Machine learning reveals distinct aquatic organic matter patterns driven by soil erosion types.

ABSTRACT: Chromophoric dissolved organic matter (CDOM), characterized by unique optical properties, is an essential indicator for understanding aquatic organic matter dynamics within global carbon cycles. Soil erosion, a major source of CDOM received by lakes, transports terrestrial organic matter to water bodies, altering sources, bioavailability and molecular complexity of CDOM significantly. Yet, the spatial patterns of CDOM in lakes from different soil erosion regions are still unknown. Here, we developed a robust machine learning framework (RMSE_calibration = 0.87 m^-1) to estimate CDOM concentrations in lakes by integrating over 1300 in situ water samples with Landsat 8 OLI surface reflectance data. We then applied this model to map the spatial distribution of CDOM a

SUBMITTER: Shang Y 

PROVIDER: S-EPMC12145842 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature