Linking vegetation changes to Arctic methane efflux

Xiaoqi Zhou; Wensheng Xiao; Josep Peñuelas

doi:10.1016/j.tplants.2025.06.005

Linking vegetation changes to Arctic methane efflux

Xiaoqi Zhou^*
, Wensheng Xiao
, Josep Peñuelas

^*此作品的通讯作者

生态与环境科学学院

East China Normal University
Cerdanyola del Vallès

科研成果: 期刊稿件 › 短篇评述 › 同行评审

2 引用（Scopus）

摘要

Arctic methane emissions are uncertain, impacting climate models. We propose combining vegetation data with machine learning to improve methane process predictions, offering more reliable insights. This approach can better inform global policies to reduce warming and address climate change effectively.

源语言	英语
页（从-至）	937-940
页数	4
期刊	Trends in Plant Science
卷	30
期	9
DOI	https://doi.org/10.1016/j.tplants.2025.06.005
出版状态	已出版 - 9月 2025

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 13 气候行动

访问文件

10.1016/j.tplants.2025.06.005

其它文件与链接

链接到 Scopus 的出版物

引用此

@article{4080c85efaa54329b90582071bda0a54,

title = "Linking vegetation changes to Arctic methane efflux",

abstract = "Arctic methane emissions are uncertain, impacting climate models. We propose combining vegetation data with machine learning to improve methane process predictions, offering more reliable insights. This approach can better inform global policies to reduce warming and address climate change effectively.",

keywords = "Arctic region, methane efflux, methane process model, vegetation type",

author = "Xiaoqi Zhou and Wensheng Xiao and Josep Pe{\~n}uelas",

note = "Publisher Copyright: {\textcopyright} 2025 Elsevier Ltd",

year = "2025",

month = sep,

doi = "10.1016/j.tplants.2025.06.005",

language = "英语",

volume = "30",

pages = "937--940",

journal = "Trends in Plant Science",

issn = "1360-1385",

publisher = "Elsevier Ltd",

number = "9",

}

TY - JOUR

T1 - Linking vegetation changes to Arctic methane efflux

AU - Zhou, Xiaoqi

AU - Xiao, Wensheng

AU - Peñuelas, Josep

PY - 2025/9

Y1 - 2025/9

N2 - Arctic methane emissions are uncertain, impacting climate models. We propose combining vegetation data with machine learning to improve methane process predictions, offering more reliable insights. This approach can better inform global policies to reduce warming and address climate change effectively.

AB - Arctic methane emissions are uncertain, impacting climate models. We propose combining vegetation data with machine learning to improve methane process predictions, offering more reliable insights. This approach can better inform global policies to reduce warming and address climate change effectively.

KW - Arctic region

KW - methane efflux

KW - methane process model

KW - vegetation type

UR - https://www.scopus.com/pages/publications/105009688447

U2 - 10.1016/j.tplants.2025.06.005

DO - 10.1016/j.tplants.2025.06.005

M3 - 短篇评述

AN - SCOPUS:105009688447

SN - 1360-1385

VL - 30

SP - 937

EP - 940

JO - Trends in Plant Science

JF - Trends in Plant Science

IS - 9

ER -

Linking vegetation changes to Arctic methane efflux

摘要

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此