A new digital bathymetric model of the South China Sea based on the subregional fusion of seven global seafloor topography products

• Published in: Geomorphology (Amsterdam, Netherlands) Netherlands), 2020-12, Vol.370, p.107403, Article 107403
• Main Authors: Ruan, Xiaoguang, Cheng, Liang, Chu, Sensen, Yan, Zhaojin, Zhou, Xiao, Duan, Zhixin, Li, Manchun
• Format: Article
• Language: English
• Subjects: Bathymetry; Digital bathymetric model (DBM); Multi-source data fusion; Seafloor topography; South China Sea (SCS); Weighted fusion
• ISSN: 0169-555X
• DOI: 10.1016/j.geomorph.2020.107403

An understanding of ocean bathymetry is important in marine planning, navigation, military activities, and environmental monitoring. The fusion of spatial data, such as those from multi-source digital bathymetric models (DBMs), can effectively improve the efficiency of large-scale seafloor topographical research and the accuracy of the results obtained therein. On the one hand, with the release of 15arc-seconds resolution DBMs, it is now necessary to verify the quality of these new products. On the other hand, in order to generate a new, high-quality, seamless DBM in the South China Sea (SCS) and adjacent areas, an adaptive subregional spatial-domain-weighted fusion framework is proposed. First, seven of the most widely used global DBMs are selected, and multi-source subregional measured depth-sounding data undergo data cleaning and other preprocessing. Next, based on the homogeneity of the terrain features, an adaptive subregional topographical analysis is performed, and the subregional data are weight-fused. Finally, the fusion dataset is post-processed via model smoothing and other procedures. In addition, the advantages and limitations of the DBMs of the SCS are compared. The results show that SRTM15_PLUS V2 is the most reliable of the original DBMs. The updated seamless SCS DBM is void-free and more similar to SRTM15_PLUS V2 with a resolution of 15arc-seconds. The root mean square error (RMSE) of the new model is 99.60 m. Its accuracy is 13%, 40%, 15%, and 1% higher than those achieved by the GEBCO_2019, GEBCO_2014, SRTM30_PLUS, and SRTM15_PLUS models, respectively, and its expression of the topography is more detailed and realistic. The feasibility and limitations of the proposed fusion framework are demonstrated. The present findings provide a useful reference for the timely reconstruction and updating of large-scale seafloor topography from multiple datasets. [Display omitted] •Newly established subregional weighted fusion of 7 global DBMs on large-scale seafloor mapping•Improved seafloor mapping via a high-quality and seamless DBM in the South China Sea•Rapid reconstruction/update of large-scale seafloor models via multiple datasets•Easily readable expression of sea-floor topography