Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Author ORCID Identifier
https://orcid.org/0000-0003-1959-7903
Abstract
The ICESat-2 satellite collects LiDAR data along linear orbital tracks using a photon-counting green wavelength (532.27 nm) instrument. The utility of combining ICESat-2 data with satellite imagery for training and subsequently applying satellite-derived bathymetry models to provide estimates of shallow water depth is well-established. However, automating and improving the accuracy of the identification of ICESat-2 photon events (PEs) representing bathymetry remains a challenge. This article presents an algorithm for automated extraction of PEs reflected from the ocean floor (rather than the ocean surface or noise in the water column). The algorithm is unique in examining both the density of PEs surrounding a subject PE and their position relative to the subject PE. This is accomplished by establishing three concentric ellipses around the subject PE, dividing them into radial “sectors” in 2D space (along-track vs. PE depth/height), recording the number of neighboring PEs in each sector, and using this information to fit a LightGBM model. Agreement with PEs identified by an image interpreter is approximately 98%. Testing suggests that the accuracy of the algorithm is relatively insensitive to the size and shape of the ellipses used to define a PE’s neighborhood and to the number of radial sectors used. The model produced also appears to be robust across different geographic areas and data densities.
Date Created
6/23/2026
Department
Center for Coastal and Ocean Mapping
Publication Date
Winter 12-22-2025
Subject
Bathymetry detection from ICESat-2 data
Grant/Award Number and Agency
NOAA grant NA20NOS4000196
Journal Title
Remote Sensing
Language
English
Publisher
MDPI
Medium
Digital Object Identifier (DOI)
Document Type
Article
Recommended Citation
Rzhanov, Yuri and Lowell, Kim, "An Algorithm for Extracting Bathymetry from ICESat-2 Data That Employs Structure and Density Using Concentric Ellipses" (2025). Remote Sensing. 2481.
https://scholars.unh.edu/faculty_pubs/2481
Comments
This is an open access article published by MDPI in Remote Sensing in 2025, available online: https://dx.doi.org/10.3390/rs18010025