This paper proposed an MSC-Transformer model based on the Transformer’s neural network, which was applied to seabed sediment classification. The data came from about 2900 km² of seabed area on the northern slope of the South China Sea. Using the submarine backscattering intensity and depth data obtained by the sub-bottom profiler, combined with latitude and longitude information, a seabed dataset of the slope area of the South China Sea was constructed. Moreover, using the MSC-Transformer, the accurate identification and judgment of sediment types such as calcareous bio-silt, calcareous bio-clay silt, silty sand, medium sand and gravel sand were realized. Compared with the conventional deep neural network CNN, RNN, etc., the model shows advantages when applied to the sediment dataset of the shallow sea slope region of the South China Sea. This confirms the feasibility and validity of the model and provides a reliable and accurate tool for seabed sediment classification in the field of marine science. The completeness and accuracy of the dataset and the good performance of the model provide a solid foundation for the scientificalness and practicability of the study.

In recent years, the extent of Antarctic sea ice has frequently reached historically low levels. Melt ponds significantly affect the sea ice heat balance and mass balance while posing challenges to ice surface transportation. Melt ponds in the Arctic have been extensively monitored using satellite remote sensing. However, the Antarctic has received very little attention, and small-scale melt pond distribution patterns remain virtually unexplored. This study combines high-resolution Unmanned Aerial Vehicle (UAV) imagery with satellite remote sensing to investigate the characteristics of melt ponds on landfast ice near Zhongshan Station, East Antarctica. UAV-derived data reveal that the melt ponds in this region are small (median area = 0.34 m²) and irregular in shape (mean roundness = 4.97). Validation against the UAV data suggests poor performance of the traditional melt pond fraction (MPF) retrieval model in the Antarctic region. Therefore, we develop an MPF retrieval algorithm by integrating UAV and Landsat 8 images. The correlation coefficient is as high as 0.82, substantially outperforming the traditional method. Dynamic MPF monitoring indicates that melt ponds concentrate near the coastline, especially to the west of bare rock areas, as the persistent summer easterly winds transport low-albedo dust westward. The integrated melt-induced risk assessment, combining MPF and Normalized Difference Water Index for ice, shows that high-risk areas are concentrated in Nella Fjord, west of the Mirror Peninsula. Ice surface transport should avoid this area and be completed before mid-November.