Work

SCBMLD: a photon signal extraction method for ICESat-2 satellite-derived bathymetry

Year: 2025

Type: article

Abstract: The integration of satellite remote sensing imagery with ICESat-2 photons offers an effective, economical, and large-scale approach for mapping shallow seabed topography. However, ICESat-2 photon data are vulnerable to noise from sea surface fluctuations, water quality, and instrument errors, which can compromise the accuracy of bathymetric products. To address this challenge, we propose a scale compression-based machine learning denoising (SCBMLD) method for accurate seafloor photon signal extraction from ICESat-2 data, enhancing bathymetry inversion in multispectral imagery. Results show that SCBMLD achieves an average accuracy improvement of 21%, and provides high-quality bathymetric data across diverse conditions while reducing the need for complex parameter tuning and lowering computational costs. Using this approach, reliable bathymetric products were generated for two study regions, with RMSE of 0.51 m and 1.09 m, meeting C-level zone of confidence requirements.

Source: Optics Express

DOI: 10.1364/oe.547702

Authors: Ningning Li, Mi Wang, Bo Yang, Jun Pan, Yingdong Pi, Zhongli Fan

Language: English

Topic: Remote Sensing and LiDAR Applications

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Life below water

Open Access status: gold

Estimated APC paid: $2,270

Funders: National Natural Science Foundation of China, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing