The objective of the present study is to propose a new vegetation index for corn canopy fresh biomass estimation, which improves the ability to accurately estimate high biomass levels by remote sensing technology. For this purpose, hyperspectral reflectance data of corn canopies were collected using a ground-based spectroradiometer during different field campaigns in a region of Quebec (Canada), from 2004 to 2008. Corresponding fresh biomass values were obtained by destructive measurements, and a hyperspectral image was also acquired using the Compact Airborne Spectrographic Imager (CASI) in 2005. A new biomass index named red-edge triangular vegetation index (RTVI) was designed and compared to existing indices used for fresh biomass estimation. The results showed that RTVI was the best vegetation index for predicting canopy fresh biomass, with sustained sensitivity at high fresh biomass levels. The best regression model between RTVI and canopy fresh biomass was the power fit, with determination coefficient (R2) of 0.96. With the validation by CASI imagery in 2005, good results were obtained. The relationship between CASI predicted biomass and actual biomass was 0.58 (R2), with the RMSE of 0.44 kg?m-2.