Remote sensing of grass/herb quantity is essential for rangeland management of livestock and wildlife. Spectral indices such as NDVI, determined from red and near infrared bands are affected by variable soil and atmospheric conditions and saturate in dense vegetation. Alternatively, the wavelength of maximum slope in the red-NIR transition, termed the red edge position (REP) has potential to mitigate these effects. But the utility of the REP using air-and spaceborne imagery is determined by the availability of narrow bands in the region of the red edge and the simplicity of the extraction method. Very recently, we proposed a simple technique for extracting the REP called the linear extrapolation method [Cho and Skidmore, Remote Sens. Environ., 101(2006)118.]. The purpose of this study was to evaluate the potential of the linear extrapolation method for estimating fresh grass/herb biomass and compare its performance with the four-point linear interpolation and three-point Lagrangian interpolation methods. The REPs were derived from atmospherically corrected HYMAP images collected over Majella National Park, Italy in July 2004. The predictive capabilities of various REP linear regression models were evaluated using leave-one-out cross validation and test set validation methods. For both validation methods, the linear extrapolation REP models produced higher correlations with grass/herb biomass and lower prediction errors compared with the linear interpolation and Lagrangian REP models. This study demonstrates the potential of REPs extracted by the linear extrapolation method using HYMAP data for estimating fresh grass/herb biomass.