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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传感器能够长时间收集全球陆地表面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。论文对中国区域MODIS LAI的标准产品进行了分析和总结,指出造成目前发布的中国区域MO-DIS 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 important parameter to characterize the growth of surface vegetation and system simulation of land surface processes. MODIS sensors mounted on Terra and Aqua satellites can collect LAI information of the global land surface over a long period of time. However, the temporal and spatial discontinuity of the MODIS LAI data products currently released restricts the application of MODIS LAI products in such fields as crop growth monitoring and yield estimation, earth surface process simulation and global change research. The paper analyzes and summarizes the standard products of MODIS LAI in China, and points out the reasons for the discontinuity in time and space of the standard products of MO-DIS LAI currently released in China. It not only has the reason of MODIS LAI inversion algorithm but also Reason for the quality of MODIS reflectivity data. Aiming at the problem of temporal and spatial discontinuity existing in MODIS LAI standard products in China, the paper further considers the influence of surface reflectivity data quality on MODIS LAI standard products based on the TSF filtering algorithm, and proposes an improved TSF filtering algorithm. The MODIS LAI improvement product based on this algorithm is generated in China region which is more temporally and spatially more continuous. The new algorithm developed in this paper and LAI improved products provide LAI data and product algorithm reference for related research.