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Article

Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine

1
Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85281, USA
2
Remote Sensing Research Centre, School of Earth and Environmental Sciences, University of Queensland, Brisbane QLD 4072, Australia
3
The Nature Conservancy, Caribbean Division, Coral Gables, FL 33134, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Dimitris Poursanidis
Remote Sens. 2021, 13(8), 1469; https://doi.org/10.3390/rs13081469
Received: 22 March 2021 / Revised: 8 April 2021 / Accepted: 8 April 2021 / Published: 10 April 2021
(This article belongs to the Section Ocean Remote Sensing)
Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed an automated bathymetry mapping approach based on the Sentinel-2 surface reflectance dataset in Google Earth Engine. We created a new method for generating a clean-water mosaic and a tailored automatic bathymetric estimation algorithm. We then evaluated the performance of the models at six globally diverse sites (Heron Island, Australia; West Coast of Hawaiʻi Island, Hawaiʻi; Saona Island, Dominican Republic; Punta Cana, Dominican Republic; St. Croix, United States Virgin Islands; and The Grenadines) using 113,520 field bathymetry sampling points. Our approach derived accurate bathymetry maps in shallow waters, with Root Mean Square Error (RMSE) values ranging from 1.2 to 1.9 m. This automatic, efficient, and robust method was applied to map shallow water bathymetry at the global scale, especially in areas which have high biodiversity (i.e., coral reefs). View Full-Text
Keywords: Allen Coral Atlas; Google Earth Engine; Sentinel-2; bathymetry; coral reef; seagrass; benthic; coastal region; shallow water Allen Coral Atlas; Google Earth Engine; Sentinel-2; bathymetry; coral reef; seagrass; benthic; coastal region; shallow water
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MDPI and ACS Style

Li, J.; Knapp, D.E.; Lyons, M.; Roelfsema, C.; Phinn, S.; Schill, S.R.; Asner, G.P. Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine. Remote Sens. 2021, 13, 1469. https://doi.org/10.3390/rs13081469

AMA Style

Li J, Knapp DE, Lyons M, Roelfsema C, Phinn S, Schill SR, Asner GP. Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine. Remote Sensing. 2021; 13(8):1469. https://doi.org/10.3390/rs13081469

Chicago/Turabian Style

Li, Jiwei, David E. Knapp, Mitchell Lyons, Chris Roelfsema, Stuart Phinn, Steven R. Schill, and Gregory P. Asner 2021. "Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine" Remote Sensing 13, no. 8: 1469. https://doi.org/10.3390/rs13081469

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