文献类型： 外文期刊

作者： Zhu, Xian-Jin ¹ ; Yu, Gui-Rui ² ; Chen, Zhi ² ; Zhang, Wei-Kang ² ; Han, Lang ⁴ ; Wang, Qiu-Feng ² ; Chen, Shi-Ping ⁵ ; Liu, Shao-Min ⁶ ; Yan, Jun-Hua ⁷ ; Zhang, Fa -Wei ⁹ ; Zhao, Feng-Hua ² ; Li, Ying-Nian ⁹ ; Zhang, Yi-Ping ¹⁰ ; Shi, Pei -Li ² ; Zhu, Jiao-Jun ¹¹ ; Wu, Jia-Bing ¹¹ ; Zhao, Zhong-Hui ¹² ; Hao, Yan-Bin ¹³ ; Sha, Li-Qing ¹⁰ ; Zhang, Yu-Cui ¹⁴ ; Jiang, Shi-Cheng ¹ ; Gu, Feng-Xue ¹⁵ ; Wu, Zhi-Xiang ¹ ; Wang, Hui-Min ² ; Tan, Jun-Lei ⁸ ; Zhang, Yang-Jian ² ; Zhou, Li ¹⁷ ; Tang, Ya-Kun ¹⁸ ; Jia, Bing-Rui ⁵ ; Li, Yu-Qiang ¹ ; Song, Qing-Hai ¹ ; Dong, Gang ¹⁹ ; Gao, Yan-Hong ¹ ; Jiang, Zheng-De ¹ ; Sun, Dan ¹ ; Wang, Jian-Lin ¹ ; He, Qi-Hua ²⁰ ; Li, Xin-Hu ²¹ ; Wang, Fei ²³ ; Wei, Wen-Xue ²⁴ ; Deng, Zheng-Miao ²⁵ ; Hao, Xiang-Xiang ¹ ; Li, Yan ¹ ; Liu, Xiao-Li ¹ ; Zhang, Xi-Feng ²⁶ ; Zhu, Zhi-Lin ² ;

作者机构： 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

关键词： Carbon cycle; Climate change; Eddy covariance; Terrestrial ecosystem; Machine learning; Scale extension

期刊名称：SCIENCE OF THE TOTAL ENVIRONMENT （ 影响因子：9.8； 五年影响因子：9.6 ）

ISSN： 0048-9697

年卷期： 2023 年 857 卷

页码：

收录情况： SCI

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.