Mapping Chinese annual gross primary productivity with eddy covariance measurements and machine learning

doi:10.1016/j.scitotenv.2022.159390

	Mapping Chinese annual gross primary productivity with eddy covariance measurements and machine learning
	Zhu, Xian-Jin ; Yu, Gui-Rui ; Chen, Zhi ; Zhang, Wei-Kang ; Han, Lang 3; Wang, Qiu-Feng ; Chen, Shi-Ping; Liu, Shao-Min ; Yan, Jun-Hua ; Zhang, Fa -Wei ; Zhao, Feng-Hua ; Li, Ying-Nian ; Zhang, Yi-Ping ; Shi, Pei -Li ; Zhu, Jiao-Jun ; Wu, Jia-Bing ; Zhao, Zhong-Hui ; Hao, Yan-Bin ; Sha, Li-Qing ; Zhang, Yu-Cui ; Jiang, Shi-Cheng ; Gu, Feng-Xue ; Wu, Zhi-Xiang ; Wang, Hui-Min ; Tan, Jun-Lei ; Zhang, Yang-Jian ; Zhou, Li 16; Tang, Ya-Kun ; Jia, Bing-Rui ; Li, Yu-Qiang ; Song, Qing-Hai ; Dong, Gang 18; Gao, Yan-Hong ; Jiang, Zheng-De ; Sun, Dan ; Wang, Jian-Lin ; He, Qi-Hua ; Li, Xin-Hu ; Wang, Fei 22; Wei, Wen-Xue ; Deng, Zheng-Miao ; Hao, Xiang-Xiang ; Li, Yan ; Liu, Xiao-Li ; Zhang, Xi-Feng ; Zhu, Zhi-Lin
	2023
发表期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
卷号	857
摘要	Annual gross primary productivity (AGPP) is the basis for grain production and terrestrial carbon sequestration. Map-ping regional AGPP from site measurements provides methodological support for analysing AGPP spatiotemporal var-iations thereby ensures regional food security and mitigates climate change. Based on 641 site-year eddy covariance measuring AGPP from China, we built an AGPP mapping scheme based on its formation and selected the optimal map-ping way, which was conducted through analysing the predicting performances of divergent mapping tools, variable combinations, and mapping approaches in predicting observed AGPP variations. The reasonability of the selected op-timal scheme was confirmed by assessing the consistency between its generating AGPP and previous products in spa-tiotemporal variations and total amount. Random forest regression tree explained 85 % of observed AGPP variations, outperforming other machine learning algorithms and classical statistical methods. Variable combinations containing climate, soil, and biological factors showed superior performance to other variable combinations. Mapping AGPP through predicting AGPP per leaf area (PAGPP) explained 86 % of AGPP variations, which was superior to other ap-proaches. The optimal scheme was thus using a random forest regression tree, combining climate, soil, and biological variables, and predicting PAGPP. The optimal scheme generating AGPP of Chinese terrestrial ecosystems decreased from southeast to northwest, which was highly consistent with previous products. The interannual trend and interan-nual variation of our generating AGPP showed a decreasing trend from east to west and from southeast to northwest, respectively, which was consistent with data-oriented products. The mean total amount of generated AGPP was 7.03 +/- 0.45 PgC yr-1 falling into the range of previous works. Considering the consistency between the generated AGPP and previous products, our optimal mapping way was suitable for mapping AGPP from site measurements. Our results provided a methodological support for mapping regional AGPP and other fluxes.
关键词	Carbon cycle Climate change Eddy covariance Terrestrial ecosystem Machine learning Scale extension
学科领域	Environmental Sciences
DOI	10.1016/j.scitotenv.2022.159390
收录类别	SCI
语种	英语
WOS关键词	TERRESTRIAL ECOSYSTEMS ; CARBON FLUXES ; NEURAL-NETWORKS ; USE EFFICIENCY ; CLIMATE ; REGRESSION ; MODEL ; MODIS ; SOIL ; DRIVERS
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000880035700009
出版者	ELSEVIER
文献子类	Article
出版地	AMSTERDAM
EISSN	1879-1026
资助机构	Special Foundation for National Science and Technology Basic Research Program of China [2019FY101303-2] ; National Natural Science Foundation of China [32071585, 32071586, 31500390] ; CAS Strategic Priority Research Program [XDA19020302]
作者邮箱	yugr@igsnrr.ac.cn ; qfwang@igsnrr.ac.cn
引用统计	被引频次：11[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/2S10CLM1/29141
专题	植被与环境变化国家重点实验室
作者单位	1.Shenyang Agr Univ, Coll Agron, Shenyang 110866, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Synth Res Ctr Chinese Ecosyst Res Network, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Tianjin Univ, Inst Surface Earth Syst Sci, Sch Earth Syst Sci, Tianjin 300072, Peoples R China 5.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 6.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China 7.Chinese Acad Sci, South China Bot Garden, Guangzhou 510650, Peoples R China 8.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China 9.Chinese Acad Sci, Northwest Inst Plateau Biol, Xining 810008, Peoples R China 10.Chinese Acad Sci, Xishuangbanna Trop Bot Garden, Mengla 666303, Peoples R China 11.Chinese Acad Sci, Inst Appl Ecol, Shenyang 110016, Peoples R China 12.Cent South Univ Forestry & Technol, Changsha 410004, Peoples R China 13.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 14.Chinese Acad Sci, Inst Genet & Dev Biol, Ctr Agr Resources Res, Shijiazhuang 050021, Peoples R China 15.Chinese Acad Agr Sci, Inst Environm & sustainable Dev Agr, Beijing 100081, Peoples R China 16.Chinese Acad trop Agr Sci, Rubber Res Inst, Haikou 570100, Peoples R China 17.Chinese Acad Meteorol Sci, China Meteorol Adm, Beijing 100081, Peoples R China 18.Shanxi Univ, Taiyuan 030006, Peoples R China 19.Qingdao Agr Univ, Qingdao 266109, Peoples R China 20.Chinese Acad Sci, Chengdu Inst Biol, Chengdu 610041, Peoples R China 21.Inner Mongolia Agr Univ, Hohhot 010018, Peoples R China 22.Chinese Acad Sci, Inst Subtrop Agr, Changsha 410125, Peoples R China 23.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Changchun 130102, Peoples R China 24.Chinese Acad Sci, Inst Soil Sci, Nanjing 210008, Peoples R China 25.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China 26.Liaoning Panjin Wetland Ecosyst Natl Observat & Re, Shenyang 110866, Peoples R China 27.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Xian-Jin,Yu, Gui-Rui,Chen, Zhi,et al. Mapping Chinese annual gross primary productivity with eddy covariance measurements and machine learning[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2023,857.
APA	Zhu, Xian-Jin.,Yu, Gui-Rui.,Chen, Zhi.,Zhang, Wei-Kang.,Han, Lang.,...&Zhu, Zhi-Lin.(2023).Mapping Chinese annual gross primary productivity with eddy covariance measurements and machine learning.SCIENCE OF THE TOTAL ENVIRONMENT,857.
MLA	Zhu, Xian-Jin,et al."Mapping Chinese annual gross primary productivity with eddy covariance measurements and machine learning".SCIENCE OF THE TOTAL ENVIRONMENT 857(2023).

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
1705462975_4eeef8d8e（7504KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览请求全文