The increasing number of flood events combined with coastal urbanization has contributed to significant economic losses and damage to buildings and infrastructure. Development of higher resolution SAR flood mapping that accurately identifies flood features at all scales can be incorporated into operational flood forecasting tools, improving response and resilience to large flood events. Here, we present a comparison of several methods for characterizing flood inundation using a combination of synthetic aperture radar (SAR) remote sensing data and machine learning methods. We implement two applications with SAR GRD data, an amplitude thresholding technique applied, for the first time, to Sentinel-1A/B SAR data, and a machine learning technique, DeepLabv3+. We also apply DeepLabv3+ to a false color RGB characterization of dual polarization SAR data. Analyses at 10 m pixel spacing are performed for the major flood event associated with Hurricane Harvey and associated inundation in Houston, TX in August of 2017. We compare these results with high-resolution aerial optical images over this time period, acquired by the NOAA Remote Sensing Division. We compare the results with NDWI produced from Sentinel-2 images, also at 10 m pixel spacing, and statistical testing suggests that the amplitude thresholding technique is the most effective, although the machine learning analysis is successful at reproducing the inundation shape and extent. These results demonstrate the effectiveness of flood inundation mapping at unprecedented resolutions and its potential for use in operational emergency hazard response to large flood events.