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作 者:李正国[1,2] 唐华俊[1,2] 杨鹏[1,2] 吴文斌[1,2] 陈仲新[1,2] 周清波[1,2] 张莉[1,2] 邹金秋[1,2]
机构地区:[1]农业部资源遥感与数字农业重点开放实验室,北京100081 [2]中国农业科学研究院农业资源与农业区划研究所
出 处:《中国农业资源与区划》2012年第5期20-28,共9页Chinese Journal of Agricultural Resources and Regional Planning
基 金:农业部"948"项目(2011-G6)国家重点基础研究发展计划项目("973"计划)(2010CB951502);国家自然科学基金项目(40930101;41171328;41201089);中央级公益性科研院所专项资金项目(IARRP-2011-15)
摘 要:多时相遥感数据因其高时效、宽范围和低成本等优点正被广泛应用于对地观测活动中,为大区域尺度掌握植被/作物空间格局提供了新的科学技术手段。通过对相关研究进展的回顾,该文系统归纳了植被物候特征遥感提取的关键步骤。首先从数据获取与处理方面,整理了目前常用的NDVI数据集以及预处理方法等相关背景信息;其次从提取原理、假设条件以及适用性等角度,比较了目前用于时序遥感数据重构和特征提取的常用技术方法;在此基础上,归纳了基于时序遥感数据拟合曲线提取的植被物候特征及其农业内涵意义;最后,结合目前的农业应用,探讨了其在识别作物物候期、确定种植制度以及提高遥感估产精度等专业领域的应用方向,以期为区域尺度上生态管理与农业生产提供科学支持。spatial sensing technique had been proven to offer an optimal approach for patterns of vegetation because of its high temporal resolution, this paper was to address the methodological techniques acquiring detecting and monitoring the wide spatial coverage and low cost. The aim of vegetation phenological information based on multi - temporal remote sensing archives and derive a more thorough understanding of the currently on - going re- search. First, the historical background of the processing methods for generalizing NDVI dataset from remote sens- ing dataset was reviewed; Second, this paper compared the current performed NDVI time series fitting techniques and phenological parameters extraction algorithms from the viewpoint of theorical hypothesis and suitability; On this basis, it summarized and interpreted the applications of employing NDVI time series dataset and phenological ex- traction algorithms into identifying agri search concerns of scientific communi cultural phenological characteristics. Finally, with respect to the current re- ty, the applications of the extracted phenological character/stics were dis-cussed, including how to identify major crop phenophases, to determine cropping systems and to improve crop yield estimation. The results were expected to provide scientific knowledge for regional ecological management and agri- cultural production.
分 类 号:S127[农业科学—农业基础科学]
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