Synthetic Aperture Radar images are routinely used for delineating flooded areas. Processing algorithms are often based on change detection techniques that enable a comparison of backscattering signals between the flood image and a reference image. However, as of today, there is little guidance on how to rapidly and reliably extract the most adequate reference image from an online archive. Our study proposes a method that allows the automatic and objective identification of the best reference image with respect to a given flood image. The proposed method consists of two processing steps. First, a subset of archived candidate images acquired on the same track, with the same polarization and in the same period of year as the flood image is created. Next, site-specific time series of regional backscattering values are established and the effects of flooding on the backscattering behaviour are statistically evaluated. We propose two complementary anomaly indexes and their combination in a single index as a means to identify the most adequate reference image for flooding-related change detection applications. The reliability of the proposed method is demonstrated in three representative case studies targeting the flood prone areas of the Severn River (United Kingdom), the Red River (United States) and the Meghna River (Bangladesh).