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.