Radar sounding across a wide range of frequencies regularly generates rich datasets for local-to-regional-scale investigation of the properties and processes that govern ice flow. However, beyond measurements of ice thickness, little of this richness is directly incorporated into continental-scale models that project the future of Earth’s ice sheets amid anthropogenic climate change. Ice sheets have long memories, and their isochronal radiostratigraphy memorializes and integrates an ice sheet’s response to past centennial-to-millennial-scale climatic and dynamic events. These memories are often cast aside in modeling studies to focus on reproducing recent observations of dramatic change, but at the expense of a more reliable initial state. Isochronal radiostratigraphy is thus an obvious target for next-generation continental-scale validation of the initial state of ice-sheet models and evaluation of their sensitivity to past climate changes. Here we describe the second version of a VHF radiostratigraphy of the Greenland Ice Sheet from 26 NASA and NSF airborne campaigns between 1993 and 2019 and its value for identifying well-tuned modern instances of the Parallel Ice Sheet Model (PISM). We incorporated several lessons learned from the generation of the first version (1993–2013), improved quality control, reviewed and augmented the entire 1993–2013 radiostratigraphy, and applied an independently developed method for predicting radiostratigraphy (ARESELP) to the previously untraced campaigns (2014–2019) to accelerate their semi-automatic tracing. The result is a substantially more robust and accessible radiostratigraphy of the Greenland Ice Sheet that highlights the tradeoff between speed and sophistication for generating continental-scale observational constraints from radar sounding. We upgraded PISM to generate and record ice age non-diffusively, and then generated an ensemble of PISM simulations initialized during the Last Glacial Period through to the present. This ensemble is compared against our new radiostratigraphy to evaluate its basin-level sensitivity to deglaciation and to identify a best-fit simulation to use as an initial state for future projections.

How to cite: MacGregor, J. A., Fahnestock, M. A., Aschwanden, A., Paden, J. D., Li, J., Harbeck, J. P., and Khrulev, C.: An improved radiostratigraphy of the Greenland Ice Sheet and its value for ice-sheet model initialization, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4553, https://doi.org/10.5194/egusphere-egu25-4553, 2025.

How to cite: Young, D., Yan, S., Vega González, A., Singh, S., Kerr, M., Li, D., Ng, G., Buhl, D., Kempf, S., and Blankenship, D.: Using delay doppler processing to separate stratigraphic and basal ice at Dome A, Antarctica, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14008, https://doi.org/10.5194/egusphere-egu25-14008, 2025.

Attenuation rates derived from radar data offer valuable insights into subsurface ice sheet conditions, revealing information about the ice sheet temperature, chemical composition, and physical structure. Accurate attenuation estimates are also essential for interpreting basal conditions. However, established methods for estimating attenuation rates perform poorly in certain ice sheet regions, and uncertainties remain in the physical interpretations of attenuation results.

In this study, we develop a novel frequency-based method for deriving ice sheet attenuation rates, adapting techniques from planetary radio science and seismology. We apply this method to airborne radar sounding data collected across multiple Antarctic basins, enabling new interpretations of the englacial and subglacial environment in regions where subsurface information is sparse. Not only do these frequency-based attenuation estimates offer valuable englacial and subglacial insight in new regions of the Antarctic ice sheet, but we show how leveraging the attenuation results to train neural networks can facilitate predictions and constraints on subglacial conditions. Such constraints are useful for better resolving subsurface processes in numerical models. Our study highlights the potential of advancing conventional geophysical methods in combination with AI-driven approaches and model validation to enhance our understanding of ice sheet subsurface conditions and ice dynamics.

How to cite: Dawson, E., Chu, W., Christoffersen, M., and Yang, D.: Advancing Radar Sounding Attenuation Estimates with Frequency-Based Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7569, https://doi.org/10.5194/egusphere-egu25-7569, 2025.

Spring in cold regions is a critical time for floods, as snowmelt releases large amounts of water into watersheds. Seasonally frozen ground reduces soil infiltration and increases runoff by blocking pores in the soil. This limited infiltration causes rivers to respond faster to rain or meltwater, heightening flood risks. Most hydrological models used to project flood risks in a future climate are built on the assumption that, for a given land use, soil infiltrability is somewhat homogeneous. We challenge that assumption by measuring frozen ground thickness distribution in an agricultural field over an entire winter. For that purpose, we measured frost thickness at one specific point of the field at a sub hour frequency and over a +/- 120m transect on a weekly basis. Point measurements were done using TDR sensors.   The transect measurements were performed with a drone-based ground penetrating radar (GPR). The use of a drone based GPR allowed repetitive measurements over a given transect in a nondestructive way. Unlike a drone based GPR, the use of a ground based GPR would have altered the snow cover over the studied transect with potential perturbations of the heat exchanges at the ground surface.

Field measurements show that the ground frost depth is not spatially uniform all winter long. During the snowmelt period, the ground frost depth is particularly heterogeneous. We found that 78.11% of the transect that we were able to interpret had an unfrozen layer on top of the frozen ground. If the top layer of the ground is unfrozen during the snowmelt period, it forms a zone where there can be liquid water infiltration and/or storage. Furthermore, because of the spatial variability of ground frost, some areas thaw completely before others. The matric potential of these areas increases and allow preferential infiltration in the thawed zone while the ground is still considered frozen. We conclude that it is important to account for spatial variability of ground frost to better understand how seasonally frozen ground impacts infiltration and flooding. The study shows that drone based GPR is a well-adapted tool to evaluate frozen ground thickness variability in a repetitive and non-destructive way.

How to cite: Michaud, L., Baraër, M., Kinnard, C., Poulin, A., and Goujon, M.: Variability of seasonnally frozen ground in an agricultural field using drone-based GPR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-277, https://doi.org/10.5194/egusphere-egu25-277, 2025.

How to cite: Siebenbrunner, A., Delleske, R., and Keuschnig, M.: Unveiling Hidden Heterogeneity: UAV-borne GPR for Investigating Alpine Snowpack Variability, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16306, https://doi.org/10.5194/egusphere-egu25-16306, 2025.

Snow on sea ice plays a critical role in modulating ice mass changes in response to anthropogenic warming, with significant implications for ocean mixed layer processes, the surface energy budget, and marine ecosystems. Most importantly, accurate snow depth measurements are essential for deriving reliable sea ice thickness estimates from all altimetry satellites. Operation IceBridge (OIB), which collected snow depth data using the airborne CReSIS FMCW C/S-band snow radar for a decade, remains a pivotal reference for understanding pan-Arctic snow depth changes and validating remote sensing snow retrievals. Despite its importance, significant concerns persist regarding snow retrieval algorithms from snow radar, particularly around algorithm performance and the representation of snow properties.

In this study, we revisit OIB snow depth retrieval algorithms by comparing them with underutilized in-situ snow depth measurements from MagnaProbe surveys conducted near Eureka, Canada, in 2016. To enhance the spatial representation of the in-situ data, we employ Kriging interpolation methods. Additionally, we make use of the co-collected conical laser scanner data. A detailed comparison of retrieval algorithms - focusing on the detection of the air-snow and snow-ice interfaces as well as the derived snow depth - reveals that the Continuous Wavelet Transform (CWT) algorithm performs best for the 2-8 GHz snow radar version, yielding a correlation of R=0.72 over undeformed sea ice. However, the CWT algorithm predominantly detects snow depths within the 80-90% quantile of the in-situ distribution within the radar footprint. This bias is attributed to the air-snow interface being identified as the first rise above the radar noise floor, which typically corresponds to the highest snow elevations within the footprint. Finally, we compare a subset of newly derived snow depth data from OIB  including highly-valuable uncertainties with existing datasets, highlighting potential improvements.

Looking ahead, we propose a framework to enhance snow depth retrieval algorithms, offering robust pathways for validating and improving satellite-based snow datasets. This approach holds significant promise for advancing the accuracy of snow depth measurements critical to polar science in the future campaigns.

How to cite: Kagel, T. and Zhou, L.: Revisiting NASA's Operation IceBridge Snow on Sea Ice Radar Measurements in the Arctic, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4482, https://doi.org/10.5194/egusphere-egu25-4482, 2025.

Estimating water content in ice is critical to our understanding of subsurface conditions and processes in both terrestrial and planetary ice masses. Knowledge of ice sheet hydrology, rheology, and thermal configuration can define more accurate models for informing sea level projections. Additionally, the presence and distribution of liquid water in ice serves as an important indicator for habitability on other planetary bodies. Past attempts to quantify water content using ice-penetrating radar tools of reflectivity, attenuation, and polarimetry have not accounted for melt inclusion geometry, leading to observational uncertainties. For instance, recent discussions regarding Mars and the Devon ice cap have highlighted the non-uniqueness of highly reflecting radar signals as being indicative of large water bodies. Other radar observables such as attenuation and polarimetry – commonly attributed to englacial water and ice fabric, respectively – may be similarly non-unique. Here, we use geometric mixing models to show how a variety of geophysical conditions can be replicated by small volume fractions of geometrically oriented melt, with strong implications for water content in both temperate and sub-temperate ice as well as ice fabric orientation. We further discuss how the combination of geometric mixing models with polarimetric radar can be a valuable tool in clarifying melt volume fraction and orientation.

How to cite: Cheng, A., Schroeder, D., Wolfenbarger, N., and Shaper, R.: The effect of melt geometry in ice on radar reflectivity, attenuation, and polarimetry, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13781, https://doi.org/10.5194/egusphere-egu25-13781, 2025.

A major milestone for the commercial lunar sector was attained by Intuitive Machines’ lander Odysseus (IM-1), which successfully soft-landed near the lunar south pole on February 22, 2024. The Odysseus mission was the second launch of the NASA’s lunar CLPS (Commercial Lunar Payload Services) program and the first to successfully reach the Moon. Despite coming to rest at an unplanned angle of ~30°, the spacecraft was able to communicate with Earth and remained operational for a week on the lunar surface.

Here we use available orbital data to characterize the geologic context of the IM-1 landing site, with an emphasis on Mini-RF bistatic radar data, LROC image data, and LOLA and LROC topographic data. One of the science goals of the Lunar Reconnaissance Orbiter (LRO)’s extended mission is to support future lunar landings by analyzing orbital data over future, current, and past landed missions in order to better constrain and “ground truth” the orbital data. Landing site characterization is a key element in planetary surface exploration as a mission that does not land safely is over before it begins.

Odysseus touched down in an intercrater region about 10° latitude from the lunar south pole (~300 km) at 80.13°S, 1.44°E. The landing site lies on a ~12° slope east of the irregular, degraded Malapert A crater (33 km in diameter) and roughly equidistant between craters Malapert B, Malapert C, and Malapert K (32, 38, and 39 km in diameter, respectively). Notably, the site is very close to the ring of discontinuous massifs that constitute the outer rim of South Pole-Aitken Basin.

The landing site was as expected in that it is a typical highlands site that consists of mostly low, rolling terrain, formed by an accumulation of ancient ejecta and interrupted by craters; steep slopes are largely limited to crater interior walls. Odysseus landed adjacent to the rim of an ancient crater measuring about 1.2 km in diameter. No high concentrations of rough-textured ejecta or hazardous boulders that are commonly found surrounding fresh craters were observed in the Mini-RF radar data, a finding consistent with the view from the lander. Unlike on the mare, small craters in the highlands (<5 to 10 km in diameter) tend not to excavate many boulders. Therefore, the lack of a radar signature consistent with abundant rocks is in agreement with Diviner-derived rock abundance measurements for non-polar terra regions that are generally low (rocks occupy <0.5% of the surface). If Odysseus had been a sample-centric mission, those samples would likely have been regolith-dominated (unlike Apollo 16 in the highlands where there were abundant boulders).

How to cite: Thomson, B., Nypaver, C., Patterson, G. W., Stickle, A., Fruh, T., and Cahill, J.: Characterization of the Intuitive Machines (IM-1) Lunar Landing Site Near the South Pole, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20315, https://doi.org/10.5194/egusphere-egu25-20315, 2025.