With growing urbanisation and climate change, flooding is likely to become even more frequent and severe. Therefore, it is essential to constantly monitor water level changes at a large scale. Synthetic Aperture Radar (SAR) images are often used for flood mapping as they allow a rather straightforward detection of water bodies, at almost all weather and illumination conditions. As the number of satellite observations and global digital elevation models (DEMs) are becoming more available, this mapping approach is gaining in popularity. In this study, we propose three different approaches to retrieve water level maps based on the combination of satellite and topographic data, hereby referred to as the global Height Above Nearest Drainage (HAND), the local HAND and the local DEM methods. The three approaches are based on the optimization of a threshold applied on the topography (HAND or DEM) data enabling a best fit with the SAR-extracted flood map. The optimized threshold values provide at the same time the normalized water levels (with respect to the drainage network). The water depth map is thus computed from the difference between the water level and the DEM. The global HAND method applies a single optimized threshold to the HAND map, over the entire area of interest. The local HAND method is based on the same concept but optimizes and applies the HAND threshold value locally, using a sliding window. The local DEM thresholding method employs the same principle as the second method but directly on the DEM. We evaluate these methods using hydraulic model simulation results and ground truth data, and we carry out several experiments using various SAR images (Envisat, TerraSAR-X and Sentinel-1) and topographic datasets (SRTM, CopDEM and LiDAR). The best results are obtained using the local DEM approach the Lidar DEM and the TerraSAR-X data. The RMSD on the derived water depth maps reaches a minimum of 0.5m.