Land cover mapping is of great interest in the Alps region for monitoring the surface occupation changes (e.g. forestation, urbanization, etc). In this pilot study, we investigate how time series of radar satellite imaging (C-band single-polarized SENTINEL-1 Synthetic Aperture Radar, SAR), also acquired through clouds, could be an alternative to optical imaging for land cover segmentation. Concretely, we compute for every location (using SAR pixels over 45 × 45 m) the temporal coherence matrix of the Interferometric SAR (InSAR) phase over 1 year. This normalized matrix of size 60, ×, 60 (60 acquisition dates over 1 year) summarizes the reflectivity changes of the land. Two machine learning models, a Support Vector Machine (SVM) and a Convolutional Neural Network (CNN) have been developed to estimate land cover classification performances of 6 main land cover classes (such as forests, urban areas, water bodies, or pastures). The training database was created by projecting to the radar geometry the reference labeled CORINE Land Cover (CLC) map on the mountainous area of Grenoble, France. Upon evaluation, both models demonstrated good performances with an overall accuracy of 78% (SVM) and of 81% (CNN) over Chambéry area (France). We show how, even with a spatially coarse training database, our model is able to generalize well, as a large part of the misclassifications are due to a low precision of the ground truth map. Although some less computationally expensive approaches (using optical data) could be available, this land cover mapping based on very different information, i.e., patterns of land changes over a year, could be complementary and thus beneficial; especially in mountainous regions where optical imaging is not always available due to clouds. Moreover, we demonstrated that the InSAR temporal coherence matrix is very informative, which could lead in the future to other applications such as automatic detection of abrupt changes as snow fall or landslides.