Monitoring dynamic alluvial rivers is essential for safe inland navigation, yet traditional bathymetric surveys are costly and infrequent. This paper presents an automated method for detecting migrating sandbars by integrating Sentinel-2 satellite imagery with daily water gauge data. Implemented in Google Earth Engine (GEE), the algorithm matches specific water levels with cloud-optimized images to map emerging shoals. Water and sediment were separated using the Sentinel Water Mask (SWM) index, while a 30-meter internal channel buffer mitigated shoreline mixed-pixel errors. The method’s accuracy was validated using 3-meter resolution PlanetScope imagery. Results demonstrated high geometric agreement (mean Intersection over Union = 0.71) and a strong area correlation (R² = 0.97). Notably, the 10-meter Sentinel-2 resolution caused a systematic 26% overestimation of sandbar size. However, for navigation, this overestimation provides a beneficial safety margin that prevents the underestimation of submerged obstacles. By correlating specific gauge levels with sandbar emergence, the extracted 2D contours provide a vital spatial baseline that enables the future estimation of available water columns over specific bottlenecks. Ultimately, this cost-effective procedure allows for the continuous generation of spatial databases, forming a practical foundation for dynamic relative depth mapping within River Information Services (RIS).