Assessment of Chlorophyll Content Using a New Vegetation Index Based on Multi-Angular Hyperspectral Image Data

The fast estimation of chlorophyll content is significant for understanding the crops growth, monitoring the disease and insect, and assessing the yield of crops. This study gets the hyperspectral imagery data by using a self-developed multi-angular acquisition system during the different maize growth period, the reflectance of maize canopy was extracted accurately from the hyperspectral images under different view angles in the principal plane. The hot-dark-spot index (HDS) of red waveband was calculated through the analysis of simulated values by ACRM model and measured values, then this index was used to modify the vegetation index (TCARI), thus a new vegetation index (HD-TCARI) based on the multi-angular observation was proposed. Finally, the multi-angular hyperspectral imagery data was used to validate the vegetation indexes. The result showed that HD-TCARI could effectively reduce the LAI effects on the assessment of chlorophyll content. When the chlorophyll content was greater than 30 mu g.cm(-2), the correlation (R-2) between HD-TCARI and LAI was only 26.88%similar to 28.72%. In addition, the HD-TCARI could resist the saturation of vegetation index during the assessment of high chlorophyll content. When the LAI varied from 1 to 6, the linear relation between HD-TCARI and chlorophyll content could be improved by 9% compared with TCARI. The ground validation of HD-TCARI by multi-angular hyperspectral image showed that the linear relation between HD-TCARI and chlorophyll content (R-2=66.74%) was better than the TCARI (R-2=39.92%), which indicated that HD-TCARI has good potentials for estimating the chlorophyll content.