Multi-Source SAR Ocean Front Monitoring Capability Assessment and Analysis

Monitoring the ocean front 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 key areas of Gulf Stream and the South China Sea key region which are 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. 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. Results indicate that Radarsat-2 has the strongest capability to identify multi-scale ocean front boundary and the highest internal correlation in both study areas. The highest grey relation degree is 0.75 and the highest comprehensive efficiency is 0.70. It also demonstrates strong monitoring capability for long ocean fronts. GF-3 proves more suitable for monitoring ocean fronts in the key region of the South China Sea influenced by the Kuroshio Current, with a grey relation degree of 0.67 and a 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.