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Spatiotemporal Pattern of Ecosystem Respiration in China Estimated by Integration of Machine Learning With Ecological Understanding

文献类型：期刊论文

作者Han, Lang4,5,6; Yu, Gui-Rui1,6; Chen, Zhi1,2,6; Zhu, Xian-Jin3; Zhang, Wei-Kang1,6; Wang, Tie-Jun4,5; Xu, Li1,6; Chen, Shi-Ping14; Liu, Shao-Min15; Wang, Hui-Min6
刊名GLOBAL BIOGEOCHEMICAL CYCLES
出版日期2022-11-01
卷号36期号:11页码:15
ISSN号0886-6236
关键词ecosystem respiration eddy covariance terrestrial ecosystem machine learning substrate scale extension
DOI10.1029/2022GB007439
通讯作者Chen, Zhi(chenz@igsnrr.ac.cn)
英文摘要Accurate estimation of regional and global patterns of ecosystem respiration (ER) is crucial to improve the understanding of terrestrial carbon cycles and the predictive ability of the global carbon budget. However, large uncertainties still exist in regional and global ER estimation due to the drawbacks of modeling methods. Based on eddy covariance ER data from 132 sites in China from 2002 to 2020, we established Intelligent Random Forest (IRF) models that integrated ecological understanding with machine learning techniques to estimate ER. The results showed that the IRF models performed better than semiempirical models and machine learning algorithms. The observed data revealed that gross primary productivity (GPP), living plant biomass, and soil organic carbon (SOC) were of great importance in controlling the spatiotemporal variability of ER across China. An optimal model governed by annual GPP, living plant biomass, SOC, and air temperature (IRF-04 model) matched 93% of the spatiotemporal variation in site-level ER, and was adopted to evaluate the spatiotemporal pattern of ER in China. Using the optimal model, we obtained that the annual value of ER in China ranged from 5.05 to 5.84 Pg C yr(-1) between 2000 and 2020, with an average value of 5.53 +/- 0.22 Pg C yr(-1). In this study, we suggest that future models should integrate process-based and data-driven approaches for understanding and evaluating regional and global carbon budgets.
WOS关键词EDDY COVARIANCE MEASUREMENTS ; SOIL-WATER CONTENT ; TEMPERATURE-DEPENDENCE ; TERRESTRIAL ECOSYSTEMS ; MODEL ; DECOMPOSITION ; ASSIMILATION ; MAINTENANCE ; SENSITIVITY ; SEPARATION
资助项目National Natural Science Foundation of China[42141005] ; Science and Technology Basic Investigation Program of China[2019FY101301] ; Youth Innovation Promotion Association of Chinese Academy of Sciences[2022050] ; Young Talents Project of Institute of Geographic Sciences and Natural Resources Research[2021RC004]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Meteorology & Atmospheric Sciences
语种英语
出版者AMER GEOPHYSICAL UNION
WOS记录号WOS:000885881500001
资助机构National Natural Science Foundation of China ; Science and Technology Basic Investigation Program of China ; Youth Innovation Promotion Association of Chinese Academy of Sciences ; Young Talents Project of Institute of Geographic Sciences and Natural Resources Research
源URL[http://ir.igsnrr.ac.cn/handle/311030/187282]  
专题中国科学院地理科学与资源研究所
通讯作者Chen, Zhi
作者单位1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Yanshan Earth Crit Zone & Surface Fluxes Res Stn, Beijing, Peoples R China
3.Shenyang Agr Univ, Coll Agron, Beijing, Peoples R China
4.Tianjin Univ, Inst Surface Earth Syst Sci, Sch Earth Syst Sci, Tianjin, Peoples R China
5.Tianjin Univ, Tianjin Bohai Rim Coastal Earth Crit Zone Natl Ob, Tianjin, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China
7.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Urumqi, Peoples R China
8.Inner Mongolia Agr Univ, Coll Forestry, Hohhot, Peoples R China
9.Chinese Acad Sci, Inst Subtrop Agr, Changsha, Peoples R China
10.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Harbin, Peoples R China
推荐引用方式
GB/T 7714		 Han, Lang,Yu, Gui-Rui,Chen, Zhi,et al. Spatiotemporal Pattern of Ecosystem Respiration in China Estimated by Integration of Machine Learning With Ecological Understanding[J]. GLOBAL BIOGEOCHEMICAL CYCLES,2022,36(11):15.
APA Han, Lang.,Yu, Gui-Rui.,Chen, Zhi.,Zhu, Xian-Jin.,Zhang, Wei-Kang.,...&Zhu, Zhi-Lin.(2022).Spatiotemporal Pattern of Ecosystem Respiration in China Estimated by Integration of Machine Learning With Ecological Understanding.GLOBAL BIOGEOCHEMICAL CYCLES,36(11),15.
MLA Han, Lang,et al."Spatiotemporal Pattern of Ecosystem Respiration in China Estimated by Integration of Machine Learning With Ecological Understanding".GLOBAL BIOGEOCHEMICAL CYCLES 36.11(2022):15.

入库方式： OAI收割

来源：地理科学与资源研究所

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