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...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Alex Horton, Martin Ewart, Noel Gourmelen, Xavier Fettweis, Amos Storkey
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2022
Subjects:
SARIn
interferometry
CryoSat
swath
ICESat-2
IceBridge
Science
Q
Greenland
Online Access:https://doi.org/10.3390/rs14246210
https://doaj.org/article/10cfa237cff64197bc3290c94f4ca1df
Description
Summary: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.

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