A New Method for Ocean Fronts’ Identification With Res-U-Net and Remotely Sensed Data in the Northwestern Pacific Area

Accurately identifying ocean fronts is essential for advancing marine science and effectively managing aquatic resources. Traditional identification methods and certain deep learning approaches often struggle to capture the dynamic complexity of oceanic phenomena due to the limitations in identification efficiency, detail sensitivity, and real-world adaptability. This study develops a new residual U-network (Res-U-Net) architecture for ocean fronts’ identification to overcome these limitations, incorporating residual blocks to improve feature extraction and gradient flow of deep learning networks, which integrates deeper network architectures and comprehensive dataset analysis methods. The study utilizes daily sea surface temperature (SST) data from the Copernicus Marine Environment Monitoring Service (CMEMS), with a 0.05° spatial resolution, covering the Northwestern Pacific (100 °E–150 °E, 0 °N–50 °N) from 1981 to 2022. This strategy significantly improves the identification accuracy and generalizability under versatile oceanic conditions. The experimental results demonstrated that Res-U-Net significantly outperformed other vanilla U-Net baselines. For overall ocean fronts’ identification in the Northwestern Pacific area, the accuracy of Res-U-Net improved by 14% compared to the baseline model. Moreover, for fine-scale ocean fronts’ challenging to identify with vanilla U-Net, Res-U-Net achieved an improvement of approximately 67% in recognition accuracy over U-Net. These results are based on the best-performing models from the experimental Res-U-Net and U-Net groups. These advances not only address the degradation issues caused by gradient explosion in deeper U-Net architectures but also enhance the identification of subtle frontal details. This improvement significantly improves the adaptability of Res-U-Net for the large-scale, multiobjective task of ocean fronts’ identification.