High-resolution satellite imagery plays a crucial role in remote sensing, enabling a wide range of applications such as land-use monitoring, urban planning, precision agriculture, and environmental change detection. Super-resolution is a computer vision task aimed at reconstructing high-resolution images from their low-resolution counterparts. Applying it to multi-temporal, multi-spectral satellite imagery, such as that provided by Sentinel-2, can yield promising results; however, it is often not suitable as input for downstream tasks, such as land-cover segmentation. Multi-task learning offers a viable alternative, providing improved regularization and better preservation of spatial structures when generating high-resolution land-cover maps. Moreover, information carried by shared feature representations proves more effective than relying on super-resolved RGB images.