The Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA) mission has provided an unprecedented opportunity to study the potential of hyperspectral multiangular data for improved vegetation characterisation at a fine spatial scale. This study exploits to what degree the spectral anisotropy contained in the data can contribute to a more robust retrieval of chlorophyll and leaf area index of cotton compared to retrievals based on single angle observations. To do so, a simplified automated radiative transfer model (RTM) inversion scheme was applied both to the single observation angles and to various combinations of multiple view angles. It is shown that significant increase in accuracy is obtained when directional information is included. However, the improvement obtained by assimilating a certain observation angle appears to be strongly related to the ability of the RTM in reconstructing canopy reflectance for this constellation. Hence, due to the deficiency of the 1-D RTM to reconstruct observed reflectance for the row crop in the extreme forward looking direction, the result strongly deteriorated when this view angle was included. Model inversion based on all but the extreme forward looking view angle provided a root-mean-square error of 18.5% (6.6 μg/cm2 ) and 32.7% (0.55 m2 /m2) for chlorophyll and leaf area index, respectively.