SAR-to-optical remote sensing modality translator neural networks are mainly trained on flat areas preventing their use to detect gravitational movements as landslides in steep sloped areas. In this paper, we first propose a new SAR-DEM-optical dataset in mountainous regions to improve performances of SAR-to-optical image translators under these extreme conditions. Then we upgrade SARDINet (SAR Distorted Image translator Network) model previously developped for urban areas, to take a Digital Elevation Model (DEM) together with the SAR image as input and perform translation in natural mountainous environment. Multiple fusion strategies are explored to merge efficiently SAR and DEM images: late fusion, early fusion but also an intermediate fusion based on balanced separable convolutions. These approaches are compared to the original SARDINet and two standard adversarial networks -Pix2pix and CycleGAN -showing improvements in distorted regions.