Estimating Global Wheat Yields at 4 km Resolution during 1982-2020 by a Spatiotemporal Transferable Method
文献类型：期刊论文
| 作者 | Zhang, Zhao4; Luo, Yuchuan3,4; Han, Jichong4; Xu, Jialu4; Tao, Fulu1,2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-07-01 |
| 卷号 | 16期号:13页码:16 |
| 关键词 | spatiotemporal transferable method global wheat yield estimates remote sensing |
| DOI | 10.3390/rs16132342 |
| 英文摘要 | Reliable and spatially explicit information on global crop yield has paramount implications for food security and agricultural sustainability. However, most previous yield estimates are either coarse-resolution in both space and time or are based on limited studied areas. Here, we developed a transferable approach to estimate 4 km global wheat yields and provide the related product from 1982 to 2020 (GlobalWheatYield4km). A spectra-phenology integration method was firstly proposed to identify spatial distributions of spring and winter wheat, followed by choosing the optimal yield prediction model at 4 km grid scale, with openly accessible data, including subnational-level census data covering similar to 11,000 political units. Finally, the optimal models were transferred at both spatial and temporal scales to obtain a consistent yield dataset product. The results showed that GlobalWheatYield4km captured 82% of yield variations with an RMSE of 619.8 kg/ha, indicating good temporal consistency (r and nRMSE ranging from 0.4 to 0.8 and 13.7% to 37.9%) with the observed yields across all subnational regions covering 40 years. In addition, our dataset generally had a higher accuracy (R-2 = 0.71) as compared with the Spatial Production Allocation Model (SPAM) (R-2 = 0.49). The method proposed for the global yield estimate would be applicable to other crops and other areas during other years, and our GlobalWheatYield4km dataset will play important roles in agro-ecosystem modeling and climate impact and adaptation assessment over larger spatial extents. |
| WOS关键词 | CROP YIELD ; CLIMATE DATA ; SATELLITE ; PHENOLOGY ; PREDICTION ; WEATHER ; AREA ; VEGETATION ; RESPONSES ; MODELS |
| 资助项目 | National Natural Science Foundation of China[42061144003] ; National Natural Science Foundation of China[41977405] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001270336000001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/207408]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Luo, Yuchuan |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Southwest Univ, Sch Geog Sci, Chongqing Jinfo Mt Karst Ecosyst Natl Observat & R, Chongqing 400715, Peoples R China 4.Beijing Normal Univ, Sch Natl Safety & Emergency Management, Joint Int Res Lab Catastrophe Simulat & Syst Risk, Zhuhai 519087, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Zhao,Luo, Yuchuan,Han, Jichong,et al. Estimating Global Wheat Yields at 4 km Resolution during 1982-2020 by a Spatiotemporal Transferable Method[J]. REMOTE SENSING,2024,16(13):16. |
| APA | Zhang, Zhao,Luo, Yuchuan,Han, Jichong,Xu, Jialu,&Tao, Fulu.(2024).Estimating Global Wheat Yields at 4 km Resolution during 1982-2020 by a Spatiotemporal Transferable Method.REMOTE SENSING,16(13),16. |
| MLA | Zhang, Zhao,et al."Estimating Global Wheat Yields at 4 km Resolution during 1982-2020 by a Spatiotemporal Transferable Method".REMOTE SENSING 16.13(2024):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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