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Monitoring the snow cover on the Qinghai Tibet Plateau holds great significance for climate prediction and snow disaster prediction, among other things. With its high temporal resolution and high spatial resolution, FY-4 data is providing a new field in snow monitoring service by geostationary satellite. Constructing snow monitoring methods and models based on FY-4A not only expands the application field of geostationary satellites but also enriches the means of snow monitoring application. The high temporal resolution of FY-4 provides minute-level data for research on snow monitoring, offering a more detailed understanding of changes in snow cover and clouds. To facilitate application for producers and reference for decision-makers, and to further improve the accuracy of snow depth inversion products, this paper is based on the hourly field snow depth observation data, daily FY-3D_SNC data, and the hourly FY-4A satellite data. A snow identification method based on NDSI (Normalized Difference Snow Index) is being constructed, as well as a snow depth monitoring model. In the end, referring to the existing snow depth classification standards on the Qinghai Tibet Plateau, a classification standard for snow depth levels in shallow snow areas using FY-4 satellite is proposed, based on NDSI and the linear estimation equation of snow depth. Mapping examples of different snow depth levels in plateau areas have been completed to better provide reference for practical business monitoring services and applications. The results show that NDSI≥0.20 is the reasonable threshold for FY-4A satellite snow detection in the Qinghai Tibet Plateau region, with a missing detection rate of less than 8.0% regardless of cloud conditions. The ground station verification results show that the accuracy of snow recognition is over 83.33%. After the cloud is directly removed in the spatial range, the accuracy of snow identification is more than 82.48%, verified by the confusion matrix. Therefore, the FY4 satellite has the ability to monitor snow cover in the Qinghai Tibet Plateau region. Although the FY-4A satellite does not have the ability to distinguish snow depths exceeding 10 cm, it can effectively identify shallow snow depths below 10 cm, with a correlation coefficient of 0.745, passing the 0.001 significance level. As a result, the FY-4A satellite snow depth level index of 0 to 10 cm has been established, with an overall classification accuracy of 87.50%. Hence, the FY-4A satellite snow depth inversion method has good estimation ability for 0 to 10 cm shallow snow depths in the Qinghai Tibet Plateau region.