The main goal of this study is to investigate the potential of the Sentinel-1 sensor in accurately mapping different types of freshwater ice. The research was conducted at Saint-Pierre Lake in Quebec, Canada. We used a dataset that included backscatter data and grey level co-occurrence matrix (GLCM) properties. To classify the freshwater ice types, we employed the Fuzzy C-Means (FCM) unsupervised classification scheme. Furthermore, a feature selection analysis was done to identify the most relevant GLCM parameters that could effectively map the different ice types with accuracy. The preliminary findings demonstrate that incorporating both the Sentinel-1 VV polarization band and the GLCM Mean parameter leads to a significant improvement in distinguishing between the different types of ice cover.