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Unraveling the impacts of climate factors on leaf area index of Chinese grasslands using interpretable machine learning models

文献类型：期刊论文

作者Liu, Wei1,2; Gao, Chengxi1,2; Lin, Shaozhi1,2; Zhou, Yu2; Bai, Wenrui1,2; Dai, Junhu2; Wang, Huanjiong2
刊名THEORETICAL AND APPLIED CLIMATOLOGY
出版日期2025-12-18
卷号157期号:1页码:23
ISSN号0177-798X
DOI10.1007/s00704-025-05970-6
产权排序1
文献子类Article
英文摘要The ecosystem services provided by grasslands depend on their biomass or leaf area index (LAI). Under the background of climate change, the impact of preseason climate and extreme weather events, such as high temperature and drought, on the spatiotemporal dynamics of grassland LAI remains unclear. Here, we constructed three interpretable machine learning models (including a Bayesian model, an interpretable neural network model, and a random forest model), to investigate the impact mechanisms of climate factors on grassland LAI in China from 2001 to 2020. The results showed that all three models performed well in simulating LAI (with R2 ranging from 0.540 to 0.963). The random forest model performed the best. Preseason climate was the most important factor driving LAI changes. The increase in preseason temperature, precipitation, and radiation could lead to an increase in grassland LAI. Regarding extreme weather events, heat events and heavy-rainfall events had positive effects on LAI, while frost events and no-rainfall events had negative impacts. CO2 showed a significant fertilization effect. Grazing intensity had a relatively small impact on LAI. The impact of precipitation on LAI was greater in spring than in autumn, whereas the impacts of temperature and radiation were greater in autumn than in spring. This study develops a climate change-adaptive framework for predicting grassland growth dynamics based on machine learning models, which can provide scientific support for grassland ecological protection and adaptive management.
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WOS研究方向Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001642642000002
出版者SPRINGER WIEN
源URL[http://ir.igsnrr.ac.cn/handle/311030/219432]  
专题陆地表层格局与模拟院重点实验室_外文论文
通讯作者Wang, Huanjiong
作者单位1.Univ Chinese Acad Sci, 19 A Yuquan Rd, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, 11 A Datun Rd, Beijing 100101, Peoples R China;
推荐引用方式
GB/T 7714		 Liu, Wei,Gao, Chengxi,Lin, Shaozhi,et al. Unraveling the impacts of climate factors on leaf area index of Chinese grasslands using interpretable machine learning models[J]. THEORETICAL AND APPLIED CLIMATOLOGY,2025,157(1):23.
APA Liu, Wei.,Gao, Chengxi.,Lin, Shaozhi.,Zhou, Yu.,Bai, Wenrui.,...&Wang, Huanjiong.(2025).Unraveling the impacts of climate factors on leaf area index of Chinese grasslands using interpretable machine learning models.THEORETICAL AND APPLIED CLIMATOLOGY,157(1),23.
MLA Liu, Wei,et al."Unraveling the impacts of climate factors on leaf area index of Chinese grasslands using interpretable machine learning models".THEORETICAL AND APPLIED CLIMATOLOGY 157.1(2025):23.

入库方式： OAI收割

来源：地理科学与资源研究所

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