In order to explore the differences between different observational data of snow cover on the Qinghai-Tibet Plateau, this paper compares the climate states and interannual variations of the NOAA-CDR satellite visible light remote sensing snow cover data, satellite passive microwave remote sensing snow cover data and snow cover data observed from 146 stations in China by defining the Snow Cover Percentage (SCP). From the angle of annual average climatic state, the SCP distributions of microwave and visible light data are relatively close, and the area of high SCPs are in the mountainous area between the Nyainqentanglha Mountain and the southern edge of the Himalayas. The area of high SCPs based on the station data is relatively small and in the eastern part of the plateau and the Nyainqentanglha Mountain in the south. The areas of low SCPs of the three datasets are similar; they are along the Yarlung Zongbo River in the middle-south of the Plateau, in the northern part of Alton Mountain and in the inland provinces at the eastern edge of the plateau. From the angle of seasonal mean field, the distributions of snow cover of all the datasets are similar in winter and autumn, whether in terms of climatic state or interannual variation. In spring, the distributions are similar only between the satellite microwave and station data. In summer, the data vary greatly, and the correlation between different data is close to zero or even negative. The paper selects our typical stations in the Qinghai-Tibet Plateau (Suoxian, Qingshuihe, Kangding, Ganzi), and interpolates the satellite data on the stations to compare the similarities and differences between the three data and to investigate their consistencies with the Surface Air Temperature (SAT) anomalies. Results show that the SCPs based on the station data show linearly decreasing trends in the past 36 years while those based on the satellite data show the increasing trends. Compared to the satellite observations, the annual-mean SCPs based on the station observations have better consistencies with the SAT anomalies.