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 one of the key parameters that describe the plant physical structure. However, the LAI product is consistently discontinuous at spatial and temporal scales due to the contamination of atmospheric factors, which limits its application. In this paper, multi-year historical LAI datasets were used as a priori knowledge to establish the LAI background library, based on which, the improved Savitzky-Golay (SG) algorithm was designed to reconstruct the high quality LAI prof iles. The results indicated that by comparison with a traditional SG algorithm, the new algorithm performed better in aspects of both robustness and efficiency.