红外与毫米波学报2011，Vol.30Issue(2)：124-130,7.
基于数据分割与主成分分析的LAI遥感估算
Estimating leaf area index from remote sensing data: based on data segmentation and principal component analysis
摘要
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.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)Key words
principal component analysis (PCA) /phenology /data segmentation /multi-scale modeling /leaf area index (LAI)分类
信息技术与安全科学引用本文复制引用
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):124-130,7.基金项目
国家自然科学基金项目(40701120) （40701120）
国家863计划项目(2006AA120108) （2006AA120108）
北京市自然科学基金项目(4092016) （4092016）
北京市科技新星计划(2008B33) （2008B33）
