Abstract: Due to cloudy days often in winter and an influence of topography and vegetation as well as uneven snow cover distribution in the area over northern Xinjiang,it is very hard to achieve a snow fraction mapping product with high accuracy using remote sensing images. In order to make snow fraction mapping more accurate,a snow quantitative inversion method based on hierarchical dynamic endmember spectral mixture analysis (DESMA) technology for snow fraction mapping using MODIS data acquired in the study areas suggested. The details of the proposed method areas follows: Image endmembers libraries and reference endmembers libraries were initially built. Then,multi-level category analysis from a coarse to a fine scale on the entire image of the study area was carried out. Two-endmember or three-endmember models for the whole MODIS data was used to unmix each pixel at each level and all pixels were classified into one of two categories: pixels containing snow endmembers and pixels without snow endmembers. Only those pixels containing snow endmembers were further unmixed using DESMA technology to achieve a more refined classification in a finer layer. Unmixing layer by layer,it produced a classification result for each layer. Finally,a snow fraction mapping product with higher precision was generated by combining classification results from each layer. The experiments showed that the suggestion of unmixing using DESMA technology when a smaller number of endmember models(e.g. two- or three-endmembers),is selected then a snow fraction mapping product with the highest overall classification accuracy of 87% can be achieved. The accuracy of snow fraction mapping will be lower if the number of endmembers used in the unmixing model is larger than three. This suggestion has also been verified by snow-cover and vegetation distribution maps derived from HJ CCD remote sensing data and field data of snow-cover.