Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy

The research evaluated the information content of hyperspectral laboratory and image data for the estimation of chlorophyll (CAB) and nitrogen (CN) concentration in Norway spruce (Picea abies L. Karst.) needles. The aims were to find reliable predictive models to estimate biochemical concentrations and to systematically compare different types of spectral transformations regarding the accuracy of prediction. The results of the analysis showed that CAB can be well estimated from laboratory and canopy reflectance data. The best predictive model to estimate CAB at the laboratory level yielded a cross-validated R2cv of 0.81 and a relative RMSEcv of 8.7 % (n =78). The best model to estimate CAB at the canopy level using hyperspectral HyMap data was achieved using band depth normalised spectra with a R2cv of 0.90 and a relative RMSEcv of 2.8 % (n = 13). Concerning the nitrogen concentration, we observed somewhat weaker relations, with however still acceptable accuracies (canopy level: R2cv = 0.57, relative RMSEcv = 4.6 %). The relatively low R2cv for CN can be partly attributed to the relative small coefficient of variation of this variable (CV: 10 percent) compared to chlorophyll (CV: 20 percent). The wavebands selected in the regression models to estimate CAB were typically located in the red edge region and along the edge of the chlorophyll absorption feature. This result coincides with a general understanding that the maximum correlations occur on the edge of absorption features as the central wavelengths become saturated. For CN, additional wavebands related to known protein absorption features at 1730 nm, 2180 nm, and 2350 nm were selected. The portion of selected wavebands attributable to known absorption features strongly depends on the type of spectral transformation applied. A method called ¿derivative of water removed spectra¿ (DWRS) produced the largest percentage of wavebands directly or indirectly related to known absorption features.JRC.H.4-Monitoring Agricultural Resource