Coastal zones are constantly exposed to environmental and human pressures. Maintaining detailed, up-to-date information under such conditions requires efficient methodologies capable of capturing these dynamic changes. Satellite-derived bathymetry (SDB) offers notable advantages but requires suitable imagery, specialised algorithms, highly skilled personnel, and powerful computing infrastructure, especially for temporal analyses. These requirements can create barriers to access and limit the use of these techniques across different stakeholders. Cloud-based platforms have emerged as promising alternatives, providing accurate, rapid, cost-effective, and regularly updated analysis capabilities. In this context, this research aims to develop an automated image-ranking approach in Google Earth Engine (GEE) to identify and curate suitable Sentinel-2 and Landsat-8 imagery for shallow-water along the Irish coast; to develop an open-access GEE application that encourages a broader and non-specialist user base to derive bathymetric data from optical satellites using Lyzenga's 1978 log-linear model, and to produce materials for training and outreach. The app enables users to analyse a curated list of pre-ranked satellite images based on the best-performing R-squared (R²) and Root Mean Square Error (RMSE) results. It provides SDB maps, validation plots, and metadata summarising information for five pilot bays: Dublin, Dungarvan, Portrane, Rosses, and Tramore. The findings confirm the pre-ranking strategy is effective, as it prioritises images with high correlation/low error, ensuring only the most appropriate are provided to the end-user for bathymetric modelling.