Abstract: Photosynthetic pigments of vegetation are a primary product and important materials that can indirectly reflect health status and photosynthetic capacity of vegetation. Hyper-spectral remote sensing provides the possibility for rapid, large-scale monitoring of vegetation chlorophyll change. In this study, canopy reflectance spectra and chlorophyll content of cool-season Festuca arundinacea were measured and their relationship analyzed. The top-five chlorophyll sensitive parameters were selected from 12 hyperspectral characteristic variables, and then used to establish a vegetation index model to estimate photosynthetic pigments. 1) The relationship between Chlb and the original spectrum was the best among the photosynthetic pigments: Chla, Chlb, Chls, and Cars. 2) The first derivative spectra gave a better relationship (correlation coefficient: -0.897) with vegetation photosynthetic pigments near 700 nm wavelength than the other two hyperspectrum forms {R and D[log(1/R)]}. Finally, after comparing the correlation between photosynthetic pigment concentrations and hyperspectral data, the most significant variables {R_g, Dλ₇₀₀, D[log(1/R₇₃₀)], RVI_b, R_ch, CARI} were selected from twelve variables to establish the regression model of photosynthetic pigments. All of these provide a theoretical basis for rapid, non-invasive detection of nutritional status and meadow quality of F. arundinacea.