Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine
文献类型：期刊论文
| 作者 | Cao, Juan1; Zhang, Zhao1; Luo, Yuchuan1; Zhang, Liangliang1; Zhang, Jing1; Li, Ziyue1; Tao, Fulu2,3
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| 刊名 | EUROPEAN JOURNAL OF AGRONOMY
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| 出版日期 | 2021-02-01 |
| 卷号 | 123页码:12 |
| 关键词 | Winter wheat Machine learning Deep learning Google earth engine (GEE) Yield estimation |
| ISSN号 | 1161-0301 |
| DOI | 10.1016/j.eja.2020.126204 |
| 通讯作者 | Zhang, Zhao(zhangzhao@bnu.edu.cn) |
| 英文摘要 | To meet the challenges of climate change, increasing population and food demand, a timely, accurate and reliable estimation of crop yield at a large scale is more imperative than ever for crop management, food security evaluation, food trade and policy-making. In this study, taking the major winter wheat production regions of China as an example, we compared a traditional machine learning method (random forest, RF) and three deep learning (DL) models, including DNN (deep neural networks), 1D-CNN (1D convolutional neural networks), and LSTM (long short-term memory networks) to predict crop yields by integrating publicly available data within the GEE (Google Earth Engine) platform, including climate, satellite, soil properties, and spatial information data. The results showed that all four models could capture winter wheat yield variations in all the county-years, with R-2 of recorded and simulated yields ranging from 0.83 to 0.90 and RMSE ranging from 561.18 to 959.62 kg/ha. They all performed well for winter wheat yield prediction at a county level from 2011 to 2015, with mean R-2 >= 0.85 and RMSE <= 768 kg/ha. At a field level, the spatial pattern of estimated winter wheat yield could capture the spatial heterogeneity and yield differences between individual fields across a county fairly well. However, only the DNN and RF models had relatively good performance at the field level, with mean R2 values of 0.71, 0.66 and RMSE values of 1127 kg/ha and 956 kg/ha, respectively. The model comparisons showed that the performance of RF was not always worse than DL at both the county and field levels. Our findings demonstrated a scalable, simple and inexpensive framework for estimating crop yields at various scales in a timely manner and with reliable accuracy, which has important implications for crop yield forecasting, agricultural disaster monitoring, food trade policy, and food security warning. |
| WOS关键词 | LEAF-AREA INDEX ; RESOLUTION GLOBAL-MAPS ; CROP YIELDS ; CLASSIFICATION ; ASSIMILATION ; PERFORMANCE ; PHENOLOGY ; CORN ; GAP |
| 资助项目 | National Basic Research Program of China[41977405] ; National Basic Research Program of China[31761143006] |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| WOS记录号 | WOS:000612213600002 |
| 出版者 | ELSEVIER |
| 资助机构 | National Basic Research Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/160683]  |
| 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, Fac Geog Sci, State Key Lab Earth Surface Proc & Resources Ecol, Key Lab Environm Change & Nat Disaster MOE, Beijing 100875, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cao, Juan,Zhang, Zhao,Luo, Yuchuan,et al. Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine[J]. EUROPEAN JOURNAL OF AGRONOMY,2021,123:12. |
| APA | Cao, Juan.,Zhang, Zhao.,Luo, Yuchuan.,Zhang, Liangliang.,Zhang, Jing.,...&Tao, Fulu.(2021).Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine.EUROPEAN JOURNAL OF AGRONOMY,123,12. |
| MLA | Cao, Juan,et al."Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine".EUROPEAN JOURNAL OF AGRONOMY 123(2021):12. |
入库方式： OAI收割
来源：地理科学与资源研究所
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