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摘要： 叶面积指数Leaf Area Index(LAI)是表征植被冠层结构的一个重要参数，因大气条件等因素影响，使MODIS LAI数据产品中存在数据缺失、质量较低等问题，严重影响LAI数据集的应用。以江西省为研究区，综合利用像元质量分析、S-G滤波和年序列异常值检测滤波技术对2009~2013年MODIS LAI时序产品数据集进行重建研究。结果表明：阔叶林高质量像元占比最低，仅为51.76%，各类别低质量与反演失败像元整体占比达到20%~30%。针对数据集质量偏低的问题，提出了综合滤波方法。相较于S-G滤波法，重建后的高质量像元的LAI均值与原始均值更趋一致，中高质量像元重建后与原始数据的相关系数达到0.97，具有更好的保真性。对中低质量像元重建的异常值进行了滤波，填充了空值区，降低了标准偏差，较好地识别和修复了低值区或异常点，整体稳定性更好，能有效地拟合时序变化曲线。 Abstract: The Leaf Area Index(LAI) is an important parameter of the canopy structure of the vegetation.There are problems such as missing data and low quality in the MODIS LAI data product because of the influence of atmospheric conditions and other factors，which seriously affects its the application.As a case study of Jiangxi Province，MODIS LAI time series products data set in 2009-2013 were reconstructed by integrating pixel quality analysis，S-G filter and annual sequence abnormal value detection and filtering technology.The results show that the high quality of the broad-leaved forest was the lowest，only 51.76%，and proportion of low quality and retrieval failed pixels reached to 20% to 30%.The integrated filtering method proposed for low quality data set reconstruction.Compared with S-G filtering，the mean value of the reconstructed LAI by the integrated filtering method is more consistent with the original mean for high quality pixel.And its correlation coefficient between the high quality pixel and the original data reaches 0.97.So it has better fidelity.The outliers of the low- and medium-quality pixel reconstruction are filtered，and the area of null value is filled.Meanwhile，the standard deviation is reduced，and the low-value area or abnormal point is better identified and repaired.The overall stability becomes better and it can fit the time series change curve well.

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