The increasing number of flood events, combined with coastal urbanization, has contributed to significant economic losses and damage to buildings and infrastructure. Here I present two several methods for characterizing flood inundation using Sentinel-1A/B synthetic aperture radar (SAR). In the first part, an amplitude thresholding technique is developed to characterize flood inundation and compared to machine learning (ML) methods. Both are very effective at identifying both large- and small-scale flood inundation at very high-resolution. In the second part, differential interferometric synthetic aperture radar (DInSAR) is used to map subsidence of the Ganges-Brahmaputra-Meghna (GBM) delta. The GBM delta is the largest delta in the world with an area of ~150,000 km^2 and approximately 200 million inhabitants (Becker et al., 2020) subject to both subsidence and coastal flooding (Higgins et al., 2014; Rogers et al. 2018). Thus, the need to quantify spatial patterns of deformation rates of the GBM is critical for future planning to mitigate the impacts of changes in sedimentation rates and sea level rise in the Bangladesh coastal region. In this study, DInSAR images at ~100 meter pixelization are used to generate interferograms and associated time series to determine current deformation rates over the GBM. Approximately 650 ascending and 550 descending Sentinel-1A Single Look Complex (SLC) images were acquired spanning from March 2017 to June 2021 resulting in approximately 2300 interferograms. The availability of both ascending and descending images in the regions allows for construction of 11 vertical time series using MSBAS which are then mosaicked into a singular deformation map. Overall, the region is subsiding with a rate of 3.36 mm/yr, with 95% of the vertical linear deformation rates ranging between -28.11 and 22.09 mm/yr. Time series display a strong sinusoidal signal with a local maximum during the middle of the year (June) and a local minimum at the start of the year (January) and an amplitude ranging from 8-12 cm. Minimal correlation between the vertical linear deformation rates and either topography or vegetation (as quantified by NDVI) was found, while a strong correlation with geologic units was observed. Comparison of GPS-derived rates and MSBAS-derived rates shows reasonable comparison between some stations. Removal of the monsoon sinusoidal signal shows a more stable time series with potential underlying deformation dynamics.