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叶面积指数LAI(Leaf Area Index)是表征植被冠层结构的重要参数,然而由于云等大气因素的影响,MODISLAI时间序列产品在时间与空间尺度的连续性仍存在问题。随着先验知识在遥感反演中的地位不断得到加强,本文将多年LAI历史数据作为先验知识,用以建立LAI背景库,并提出了基于LAI背景库的Savitzky-Golay(SG)滤波算法来实现LAI时间序列数据的降噪工作。结果表明,与传统SG滤波相比,新算法能够更加客观有效地重建LAI时间序列。
Leaf Area Index (LAI) is an important parameter to characterize the vegetation canopy structure. However, due to the influence of atmospheric factors such as clouds, the continuity of MODISLAI time-series products on time and space scales remains a problem. As prior knowledge has been continuously strengthened in the retrieval of remote sensing, this paper uses LAI history data from many years as a priori knowledge to build the LAI background library and proposes a Savitzky-Golay (SG) filtering algorithm based on the LAI background library To achieve the LAI time series data noise reduction. The results show that the new algorithm can reconstruct the LAI time series more objectively and effectively than the traditional SG filtering.