Floods are one of the deadliest natural disasters causing devastating socio-economic and ecological losses worldwide. Recent advances in satellite remote sensing, especially in sensor technologies and application strategies have facilitated improved understandings of near real-time flooding dynamics and decreased knowledge gaps in long-term flood variability and trends analyses.

For this session, we welcome studies related to, but not limited to (1) novel techniques (e.g. machine learning and other data-driven methods) that improve understandings of flood characteristics (e.g. time-lag, depth, and extent); (2) remote sensing datasets for flood hazard and risk mapping; (3) integration of remote sensing observations in emergency response services and global flood forecasting systems; (4) use of remote sensing in calibration/validation of hydrological/hydraulic models; (5) current limitations of real-time flood inundation monitoring and data assimilation for hydrologic/hydraulic models. Contributions can be efforts ranging from local to global scale focusing on different types of floods: fluvial, pluvial, and coastal.