Given a worldwide increased energy demand with a consequent increased use of biofuels, it is necessary to develop new technologies and techniques to improve efficiency of the biomass screening and the biomass-to-energy conversion systems. Gross calorific value of biomass is one of the critical properties of interest and it is inherently influenced by its chemical composition. Therefore, non-destructive spectroscopic sensing techniques have the potential to be an accurate method to rapidly determine the gross calorific value of biomass to be used in biofuel industry.  In this study, we used Near-infrared Reflectance Spectroscopy (NIRS) to determine the gross calorific value of woody biomass of 8-17 years old loblolly pine (Pinus taeda) and slash pine (Pinus elliotti) trees from Piedmont and Coastal Plains of Georgia. The 284 samples included in this study were chips of bark, branch, wood, needle as well as whole tree. The samples were oven-dried (at 60 ^oC), coarse-ground, and passed through 2 mm sieve. The processed samples were scanned on a Foss NIRSystem model 6500 scanning monochromator in the reflectance mode. The scan covered both visible and near-infrared regions in the wavelength range from 400 to 2500 nm at 2 nm intervals to give a total of 1050 data points per sample. The reference gross calorific values were determined by an Adiabatic Bomb Calorimeter.  Calibration equation was developed using modified partial least-squares regression with internal cross validation using 192 randomly chosen samples. The reference gross calorific values of these calibration samples ranged from 4270 to 5118 cal/g. The equation developed had low standard error of both calibration (SEC = 61.86 cal/g) and cross-validation (SECV = 67.33 cal/g) with high coefficient of determination in both calibration (R² = 0.8775) and cross validation (1-VR = 0.8930). Prediction of an independent validation set of 92 samples showed significant correlation between the NIRS predicted values and the reference gross calorific values based on the standard error of prediction (SEP = 60.42 cal/g), coefficient of determination in prediction (r² = 0.8450), and the ratio of standard deviation for prediction (RSP = the ratio of standard deviation of reference data to SEP(c) = 2.52), thereby characterizing the equation as having good quantitative information. The results suggest that NIRS could be used to rapidly determine gross calorific value of woody biomass in bioenergy research and industry.