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| 基于遥感数据与作物生长模型同化的冬小麦长势监测与估产方法研究 | |
| 引用本文： | 闫岩,柳钦火,刘强,李静,陈良富.基于遥感数据与作物生长模型同化的冬小麦长势监测与估产方法研究[J].遥感学报,2006,10(5):804-811. |
| 作者姓名： | 闫岩 柳钦火 刘强 李静 陈良富 |
| 作者单位： | 1. 遥感科学国家重点实验室,中国科学院,遥感应用研究所,北京,100101;中国科学院,研究生院,北京,100049 2. 遥感科学国家重点实验室,中国科学院,遥感应用研究所,北京,100101 3. 遥感科学国家重点实验室,中国科学院,遥感应用研究所,北京,100101;国家航天局,航天遥感论证中心,北京,100101;江西师范大学,地理学院,江西,南昌,330027 |
| 基金项目： | 国家自然科学基金;中国科学院知识创新工程项目;中国科学院"百人计划";国家教育部留学回国人员科研启动基金;国防科工委资助项目 |
| 摘 要： | 本文以LAI作为结合点，讨论了利用复合型混合演化（SCE—UA）算法实现CERES—Wheat模型与遥感数据同化的可行性。CERES—Wheat模型同化后主要生育期和产量的模拟值分别与真实条件下模型相应模拟值以及实测值进行比较。结果表明，同化后CERES—Wheat模型的模拟精度对LAI外部同化数据的误差并不十分敏感。并且在LAI同化数据较少时，也可获得较好的同化结果。这一特点体现了SCE—UA算法应用于同化过程的优越性，为同化策略在区域冬小麦长势监测及估产中的应用提供了基础。 |
| 关 键 词： | 遥感 作物生长模型 同化 冬小麦 长势监测 估产 |
| 文章编号： | 1007-4619(2006)05-0804-08 |
| 收稿时间： | 2006-04-10 |
| 修稿时间： | 2006-05-26 |
Methodolagy of Winter Wheat Yield Prediction based on Assimilation of Remote Sensing Data with Crop Growth Model |
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| YAN Yan,LIU Qin-huo,LIU Qian,LI Jing and CHEN Liang-fu.Methodolagy of Winter Wheat Yield Prediction based on Assimilation of Remote Sensing Data with Crop Growth Model[J].Journal of Remote Sensing,2006,10(5):804-811. | |
| Authors: | YAN Yan LIU Qin-huo LIU Qian LI Jing and CHEN Liang-fu |
| Institution: | 1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Bcijing Normal University, Beijing 100101, China; 2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China; 3. The Center for National Spaceborne Demonstration, Beijing 10010l, China; 4. Geography and Environment College, Jiangxi Normal University, Jiangxi Nanchang 330027, China |
| Abstract: | In this paper,the shuffled complex evolution(SCE_UA) method was used to assimilate remotely sensed data into CERES_Wheat model.In the process of model assimilation,leaf area index(LAI) was considered as the state variable.The simulated main growth stages and yields after assimilation were compared with simulated growth stages and yields with CERES_Wheat using actual input,and with measured data in the fields.The measured data was collected from four fields in different locations and planting conditions in Shunyi district and Beijing.The results show that the accuracy of simulation results of CERES_Wheat model after assimilation is not very sensitive to LAI errors and the number of LAI data.The advantage of the SCE_UA method will help to realize wheat growth monitoring and yield prediction. |
| Keywords: | remotely sensed data crop growth model assimilation winter wheat growth monitoring yield prediction |
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