Machine learning reveals distinct aquatic organic matter patterns driven by soil erosion types
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 bo...
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| Language: | English |
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Elsevier
2025-05-01
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| Series: | Environmental Science and Ecotechnology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666498425000481 |
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| author | Yingxin Shang Kaishan Song Zhidan Wen Fengfa Lai Ge Liu Hui Tao Xiangfei Yu |
| author_facet | Yingxin Shang Kaishan Song Zhidan Wen Fengfa Lai Ge Liu Hui Tao Xiangfei Yu |
| author_sort | Yingxin Shang |
| collection | DOAJ |
| description | 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 (RMSEcalibration = 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 across lakes larger than 0.1 km2 in 2020. Our analysis revealed distinct spatial patterns, with mean CDOM absorption coefficients at 355 nm of 3.73 m-1 in freeze-thaw erosion regions, 6.31 m-1 in wind erosion regions, and 3.72 m-1 in hydraulic erosion regions, reflecting significant variations driven by erosion intensity. Two axes of PCA analysis explained over 48% variations of CDOM for different soil erosion types. Chemical characterization indicated that polycyclic aromatic predominated in wind and hydraulic erosion regions, whereas freeze-thaw erosion regions exhibited higher proportions of peptides and unsaturated aliphatic compounds. This study highlights the crucial connection between terrestrial soil erosion processes and aquatic DOM composition, providing vital insights for evaluating global carbon cycling and carbon storage within inland ecosystems. |
| format | Article |
| id | doaj-art-acd2046cd1bc4cecb32d91124bff2d3e |
| institution | Kabale University |
| issn | 2666-4984 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Environmental Science and Ecotechnology |
| spelling | doaj-art-acd2046cd1bc4cecb32d91124bff2d3e2025-08-20T03:24:47ZengElsevierEnvironmental Science and Ecotechnology2666-49842025-05-012510057010.1016/j.ese.2025.100570Machine learning reveals distinct aquatic organic matter patterns driven by soil erosion typesYingxin Shang0Kaishan Song1Zhidan Wen2Fengfa Lai3Ge Liu4Hui Tao5Xiangfei Yu6Northeast Institute of Geography and Agroecology, CAS, Changchun, 130102, ChinaNortheast Institute of Geography and Agroecology, CAS, Changchun, 130102, China; Corresponding author.Northeast Institute of Geography and Agroecology, CAS, Changchun, 130102, ChinaNortheast Institute of Geography and Agroecology, CAS, Changchun, 130102, ChinaNortheast Institute of Geography and Agroecology, CAS, Changchun, 130102, ChinaNortheast Institute of Geography and Agroecology, CAS, Changchun, 130102, ChinaJilin Jianzhu University, Changchun, 130118, ChinaChromophoric 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 (RMSEcalibration = 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 across lakes larger than 0.1 km2 in 2020. Our analysis revealed distinct spatial patterns, with mean CDOM absorption coefficients at 355 nm of 3.73 m-1 in freeze-thaw erosion regions, 6.31 m-1 in wind erosion regions, and 3.72 m-1 in hydraulic erosion regions, reflecting significant variations driven by erosion intensity. Two axes of PCA analysis explained over 48% variations of CDOM for different soil erosion types. Chemical characterization indicated that polycyclic aromatic predominated in wind and hydraulic erosion regions, whereas freeze-thaw erosion regions exhibited higher proportions of peptides and unsaturated aliphatic compounds. This study highlights the crucial connection between terrestrial soil erosion processes and aquatic DOM composition, providing vital insights for evaluating global carbon cycling and carbon storage within inland ecosystems.http://www.sciencedirect.com/science/article/pii/S2666498425000481CDOMRemote sensingFT ICRMSSoil erosionLake |
| spellingShingle | Yingxin Shang Kaishan Song Zhidan Wen Fengfa Lai Ge Liu Hui Tao Xiangfei Yu Machine learning reveals distinct aquatic organic matter patterns driven by soil erosion types Environmental Science and Ecotechnology CDOM Remote sensing FT ICRMS Soil erosion Lake |
| title | Machine learning reveals distinct aquatic organic matter patterns driven by soil erosion types |
| title_full | Machine learning reveals distinct aquatic organic matter patterns driven by soil erosion types |
| title_fullStr | Machine learning reveals distinct aquatic organic matter patterns driven by soil erosion types |
| title_full_unstemmed | Machine learning reveals distinct aquatic organic matter patterns driven by soil erosion types |
| title_short | Machine learning reveals distinct aquatic organic matter patterns driven by soil erosion types |
| title_sort | machine learning reveals distinct aquatic organic matter patterns driven by soil erosion types |
| topic | CDOM Remote sensing FT ICRMS Soil erosion Lake |
| url | http://www.sciencedirect.com/science/article/pii/S2666498425000481 |
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