Driven by the climate crisis, the frequency and intensity of flood events are increasing. Electro-optical (EO) satellite imagery is commonly utilized for rapid disaster response. However, its utilities in flood situations are hampered by cloud cover and limited during night-time. Alternative flood detection methods utilize Synthetic Aperture Radar (SAR) data. Despite the advantages of SAR over EO in the aforementioned situations, SAR presents a distinct drawback: human analysis often struggles with data interpretation. This paper proposes a novel framework, Diffusion-based SAR-to-EO Image Translation (DSE). The DSE framework converts SAR into EO imageries, thereby enhancing the interpretability of flood insights for humans. Experimental results on the Sen1Floods11 and SEN12-FLOOD datasets confirm that the DSE framework delivers enhanced visual information and improves performance across all flood segmentation tests.