The nitrogen content of canopy leaves of wheat (Triticum aestivum L.) is one of the most important indices for monitoring wheat growth and yield. The Kjeldahl procedure as the common method for N assays is time-consuming, labor intensive and invasive. As a modern technique, hyperspectral remote sensing is an effective and non-invasive method for rapid estimation of plant nitrogen contents. In this study, a novel hyperspectral index, first derivative normalized difference nitrogen index (FD-NDNI), was developed to estimate the nitrogen content of wheat canopy by hyperspectral remote sensing technology. A total of 190 canopy samples of wheat ranging from jointing to booting stage were scanned by a FieldSpec Pro FR spectrometer, and the spectral data were then assigned randomly to calibration (142 data) and prediction (48 data), respectively. The data were pretreated by wavelet threshold denoising before analysis. Using the fist derivative spectra at 525, 570 and 730 nm and the methods of difference, ratio and normalization, 12 new hyperspectral indices were developed to quantify the nitrogen content of wheat canopy. These indices were then compared with 22 commonly used hyperspectral indices including mNDVI705, mSR and NDVI705. The accuracy of the index FD-NDNI developed was higher than that by the hyperspectral indices commonly used, as indicated by a calibration coefficient of determination (C-R²) of 0.818 and a predicted coefficient of determination (P-R²) of 0.811 of the estimation predicted by FD-NDNI. A further analysis showed that the FD-NDNI index described an exponential equation, thererfore the FD-NDNI prediction was concise and unaffected by passivation. The sensitivity analysis of the susceptibility of FD-NDNI to interference of canopy density showed that the R² of the correlation between FD-NDNI and the leaf area index (LAI) was 0.536, which was lower than that between the commonly used hyperspectral indices and LAI. FD-NDNI was least sensitive to LAI among the hyperspectral indices and therefore least affected by canopy density when used to estimate the nitrogen content of wheat canopy. FD-NDNI was therefore an ideal spectral index sensitive to prediction, but insensitive to interference. An algorithm of the least squares support vector regression (LS-SVR) was finally used to optimize the FD-NDNI model. A step-search procedure in which a long step size was set first to determine the range of values and then followed by a short step size to determine the specific values, was carried out for rapid optimization of the penalty coefficient C and the RBF kernel function parameter g of LS-SVR models. When the parameters C and g reached the optimal values of 6.4 and 1.6, respectively, the C-R² and P-R² of the model reached 0.846 and 0.838, respectively, which were higher than those of the exponential model, and indicated that the LS-SVR model was more accurate. The results suggested that FD-NDNI was an optimal hyperspectral index for estimation of the nitrogen content of wheat canopy, and LS-SVR algorithm was a preferred modeling method.