The coastal zones consist of the interfaces between land and sea, undergoing the mobility of the shoreline at an unprecedented pace over the last centuries. Such a trajectory, at the global scale, exacerbates the coastal risks (intersecting hazards, exposure and vulnerability), calling upon a scalable methodology to ensure the precise and accurate monitoring. One of the observation solutions resides in the satellite platform provided with the finest spatial and spectral resolutions. Because remote sensing is a science of trade-offs, no sensors can be both excellent in spatial and spectral specificities.<br />We propose an original research study to create an imagery endowed with both high spatial and spectral characteristics, purposed to classify a representative coastal zone (12 habitat classes) in a temperate area in Brittany, France. The methodology highlights a transferable fusion procedure based on the simultaneous acquisition (10-min difference) of the 30-m hyperspectral PRISMA satellite imagery and the 3-m PlanetScope (Dove-R) imagery, made possible given the very high temporal resolution of the PlanetScope constellation. The spatial resolution of the hyperspectral PRISMA imagery, in the visible and near-infrared spectrum (63 bands), was successfully upscaled at 3 m, using a bandwise linear prediction from the 4 PlanetScope Dove-R bands (collected at 3 m). The model residuals showed that the pansharpened PRISMA imagery (5 m) was better enhanced (absolute deviation of 0,011) than the original PRISMA imagery (30 m, absolute deviation of 0,015). Seawater and mudflat were the best habitats upscaled, whereas the road and the roof were the worst classes predicted.