“…The proposed method can fill cloud gaps without a significant loss of accuracy, especially during snow cover transition periods (autumn and spring), which may provide more accurate cloud-free NDSI data for climate change and energy balance studies. distribution and its spatiotemporal changes has become the focus of numerous studies [5][6][7][8][9][10][11][12][13], such that the timely and accurate acquisition of snow distribution information has become critical [9,14,15].Satellite images acquired using remote sensing can provide continuous spatiotemporal information on snow coverage over long time series and on a global scale, which is advantageous to a large number of researchers [16][17][18][19][20][21][22]. Among other widely-utilized snow cover assessment methods, MODIS products have become one of the main data sources for ice and snow research due to their global coverage, long time series (i.e., the databases are currently updated and have been maintained since 2000), high spatial (e.g., 500 m) and temporal (e.g., daily) resolutions, and free access, which allow for real-time, accurate, and large-scale snow cover variation monitoring [14].…”