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

doi:10.1029/2022GB007439

CORC > 植物研究所 > 中国科学院植物研究所 > 植被与环境变化国家重点实验室

	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
刊名	GLOBAL BIOGEOCHEMICAL CYCLES
	2022
卷号	36 期号:11
关键词	ecosystem respiration eddy covariance terrestrial ecosystem machine learning substrate scale extension
ISSN号	0886-6236
DOI	10.1029/2022GB007439
文献子类	Article
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.
学科主题	Environmental Sciences ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
电子版国际标准刊号	1944-9224
出版地	WASHINGTON
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)
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 [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]
内容类型	期刊论文
源URL	[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.Chinese Acad Sci, Inst Soil Sci, Nanjing, 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
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).