Abstract: Due to the harsh climatic conditions and remote geographical location, there are few meteorological stations in the Sanjiangyuan region, making snow monitoring difficult. Although the development of satellite remote sensing technology provides a new way to solve this problem, existing satellite sensors still face challenges in meeting the needs of large-scale and real-time monitoring. Based on remote sensing data from the MERSI-II sensor onboard the Fengyun-3 (FY-3) satellite, including the 1000 m resolution short-wave infrared band and the 250 m resolution visible band, the 250 m high-resolution panchromatic band image is used as a reference. Through similarity matching between the 250 m high-resolution and 1000 m low-resolution images, super-resolution reconstruction of the image is achieved. The Normalized Difference Snow Index (NDSI) value is calculated using the green light and short-wave infrared bands of the reconstructed remote sensing images to extract snow cover in the Sanjiangyuan region. The results show that the super-resolution reconstruction method for remote sensing image has excellent performance in both visual effect and objective accuracy. It can not only retain the spectral information of the original band, but also provide fine and accurate spatial information. The accuracy of snow extraction and other precision metrics are significantly improved after reconstruction. The accuracy of the reconstructed 250 m snow product reaches 90.79%, which is 20.6% higher than that of the pre-reconstruction 1000 m snow product. The research provides more reliable data support for snow monitoring in the Sanjiangyuan area.