Geostatistical Analysis and Mitigation of the Atmospheric Phase Screens in Ku-Band Terrestrial Radar Interferometric Observations of an Alpine Glacier

Terrestrial radar interferometry (TRI) can measure displacements at high temporal resolution, potentially with high accuracy. An application of this method is the observation of the surface flow velocity of steep, fast-flowing aglaciers. For these observations, the main factor limiting the accuracy...

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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing 2020-11, Vol.58 (11), p.7533-7556
Main Authors:	Baffelli, Simone, Frey, Othmar, Hajnsek, Irena
Format:	Article
Language:	eng
Subjects:	Accuracy Atmospheric measurements Atmospheric modeling Atmospheric models atmospheric phase screen (APS) Atmospheric water Covariance Delays differential radar interferometry (D-InSAR) Flow velocity Geostatistics Glaciers Interferometry Mitigation Phase measurement Radar Radar interferometry Regression analysis Spaceborne radar Spatial analysis Statistical analysis Statistical methods Stratification Superhigh frequencies Surface flow synthetic aperture radar (SAR) Temporal resolution Terrestrial environments terrestrial radar interferometry (TRI) Variance analysis Velocity Water vapor Water vapour
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Summary:	Terrestrial radar interferometry (TRI) can measure displacements at high temporal resolution, potentially with high accuracy. An application of this method is the observation of the surface flow velocity of steep, fast-flowing aglaciers. For these observations, the main factor limiting the accuracy of TRI observations is the spatial and temporal variabilities in the distribution of atmospheric water vapor content, causing a phase delay [atmospheric phase screen (APS)] whose magnitude is similar to the displacement phase. This contribution presents a geostatistical analysis of the spatial and temporal behaviors of the APS in Ku-Band TRI. The analysis is based on the assumption of a separable spatiotemporal covariance structure, which is tested empirically using variogram analysis. From this analysis, spatial and temporal APS statistics are estimated and used in a two-step procedure combining regression-Kriging with generalized least squares (GLS) inversion to estimate a velocity time-series. The performance of this method is evaluated by cross-validation using phase observations on stable scatterers. This analysis shows a considerable reduction in residual phase variance compared with the standard approach of combining the linear models of APS stratification and interferogram stacking.
ISSN:	0196-2892 1558-0644