基于集合卡尔曼滤波的帽儿山森林多源LAI产品重建及融合校正方法  

Reconstruction and fusion correction method of multi-source LAI products in Maoershan forest based on ensemble Kalman filter

作　　者：包塔娜 范文义[1,2] BAO Ta’na;FAN Wenyi(College of Forestry,Northeast Forestry University,Harbin 150040,Heilongjiang,China;Key Laboratory of Forest Ecosystem Sustainable Management of Ministry of Education,Northeast Forestry University,Harbin 150040,Heilongjiang,China)

机构地区：[1]东北林业大学林学院,黑龙江哈尔滨150040 [2]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

出　　处：《浙江农林大学学报》2024年第4期841-849,共9页Journal of Zhejiang A&F University

基　　金：国家自然科学基金资助项目(31971654)。

摘　　要：【目的】现有叶面积指数(LAI)产品大多存在分辨率低、数据异常和精度低等问题,难以满足某些应用需求。因此,本研究提出一种多源LAI数据的融合方法,以减少不同来源数据的差异并提高产品精度。【方法】以帽儿山实验林场的阔叶林和针叶林区域为研究区,基于2017年的MODIS、VIIRS和PROBA-V的LAI产品,利用多年LAI数据作为先验知识建立LAI背景库修正低质量数据,对3种LAI数据集进行混合像元分解的降尺度处理,基于Sentinel-2反射率产品耦合集合卡尔曼滤波(EnKF)算法、LAI动态模型和辐射传输模型进行数据同化,最后对同化后的3种LAI数据进行赋权融合,使用实测数据进行精度评价。【结果】在阔叶林,同化后的MODIS、VIIRS和PROBA-V LAI与实测数据的相关系数分别为0.59、0.56和0.62,比原始数据提升了0.57、0.52和0.57;均方根误差分别为0.37、0.31和0.14,比原始数据减小了1.23、1.69和1.06。在针叶林,同化后的MODIS、VIIRS和PROBA-V LAI与实测数据的相关系数分别为0.59、0.49和0.56,比原始数据提升了0.52、0.30和0.40;均方根误差分别为0.24、0.28和0.19,比原始数据减小了1.22、0.67和1.35。通过融合方法,阔叶林LAI和针叶林LAI的相关系数分别为0.83和0.76,比同化后数据的相关性更高;均方根误差分别为0.15和0.13,比同化后数据的误差更小。【结论】通过数据同化提升了3种LAI产品精度,融合后LAI较同化后单一LAI具有更高的精度和可靠性。[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

关 键 词：叶面积指数(LAI) MODIS VIIRS PROBA-V 重建 集合卡尔曼滤波(EnKF) 数据融合 

分 类 号：S758[农业科学—森林经理学]