The study presented here builds on previous synthetic aperture radar (SAR) burnt area estimation models and presents the first U-Net (a convolutional network architecture for fast and precise segmentation of images) combined with ResNet50 (Residual Networks used as a backbone for many computer vision tasks) encoder architecture used with SAR, Digital Elevation Model, and land cover data for burnt area mapping in near-real time. The Santa Cruz Mountains Lightning Complex (CZU) was one of the most destructive fires in state history. The results showed a maximum burnt area segmentation F1-Score of 0.671 in the CZU, which outperforms current models estimating burnt area with SAR data for the specific event studied models in the literature, with an F1-Score of 0.667. The framework presented here has the potential to be applied on a near real-time basis, which could allow land monitoring as the frequency of data capture improves.