DORSL-FIN: A Self-supervised Neural Network for Recovering Missing Bathymetry from ICESat-2

Abstract

Bathymetric data, comprising elevations of submerged surfaces (e. g., seafloor or lake bed), constitute a critical need for a wide range of science and application focus areas, such as safety of marine navi- gation, benthic habitat mapping, flood inundation modeling, and coastal engineering. Over the past decade, the availability of near- shore bathymetric data has increased dramatically due to advances in satellite-derived bathymetry (SDB). One notable advance occurred with the 2018 launch of NASA's Ice, Cloud, and land Elevation Satellite 2 (ICESat-2), carrying the Advanced Topographic Laser Altimeter System (ATLAS). However, much like other Earth observing satellites, ATLAS is often hampered by obstructions, such as clouds, which block the sensor's view of the Earth's surface. In this study, we introduce the Deep Occlusion Recovery of Satellite Lidar From ICESat-2 Network (DORSL-FIN) to recover partially occluded bathymetric profiles. We show that DORSL-FIN is able to accurately recover occluded bathymetry and outperforms other methods of interpolation.