The May 2023 Emilia-Romagna flood stands among the most severe hydrometeorological events to affect northern Italy in recent years, underscoring the urgent need for satellite-based flood characterization frameworks that are reliable, rapid, and sensitive to spatial resolution. In response to this need, the present study develops and evaluates a reproducible multi-sensor workflow implemented in Google Earth Engine (GEE) for both flood-extent mapping and floodwater-depth estimation. The framework integrates Sentinel-1 SAR, Sentinel-2 optical imagery, and very-high-resolution (VHR) SkySat data. Flood-extent maps were generated independently from each sensor and subsequently validated against Copernicus Emergency Management Service (CEMS) EMSR664 reference delineations using a vector-based accuracy assessment approach.