Estimating leaf area index from remote sensing data: Based on data segmentation and principal component analysis

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.