Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry
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 satel...
Published in: | Remote Sensing |
---|---|
Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
MDPI AG
2022
|
Subjects: | |
Online Access: | https://doi.org/10.3390/rs14246210 https://doaj.org/article/10cfa237cff64197bc3290c94f4ca1df |
id |
ftdoajarticles:oai:doaj.org/article:10cfa237cff64197bc3290c94f4ca1df |
---|---|
record_format |
openpolar |
spelling |
ftdoajarticles:oai:doaj.org/article:10cfa237cff64197bc3290c94f4ca1df 2023-05-15T16:29:35+02:00 Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry Alex Horton Martin Ewart Noel Gourmelen Xavier Fettweis Amos Storkey 2022-12-01T00:00:00Z https://doi.org/10.3390/rs14246210 https://doaj.org/article/10cfa237cff64197bc3290c94f4ca1df EN eng MDPI AG https://www.mdpi.com/2072-4292/14/24/6210 https://doaj.org/toc/2072-4292 doi:10.3390/rs14246210 2072-4292 https://doaj.org/article/10cfa237cff64197bc3290c94f4ca1df Remote Sensing, Vol 14, Iss 6210, p 6210 (2022) SARIn interferometry CryoSat swath ICESat-2 IceBridge Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14246210 2022-12-30T19:30:26Z 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 Directory of Open Access Journals: DOAJ Articles Greenland Remote Sensing 14 24 6210 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
SARIn interferometry CryoSat swath ICESat-2 IceBridge Science Q |
spellingShingle |
SARIn interferometry CryoSat swath ICESat-2 IceBridge Science Q Alex Horton Martin Ewart Noel Gourmelen Xavier Fettweis Amos Storkey Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry |
topic_facet |
SARIn interferometry CryoSat swath ICESat-2 IceBridge Science Q |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Alex Horton Martin Ewart Noel Gourmelen Xavier Fettweis Amos Storkey |
author_facet |
Alex Horton Martin Ewart Noel Gourmelen Xavier Fettweis Amos Storkey |
author_sort |
Alex Horton |
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 AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14246210 https://doaj.org/article/10cfa237cff64197bc3290c94f4ca1df |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland |
genre_facet |
Greenland |
op_source |
Remote Sensing, Vol 14, Iss 6210, p 6210 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/24/6210 https://doaj.org/toc/2072-4292 doi:10.3390/rs14246210 2072-4292 https://doaj.org/article/10cfa237cff64197bc3290c94f4ca1df |
op_doi |
https://doi.org/10.3390/rs14246210 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
24 |
container_start_page |
6210 |
_version_ |
1766019297283407872 |