A fully automated method uses Landsat Thematic Mapper data to map snow cover in the Sierra Nevada and make quantitative estimates of the fractional snow-covered area within each pixel. We model winter and spring reference scenes as linear mixtures of image end member spectra to produce the response variables for tree-based regression and classification models. Decision trees identify cloud cover and fractional snow-covered area. We test the algorithm on a different Thematic Mapper scene and verify with high-resolution, large-format, color aerial photography. The accuracy of the automated classification of Thematic Mapper data equals that obtainable from the aerial photographs but is faster, cheaper, and covers a vastly larger area. The mapping method is insensitive to the choice of lithologic or vegetation end members, the water equivalent of the snow pack, snow grain size, or local illumination angle.