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Cloud obscuration is a major problem for using Moderate Resolution Imaging Spectroradiometer (MODIS) images in different applications. This issue poses serious difficulties in monitoring the snow cover in mountainous regions due to high cloudiness in such areas. To overcome this, different cloud removal methods have been developed in the past where most of them use MODIS snow cover products and spatiotemporal dependencies of snow to estimate the undercloud coverage. In this study, a new approach is adopted that uses surface reflectance data in the cloud-free pixels and estimates the surface reflectance of a cloudy pixel as if there were no cloud. This estimation is obtained by subsequently applying the k-nearest neighbor and dynamic time compositing methods. The modified surface reflectance data are then utilized as inputs of a Normalized Difference Snow Index (NDSI)-based algorithm to map snow cover in the study area. The results indicate that the suggested approach is able to appropriately estimate undercloud surface reflectance in bands 2, 4 and 6, and can map the snow cover with 97% accuracy, which is a substantial improvement over the conventional method with an accuracy of 86%. Finally, although a clear underestimation of snow cover (about 15%) is observed by applying the proposed approach, still, it is much better than the 30% underestimation obtained by the conventional method.

Developing a Cloud-Reduced MODIS Surface Reflectance Product for Snow Cover Mapping in Mountainous Regions