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

Yingxin Shang Kaishan Song Zhidan Wen Fengfa Lai Ge Liu Hui Tao Xiangfei Yu

环境科学与生态技术（英文）Issue(3)：124-131,8.

环境科学与生态技术（英文）Issue(3)：124-131,8.DOI:10.1016/j.ese.2025.100570

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

Yingxin Shang ¹Kaishan Song ¹Zhidan Wen ¹Fengfa Lai ¹Ge Liu ¹Hui Tao ¹Xiangfei Yu²

作者信息

1. Northeast Institute of Geography and Agroecology,CAS,Changchun,130102,China
2. Jilin Jianzhu University,Changchun,130118,China
折叠

摘要

关键词

CDOM/Remote sensing/FT ICRMS/Soil erosion/Lake

Key words

CDOM/Remote sensing/FT ICRMS/Soil erosion/Lake

引用本文复制引用

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[J].环境科学与生态技术（英文）,2025,(3):124-131,8.

基金项目

The research was jointly supported by the National Natural Science Foundation of China(42371390,42471358),the Science &Technology Fundamental Resources Investigation Program(2021FY100406),the Jilin Provincial Department of Ecology and Environment(2024-01),Youth Innovation Promotion Association of Chinese Academy of Sciences of China granted for Dr.Yingxin Shang,the Staying Postdoctoral Researcher Support Program of Jilin Province granted for Dr.Yingxin Shang(2024),the Natural Science Foundation of Jilin Province,China(20220508017RC),the National funded postdoctoral researcher program(GZC20232638)and Young Scientist Group Project of Northeast Institute of Geography and Agroecology,China(2023QNXZ01).The authors thank all staff and students for their persistent assistance with field sampling and laboratory analysis.We thank the three anonymous reviewers for their constructive comments and suggestions. （42371390,42471358）

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