High-accuracy shallow-water bathymetric method including reliability evaluation based on Sentinel-2 time-series images and ICESat-2 data
Shallow-water bathymetric maps provide vital geographic information for various coastal and marine applications such as environmental management, engineering construction, oil and gas resource exploration, and ocean fisheries. Recently, satellite-derived bathymetry (SDB) has emerged as an alternative approach to shallow-water bathymetry, particularly in hard-to-reach areas. In this research, an innovative approach to bathymetry was introduced. This method provides a reliable approach for generating high-accuracy and high-reliability shallow water bathymetry results. By using Sentinel-2 time series imagery combined with ICESat-2 data, four bathymetry results at different time points are produced based on four traditional bathymetry methods. For the results at each location, a statistical method is applied to evaluate the bathymetry results, remove erroneous data, and generate high-confidence bathymetry results. The validation results indicated that the accuracy of the proposed bathymetric method achieved an R² range of 0.96 to 0.99 and an RMSE between 0.42 and 1.18 meters. When contrasted with traditional methods that utilize a single temporal image, a notable enhancement in bathymetric accuracy was observed.
Authors
- Mengzhi Hu
- Yuan Le (ORCID: https://orcid.org/0000-0002-3959-9693)
- Yifu Chen (ORCID: https://orcid.org/0000-0003-4997-8563)
- Hai Liu (ORCID: https://orcid.org/0000-0003-3421-1589)
- Lin Wu (ORCID: https://orcid.org/0000-0003-3991-4574)
- Dongfang Zhang (ORCID: https://orcid.org/0000-0001-9219-6829)
- Xiaoyu Sun (ORCID: https://orcid.org/0009-0000-9365-3171)
Institutions
- China University of Geosciences (CN)
Publication Details
- Journal
- Frontiers in Marine Science
- Published
- 2024-11-21
- DOI
- https://doi.org/10.3389/fmars.2024.1470859
- Citations
- 3
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.62
Funders
- National Natural Science Foundation of China