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-Jin; Yu, Gui-Rui; Chen, Zhi; Zhang, Wei-Kang; Han, Lang22; Wang, Qiu-Feng; Chen, Shi-Ping; Liu, Shao-Min; Yan, Jun-Hua; Zhang, Fa -Wei |
| 刊名 | SCIENCE OF THE TOTAL ENVIRONMENT
 |
| 出版日期 | 2023 |
| 卷号 | 857 |
| 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 |
| 文献子类 | Article |
| 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. |
| 学科主题 | Environmental Sciences |
| 电子版国际标准刊号 | 1879-1026 |
| 出版地 | AMSTERDAM |
| 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) |
| 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 [2019FY101303-2] ; National Natural Science Foundation of China [32071585, 32071586, 31500390] ; CAS Strategic Priority Research Program [XDA19020302] |
| 源URL | [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.Chinese Acad Sci, Inst Soil Sci, Nanjing 210008, Peoples R China 2.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Changchun 130102, Peoples R China 3.Chinese Acad Sci, Inst Subtrop Agr, Changsha 410125, Peoples R China 4.Inner Mongolia Agr Univ, Hohhot 010018, Peoples R China 5.Chinese Acad Sci, Chengdu Inst Biol, Chengdu 610041, Peoples R China 6.Qingdao Agr Univ, Qingdao 266109, Peoples R China 7.Shanxi Univ, Taiyuan 030006, Peoples R China 8.Chinese Acad Meteorol Sci, China Meteorol Adm, Beijing 100081, Peoples R China 9.Chinese Acad trop Agr Sci, Rubber Res Inst, Haikou 570100, Peoples R China 10.Chinese Acad Agr Sci, Inst Environm & sustainable Dev Agr, Beijing 100081, 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). |
入库方式： OAI收割
来源：植物研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。