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: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-03-01
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| Series: | Geophysical Research Letters |
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| 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. |
| format | Article |
| id | doaj-art-2c7f3ac4635f4b829bbe698b01fb3274 |
| institution | OA Journals |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Wiley |
| record_format | Article |
| 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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