Compensating for penetration in radar altimetry cryosphere elevation estimates using deep learning
dc.contributor.advisor
Gourmelen, Noel
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dc.contributor.author
Ewart, Martin
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dc.date.accessioned
2019-02-22T16:09:17Z
dc.date.available
2019-02-22T16:09:17Z
dc.date.issued
2018-11-29
dc.description.abstract
Radar altimetry is commonly used for monitoring changes within the cryosphere and itscontribution to sea-level rise. Recent advances in Swath altimetry processing, using theinterferometric mode of CryoSat-2, have enabled fine 500m spatial resolution surface elevationmodels from each satellite pass. However, there is variability in radar elevation estimates, oftendue to the penetration of radar waves into snow and firn, yielding differences compared tolocal airborne LiDAR altimeters which are less impacted by penetration. While neuralnetworks are increasingly being used in a wide variety of domains to enhance predictions anddetect changes, they have not previously been applied to radar altimetry to correct forelevation biases within the cryosphere. In this study, we present a novel approach to adjustingfor elevation bias by creating a neural network that is trained to predict the elevation providedby local airborne LiDAR from CryoSat-2 radar elevation data where both data are availableand then apply that model where only radar data are present. We investigate the challengeswith building such models and review the variety of configurations and considerations. Finally,we present two proof of concepts that show good spatial and temporal transfer ability andcompensate for 70-90% of the mean penetration, while reducing the root mean squared errorby 10-17%.
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dc.identifier.uri
http://hdl.handle.net/1842/35458
dc.language.iso
en
dc.publisher
The University of Edinburgh
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dc.subject
Radar altimetry
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dc.subject
CryoSat-2
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dc.subject
Greenland
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dc.subject
Swath processing
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dc.subject
Interferometry
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dc.subject
Cryosphere
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dc.subject
Ice-sheet
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dc.subject
Glaciers
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dc.subject
DEM
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dc.subject
Surface elevation change
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dc.subject
Climate change
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dc.subject
Sea level change
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dc.subject
Operation IceBridge
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dc.subject
IceSat-2
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dc.subject
Neural network
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dc.subject
Machine learning
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dc.subject
Deep learning
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dc.subject
MSc Geographical Information Science
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dc.subject
GIS
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dc.title
Compensating for penetration in radar altimetry cryosphere elevation estimates using deep learning
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dc.type
Thesis or Dissertation
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dc.type.qualificationlevel
Masters
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dc.type.qualificationname
MSc Master of Science
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dcterms.accessRights
RESTRICTED ACCESS
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