叶面积指数(LAI)是表征地表植被生长状况和进行陆面过程系统模拟的一个重要参数,搭载在Terra和Aqua两颗卫星上的MODIS传感器能够长时问收集全球陆地表面LAI的变化信息.然而,目前发布的MODIS LAI数据产品的时空不连续性制约着MODIS LAI产品在农作物长势监测与产量估计、地球表面过程模拟、全球变化研究等领域的应用.论文对中围区域MODIS LAI的标准产品进行了分析和总结,指出造成日前发布的中国区域MODIS LAI的标准产品在时间和空间上的不连续性,既有MODIS LAI反演算法的原因,更有MODIS反射率数据质量的原因.针对中国区域MODIS LAI标准产品存在的时空不连续性问题,论文在TSF滤波算法的基础上,进一步考虑地表反射率数据质量对MODIS LAI标准产品的影响,提出了改进的TSF滤波算法,并给出了基于该算法生成的时间上和空间上更具连续性的中国区域的MODIS LAI改进产品.本文发展的新算法和LAI改进产品可为相关研究提供LAI数据和产品算法参考.

Leaf area index (LAI) is an mi portantvegetation biophysicalvariable, and hasbeenwidely applied to the est miation ofcrop yield evapotranspiration, and netphotosynthesis. Meanwhile as an mi portant inputorout putparameter of some dynamic processmodels such as crop growthmodel and common landmode,l LAI is a connecting bridge to couple the processmodelswith remote sensingmodels. The Moderate Resolution Imaging Spectroradiometer(MODIS),carried aboard theTerra andAqua satellites, can collect the information ofLAI change continuously. The MODIS LAI standard product,issued since 2000, have been used in themeteorological and hydrologic field. However, spatio-temporaldiscon-tinuity exit in the MODIS standard LAI product,which restricts its application in the crop growthmonitoring and yield estmi ation, land surface process smi ulation and global change research. The paper analyzed the MODIS standard LAI product in China region, and pointed out that the spatio-temporal discontinuity of theMODIS standard LAI product in China region is caused notonly by theMODIS LAI reverse algorithm but also by the MODIS reflectance data quality. Based on theTSF filter algorithm a new mi provedTSF filteralgorithm isput forward in thispaper, inwhichmore attention ispaid to the relatively serious cloud covering in the crops growth season that results in poor quality of reflection data in China region.The mi proved TSF filter algorithm makes fulluse of the data quality control information in theMODIS data product and takes further consideration of the influence ofsurface reflectance data quality on MODISLAI standard product. Exper miental results show that the mi proved TSF algorithm can better process MODIS LAI standard product. Further, the miproved TSF algorithm hasbeen used to produce a new suit of MODISLAI product in China regionwithmuch better spatiotemporal continuity, which ismore reasonable and reliable dataset for integrating the remote sensing data productwith application models.