ArticleEnvironmental science and ecotechnology2025
Machine learning reveals distinct aquatic organic matter patterns driven by soil erosion types.
Article in Environmental science and ecotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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
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