| 基于背景库的高质量LAI时间序列数据重建 |
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| 引用本文： | 张慧芳,高炜,施润和.基于背景库的高质量LAI时间序列数据重建[J].遥感学报,2012,16(5):986-999. |
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| 作者姓名： | 张慧芳 高炜 施润和 |
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| 作者单位： | 华东师范大学 地理信息科学教育部重点实验室,上海 200062;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;USDA UVB Monitoring and ResearchProgram, Natural Resource Ecology Laboratory, Colorado State University, FortCollins, CO, 80525, USA;华东师范大学 地理信息科学教育部重点实验室,上海 200062;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;USDA UVB Monitoring and ResearchProgram, Natural Resource Ecology Laboratory, Colorado State University, FortCollins, CO, 80525, USA;华东师范大学 地理信息科学教育部重点实验室,上海 200062;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 |
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| 基金项目： | 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) |
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| 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。
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| 关 键 词： | LAI算法 时间序列 背景库 SG滤波 |
| 收稿时间： | 6/1/2010 12:00:00 AM |
| 修稿时间： | 2012/2/15 0:00:00 |
Reconstruction of high-quality LAI time-series product based on long-term historical database |
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| ZHANG Huifang,GAO Wei and SHI Runhe.Reconstruction of high-quality LAI time-series product based on long-term historical database[J].Journal of Remote Sensing,2012,16(5):986-999. |
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| Authors: | ZHANG Huifang GAO Wei and SHI Runhe |
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| Institution: | Key laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200062, China;Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University & CEODE/CAS, Shanghai 200062, China;USDA UVB Monitoring and ResearchProgram, Natural Resource Ecology Laboratory, Colorado State University, FortCollins, CO 80525, USA;Key laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200062, China;Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University & CEODE/CAS, Shanghai 200062, China;USDA UVB Monitoring and ResearchProgram, Natural Resource Ecology Laboratory, Colorado State University, FortCollins, CO 80525, USA;Key laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200062, China;Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University & CEODE/CAS, Shanghai 200062, China |
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| 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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| Keywords: | LAI algorithm time-series background library SG f ilter |
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