Many communities coexist with wildfires that can lead to loss of lives, property, and ecosystem services. The increasing usage of remote sensing tools to aid disaster response and post-event assessment offers fire agencies an opportunity for additional surveillance. The adaptability of radar instruments in their ability to see through smoke, haze, and clouds during the day or night is especially relevant when cloud cover or lack of solar illumination inhibits traditional visual surveys of damage. The Station (2009) and Bobcat (2020) Fires are the two largest fires in Los Angeles County history, each burning over 100,000 acres. These areas are imaged with NASA’s UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) L-band synthetic aperture radar. For these neighboring fires, we investigate the usage of polarimetric radar products to detect fire scars, burn severity, and different fuel (vegetation) types. These fire characteristics are observed using individual HV (horizontally emitted, vertically collected) images and in eigenvector decomposition products derived from quad-polarimetric data. Traditionally unintuitive, yet powerful PolSAR (polarimetric SAR) products are moved into GIS-friendly (geographic information system) formats to be analyzed alongside agency data such as fire perimeters, burn progression outlines, and soil burn severity. We demonstrate the advantages of combining PolSAR with GIS datasets and methods to understand the fuel loads which contributed to the fires and to monitor post-fire vegetation recovery.