The rapid and accurate mapping of desert type distribution is of great significance for environmental protection and ecological restoration. However， due to the influence of spectral and resolution factors， the current research on the extraction of information about different desert types is obviously insufficient. In this study， Dulan County of Qinghai Province was selected as a typical area. The desert was classified based on GEE platform and multi-source data， and the classification performance of different classification features combined with three machine learning methods （RF， SVM and CART） was compared and evaluated. The results show that （1） RF outperforms CART and SVM， and the overall classification accuracy using the RF classifier and spectral features， radar features， terrain features and texture features is the highest， with an overall accuracy of 95.68%， a Kappa coefficient of 0.95， an F_M score of 94.28%， and an obtained desert area of Dulan County is 29 039 km2. （2） In the assessment of feature importance scores， elevation and VH contribute more to the desert classification， while other features do not contribute much. （3） Based on the use of spectral data， radar features are the most helpful for identifying gravel and loamy deserts， while terrain features are more suitable for identifying other types of deserts.