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

doi:10.1029/2022GB007439

	Spatiotemporal Pattern of Ecosystem Respiration in China Estimated by Integration of Machine Learning With Ecological Understanding
	Han, Lang ; Yu, Gui-Rui ; Chen, Zhi 4; Zhu, Xian-Jin ; Zhang, Wei-Kang ; Wang, Tie-Jun ; Xu, Li ; Chen, Shi-Ping; Liu, Shao-Min ; Wang, Hui-Min ; Yan, Jun-Hua ; Tan, Jun-Lei ; Zhang, Fa-Wei ; Zhao, Feng-Hua ; Li, Ying-Nian ; Zhang, Yi-Ping ; Sha, Li-Qing ; Song, Qing-Hai ; Shi, Pei-Li ; Zhu, Jiao-Jun ; Wu, Jia-Bing ; Zhao, Zhong-Hui ; Hao, Yan-Bin ; Ji, Xi-Bin ; Zhao, Liang 10; Zhang, Yu-Cui ; Jiang, Shi-Cheng ; Gu, Feng-Xue ; Wu, Zhi-Xiang ; Zhang, Yang-Jian ; Li, Zhou 19; Tang, Ya-Kun ; Jia, Bing-Rui ; Dong, Gang 21; Gao, Yan-Hong ; Jiang, Zheng-De ; Sun, Dan 8; Wang, Jian-Lin ; He, Qi-Hua ; Li, Xin-Hu ; Wang, Fei 25; Wei, Wen-Xue ; Deng, Zheng-Miao ; Hao, Xiang-Xiang ; Liu, Xiao-Li ; Zhang, Xi-Feng ; Mo, Xing-Guo ; He, Yong-Tao ; Liu, Xin-Wei ; Du, Hu 26; Zhu, Zhi-Lin
	2022
发表期刊	GLOBAL BIOGEOCHEMICAL CYCLES
ISSN	0886-6236
卷号	36 期号:11
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.
关键词	ecosystem respiration eddy covariance terrestrial ecosystem machine learning substrate scale extension
学科领域	Environmental Sciences ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
DOI	10.1029/2022GB007439
收录类别	SCI
语种	英语
WOS关键词	EDDY COVARIANCE MEASUREMENTS ; SOIL-WATER CONTENT ; TEMPERATURE-DEPENDENCE ; TERRESTRIAL ECOSYSTEMS ; MODEL ; DECOMPOSITION ; ASSIMILATION ; MAINTENANCE ; SENSITIVITY ; SEPARATION
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000885881500001
出版者	AMER GEOPHYSICAL UNION
文献子类	Article
出版地	WASHINGTON
EISSN	1944-9224
资助机构	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]
作者邮箱	chenz@igsnrr.ac.cn
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ibcas.ac.cn/handle/2S10CLM1/28931
专题	植被与环境变化国家重点实验室
作者单位	1.Tianjin Univ, Inst Surface Earth Syst Sci, Sch Earth Syst Sci, Tianjin, Peoples R China 2.Tianjin Univ, Tianjin Bohai Rim Coastal Earth Crit Zone Natl Ob, Tianjin, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Yanshan Earth Crit Zone & Surface Fluxes Res Stn, Beijing, Peoples R China 6.Shenyang Agr Univ, Coll Agron, Beijing, Peoples R China 7.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing, Peoples R China 8.Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol, Fac Geog Sci, Beijing, Peoples R China 9.Chinese Acad Sci, South China Bot Garden, Guangzhou, Peoples R China 10.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou, Peoples R China 11.Chinese Acad Sci, Northwest Inst Plateau Biol, Xining, Peoples R China 12.Chinese Acad Sci, Xishuangbanna Trop Bot Garden, Menglun, Peoples R China 13.Chinese Acad Sci, Inst Appl Ecol, Shenyang, Peoples R China 14.Cent South Univ Forestry & Technol, Changsha, Peoples R China 15.Univ Chinese Acad Sci, Beijing, Peoples R China 16.Chinese Acad Sci, Inst Genet & Dev Biol, Ctr Agr Resources Res, Shijiazhuang, Hebei, Peoples R China 17.Northeast Normal Univ, Sch Life Sci, Changchun, Peoples R China 18.Chinese Acad Agr Sci, Inst Environm & Sustainable Dev Agr, Beijing, Peoples R China 19.Chinese Acad Trop Agr Sci, Rubber Res Inst, Danzhou, Peoples R China 20.Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China 21.Northwest A&F Univ, Xianyang, Peoples R China 22.Shanxi Univ, Taiyuan, Peoples R China 23.Qingdao Agr Univ, Coll Agron, Qingdao, Peoples R China 24.Chinese Acad Sci, Chengdu Inst Biol, Chengdu, Peoples R China 25.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, Urumqi, Peoples R China 26.Inner Mongolia Agr Univ, Coll Forestry, Hohhot, Peoples R China 27.Chinese Acad Sci, Inst Subtrop Agr, Changsha, Peoples R China 28.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Harbin, Peoples R China 29.Chinese Acad Sci, Inst Soil Sci, Nanjing, Peoples R China 30.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu, Peoples R China 31.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Its Related Land Proc, Beijing, 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).
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).
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).

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