The monitoring of heavy metal pollution in crops is one important application of hyperspectral remote sensing study. The objective of this work was to develop a new narrow-band vegetation index to characterize the Cu (copper) stress degree in two corn species at two growing years. The experiment on the copper pollution was designed based on its different concentrations, meanwhile, the hyperspectral reflectance of corn leaves stressed by different Cu2+ concentrations were measured using hand-held spectrometer(SVC, USA) and leaf Cu2+ contents were also measured. The first difference reflectance and biochemical data of corn were analyzed using Pearson correlation coefficient (r) to select wavelengths sensitive to Cu stress. The calculated Pearson correlation coefficients suggested that the first difference reflectance near 489~497, 632 and 677 nm wavelengths was significantly correlated with Cu2+ contents in leaves. The selected wavelengths of 489~497, 632 and 677 nm were used to establish the Cu stress vegetation index based on the first difference reflectance (dVI). To select index with the highest possible correlation to Cu stress, all possible dVIs were related through simple regression models with Cu2+ contents andthe predictive abilities of those models were evaluated through the R2 values and the root mean square error (RMSE). The stability of the sensitive bands and the applicability of dVI were assessed using corn data from different growth years. Meanwhile, the performance of dVI was compared with that of existing popular vegetation index (VIs) related to heavy metal stress, such asnormalized difference vegetation index (NDVI), red-edge chlorophyll index (CIred-edge), red-edge position (REP), photochemical reflectance index (PRI). The results suggest that the corn spectral characteristics in response to copper stress are enhanced with the first-order difference treatment. Compared with the original reflectance, the correlation coefficient between first difference reflectance at wavelengths of 450~500, 630~680 and 677 nm and Cu2+ content increases. The wavelength position of copper stress sensitive band based on the first-order differential reflectance is stable for the data sets of different growth years. The index that combined the first difference reflectance in 497, 632 and 677 nm wavelengths is found to be a potential useful index to predict leave Cu concentration for different data sets. And the correlation of dVI was much stronger than that of other VIs for all the tested data sets from two corn species at two growing years. The proposed dVI characterizes the Cu stress degree on vegetation with advantages of better effectiveness and robustness. This study focuses on the spectral reflectance at the leaf scale, so it is expected that future work will extend it to canopy scale.