Hyperspectral reflectance (438 to 884 nm) data were recorded at five different growth stages of winter wheat in a field experiment including two cultivars, three plant densities, and four levels of N application. All two-band combinations in the normalized difference vegetation index ( λ1− λ2)/( λ1+ λ2) were subsequently used in a linear regression analysis against green biomass (GBM, g fresh weight m −2 soil), leaf area index (LAI, m 2 green leaf m −2 soil), leaf chlorophyll concentration (Chl conc, mg chlorophyll g −1 leaf fresh weight), leaf chlorophyll density (Chl density, mg chlorophyll m −2 soil), leaf nitrogen concentration (N conc, mg nitrogen g −1 leaf dry weight), and leaf nitrogen density (N density, g nitrogen m −2 soil). A number of grouped wavebands with high correlation ( R 2>95%) were revealed. For the crop variables based on quantity per unit surface area, i.e. GBM, LAI, Chl density, and N density, these wavebands had in the majority (87%) of the cases a center wavelength in the red edge spectral region from 680 to 750 nm and the band combinations were often paired so that both bands were closely spaced in the steep linear shift between R red and R nir. The red edge region was almost absent for bands related to Chl conc and N conc, where the visible spectral range, mainly in the blue region, proved to be better. The selected narrow-band indices improved the description of the influence of all six-crop variables compared to the traditional broad- and short-band indices normally applied on data from satellite, aerial photos, and field spectroradiometers. For variables expressed on the basis of soil or canopy surface area, the relationship was further improved when exponential curve fitting was used instead of linear regression. The best of the selected narrow-band indices was compared to the results of a partial least square regression (PLS). This comparison showed that the narrow-band indices related to LAI and Chl conc, and to some extent also Chl density and N density, were optimal and could not be significantly improved by PLS using the information from all wavelengths in the hyperspectral region. However, PLS improved the prediction of GBM and N conc by lowering the RMSE with 22% and 24%, respectively, compared to the best narrow-band indices. It is concluded that PLS regression analysis may provide a useful exploratory and predictive tool when applied on hyperspectral reflectance data.