Snow cover,as the most widely distributed element in the cryosphere,plays a critical role in the climate change and hydrological cycle.Microwave remote sensing is an important technique to monitor snow cover,because of its all-weather,all-time capability and ability to penetrate.In this study,FY-3Csatellites passive microwave brightness temperature data acquired by FY-3C MWRI,snow cover products obtained by MERSI and VIRR,MOD10C1and MOD11C1,are used to develop a new Snow identification algorithm in western China.In this algorithm,the passive microwave brightness temperature of different land types are firstly extracted,and then they are analyzed using cluster analysis.The analysis results exhibit that TB19V-TB19H,TB19V-TB37V,TB22V,TB22V-TB89V,(TB22V-TB89V)-(TB19V-TB37V)can be used as the criterion for identifying snow cover from other scatters.Finally,MODIS snow cover products are used to validate the identification accuracy as a reference,and the results show that the overall accuracy of this algorithm in western China is 87.1%,the omission rate is 4.6%,the commission rate is 23.3%.The overall accuracy of Grody algorithm is 78.6%,the omission rate is 9.8%,and the commission rate is 30.7%. The accuracy of this algorithm is higher than the Grody algorithm.The Kappa coefficient of this algorithm is 47.3%,which is higher than the Grody algorithms Kappa coefficient of 39.9%,indicates that the algorithm' s snow identification results are more consistent with the MODIS snow product identification results.