Space-borne cloud-native satellite-derived bathymetry (SDB) models using ICESat-2 and Sentinel-2

Nathan Thomas, Avi Putri Pertiwi, Dimosthenis Traganos, David Lagomasino, Dimitris Poursanidis, Shalimar Moreno, Lola Fatoyinbo

Research output: Contribution to journalArticle (journal)peer-review

68 Citations (Scopus)


Shallow nearshore coastal waters provide a wealth of societal, economic, and ecosystem services, yet their topographic structure is poorly mapped due to a reliance upon expensive and time intensive methods. Space-borne bathymetric mapping has helped address these issues, but has remained largely dependent upon in situ measurements. Here we fuse ICESat-2 lidar data with Sentinel-2 optical imagery, within the Google Earth Engine cloud platform, to create openly available spatially continuous high-resolution bathymetric maps at regional-to-national scales in Florida, Crete and Bermuda. ICESat-2 bathymetric classified photons are used to train three Satellite Derived Bathymetry (SDB) methods, including Lyzenga, Stumpf, and Support Vector Regression algorithms. For each study site the Lyzenga algorithm yielded the lowest RMSE (approx. 10%–15%) when compared with validation data. We demonstrate a means of using ICESat-2 for both model calibration and validation, thus cementing a pathway for fully space-borne estimates of nearshore bathymetry in shallow, clear water environments.

Original languageEnglish
Article numbere2020GL092170
JournalGeophysical Research Letters
Issue number6
Publication statusPublished - 28 Mar 2021


  • ICESat-2
  • Sentinel-2
  • bathymetry
  • seascapes


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