The snow coverage area (SCA) is one of the most important parameters for cryospheric studies. The use of remote sensing imagery can complement field measurements by providing means to derive SCA with a high temporal frequency and covering large areas. Images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) are perhaps the most widely used data to retrieve SCA maps. Some MODIS derived algorithms are available for subpixel SCA estimation, as MODSCAG and MODImLab. Both algorithms make use of spectral unmixing techniques using a fixed set of snow, rocks and other materials spectra (endmembers). We aim to improve the performance of a modified version of MODImLab algorithm by exploring advanced spectral unmixing techniques. Furthermore, we make use of endmember induction algorithms to obtain the endmembers from the data itself instead of using a fixed spectral library. We validate the proposed approach on a case study in the mountainous region of the Alps.