Extreme flooding, whether due to natural or anthropogenic causes, poses a significant threat to human society, the economy, and the environment, often resulting in the loss of human lives. The availability of data from Earth-observation satellites enhances communities’ capacity for a timely response to flood events, as they offer an efficient means of mapping flood extent over a large area. Synthetic Aperture Radar (SAR) imaging provides an all-weather sensing technique that is well-suited for near-real-time disaster mapping, especially for events like floods. As part of my doctoral degree, I worked on a probabilistic flood-mapping approach to investigate hazard and exposure. Most flood mapping algorithms provide an estimate of flood extent in the form of a binary map. Despite their usefulness, such binary maps do not provide any information on the uncertainty associated with the pixel class. Due to the ability to characterize the uncertainty associated with each pixel class, compared with the traditional deterministic approach, I applied Bayesian inference approach alongside machine learning-based hierarchical split-based image segmentation to a set of SAR images acquired by Sentinel-1 C-band satellites over the Kerala state of India before, during, and after the flood event in August 2018. My method generates a priori distribution functions of backscattering values for flooded and non-flooded pixels using ensembles of observed SAR amplitude histograms. Next, I apply a Bayesian framework to update a priori distribution functions using observed SAR backscattering values at individual pixels and obtain the posterior flood probability distribution of a pixel. I compare the probabilistic flood map obtained from the SAR-based Bayesian approach against the flood extent obtained from visual inspection of available optical data acquired by Sentinel-2, Landsat satellites, and images from Moderate Resolution Imaging Spectroradiometer (MODIS). Following figure shows an example of this work from Sherpa et al 2020 (IEEE).