In: Proceedings of the 8th EARSeL Workshop on Imaging Spectroscopy, Nantes, France. . 2013, 6. S.

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Abstract

The precise assessment of canopy nitrogen status is one of the key parameters in agriculture for high accuracy yield estimations. The increasing availability of airborne imaging hyperspectral sensors (e.g. HyMap, HySpex, CASI, AISA) provides the required data to derive canopy nitrogen status for large agricultural areas with a high spatial resolution. In this study the potential of vegetation indices – red edge inflection point, normalized difference red edge index and normalized difference nitrogen index – and empirical regression models – support vector regression, partial least squares regression – have been compared for the prediction of biomass nitrogen concentration of wheat from AISA-DUAL data. For empirical regression models the best result was found for support vector regression (r2cv=0.86, RMSEcv=0.25, RPD=2.52) while the best result for vegetation indices was found for red edge inflection point (r2cv=0.69, RMSEcv=0.35, RPD=1.83). The comparison proves a higher potential of empirical regression models to deliver predictions for biomass nitrogen concentration of wheat. The transfer of the SVR model to the AISA-DUAL data allowed to map the spatial distribution of N concentration with reasonable accuracy and reflected the spatial pattern of N of the investigated fields very well.