XTBG OpenIR  > 全球变化研究组
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, Lang3; 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, Li16; Tang, Ya-Kun; Jia, Bing-Rui; Li, Yu-Qiang; Song, Qing-Hai; Dong, Gang18; Gao, Yan-Hong; Jiang, Zheng-De; Sun, Dan; Wang, Jian-Lin; He, Qi-Hua; Li, Xin-Hu; Wang, Fei22; Wei, Wen-Xue; Deng, Zheng-Miao; Hao, Xiang-Xiang; Li, Yan; Liu, Xiao-Li; Zhang, Xi-Feng; Zhu, Zhi-Lin
2023
Source PublicationSCIENCE OF THE TOTAL ENVIRONMENT
Volume857Issue:xPages:-
Abstract

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.

KeywordCarbon cycle Climate change Eddy covariance Terrestrial ecosystem Machine learning Scale extension
Subject AreaEnvironmental Sciences
DOI10.1016/j.scitotenv.2022.159390
Indexed BySCI
Language英语
WOS IDWOS:000880035700009
Citation statistics
Document Type期刊论文
Identifierhttps://ir.xtbg.ac.cn/handle/353005/13349
Collection全球变化研究组
Affiliation1.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
Recommended Citation
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(x):-.
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(x),-.
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.x(2023):-.
Files in This Item: Download All
File Name/Size DocType Version Access License
Mapping Chinese annu(7504KB)期刊论文出版稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhu, Xian-Jin]'s Articles
[Yu, Gui-Rui]'s Articles
[Chen, Zhi]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhu, Xian-Jin]'s Articles
[Yu, Gui-Rui]'s Articles
[Chen, Zhi]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhu, Xian-Jin]'s Articles
[Yu, Gui-Rui]'s Articles
[Chen, Zhi]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Mapping Chinese annual gross primary productivity with eddy covariance measurements and machine learning.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.