Four-Decade CDOM Dynamics in Amur River Basin Lakes from Landsat and Machine Learning

Lakes in the Amur River Basin (ARB) are increasingly influenced by climate variability and human activities, yet long-term basin-scale patterns of colored dissolved organic matter (CDOM) remain unclear. In this study, we developed a support vector regression (SVR) model to retrieve lake CDOM from Landsat 5/7/8 imagery and generated a 40-year (1984–2023) CDOM dataset for 69 large lakes. The model provides a reliable tool for multi-decadal, large-area water quality monitoring considering its robust performance (R² = 0.88, rRMSE = 22.4%, MAE = 2.63 m⁻¹). Trend analysis revealed a significant rise in CDOM since 1999, particularly across the Mongolian Plateau and Northeast China Plain. Among the 69 lakes, 27 exhibited increasing CDOM, while 4 showed declines, highlighting pronounced regional variability. Variance partitioning indicated that human activities, especially irrigation and grazing, account for ~30% of CDOM variation, exceeding the contribution of any single climatic driver, whereas temperature represents the dominant climate driver (12.8%). Shallow systems were more sensitive to external disturbances, while deep lakes responded more strongly to thermal conditions. This study delivers the first long-term satellite-based CDOM assessment in the ARB and underscores the combined impacts of climate change and land-use pressures on lake optical dynamics.