Morphological approaches to understanding Antarctic Sea ice thickness
type: CreativeWork
description: Sea ice thickness has long been an under-measured quantity, even in the satellite era. The snow surface elevation, which is far easier to measure, cannot be directly converted into sea ice thickness estimates without knowledge or assumption of what proportion of the snow surface consists of snow and ice. We do not fully understand how snow is distributed upon sea ice, in particular around areas with surface deformation. Here, we show that deep learning methods can be used to directly predict snow depth, as well as sea ice thickness, from measurements of surface topography obtained from laser altimetry. We also show that snow surfaces can be texturally distinguished, and that texturally-similar segments have similar snow depths. This can be used to predict snow depth at both local (sub-kilometer) and satellite (25 km) scales with much lower error and bias, and with greater ability to distinguish inter-annual and regional variability than current methods using linear regressions. We find that sea ice thickness can be estimated to ∼20% error at the kilometer scale. The success of deep learning methods to predict snow depth and sea ice thickness suggests that such methods may be also applied to temporally/spatially larger datasets like ICESat-2.
abstract: Sea ice thickness has long been an under-measured quantity, even in the satellite era. The snow surface elevation, which is far easier to measure, cannot be directly converted into sea ice thickness estimates without knowledge or assumption of what proportion of the snow surface consists of snow and ice. We do not fully understand how snow is distributed upon sea ice, in particular around areas with surface deformation. Here, we show that deep learning methods can be used to directly predict snow depth, as well as sea ice thickness, from measurements of surface topography obtained from laser altimetry. We also show that snow surfaces can be texturally distinguished, and that texturally-similar segments have similar snow depths. This can be used to predict snow depth at both local (sub-kilometer) and satellite (25 km) scales with much lower error and bias, and with greater ability to distinguish inter-annual and regional variability than current methods using linear regressions. We find that sea ice thickness can be estimated to ∼20% error at the kilometer scale. The success of deep learning methods to predict snow depth and sea ice thickness suggests that such methods may be also applied to temporally/spatially larger datasets like ICESat-2.
author: Mei, M. Jeffrey
language: en_US
keywords: Sea ice, Antarctic, Snow depth
date published: 2020-09
date modified: 2026-07-27T22:05:19Z
provider: Woods Hole Oceanographic Institute
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Massachusetts Institute of Technology and Woods Hole Oceanographic Institution

PropertyValue
property ID: Handle
value: https://hdl.handle.net/1912/26115

PropertyValue
property ID: DOI
value: 10.1575/1912/26115