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

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.结果显示,该方法既能有效地提高整体估算精度,又能显著地改善数据饱和问题,且在全样本遍历时体现了稳定鲁棒性.

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.