Reliable assessment and classification of natural disasters, particularly floods, are vital but often obstructed by the absence of timely and precise data, especially in adverse weather conditions. However, Synthetic Aperture Radar (SAR) images are instrumental in overcoming these obstacles, as they can collect data regardless of weather constraints. This paper presents a study on flood classification using multi-source satellite data, integrating SAR data from Sentinel-1A, the ESA World Cover map, and Global Surface Water Probability. In this study, only 0.6% of the data was used for creating the model, while the remaining data was utilized for testing. The comparative analysis demonstrated a significant improvement in accuracy from 0.799 to 0.8688, and the F1-score increased from 0.82 to 0.8813. These findings underscore the crucial role of the ESA World Cover Map and Global Surface Water Probability in enhancing flood classification using SAR imagery.