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Snow depth is one of the most important physical properties of snow, and accurate estimation of snow depth is critical to human production and life, such as water resources management, climate change research, disaster early warning and management. At present, snow depth data observed by meteorological stations and retrieved by passive microwave re-mote sensing (SMMR, SSM/I and SSMI/S) have been widely used for long time series snow depth research. In order to clarify the advantages and disadvantages of these data in the study of snow depth change, this paper compares the spa-tial distribution and inter-annual variation of the maximum snow depth and mean snow depth of these in China. The results show that the distribution of the two types of snow depth is consistent in the stable snow cover areas, but the maximum snow depth observed by metrological stations is significantly greater than the maximum retrieved by remote sensing in the deep snow area of more than 40cm and the snow depth of less than 5cm in southern China. The correlation between the two snow depth data is the best in northeast China, the second in Xinjiang, and the worst in Qing-hai-Tibet Plateau. It is more suitable to use the passive microwave snow depth data after 1988 to study the snow depth changes in China. Because the SMMR sensor (1978.10.26~1987.8.20) has low time resolution and serious data loss in the middle and low latitudes, resulting in poor quality of the corresponding snow depth data. Forthemore， comparing the changes of the two types of snow depth in China in recent 30 years, the results show that the changes of those are basically the same in different regions, with a significant increase in the northeast Plain and a significant decrease in the western and southeastern parts of the Qinghai-Tibet Plateau. Meteorological stations, influenced by their site selection, cannot reflect the high altitudes snow depth and mountain district time distribution and the change of situation. However, the snow depth retrieved by passive microwave remote sensing is affected by snow thickness, sea-sonal variation of snow density, liquid-water content of snowpacks, snow grain size and other factors. Therefore, it can-not reflect the extreme snowfall events with rapid changes of snow attribute in a short time.