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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 Giovanna Moreno

Geophysical Research Letters · 2021 · 130 citations · 49 references

DOI Full text

Open access

TL;DR

Shallow nearshore coastal waters provide essential societal, economic, and ecosystem services, yet their topography remains poorly mapped because traditional methods are expensive and time‑consuming, and even space‑borne bathymetric mapping still largely depends on in‑situ measurements. This study fuses ICESat‑2 lidar data with Sentinel‑2 optical imagery in Google Earth Engine to generate openly available, high‑resolution bathymetric maps covering Florida, Crete, and Bermuda. The authors train three satellite‑derived bathymetry methods—Lyzenga, Stumpf, and support vector regression—using ICESat‑2 classified photons. Across all sites the Lyzenga algorithm achieved the lowest RMSE (≈10–15%), and the work demonstrates that ICESat‑2 can serve both as a calibration and validation source, establishing a pathway for fully space‑borne nearshore bathymetry in shallow, clear waters.

Abstract

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.