Sensitivity of polarimetric SAR interferometry data to different vertical subsurface structures of the Greenland ice sheet

Fischer, Georg; Parrella, Giuseppe; Papathanassiou, Konstantinos; Hajnsek, Irena

doi:10.1109/igarss.2017.8127774

Cited by 4 publications

(11 citation statements)

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“…All of them are able to describe the data better than the conventional UV model, which is forced to start at the surface and thus has less flexibility. This is in line with previous studies which concluded that interferometric phase center depths cannot be explained with a UV model without accounting for a shift [5], [9]. The present analysis illustrates the need for this type of shift, as tomographic measurements revealed that the first 1-5 m meters below the surface are widely transparent.…”

Section: Discussionsupporting

confidence: 92%

“…As density increases even further with depth, the volume fraction of the ice-air mixture exceeds 50% and the firn becomes more homogeneous and the scattering coefficient decreases again [12]. Such a model was shown to improve interferometric phase center modeling, but requires extensive in situ data [5]. A similar behavior can be approximated by a Gaussian function…”

Section: ) Gaussian Volume Modelmentioning

confidence: 99%

“…The bias depends on the vertical distribution of the backscattered power in the subsurface and varies with the snow, firn, and ice conditions (e.g. density or temperature), as well as with polarization, frequency, and the interferometric baseline [5]. A large range of values has been reported for this bias, e.g., -1 m to -10 m at X-band (in the transition from the percolation to the dry snow zone in Greenland) [2] and -14 m at L-band (Greenland Summit) [6], with rare cases down to 120 m (cold marginal ice) [6].…”

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confidence: 99%

“…The effect of refrozen melt layers within the firn column in the percolation zone of Greenland on interferometric coherences was modelled in [14] using Dirac deltas in combination with a UV model for the background volume. Although this model accurately reproduces coherence magnitudes, it also fails to explain the location of the interferometric phase center, which motivated the use of nonuniform volume models [5]. An indication for non-uniform volume models can be found in snow scattering models based on the improved Born approximation [12], where the scattering coefficient in snow or firn is largest for 50% volume fraction of the mixture of ice particles and air.…”

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confidence: 99%

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Modeling the Vertical Backscattering Distribution in the Percolation Zone of the Greenland Ice Sheet With SAR Tomography

Fischer

Jäger

Papathanassiou

et al. 2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The penetration of microwave signals into snow and ice, especially in dry conditions, introduces a bias in digital elevation models generated by means of synthetic aperture radar (SAR) interferometry. This bias depends directly on the vertical backscattering distribution in the subsurface. At the same time, the sensitivity of interferometric SAR measurements on the vertical backscattering distribution provides the potential to derive information about the subsurface of glaciers and ice sheets from SAR data, which could support the assessment of their dynamics. The aim of this paper is to improve the interferometric modeling of the vertical backscattering distribution in order to support subsurface structure retrieval and penetration bias estimation. Vertical backscattering distributions are investigated at different frequencies and polarizations on two test sites in the percolation zone of Greenland using fully polarimetric X-, C-, Land nd P-band SAR data. The vertical backscattering distributions were reconstructed by means of SAR tomography and compared to different vertical structure models. The tomographic assessment indicated that the subsurface in the upper percolation zone is dominated by scattering layers at specific depths, while a more homogeneous scattering structure appears in the lower percolation zone. The performance of the evaluated structure models, namely an exponential function with a vertical shift, a Gaussian function and a Weibull function, was evaluated. The proposed models improve the representation of the data compared to existing models while the complexity is still low to enable potential model inversion approaches. The tomographic analysis and the model assessment is therefore a step forward towards subsurface structure information and penetration bias estimation from SAR data.

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Section: Discussionsupporting

confidence: 92%

Section: ) Gaussian Volume Modelmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Modeling the Vertical Backscattering Distribution in the Percolation Zone of the Greenland Ice Sheet With SAR Tomography

Fischer

Jäger

Papathanassiou

et al. 2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Studies have shown the retrieval of InSAR penetration depths [4] and signal extinction coefficients [5] at different frequencies and polarizations based on the assumption of a lossy signal propagation through an isotropic, homogeneous volume in the subsurface of glaciers or ice sheets, which is referred to as Uniform Volume (UV) model. However, InSAR phase centers have been found to be deeper than predicted by the UV model [6] [7]. This indicates the necessity of refined InSAR models for the vertical distribution of backscattering in the subsurface of glaciers and ice sheets.…”

Section: Introductionmentioning

confidence: 97%

Investigating the Potential to Estimate Insar Penetration Depth Over Ice Sheets from Pol-Insar Data

Fischer¹,

Parrella²,

Papathanassiou³

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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Digital elevation models generated with SAR interferometry (InSAR) are an important information source for glacier and ice sheet mass balance. However, the measured elevations suffer from a penetration bias due to the interferometric phase center being up to several tens of meters below the surface. The penetration of the microwave signals depends on SAR parameters (e.g. frequency) and snow and ice conditions. There is potential to estimate this penetration bias directly from the data by means of polarimetric InSAR models. Existing models fail to describe the data across different test sites and ice conditions and phase centers were found to be deeper than predicted by these models. SAR tomography is employed to assess the vertical distribution of backscattering in the data from an airborne campaign. The data are compared to refined models in order to find better representations of the vertical backscattering distribution, while the model complexity is purposely kept simple to make a phase center estimation possible. Additionally, recent work showed the importance of strong subsurface layers which influence phase center depth. Combining the refined subsurface structure models and dominant subsurface layers allows simulating a variety of ice sheet subsurface scenarios and can be used to assess the potential to estimate the InSAR phase center depth directly from the data.

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Modeling and Compensation of the Penetration Bias in InSAR DEMs of Ice Sheets at Different Frequencies

Fischer

Papathanassiou

Hajnsek

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic Aperture Radar Interferometry (InSAR) is able to provide important information for the characterization of the surface topography of glaciers and ice sheets. However, due to the inherent penetration of microwaves into dry snow, firn, and ice, InSAR elevation models are affected by a penetration bias. The fact that this bias depends on the snow and ice conditions as well as on the interferometric acquisition parameters complicates its assessment and makes it also relevant for measuring topographic changes. Recent studies indicated the potential for model based compensation of this penetration bias. This paper follows this approach and investigates the performance of two subsurface volume models for this task. Single-channel and polarimetric approaches are discussed for random and oriented volume scenarios. The model performance is assessed on two test sites in the percolation zone of the Greenland ice sheet using fully polarimetric airborne X-, C-, L-, and P-band InSAR data. The results indicate that simple models are able to partially compensate the penetration bias and provide more accurate topographic information than the interferometric phase center measurements alone.

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Sensitivity of polarimetric SAR interferometry data to different vertical subsurface structures of the Greenland ice sheet

Cited by 4 publications

References 4 publications

Modeling the Vertical Backscattering Distribution in the Percolation Zone of the Greenland Ice Sheet With SAR Tomography

Modeling the Vertical Backscattering Distribution in the Percolation Zone of the Greenland Ice Sheet With SAR Tomography

Investigating the Potential to Estimate Insar Penetration Depth Over Ice Sheets from Pol-Insar Data

Modeling and Compensation of the Penetration Bias in InSAR DEMs of Ice Sheets at Different Frequencies

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