) and CNKI core databases (including Peking University Core, EI, and SCI) between 2005 and 2024.Focusing on research trends in inland water quality remote sensing, CiteSpace was employed to visualize co-citation networks, author collaborations, and keyword timelines.The distribution of publications by time, discipline, journal, and country was analyzed, identifying high-impact institutions, productive authors, and significant literature.Keyword evolution across stages was investigated through burst detection.Key findings are: ①Global publications on inland water quality remote sensing showed fluctuating growth during 2005~2024, with annual growth rates of 4.58% (Chinese publications) and 11.62% (international publications).② China produced the most publications (700 English; 589 Chinese), followed by the United States (569) and India (176).③During 2005~2016, research focused on remote sensing water quality monitoring, multispectral inversion of water quality parameters, and lake color remote sensing, primarily using band-based empirical/semi-empirical models with inversion accuracy for chlorophyll-a and CDOM below 65%.From 2016~2022, emphasis shifted to remote-sensing-reflectance-based parameter estimation, UAV remote sensing, and turbidity monitoring, employing bio-optical analytical/semi-analytical algorithms.Chlorophyll-a inversion accuracy reached 90% in specific areas like Meiliang Bay in Taihu Lake, while turbidity and total nitrogen accuracy improved to 70%.Post-2022, research hotspots include hyperspectral parameter inversion, algal bloom monitoring, and random forest algorithms, achieving R2>0.8 for DOC, COD, total suspended solids, and water transparency.This study clarifies global research trajectories and emerging trends, providing references for scientific water resource management and protection in China.
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