Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine

Abstract

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).

Application of Earth Observation Data and Google Earth Engine for Monitoring Coral Reef Exposure to Environmental Stressors

“…In addition, the Automated Global Shallow Water Bathymetry Mapping method [52] has a maximum limitation of 20 m. As it measures the light differences between blue and green bands, and no light differences are seen between blue and green light at 20 m and above, deep water areas are derived at 20m. As our threshold cut offs were at 20m for depth and considering the low spatial resolution of the alternative in GEE, this was not considered a problem for this study.…”

“…Only one bathymetry product, ETOPO1, was available in GEE, which, with a spatial resolution of 1 arc-minute, was deemed too coarse for this study. Instead we utilised the method developed by Li et al [52] for automatic global shallow water depth mapping, which utilises Sentinel-2 reflectance data to obtain shallow water bathymetry at a resolution of 10 m in GEE. We used Sentinel-2 data from 1 st January 2019 to 31 st December 2020 to generate a fixed depth map, as in Li et al [52], since depth changes over very long time scales.…”

“…Instead we utilised the method developed by Li et al [52] for automatic global shallow water depth mapping, which utilises Sentinel-2 reflectance data to obtain shallow water bathymetry at a resolution of 10 m in GEE. We used Sentinel-2 data from 1 st January 2019 to 31 st December 2020 to generate a fixed depth map, as in Li et al [52], since depth changes over very long time scales. To speed up processing in GEE this data was downloaded as a product to be called on in GEE and has been included as public asset in GEE (https://code.earthengine.google.com/?asset=users/mjw1280/Automated_Shallow_Water_Ba-thymetry_Mapping) and included in github for free and open use (https://github.com/mjw-marine/Coral-Reef-Stress-Exposure-Index).…”

The Future Is Big—and Small: Remote Sensing Enables Cross-Scale Comparisons of Microbiome Dynamics and Ecological Consequences

“…Seagrasses create habitat that supports biodiversity but are declining globally due to multiple stressors, including disease ( 29 , 30 ). Meadows grow in shallow coastal waters; intertidal seagrass can be mapped at low tide by UAVs ( 17 , 31 ), and water correction algorithms allow mapping of subtidal meadows ( 32 , 33 ). We can derive ecosystem-level characteristics such as plant biomass and above-ground carbon stocks from UAVs and satellite measurements in response to disease outbreaks ( 34 , 35 ).…”

Data_Sheet_3.DOCX

“…Thanks to the considerable advances in SDB methods in recent years various research projects have been carried out, such as the Allen Coral Atlas which provides satellite-derived bathymetry in global coral reef regions (Li et al, 2021). A number of private companies have also emerged to exploit this potential (Supplementary Appendix 1).…”