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_W heat模型与遥感数据同化的可行性。CERES_W heat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_W heat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。
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.
出处
《遥感学报》
EI
CSCD
北大核心
2006年第5期804-811,共8页
NATIONAL REMOTE SENSING BULLETIN
基金
国家自然科学基金项目(编号:40371087
40401042)
中国科学院知识创新工程重要方向性项目(编号:KZCX3-SW-338-2
KZCX3-SW-334)
中国科学院百人计划项目(编号:KZCX0415)
国家教育部留学回国人员科研启动基金重点项目(编号:HX040013)
国防科学技术工业委员会项目(编号:KJSX0401)
关键词
遥感
作物生长模型
同化
冬小麦
长势监测
估产
remotely sensed data
crop growth model
assimilation
winter wheat
growth monitoring
yield prediction
作者简介
闫岩（1981-），女，现为中国科学院遥感应用研究所硕士生，主要从事遥感农情监测方面的研究工作。E-mail：yanyan_work@163．com.
