Abstract: The 2002 to 2008 MODIS/Terra-Aqua daily snow products (MOD10A1 and MYD10A1) and AMSR-E/Aqua daily snow water equivalent product AE_DySno from the Tibetan Plateau were used to study the daily composition algorithm of MODIS and AMSR-E data. The accuracies of composited daily cloud-free snow classification image MATS10A1 were validated based on snow depth data from climate stations. 1) In the Tibetan Plateau, snow classification accuracy of MODIS daily snow product is high (reaching 80.82% when the snow depth>3 cm) under clear sky conditions, but MOD10A1 and MYD10A1 are not suitable for monitoring the snow distribution in real time in pastoral areas because the average cloud ratios are 39.74% and 48.74%, respectively; 2) Composited images (MOYDTS10A1) of MOD10A1 and MYD10A1 not only eliminate most of the cloud’s impact (the average cloud ratio is 24.13%) but also improves the snow classification accuracy (reaching 81.67% when the snow depth >3 cm); 3) Daily cloud-free snow classification image MATS10A1 combines the advantages of AMSR-E (cannot be affected by weather conditions) and MODIS (relatively high resolution), completely eliminates the interference of clouds, and also has a relatively high snow classification accuracy (reaching 79.36% when the snow depth >3 cm). Therefore, the daily cloud-free images generated by the improved algorithm in this study will play an important role in snow disaster monitoring and evaluation in the Tibetan Plateau pastoral areas in the future.