Oil spills represent a serious threat to marine ecosystems. Remote sensing monitoring, especially based on synthetic aperture radar (SAR), have been extensively employed in marine environments due to its unique advantages. However, SAR images of marine oil spills exhibit challenges of weak boundaries, confusion with look-alike phenomena, and the difficulty of detecting small-scale targets. To address these issues, we propose LRA-UNet, a Lightweight Residual Attention UNet for semantic segmentation in SAR images. Our model integrates depthwise separable convolutions to reduce feature redundancy and computational cost, while adopting a residual encoder enhanced with the Simple Attention Module (SimAM) to improve the precise extraction of target features. Additionally, we design a joint loss function that incorporates Sobel-based edge information, emphasizing boundary features during training to enhance edge sharpness. Experimental results show that LRA-UNet achieves superior segmentation results, with a mIoU of 67.36%, surpassing the original UNet by 4.41%, and a 5.17% improvement in IoU for the oil spill category. These results confirm the effectiveness of our approach in accurately extracting oil spill regions from complex SAR imagery.

Ocean wave evolution reflects delayed wind forcing, nonlinear wind–wave interactions, and wave persistence, which complicate multi-horizon significant wave height (SWH) forecasting. This study analyzes wind–wave lag relationships and wave memory characteristics in the South China Sea and the Bay of Bengal using ERA5 data from 2022 to 2024, leading to region-specific input windows of 12 h and 21 h. Based on these findings, SSA-STLSTM-LT integrates Wind–Wave Lag Attention, ST-LSTM, a Temporal Transformer, and spatial self-attention within a direct six-horizon forecasting framework. Feature ablation further identifies a nine-variable input configuration. Under a common ERA5-based evaluation protocol, the model achieved horizon-averaged RMSE values of 0.098 m and 0.065 m in Regions A and B, respectively. It obtained the lowest MAE and RMSE across all six horizons in Region A. In Region B, it achieved the lowest RMSE from 3 to 24 h, remained comparable to the best model at 48 h, and was less accurate than the LT-enhanced recurrent baselines at 72 h. Independent altimeter validation further showed favorable observational consistency relative to PredRNN-LT and TrajGRU-LT, with smaller differences in Region B. The results indicate strong but region- and horizon-dependent forecasting advantages.