Linking vegetation changes to Arctic methane efflux.

IF 17.3 1区生物学 Q1 PLANT SCIENCES

Trends in Plant Science Pub Date : 2025-07-04 DOI:10.1016/j.tplants.2025.06.005

Xiaoqi Zhou, Wensheng Xiao, Josep Peñuelas

引用次数: 0

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.

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将植被变化与北极甲烷外流联系起来。

北极的甲烷排放是不确定的，影响着气候模型。我们建议将植被数据与机器学习相结合，以改进甲烷过程的预测，提供更可靠的见解。这种方法可以更好地为全球政策提供信息，以有效地减少变暖和应对气候变化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Trends in Plant Science 生物-植物科学

CiteScore

31.30

自引率

2.00%

发文量

196

审稿时长

6-12 weeks

期刊介绍： Trends in Plant Science is the primary monthly review journal in plant science, encompassing a wide range from molecular biology to ecology. It offers concise and accessible reviews and opinions on fundamental plant science topics, providing quick insights into current thinking and developments in plant biology. Geared towards researchers, students, and teachers, the articles are authoritative, authored by both established leaders in the field and emerging talents.