Quantifying the impact of X-band InSAR penetration bias on elevation change and mass balance estimation

Abstract Interferometric synthetic aperture radar (InSAR) data suffer from an elevation bias due to signal penetration into the firn and ice surface, rendering the height information unusable for elevation and mass-change detection. This study estimates the penetration bias in X-band InSAR data to q...

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Bibliographic Details
Published in:Annals of Glaciology
Main Authors: Abdullahi, Sahra, Burgess, David, Wessel, Birgit, Copland, Luke, Roth, Achim
Format: Article in Journal/Newspaper
Language:English
Published: Cambridge University Press (CUP) 2024
Subjects:
Canada
Devon Ice Cap
Sverdrup Glacier
Annals of Glaciology
glacier*
Ice cap
Online Access:http://dx.doi.org/10.1017/aog.2024.7
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0260305524000077
Description
Summary:Abstract Interferometric synthetic aperture radar (InSAR) data suffer from an elevation bias due to signal penetration into the firn and ice surface, rendering the height information unusable for elevation and mass-change detection. This study estimates the penetration bias in X-band InSAR data to quantify its impact on elevation and mass-change detection and to demonstrate the applicability of TanDEM-X digital elevation models (DEMs) for cryosphere research. To achieve this, a multiple linear regression model is applied to a time series of four TanDEM-X DEMs acquired between 2010 and 2018 over the Sverdrup Glacier basin (SGB), Devon Ice Cap, Canada. The resulting penetration corrected TanDEM-X DEMs agreed to within ±14 cm of spatially and temporally coincident precise in situ kinematic dGPS data (±10 cm RMSE). Additionally, multi-year estimations of mass change for the SGB derived from differencing TanDEM-X DEMs over multi-year periods between 2010 and 2018, showed good agreement with mean deviation of 338 ± 166 mm w.e. with independent measurements of mass change derived from annual in situ surface mass balance over the same time periods. The results show that the penetration bias can vary significantly, leading to random under- and overestimations in the detection of elevation and mass changes.

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