Abstract: Ocean front monitoring plays a critical role in fisheries, underwater navigation, and marine environmental sensing. To quantitatively, objectively, and accurately evaluate the performance of different Synthetic Aperture Radar (SAR) satellites in ocean front detection accuracy and boundary identification capabilities, this study utilizes data from mainstream international SAR satellites (GF-3, Radarsat-2, and Sentinel-1). The Canny edge detection algorithm is applied to identify ocean fronts in the Gulf Stream key area and the South China Sea key region influenced by the Kuroshio Current. Sea Surface Temperature (SST) reanalysis data are used to validate the detection results. An evaluation index system is established based on the satellites' ocean front monitoring capabilities, and three comprehensive decision-making methods, Grey Relation Analysis (GRA), Data Envelopment Analysis (DEA), and Fuzzy Comprehensive Evaluation (FCE), are employed to assess the monitoring performance of the three SAR satellites. The results indicate that Radarsat-2 exhibits the strongest multi-scale ocean front boundary identification capability and internal correlation in both study areas, with the highest grey relation degree reaching 0.75 and the highest comprehensive efficiency reaching 0.70. It also demonstrates strong monitoring capability for long ocean fronts. GF-3 is more suitable for monitoring ocean fronts in the South China Sea key region influenced by the Kuroshio Current, with a grey relation degree of 0.67 and comprehensive efficiency of 0.68. Sentinel-1, with its medium-resolution wide coverage and rapid revisit capability, is suitable for monitoring mesoscale steady-state frontal zones. This study provides scientific technical support and decision-making references for evaluating the effectiveness of SAR-based ocean front monitoring, selecting optimal monitoring approaches, and integrating multi-source information.