| 采用DBM方法的时间序列LAI建模与估算 |
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| 引用本文： | 陈平,王锦地,梁顺林.采用DBM方法的时间序列LAI建模与估算[J].遥感学报,2012,16(3):505-519. |
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| 作者姓名： | 陈平 王锦地 梁顺林 |
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| 作者单位： | 遥感科学国家重点实验室 北京师范大学, 北京 100875; 北京师范大学 地理学与遥感科学学院, 北京 100875;遥感科学国家重点实验室 北京师范大学, 北京 100875; 北京师范大学 地理学与遥感科学学院, 北京 100875;北京师范大学 全球变化和地球系统科学研究院, 北京 100875; 美国马里兰大学 地理系, 马里兰 MD20742 |
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| 基金项目： | 国家重点基础研究发展计划(973计划)(编号:2007CB714407);国家自然科学基金(编号:40871163);国家高技术研究发展计划(863计划)(编号:2009AA122103) |
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| 摘 要： | 运用DBM(Data Based Mechanistic)方法,使用MODIS数据,建立了遥感观测反射率数据与叶面积指数(LAI)在时间序列上的统计关系模型(LAI_DBM模型),并结合部分Bigfoot站点实测LAI数据进行了模型检验。结果显示,LAI_DBM模型能够较好表达时间序列反射率与LAI的动态变化关系。LAI_DBM模型使用遥感观测数据实时估算得到的LAI,在数据质量和时间连续性上比MODISLAI有改进。
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| 关 键 词： | 叶面积指数 时间序列 MODIS DBM |
| 收稿时间： | 5/3/2011 12:00:00 AM |
| 修稿时间： | 2011/10/20 0:00:00 |
A data-based mechanistic approach to time-series LAI modeling and estimation |
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| CHEN Ping,WANG Jindi and LIANG Shunlin.A data-based mechanistic approach to time-series LAI modeling and estimation[J].Journal of Remote Sensing,2012,16(3):505-519. |
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| Authors: | CHEN Ping WANG Jindi and LIANG Shunlin |
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| Institution: | State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote SensingApplications of Chinese Academy of Science, Beijing 100875, China; School of Geography, Beijing Normal University, Beijing 100875, China;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and the Institute of Remote SensingApplications of Chinese Academy of Science, Beijing 100875, China; School of Geography, Beijing Normal University, Beijing 100875, China;College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China; Department of Geography, University of Maryland, College Park, MD20742, USA |
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| Abstract: | A data-based mechanistic (DBM) modeling approach is used to model the statistical relationship between time-series reflectance and leaf area index (LAI). This relationship model is referred to as LAI_DBM model. Moderate Resolution Imaging Spectroradiometer (MODIS) data products are utilized as example data to implement DBM modeling and validation. LAI field measurements from the Bigfoot project were used to further validate LAI_DBM model. The results show that LAI_DBM model provid a very good explanation of the relationship between time-series refl ectance and LAI. The LAI estimated by LAI_DBM model is better than MODIS LAI in terms of data quality and continuity. |
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| Keywords: | leaf area index time-series MODIS data-based mechanistic (DBM) |
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