摘要
利用定量遥感技术反演的叶面积指数(LAI)在中国北方黄淮海地区典型县市进行冬小麦单产预测研究.为提高数据质量和减少估产误差,利用Savitzky-Golay滤波技术降低云对NDVI数据的影响及数据缺失;通过冬小麦实测LAI进行时序内插,模拟得到实测点每日冬小麦LAI,继而获得实测点主要生育时期平均LAI;在此基础上,建立了冬小麦主要生育时期平均LAI与作物单产关系模型,改变目前利用生育时期内某一时间点LAI代替整个生育时期LAI的方法;在模型择优基础上,得到最佳遥感估产关键期——开花期LAI与单产统计模型;最后,利用MODIS-NDVI经验模型反演得到的开花期平均LAI进行2008年冬小麦单产预测.结果表明:与地面实测的冬小麦单产相比,研究区估产平均相对误差为1.21%,RMSE达到257.33kg·hm-2,可以满足大范围估产的要求.利用上述方法可以在研究区冬小麦收获前20～30d进行准确的单产估计.
By using retrieved LAI from remotely-sensed imagery,this paper studied the regional winter wheat yield estimation in Huanghuaihai Plain of North China.In order to improve the quality of remotely sensed data for winter wheat yield estimation,a Savitzky-Golay filter was used to smooth the MODIS-NDVI time series data to reduce the cloud contamination and remove the abnormal data.Then,a Gaussian model was used to simulate the daily crop LAI which was corrected by interpolating the measured LAI to get the average LAI values for each phenological stage.Using these LAI data,the relationships between LAI and crop yield at the main phenological stages of winter wheat was established.After optimizing the yield estimation model,the optimal time period and the best model parameters for winter wheat yield estimation in the study area were selected out.Finally,the established model was applied to estimate winter wheat yield based on the retrieved LAI from MODIS-NDVI,and the model accuracy was tested.Through the comparison of the predicted yield with the measured yield in the field,the mean relative error was 1.21%,and the RMSE was 257.33 kg·hm-2.The model and the method proposed in this study were promising,and could help to get the accurate estimated yield of winter wheat in about 20-30 days ahead of the harvest.
出处
《应用生态学报》
CAS
CSCD
北大核心
2010年第11期2883-2888,共6页
Chinese Journal of Applied Ecology
基金
国家高技术研究发展计划项目(2006AA120101)
农业部"948"计划项目(2009-Z31)
农业部"全国农情遥感监测业务化运行"项目资助
国际科技合作项目(2010DFB10030)
中国农业科学院农业资源与农业区划研究所中央级公益性科研院所基本科研业务费专项(IARRP-2010-02
IARRP-2009-27)
