Abstract: Mapping the fractional snow cover (FSC) is significant for water resource management, especially in the upper reaches of the Indus River, where water resources are highly dependent on alpine snowmelt. This study proposed a RepVGG network-based FSC inversion model, RepFNet, which used FY-4A/AGRI remote sensing data and Landsat8 OLI images for FSC feature extraction. Moreover, this model incorporated upsampling and downsampling modules, an innovative attention mechanism, and a specific loss function, thereby enabling the FSC mapping of the upper reaches of the Indus River at 2 000-m resolution. The mapping results were validated using the MODIS data. The experimental results show that the RepFNet achieved a coefficient of determination of 0.667, a root mean square error (RMSE) of 0.090, a correlation coefficient (r) of 0.890, an explained variance score (EVS) of 0.683, and a Kappa coefficient of 0.468, remarkably outperforming the classical algorithms such as random forest and U-Net. Overall, the RepFNet model demonstrates excellent performance in FSC inversion, offering a novel technical solution for high-accuracy FSC monitoring.