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

^*Corresponding author for this work

School of Ecology and Environmental Sciences

Research output: Contribution to journal › Short survey › peer-review

2 Scopus citations

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.

Original language	English
Pages (from-to)	937-940
Number of pages	4
Journal	Trends in Plant Science
Volume	30
Issue number	9
DOIs	https://doi.org/10.1016/j.tplants.2025.06.005
State	Published - Sep 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Keywords

Arctic region
methane efflux
methane process model
vegetation type

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10.1016/j.tplants.2025.06.005

Cite this

@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

Abstract

UN SDGs

Keywords

Access to Document

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