Reconstruction and fusion correction method of multi-source LAI products in Maoershan forest based on ensemble Kalman filter
[Objective]Most of the existing leaf area index(LAI)products have some problems,such as low resolution,abnormal data and low accuracy,which are difficult to meet the requirements of some applications.Therefore,this study proposes a method of fusing multi-source LAI data to reduce the differences of data from different sources and improve product accuracy.[Method]The broad-leaved forest and coniferous forest in Maoershan experimental forest farm were taken as the research area.Based on MODIS LAI,VIIRS LAI and PROBA-V LAI products in 2017,the LAI background database was established to correct low-quality data by using years of LAI data as prior knowledge,and 3 LAI data sets were downscaled by mixed pixel decomposition.Based on Sentinel-2 reflectivity product coupling ensemble Kalman filter(EnKF)algorithm,LAI dynamic model and radiative transfer model,data assimilation was carried out.Finally,3 LAI data after assimilation were weighted and fused,and the accuracy was evaluated by using measured data.[Result]In broad-leaved forest,the correlation coefficients between the assimilated MODIS,VIIRS and PROBA-V LAI and the measured data were 0.59,0.56 and 0.62,respectively,which were 0.57,0.52 and 0.57 higher than the original data.The root mean square error(ERMSE)were 0.37,0.31 and 0.14 respectively,which were 1.23,1.69 and 1.06 lower than the original data.In coniferous forest,the correlation coefficients between the assimilated MODIS,VIIRS and PROBA-V LAI and the measured data were 0.59,0.49 and 0.56,respectively,which were 0.52,0.30 and 0.40 higher than the original data.ERMSE were 0.24,0.28 and 0.19 respectively,which were 1.22,0.67 and 1.35 lower than the original data.Through the fusion method,the correlation coefficients of LAI in broad-leaved forest and coniferous forest were 0.83 and 0.76 respectively,which were higher than the data after assimilation.ERMSE were 0.15 and 0.13,respectively,which were smaller than the error of the assimilated data.[Conclusion]Through data assimilation,the accuracy of 3 LAI products is improved,and the fused LAI data has higher accuracy and reliability than the single LAI data after assimilation.[Ch,4 fig.2 tab.30 ref.]
leaf area index(LAI)MODISVIIRSPROBA-Vreconstructionensemble Kalman filter(EnKF)data fusion
包塔娜、范文义
展开 >
东北林业大学林学院,黑龙江哈尔滨 150040
东北林业大学森林生态系统可持续经营教育部重点实验室,黑龙江哈尔滨 150040
叶面积指数(LAI) MODIS VIIRS PROBA-V 重建 集合卡尔曼滤波(EnKF) 数据融合
国家科技期刊平台
NSTL
万方数据
