Flood maps are essential for loss estimation. Ground observations are often limited, resulting in poor estimates of flood impacts. Airborne images from satellite synthetic aperture radar (SAR) are considered as an alternative. In this study, given the flood extent extracted from Sentinel-1 SAR imagery via the Google Earth Engine (GEE) platform, a digital elevation model (DEM)-based inundation model, Simple Inundation Model (SPM)-GEE, was proposed to generate inundation depths simultaneously. Flood events of varying spatial scales were used to evaluate the model’s performance. These included three historical events: the short-duration, high-intensity rainfalls on August 23, 2018, and July 31, 2021, and the heavy rainfall induced by the outer circulation of Typhoon Hagupit on August 3, 2020. The results indicate that the root mean square error (RMSE) for a village-scale event is 0.46 m. In contrast, previous studies reported RMSE values of 0.72 m and 0.64 m, representing improvements of 36% and 28%, respectively. For another event at the district level, the absolute error (AE) was found to be 0.16 m, compared to 0.4 m and 0.23 m in earlier studies, showing improvements of 60% and 30%. With the current temporal and spatial limitations of satellite imagery, accurate estimation of flood depths remains challenging. By integrating GEE with the SPM-GEE model, this study provides comprehensive mapping of maximum flood extent and depth in areas with limited ground observation data.