Evaluation of a Statistical Approach for Extracting Shallow Water Bathymetry Signals from ICESat-2 ATL03 Photon Data

In this study we present and validate a simple empirical method to obtain bathymetry profiles using the geolocated photon data from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission, which was launched by NASA in September 2018. The satellite carries the Advanced Topographic Laser Al...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Heidi Ranndal, Philip Sigaard Christiansen, Pernille Kliving, Ole Baltazar Andersen, Karina Nielsen
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2021
Subjects:
ICESat-2
ATLAS
ATL03
lidar
bathymetry
Sisimiut
Science
Q
Greenland
Online Access:https://doi.org/10.3390/rs13173548
https://doaj.org/article/4969a39b953d4edeada0ac75fbf59065
Description
Summary:In this study we present and validate a simple empirical method to obtain bathymetry profiles using the geolocated photon data from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission, which was launched by NASA in September 2018. The satellite carries the Advanced Topographic Laser Altimeter System (ATLAS), which is a lidar that can detect single photons and calculate their bounce point positions. ATLAS uses a green laser, causing some of the photons to penetrate the air–water interface. Under the right conditions and in shallow waters (<40 m), these photons are reflected back to ATLAS after interaction with the ocean bottom. Using ICESat-2 data from four different overflights above the Heron Reef, Australia, a comparison with SDB data showed a median absolute deviation of approximately 18 cm and Root Mean Square Errors (RMSEs) down to 28 cm. Crossovers between two different overflights above the Heron Reef showed a median absolute difference of 13 cm. For an area north-west of Sisimiut, Greenland, the comparison was done with multibeam echo sounding data, with RMSEs down to between 35 cm, and correspondingly showed median absolute deviations between 33 and 49 cm. The proposed method works well under good conditions with clear waters such as in the Great Barrier Reef; however, for more difficult areas a more advanced machine learning technique should be investigated in order to find an automated method that can distinguish between bathymetry and other signals and noise.

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