ESTIMATION OF LEAF AREA INDEX BY USING MULTI-ANGULAR HYPERSPECTRAL IMAGING DATA BASED ON THE TWO-LAYER CANOPY REFLECTANCE MODEL
This study aims to investigate the effects of observation angle on the estimation of leaf area index (LAI) by using multi-angular hyperspectral imaging data. First, the bidirectional reflectance was simulated with a two-layer canopy reflectance model (ACRM), the obvious bell-shaped and bowl-shaped pattern can be found in the blue, red and NIR wavebands. Subsequently, the three most commonly used vegetation indexes, the normalized difference vegetation index (NDVI), the simple ratio index (SRI) and enhanced vegetation index (EVI) were used to exploit the effect of different observation angles. Through the analysis of simulated data, SRI and EVI displayed a greater potential for estimating LAI due to the fact that they are more sensitive to the variation of observation angle, thus the partial least square regression (PLS) based on the cross validation was applied both to the single observation angle and to various combinations of multiple observation angles. The result shows that SRI has obtained the highest estimation accuracy (R-2 = 0.47, RMSE = 0.30) by the combination of six observation angles, which agreed well with the simulated result, indicating that multi-angular observation can improve the estimation accuracy of LAI.
leaf area index multi-angular hyperspectral image canopy reflectance model vegetation index partial least square regression