Hyperspectral remote sensing is a promising tool for the analysis of vegetation and soils in remote sensing imagery. The purpose of this study is to ascertain how well hyperspectral remote sensing data can retrieve vegetation cover, vegetation type, and soil type in areas of low vegetation cover. We use multiple endmember spectral mixture analysis (MESMA), high-quality field spectra, and AVIRIS data to determine how well full-range spectral mixture analysis (SMA) techniques can retrieve vegetation and soil information. Using simulated AVIRIS-derived reflectance spectra, we find that, in areas of low vegetation cover, MESMA is not able to provide reliable retrievals of vegetation type when covers are less than at least 30%. Overestimations of vegetation are likely, but vegetation cover in many circumstances can be estimated reliably. Soil type retrievals are more than 90% reliable in discriminating dark-armored desert soils from blown sands. This simulation comprises a best-case scenario in which many typical problems with remote sensing in areas of low cover or desert areas are minimized. Our results have broad implications for the applicability of full-range SMA techniques in analysis of data from current and planned hyperspectral sensors. Several phenomena contribute to the unreliability of vegetation retrievals. Spectrally indeterminate vegetation types, characterized by low spectral contrast, are difficult to model correctly even at relatively high covers.