Reconstruction of high-quality LAI time-series product based on long-term historical database
基于背景库的高质量LAI时间序列数据重建

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.