Improving pedotransfer functions for predicting soil mineral associated organic carbon by ensemble machine learning

Yi Xiao; Jie Xue; Xianglin Zhang; Nan Wang; Yongsheng Hong; Yefeng Jiang; Yin Zhou; Hongfen Teng; Bifeng Hu; Emanuele Lugato; Anne Richer-De-Forges; Dominique Arrouays; Zhou Shi; Songchao Chen

doi:10.1016/j.geoderma.2022.116208

Journal Articles Geoderma Year : 2022

Improving pedotransfer functions for predicting soil mineral associated organic carbon by ensemble machine learning

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Yi Xiao

Function : Author

ZJU-Hangzhou Global Scientific and Technological Innovation Center

Institute of Applied Remote Sensing and Information Technology

Jie Xue

Function : Author

Institute of Applied Remote Sensing and Information Technology

Xianglin Zhang

Function : Author

Institute of Applied Remote Sensing and Information Technology

Nan Wang

Function : Author

Institute of Applied Remote Sensing and Information Technology

Yongsheng Hong

Function : Author

Institute of Applied Remote Sensing and Information Technology

Yefeng Jiang

Function : Author

Institute of Applied Remote Sensing and Information Technology

Yin Zhou

Function : Author

Institute of Land and Urban-Rural Development

Hongfen Teng

Function : Author

School of Environmental Ecology and Biological Engineering

Bifeng Hu

Function : Author

Department of Land Resource Management

Emanuele Lugato

Function : Author

European Commission - Joint Research Centre [Ispra]

Anne Richer-De-Forges

Function : Author

InfoSol

Dominique Arrouays

Function : Author

InfoSol

Zhou Shi

Function : Author

Institute of Applied Remote Sensing and Information Technology

Songchao Chen

Function : Author

ZJU-Hangzhou Global Scientific and Technological Innovation Center

Institute of Applied Remote Sensing and Information Technology

Keywords

algorithm carbon dioxide climate model validation model nitrogen particulate organic carbon pedotransfer function prediction sand soil erosion soil minerals soil organic carbon topsoil Europe

Domains

Sciences of the Universe [physics] Earth Sciences Environmental Sciences Global Changes

Anne Richer-de-Forges : Connect in order to contact the contributor

https://hal.inrae.fr/hal-03830619

Submitted on : Wednesday, October 26, 2022-2:24:07 PM

Last modification on : Tuesday, November 22, 2022-10:57:46 AM

Dates and versions

hal-03830619 , version 1 (26-10-2022)

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Attribution - NonCommercial - NoDerivatives - CC BY 4.0

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HAL Id : hal-03830619 , version 1
DOI : 10.1016/j.geoderma.2022.116208

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Yi Xiao, Jie Xue, Xianglin Zhang, Nan Wang, Yongsheng Hong, et al.. Improving pedotransfer functions for predicting soil mineral associated organic carbon by ensemble machine learning. Geoderma, 2022, pp.116208. ⟨10.1016/j.geoderma.2022.116208⟩. ⟨hal-03830619⟩

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Improving pedotransfer functions for predicting soil mineral associated organic carbon by ensemble machine learning

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