A procedure was developed for analyzing remote reflectance spectra, including multispectral images, that quantifies parameters such as types of mineral mixtures, the abundances of mixed minerals, and particle sizes. Principal components analysis (PCA) reduced the spectral dimensionality and allowed testing the uniqueness and validity of spectral mixing models. By analyzing variations in the overall spectral reflectance curves we identified the type of spectral mixture, quantified mineral abundances, and identified the effects of particle size. The results demonstrate an advantage in classification accuracy over classical forms of analysis that ignore effects of particle-size or mineral-mixture systematics on spectra. The approach is applicable to remote sensing data of planetary surfaces for quantitative determinations of mineral abundances.