Abstract: In order to monitor peanut canopy effectively and nondestructively and to get their growth information, hyperspectral data of peanut canopy was measured with ASD FieldSpec and canopy leaf area index was measured with SUNSCAN during different growing stages in the field experiment, including five cultivars with different ecological types. The correlations between four spectral forms, six vegetation index and LAI were analyzed, and the estimation models were established, by using spectral derivative technique and statistical analysis technique. The results showed that the correlation between the optimal bands of hyperspectral reflectance, its transformation forms and LAI were 1% significant, LAI could be estimated better by the first derivative spectra ρ' at 793 nm （r=-0.5391, P＜0.01, RE=0.2497）, the correlation coefficient between simulating data and testing data was 1% significant （R=0.4435, P＜0.01）; simulating LAI and testing LAI could be fitted mostly by the first derivative sPectra ρ ' at 734nm （R=0.5485, P＜0.01）. The correlations between the optimal bands of six vegetation index and LAI were 1% significant （r≥0.5731, P＜0.01）, LAI could be estimated mostly by NDVI [760, 976] （r=-0.6421，P＜0.01, RE=0.2167）, the correlation coefficient between simulating data and testing data was 1% significant （R=0.6731, P＜0.01）; simulating LAI and testing LAI could be fitted mostly by DVI [760, 976] （R=0.6893, P＜0.01）. The results indicated that the estimation accuracy of peanut canopy was higher than that of with ρ' and vegetation index, especially vegetation index. It confirmed further that ρ' and vegetation index played a great role on eliminating the effect of environment background including soil and atmosphere.

Key words: Peanut, Hyperspectral remote sensing, LAI, Estimation models