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| 基于数据分割与主成分分析的LAI遥感估算 | |
| 引用本文： | 董莹莹,王纪华,李存军,杨贵军,宋晓宇,顾晓鹤,黄文江.基于数据分割与主成分分析的LAI遥感估算[J].红外与毫米波学报,2011,30(2):124-130. |
| 作者姓名： | 董莹莹 王纪华 李存军 杨贵军 宋晓宇 顾晓鹤 黄文江 |
| 作者单位： | 1. 浙江大学,农业遥感与信息技术应用研究所,浙江,杭州,310029;国家农业信息化工程技术研究中心,北京,100097 2. 浙江大学,农业遥感与信息技术应用研究所,浙江,杭州,310029国家农业信息化工程技术研究中心,北京,100097 3. 国家农业信息化工程技术研究中心,北京,100097 |
| 基金项目： | 国家高技术研究发展计划（863计划）,国家自然科学基金项目（面上项目，重点项目，重大项目） |
| 摘 要： | 针对叶面积指数(LAI)经典统计反演模型存在估算效果不理想以及反演效率低等问题,提出了一种基于农学物候的数据分割与主成分分析结合的遥感估算方法.综合了原始光谱和微分(或差分)光谱主成分信息作为自变量,融入了以农学物候为先验的数据分割思想,并引入了多尺度建模方式参与反演过程.以冬小麦为实验对象,进行数值模拟和比较分析.结... |
| 关 键 词： | 主成分分析(PCA) 农学物候 数据分割 多尺度建模 叶面积指数(LAI) |
| 收稿时间： | 2010/5/24 0:00:00 |
| 修稿时间： | 2010/10/9 0:00:00 |
Estimating leaf area index from remote sensing data: based on data segmentation and principal component analysis |
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| DONG Ying-Ying,WANG Ji-Hu,LI Cun-Jun,YANG Gui-Jun,SONG Xiao-Yu,GU Xiao-He and HUANG Wen-Jiang.Estimating leaf area index from remote sensing data: based on data segmentation and principal component analysis[J].Journal of Infrared and Millimeter Waves,2011,30(2):124-130. | |
| Authors: | DONG Ying-Ying WANG Ji-Hu LI Cun-Jun YANG Gui-Jun SONG Xiao-Yu GU Xiao-He and HUANG Wen-Jiang |
| Affiliation: | Institute of Agricultural Remote Sensing & Information System Application, Zhejiang University,Institute of Agricultural Remote Sensing & Information System Application, Zhejiang University,National Engineering Research Center for Information Technology in Agriculture,National Engineering Research Center for Information Technology in Agriculture,National Engineering Research Center for Information Technology in Agriculture,National Engineering Research Center for Information Technology in Agriculture and National Engineering Research Center for Information Technology in Agriculture |
| Abstract: | According to the unsatisfactory and lower efficiency of classical statistical models in leaf area index (LAI) estimation, a new inversion method combined with phenology-based data segmentation and principal component analysis was proposed in this paper. In the method, principal components of spectral data and differential (or difference) spectral data were chosen as independent variables, and phenology-based data segmentation was integrated into data processing in order to improve estimation accuracy. In addition, multi-scale was involved in modeling. Winter wheat was selected as experimental object for numerical simulation and comparative analysis. Results not only showed high precision in whole estimation and effectively improved data saturation, but also manifested stability and robustness under full scan. |
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