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During the concluding phase of the NASA Operation IceBridge (OIB), we successfully completed two airborne measurement campaigns (in 2018 and 2021, respectively) using a compact S and C band radar installed on a Single Otter aircraft and collected data over Alaskan mountains, ice fields, and glaciers. This paper reports seasonal snow depths derived from radar data. We found large variations in seasonal radar-inferred depths with multi-modal distributions assuming a constant relative permittivity for snow equal to 1.89. About 34 % of the snow depths observed in 2018 were between 3.2 and 4.2 m, and close to 30 % of the snow depths observed in 2021 were between 2.5 and 3.5 m. We observed snow strata in ice facies, combined percolation and wet-snow facies, and dry-snow facies from radar data and identified the transition areas from wet-snow facies to ice facies for multiple glaciers based on the snow strata and radar backscattering characteristics. Our analysis focuses on the measured strata of multiple years at the caldera of Mount Wrangell (K'elt'aeni) to estimate the local snow accumulation rate. We developed a method for using our radar readings of multi-year strata to constrain the uncertain parameters of interpretation models with the assumption that most of the snow layers detected by the radar at the caldera are annual accumulation layers. At a 2004 ice core and 2005 temperature sensor tower site, the locally estimated average snow accumulation rate is ∼2.89 m w.e. a−1 between the years 2003 and 2021. Our estimate of the snow accumulation rate between 2005 and 2006 is 2.82 m w.e. a−1, which matches closely to the 2.75 m w.e. a−1 inferred from independent ground-truth measurements made the same year. The snow accumulation rate between the years 2003 and 2021 also showed a linear increasing trend of 0.011 m w.e. a−2. This trend is corroborated by comparisons with the surface mass balance (SMB) derived for the same period from the regional atmospheric climate model MAR (Modèle Atmosphérique Régional). According to MAR data, which show an increase of 0.86 ∘C in this area for the period of 2003–2021, the linear upward trend is associated with the increase in snowfall and rainfall events, which may be attributed to elevated global temperatures. The findings of this study confirmed the viability of our methodology, as well as its underlying assumptions and interpretation models.

Over the past 60 years, scientists have strived to understand the past, present and future of the Antarctic Ice Sheet. One of the key components of this research has been the mapping of Antarctic bed topography and ice thickness parameters that are crucial for modelling ice flow and hence for predicting future ice loss and ensuing sea level rise. Supported by the Scientific Committee on Antarctic Research (SCAR), the Bedmap3 Action Group aims not only to produce new gridded maps of ice thickness and bed topography for the international scientific community, but also to standardize and make available all the geophysical survey data points used in producing the Bedmap gridded products. Here, we document the survey data used in the latest iteration, Bedmap3, incorporating and adding to all of the datasets previously used for Bedmap1 and Bedmap2, including ice-bed, surface and thickness point data from all Antarctic geophysical campaigns since the 1950s. More specifically, we describe the processes used to standardize and make these and future survey and gridded datasets accessible under the ‘Findable, Accessible, Interoperable and Reusable’ (FAIR) data principles. With the goals to make the gridding process reproducible and to allow scientists to re-use the data freely for their own analysis, we introduce the new SCAR Bedmap Data Portal (bedmap.scar.org, last access: 18 October 2022) created to provide unprecedented open access to these important datasets, through a user-friendly webmap interface. We believe that this data release will be a valuable asset to Antarctic research and will greatly extend the life cycle of the data held within it. Data are available from the UK Polar Data Centre: https://data.bas.ac.uk.

Dome Fuji, inland East Antarctica is one of only few regions where 1.5-Ma old ice can be preserved for investigating the mid-Pleistocene Transition. We used stochastic simulation and various radar datasets to generate a bed topography ensemble with the continuous, realistic roughness necessary to assess basal conditions. Ensemble analysis reveals the magnitude and spatial distribution of topographic uncertainty, facilitating uncertainty-constrained assessments of subglacial drainage and topographic adjustments to geothermal heat flow. We find that topographic variability can lead to widespread local geothermal heat flow variations of ± 20% the background value, which aggregate to raise the regional value and suggest previously underestimated distributions and rates of basal melting. We also find that survey profile spacing has an increasing influence on topographic uncertainty for rougher bed, deriving an empirical relationship that could guide future survey planning based on uncertainty tolerance.

Funding: This research was undertaken, in part, thanks to funding from the Canada Excellence Research Chairs Programme and has been financially supported by the Villum Investigator Project IceFlow (Grant No. 16572 to D.D.-J.). Radar development was further supported by funding from the University of Alabama. EGRIP is directed and organised by the Centre for Ice and Climate at the Niels Bohr Institute, University of Copenhagen. S.F. received funding from the German Academic Exchange Service (DAAD): Forschungsstipendien für promovierte Nachwuchswissenschaftlerinnen und -wissenschaftler. M.R.E. was supported by a DFG Emmy Noether grant (grant no. DR 822/3-1). The dynamic mass loss of ice sheets constitutes one of the biggest uncertainties in projections of ice-sheet evolution. One central, understudied aspect of ice flow is how the bulk orientation of the crystal orientation fabric translates to the mechanical anisotropy of ice. Here we show the spatial distribution of the depth-averaged horizontal anisotropy and corresponding directional flow-enhancement factors covering a large area of the Northeast Greenland Ice Stream onset. Our results are based on airborne and ground-based radar surveys, ice-core observations, and numerical ice-flow modelling. They show a strong spatial variability of the horizontal anisotropy and a rapid crystal reorganisation on the order of hundreds of years coinciding with the ice-stream geometry. Compared to isotropic ice, parts of the ice stream are found to be more than one order of magnitude harder for along-flow extension/compression while the shear margins are potentially softened by a factor of two for horizontal-shear deformation. Publisher PDF

The dynamic mass loss of ice sheets due to ice flow constitutes one of the biggest uncertainties in projections of ice-sheet evolution and sea-level rise. One central, understudied aspect of ice flow is how the bulk orientation of the ice-crystal lattice (fabric) translates to the mechanical anisotropy of ice. Here, we present a comprehensive analysis of the depth-averaged fabric distribution and corresponding directional flow enhancement factors covering a large area of the Northeast Greenland Ice Stream onset region. Our results are based on a combination of methods applied to extensive airborne and ground-based radar surveys, ice-core observations, and numerical ice-flow modelling. They show a strong spatial variability of the horizontal fabric anisotropy and a rapid crystal reorganization on the order of hundreds of years coinciding with the ice-stream geometry. The ice in parts of the ice stream is found to be up to one order of magnitude harder for pure-shear deformation in flow direction compared to isotropic ice, while the shear margins are potentially softened for horizontal simple shear by a factor of two. The high spatial resolution and large-scale coverage of our results is unprecedented and significantly contributes towards more accurate ice-flow models and a better understanding of ice-stream dynamics. Moreover, our estimated time scale of crystal reorganization and methodological approaches can potentially also be transferred to the investigation of ice on extraterrestrial bodies and other geologic materials.

Monitoring properties of ice sheets in polar regions is one of the main challenges in glaciology. There is a large amount of heterogeneous radar data from the polar regions that have been gathered through expensive missions. However, retrieving meaningful information from this large volume of data is still a great challenge. With the advancement of machine learning techniques in recent years, many scientists are eager to take advantage of these algorithms and techniques to explore and mine Arctic and Antarctic data. These advancements, however, have happened mainly in the area of supervised learning where the models are data hungry and require large amounts of annotated data. Generating simulated data can be an effective and inexpensive approach to provide large labeled datasets for training machine learning models. In this work, we explore two approaches to simulate arctic snow radar echogram images, namely a radar scattering physics based approach combined with some statistical measures and a purely data-driven approach based on a conditional generative adversarial network. Using several image comparison metrics, we analyze the utility of both methods for the purpose of simulating echograms. Our results show that the physics simulator generates images with good structural similarities, while the purely data-driven approach achieves better textural similarities for simulated image. Finally, we also show that by augmenting our real dataset by the simulated echograms, we can improve our deep learning model for tracking internal layers of snow.

During the concluding phase of the NASA Operation IceBridge (OIB), we successfully completed two airborne measurement campaigns (in 2018 and 2021, respectively) using a compact S/C band radar installed on a Single Otter aircraft and collected data over Alaskan mountains, ice fields, and glaciers. We observed snow strata in ice facies, wet-snow/percolation facies and dry snow facies from radar data. This paper reports seasonal snow depths derived from our observations. We found large variations in seasonal radar-inferred depths assuming a constant relative permittivity for snow equal to 1.89. The majority of the seasonal depths observed in 2018 were between 3.2 m and 4.2 m, and around 3 m in 2021. We also identified the transition areas from wet-snow facies to ice facies for multiple glaciers based on the snow strata and radar backscattering characteristics. Our analysis focuses on the measured strata of multiple years at the caldera of Mount Wrangell to estimate the local snow accumulation rate. We developed a method for using our radar readings of multi-year strata to constrain the uncertain parameters of interpretation models with the assumption that most of the snow layers detected by the radar at the caldera are annual accumulation layers. At a 2004 ice core and 2005 temperature sensor tower site, the locally estimated average snow accumulation rate is ~2.89 m w. e. a-1 between the years 2002 and 2021. Our estimate of the snow accumulation rate between 2005 and 2006 is 2.82 m w. e. a-1, which matches closely to the 2.75 m w. e. a-1 inferred from independent ground-truth measurements made the same year. We also found a linear increasing trend of 0.011 m w. e. a-1 per year between the years 2002 and 2021. With this trend, we extrapolated the snow accumulation back to 1992 and obtained an average accumulation rate of 2.74 w. e. a-1 between the years 1992 and 2004, which agrees well with the value of 2.66 w. e. a-1 for the same period determined from the ice core data retrieved at the caldera in 2004. The results reported here verified the efficacy of our method, its assumption, and the interpretation models.

In this study, our goal is to track internal ice layers on the Snow Radar data collected by NASA Operation IceBridge. We examine the application of deep learning methods on radar data gathered from polar regions. Artificial intelligence techniques have displayed impressive success in many practical fields. Deep neural networks owe their success to the availability of massive labeled data. However, in many real-world problems, even when a large dataset is available, deep learning methods have shown less success, due to causes such as lack of a large labeled dataset, presence of noise in the data or missing data. In our radar data, the presence of noise is one of the main obstacles in utilizing popular deep learning methods such as transfer learning. Our experiments show that if the neural network is trained to detect contours of objects in electro-optical imagery, it can only track a low percentage of contours in radar data. Fine-tuning and further training do not provide any better results. However, we show that selecting the right model and training it on the radar imagery from the start yields far better results.

We present an ultrawideband (UWB) frequency-modulated (FM) microwave radar instrument capable of preserving cm-scale vertical resolution from a very long range (verified up to ~6 km). The system can operate either as a conventional continuous-wave FM radar (2–18 GHz) or in time-delayed stretch processing mode (2–14 GHz), providing high sensitivity, fine range resolution, and very low-range sidelobes. We present an overview of the system and laboratory test results to validate its performance. As a practical demonstration of its capabilities, we tested the system’s ability to map snow cover thickness on the Arctic marine ice. We present sample results from airborne measurements carried out at altitudes between ~500 m and ~6 km above the ground level onboard a long-range fixed-wing platform.

Radar sounding is a powerful geophysical approach for characterizing the subsurface conditions of terrestrial and planetary ice masses at local to global scales. As a result, a wide array of orbital, airborne, ground-based, and in situ instruments, platforms and data analysis approaches for radioglaciology have been developed, applied or proposed. Terrestrially, airborne radar sounding has been used in glaciology to observe ice thickness, basal topography and englacial layers for five decades. More recently, radar sounding data have also been exploited to estimate the extent and configuration of subglacial water, the geometry of subglacial bedforms and the subglacial and englacial thermal states of ice sheets. Planetary radar sounders have observed, or are planned to observe, the subsurfaces and near-surfaces of Mars, Earth's Moon, comets and the icy moons of Jupiter. In this review paper, and the thematic issue of theAnnals of Glaciologyon ‘Five decades of radioglaciology’ to which it belongs, we present recent advances in the fields of radar systems, missions, signal processing, data analysis, modeling and scientific interpretation. Our review presents progress in these fields since the last radio-glaciologicalAnnals of Glaciologyissue of 2014, the context of their history and future prospects.

Ice sheets reshape Earth’s surface. Maps of the landscape formed by past ice sheets are our best tool for reconstructing historic ice sheet behavior. But models of glacier erosion and deposition that explain mapped features are relatively untested, and without observations of landforms developing in situ, postglacial landscapes can provide only qualitative insight into past ice sheet conditions. Here we present the first swath radar data collected in Antarctica, demonstrating the ability of swath radar technology to map the subglacial environment of Thwaites Glacier (West Antarctica) at comparable resolutions to digital elevation models of deglaciated terrain. Incompatibility between measured bedform orientation and predicted subglacial water pathways indicates that ice, not water, is the primary actor in initiating bedform development at Thwaites Glacier. These data show no clear relationship between morphology and glacier speed, a weak relationship between morphology and basal shear stress, and highlight a likely role for preexisting geology in glacial bedform shape.

We developed a high-performance, multichannel, ultra-wideband radar system for measurements of the base and interior of the East Antarctic Ice Sheet. We designed the radar to be of high power (4000-W peak) yet portable and to be able to operate with 60-MHz bandwidth at a center frequency of 200 MHz, providing high sensitivity and fine vertical resolution relative to current technology. We used the radar to perform extensive mea- surements as a part of a multinational collaboration. We collected data onboard a tracked vehicle outfitted with an array of high-gain antennas. We sounded 2- to 3-km thick ice near Dome Fuji. Prelim- inary ice thickness data match those obtained via semicoincident measurements performed with a different surface-based pulse- modulated radar system operated during the same field campaign, as well as previous airborne measurements. In addition, we mapped internal reflection horizons with fine vertical resolution from 300 m below the ice surface to ∼100 m above the bed. In this article, we provide a detailed overview of the radar instrument design, implementation, and field measurement setup. We present sample data to illustrate its capabilities and discuss how the data collected with it will be valuable for the assessment of promising drilling sites to recover ice cores that are 0.9–1.5 million years old.

We present a high-resolution airborne radar data set (EGRIP-NOR-2018) for the onset region of the Northeast Greenland Ice Stream (NEGIS). The radar data were acquired in May 2018 with Alfred Wegener Institute’s multichannel ultra-wideband (UWB) radar mounted on the Polar6 aircraft. Radar profiles cover an area of ~24000 km2 and extend over the well-defined shear margins of the NEGIS. The survey area is centred at the location of the drill site of the East Greenland Ice-Core Project (EastGRIP) and several radar lines intersect at this location. The survey layout was designed to: (i) map the stratigraphic signature of the shear margins with radar profiles aligned perpendicular to ice flow, (ii) trace the radar stratigraphy along several flow lines and (iii) provide spatial coverage of ice thickness and basal properties. While we are able to resolve radar reflections in the deep stratigraphy, we can not fully resolve the steeply inclined reflections at the tightly folded shear margins in the lower part of the ice column. The NEGIS is causing the most significant discrepancies between numerically modelled and observed ice surface velocities. Given the high likelihood of future climate and ocean warming, this extensive data set of new high-resolution radar data in combination with the EastGRIP ice core will be a key contribution to understand the past and future dynamics of the NEGIS. The EGRIP-NOR-2018 radar data products can be obtained at the PANGAEA Data Publisher (https://doi.pangaea.de/10.1594/PANGAEA.928569; Franke et al. 2021a).

During the past 20 years, multi-channel radar emerged as a key tool for deciphering an ice sheet's internal architecture. To assign ages to radar reflections and connect them over large areas in the ice sheet, the layer genesis has to be understood on a microphysical scale. Synthetic radar trace modelling based on the dielectric profile of ice cores allows for the assignation of observed physical properties’ variations on the decimetre scale to radar reflectors extending from the coring site to a regional or even whole-ice-sheet scale. In this paper we rely on the available dielectric profiling data of the northern Greenland deep ice cores: NGRIP, NEEM and EGRIP. The three records are well suited for assigning an age model to the stratigraphic radar-mapped layers, and linking up the reflector properties to observations in the cores. Our modelling results show that the internal reflections are mainly due to conductivity changes. Furthermore, we deduce fabric characteristics at the EGRIP drill site from two-way-travel-time differences of along and across-flow polarized radarwave reflections of selected horizons (below 980 m). These indicate in deeper parts of the ice column an across-flow concentrated c-axis fabric.

Crary Ice Rise formed after the Ross Ice Shelf re-grounded ~1 kyr BP. We present new ice-penetrating radar data from two systems operating at center frequencies of 7 and 750 MHz that confirm the ice rise is composed of a former ice shelf buried by subsequent accumulation. Stacks of englacial diffraction hyperbolas are present almost everywhere across the central ice rise and extend up to ~350 m above the bed. In many cases, bed reflections beneath the diffraction hyperbolas are obscured for distances up to 1 km. Waveform modeling indicates that the diffraction hyperbolas are likely caused by marine ice deposits in former basal crevasses and rifts. The in-filling of rifts and basal crevasses may have strengthened the connection between the ice rise and the surrounding ice shelf, which could have influenced local and regional ice dynamics. Three internal reflection horizons mark the upper limit of disturbed ice and diffraction hyperbolas in different sections of the ice rise, indicating at least three stages of flow stabilization across the ice rise. A surface lineation visible in MODIS imagery corresponds spatially to deepening and strong deformation of these layers, consistent with the characteristics of former grounding lines observed elsewhere in Antarctica.

Tree heights are important input for many inventory and ecosystem models. While optical and infrared sensors such as LiDAR are widely used in forest surveys, microwave radar has the unique advantage of being able to operate in bad weather or poor visibility conditions. In this work, we employed a LiDAR combined with a compact ultra-wideband frequency modulated continuous wave (FMCW) radar onboard a Single Otter aircraft to collect data over forested areas in Alaska. While the primary focus of the mission was to map the surface elevation and snow thickness of Alaskan glaciers as a part of NASA Operation IceBridge (OIB), we recorded LiDAR and radar returns during the transit flights to analyze backscattering signatures from tree-covered areas, thereby assessing the potential application of our radar to forest and vegetation remote sensing. We analyzed these measurements and estimated the tree heights along the flight trajectory. The very good agreement between the tree height profiles from the two instruments shows promising results for wide area forestry studies using microwave radar.

Array manifold calibration improves parametric direction of arrival angle estimator performance by reducing mismatch between the model of the array transfer function and truth. This paper demonstrates the estimation and application of a measured manifold for a multichannel ice penetrating synthetic aperture radar (SAR) used to image subglacial features with tomography. We apply SAR tomography to produce digital elevation models of exposed rock and sea ice in the Canadian Arctic Archipelago using both nominal and measured manifolds in angle estimation. We compare reconstructed topography to a fine resolution satellite-derived digital elevation model of the Arctic and demonstrate improvements realized when a measured manifold is used in 3D image formation.

Understanding the structure of Earth's polar ice sheets is important for modeling how global warming will impact polar ice and, in turn, the Earth's climate. Ground-penetrating radar is able to collect observations of the internal structure of snow and ice, but the process of manually labeling these observations is slow and laborious. Recent work has developed automatic techniques for finding the boundaries between the ice and the bedrock, but finding internal layers - the subtle boundaries that indicate where one year's ice accumulation ended and the next began - is much more challenging because the number of layers varies and the boundaries often merge and split. In this paper, we propose a novel deep neural network for solving a general class of tiered segmentation problems. We then apply it to detecting internal layers in polar ice, evaluating on a large-scale dataset of polar ice radar data with human-labeled annotations as ground truth., ICME version

Author(s): MacGregor, JA; Boisvert, LN; Medley, B; Petty, AA; Harbeck, JP; Bell, RE; Blair, JB; Blanchard-Wrigglesworth, E; Buckley, EM; Christoffersen, MS; Cochran, JR; Csatho, BM; De Marco, EL; Dominguez, RAT; Fahnestock, MA; Farrell, SL; Gogineni, SP; Greenbaum, JS; Hansen, CM; Hofton, MA; Holt, JW; Jezek, KC; Koenig, LS; Kurtz, NT; Kwok, R; Larsen, CF; Leuschen, CJ; Locke, CD; Manizade, SS; Martin, S; Neumann, TA; Nowicki, SMJ; Paden, JD; Richter-Menge, JA; Rignot, EJ; Rodriguez-Morales, F; Siegfried, MR; Smith, BE; Sonntag, JG; Studinger, M; Tinto, KJ; Truffer, M; Wagner, TP; Woods, JE; Young, DA; Yungel, JK | Abstract: The National Aeronautics and Space Administration (NASA)’s Operation IceBridge (OIB) was a 13-year (2009–2021) airborne mission to survey land and sea ice across the Arctic, Antarctic, and Alaska. Here, we review OIB’s goals, instruments, campaigns, key scientific results, and implications for future investigations of the cryosphere. OIB’s primary goal was to use airborne laser altimetry to bridge the gap in fine-resolution elevation measurements of ice from space between the conclusion of NASA’s Ice, Cloud, and land Elevation Satellite (ICESat; 2003–2009) and its follow-on, ICESat-2 (launched 2018). Additional scientific requirements were intended to contextualize observed elevation changes using a multisensor suite of radar sounders, gravimeters, magnetometers, and cameras. Using 15 different aircraft, OIB conducted 968 science flights, of which 42% were repeat surveys of land ice, 42% were surveys of previously unmapped terrain across the Greenland and Antarctic ice sheets, Arctic ice caps, and Alaskan glaciers, and 16% were surveys of sea ice. The combination of an expansive instrument suite and breadth of surveys enabled numerous fundamental advances in our understanding of the Earth’s cryosphere. For land ice, OIB dramatically improved knowledge of interannual outlet-glacier variability, ice-sheet, and outlet-glacier thicknesses, snowfall rates on ice sheets, fjord and sub-ice-shelf bathymetry, and ice-sheet hydrology. Unanticipated discoveries included a reliable method for constraining the thickness within difficult-to-sound incised troughs beneath ice sheets, the extent of the firn aquifer within the Greenland Ice Sheet, the vulnerability of many Greenland and Antarctic outlet glaciers to ocean-driven melting at their grounding zones, and the dominance of surface-melt-driven mass loss of Alaskan glaciers. For sea ice, OIB significantly advanced our understanding of spatiotemporal variability in sea ice freeboard and its snow cover, especially through combined analysis of fine-resolution altimetry, visible imagery, and snow radar measurements of the overlying snow thickness. Such analyses led to the unanticipated discovery of an interdecadal decrease in snow thickness on Arctic sea ice and numerous opportunities to validate sea ice freeboards from satellite radar altimetry. While many of its data sets have yet to be fully explored, OIB’s scientific legacy has already demonstrated the value of sustained investment in reliable airborne platforms, airborne instrument development, interagency and international collaboration, and open and rapid data access to advance our understanding of Earth’s remote polar regions and their role in the Earth system.

This paper provides an update and overview of the Center for Remote Sensing of Ice Sheets (CReSIS) radars and platforms, including representative results from these systems. CReSIS radar systems operate over a frequency range of 14–38 GHz. Each radar system's specific frequency band is driven by the required depth of signal penetration, measurement resolution, allocated frequency spectra, and antenna operating frequencies (often influenced by aircraft integration). We also highlight recent system advancements and future work, including (1) increasing system bandwidth; (2) miniaturizing radar hardware; and (3) increasing sensitivity. For platform development, we are developing smaller, easier to operate and less expensive unmanned aerial systems. Next-generation platforms will further expand accessibility to scientists with vertical takeoff and landing capabilities.

Multichannel radar depth sounding systems are able to produce two-dimensional (2D) and three-dimensional (3D) imagery of the internal structure of polar ice sheets. Information such as ice thickness and surface elevation is extracted from these data and applied to research in ice flow modeling and ice mass balance calculations. Due to a large amount of data collected, we seek to automate the ice-bottom layer tracking and allow for efficient manual corrections when errors occur in the automated method. We present improvements made to previous implementations of the Viterbi and sequential tree-reweighted message passing (TRW-S) algorithms for ice-bottom extraction in 2D and 3D radar imagery. These improvements are in the form of novel cost functions that allow for the integration of further domain-specific knowledge into the cost calculations and provide additional evidence of the characteristics of the ice sheets surveyed. Along with an explanation of our modifications, we demonstrate the results obtained by our modified implementations of the two algorithms and by previously proposed solutions to this problem, when compared to manually corrected ground truth data. Furthermore, we perform a self-assessment of tracking results by analyzing differences in the estimated ice-bottom for surveyed locations where flight paths have crossed and, thus, two separate measurements have been made at the same location. Using our modified cost functions and preprocessing routines, we obtain significantly decreased mean error measurements from both algorithms, such as a 47% reduction in average tracking error in the case of 3D imagery between the original and our proposed implementation of TRW-S.

Ice thickness estimation is an important aspect of ice sheet studies. In this work, we use convolutional neural networks with multiple output nodes to regress and learn the thickness of internal ice layers in Snow Radar images collected in northwest Greenland. We experiment with some state-of-the-art networks and find that with the residual connections of ResNet50, we could achieve a mean absolute error of 1.251 pixels over the test set. Such regression-based networks can further be improved by embedding domain knowledge and radar information in the neural network in order to reduce the requirement of manual annotations.

The North East Greenland Ice Stream clearly stands out in the surface velocity field of the ice flow of Greenland, with its sharp and narrow shear margins visible in the flow field almost up to the central divide. While the current extent and strength of the streaming can be determined from remotely sensed velocities of the ice surface, it is less known how the ice stream is affecting the deeper layers of ice in its catchment area, and how it may have evolved over time. The deformation of the ice due to streaming can be made visible by mapping the distortion of the isochronous stratigraphy of the ice. This has been done by an airborne radar survey centering on the location of the EGRIP drilling camp, carried out with the ultra wide band radar system (AWI UWB). The dense grid of profiles arranged mainly perpendicular to the ice flow reveals the imprint that the strong shearing leaves within the layering of the ice. Although the layers are tightly folded and distorted within the shear zones, it is possible to continuously trace reflections within the upper half of the ice column throughout the entire survey area. It can be shown that the intensity of the folding is linked to the strain rate field derived from the surface velocities, and that the deformation history of the ice is preserved in the folded layers, even after it is no longer affected by high strain rates. The advection patterns of the mapped stratigraphic features reveal how the streaming of the ice and the resulting local changes of surface topography may have affected the inflow into the stream and the position of the shear margins over time.

Crystal anisotropy of ice causes slight birefringence for electromagnetic waves. At the same time, the mechanical anisotropy amounts to several orders of magnitude, thus making fabric properties highly-relevant for internal deformation. To date, bulk anisotropy of glaciers and ice sheets can be determined by geophysical methods, such as polarimetric radar, or direct sampling from ice cores. A shortcoming has been so far that changes of bulk anisotropy could mainly be inferred at single point observations, but less so as continuous profiles. Here, we exploit the effect of birefringence caused by bulk anisotropy in co-polarized airborne radar data to determine the horizontal anisotropy across the North-East Greenland Ice Stream. We base our analysis on the fact that birefringence causes a second-order effect on radar amplitudes, which leads to a beat frequency in the low and medium frequency range (O(100 kHz)), which is proportional to the horizontal anisotropy. Complementing our radar analysis with direct fabric and dielectric property observations we can constrain the range of all three fabric eigenvalues as a function of space across and along the ice stream. Finally, we assess the effect of the inferred fabric distribution on the overall ice rheology in the context of ice stream dynamics and compare it with numerical model results. Our overall approach has the advantage that it can be applied to co-polarized radar systems, as commonly used in profiling surveys, and does not require dedicated cross-polarized radar set-up. This provides the opportunity to revisit older data, especially from Greenland and Antarctica, to map fabric anisotropy in ice-dynamically interesting regions.

The ice stream geometry and large ice surface velocities at the onset region of the Northeast Greenland Ice Stream (NEGIS) are not yet well reproduced by ice sheet models. The quantification of basal sliding and a parametrization of basal conditions remains a major gap. In this study, we assess the basal conditions of the onset region of the NEGIS in a systematic analysis of airborne ultra‐wideband radar data. We evaluate basal roughness and basal return echoes in the context of the current ice stream geometry and ice surface velocity. We observe a change from a smooth to a rougher bed where the ice stream widens, and a distinct roughness anisotropy, indicating a preferred orientation of subglacial structures. In the upstream region, the excess ice mass flux through the shear margins is evacuated by ice flow acceleration and along‐flow stretching of the ice. At the downstream part, the generally rougher bed topography correlates with a decrease in flow acceleration and lateral variations in ice surface velocity. Together with basal water routing pathways, this hints to two different zones in this part of the NEGIS: the upstream region collecting water, with a reduced basal traction, and downstream, where the ice stream is slowing down and is widening on a rougher bed, with a distribution of basal water toward the shear margins. Our findings support the hypothesis that the NEGIS is strongly interconnected to the subglacial water system in its onset region, but also to the subglacial substrate and morphology., Plain Language Summary: The Northeast Greenland Ice Stream (NEGIS) transports a large amount of ice mass from the interior of the Greenland Ice Sheet (GrIS) toward the ocean. The extent and geometry of the NEGIS are difficult to reproduce in current ice sheet models because many boundary conditions, such as the properties of the ice base, are not well known. In this study, we present new characteristics of the ice base from the onset region of the NEGIS derived by airborne radio‐echo sounding data. Our data yield a smooth and increasingly lubricated bed in the upstream part of our survey area, which enables the ice to accelerate. Our results confirm the hypothesis that the position of the ice stream boundaries are coupled to the subglacial hydrology system., Key Points: Basal roughness at the onset of the NEGIS hints to a geomorphic anisotropy and a change in the geomorphological regime. Basal water is funneled into the ice stream upstream and redistributed toward the shear margins further downstream. A smooth and progressively lubricated bed reduces basal traction and favors the acceleration of the NEGIS at its onset., A. P. Møller Foundation, US National Science Foundation, Alfred Wegener Institute, National Institute of Polar Research and Arctic Challenge for Sustainability, University of Bergen and Bergen Research Foundation, Swiss National Science Foundation, French Polar Institute Paul‐Emile Victor, Chinese Academy of Sciences and Beijing Normal University, NASA Operation IceBridge, NSF

We present new high-resolution snow depth data on Arctic sea ice derived from airborne microwave radar measurements from the IceBird campaigns of the Alfred Wegener Institute (AWI) together with a new retrieval method using signal peakiness based on an intercomparison exercise of colocated data at different altitudes. We aim to demonstrate the capabilities and potential improvements of radar data, which were acquired at a lower altitude (200 ft) and slower speed (110 kn) and had a smaller radar footprint size (2-m diameter) than previous airborne snow radar data. So far, AWI Snow Radar data have been derived using a 2-18-GHz ultrawideband frequency-modulated continuous-wave (FMCW) radar in 2017-2019. Our results show that our method in combination with thorough calibration through coherent noise removal and system response deconvolution significantly improves the quality of the radar-derived snow depth data. The validation against a 2-D grid of in situ snow depth measurements on level landfast first-year ice indicates a mean bias of only 0.86 cm between radar and ground truth. Comparison between the radar-derived snow depth estimates from different altitudes shows good consistency. We conclude that the AWI Snow Radar aboard the IceBird campaigns is able to measure the snow depth on Arctic sea ice accurately at higher spatial resolution than but consistent with the existing airborne snow radar data of NASA Operation IceBridge. Together with the simultaneous measurements of the total ice thickness and surface freeboard, the IceBird campaign data will be able to describe the whole sea-ice column on regional scales.

We present an ice-penetrating radar data set which consists of 26 internal reflecting horizons (IRHs) that cover the entire Dome C area of the East Antarctic plateau, the most extensive to date in the region. This data set uses radar surveys collected over the space of 10 years, starting with an airborne international collaboration in 2008 to explore the region, up to the detailed ground based surveys in support of the Beyond EPICA – Oldest Ice (BE-OI) European Consortium. Through direct correlation with the EPICA-DC ice core, we date 19 IRHs that span the past four glacial cycles, from the beginning of the Holocene to over 350 ka. We indirectly date and provide stratigraphic information for seven older IRHs using an 1-D ice flow inverse model, going back to an estimated 700 ka. Depth and age uncertainties are quantified for all IRHs and provided as part of the data set. The IRH data set presented in this study is available at the U.S. Antarctic Program Data Center (USAP-DC) (https://doi.org/10.15784/601411, Cavitte et al., 2020) and represents a contribution to the SCAR AntArchitecture program.

Global warming is rapidly reducing glaciers and ice sheets across the world. Real time assessment of this reduction is required so as to monitor its global climatic impact. In this paper, we introduce a novel way of estimating the thickness of each internal ice layer using Snow Radar images and Fully Convolutional Networks. The estimated thickness can be used to understand snow accumulation each year. To understand the depth and structure of each internal ice layer, we perform multi-class semantic segmentation on radar images, which hasn't been performed before. As the radar images lack good training labels, we carry out a pre-processing technique to get a clean set of labels. After detecting each ice layer uniquely, we calculate its thickness and compare it with the processed ground truth. This is the first time that each ice layer is detected separately and its thickness calculated through automated techniques. Through this procedure we were able to estimate the ice-layer thicknesses within a Mean Absolute Error of approximately 3.6 pixels. Such a Deep Learning based method can be used with ever-increasing datasets to make accurate assessments for cryospheric studies.

Airborne sounders with cross-track antenna arrays coherently combine measurements from multiple sensors to isolate nadir echoes from co-range lateral surface clutter. The array size, which is limited by the constraints of the platform, determines the degree to which clutter may be suppressed. Array processing techniques offer strategic advantages for suppressing interference in the cross-track dimension but require a well constrained model of the array's response to directional sources (referred to as the array manifold). We propose and test an empirical array manifold characterization using a fine-resolution digital elevation model of the Arctic and multibeam data collected by a Center for Remote Sensing of Ice Sheets (CReSIS) sensor, the Multichannel Coherent Radar Depth Sounder (MCoRDS), during NASA's Operation Ice-Bridge. The results outline our first efforts to estimate the manifold and evidence improvements observed in combined images.

We designed a Ka-band prototype radar altimeter operated at a center frequency of 35 GHz with a 6 GHz bandwidth. The instrument was intended for fine-resolution verification and validation of space borne altimetry datasets. We installed it onboard the NASA C-130 aircraft in conjunction with two other wideband microwave instruments and collected airborne altimetry data over Greenland land ice and arctic sea ice during the 2015 NASA Operation IceBridge arctic campaign. Apart from the major application of verification and calibration of satellite-based measurements, data from this instrument can be used to derive snow grain size because of the dominant effect of volume scattering in radar signatures. In this paper, we briefly describe the system design and the installation on the NASA C-130, discuss the observed penetration depths of Ka-band signals into the snowpack, present sample results of optical-equivalent snow grain size estimates from radar measurements over the dry snow zone using a simplified snowpack model. We show that the observed penetration depths and the snow grain size estimates from the airborne Ka-band radar retrievals agree well with the model and in-situ snow-pit measurements.

Pragmatic Deep Learning techniques in recent years have greatly influenced our approaches to data analysis. However, in many real-world problems, even when a large dataset is available, Deep Learning methods have shown less success, for the lack of large labeled dataset, presence of noise, or missing data. In this work, our goal is to track internal ice layers in radar images gathered with various sensors in different years. We will show that transfer learning will not generally work well. However, if the Deep Learning model gets trained on noisy images, there would be a significant improvement. Unlike spatial Transfer Learning, our experiments show that temporal Transfer Learning can provide considerably better results.

This paper presents preliminary results using a fully connected neural network (NN) to automatically track the internal layers of snow radar echograms using an iterative “row-block-column” approach. Snow radar images, when accurately tracked, provide relevant information for estimating snow accumulation rates in polar regions which is a key measurement needed to understand and predict the impact of climate warming in Greenland and Antarctica. A multiclass NN was designed and trained with a training set of 121,408 columns of simulated snow radar data and learns to automatically track the internal layers with an accuracy of 92.8%, a RMSE of 0.24 pixels, and with 98% of pixel errors less than or equal to 1 pixel.

Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. In this research, we evaluated the performance of synthetically generated snow radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. Our experiments show a very good similarity between real and synthetic snow radar images.

Differential Interferometric Synthetic Aperture Radar (DIn-SAR) processing techniques applied to ice penetrating radar enable precise measurement of the vertical displacement of englacial layers within an ice sheet. This technique has primarily been applied using ground based ice-penetrating radar due to the ability to achieve a near-zero spatial baseline. We investigate this technique on data from the Multichannel Coherent Radar Depth Sounder (MCoRDS), an airborne ice penetrating radar, and produce initial results from a high accumulation region near Camp Century in northwest Greenland. We estimate the vertical displacement by compensating for the spatial baseline using precise trajectory information and estimates of the cross-track layer slope from direction of arrival analysis. The measurement accuracy is still being investigated.

Ice shelf buttressing plays a critical role in the long-term stability of ice sheets. The underlying bathymetry and cavity thickness therefore is a key to accurate models of future ice sheet evolution. However, direct observation of sub-ice shelf bathymetry is time consuming, logistically risky, and in some areas simply not possible, meaning there is a blind-spot in our understanding of this key system. Here we use airborne gravity anomaly data to provide new estimates of sub-ice shelf bathymetry outboard of the rapidly changing West Antarctic Thwaites Glacier, and beneath the adjacent Dotson and Crosson Ice Shelves. These regions are of especial interest as the low-lying inland reverse slope of the Thwaites glacier system makes it vulnerable to collapse through marine ice sheet instability, with rapid grounding-line retreat observed since 1993 suggesting this process may be underway. Our results confirm a major marine channel > 800 m deep extends to the front of Thwaites Glacier, while the adjacent ice shelves are underlain by more complex bathymetry. Comparison of our new bathymetry with ice shelf draft reveals that ice shelves formed since 1993 comprise a distinct population where the draft conforms closely to the underlying bathymetry, unlike the older ice shelves which show a more uniform depth of the ice base. This indicates that despite rapid basal melting in some areas, these “new” ice shelves are not yet in equilibrium with the underlying ocean system. We propose qualitative models of how this transient ice-shelf configuration may have developed, but further investigation is required to constrain the longevity and full impact of these newly recognised systems.

The Northeast Greenland Ice Stream (NEGIS) is an important dynamic component for the total mass balance of the Greenland ice sheet, as it reaches up to the central divide and drains 12% of the ice sheet. The geometric boundary conditions and in particular the nature of the subglacial bed of the NEGIS are essential to understand its ice flow dynamics. We present a record of more than 8000 km of radar survey lines of multi-channel, ultra-wideband radio echo sounding data covering an area of 24 000 km2, centered on the drill site for the East Greenland Ice-core Project (EGRIP), in the upper part of the NEGIS catchment. Our data yield a new detailed model of ice-thickness distribution and basal topography in the region. The enhanced resolution of our bed topography model shows features which we interpret to be caused by erosional activity, potentially over several glacial–interglacial cycles. Off-nadir reflections from the ice–bed interface in the center of the ice stream indicate a streamlined bed with elongated subglacial landforms. Our new bed topography model will help to improve the basal boundary conditions of NEGIS prescribed for ice flow models and thus foster an improved understanding of the ice-dynamic setting.

The Antarctic ice sheet has been losing mass over past decades through the accelerated flow of its glaciers, conditioned by ocean temperature and bed topography. Glaciers retreating along retrograde slopes (that is, the bed elevation drops in the inland direction) are potentially unstable, while subglacial ridges slow down the glacial retreat. Despite major advances in the mapping of subglacial bed topography, significant sectors of Antarctica remain poorly resolved and critical spatial details are missing. Here we present a novel, high-resolution and physically based description of Antarctic bed topography using mass conservation. Our results reveal previously unknown basal features with major implications for glacier response to climate change. For example, glaciers flowing across the Transantarctic Mountains are protected by broad, stabilizing ridges. Conversely, in the marine basin of Wilkes Land, East Antarctica, we find retrograde slopes along Ninnis and Denman glaciers, with stabilizing slopes beneath Moscow University, Totten and Lambert glacier system, despite corrections in bed elevation of up to 1 km for the latter. This transformative description of bed topography redefines the high- and lower-risk sectors for rapid sea level rise from Antarctica; it will also significantly impact model projections of sea level rise from Antarctica in the coming centuries., SCOPUS: ar.j, info:eu-repo/semantics/published

Ice shelves play a critical role in the long-term stability of ice sheets through their buttressing effect. The underlying bathymetry and cavity thickness are key inputs for modelling future ice sheet evolution. However, direct observation of sub-ice-shelf bathymetry is time-consuming, logistically risky, and in some areas simply not possible. Here we use new compilations of airborne and marine gravity, radar depth sounding, and swath bathymetry to provide new estimates of sub-ice-shelf bathymetry outboard of the rapidly changing West Antarctic Thwaites Glacier and beneath the adjacent Dotson and Crosson ice shelves. This region is of special interest, as the low-lying inland reverse slope of the Thwaites Glacier system makes it vulnerable to marine ice sheet instability, with rapid grounding line retreat observed since 1993 suggesting this process may be underway. Our results confirm a major marine channel >800 m deep extends tens of kilometres to the front of Thwaites Glacier, while the adjacent ice shelves are underlain by more complex bathymetry. Comparison of our new bathymetry with ice shelf draft reveals that ice shelves formed since 1993 comprise a distinct population where the draft conforms closely to the underlying bathymetry, unlike the older ice shelves, which show a more uniform depth of the ice base. This indicates that despite rapid basal melting in some areas, these recently floated parts of the ice shelf are not yet in dynamic equilibrium with their retreated grounding line positions and the underlying ocean system, a factor which must be included in future models of this region's evolution.

In this paper, we present a brief overview of the state of the art in radar systems for ice and snow measurements. Emphasis is given to the instrumentation developed at the University of Kansas for multi-band data retrieval at frequencies ranging from 14 MHz to 38 GHz, which is operated onboard manned and unmanned aircraft. The information collected with these systems is used to estimate parameters such as ice thickness, ice surface and bedrock topography, snow cover thickness on sea ice, and annual snow accumulation. We give a summary of recent field programs (including operations out of Punta Arenas, Chile), present sample results, and discuss recent collaborations with Chilean institutions.

Significant resources have been spent in collecting and storing large and heterogeneous radar datasets during expensive Arctic and Antarctic fieldwork. The vast majority of data available is unlabeled, and the labeling process is both time-consuming and expensive. One possible alternative to the labeling process is the use of synthetically generated data with artificial intelligence. Instead of labeling real images, we can generate synthetic data based on arbitrary labels. In this way, training data can be quickly augmented with additional images. In this research, we evaluated the performance of synthetically generated radar images based on modified cycle-consistent adversarial networks. We conducted several experiments to test the quality of the generated radar imagery. We also tested the quality of a state-of-the-art contour detection algorithm on synthetic data and different combinations of real and synthetic data. Our experiments show that synthetic radar images generated by generative adversarial network (GAN) can be used in combination with real images for data augmentation and training of deep neural networks. However, the synthetic images generated by GANs cannot be used solely for training a neural network (training on synthetic and testing on real) as they cannot simulate all of the radar characteristics such as noise or Doppler effects. To the best of our knowledge, this is the first work in creating radar sounder imagery based on generative adversarial network.

The Tenth Symposium on Polar Science/Ordinary sessions: [OM] Polar Meteorology and Glaciology, Wed. 4 Dec. / 2F Auditorium, National Institute of Polar Research

Artificial intelligence (AI) techniques have displayed impressive success in many practical fields. Deep neural networks (DNNs) owe their success to the availability of massive labeled data. However, in many real-world problems, even when a large dataset is available, deep learning methods have shown less success, due to causes such as lack of large labeled dataset, presence of noise in data, or missing data. In the present work, we intend to examine the application of deep learning methods on radar data gathered from polar regions. Our goal is to track internal ice layers in radar imagery. In such data, the presence of noise is one of the main obstacles in utilizing popular deep learning methods such as transfer learning. Our experiments show that if the neural network is trained to detect contours of objects in electro-optical imagery, it can only track a low percentage of contours in radar data. Fine-tuning and further training do not provide any better results. However, we will show that selecting the right model and training the model on the radar imagery from the base, is going to yield far better results. We also discuss another possible learning approach that can save us time for data annotation.