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To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains for the period of 2021-2023. The experiment objective was to improve understanding of the sensitivity of Sentinel-1 C-band backscatter radar signals to snow. The data were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The data show that scattering is present throughout the snow volume, although it is limited for low snow densities. Contrasting layer interfaces, ice features and metamorphic snow can have considerable impact on the backscatter signal. During snow melt periods, wet snow absorbs the signal and the soil backscatter becomes negligible. A comparison of the vertically integrated tower radar data with Sentinel-1 data shows that both systems have a similar temporal behavior, and both feature an increase in backscatter during the dry snow period in 2021-2022, even during weeks of nearly constant snow depth, likely due to morphological changes in the snowpack. The results demonstrate that C-band radar is sensitive to the dominant seasonal patterns in snow accumulation, but that changes in microstructure, stratigraphy, melt-freeze cycles, and snow wetness may complicate satellite-based snow depth retrievals. [ABSTRACT FROM AUTHOR]

Surface mass balance (SMB) over the Antarctic Ice Sheet must be better understood to document current Antarctic contribution to sea-level rise. Field point data using snow stakes and ice cores are often used to evaluate the state of the ice sheet's mass balance as well as to validate SMB derived from regional climate models, which are then used to produce future climate projections. However, spatial representativeness of individual point data remains largely unknown, particularly in the coastal regions 5 of Antarctica with highly variable terrains. Here, we compare ice core data collected at the summit of eight ice rises along the coast of Dronning Maud Land, as well as at the Dome Fuji site, and shallow ice-penetrating radar data over these regions. Shallow radar data has the advantage of being spatially extensive with a temporal resolution that varies between annual and sub-decadal resolution from which we can derive a SMB record over the entire radar survey. This comparison allows us therefore to evaluate the spatial variability of SMB and the spatial representativeness of ice-core derived 10 SMB. We found that ice core mean SMB is very local and the difference with radar-derived SMB increases in a logarithmicfashion as the surface covered by the radar data increases, with for most ice rises a plateau ~1-2 km away from the ice crest where there are strong wind-topography interactions, and ~10 km where the ice shelves begin. The relative uncertainty in measuring SMB also increases rapidly as we move away from the ice core sites. Five of our ice rise sites show a strong spatial representativeness in terms of temporal variability, while the other three sites show it is limited to a surface areas between 15 20-120 km2. The Dome Fuji site on the other hand shows a small difference between pointwise and area mean SMB, as well as a strong spatial representativeness in terms of temporal variability. We found no simple parameterization that could represent the spatial variability observed at all the sites. However, these data clearly indicate that local spatial SMB variability must be considered when assessing mass balance as well as comparing modeled SMB values to point field data. [ABSTRACT FROM AUTHOR]

Radio-echo sounding is a standard technique for imaging the englacial stratigraphy of glaciers and ice sheets. In most cases, internal reflection horizons (IRHs) represent former glacier surfaces and comprise information about past accumulation, ice deformation and allow to link ice core chronologies. IRHs in the lower third of the ice column are often difficult to detect or coherently trace. In the polar ice sheets, progress in IRH detection has been made by using multistatic, phase coherent radars, enabling synthetic-aperture radar (SAR) processing. However, these radar systems are often not suitable for deployment on mountain glaciers. We present a proof-of-concept study for a lightweight, phase-coherent, and ground-based radar system, based on the phase-sensitive radio echo-sounder (pRES). To improve the detectability of IRHs we additionally adapted a layer-optimized SAR (LO-SAR) processing scheme to this setup. We showcase the system capability at Colle Gnifetti, Switzerland/Italy, and detect significantly deeper and older IRHs compared to previously deployed pulsed radar sys tems. Continuous IRHs are now apparent down to the base of the glacier. Corresponding reflection mechanisms for this glacier are linked to a stratified acidic impurity which was deposited at a higher rate due to increased industrial activity in the area. Possible improvements of the system are discussed. If successfully implemented, these may provide a new way to map the deep internal structure of Colle Gnifetti and other mountain glaciers more extensively in future deployments. [ABSTRACT FROM AUTHOR]

Observations of glacier mass changes are key to understanding the response of glaciers to climate change and related impacts, such as regional runoff, ecosystem changes, and global sea-level rise. Spaceborne optical and radar sensors make it possible to quantify glacier elevation changes, and thus multi-annual mass changes, on a regional and global scale. However, estimates from a growing number of studies show a wide range of results with differences often beyond uncertainty bounds. Here, we present the outcome of a community-based inter-comparison experiment using spaceborne optical stereo (ASTER) and synthetic aperture radar interferometry (TanDEM-X) data to estimate elevation changes for defined glaciers and target periods that pose different assessment challenges. Using provided or self-processed digital elevation models (DEMs) for five test sites, 12 research groups provided a total of 97 spaceborne elevation-change datasets using various processing strategies. Validation with airborne data showed that using an ensemble estimate is promising to reduce random errors from different instruments and processing methods, but still requires a more comprehensive investigation and correction of systematic errors. We found that scene selection, DEM processing, and co-registration have the biggest impact on the results. Other processing steps, such as treating spatial data voids, differences in survey periods, or radar penetration, can still be important for individual cases. Future research should focus on testing different implementations of individual processing steps (e.g. co-registration) and addressing issues related to temporal corrections, radar penetration, glacier area changes, and density conversion. Finally, there is a clear need for our community to develop best practices, use open, reproducible software, and assess overall uncertainty in order to enhance inter-comparison and empower physical process insights across glacier elevation-change studies. [ABSTRACT FROM AUTHOR]

Ice-penetrating radar (IPR) imaging is a valuable tool for observing the internal structure and bottom of ice sheets. Subglacial water bodies, also known as subglacial lakes, generally appear as distinct, bright, flat, and continuous reflections in IPR images. In this study, we collect and generate a dataset of one-dimensional reflector waveform features from IPR images of the Gamburtsev Subglacial Mountains region in the CReSIS database, to investigate these features. We apply a deep learning method to reconstruct the reflector features, and subsequently downsample the features to a low-dimensional vector representation. An unsupervised clustering method is then used to separate different types of reflector features, including reflector type corresponding to subglacial water bodies. The derived clustering labels are used to detect features of subglacial water bodies in IPR images. Using this method, we compare the new detections with the known lakes inventory. The results indicate that this new method identified additional subglacial lakes that were not previously detected, and some previously known lakes are found to correspond to other reflector clusters. This method can offer automatic detections of subglacial lakes and provide new insight for subglacial studies. [ABSTRACT FROM AUTHOR]

The Dome Fuji (DF) region in Antarctica is a potential site for an ice core with a record of over one million years. Here, we combine the internal airborne radar stratigraphy with a 1-D model to estimate the age of basal ice in the DF region. The radar data used in the study were collected in a survey during the 2016-2017 Antarctica season. We transfer the newest age-depth scales from the DF ice core to isochrones in the surrounding 500 km × 550 km region through traced radar isochrones. At each point of the survey the 1-D model uses the ages of isochrones to construct the age-depth scale at depths where dated isochrones do not exist, the basal thermal conditions, including the thickness of a potentially present basal layer and surface accumulation rates. Our resulting age distribution and age density suggest that a few promising sites with ice older than 1.5 million years in the DF region might exist. The deduced melt rates and presence of stagnant ice map provides more constraints for finding sites with a cold base. The accumulation rates range from 0.015 to 0.038 m a-1 ice equivalent. The numbers of picked isochrones and the timescale of the ice core severely affect the model results according to sensitivity studies. Our study demonstrates it is possible to find the old ice in the DF region, the constraint from deep radar isochrones and a trustworthy timescale could improve the model estimation. [ABSTRACT FROM AUTHOR]

The bulk crystal orientation in ice influences the flow of glaciers and ice streams. The ice c-axes fabric is most reliably derived from ice cores. Because these are sparse, the spatial and vertical distribution of the fabric in the Greenland and Antarctic ice sheets is largely unknown. In recent years, methods have been developed to determine fabric characteristics from radar measurements. The aim of this paper is to present an improved method to infer the horizontal fabric asymmetry by precisely determining the travel-time difference using co-polarised phase-sensitive radar data. We applied this method to six radar measurements from the EastGRIP drill site on Greenland's largest ice stream to give a proof-of-concept by comparing the results with the horizontal asymmetry of the bulk crystal anisotropy derived from the ice core. This comparison shows an almost perfect agreement, which is a large improvement compared to previously used methods. Our approach is particularly useful for determining the vertical profile of the fabric asymmetry in higher resolution and over larger depths than was achievable with previous methods, especially in regions with strong asymmetry. [ABSTRACT FROM AUTHOR]

Sea ice volume significant interannual variability requires long-term series of observations to identify trends in its evolution. Despite improvements in sea ice thickness estimations from altimetry during the past few years thanks to CryoSat-2 and ICESat-2, former ESA radar altimetry missions such as Envisat and especially ERS-1 and ERS-2 have remained under exploited so far. Although solutions have already been proposed to ensure continuity of measurements between CryoSat-2 and Envisat, there is no time series integrating ERS. The purpose of this study is to extend the Arctic freeboard time series back to 1995. The difficulty to handle ERS measurements comes from a technical issue known as the pulse-blurring effect, altering the radar echos over sea ice and the resulting surface height estimates. Here we present and apply a correction for this pulse-blurring effect. To ensure consistency of the CryoSat-2/Envisat/ERS-2 time series, a multi-parameters neural network-based method to calibrate Envisat against CryoSat-2 and ERS-2 against Envisat is presented. The calibration is trained on the discrepancies observed between the altimeter measurements during the missions-overlap periods and a set of parameters characterizing the sea ice state. Monthly radar freeboards are provided with uncertainty estimations based on a Monte Carlo approach to propagate the uncertainties all along the processing chain, including the neural network. Comparisons of corrected radar freeboards during overlap periods reveal good consistencies between missions, with a mean bias of 3 mm for Envisat/CryoSat-2 and 2 mm for ERS-2/Envisat. The monthly maps obtained from Envisat and ERS-2 are then validated by comparison with several independent data such as airborne, moorings, direct measurements and other altimeter products. Except for two data sets, comparisons lead to correlation ranging from 0.42 to 0.94 for Envisat, and 0.6 to 0.76 for ERS-2. The study finally provides radar freeboard estimation for winters from 1995 to 2021 (from ERS-2 mission to CryoSat-2). [ABSTRACT FROM AUTHOR]

Monitoring active layer dynamic is critical for improving the near surface thermal and hydrological process understanding. This study presents the laboratory test of a low-cost Monitoring Ground-Penetrating Radar (GPR) system within a laboratory experiment of active layer freezing and thawing monitoring. The system is a in-house built low power monostatic GPR antenna coupled with a reflectometer piloted by a single board computer, tested prior to field deployment. The correspondence between the frozen front electromagnetic reflection and temperature allowed the better understanding of the frozen front/bottom of the active layer reflection and the intrinsic permittivity of the frozen layer. [ABSTRACT FROM AUTHOR]

The Arctic sea ice thicknesses retrieved from the CryoSat-2 satellite are significantly influenced by sea ice surface roughness, snow backscatter, and snow depth on the sea ice. This study is the first to improve the retrieval of sea ice thickness from CryoSat-2 data derived by the Alfred Wegener Institute (AWI CS2) through applying a comprehensive optimization of an improved retracking algorithm, corrected radar penetration rate, and new snow depth. The radar freeboard data was obtained from the improved retracking algorithm of the Lognormal Altimeter Retracker Model (LARM). The radar penetration rates were corrected to 0.77 for first-year ice (FYI), 0.96 for multi-year ice (MYI), and 0.91 for all ice types. The new snow depth data was derived from the Chinese satellite Feng Yun-3B with the MicroWave Radiometer Imager (FY3B/MWRI). Three individual and one combined optimization cases were created by focusing on the retracking algorithm, radar penetration rate, and snow depth, which were validated with in situ observations collected from the National Aeronautics and Space Administration Operation IceBridge (OIB), Ice Mass Balance buoys (IMB), CryoSat Validation Experiment (CryoVEX), and Alfred Wegener Institute IceBird Program (AWI IceBird). The assessment results showed that all the optimization cases had the ability to effectively improve the sea ice thickness, with similar correlation coefficients. In the validation with the four kinds of observed data, the optimization cases reduced the RMSE of AWI CS2 up to 0.23 m (25.0 %), 0.27 m (29.7 %), 0.26 m (25.5 %), and 0.22 m (23.9 %). The improved sea ice thickness retrieval retained the major distribution patterns generated by AWI CS2, but generally showed thinner sea ice thickness. In particular, in the MYI region, the difference in thickness became increasingly evident from fall to spring. The differences in the variation trend between the optimization cases and AWI CS2 were significant in some coastal regions and the central Arctic. The experiments revealed that the radar penetration rate calculation was more sensitive to sea ice density than to snow density. The sensitivity experiments suggested that the snow depth of TOPAZ4, in addition to that of FY3B/MWRI, was also applicable in improving the retrieval of sea ice thickness. The updated scheme of sea ice densities (FYI = 925 kg m-3 and MYI = 902 kg m-3) can be combined with the use of comprehensive optimization to improve the retrieval of sea ice thickness. This successful optimization provided new insights into improving the sea ice thickness retrieved from CryoSat-2 and helped to further understand and quantify the spatiotemporal variations of sea ice thickness. [ABSTRACT FROM AUTHOR]

The fracturing of glaciers and ice shelves in Antarctica influences their dynamics, and may introduce as-yet poorly understood feedbacks and hysteresis into the ice sheet system. Therefore, data on the evolving distribution of crevasses is required to better understand the evolution of the ice sheet, though such data has traditionally been difficult and time consuming to generate. Here, we present an automated method of mapping crevasses on grounded and floating ice with the application of convolutional neural networks to Sentinel-1 synthetic aperture radar backscatter images acquired between 2015 and 2022. We apply this method across Antarctica to produce a 7-and-a-half year record of composite fracture maps at monthly intervals and 50 m spatial resolution, showing the distribution of crevasses around the majority of the ice sheet margin. We develop a method of quantifying changes to the density of ice shelf fractures using the timeseries of crevasse maps, and show increases in crevassing on the Thwaites and Pine Island ice shelves over the observational period, with observed changes elsewhere in the Amundsen Sea dominated by the advection of existing crevasses. Using stress fields computed using the BISICLES ice sheet model, we show that much of this structural change has occurred in strongly buttressing regions of these ice shelves, indicating a recent and ongoing link between fracturing and the developing dynamics of the Amundsen Sea Sector. [ABSTRACT FROM AUTHOR]

Melting and refreezing processes in the firn of Devon Ice Cap control meltwater infiltration and runoff across the ice cap, but their full spatial extent and effect on near-surface structure is difficult to measure with ground-based traverses or existing satellite remote sensing. Here, we derive the coherent component of the near-surface return from airborne icepenetrating radar over Devon Ice Cap, Canadian Arctic, to characterize firn containing centimeter to meter-thick ice layers (i.e., ice slabs) formed from refrozen meltwater in firn. Comparison with reflectivities using a thin layer reflectivity model, informed by ground-based radar and firn core measurements, indicate that the coherent component is sensitive to the nearsurface firn structure composed of quasi-specular ice and firn layers, limited by the bandwidth-constrained radar range resolution. By leveraging their differences in range resolution, we assess the use of dual-frequency airborne ice-penetrating radar to characterize the spatial and vertical near-surface structure of Devon Ice Cap. Our results suggest that average ice slab thickness throughout the Devon Ice Cap percolation zone ranges from 4.2 to 5.6 m. This implies conditions that can enable lateral meltwater runoff and potentially contribute to the total surface runoff routed through supraglacial rivers down glacier. Together with the incoherent component of the surface return previously studied, our dual-frequency approach provides an alternative method for characterizing bulk firn properties, particularly where ground-based and higher frequency radar data are not available. [ABSTRACT FROM AUTHOR]

We investigate the application of ground-based radar interferometry for measuring flexural-gravity waves in sea ice. We deployed a Gamma Portable Radar Interferometer (GPRI) on top of a grounded iceberg surrounded by landfast sea ice near Utqiaġvik, Alaska. The GPRI collected 238 acquisitions in stare-mode during a period of moderate lateral ice motion during 23-24 Apr 2021. Individual 30-second interferograms exhibit ~20-50 s periodic motion indicative of propagating infragravity waves with ~1 mm amplitudes. Results include examples of onshore wave propagation at the speed predicted by the water depth and a possible edge wave along an ice discontinuity. Findings are supported through comparison with on-ice Ice Wave Rider (IWR) accelerometers and modeled wave propagation. These results suggest that the GPRI can be a valuable tool to track wave propagation through sea ice and possibly detect changes in such properties across variable ice conditions. [ABSTRACT FROM AUTHOR]

Modelling the past and future evolution of the West Antarctic Ice Sheet (WAIS) to atmospheric and ocean forcing is challenged by the availability and quality of observed palaeo-boundary conditions. Key potential data for reconstructing past ice-sheet processes on large spatial scales are Internal Reflecting Horizons (IRHs) detected by Radio-Echo Sounding (RES) techniques. When isochronal and dated at ice cores, IRHs can be used to determine palaeo-accumulation rates and patterns. Using a spatially extensive IRH over Pine Island Glacier, Thwaites Glacier, Institute and Möller Ice Streams, and a local layer approximation model, we infer mid-Holocene accumulation rates over the slow-flowing parts of these catchments for the past ~5000 years. By comparing our results with modern climate reanalysis models and observational syntheses, we estimate that accumulation rates over the Amundsen-Weddell-Ross divide were on average 18% higher than modern rates during the mid-Holocene. However, no significant spatial changes in the accumulation pattern were observed. These higher mid-Holocene accumulation-rate estimates match previous palaeo-accumulation estimates from ice-core and targeted RES surveys over the ice divide, and also coincide with periods of grounding-line re-advance during the Holocene over the Weddell and Ross Sea sectors. Our results highlight the need for ice-sheet models to account for time-varying accumulation rates across the WAIS during the Holocene to provide better estimates of its contribution to past sea-level rise. [ABSTRACT FROM AUTHOR]

Wind transport alters the snow topography and microstructure on sea ice through snow redistribution controlled by deposition and erosion. The impact of these processes on radar signatures is poorly understood. Here, we examine the effects of snow redistribution on Arctic sea ice from Ka- and Ku-band radar signatures. Measurements were obtained during two wind events in November 2019 during the MOSAiC expedition. During both events, changes in Ka- and Ku-band radar waveforms and backscatter coincident with surface height changes measured from a terrestrial laser scanner are observed. At both frequencies, snow redistribution events increased the dominance of the air/snow interface at nadir as the dominant radar scattering surface, due to wind densifying the snow surface and uppermost layers. The radar waveform data also detect the presence of previous air/snow interfaces, buried beneath newly deposited snow. The additional scattering from previous air/snow interfaces could therefore affect the range retrieved from Ka- and Ku-band satellite radar altimeters. The relative scattering contribution of the air/snow interface decreases, and the snow/sea ice interface increases with increasing incidence angles. Relative to pre-wind conditions, azimuthally averaged backscatter at nadir during the wind events increases by up to 8 dB (Ka-band) and 5 dB (Ku-band). Binned backscatter within 5° azimuth bins reveals substantial backscatter variability in the radar footprint at all incidence angles and polarizations. The sensitivity of the co-polarized phase difference is linked to changes in snow settling and temperature-gradient induced grain metamorphism, demonstrating the potential of the radar to discriminate between newly deposited and older snow on sea ice. Our results reveal the importance of wind, through its geophysical impact on Ka- and Ku-band radar signatures of snow on sea ice and has implications for reliable interpretation of airborne and satellite radar measurements of snow-covered sea ice. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Exploring the snow thickness at the Mount Everest has long been a topic of interest for studying geodesy, cryosphere and climate change, but has not yet been measured successfully. Here, we report the ground-penetrating radar survey of snow thickness along the northern slope of the Mount Everest in May 2022. Our radar measurements display a gradual increasing transition of snow thickness along the north slope, and the mean snow thickness estimates at the Mount Everest is approximately 9.5 m. This updated snow thickness at the Mount Everest is much deeper than previously reported values (0.9~3.5m). [ABSTRACT FROM AUTHOR]

Ice thickness across lake ice is influenced mainly by the presence of snow and its distribution, as it directly impacts the rate of lake ice growth. The spatial distribution of snow depth over lake ice varies and is driven by wind redistribution and snowpack metamorphism, creating variability in the lake ice thickness. The accuracy and consistency of snow depth measurement data on lake ice are challenging and sparse to obtain. However, high spatial resolution lake snow depth observations are necessary for the next generation of thermodynamic lake ice models. Such information is required to improve the knowledge and understanding of snow depth distribution over lake ice. This study maps snow depth distribution over lake ice using ground penetrating radar (GPR) two-way travel-time (TWT) with ~9 cm spatial resolution along transects totalling ~44 km over four freshwater lakes in Canada's sub-arctic. The accuracy of the snow depth retrieval is assessed using in situ snow depth observations (n =2,430). On average, the snow depth derived from GPR TWTs for the early winter season is estimated with a root mean square error (RMSE) of 1.58 cm and a mean bias error of -0.01 cm. For the late winter season on a deeper snowpack, the accuracy is estimated with RMSE of 2.86 cm and a mean bias error of 0.41 cm. The GPR-derived snow depths are interpolated to create 1 m spatial resolution snow depth maps. Overall, this study improved lake snow depth retrieval accuracy and introduced a fast and efficient method to obtain high spatial resolution snow depth information, which is essential for the lake ice modelling community. [ABSTRACT FROM AUTHOR]

Snowmelt runoff serves both human needs and ecosystem services and is an important parameter in operational forecasting systems. Sentinel-1 Synthetic Aperture Radar (SAR) observations can estimate the timing of melt within a snowpack; however, these estimates have not been applied on large spatial scales. We present here a workflow to fuse Sentinel-1 SAR and optical data from Landsat-8 and Sentinel-2 to estimate the onset and duration of snowmelt in the Lajoie Basin, a large watershed in the Southern Coast Mountains of British Columbia. A backscatter threshold is used to infer the point at which snowpack saturation occurs, and the snowpack begins to produce runoff. Multispectral imagery is used to estimate snow free dates across the basin to define the end of the snowmelt period. SAR estimates of snowmelt onset form consistent trends by elevation and aspect on the watershed scale and reflect snowmelt records from continuous SWE observations. SAR estimates of snowpack saturation are most effective on moderate to low slopes (< 30°) in open areas. The accuracy of snowmelt durations is reduced due to persistent cloud cover in optical imagery. Despite these challenges, snowmelt durations agree with trends in snow depths observed in the Lajoie. This approach has high potential for adaptability to other alpine regions and can provide estimates of snowmelt timing in ungauged basins. [ABSTRACT FROM AUTHOR]

Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth Observation mission. The snow scientific community has shown that Ku-Band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent de velopments of tools like the Snow MicroPenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer Model (SMRT), it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-Band radar backscatter at a 100-m scale. Airborne Ku-Band data was acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using Radarsat-2 and TerraSAR-X data, soil background roughness properties were retrieved (msssoil = 0.010±0.002) and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, was retrieved. Using the Geometrical Optics surface backscatter model, the retrieved effective soil permittivity increased from C-Band (εsoil = 2.47) to X-Band (εsoil = 2.61), to Ku-Band (εsoil = 2.77) for the TVC domain. Using SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-Band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-Band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve SWE at the 100 m scale. [ABSTRACT FROM AUTHOR]

We present a 1D numerical model which calculates the age of ice around Dome C. It accounts either for melting or for a layer of stagnant ice above the bedrock. It is constrained by horizons picked from radar observations and dated using the EPICA Dome C (EDC) ice core age profile. We used 3 different radar datasets with the widest reaching airborne radar system covering an area of 10,000 km² and zooming in to 5 km transects over Little Dome C (LDC) with a ground based system. We find that stagnant ice exists in many places including above the LDC relief where the new Beyond EPICA drill site (BELDC) is located. The modelled thickness of this layer of stagnant ice roughly corresponds to the thickness of the basal unit observed in one of the radar surveys and observations made with Autonomous phase-sensitive Radio-Echo Sounder (ApRES). At BELDC, the modelled stagnant ice thickness is 182±63 m and the modelled maximum age (that we define as the age at a maximum age density of 20 kyr m-1) is 1.49±0.18 Ma at a depth of 2505±34 m. This is very similar to all sites situated on the LDC relief such as that of the Million Year Ice Core project being conducted by the Australian Antarctic Division. The model was also applied to radar data in the area 10-20 km north of EDC (North Patch, NP), where we find either a thin layer of stagnant ice (generally <60 m) or a very low melt rate (<0.1 mm yr-1). The modelled maximum age at NP is over 2 Ma in most places, with ice at 1.5 Ma having a resolution of 9-12 kyr m-1, making it an exciting prospect for a future oldest ice drill site. [ABSTRACT FROM AUTHOR]

Since distributed satellite observations of elevation change and velocity became available in the 1990s, Thwaites, Pine Island, Haynes, Pope, and Kohler Glaciers, located in Antarctica's Amundsen Sea Embayment, have thinned and accelerated in response to ocean-induced melting and grounding-line retreat. We develop a crevasse image segmentation algorithm to identify and map surface crevasses on the grounded portions of Thwaites, Pine Island, Haynes, Pope, and Kohler Glaciers between 2015 and 2022 using Sentinel-1A satellite synthetic aperture radar (SAR) imagery. We also develop a geometric 5 model for firn tensile strength dependent on porosity and the tensile strength of ice. On Pine Island and Thwaites Glaciers, which have both accelerated since 2015, crevassing has expanded tens of kilometers upstream of the 2015 extent. From the crevasse time series, we find that crevassing is strongly linked to principal surface stresses and consistent with von Mises fracture theory predictions. Our geometric model, analysis of SAR, and optical imagery, together with ice-penetrating radar data, suggest that these crevasses are near-surface features restricted to the firn. The porosity dependence of the near-surface tensile 10 strength of the ice sheet may explain discrepancies between the tensile strength inferred from remotely-sensed surface crevasse observations and tensile strength measured in laboratory experiments, which often focus on ice (rather than firn) fracture. The near-surface nature of these features suggests that the expansion of crevasses inland has a limited direct impact on glacier mechanics. [ABSTRACT FROM AUTHOR]

Satellite Synthetic Aperture Radar (SAR) data are commonly utilized for calculating sea ice displacements and, consequently, sea ice deformation strain rates. However, strain rate calculations often suffer from a poor signal-to-noise ratio, especially for products with a spatial resolution higher than 1 km. In this study, we applied a new filtering method to strain rate calculations derived from Sentinel-1 SAR image pairs with a spatial resolution of 800 m. Subsequently, we employed a power law to evaluate the deformation rates at decreasing spatial resolutions, assessing the quality of the filtered data. Upon positive evaluation of the filtered data, we introduced two innovative methods for sea ice deformation assessment. The first method, named 'damage parcels' (DP) tracking, involved the combined analysis of displacements and deformation strain rates to monitor divergence and convergence within the sea ice cover. Additionally, we proposed a new term to describe the behavior of the winter pack: 'Coherent Dynamic Elements' (CDE). CDEs are cohesive clusters of ice plates within the pack ice that move coherently along Linear Kinematic Features (LKFs). The second novel method developed in this study focused on exploring the geometrical properties of these CDEs. Both methods were applied to the winter collection of Sentinel-1 SAR imagery available during the N-ICE2015 campaign. Our results revealed a cyclically changing winter sea ice cover, marked by synoptic events and transitions from pack ice to the marginal ice zone. The DP were continuously tracked over a period of three weeks, including a major storm, revealing a remarkably slow healing process of existing LKFs. Furthermore, the CDE analysis demonstrated the presence of elongated CDEs with a density ranging from 5 to 20 per 100 km by 100 km, and the shortest distance between LKFs was found to be 5-10 km. [ABSTRACT FROM AUTHOR]

Ice shelves surrounding the Antarctic perimeter decelerate ice discharge towards the ocean through buttressing. Iceshelf evolution and integrity depend on the local surface accumulation, basal melting and on the spatially variable ice-shelf viscosity. These parameters are often poorly constrained by observations and introduce uncertainties in ice-sheet projections for the ice-sheet evolution. Isochronal radar stratigraphy is an observational archive for the atmospheric, oceanographic and ice-flow history of ice shelves with potential to assist model calibration. Here, we explore the possibility of using a simple and observationally driven ice-flow forward model to predict the ice-shelf stratigraphy for a given atmospheric- and oceanographic scenario. We validate this approach with the full Stokes ice-flow model Elmer/Ice and present a test case for the Roi Baudouin Ice Shelf (East Antarctica), where we compare model predictions with radar observations. The presented method enables us to investigate whether ice shelves are in steady-state and to delineate how much of the ice-shelf volume is determined by its local surface accumulation. This can be used to better understand variability in ice-shelf rheology and for estimations which ice shelves are particularly susceptible to changes of surface accumulation rates in the future. Moreover, the numerically efficient prediction of isochronal stratigraphy is a step forward towards integrating radar data into ice-flow models using inverse methods. This has potential to constrain ocean-induced melting beneath Antarctic ice shelves using the ever-growing archive of radar observations of internal ice stratigraphy. [ABSTRACT FROM AUTHOR]

The sea ice topography is essential for understanding the interactions within the air-ocean-ice system. Single-pass interferometric synthetic aperture radar (InSAR) allows for the generation of digital elevation model (DEM) over the drift sea ice. However, accurate sea ice DEMs (i.e., snow freeboard) derived from InSAR are impeded due to the radar signals penetrating the snow and ice layers. This research introduces a novel methodology for retrieving sea ice DEMs using dual-polarization interferometric SAR images, considering the variation in radar penetration bias across multiple ice types. The accuracy of the method is verified through photogrammetric measurements, demonstrating the derived DEM with a root-meansquare error of 0.26m over a 200×19km area. The method is further applied to broader regions in the Weddell and the Ross Sea, offering new insights into the regional variations of sea ice topography in the Antarctic. We also characterize the non-Gaussian statistical behavior of sea ice elevations using log-normal and exponential-normal distributions. The results suggest that the exponential-normal distribution is superior in the thicker sea ice region (average elevation > 0.5m), whereas the two distributions exhibit similar performance in the thinner ice region (average elevation < 0.5m). These findings offer an in-depth representation of sea ice elevation and roughness in the Weddell and Ross Sea, which can be conducted in time series data to comprehend sea ice dynamics, including its growth and deformation. [ABSTRACT FROM AUTHOR]

In this work, we assess the performance of Persistent Scatterer Interferometry (PSI) based on Sentinel-1 data in the Swiss Alps, focusing on the analysis of surface displacement in areas located in permafrost. To this end, we exploit a PSI dataset over Canton Wallis and the recently published permafrost and ground ice map of Switzerland (PGIM). First we evaluate the sensitivity to radar detection of the terrain, given a particular slope distribution and given satellite orbital geometries. We find that 92% of the areas currently labeled as permafrost satisfy the geometric visibility criteria, which is a necessary but not sufficient condition for detection of ground movement by satellite radar. Second, we analyse the PSI surface velocity and displacement time series observed in Canton Wallis in the period 2015 to 2022. We find that the displacements rates appear to be correlated with the occurrence of permafrost and that there is a significant difference in displacement rates between ice-poor and ice-rich permafrost compared to no-permafrost zones. However, the difference between the surface velocities retrieved in ice-poor permafrost and no-permafrost zones are small and additional information would be needed to discriminate ice-poor permafrost zones. [ABSTRACT FROM AUTHOR]

Snow Water Equivalent (SWE) is identified as the key element of the snowpack that impacts rivers' streamflow and water cycle. Both active and~passive microwave remote sensing methods have been used to retrieve SWE, but there does not currently exist a SWE product that provides useful estimates in mountainous terrain. Active sensors provide higher-resolution observations, but the optimal radar frequencies and temporal repeat intervals have not been available until recently. Interferometric Synthetic Aperture Radar (InSAR) has been shown to have the potential to estimate SWE change. In this study, we apply this technique to a long time series of Sentinel-1 data from the 2020-2021 winter. The retrievals show statistically significant correlations both temporally and spatially with independent measurements of snow depth and SWE. The Pearson correlation and RSME between retrieved SWE change observations and in situ stations measurements are 0.82, and 0.76 cm, respectively. The total retrieved SWE in the entire 2020-2021 time series shows SWE error less than 2 cm for the 16 in situ stations in the scene. Additionally, the retrieved SWE using Sentinel-1 data is highly correlated with LIDAR snow depth data with correlation of more than 0.5. Low temporal coherence is the main reason for degrading the performance of SWE retrieval using InSAR data. Low temporal coherence also causes the degradation of phase unwrapping algorithms. [ABSTRACT FROM AUTHOR]

The Greenlandic and Antarctic Ice Sheet are important indicators of climate change and major contributors to sea level rise. Hence, precise, long-term observations of surface elevation change are required to assess changes and their contribution to sea level rise. Satellite radar altimetry has been used by various missions to measure surface elevation change since 1992. It has been shown that, next to the surface slope and complex topography, one of the most challenging issues is the spatial and temporal variability of radar pulse penetration into the snow pack, especially over the vast East Antarctic plateau. This results in an inaccurate measurement of the true surface elevation and consequently affects surface elevation change (SEC) estimates. To increase the accuracy of surface elevations retrieved by retracking the radar return waveform and thus reduce the uncertainty in SEC, we developed a deep convolutional neural network architecture (AWI-ICENet1). The AWI-ICENet1 is trained using a simulated reference data set with 3.8 million waveforms, taking into account different surface slopes, topography, and attenuation. The successfully trained network is finally applied as AWI-ICENet1-retracker to the full time series of CryoSat-2 Low Resolution Mode (LRM) waveforms over both ice sheets. We compare the AWIICENet1 retrieved SEC with estimates of conventional retrackers like TFMRA and ESA ICE1 and ESA ICE2 products. Our results show less uncertainty and a greatly diminished effect of time variable radar penetration, reducing the need to apply corrections based on a close relationship with backscatter- and/or leading edge width, as typically done in SEC processing. This technique provides new opportunities to utilize convolutional neural networks in altimetry, waveform retracking, and processing of satellite altimetry data, which can be applied to historical, recent, and future missions. [ABSTRACT FROM AUTHOR]

Landscapes buried beneath the Antarctic Ice Sheet preserve information about the geologic and geomorphic evolution of the continent both before and during the wide-scale glaciation that began roughly 34 million years ago. Throughout this time, some areas of the ice sheet have remained cold-based and non-erosive, preserving ancient landscapes remarkably intact. The Gamburtsev Subglacial Mountains in central East Antarctica are one such landscape, maintaining evidence of tectonic, fluvial and glacial controls on their distinctly alpine morphology. The central Gamburtsevs have previously been surveyed using airborne ice-penetrating radar, however, many questions remain as to their evolution and their influence on the East Antarctic Ice Sheet, including where in the region to drill for a 1.5-million-year-long ‘Oldest Ice’ core. Here, we derive new maps of the planform geometry of the Gamburtsev Subglacial Mountains from satellite remote sensing datasets of the ice sheet surface, based on the relationship between bed roughness and ice surface morphology. Automated and manual approaches to mapping were tested and validated against existing radar data and elevation models. Manual mapping was more effective than automated approaches at reproducing bed features observed in radar data, but a hybrid approach is suggested for future work. The maps produced here show detail of mountain ridges and valleys on wavelengths significantly smaller than the spacing of existing radar flightlines, and mapping has extended well beyond the confines of existing radar surveys. Morphometric analysis of the mapped landscape reveals that it constitutes a preserved (> 34 Ma) dendritic valley network, with some evidence for modification by topographically confined glaciation prior to ice sheet inception. The planform geometry of the landscape is a significant control on locations of basal melting, subglacial hydrological flows, and the stability of the ice sheet over time, so the maps presented here may help to guide decisions about where to search for Oldest Ice. [ABSTRACT FROM AUTHOR]

The regular and robust mapping of grounding lines is essential for various applications related to the mass balance of marine ice sheets and glaciers, especially in Antarctica and Greenland. With Differential Interferometric Synthetic Aperture Radar (DInSAR) interferograms, it is possible to accurately capture the tide-induced bending of the ice shelf at a continent-wide scale and a temporal resolution of a few days. While current processing chains typically automatically generate differential interferograms, grounding lines are still primarily identified and delineated on the interferograms by a human operator. This method is time-consuming and inefficient, considering the volume of data from current and future SAR missions. We developed a pipeline that utilizes the Holistically-Nested Edge Detection (HED) neural network to delineate DInSAR interferograms automatically. We trained HED in a supervised manner using 421 manually annotated grounding lines for outlet glaciers and ice shelves on the Antarctic Ice Sheet. We also assessed the contribution of non-interferometric features like elevation, ice velocity and differential tide levels towards the delineation task. Our best-performing network generated grounding lines with a median distance of 186 m from the manual delineations. Additionally, we applied the network to generate grounding lines for undelineated interferograms, demonstrating the network's generalization capabilities and potential to generate high-resolution temporal and spatial mappings. [ABSTRACT FROM AUTHOR]

In recent years, the existence of perennial firn aquifers in the Antarctic Peninsula (AP) has been confirmed by in situ observations. Previous studies have suggested that these subsurface aquifers, together with meltwater ponds at the surface, provide a reservoir of liquid water to feed propagating fractures, promoting hydrofracture-driven ice-shelf disintegration. This study maps perennial firn aquifers in the AP from space using C-band Synthetic Aperture Radar imagery from ESA's Sentinel-1 (S1) mission. With these observations, we detect firn aquifers at 1 km x 1 km resolution, for the period 2017 to 2020. Existing methods, that use S1 data and rely on a backscatter intensity difference threshold approach, are prone to misclassify late-melt events as aquifers, when applied to the AP. Therefore, we have developed and evaluated a new approach that is better suited to the Antarctic environment. The new method exploits the characteristic, gradual backscatter increase during the (partial) refreezing of the liquid water in the firn layer after the peak of the melt season. Most firn aquifers are detected in the north and northwest of the AP, as well as on the Wilkins and George VI ice shelves. Aquifer locations detected with the present methodology agree with in situ observations and with model simulations of firn water content. [ABSTRACT FROM AUTHOR]

Leads and fractures in sea ice play a crucial role in the heat and gas exchange between the ocean and atmosphere, impacting atmospheric, ecological, and oceanic processes. Our aim was to estimate lead fractions from high-resolution divergence obtained from satellite synthetic-aperture radar (SAR) data and to evaluate it against existing lead products. We derived two new leadfraction products from divergence with a spatial resolution of 700m calculated from daily Sentinel-1 images. For the first lead product, we advected and accumulated the lead fractions of individual time steps. With those accumulated divergence-derived lead fractions, we described comprehensively the presence of up to 10-day-old leads and analyzed their deformation history. For the second lead product, we used only divergence pixels that were identified as part of linear kinematic features (LKFs). Both new lead products accurately captured the formation of new leads with widths of a few hundred meters. We presented a Lagrangian time series of the divergence-based lead fractions along the drift of the MOSAiC expedition in the central Arctic Ocean during winter 2019/2020. Lead activity was high in fall and spring, consistent with wind forcing and ice pack consolidation. At larger scales of 50-150 km around the MOSAiC expedition, lead activity on all scales was similar, but differences emerged at smaller scales (10 km). We compared our lead products with 6 others from satellite and airborne sources, including classified SAR, thermal infrared, microwave radiometer, and altimeter data. We found that the mean lead fractions varied by 1 magnitude across different lead products due to different physical lead and sea ice properties observed by the sensors and methodological factors such as spatial resolution. Thus, the choice of lead product should align with the specific application. [ABSTRACT FROM AUTHOR]

Dobšiná Ice Cave has attracted the attention of many researchers since its discovery more than 150 years ago. Although the cave is located outside the high-mountain area, it hosts one of the largest blocks of underground perennial ice. The topographic mapping of this unique UNESCO Natural Heritage site has led to several historical surveys. In the last decades of rapid climate change, this natural formation has been subject to rapid changes that are dynamically affecting the shape of the ice body. Not only increased precipitation, the rise in year-round surface temperatures, but also the gravity cause significant shape changes in the ice filling. This paper describes modern technological tools to comprehensively survey and evaluate interannual changes in both the floor and wall of the underground ice block. Technologies such as digital photogrammetry, in conjunction with precise digital tacheometry, make it possible to detect ice accumulation and loss, including the effect of sublimation due to airflow, as well as sliding movements of the ice block to the lower part of the cave. In the last two years, geophysical methods (microgravimetry and ground penetrating radar) have been added to determine the thickness of the floor ice in the upper parts of the cave due to the complexity of the measurements. The paper not only highlights the current technological possibilities but also points out the limitations of these technologies and then sets out solutions with a proposal of technological procedures for obtaining accurate geodetic and geophysical data. [ABSTRACT FROM AUTHOR]

In 2018 the first subglacial lake in the Canadian Artic was proposed to exist beneath Devon Ice cap, based on the analysis of airborne radar data. Here, we report a new interpretation of the subglacial material beneath Devon Ice Cap, supported by data acquired from multiple surface-based geophysical methods in 2022. The geophysical data recorded included km of active source seismic reflection profiles, 7 transient electromagnetic soundings and 17 magnetotellurics stations. These surface-based geophysical datasets were collected above the inferred locations of the subglacial lakes and show no evidence for the presence of subglacial water. The acoustic impedance of the subglacial material, estimated from the seismic data, is 9.49 ± 1.92 x 106 kg m-2 s-1, comparable to consolidated or frozen sediment. The resistivity models obtained by inversion of both the transient electromagnetic and magnetotelluric measurements show the presence of highly resistive rock layers (1000 100000 O.m) directly beneath the ice. Re-evaluation of the airborne reflectivity data show that the radar attenuation rates were likely overestimated, leading to an overestimation of the basal reflectivity in the original radar studies. Here, we derive new radar attenuation rates using the temperature- and chemistry-dependent Arrhenius equation, and when applied to correct the returned bed power, the bed power does not meet the basal reflectivity threshold expected over subglacial water. Thus, the radar interpretation is now consistent with the seismic and electromagnetic observations of dry or frozen, non-conductive basal material. [ABSTRACT FROM AUTHOR]

Vermicular Ridge Features (VRFs) comprise a series of ridges and troughs with a circular, sinuous, and anastomosing morphology composed of clast-rich sandy diamict. VRFs were first reported on the south coast of Devon Island, Nunavut, Canada, in the Dundas Harbour region. Here, we document the presence of VRFs near Mokka Fjord on Axel Heiberg Island, Nunavut, Canada. We utilize field observations, ultra high resolution LiDAR, and ground penetrating radar to characterize and compare the morphometry and sedimentology of VRFs near Mokka Fjord to other periglacial, paraglacial, and glacial landforms. VRFs near Mokka Fjord have a diameter ranging from 6 to 37 m and reach up to 1.5 m in height. They comprise clast-rich glaciofluvial sediment and till. A leading periglacial (i.e., segregation ice features/lithalsas) and glacial (i.e., ring-ridge moraines and kame/kettled terraces) origin are presented. We interpret Mokka Fjord VRFs to be an icemarginal feature resulting from paraglacial ablation of buried glacial ice producing a hummocky ring-ridge moraine comprised of ice marginal and supra- and englacial debris. This formation mechanism would infer a largely polythermal glacial environment with limited water supply. Likely from occasional warm-based periods at the ice margins which may allow sediment output and ice burial from basal ice debris redistribution or the thinning and subsequent burial of snout ice from glaciofluvial outwash. [ABSTRACT FROM AUTHOR]

Arctic sea ice monitoring is a fundamental prerequisite for anticipating and mitigating the impacts of climate change. Satellite-based sea ice observations have been subject to intense attention over the last few decades, with passive microwave PMW) radiometers being the primary sensors for retrieving pan-Arctic sea ice concentration, albeit with coarse spatial resolutions of a few or even tens of kilometers. Space-borne Synthetic Aperture Radar (SAR) missions, such as Sentinel-1, provide dual-polarized C-band images with <100 meter spatial resolution, which are particularly well-suited for retrieving high-resolution sea ice information. In recent years, deep learning-based vision methodologies have emerged with promising results for SAR-based sea ice concentration retrievals. Despite recent advancements, most contributions focus on regional or local applications without empirical studies on the generalization of the algorithms to the pan-Arctic region. Furthermore, many contributions omit uncertainty quantification from the retrieval methodologies, which is a prerequisite for the integration of automated SAR-based sea ice products into the workflows of the national ice services, or for the assimilation into numerical ocean-sea-ice coupled forecast models. Here, we present ASIP (Automated Sea Ice Products): a new and comprehensive deep learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from Sentinel-1 SAR and Advanced Microwave Scanning Radiometer 2 (AMSR2) passive microwave observations at a pan-Arctic scale for all seasons. We compiled a vast matched dataset of Sentinel-1 HH/HV imagery and AMSR2 brightness temperatures to train ASIP with regional ice charts as labels. ASIP achieves an R2-score of 95% against a held-out test dataset of regional ice charts. In a comparative study against pan-Arctic ice charts and PMW-based sea ice products, we show that ASIP generalizes well to the pan-Arctic region. Additionally, the comparison reveals that ASIP consistently produces relatively higher sea ice concentration than the PMW-based sea ice product, with mean biases ranging from 1.45% to 8.55%, and that the discrepancies are primarily attributed to disparities in the marginal ice zone. [ABSTRACT FROM AUTHOR]

The 79° North Glacier (Nioghalvfjerdsbrae, 79NG) is one of three remaining glaciers with a floating tongue in Greenland. Although the glacier was considered exceptionally stable in the past, earlier studies indicate that the ice tongue has thinned in recent decades. By conducting high-resolution ground-based and airborne radar measurements in conjunction with satellite remote sensing observations, we find significant changes in the geometry of 79NG. In the vicinity of the grounding line, a 500 m high subglacial channel has grown since ~2010 and caused surface lowering of up to 7.6 ma-1. Our results show extreme basal melt rates exceeding 150 ma-1 within a distance of 5 km from the grounding line, where the ice has thinned by 42% since 1998. We found a heterogeneous distribution of melt rates likely due to variability in water column thickness and channelization of the ice base. Time series of melt rates show a decrease in basal melting since 2018, indicating an inflow of colder water into the cavity below 79NG. We discuss the processes that have led to the changes in geometry and conclude that the inflow of warm ocean currents has led to the extensive thinning of 79NG's floating ice tongue near the grounding line in the last two decades. In contrast, we hypothesize that the growth of the channel results from increased subglacial discharge due to a considerably enlarged area of summer surface melt due to the warming of the atmosphere. [ABSTRACT FROM AUTHOR]

Automated sea ice charting from Synthetic Aperture Radar (SAR) has been researched for more than a decade and still, we are not close to unlocking the full potential of automated solutions in terms of resolution and accuracy. The central complications arise from ground truth data not being readily available in the polar regions. In this paper, we build a dataset from 20 near coincident X-Band SAR acquisitions and as many Airborne Laser Scanner (ALS) measurements from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), between October and May. This dataset is then used to assess the accuracy and robustness of five machine learning based approaches, by deriving classes from the freeboard, surface roughness (standard deviation at 0.5 m correlation length) and reflectance. It is shown that there is only a weak correllation of the radar backscatter and the sea ice topography. Accuracies between 40 % and 69 % percent and robustnesses between 68 % and 85 % give a realistic insight into modern classifiers' performance across a range of ice conditions over 8 months. It also marks the first time algorithms are trained entirely with labels from coincident measurements, allowing for a probabilistic class retrieval. The results show that segmentation models able to learn from the class distribution significantly perform pixel-wise classification approaches. [ABSTRACT FROM AUTHOR]

The Sentinel-1A/B synthetic aperture radar (SAR) imagery archive between 14 October 2014 and 29 June 2023 was used in combination with a segmentation algorithm to create a series of binary ice/open water maps of Hornsund fjord, Svalbard at 50 m resolution for nine seasons (2014/15 to 2022/23). The near-daily (1.57 day mean temporal resolution) maps were used to calculate sea ice coverage for the entire fjord and its parts: the main basin and three major bays: Burgerbukta, Brepollen and Samarinvågen. The average length of the sea ice season was 158 days (range: 105–246 days). Drift ice first arrived from the south-west between October and March and the fast ice onset was on average 24 days later. The fast ice typically disappeared in June, around 20 days after the last day with drift ice. The average sea ice coverage over the sea ice season was 41 % (range: 23–56 %), but it was lower in the main basin (27 %) compared to the bays (63 %). Of the bays, Samarinvågen had the highest sea ice coverage (69 %) likely due to the location in southern Hornsund protected from the incoming wind-generated waves and a narrow opening. Seasonally, the highest sea ice coverage was observed in April for the entire fjord and the bays, and in March for the main basin. The highest sea ice coverage characterised 2019/20, 2021/22 and 2014/15, which were also the seasons with the largest number of negative air temperature days in October – December. The season 2019/20 was characterised by the lowest mean daily and monthly air temperatures. We observed a remarkable inter-annual variability in the sea ice coverage but at the nine-season scale we did not record any gradual trend of decreasing sea ice coverage. These high-resolution data can be used to e.g., better understand the spatio-temporal trends in the sea ice distribution in Hornsund, facilitate comparison between Svalbard fjords and improve modelling of nearshore wind wave transformation and coastal erosion. [ABSTRACT FROM AUTHOR]

Mapping sea ice in the Arctic is essential for maritime navigation, and growing vessel traffic highlights the necessity of timeliness and accuracy of sea ice charts. In addition, with the increased availability of satellite imagery, automation is becoming more important. The aim of the AutoICE Challenge was to encourage the creation of models capable of mapping sea ice automatically from spaceborne Synthetic Aperture Radar (SAR) imagery using deep learning while inspiring participants to move towards multiple sea ice parameter model retrieval instead of the current focus on a single sea ice parameter, such as concentration. Participants were tasked with the development of machine learning algorithms mapping the total sea ice concentration, stage of development and floe size using a state-of-the-art sea ice dataset with dual-polarised Sentinel-1 SAR images and 22 other relevant variables while using professionally labelled sea ice charts from multiple national ice services as reference data. The challenge had 129 teams representing a total of 179 participants, with 34 teams delivering 494 submissions, resulting in a participation rate of 26.4%, and was won by a team from the University of Waterloo. Participants were successful in training models capable of retrieving multiple ice parameters with convolutional neural network and vision transformer models. The top participants scored best on the total sea ice concentration and stage of development, while the floe size was more difficult. Furthermore, participants offered intriguing approaches and ideas that could help propel future research within automatic sea ice mapping, such as applying high downsampling of SAR data to improve model efficiency and produce better results. [ABSTRACT FROM AUTHOR]

Ice slabs are multi-meter thick layers of refrozen ice that limit meltwater storage in firn, leading to enhanced surface runoff and ice sheet mass loss. To date, ice slabs have largely been mapped using airborne ice-penetrating radar, which has limited spatial and temporal coverage. This makes it difficult to fully assess the current extent and continuity of ice slabs or to validate predictive models of ice slab evolution that are key to understanding their impact on Greenland's surface mass balance. Here, for the 5 first time, we map the extent of ice slabs and similar superimposed ice facies across the entire Greenland Ice Sheet at 500 m resolution using dual-polarization Sentinel-1 (S-1) synthetic aperture radar data collected in winter 2016-2017. The S-1 inferred ice slab extent is in excellent agreement with ice-penetrating radar ice slab detections from spring 2017, as well as the extent of the visible runoff zone as mapped from optical imagery. Our results show that ice slabs are nearly continuous around the entire margin of the ice sheet, including regions in Southwest Greenland where ice slabs have not been previously identified. The algorithm developed here also lays the groundwork for long-term monitoring of ice slab expansion with current and future C-band satellite systems and highlights the added value of future L-band missions for near-surface studies in Greenland. [ABSTRACT FROM AUTHOR]

In recent years, the existence of firn aquifers in the Antarctic Peninsula (AP) has been confirmed by in-situ observations. Given their importance for understanding the hydrology of the Antarctic ice sheet, a more spatially comprehensive assessment of AP firn aquifers is desirable. The purpose of this study is to map firn aquifers in the AP from space using C-band Synthetic Aperture Radar imagery from ESA's Sentinel-1 mission. These observations enable the detection of firn aquifers at 1 x 1 km² resolution. The method presented here is based on quantifying the characteristic, gradual backscatter increase during the (partial) refreezing of the liquid water in the firn layer after the peak melt season. When applied to the available time series, it detects perennial aquifers (existing year-round) for the period 2017 to 2020, as well as seasonal aquifers which do not persist through winter. We acknowledge that the backscatter signature in any given year is indistinguishable for seasonal and perennial aquifers. We detect seasonal firn aquifers in the north and northwest of the AP, as well as on the Wilkins and George VI ice shelves. Only in the north and northwest of the AP, aquifers are detected each year in the observation period, here taken as a proxy for perennial firn aquifers. Both distributions agree with model simulations. Further in situ and modelling studies and longer time series of satellite observations are needed to validate the results of this study. [ABSTRACT FROM AUTHOR]

Perennial snowfields are a critical part of the alpine ecosystem, serving as habitat for an array of wildlife species, and influencing downslope hydrology, vegetation, geology, and permafrost. In this study, perennial snowfield extents in the Brooks Range of Arctic Alaska are derived from Synthetic Aperture Radar (SAR) and multi-spectral satellite remote sensing via the Sentinel-1 (S1) and Sentinel-2 (S2) constellations. Snow cover area (SCA) is mapped using multi-spectral analysis in S2 and via the creation of a SAR backscatter change detection algorithm with S1. Results of the remote sensing techniques are evaluated by comparison with field data acquired across multiple spatial resolutions and geographic domains, including helicopter points and manual, on-the-ground collected SCA. Evaluations of the SAR change detection algorithm via comparison with results from multi-spectral imagery analysis, and field acquired data, indicate that the SAR algorithm performs best in small, focused geographic sub-domains. This may be the result of SAR algorithm dependency on thresholding and slope corrections in mountainous terrain. An alternative approach to mapping the perennial snowfields is also presented, as a synthesis of the S1 and S2 results, wherein S1 results are used to fill voids left in the S2 data from cloud masking processes. [ABSTRACT FROM AUTHOR]

Elevation changes of the Antarctic Ice Sheet (AIS) related to surface mass balance (SMB) and firn processes vary strongly in space and time. Their short-term natural variability is large and hampers the detection of long-term climate trends. Firn models or satellite altimetry observations are typically used to investigate such firn thickness changes. However, there is a large spread among firn models. Further, they do not fully explain observed firn thickness changes, especially on smaller temporal and spatial scales. Reconciled firn thickness variations will facilitate the detection of long-term trends from satellite altimetry, the resolution of the spatial patterns of such trends and, hence, their attribution to the underlying mechanisms. This study has two objectives: First, we quantify interannual Antarctic firn thickness variations on a 10km grid scale. Second, we characterise errors in both the altimetry products and firn models. To achieve this, we jointly analyse satellite altimetry and firn modelling results in time and space. We use the timing of firn thickness variations from firn models and the satelliteobserved amplitude of these variations to generate a combined product ('adjusted firn thickness variations') over the AIS for 1992--2017. The combined product characterises spatially resolved variations better than either firn models alone or altimetry alone. We detect highest absolute differences between the adjusted and modelled variations at lower elevations near the AIS margins, probably influenced by the lower resolution, more blurred spatial distribution of the modelled variations. In a relative sense, the largest mismatch between the adjusted and modelled variations is found in the dry interior of the East Antarctic Ice Sheet (EAIS), in particular across large megadune fields. Here, the low signal-to-noise ratio poses a challenge for both models and altimetry to resolve firn thickness variations. The altimetric residuals still contain a large part of the altimetry variance and include firn model errors, such as firn signals not captured by the models, and altimetry errors. Apart from time-variable penetration effects of radar altimetry signals, the residuals disclose patterns indicating uncertainties in intermission calibration. [ABSTRACT FROM AUTHOR]

This study evaluates using interferometry on low frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 days temporal baseline, captured by an L-band aerial platform, NASA's UAVSAR, over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved shows a strong Pearson correlation (R = 0.80) and low RMSE (0.1 m, n = 64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R = 0.52, n = 57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R = 0.60, RMSD = 0.023 m, n = 3.2e6) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R = 0.72, RMSD = 0.013 m, n = 6.5e4). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy. [ABSTRACT FROM AUTHOR]

An airborne microwave wide-band radiometer (500-2000 MHz) was operated for the first time in Antarctica to better understand the emission properties of sea ice, outlet glaciers and the interior ice sheet from Terra Nova Bay to Dome C. The different glaciological regimes were revealed to exhibit unique spectral signatures in this portion of the microwave spectrum. Generally, the brightness temperatures over the inland ice sheet were warmest at the lowest frequencies consistent with models that predict that those channels sensed the deeper, warmer parts of the ice sheet. Spectra along the lengths of outlet glaciers were modulated by the deposition and erosion of snow, driven by strong katabatic winds. Similar to previous experiments in Greenland, the brightness temperatures across the frequency band were low in crevasse areas. Variations in brightness temperature were consistent with spatial changes in sea ice type identified in satellite imagery and in situ ground penetrating radar data. The results contribute to a better understanding of the utility of microwave wide-band radiometry for cryospheric studies and also advance knowledge of the important physics underlying existing L-band radiometers operating in space. [ABSTRACT FROM AUTHOR]

Radar at high frequency is a promising technique for fine-resolution snow water equivalent (SWE) mapping. In this paper, we extend the Bayesian-based Algorithm for SWE Estimation (BASE) from passive to active microwave (AM) application and test it using ground-based backscattering measurements at three frequencies (X- and dual Ku-bands, 10.2, 13.3 and 16.7 GHz), VV polarization obtained at 50° incidence angle from the Nordic Snow Radar Experiment (NoSREx) in Sodankylä, Finland. We assume only an uninformative prior for snow microstructure, in contrast with an accurate prior required in previous studies. Starting from a biased SWE prior from land surface model simulation, two-layer snow state variables and single-layer soil variables are iterated until their posterior distribution could stably reproduce the observed microwave signals. The observation model is the Microwave Emission Model of Layered Snowpacks 3 and Active (MEMLS3&a). Results show that BASE-AM achieved a RMSE of ~10 cm for snow depth (SD) and less than 30 mm for SWE, compared with the RMSE of ~20 cm SD and ~50 mm SWE from priors. Retrieval errors are significantly larger when BASE-AM is run using a single snow layer. The results support the potential of X- and Ku-band radar for SWE retrieval and shows that providing a fully-unbiased snow microstructure prior is not the only promise to obtain accurate SWE retrievals. [ABSTRACT FROM AUTHOR]

Melt ponds are pools of open water that form during summer on sea ice surface, playing a key role in the Arctic climate and sea ice energy budget. Due to their lower albedo, melt ponds absorb more radiation than un-ponded ice, spurring further ice melt and enhancing the positive ice-albedo feedback. This feedback is expected to increase as the Arctic ice cover is moving towards a regime where multiyear ice (MYI) is being replaced by first-year ice (FYI), which presents wider melt pond coverage as well as more energy absorption with profound consequences from an energy balance perspective. Nevertheless, the lack of knowledge or inclusion of melt pond fraction (MPF) on global climate and sea ice models, is pointed as their main source of uncertainty and disparity of predictions results. This, along with the recent conclusions on the potential of MPF for enhancing the forecasting ability of summer sea ice extent, underscores the importance of accurately obtaining large and spatiotemporal scale of MPF, across the Arctic. However, observations of melt ponds are far from adequate for the Arctic ocean and for both MYI and FYI, and on a large scale this is only possible through satellite-based Earth observations (EO). This paper provides an overview of efforts in EO remote sensing studies of melt ponds, for both optical sensors and radar-based approaches. The main algorithms used for melt pond identification and the different methods for MPF retrievals are outlined, ranging from the early traditional techniques to the increasingly prevalent use of Artificial intelligence (AI), namely machine and deep learning. The current large-scale optical-based pan-Arctic MPF datasets are intercompared along with the main advantages and disadvantages of various optical and radar data-based methods for MPF retrievals. The potential of radar, namely Synthetic Aperture Radar (SAR) technical abilities to the enhancement of reliability is analysed, since optical approaches, despite being more used, are hampered by cloud cover, spectral representativeness and resolution. Finally, current gaps in melt pond knowledge and MPF retrievals are discussed and summarised leading to the outline of further directions of research development. [ABSTRACT FROM AUTHOR]

Monitoring of lake ice is important to maintain transportation routes but in recent decades the number of in situ observations have declined. Remote sensing has worked to fill this gap in observations, with active microwave, particularly synthetic aperture radar (SAR), being a crucial technology. However, the impact of wet conditions on radar and how interactions change under these conditions has been largely ignored. It is important to understand these interactions as warming conditions are likely to lead to an increase in the occurrence of slush layers. This study works to address this gap using the snow microwave radiative transfer (SMRT) model to conduct forward modelling experiments of backscatter for Lake Oulujärvi in Finland. Experiments were conducted under dry conditions, under moderate wet conditions, and under saturated conditions. These experiments reflected field observations during the 2020-2021 ice season. Results of the dry snow experiments support the dominance of surface scattering from the ice-water interface. However, conditions where layers wet snow are introduced show that the primary scattering interface changes depending on the location of the wet layer. The addition of a saturated layer at the ice surface results in the highest backscatter values due to the larger dielectric contrast created between the overlying dry snow and the slush layer. Improving the representation of these conditions in SMRT can also aid in more accurate retrievals of lake ice properties such as roughness, which is key for inversion modelling of other properties such as ice thickness. [ABSTRACT FROM AUTHOR]