Estimating crop yield from multi-temporal satellite data using multi-variate regression and neural network techniques
文献类型：会议论文
| 作者 | Ainong Li（李爱农） ; Shunlin Liang ; Angsheng Wang ; Jun Qin |
| 出版日期 | 2007 |
| 卷号 | 73 |
| 期号 | 10 |
| 页码 | 1149–1157 |
| 英文摘要 | Accurate, objective, reliable, and timely predictions of crop yield over large areas are critical to helping ensure the adequacy of a nation’s food supply and aiding policy makers on import/export plans and prices. Development of objective mathematical models of crop yield prediction using remote sensing is highly desirable. In this study, we develop a new methodology using an artificial neural network (ANN) to estimate and predict corn and soybean yields on a county-by-county basis, in the “corn belt” area in the Midwestern and Great Plains regions of the United States. The historical yield data and long time-series NDVI derived from AVHRR and MODIS are used to develop the models. A new procedure is developed to train the ANN model using the SCE-UA optimization algorithm. The performance of ANN models is compared with multivariate linear regression (MLR) models and validation is made on the model’s stability and forecasting ability. The new algorithms can effectively train ANN models, and the prediction accuracy can be as high as 85 percent. |
| 合作状况 | 其它 |
| 会议录 | Photogrammetric Engineering & Remote Sensing
 |
| 语种 | 英语 |
| 源URL | [http://ir.imde.ac.cn/handle/131551/2463]  |
| 专题 | 成都山地灾害与环境研究所_成都山地所知识仓储（2009年以前） |
| 推荐引用方式 GB/T 7714 | Ainong Li,Shunlin Liang,Angsheng Wang,et al. Estimating crop yield from multi-temporal satellite data using multi-variate regression and neural network techniques[C]. 见:. |
入库方式： OAI收割
来源：成都山地灾害与环境研究所
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