Integrating climate and satellite remote sensing data for predicting county-level wheat yield in China using machine learning methods
文献类型：期刊论文
| 作者 | Zhou, Weimo1,2; Liu, Yujie1,2,6; Ata-Ul-Karim, Syed Tahir3; Ge, Quansheng1,2; Li, Xing4; Xiao, Jingfeng5 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
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| 出版日期 | 2022-07-01 |
| 卷号 | 111页码:11 |
| ISSN号 | 1569-8432 |
| 关键词 | Wheat yield prediction Large scale Climate data Remote sensing Artificial intelligence |
| DOI | 10.1016/j.jag.2022.102861 |
| 通讯作者 | Liu, Yujie(liuyujie@igsnrr.ac.cn) |
| 英文摘要 | Early and reliable crop yield prediction on a large scale is imperative for making in-season crop management decisions as well as for ensuring global food security. The integrated use of climate and remote sensing data for predicting yield at regional and national scales has been previously investigated in various parts of the world. However, such attempts for national scale yield prediction, particularly in different planting zones in China have been rarely reported. For this purpose, this study explored the potential of nine climate variables, three remote sensing-derived metrics, and three machine learning methods (random forest, support vector machine, and least absolute shrinkage and selection operator) for predicting wheat yield based on data acquired during 2002-2010 from 1582 counties across China's three wheat planting zones. Our results illustrated large spatial divergences for yield prediction. The best performance (R-2 = 0.79 and R-2 = 0.66) was achieved for the northern winter wheat and northern spring wheat planting zones, respectively. Water-related climatic variables outperformed temperature-related variables, with the best individual predictive performance (R-2 = 0.67). Solar-induced chlorophyll fluorescence had better performance (R-2 = 0.60) for predicting the crop yield than NDVI and EVI. Climate data across the whole growing season has provided additional information for yield prediction as compared to remote sensing data. The additional contribution for yield prediction in winter wheat planting zones benefiting from climate data decreased from sowing to maturity, which was the opposite in remote sensing data. Typically, the support vector machine outperformed other models and the prediction in winter wheat planting zones performed better than the spring wheat planting zone. Our study demonstrates the effectiveness of integrating climate and remote sensing data for accurate county-level yield prediction in China. These kinds of simple and scalable machine learning methods could be targeted for further work by agricultural researchers and advisors. |
| WOS关键词 | LIGHT USE EFFICIENCY ; CROP YIELD ; CHLOROPHYLL FLUORESCENCE ; HEAT-STRESS ; PHOTOSYNTHESIS ; MODELS |
| 资助项目 | National Science Fund for Excellent Young Scholars[42122003] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA28060200] ; Youth Innovation Promotion Association, Chinese Academy of Sciences[Y202016] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000813505000001 |
| 资助机构 | National Science Fund for Excellent Young Scholars ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Youth Innovation Promotion Association, Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/180893]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Yujie |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Univ Tokyo, Grad Sch Agr & Life Sci, 1-1-1 Yayoi,Bunkyo, Tokyo 1138657, Japan 4.Seoul Natl Univ, Res Inst Agr & Life Sci, Seoul, South Korea 5.Univ New Hampshire, Inst Study Earth Oceans & Space, Earth Syst Res Ctr, Durham, NH 03824 USA 6.11 Plus,Datun Rd, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Weimo,Liu, Yujie,Ata-Ul-Karim, Syed Tahir,et al. Integrating climate and satellite remote sensing data for predicting county-level wheat yield in China using machine learning methods[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2022,111:11. |
| APA | Zhou, Weimo,Liu, Yujie,Ata-Ul-Karim, Syed Tahir,Ge, Quansheng,Li, Xing,&Xiao, Jingfeng.(2022).Integrating climate and satellite remote sensing data for predicting county-level wheat yield in China using machine learning methods.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,111,11. |
| MLA | Zhou, Weimo,et al."Integrating climate and satellite remote sensing data for predicting county-level wheat yield in China using machine learning methods".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 111(2022):11. |
入库方式： OAI收割
来源：地理科学与资源研究所
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