Improving Winter Wheat Yield Forecasting Based on Multi-Source Data and Machine Learning
文献类型：期刊论文
| 作者 | Sun, Yuexia3,4; Zhang, Shuai3,4; Tao, Fulu3,4,5; Aboelenein, Rashad2; Amer, Alia1 |
| 刊名 | AGRICULTURE-BASEL
 |
| 出版日期 | 2022-05-01 |
| 卷号 | 12期号:5页码:16 |
| 关键词 | solar induced chlorophyll fluorescence (SIF) winter wheat yield forecast random forest enhanced vegetation index (EVI) |
| DOI | 10.3390/agriculture12050571 |
| 通讯作者 | Zhang, Shuai(zhangshuai@igsnrr.ac.cn) |
| 英文摘要 | To meet the challenges of climate change, population growth, and an increasing food demand, an accurate, timely and dynamic yield estimation of regional and global crop yield is critical to food trade and policy-making. In this study, a machine learning method (Random Forest, RF) was used to estimate winter wheat yield in China from 2014 to 2018 by integrating satellite data, climate data, and geographic information. The results show that the yield estimation accuracy of RF is higher than that of the multiple linear regression method. The yield estimation accuracy can be significantly improved by using climate data and geographic information. According to the model results, the estimation accuracy of winter wheat yield increases dramatically and then flattens out over months; it approached the maximum in March, with R-2 and RMSE reaching 0.87 and 488.59 kg/ha, respectively; this model can achieve a better yield forecasting at a large scale two months in advance. |
| WOS关键词 | LEAF-AREA INDEX ; CHLOROPHYLL FLUORESCENCE ; PHOTOSYNTHETIC CAPACITY ; PRIMARY PRODUCTIVITY ; SOIL-MOISTURE ; TIME-SERIES ; MODEL ; SATELLITE ; LANDSAT ; CROPS |
| 资助项目 | National Key Research and Development Program of China[2016YFD0300201] ; National Science Foundation of China[41801078] |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| WOS记录号 | WOS:000801451800001 |
| 出版者 | MDPI |
| 资助机构 | National Key Research and Development Program of China ; National Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/178315]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhang, Shuai |
| 作者单位 | 1.Agr Res Ctr, Hort Res Inst, Med & Aromat Plants Dept, Giza 583121, Egypt 2.Agr Res Ctr, Field Crops Res Inst, Barley Res Dept, Giza 583121, Egypt 3.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 5.Nat Resources Inst Finland Luke, Helsinki 00790, Finland |
| 推荐引用方式 GB/T 7714 | Sun, Yuexia,Zhang, Shuai,Tao, Fulu,et al. Improving Winter Wheat Yield Forecasting Based on Multi-Source Data and Machine Learning[J]. AGRICULTURE-BASEL,2022,12(5):16. |
| APA | Sun, Yuexia,Zhang, Shuai,Tao, Fulu,Aboelenein, Rashad,&Amer, Alia.(2022).Improving Winter Wheat Yield Forecasting Based on Multi-Source Data and Machine Learning.AGRICULTURE-BASEL,12(5),16. |
| MLA | Sun, Yuexia,et al."Improving Winter Wheat Yield Forecasting Based on Multi-Source Data and Machine Learning".AGRICULTURE-BASEL 12.5(2022):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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