Space‐Borne Cloud‐Native Satellite‐Derived Bathymetry (SDB) Models Using ICESat‐2 And Sentinel‐2

Abstract 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 la...

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Main Authors: N. Thomas, A. P. Pertiwi, D. Traganos, D. Lagomasino, D. Poursanidis, S. Moreno, L. Fatoyinbo
Format: Article
Language:English
Published: Wiley 2021-03-01
Series:Geophysical Research Letters
Subjects:
Online Access:https://doi.org/10.1029/2020GL092170
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author N. Thomas
A. P. Pertiwi
D. Traganos
D. Lagomasino
D. Poursanidis
S. Moreno
L. Fatoyinbo
author_facet N. Thomas
A. P. Pertiwi
D. Traganos
D. Lagomasino
D. Poursanidis
S. Moreno
L. Fatoyinbo
author_sort N. Thomas
collection DOAJ
description Abstract 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.
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publishDate 2021-03-01
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series Geophysical Research Letters
spelling doaj-art-2c7f3ac4635f4b829bbe698b01fb32742025-08-20T02:11:09ZengWileyGeophysical Research Letters0094-82761944-80072021-03-01486n/an/a10.1029/2020GL092170Space‐Borne Cloud‐Native Satellite‐Derived Bathymetry (SDB) Models Using ICESat‐2 And Sentinel‐2N. Thomas0A. P. Pertiwi1D. Traganos2D. Lagomasino3D. Poursanidis4S. Moreno5L. Fatoyinbo6Earth System Science Interdisciplinary Center (ESSIC) University of Maryland College Park MD USAGerman Aerospace Center (DLR) Remote Sensing Technology Institute Berlin GermanyGerman Aerospace Center (DLR) Remote Sensing Technology Institute Berlin GermanyDepartment of Coastal Studies East Carolina University Wanchese NC USAFoundation for Research and Technology‐Hellas (FORTH) Institute of Applied and Computational Mathematics, The Remote Sensing Lab Heraklion Crete GreeceDepartment of Coastal Studies East Carolina University Wanchese NC USANASA Goddard Space Flight Center Biospheric Sciences Laboratory Greenbelt MD USAAbstract 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.https://doi.org/10.1029/2020GL092170bathymetryICESat‐2seascapesSentinel‐2
spellingShingle N. Thomas
A. P. Pertiwi
D. Traganos
D. Lagomasino
D. Poursanidis
S. Moreno
L. Fatoyinbo
Space‐Borne Cloud‐Native Satellite‐Derived Bathymetry (SDB) Models Using ICESat‐2 And Sentinel‐2
Geophysical Research Letters
bathymetry
ICESat‐2
seascapes
Sentinel‐2
title Space‐Borne Cloud‐Native Satellite‐Derived Bathymetry (SDB) Models Using ICESat‐2 And Sentinel‐2
title_full Space‐Borne Cloud‐Native Satellite‐Derived Bathymetry (SDB) Models Using ICESat‐2 And Sentinel‐2
title_fullStr Space‐Borne Cloud‐Native Satellite‐Derived Bathymetry (SDB) Models Using ICESat‐2 And Sentinel‐2
title_full_unstemmed Space‐Borne Cloud‐Native Satellite‐Derived Bathymetry (SDB) Models Using ICESat‐2 And Sentinel‐2
title_short Space‐Borne Cloud‐Native Satellite‐Derived Bathymetry (SDB) Models Using ICESat‐2 And Sentinel‐2
title_sort space borne cloud native satellite derived bathymetry sdb models using icesat 2 and sentinel 2
topic bathymetry
ICESat‐2
seascapes
Sentinel‐2
url https://doi.org/10.1029/2020GL092170
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