首页 >  2012, Vol. 16, Issue (5) : 986-999

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全文摘要次数: 3113 全文下载次数: 60
引用本文:

DOI:

10.11834/jrs.20120193

收稿日期:

2010-06-01

修改日期:

2012-02-15

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基于背景库的高质量LAI时间序列数据重建
张慧芳1,2,3, 高炜1,2,3, 施润和1,2
1.华东师范大学 地理信息科学教育部重点实验室,上海 200062;2.华东师范大学 中国科学院对地观测与数字研究中心-环境遥感与数据同化联合实验室,上海 200062;3.USDA UVB Monitoring and ResearchProgram, Natural Resource Ecology Laboratory, Colorado State University, FortCollins, CO, 80525, USA
摘要:

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.

Reconstruction of high-quality LAI time-series product based on long-term historical database
Abstract:

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.

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