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

闫岩; 柳钦火; 刘强; 李静; 陈良富

doi:10.11834/jrs.200605119

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专辑

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
Methodolagy of Winter Wheat Yield Prediction based on Assimilation of Remote Sensing Data with Crop Growth Model
2006年第5期页码：804-811
纸质出版：2006
DOI： 10.11834/jrs.200605119
稿件说明：

移动端阅览

[1]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(05):804-811. DOI： 10.11834/jrs.200605119.

YAN Yan~. Methodolagy of Winter Wheat Yield Prediction based on Assimilation of Remote Sensing Data with Crop Growth Model[J]. Journal of Remote Sensing, 2006, (5): 804-811. DOI： 10.11834/jrs.200605119.

摘要

本文以LAI作为结合点

讨论了利用复合型混合演化（SCE

A）算法实现CERES

heat模型与遥感数据同化的可行性。CERES

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

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

A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。

Abstract

In this paper

the shuffled complex evolution（SCE

A） method was used to assimilate remotely sensed data into CERES

heat 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

heat 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

heat model after assimilation is not very sensitive to LAI errors and the number of LAI data.The advantage of the SCE

A method will help to realize wheat growth monitoring and yield prediction.