Identification of hyperspectral vegetation indices for Mediterranean pasture characterization

Abstract A field experiment was carried out to assess biomass and nitrogen status in Mediterranean pastures by means of hyperspectral high resolution field radiometric data. Spectral and agronomic measurements were collected at three different pasture growth stages and in grazed–ungrazed plots distributed over an area of 14 ha. Reflectance-based vegetation indices such as simple ratio indices (SR[i,j]) and normalized difference vegetation indices (NDVI[i,j]) were calculated using all combinations of two wavelengths i and j in the spectral range 400–1000 nm. The performances of these indices in predicting green biomass (GBM, t ha−1), leaf area index (LAI, m2 m−2), nitrogen content (N, kg ha−1) and nitrogen concentration (NC, %) were evaluated by linear regression analysis using the cross validated coefficient of determination ( R CV 2 ) and root mean squared error (RMSECV). SR involving bands in near-infrared (i = 770–930 nm) and in the red edge (j = 720–740 nm) yielded the best performance for GBM ( R CV 2 = 0.73 , RMSECV = 2.35 t ha−1), LAI ( R CV 2 = 0.73 , RMSECV = 0.37 m2 m−2), and N ( R CV 2 = 0.73 , RMSECV = 7.36 kg ha−1). The best model performances for NC ( R CV 2 = 0.54 , RMSECV = 0.35%) were obtained using SR involving near-infrared bands (i = 775–820 nm) and longer wavelengths of the red edge (j = 740–770 nm). The defined indices lead to significant improvements in model predictive capability compared to the traditional SR [near-infrared, red] and NDVI [near-infrared, red] and to broad-band indices. The possibility of exploiting these results gathered at field level with high resolution spectral data (FWHM 3.5 nm) also at landscape level by means of hyperspectral airborne or satellite sensors was explored. Model performances resulted extremely sensitive to band position, suggesting the importance of using hyperspectral sensors with contiguous spectral bands.