Mapping Chinese annual gross primary productivity with eddy covariance measurements and machine learning
文献类型：期刊论文
| 作者 | Zhu, Xian-Jin6; Yu, Gui-Rui7,8,27; Chen, Zhi7,8; Zhang, Wei-Kang7; Han, Lang1; Wang, Qiu-Feng7,8,27; Chen, Shi-Ping2; Liu, Shao-Min3; Yan, Jun-Hua4; Zhang, Fa -Wei9 |
| 刊名 | SCIENCE OF THE TOTAL ENVIRONMENT
 |
| 出版日期 | 2023-01-20 |
| 卷号 | 857页码:18 |
| ISSN号 | 0048-9697 |
| 关键词 | Carbon cycle Climate change Eddy covariance Terrestrial ecosystem Machine learning Scale extension |
| DOI | 10.1016/j.scitotenv.2022.159390 |
| 通讯作者 | Yu, Gui-Rui(yugr@igsnrr.ac.cn) ; Wang, Qiu-Feng(qfwang@igsnrr.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | TERRESTRIAL ECOSYSTEMS ; CARBON FLUXES ; NEURAL-NETWORKS ; USE EFFICIENCY ; CLIMATE ; REGRESSION ; MODEL ; MODIS ; SOIL ; DRIVERS |
| 资助项目 | Special Foundation for National Science and Technology Basic Research Program of China[2019FY101303-2] ; National Natural Science Foundation of China[32071585] ; National Natural Science Foundation of China[32071586] ; National Natural Science Foundation of China[31500390] ; CAS Strategic Priority Research Program[XDA19020302] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000880035700009 |
| 资助机构 | Special Foundation for National Science and Technology Basic Research Program of China ; National Natural Science Foundation of China ; CAS Strategic Priority Research Program |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/186593]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yu, Gui-Rui; Wang, Qiu-Feng |
| 作者单位 | 1.Tianjin Univ, Inst Surface Earth Syst Sci, Sch Earth Syst Sci, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 3.Beijing Normal Univ, Fac Geog Sci, State Key Lab Earth Surface Proc & Resource Ecol, Beijing 100875, Peoples R China 4.Chinese Acad Sci, South China Bot Garden, Guangzhou 510650, Peoples R China 5.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou 730000, Peoples R China 6.Shenyang Agr Univ, Coll Agron, Shenyang 110866, Peoples R China 7.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 8.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, 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 |
| 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,2023,857:18. |
| 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,857,18. |
| 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):18. |
入库方式： OAI收割
来源：地理科学与资源研究所
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