A purely spaceborne open source approach for regional bathymetry mapping

Nathan Thomas, Brian Lee, Oliver Coutts, Pete Bunting, David Lagomasino, Lola Fatoyinbo

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

15 Citations (Scopus)


Timely and up-to-date bathymetry maps over large geographical areas have been difficult to create, due to the cost and difficulty of collecting in situ calibration and validation data. Recently, combinations of spaceborne Ice, Cloud, and Elevation Satellite-2 (ICESat-2) lidar data and Landsat/sentinel-2 data have reduced these obstacles. However, to date, there have been no means of automatically extracting bathymetry photons from ICESat-2 tracks for model calibration/validation and no well-established open source workflows for generating regional scale bathymetric models. Here we provide an open source approach for generating bathymetry maps for the shallow water region around the island of Andros, Bahamas. We demonstrate an efficient means of processing 224 ICESat-2 tracks and 221 Landsat-8 scenes, using the classification of subaquatic height extracted photons (C-SHELPh) algorithm and Extra Trees Regression to provide 30 m pixel estimates of per-pixel depth and standard error. We map bathymetry with an RMSE of 0.32 m and RMSE% of 6.7%. Our workflow and results demonstrate a means of achieving accurate regional-scale bathymetry maps from purely spaceborne data.

Original languageEnglish
Article number4708109
Pages (from-to)1-9
Number of pages9
JournalIEEE Transactions on Geoscience and Remote Sensing
Publication statusPublished - 21 Jul 2022


  • Bathymetry
  • Cloud and Elevation Satellite-2 (ICESat-2)
  • Ice
  • landsat8
  • machine learning


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