Causality guided machine learning model on wetland CH4 emissions across global wetlands

Kunxiaojia Yuan; Qing Zhu; Fa Li; William J. Riley; Margaret Torn; Housen Chu; Gavin McNicol; Min Chen; Sara Knox; Kyle Delwiche; Huayi Wu; Dennis Baldocchi; Hongxu Ma; Ankur R. Desai; Jiquan Chen; Torsten Sachs; Masahito Ueyama; Oliver Sonnentag; Manuel Helbig; Eeva-Stiina Tuittila; Gerald Jurasinski; Franziska Koebsch; David Campbell; Hans Peter Schmid; Annalea Lohila; Mathias Goeckede; Mats B. Nilsson; Thomas Friborg; Joachim Jansen; Donatella Zona; Eugenie Euskirchen; Eric J. Ward; Gil Bohrer; Zhenong Jin; Licheng Liu; Hiroki Iwata; Jordan Goodrich; Robert Jackson

doi:10.1016/j.agrformet.2022.109115

Causality guided machine learning model on wetland CH4 emissions across global wetlands

Kunxiaojia Yuan, Qing Zhu, Fa Li, William J. Riley, Margaret Torn, Housen Chu, Gavin McNicol, Min Chen, Sara Knox, Kyle Delwiche, Huayi Wu, Dennis Baldocchi, Hongxu Ma, Ankur R. Desai, Jiquan Chen, Torsten Sachs, Masahito Ueyama, Oliver Sonnentag, Manuel Helbig, Eeva-Stiina TuittilaGerald Jurasinski, Franziska Koebsch, David Campbell, Hans Peter Schmid, Annalea Lohila, Mathias Goeckede, Mats B. Nilsson, Thomas Friborg, Joachim Jansen, Donatella Zona, Eugenie Euskirchen, Eric J. Ward, Gil Bohrer, Zhenong Jin, Licheng Liu, Hiroki Iwata, Jordan Goodrich, Robert Jackson

Centret för atmosfärvetenskaper (INAR)

Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

Originalspråk	engelska
Tidskrift	Agricultural and Forest Meteorology
Volym	324
Antal sidor	10
ISSN	0168-1923
DOI	https://doi.org/10.1016/j.agrformet.2022.109115
Status	Publicerad - 15 sep. 2022
MoE-publikationstyp	A1 Tidskriftsartikel-refererad

Vetenskapsgrenar

114 Fysik
113 Data- och informationsvetenskap

Åtkomst av dokument

10.1016/j.agrformet.2022.109115Licens: CC BY

1-s2.0-S0168192322003021-mainSlutlig publicerad version, 1,61 MBLicens: CC BY

Citera det här

Yuan, K., Zhu, Q., Li, F., Riley, W. J., Torn, M., Chu, H., McNicol, G., Chen, M., Knox, S., Delwiche, K., Wu, H., Baldocchi, D., Ma, H., Desai, A. R., Chen, J., Sachs, T., Ueyama, M., Sonnentag, O., Helbig, M., ... Jackson, R. (2022). Causality guided machine learning model on wetland CH4 emissions across global wetlands. Agricultural and Forest Meteorology, 324. https://doi.org/10.1016/j.agrformet.2022.109115

@article{c2b5409816ec44039666c942fd5025f1,

title = "Causality guided machine learning model on wetland CH4 emissions across global wetlands",

keywords = "Causal inference, Eddy covariance CH4 emission, Machine learning, Wetlands, 114 Physical sciences, 113 Computer and information sciences",

author = "Kunxiaojia Yuan and Qing Zhu and Fa Li and Riley, {William J.} and Margaret Torn and Housen Chu and Gavin McNicol and Min Chen and Sara Knox and Kyle Delwiche and Huayi Wu and Dennis Baldocchi and Hongxu Ma and Desai, {Ankur R.} and Jiquan Chen and Torsten Sachs and Masahito Ueyama and Oliver Sonnentag and Manuel Helbig and Eeva-Stiina Tuittila and Gerald Jurasinski and Franziska Koebsch and David Campbell and Schmid, {Hans Peter} and Annalea Lohila and Mathias Goeckede and Nilsson, {Mats B.} and Thomas Friborg and Joachim Jansen and Donatella Zona and Eugenie Euskirchen and Ward, {Eric J.} and Gil Bohrer and Zhenong Jin and Licheng Liu and Hiroki Iwata and Jordan Goodrich and Robert Jackson",

year = "2022",

month = sep,

day = "15",

doi = "10.1016/j.agrformet.2022.109115",

language = "English",

volume = "324",

journal = "Agricultural and Forest Meteorology",

issn = "0168-1923",

publisher = "Elsevier Scientific Publ. Co",

}

Yuan, K, Zhu, Q, Li, F, Riley, WJ, Torn, M, Chu, H, McNicol, G, Chen, M, Knox, S, Delwiche, K, Wu, H, Baldocchi, D, Ma, H, Desai, AR, Chen, J, Sachs, T, Ueyama, M, Sonnentag, O, Helbig, M, Tuittila, E-S, Jurasinski, G, Koebsch, F, Campbell, D, Schmid, HP, Lohila, A, Goeckede, M, Nilsson, MB, Friborg, T, Jansen, J, Zona, D, Euskirchen, E, Ward, EJ, Bohrer, G, Jin, Z, Liu, L, Iwata, H, Goodrich, J & Jackson, R 2022, 'Causality guided machine learning model on wetland CH4 emissions across global wetlands', Agricultural and Forest Meteorology, vol. 324. https://doi.org/10.1016/j.agrformet.2022.109115

TY - JOUR

T1 - Causality guided machine learning model on wetland CH4 emissions across global wetlands

AU - Yuan, Kunxiaojia

AU - Zhu, Qing

AU - Li, Fa

AU - Riley, William J.

AU - Torn, Margaret

AU - Chu, Housen

AU - McNicol, Gavin

AU - Chen, Min

AU - Knox, Sara

AU - Delwiche, Kyle

AU - Wu, Huayi

AU - Baldocchi, Dennis

AU - Ma, Hongxu

AU - Desai, Ankur R.

AU - Chen, Jiquan

AU - Sachs, Torsten

AU - Ueyama, Masahito

AU - Sonnentag, Oliver

AU - Helbig, Manuel

AU - Tuittila, Eeva-Stiina

AU - Jurasinski, Gerald

AU - Koebsch, Franziska

AU - Campbell, David

AU - Schmid, Hans Peter

AU - Lohila, Annalea

AU - Goeckede, Mathias

AU - Nilsson, Mats B.

AU - Friborg, Thomas

AU - Jansen, Joachim

AU - Zona, Donatella

AU - Euskirchen, Eugenie

AU - Ward, Eric J.

AU - Bohrer, Gil

AU - Jin, Zhenong

AU - Liu, Licheng

AU - Iwata, Hiroki

AU - Goodrich, Jordan

AU - Jackson, Robert

PY - 2022/9/15

Y1 - 2022/9/15

KW - Causal inference

KW - Eddy covariance CH4 emission

KW - Machine learning

KW - Wetlands

KW - 114 Physical sciences

KW - 113 Computer and information sciences

U2 - 10.1016/j.agrformet.2022.109115

DO - 10.1016/j.agrformet.2022.109115

M3 - Article

VL - 324

JO - Agricultural and Forest Meteorology

JF - Agricultural and Forest Meteorology

SN - 0168-1923

ER -