Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry
peer reviewed Satellite and airborne observations of surface elevation are critical in understanding climatic and glaciological processes and quantifying their impact on changes in ice masses and sea level contribution. With the growing number of dedicated airborne campaigns and experimental and ope...
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Online Access: | https://orbi.uliege.be/handle/2268/302676 https://orbi.uliege.be/bitstream/2268/302676/1/remotesensing-14-06210.pdf https://doi.org/10.3390/rs14246210 |
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ftorbi:oai:orbi.ulg.ac.be:2268/302676 2024-04-21T08:03:37+00:00 Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry Horton, Alex Ewart, Martin Gourmelen, Noel Fettweis, Xavier Storkey, Amos SPHERES - ULiège 2022-12 https://orbi.uliege.be/handle/2268/302676 https://orbi.uliege.be/bitstream/2268/302676/1/remotesensing-14-06210.pdf https://doi.org/10.3390/rs14246210 en eng MDPI https://www.mdpi.com/2072-4292/14/24/6210/pdf urn:issn:2072-4292 https://orbi.uliege.be/handle/2268/302676 info:hdl:2268/302676 https://orbi.uliege.be/bitstream/2268/302676/1/remotesensing-14-06210.pdf doi:10.3390/rs14246210 scopus-id:2-s2.0-85144622238 open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess Remote Sensing, 14 (24), 6210 (2022-12) altimetry artificial intelligence (AI) CryoSat cryosphere Greenland IceBridge ICESat-2 interferometry SARIn swath Artificial intelligence Green land Interferometric radars Earth and Planetary Sciences (all) General Earth and Planetary Sciences Physical chemical mathematical & earth Sciences Earth sciences & physical geography Physique chimie mathématiques & sciences de la terre Sciences de la terre & géographie physique journal article http://purl.org/coar/resource_type/c_6501 info:eu-repo/semantics/article peer reviewed 2022 ftorbi https://doi.org/10.3390/rs14246210 2024-03-27T14:57:58Z peer reviewed Satellite and airborne observations of surface elevation are critical in understanding climatic and glaciological processes and quantifying their impact on changes in ice masses and sea level contribution. With the growing number of dedicated airborne campaigns and experimental and operational satellite missions, the science community has access to unprecedented and ever-increasing data. Combining elevation datasets allows potentially greater spatial-temporal coverage and improved accuracy; however, combining data from different sensor types and acquisition modes is difficult by differences in intrinsic sensor properties and processing methods. This study focuses on the combination of elevation measurements derived from ICESat-2 and Operation IceBridge LIDAR instruments and from CryoSat-2’s novel interferometric radar altimeter over Greenland. We develop a deep neural network based on sub-waveform information from CryoSat-2, elevation differences between radar and LIDAR, and additional inputs representing local geophysical information. A time series of maps are created showing observed LIDAR-radar differences and neural network model predictions. Mean LIDAR vs. interferometric radar adjustments and the broad spatial and temporal trends thereof are recreated by the neural network. The neural network also predicts radar-LIDAR differences with respect to waveform parameters better than a simple linear model; however, point level adjustments and the magnitudes of the spatial and temporal trends are underestimated. Article in Journal/Newspaper Greenland University of Liège: ORBi (Open Repository and Bibliography) Remote Sensing 14 24 6210 |
institution |
Open Polar |
collection |
University of Liège: ORBi (Open Repository and Bibliography) |
op_collection_id |
ftorbi |
language |
English |
topic |
altimetry artificial intelligence (AI) CryoSat cryosphere Greenland IceBridge ICESat-2 interferometry SARIn swath Artificial intelligence Green land Interferometric radars Earth and Planetary Sciences (all) General Earth and Planetary Sciences Physical chemical mathematical & earth Sciences Earth sciences & physical geography Physique chimie mathématiques & sciences de la terre Sciences de la terre & géographie physique |
spellingShingle |
altimetry artificial intelligence (AI) CryoSat cryosphere Greenland IceBridge ICESat-2 interferometry SARIn swath Artificial intelligence Green land Interferometric radars Earth and Planetary Sciences (all) General Earth and Planetary Sciences Physical chemical mathematical & earth Sciences Earth sciences & physical geography Physique chimie mathématiques & sciences de la terre Sciences de la terre & géographie physique Horton, Alex Ewart, Martin Gourmelen, Noel Fettweis, Xavier Storkey, Amos Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry |
topic_facet |
altimetry artificial intelligence (AI) CryoSat cryosphere Greenland IceBridge ICESat-2 interferometry SARIn swath Artificial intelligence Green land Interferometric radars Earth and Planetary Sciences (all) General Earth and Planetary Sciences Physical chemical mathematical & earth Sciences Earth sciences & physical geography Physique chimie mathématiques & sciences de la terre Sciences de la terre & géographie physique |
description |
peer reviewed Satellite and airborne observations of surface elevation are critical in understanding climatic and glaciological processes and quantifying their impact on changes in ice masses and sea level contribution. With the growing number of dedicated airborne campaigns and experimental and operational satellite missions, the science community has access to unprecedented and ever-increasing data. Combining elevation datasets allows potentially greater spatial-temporal coverage and improved accuracy; however, combining data from different sensor types and acquisition modes is difficult by differences in intrinsic sensor properties and processing methods. This study focuses on the combination of elevation measurements derived from ICESat-2 and Operation IceBridge LIDAR instruments and from CryoSat-2’s novel interferometric radar altimeter over Greenland. We develop a deep neural network based on sub-waveform information from CryoSat-2, elevation differences between radar and LIDAR, and additional inputs representing local geophysical information. A time series of maps are created showing observed LIDAR-radar differences and neural network model predictions. Mean LIDAR vs. interferometric radar adjustments and the broad spatial and temporal trends thereof are recreated by the neural network. The neural network also predicts radar-LIDAR differences with respect to waveform parameters better than a simple linear model; however, point level adjustments and the magnitudes of the spatial and temporal trends are underestimated. |
author2 |
SPHERES - ULiège |
format |
Article in Journal/Newspaper |
author |
Horton, Alex Ewart, Martin Gourmelen, Noel Fettweis, Xavier Storkey, Amos |
author_facet |
Horton, Alex Ewart, Martin Gourmelen, Noel Fettweis, Xavier Storkey, Amos |
author_sort |
Horton, Alex |
title |
Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry |
title_short |
Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry |
title_full |
Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry |
title_fullStr |
Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry |
title_full_unstemmed |
Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry |
title_sort |
using deep learning to model elevation differences between radar and laser altimetry |
publisher |
MDPI |
publishDate |
2022 |
url |
https://orbi.uliege.be/handle/2268/302676 https://orbi.uliege.be/bitstream/2268/302676/1/remotesensing-14-06210.pdf https://doi.org/10.3390/rs14246210 |
genre |
Greenland |
genre_facet |
Greenland |
op_source |
Remote Sensing, 14 (24), 6210 (2022-12) |
op_relation |
https://www.mdpi.com/2072-4292/14/24/6210/pdf urn:issn:2072-4292 https://orbi.uliege.be/handle/2268/302676 info:hdl:2268/302676 https://orbi.uliege.be/bitstream/2268/302676/1/remotesensing-14-06210.pdf doi:10.3390/rs14246210 scopus-id:2-s2.0-85144622238 |
op_rights |
open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.3390/rs14246210 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
24 |
container_start_page |
6210 |
_version_ |
1796943387636531200 |