Rapid changes in the Greenland Ice Sheet require precise elevation monitoring to understand ice dynamics and predict sea level rise. X-band Interferometric Synthetic Aperture Radar (InSAR) has the potential for this purpose but is limited by microwave signal penetration biases, which can be a few meters. We present a novel hybrid modeling approach that integrates machine learning (ML) with physical models to enhance the estimation of the elevation bias in InSAR data at X-band. Our method addresses the limitations of traditional physical modeling techniques by parameterizing the vertical structure function using a ML model. This approach combines machine learning as input for the physical model. The results demonstrate the improvements in correcting elevation biases, thus increasing the accuracy of X-band InSAR DEMs over Greenland. This advancement has the potential for more precise elevation estimation and ice-sheet monitoring.

The amount of water in a snow pack can be described by the snow water equivalent (SWE). SWE is a crucial parameter for hydrological models, for example, for flood predictions. Previous studies have shown that the interferometric phase between two repeat-pass synthetic aperture radar (SAR) measurements can be used to determine the change in SWE. However, a limitation of this method is phase wraps. To overcome this, the copolar phase difference (CPD) between the VV and HH channel can be used, which has been proven to be related to the depth of freshly accumulated snow. This study proposes an approach to incorporate the information on the fresh snow accumulation from the CPD into the interferometric SWE retrieval algorithm. The aim is to detect and correct interferometric phase wraps. The first results using airborne SAR data indicate that including the CPD improves the accuracy of the SWE retrieval.

Spaceborne differential SAR interferometry (D-InSAR) has been demonstrated to potentially allow snow water equivalent (SWE) change measurements for dry snow on a spatial scale, resolution, and accuracy unprecedented by other sensor concepts. However, its operational use is hindered mainly because of: 1) low coherence areas resulting from temporal decorrelation, complicating a robust phase unwrapping and 2) an unknown phase offset due to the$2\pi $ambiguity of the interferometric measurement and therefore a strongly biased SWE change estimate. Furthermore, conventional D-InSAR does not provide a direct measurement of the snow density, which is used in the phase-to-SWE inversion and is an important snow parameter. This article presents strategies to potentially overcome these shortcomings by exploiting simultaneously acquired interferograms with different squint angles. The different lines of sight result in differential phase delays introduced by a SWE change. The phase difference between the interferograms may be exploited to produce a low-resolution SWE estimate without the need for phase unwrapping and to resolve the$2\pi $phase ambiguity of the single interferogram. In addition to that, the ratio between the interferograms is a measure of the dielectric permittivity of the snow and can be related to the snow density. The theoretical performance and functionality of the strategies are analyzed for the planned Harmony mission (ESA’s Earth Explorer 10) based on simulated data, indicating great potential of the approach given the large squint diversity of the Harmony constellation.

Single-pass InSAR elevation measurements of dry snow, firn, and ice are known to be substantially biased downward due to a partial penetration of the radar signals into the medium, resulting in a phase center location within the volume. The so-called penetration bias, i.e., the elevation difference between surface and InSAR phase center, can be estimated from the contribution of the volume to the interferometric coherence and may be used to retrieve the surface elevation. In this paper, we show that both an additional elevation bias and a horizontal shift occur in the InSAR processing for natural media with a dielectric constant different to the one of air, originating from an uncompensated stretch of the vertical wavenumber in the medium and refraction effects at the surface. This geolocation error depends on the magnitude of the penetration bias, the dielectric constant, and the acquisition geometry. It may reach up to few meters for X- and C-band frequencies and more for lower frequencies and therefore may significantly affect cryospheric elevation products from past (SRTM), current (TanDEM-X), and future (e.g., Harmony, Tandem-L) SAR interferometers. In this paper, the geolocation error is assessed and an adapted interferometric processing allowing for an accurate geolocation (i.e., surface elevation measurement) is presented.

The Snow Water Equivalent (SWE) describes the amount of liquid water stored in a snow pack and is an important parameter for runoff predictions and flood forecasts. Differential Interferometric Synthetic Aperture Radar (DInSAR) can be used to estimate the SWE change between two temporally separated repeat pass SAR acquisitions utilizing the interferometric phase. However, only a limited range of SWE changes can be retrieved unambiguously due to phase wraps of the interferometric phase. In this study, the aim is to include information on snow depth obtained from the Co-polar Phase Difference (CPD) between the polarimetric channels to detect phase wraps and improve the SWE retrieval results. The investigations are performed using airborne SAR acquisitions over the Alps.

Active microwave sensors, such as synthetic aperture radars (SARs), offer all-weather and daylight independent operability which is of great advantage for monitoring polar regions, where extreme environmental conditions and long period of darkness strongly limit other kinds of sensors. In addition, microwaves allow penetrating into dry snow and ice, making SAR measurements sensitive to the subsurface structure of glaciers and ice sheets. However, the retrieval of glacier subsurface parameters from SAR observations remains difficult due to their sensitivity to a large number of factors, including snow and ice properties, presence of layers, etc. The objective of this study is to attempt advancing the understanding of SAR measurements of glaciers and ice sheets. A combined analysis of polarimetric and tomographic measurements is carried out to derive a 3-D characterization of the scattering mechanisms occurring in a glacier subsurface scenario. The investigation exploits a fully-polarimetric tomographic airborne dataset, acquired over Greenland by the DLR's F-SAR system in the frame of the ARCTIC15 campaign.

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.

The need of large scale observations with high temporal frequency has promoted airborne and satellite remote sensing techniques for glaciological applications. In particular, active microwave sensors, such as synthetic aperture radars (SARs), offer all-weather and daylight independent operability which is of great advantage at high latitudes, where extreme environmental conditions and long period of darkness strongly limit other kinds of sensors. Moreover, longer wavelengths allow to penetrate significantly into dry snow and ice, interacting with surface as well as subsurface features. On the one hand, this makes SAR measurements suitable to investigate the subsurface structure of glaciers and ice sheets. On the other hand, the complex interaction of microwaves with the subsurface layers makes the interpretation of SAR measurements challenging. This study investigates the potential of SAR techniques to retrieve information about glacier subsurface. SAR polarimetry and tomography are used to gain a 3-D characterization of the scattering scenario of the K-transect, a site located in the ablation zone of Greenland. For this, a fully-polarimetric tomographic airborne dataset, acquired by the DLR's F-SAR system in the frame of the ARCTIC15 campaign, is exploited.

The analysis of data from an airborne synthetic aperture radar (SAR) campaign in the percolation zone of Greenland revealed an interferometric coherence undulation behavior with respect to vertical wavenumber, which cannot be explained with existing models. We propose a model extension that accounts for scattering from distinct layers below the surface. Simulations show that the periodicity of the coherence undulation is mainly driven by the vertical distance between dominant subsurface layers, while the amplitude of the undulation is determined by the ratio between scattering from distinct layers and scattering from the firn volume. We use the model to interpret quad-pol SAR data at X-, C-, S-, L-, and P-bands. The inferred layer depths match layer detections in ground-based radar data and in situ measurements. We conclude that in the percolation zone, scattering from subsurface layers has to be taken into account to correctly interpret SAR data and demonstrate the potential to retrieve geophysical information about the vertical subsurface structure.

The vertical structure of firn below the surface of the Greenland ice sheet is driven by meteorological conditions like the intensity and duration of melting periods. The formation of ice inclusions through meltrefreeze processes leads to strong scatterers whose vertical distribution influences interferometric coherences. Interferometric coherence profiles from quad-pol SAR data show clear sensitivity to the different subsurfaces of two test sites with different melting periods. The differences in coherence are explained in this study through modelling of the different vertical subsurface structures supported by ground penetrating radar measurements. The shown sensitivity demonstrates the potential of polarimetric SAR interferometry to retrieve information about meltrefreeze processes and layering below the surface of ice sheets.