基于背景库的高质量LAI时间序列数据重建  

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

作　　者：张慧芳[1,2,3] 高炜[1,2,3] 施润和[1,2] 

机构地区：[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

出　　处：《遥感学报》2012年第5期986-999,共14页NATIONAL REMOTE SENSING BULLETIN

基　　金：上海市科委世博科技专项(编号:10DZ0581600);国家重点基础研究发展计划(973计划)(编号:2010CB951603);国家自然科学基金(编号:41101037);美国农业部全国粮食和农业研究所项目(编号:2010-34263-21075)~~

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。Leaf area index （LAI） is one of the key parameters that describe the plant physical structure. However, the LAI prod- uct 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 profiles. The results indicated that by comparison with a traditional SG algorithm, the new algorithm performed better in aspects of both robustness and efficiency.

关 键 词：LAI算法 时间序列 背景库 SG滤波 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]