The number of ground cover classes for which proportions estimates can be derived by application of spectral mixture modelling is constrained by the dimensionality of the data. This may limit the usefulness of the approach, as often there are a larger number of discriminated classes than dimensions of data. Typically, relatively few, broadly defined end-member classes have been used but this is likely to degrade the precision of the proportions estimates at the pixel level as the end-member signatures attempt to represent a spectrally diverse range of vegetation covers within each broad class. To overcome this problem, multiple end-member mixture models have been proposed that iterate through a larger number of more narrowly defined end-members. In this study, broad and multiple end-member mixture models were applied to satellite data at 1 km 2 spatial resolution of a semi-arid environment to evaluate their respective precision of proportions estimates against ground survey data for 25 1-km 2 segments. This study demonstrates improved precision of proportions estimates by application of a multiple end-member mixture model.