Is satellite-derived bathymetry vertical accuracy dependent on satellite mission and processing method?
This research focusses on three satellite-derived bathymetry methods and optical satellite instruments: (1) a stereo photogrammetry bathymetry module (SaTSeaD) developed for the NASA Ames stereo pipeline open-source software (version 3.6.0) using stereo WorldView data; (2) physics-based radiative transfer equations (PBSDB) using Landsat data; and (3) a modified composite band-ratio method for Sentinel-2 (SatBathy) with an initial simplified calibration, followed by a more rigorous linear regression against in situ bathymetry data. All methods were tested in three different areas with different geological and environmental conditions, Cabo Rojo, Puerto Rico; Key West, Florida; and Cocos Lagoon and Achang Flat Reef Preserve, Guam. It is demonstrated that all satellite derived bathymetry (SDB) methods have increased accuracy when the results are aligned with higher-accuracy ICESat-2 ATL24 track bathymetry data using the iterative closest point (ICP). SDB vertical accuracy depends more on location characteristics than the method or optical satellite instrument used. All error metrics considered (mean absolute error, median absolute deviation, and root mean square error) can be less than 5% of the maximum bathymetry depth penetration for at least one method, although not necessarily for the same method for all sites. The SDB error distribution tends to be bimodal irrespective of method, satellite instrument, alignment, site, or maximum bathymetry depth, leading to the potential ineffectiveness of traditional error metrics, such as the root mean square error. However, our analysis demonstrates that performing detrending where possible can achieve an error distribution as close to normality as possible for which error metrics are more diagnostic.
Citation Information
| Publication Year | 2026 |
|---|---|
| Title | Is satellite-derived bathymetry vertical accuracy dependent on satellite mission and processing method? |
| DOI | 10.3390/rs18020195 |
| Authors | Monica Palaseanu-Lovejoy, Jeffrey J. Danielson, Minsu Kim, Bryan Eder, Gretchen Imahori, Curt D. Storlazzi |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | Remote Sensing |
| Index ID | 70273442 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Geology, Minerals, Energy, and Geophysics Science Center |