The low spectral resolution of multispectral satellite imagery limits its capability for extracting information in arid environments with sparse vegetation cover. The higher spectral resolution of hyperspectral imagery may improve discrimination of different vegetation types, even with low cover. The aim of this study was to evaluate the potential of Earth Observing 1 (EO-1) Hyperion hyperspectral data to discriminate arid landscape components in the southern rangelands of South Australia. Hyperion imagery was analysed with spectral mixture analysis to discriminate spectrally distinct land cover components. Five distinct end-members were extracted: two associated with vegetation cover and the remaining three associated with different soils and surface gravel and stone. The end-members were characterised with field spectra collected by ASD Fieldspec Pro spectrometer. To confirm the identity of the end-members we also investigated relationships between their abundance and field cover data collected at 54 sample sites using a step-point technique. One vegetation end-member was significantly correlated with Cottonbush(Maireanaaphylla) vegetation cover (R²=0.89) that was distributed as patches throughout the study area. The second vegetation end-member mapped green and grey-green perennial shrubs (e.g. Mulga, Acacia aneura) and was significantly correlated with total vegetation cover (R²=0.68). The soil and surface gravel and stone were not significantly correlated with the field estimates of these physical components. Despite the high spectral resolution of the Hyperion scene, spectral mixture analysis was unable to identify more than five meaningful spectral end-members in this arid environment. This may be the result of low vegetation cover of the region (28%), the lack of spectral contrast in arid vegetation types, and the ground resolution of Hyperion (900 m²) that reduced the ability to identify spectrally pure end-members to represent different land cover components.