NDVI joint process-based models drive a learning ensemble model for accurately estimating cropland net primary productivity (NPP)
文献类型：期刊论文
| 作者 | Wang, Zhaosheng; Liu, Zhengjia; Huang, Mei |
| 刊名 | FRONTIERS IN ENVIRONMENTAL SCIENCE
 |
| 出版日期 | 2024-01-15 |
| 卷号 | 11页码:15 |
| 关键词 | cropland NPP (net primary productivity) learning ensemble model NDVI process-based model China |
| DOI | 10.3389/fenvs.2023.1304400 |
| 通讯作者 | Huang, Mei(huangm@igsnrr.ac.cn) |
| 英文摘要 | The accurate estimation of cropland net primary productivity (NPP) remains a significant challenge. We hypothesized that incorporating prior information on NPP simulated by process-based models into normalized difference vegetation index (NDVI) data would improve the accuracy of cropland ecosystem NPP estimations. We used NDVI, MNPP (NPP of process-based model), and SNPP (statistic-based NPP) data estimated by nine process-based models and yield statistics to build a learning ensemble of the random forest model (LERFM). We used the new model to re-evaluate the cropland NPP in China from 1982 to 2010. Large spatial discrepancies among MNPPs, which indicate uncertainties in cropland NPP estimation using different methods, were observed when compared to SNPP. The LERFM model showed a slightly underestimation of only -0.37%, while the multi-model average process-based model (MMEM) strongly underestimated -15.46% of the SNPP. LERFM accurately estimated cropland NPP with a high simulation skill score. A consistent increasing trend in the LERFM and MMEM NPP during 1982-2010 and a significant positive correlation (r = 0.795, p < 0.001) between their total NPP indicate that the LERFM model can better describe spatiotemporal dynamic changes in cropland NPP. This study suggests that a learning ensemble method that combines the NDVI and process-based simulation results can effectively improve cropland NPP. |
| WOS关键词 | GROSS PRIMARY PRODUCTION ; CARBON SEQUESTRATION ; CHINA ; ECOSYSTEM ; UNCERTAINTY ; AGRICULTURE ; EFFICIENCY ; SOILS ; SINK |
| 资助项目 | National Natural Science Foundation of China10.13039/501100001809 |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| 出版者 | FRONTIERS MEDIA SA |
| WOS记录号 | WOS:001152428400001 |
| 资助机构 | National Natural Science Foundation of China10.13039/501100001809 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/202221]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Huang, Mei |
| 作者单位 | Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Natl Ecosyst Sci Data Ctr, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Zhaosheng,Liu, Zhengjia,Huang, Mei. NDVI joint process-based models drive a learning ensemble model for accurately estimating cropland net primary productivity (NPP)[J]. FRONTIERS IN ENVIRONMENTAL SCIENCE,2024,11:15. |
| APA | Wang, Zhaosheng,Liu, Zhengjia,&Huang, Mei.(2024).NDVI joint process-based models drive a learning ensemble model for accurately estimating cropland net primary productivity (NPP).FRONTIERS IN ENVIRONMENTAL SCIENCE,11,15. |
| MLA | Wang, Zhaosheng,et al."NDVI joint process-based models drive a learning ensemble model for accurately estimating cropland net primary productivity (NPP)".FRONTIERS IN ENVIRONMENTAL SCIENCE 11(2024):15. |
入库方式： OAI收割
来源：地理科学与资源研究所
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。