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.

This study utilizes high-resolution sea surface temperature (SST) reanalysis data (0.25° × 0.25°) to investigate the relationship between SST anomalies in the South China Sea and ENSO events. The main findings are as follows: First, there is a delayed correlation between ENSO and SST anomalies in the South China Sea, with the correlation being stronger during El Niño years than during La Niña years. Second, the correlation with the peak values of the Oceanic Niño Index (ONI) is strongest for El Niño events with a 9-month lead, while for La Niña events, it is strongest with a 2-month lead. Seasonally, during El Niño events, the strongest correlations are observed in summer with a 3-month lead and in winter with a 1-month lag. For La Niña events, the strongest correlations are seen in summer with an 8-month lag and in winter with a 9-month lag. Finally, an analysis of atmospheric anomalies and shear kinetic energy anomalies relative to SST anomalies reveals a significant seasonal SST response, particularly during the summer of El Niño years and the winter of La Niña years. Overall, these results enhance our understanding of ENSO’s influence on the South China Sea and provide valuable insights for climate prediction and ecosystem protection in the region.

Seagrass beds provide essential ecosystem services, such as habitat for marine life, water quality purification, carbon sequestration, and climate regulation. For the Changshan Archipelago, which relies heavily on marine resources, the growth and development of seagrass beds are key factors affecting aquaculture. This study is based on data collected from a survey conducted in the nearshore waters of the Changshan Archipelago in August 2022, encompassing seagrass distribution and water sample data. The water samples were analyzed for various parameters, including salinity, suspended solids, pH, dissolved oxygen, sea temperature, nitrite-nitrogen, nitrate-nitrogen, and ammonia-nitrogen concentrations. A habitat suitability assessment of the seagrass beds in the Changshan Archipelago was conducted. The study calculated the suitability index for each environmental variable based on the abundance index, and then established a Habitat Suitability Index model using a weighted allocation method. The results indicate that the seagrass bed area in the study region is primarily composed of excellent and suitable habitats. The concentration of inorganic nutrients is a key factor influencing seagrass growth. The HSI model not only identifies the hierarchical distribution of habitats in seagrass areas, but also detects potential suitable habitats for seagrass. This provides scientific reference for future seagrass bed resource protection and artificial cultivation efforts.

This study utilizes the typhoon path ensemble dataset from the Marine Science Big Data Center, surface temperature data from NOAA’s COBE Sea Surface Temperature dataset, and wind field data from the NCEP/DOE Reanalysis II dataset. It employs analytical techniques such as wavelet analysis, correlation analysis, and the Mann–Kendall test to investigate the temporal and spatial variations in tropical cyclones in the Northwest Pacific, focusing on aspects such as frequency, genesis regions, and Accumulated Cyclone Energy. The research examines the influence of environmental factors, including warm pool thermal conditions and monsoon troughs, on the behavior of tropical cyclones. Findings indicate that over the past 39 years, there has been an upward trend in the frequency of tropical cyclones, whereas ACE has exhibited a slight downward trend. The results of the M-K test suggest that following a period of rapid increase, cyclone frequency experienced a significant shift in 1996, subsequently displaying a downward trend. Notably, the frequency of cyclones aligns with mutation points corresponding to warm pool thermal conditions and the Monsoon Trough Index. Wavelet analysis reveals that cyclone frequency, ACE, warm pool thermal conditions, and MTI exhibit similar small scale periodic variations. The observed differences in the genesis regions of tropical cyclones are attributed to fluctuations in warm pool thermal conditions. Specifically, years characterized by cooler warm pool conditions correspond with a stronger MTI, while warmer conditions are associated with a weaker MTI. The genesis regions of cyclones predominantly lie within the monsoon trough, where environmental conditions favorable for cyclone development are intensified during years of cooler warm pool conditions, resulting in heightened convective activity.

Chlorophyll-a concentration (Chl-a) is an important indicator of coastal eutrophication. Remote sensing technology provides a global view of it. However, different types of sensors are subject to design constraints and cannot meet the requirements of high temporal and spatial resolution on nearshore engineering simultaneously. To obtain high-spatiotemporal-resolution images, this study examines the performance of the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) on GOCI and Landsat Chl-a data fusion. Considering the rapidly changing rate and consistency of oceanic Chl-a, the ESTARFM was modified via segmented fitting and numerical conversion. The results show that both fusion models can fuse multiple data advantages to obtain high-spatiotemporal-resolution Chl-a images. Compared with the ESTARFM, the modified solution has a better performance in terms of the root mean square error and correlation coefficient, and its results have better spatial consistency for coastal Chl-a. In addition, the new solution expands the data utilization range of data fusion by reducing the influence of the time interval of original data and realizes better monitoring of nearshore Chl-a changes.