Accurately mapping global wheat production system using deep learning algorithms
文献类型：期刊论文
| 作者 | Luo, Yuchuan1,2; Zhang, Zhao1,2; Cao, Juan1,2; Zhang, Liangliang1,2; Zhang, Jing1,2; Han, Jichong1,2; Zhuang, Huimin1,2; Cheng, Fei1,2; Tao, Fulu3,4,5 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2022-06-01 |
| 卷号 | 110页码:10 |
| 关键词 | Wheat Crop mapping Yield estimation Deep learning Remote sensing |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2022.102823 |
| 通讯作者 | Zhang, Zhao(zhangzhao@bnu.edu.cn) |
| 英文摘要 | Assessing global food security and developing sustainable production systems need spatially explicit information on crop harvesting areas and yields; however the available datasets are spatially and temporally coarse. Here, we developed a general framework, Global Wheat Production Mapping System (GWPMS), to map the spatial distribution of wheat harvesting area and estimate yield using data-driven models across eight major wheat producing countries worldwide. We found GWPMS could not only generate robust wheat maps with R-2 consistently greater than 0.8, but also successfully captured a substantial fraction of yield variations with an average of 76%. The developed long short-term memory model outperformed other machine learning algorithms because it characterized the nonlinear and cumulative impacts of meteorological factors on yield. Using the derived wheat maps improved R-2 by 6.7% compared to a popularly used dataset. GWPMS is able to map spatial distribution of harvesting areas in a scalable way and further estimate gridded-yield robustly, and it can be applied globally using publicly available data. GWPMS and the resultant datasets will greatly accelerate our understanding and studies on global food security. |
| WOS关键词 | YIELD PREDICTION ; SATELLITE ; MODEL ; CORN ; MAPS |
| 资助项目 | National Natural Science Foundation of China[42061144003] ; National Natural Science Foundation of China[41977405] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000805028900003 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/177885]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhang, Zhao |
| 作者单位 | 1.Beijing Normal Univ, Sch Natl Safety & Emergency Management, Minist Educ, Beijing 100875, Peoples R China 2.Beijing Normal Univ, Acad Disaster Reduct & Emergency Management, Sch Natl Safety & Emergency Management, Minsitry Emergency Management, Beijing 100875, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 5.Natl Resources Inst Finland Luke, FI-00790 Helsinki, Finland |
| 推荐引用方式 GB/T 7714 | Luo, Yuchuan,Zhang, Zhao,Cao, Juan,et al. Accurately mapping global wheat production system using deep learning algorithms[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2022,110:10. |
| APA | Luo, Yuchuan.,Zhang, Zhao.,Cao, Juan.,Zhang, Liangliang.,Zhang, Jing.,...&Tao, Fulu.(2022).Accurately mapping global wheat production system using deep learning algorithms.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,110,10. |
| MLA | Luo, Yuchuan,et al."Accurately mapping global wheat production system using deep learning algorithms".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 110(2022):10. |
入库方式： OAI收割
来源：地理科学与资源研究所
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