Radar satellites have high utility in monitoring various earth geophysical process. Radar can collect through all weather and optical obstructions (clouds, gas, smoke, etc.), providing uninterrupted data collection. The European Space Agency’s (ESA’s) Sentinel-1A/B synthetic aperture radar (SAR) C-band data has been widely used up until this point, but with low resolution (~5 meters for C-band radar, with a 5.6 cm wavelength). We have a unique partnership with Capella Space, which provides access to SAR X-band data at much higher resolution (up to 0.5 meters for X-band, with a shorter wavelength of 3.1 cm), shorter repeat times, and tasking capabilities. Our goal is to modify and develop an algorithm for estimating lava flow height based on a building height algorithm originally developed by Liu et al., 2013. Here, we present our first steps toward that goal, where we compare Sentinel-1C-band data to commercial Capella X-band data. We test Capella SAR images against Sentinel-1 images for three test sites: the Colorado Marshall fire area, and the 2021 La Palma and Kilauea volcanic eruption areas. We utilize ESA SNAP and commercial GAMMA softwares to preprocess the Sentinel-1 and Capella imagery, respectively. We utilize Microsoft building footprints (https://www.microsoft.com/en-us/maps/bing-maps/building-footprints) as well as outlines generated from machine learning techniques. Future work includes modification of the building height algorithm for variable terrain instead of flat surfaces, so that we may apply this work to various geophysical processes such as lava flows in near-to-real time.