Monitoring wildfires has become increasingly critical due to the sharp rise in wildfire incidents in recent years. Optical satellites like Sentinel-2 and Landsat are extensively utilized for mapping burned areas. However, the effectiveness of optical sensors is compromised by clouds and smoke, which obstruct the detection of burned areas. As a result, there is growing interest in satellites equipped with Synthetic Aperture Radar (SAR), which can penetrate clouds and smoke. Previous studies have investigated the potential of Sentinel-1 and RADARSAT-1/-2 C-band SAR for burned area mapping. However, to the best of our knowledge, no published research is found using RADARSAT Constellation Mission (RCM) SAR data for this purpose. The objective of this study is to investigate RCM SAR data for burned area mapping using deep learning. We propose a deep-learning-based processing pipeline specifically for RCM data. The deep learning-based pipeline utilizes the U-Net as the segmentation model. The training samples are preprocessed to generate log-ratio images based on the same beam mode. The training labels are generated from binarized log-ratio images and Sentinel2 polygons. Our results demonstrate that RCM data can effectively detect burned areas in the 2023 Canadian Wildfires, achieving an F1 Score of 0.765 and an IoU Score of 0.620 for the study area in Alberta, and an F1 Score of 0.655 and an IoU Score of 0.487 for the study area in Quebec. These results indicate the promising potential of RCM data in wildfire monitoring.