Recently, deep convolutional networks have made great progress on the task of super-resolution, i.e., reconstructing images with finer spatial resolution. However, although the reconstructions are visually impressive, they may lack physical consistency. This aspect is sought in remote sensing, where the resolution of satellite imagery (e.g., Sentinel-2) may be too coarse to characterize the physical structure and dynamics of certain landscapes. Through the study of flooding dynamics in wet grasslands, we propose a super-resolution approach that allows deriving fine resolution patterns that are visually realistic and physically exploitable. This approach is based on an architecture, Fusion-UNet, allowing the fusion of multispectral data with a digital terrain model (DTM) associated with a loss function combining content, structure, and segmentation losses. Our results show that this model can precisely predict water levels (WLs) while restituting the fine structure of the landscape. This approach allows to refine the production of hydrological and ecological indicators to define the state of the ecosystem.