Landsat monitoring reveals the history of river organic pollution across China during 1984-2023.

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River organic pollution exhibits pronounced spatiotemporal dynamics in response to environmental changes. However, the traditional method of tracking chemical oxygen demand (COD) and/or other organic pollution indicators at fixed locations over expansive regions is labor-intensive, time-consuming, and inadequate for achieving full spatial coverage. To address this limitation, here we developed a Random Forest algorithm using Landsat satellite data in conjunction with sub-daily (every 4 h) COD data at 1,997 sites across China. The proposed model achieved high accuracy, with a root mean square error of 0.52 mg/L and a mean absolute percent difference of 13.01 %. Additionally, the model was robust across clear, algae-laden, turbid, and black-smelling waters. Then, the algorithm was applied to investigate the spatiotemporal variations of COD concentration in Chinese rivers during 1984-2023. Across China, high river COD concentrations were observed in the eastern Songliao (3.56 ± 1.11 mg/L), Haihe (3.00 ± 0.89 mg/L), and Huaihe (3.57 ± 0.67 mg/L) basins. Anthropogenic activities could explain 79.39 % of the spatial variability in COD concentrations, and the cropland distribution had a significant impact. During 1984-2023, 73.58 % of China's rivers exhibited significant changes in COD concentrations (p < 0.05). With respect to the 800 mm isoprecipitation line, 56.62 % of the southeastern rivers showed decreasing trends; in contrast, 84.25 % of the northwestern rivers displayed increasing trends in COD concentrations. The temporal variations in COD concentrations were driven by the combined effects of factors including rainfall, vegetation coverage, and human activities; their relative contributions were 0.02 - 42.45 %, 0.07 - 68.76 %, and 0.06 - 90.31 % for COD changes in different provinces. This study underscores the feasibilities of using long-term Landsat data to efficiently and dynamically monitor organic pollution in rivers on a large scale, providing crucial implications for spatiotemporal monitoring of other water quality indicators.