County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model
文献类型：期刊论文
| 作者 | Sun, Jie1,2; Di, Liping1; Sun, Ziheng1; Shen, Yonglin2,3; Lai, Zulong2 |
| 刊名 | SENSORS
 |
| 出版日期 | 2019-10-02 |
| 卷号 | 19期号:20页码:21 |
| 关键词 | soybean yield prediction county-level Google Earth Engine CNN-LSTM |
| DOI | 10.3390/s19204363 |
| 通讯作者 | Sun, Jie(jsun20@gmu.edu) ; Di, Liping(ldi@gmu.edu) ; Lai, Zulong(laizulong@cug.edu.cn) |
| 英文摘要 | Yield prediction is of great significance for yield mapping, crop market planning, crop insurance, and harvest management. Remote sensing is becoming increasingly important in crop yield prediction. Based on remote sensing data, great progress has been made in this field by using machine learning, especially the Deep Learning (DL) method, including Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM). Recent experiments in this area suggested that CNN can explore more spatial features and LSTM has the ability to reveal phenological characteristics, which both play an important role in crop yield prediction. However, very few experiments combining these two models for crop yield prediction have been reported. In this paper, we propose a deep CNN-LSTM model for both end-of-season and in-season soybean yield prediction in CONUS at the county-level. The model was trained by crop growth variables and environment variables, which include weather data, MODIS Land Surface Temperature (LST) data, and MODIS Surface Reflectance (SR) data; historical soybean yield data were employed as labels. Based on the Google Earth Engine (GEE), all these training data were combined and transformed into histogram-based tensors for deep learning. The results of the experiment indicate that the prediction performance of the proposed CNN-LSTM model can outperform the pure CNN or LSTM model in both end-of-season and in-season. The proposed method shows great potential in improving the accuracy of yield prediction for other crops like corn, wheat, and potatoes at fine scales in the future. |
| WOS关键词 | ARTIFICIAL NEURAL-NETWORK ; VEGETATION INDEXES ; SOIL PROPERTIES ; GRAIN-YIELD ; WHEAT YIELD ; MODIS NDVI ; CROP ; AREA |
| 资助项目 | National Key Research and Development Project Integrated Aerogeophysical Detection System Integration and Method Technology Demonstration Research, China[2017YFC0602201] ; State Key Laboratory of Resources and Environmental Information System ; China Scholarship Council[201806415026] |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000497864700011 |
| 出版者 | MDPI |
| 资助机构 | National Key Research and Development Project Integrated Aerogeophysical Detection System Integration and Method Technology Demonstration Research, China ; State Key Laboratory of Resources and Environmental Information System ; China Scholarship Council |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/130180]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Sun, Jie; Di, Liping; Lai, Zulong |
| 作者单位 | 1.George Mason Univ, Ctr Spatial Informat Sci & Syst, Fairfax, VA 22030 USA 2.China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Hubei, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Sun, Jie,Di, Liping,Sun, Ziheng,et al. County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model[J]. SENSORS,2019,19(20):21. |
| APA | Sun, Jie,Di, Liping,Sun, Ziheng,Shen, Yonglin,&Lai, Zulong.(2019).County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model.SENSORS,19(20),21. |
| MLA | Sun, Jie,et al."County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model".SENSORS 19.20(2019):21. |
入库方式： OAI收割
来源：地理科学与资源研究所
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