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Daily cloud-free snow cover products are important premise and foundation for hydrological simulation, climate system research, and snow disaster monitoring in the Tienshan Mountains, Central Asia. In this study, partial clouds appearing in the Moderate Resolution Imaging Spectroradiometer (MODIS) were removed by temporal and spatial filtering, and the remaining cloud pixels were replaced by Interactive Multisensor Snow and Ice Mapping System (IMS). The results show that the retrieved annual mean snow cover obtained from the improved MODIS product data is 31.5% higher than the original MODIS product. Through validation performed via in situ observations, the overall accuracy, land accuracy, and snow accuracy also increased to 88.2%, 91.9%, and 81.9%, respectively. However, the overall accuracy shows a lower accuracy in transitional months compared to other months, which derives mainly from the difference in spatial scales between IMS and in situ observations.

Plain Language Summary The Moderate Resolution Imaging Spectroradiometer (MODIS) has a high spatial and time resolution but always obscured by cloud. While the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover product can penetrate cloud but has coarse spatial resolution. In response to these strengths and limitations, this study combines MODIS and IMS to create an improved snow product which is both free from cloud interference and features high temporal and spatial resolution. The novel composite snow-mapping product is then applied over the Tienshan Mountains region in Central Asia. The retrieved annual mean snow cover for the improved MODIS product data is 31.5% higher than the original MODIS product. Furthermore, compared with in situ snow depth measurements, the overall accuracy, land accuracy, and snow accuracy of the improved snow product is 88.2%, 91.9%, and 81.9%, respectively. Given the significant improvement in its accuracy, this product can readily be applied to climate change research and snow disaster monitoring.