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Spectral mixture analysis (SMA) is an image-processing technique used for the analysis of airborne hyperspectral remote-sensing data which consist of a large number of spectral bands, typically over 100. In this paper the possibilities are examined of using SMA for the analysis of Landsat Thematic Mapper satellite data with only six bands in the visible to shortwave-infrared wavelength. We use data from a semi-arid area in the Sanmatenga province of Burkina Faso, an area known to experience land-degradation problems. In SMA, we assume that pixels in an image consist of one or more homogeneous (uniform in character) or pure surfaces, the so called end-members. The end-members were derived from the image data on the basis of specific image characteristics. Field data was collected to describe the characteristics of these end-members in terms of land cover, soil and degradation phenomena. The end-members derived from the image analysis, although statistically pure in terms of image spectral characteristics, prove to be mixtures at a field scale. This lack of purity is explained by the nature of semi-arid areas which is more heterogeneous than that of most other areas. The SMA yielded cover percentages for the end-members from which an unsupervised classification was made. Comparison of the classification with the results of SMA shows that the latter provides information on the amount and spatial distribution of land characteristics such as land degradation.