Subproject 2.2

Radar penetration of TanDEM-X on glaciers & ice caps SAR tomography for 3D imaging of snow and firn structures

The radar signal can penetrate into snow, firn, and ice bodies depending on liquid water content and internal structure of the porous medium as well as radar imaging parameters. Consequently, the interferometric phase center of the backscattered radar signal is located at a certain depth below the actual topographic surface, leading to a bias between the glacier surface and the estimated one from SAR interferometry (Rott et al 2021, Rizzoli et al 2017). Radar penetration is still a large source of uncertainty in estimates of glacier volume and mass change from bi-static SAR mission data like bi-static TanDEM-X or the upcoming HRWS MirrorSAR (Braun et al. 2019, Huber et al. 2020).

The uncertainty is highest in presence of dry snow, not affected by melting phenomena as in the accumulation area of high mountain glaciers or polar glaciers and ice caps outside the large ice sheets. Within this sub-project, we aim at estimating radar penetration by deploying a deep learning architecture using input data from SAR imagery like coherence, backscatter, frequency and geometric baseline as well as a terrain model. As reference data for training deep learning networks in a supervised manner, we will utilize the height difference between precise laser altimetric measurements (ICESat-2, Operation IceBridge, ka-band data from JPL UAVSAR system) for the surface and radar DEMs for the location of the mean interferometric phase center.

Methodologically, we suggest the use of a deep convolutional network to estimate the altimetry, phrasing it as a regression problem. In particular, we propose the adoption of AdaIn-based Tunable CycleGAN (Gu 2020). We believe the additional CycleGAN constraints in combination with the switchable adaptive instance normalization will serve as a valuable regularizer. Making the CycleGAN tunable and hence, omitting one generator from the original CycleGAN approach, leads to a significant reduction in memory requirements and a more stable training process even on small datasets. Additionally, we can incorporate other data such as the local incident angle as well as climatological information as conditional input for the discriminators, since both variables can significantly influence the interferometric SAR phase center depth below the actual surface. This SP is linked to SP 1.2 with a joint airborne survey as well as to SP 2.3 and SP 3.1.

For specific information on the sub-project please contact: Dr.-Ing. Paola Rizzoli, Münchener Straße 20, 82234 Weßling, T: 08153-28-1785, Paola.Rizzoli@dlr.de, https://www.dlr.de/hr/desktopdefault.aspx/tabid-2328/3447_read-46107

Co-PIs: M. Braun (FAU Geography), A. Maier (FAU Informatics), P. Millilo (Univ. Houston)

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Subproject 2.3

Improved volume-to-mass conversion

Glacier mass balance variations are an essential indicator of regional and global climate fluctuations. Today, glacier volume changes can be detected with satellite based systems to a reasonable degree of accuracy, while density estimates of the affected volume are still based on models and a priori assumptions. Even though, methods for volume to mass conversion are established, there exists a considerable knowledge gap with respect to the validation and temporal stability of the assumptions. Especially the transient evolution of the firn pack will strongly influence the density assumptions of the glaciers with time. During periods of negative mass balances, the shrinkage of the snow and firn resources requires an adaptation of the usual conversion techniques. In addition, there is a strong need to investigate the regional variability of density distribution, as the glacier-climate relation is strongly dependent on the regional characteristics like topography, precipitation and wind patterns.

A large data basis exists for Vernagtferner in the Ötztal, related to the temporal development of the glacier and its firn regions. Similar information is available for the adjacent Hintereisferner, making this region an ideal test site for such investigations. A detailed investigation of firn and snow deposits, based on glaciological and geophysical methods and microwave remote sensing instruments should be conducted to provide a clear knowledge about the firn pack architecture and its spatial distribution. In combination with legacy remote sensing observations of firn and snow extents and volume changes, regional climate information will be used to investigate the temporal development of glaciers and their compartments. This analysis should lead to a strongly improved approach for volume to mass conversion, especially with regard to temporal and regional variability, which is also essential for large-scale mass balance assessments. Direct employment of the results in, and exchange with the modelling-orientated sub-projects will be possible and highly welcome.

For specific information on the sub-project please contact: Dr. Christoph Mayer, Alfons-Goppel-Str. 11 (Residenz), 80533 München, T: 089-23031-1260, Christoph.Mayer@badw.de, https://geo.badw.de/das-projekt.html

Co-PIs: T. Mölg (FAU Geography), M Huss (ETH ZH), R. Hock (Univ. Oslo)

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Subproject 1.1

A lightweight multifrequency radar system for snow and frin structure measurements

Typically ground-based GPR systems for geophysical applications consist of low frequency pulse radars that operate in the MHz frequency range. As those rudimentary radar concepts have proven to be easy to use and offer enough resilience to endure in rough environments in the past, the most recent and enormous advances in radar technology have almost exclusively been made in other fields such as automotive radar or personnel security scanner systems. Innovative GPR approaches can strongly benefit from these new radar techniques e.g. by multi-functional radar systems that utilize software-defined waveforms and measuring principles best adapted to the specific environment and measuring task.

A holistic hardware-software design based on cutting-edge technology is needed to obtain the maximum performance for the intended field of use. By this, enhanced mapping and surveying of subglacial structures will be achieved, and previously unsolved challenges linked to the measurement of ice and snow parameters in remote locations will be effectively addressed. A lightweight and easy-to-transport radar system will be developed within this project which will serve as a versatile sensor setup for multiple applications in remote glacial environments. For this, a multifrequency system will be implemented which will make use of the benefits of different frequency bands. E.g., typical GPR systems for measuring ice sheet thicknesses on alpine glaciers operate in the sub 200-MHz region as signals experience less attenuation but can only obtain comparably small bandwidths and therefore low resolution. Imaging Radars working in the >1GHz frequency range suffer from higher attenuation but can reach an outstanding resolution. Those and further advantages will be combined for maximum performance and flexibility adapted to each scenario.

For this purpose, suitable high-performance and meanwhile power and weight-saving transceiver frontend concepts need to be designed and tested for their usability. Furthermore, innovative antenna concepts need to be examined in order to address the requirements of lightweight and small size while maintaining excellent radiation characteristics over a wide/multiband frequency range. With the integration of localization techniques such as differential GNSS and other advanced self-localization techniques, it will be possible to apply ground-based SAR techniques for enhanced resolution and sensitivity of the overall system. The integrated digital backend will exploit cutting-edge radar algorithms which have to be expanded in order to combine data taken in different frequency bands for extracting an even wider range of parameters from the surveyed ice structures compared to a single band evaluation.

Examples for those results are amongst other things estimated water content in the snow/firn cover or the glaciers permittivity profile which is directly linked to the ice density. Integrated into vehicles such as UAVs the system will be capable of surface-based mapping of large areas in a time and cost-effective manner. The system will be tested extensively on Vernagt- and Hintereisferner. This subproject will collaborate with SP1.2, 1.3, 2.3 and 3.1.

For specific information on the subproject please contact: Prof. Dr.-Ing. Martin Vossiek; Institute of Microwaves and Photonics (LHFT), Cauerstr. 9, 91058 Erlangen, T: 09131-85-20773, martin.vossiek@fau.de, https://www.lhft.eei.fau.de/

Co-PIs: G. Krieger (DLR HR), T. Seehaus (FAU Geography), F. Navarro (U. Madrid)

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Subproject 1.2

SAR tomography for 3D imaging of snow and firn structures

Structures in the firn and snow cover on glaciers can be caused by annual melt/freeze cycles, but also by internal water percolation or refreezing. Additionally, water channels within the glaciers are important indicators for melt water routing in geophysical and hydrological glacier models. The highest changes occur within the upper layer (first tens of meters) and are therefore of importance to be monitored. Synthetic Aperture Radar (SAR) tomography is an evolving 3D imaging technique that enables the mapping of subsurface properties of glaciers and ice sheets with high spatial resolution, taking advantage of the penetration of radar signals up to several tens of meters into dry snow, firn, and ice (Tebaldini et al., 2016, Fischer et al., 2019). The main objective of this doctoral project is to exploit and improve this powerful 3D imaging technique and to establish the relation of the vertical reflectivities to geophysical snow/ice parameters. Moreover, new tomographic imaging modes and techniques like transmission, MIMO and subaperture-based tomography will be explored in view of their potential to gain further information about the internal structure and dielectric properties of snow and glaciers. In the frame of the project dedicated campaigns on Vernagtferner and Hintereisferner will be conducted, where multiple sensors collect data to estimate and characterise the internal structures of snow and ice regions. For this, the airborne multi-modal SAR system of DLR, as well as ground-based laser and radar systems will be deployed to acquire both multi-angular and multi-temporal data. Simultaneously, point/grid-based measurements and satellite data will be collected and evaluated. Strong links exist to SP1.1, SP1.3, SP2.2, SP2.3 and SP3.1.

For specific information on the sub-project please contact: Prof. Dr.-Ing. Gehard Krieger, LHFT, Institute of Microwaves and Photonics (LHFT), Cauerstr. 9, 91058 Erlangen, T: 08153-28-3054, gerhard.krieger@fau.de

Co-PIs: I. Hajnsek (DLR HR / ETH ZH), C. Mayer (BAdW), H. Rott (ENVEO IT)

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Subproject 1.3

Machine learning on radargrams

Information on ice thickness and internal structures of ice bodies (e.g. water table, isochrones, water pockets, and channels) from ground penetrating radargrams is to date often picked manually. Often only a specific target neglecting all other information is traced since existing contour-following algorithms in standard software like REFLEX do not provide consistent and reliable output. Within this doctoral project, we aim at using and modifying machine learning techniques from medical imaging as well as natural language processing (NLP) and apply those to glaciological radargrams. Ice thickness, bedrock topography, as well as internal structures, shall be mapped ideally at once after the respective pre-processing of the radargrams has occurred. Each radargram is composed of lines denoting different structures in the ice body. Algorithmically, this represents a segmentation problem in radargrams.
Datasets are available from airborne and ground-based low-frequency radar surveys of sites in the Alps and high Mountain Asia (BAdW), planned campaigns in Patagonia and alpine-type glaciers in Antarctica (FAU). Additional material for algorithm testing and training is available through an intense collaboration with AWI from polar surveys. Ideally, the developed algorithms will also be tested on the first survey data of the developed new multi-frequent radar in sub-project 1.1. Further links exist also to SP 2.3, and 3.1.

Co-PIs: A. Maier (FAU Informatics), T. Seehaus (FAU Geography), F. Navarro (U. Madrid)

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