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The National Science Foundation Ice Core Facility (NSF-ICF, fka NICL) is in the process of building a new facility including freezer and scientist support space. The facility is being designed to minimize environmental impacts while maximizing ice core curation and science support. In preparation for the new facility, we are updating research equipment and integrating ice core data collection and processing by assigning International Generic Sample Numbers (IGSN) to advance the “FAIR”ness and establish clear provenance of samples, fostering the next generation of linked research data products. The NSF-ICF team, in collaboration with the US ice core science community, has established a metadata schema for the assignment of IGSNs to ice cores and samples. In addition, in close coordination with the US ice core community, we are adding equipment modules that expand traditional sets of physical property, visual stratigraphy, and electrical conductance ice core measurements. One such module is an ice core hyperspectral imaging (HSI) system. Adapted for the cold laboratory settings, the SPECIM SisuSCS HSI system can collect up to 224 bands using a continuous line-scanning mode in the visible and near-infrared (VNIR) 400-1000 nm spectral region. A modular system design allows expansion of spectral properties in the future. The second module is an updated multitrack electrical conductance system. These new data will guide real time optimization of sampling for planned analyses during ice core processing, especially for ice with deformed or highly compressed layering. The aim is to facilitate the collection of robust, long-term, and FAIR data archives for every future ice core section processed at NSF-ICF. The NSF-ICF is fully funded by the National Science Foundation and operated by the U.S. Geological Survey.
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Hyperspectral imaging system for ice core studies
Hyperspectral imaging (HSI) technology has been increasingly used in Earth and planetary sciences. This imaging technique has been successfully tested on ice cores using VNIR (visible and near-infrared, 380-1000 nm) (Garzonio et al., 2018) and near-infrared (900 - 1700 nm) (McDowell et al, 2023) line-scan cameras. Results show that HSI data greatly expand ice core line-scan imaging capabilities, previously used with gray or RGB cameras (see summary in Dey et al., 2023). Combinations of selected HSI bands from the hyperspectral data cube improve feature detection in ice core stratigraphy, and map distribution of volcanic material, dust, air bubbles, fractures, and ice crystals in ice cores. Captured spectral information provides unique fingerprints for specific materials present in ice cores. This method helps to guide ice core sampling because it provides non-destructive, rapid visualization of microstructural properties, layering, bubble contents, increases in dust, or presence of tephra material. Precise identification of these atmospheric components is important for understanding past climate drivers reconstructed from ice cores. As part of the COLDEX project (Brook et al., this meeting) we adapted the SPECIM SisuSCS HSI system for ice core imaging. The ice core scanning system is housed inside the ca. -20ºC main NSF ICF freezer, and externally computer-controlled. The operator monitors scanning operations and communicates with personnel inside of the freezer via radio. The system is equipped with a SPECIM FX10 camera that measures up to 224 bands in the VNIR range. We modified the ice core holder tray and installed a heated enclosure for the camera. The system uses SCHOTT DCR III Fiber Optic light sources with an OSL2BIR bulb from Thorlabs. IR filters are removed to extend the light spectral range beyond the 700 nm limit without heating the ice core surface during rapid (<5 minutes) scanning of an entire meter-long section. Emitted light enters ice at a 45º angle from two top and two bottom light sources. To calibrate absolute reflectance we use three Spectralon panels with 100, 50 and 20% reflectance values with every scan as well as several secondary reflective standards and USAF targets for geometric corrections. We are developing Python-based open source data processing routines and currently comparing HSI data with existing ice core physical and chemical measurements. The goal is to fully integrate the ice core HSI system with ice core processing at the NSF ICF. Dey et al., 2023. Application of Visual Stratigraphy from Line-Scan Images to Constrain Chronology and Melt Features of a Firn Core from Coastal Antarctica. Journal of Glaciology 69(273): 179–90. https://doi.org/10.1017/jog.2022.59.Garzonio et al., 2018. A Novel Hyperspectral System for High Resolution Imaging of Ice Cores: Application to Light-Absorbing Impurities and Ice Structure. Cold Regions Science and Technology 155: 47–57. https://doi.org/10.1016/j.coldregions.2018.07.005.McDowell et al., 2023. A Cold Laboratory Hyperspectral Imaging System to Map Grain Size and Ice Layer Distributions in Firn Cores. Preprint. Ice sheets/Instrumentation. https://doi.org/10.5194/egusphere-2023-2351.
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- Award ID(s):
- 2149519
- PAR ID:
- 10527573
- Publisher / Repository:
- EGU General Assembly 2024
- Date Published:
- Page Range / eLocation ID:
- EGU24-13878
- Format(s):
- Medium: X Other: PDF
- Sponsoring Org:
- National Science Foundation
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Abstract. The Greenland and Antarctic ice sheets are covered in a layer of porous firn. Knowledge of firn structure improves our understanding of ice sheet mass balance, supra- and englacial hydrology, and ice core paleoclimate records. While macroscale firn properties, such as firn density, are relatively easy to measure in the field or lab, more intensive measurements of microstructural properties are necessary to reduce uncertainty in remote sensing observations of mass balance, model meltwater infiltration, and constrain ice age – gas age differences in ice cores. Additionally, as the duration and extent of surface melting increases, refreezing meltwater will greatly alter firn structure. Field observations of firn grain size and ice layer stratigraphy are required to test and validate physical models that simulate the ice-sheet-wide evolution of the firn layer. However, visually measuring grain size and ice layer distributions is tedious, is time-consuming, and can be subjective depending on the method. Here we demonstrate a method to systematically map firn core grain size and ice layer stratigraphy using a near-infrared hyperspectral imager (NIR-HSI; 900–1700 nm). We scanned 14 firn cores spanning ∼ 1000 km across western Greenland’s percolation zone with the NIR-HSI mounted on a linear translation stage in a cold laboratory. We leverage the relationship between effective grain size, a measure of NIR light absorption by firn grains, and NIR reflectance to produce high-resolution (0.4 mm) maps of effective grain size and ice layer stratigraphy. We show the NIR-HSI reproduces visually identified ice layer stratigraphy and infiltration ice content across all cores. Effective grain sizes change synchronously with traditionally measured grain radii with depth, although effective grains in each core are 1.5× larger on average, which is largely related to the differences in measurement techniques. To demonstrate the utility of the firn stratigraphic maps produced by the NIR-HSI, we track the 2012 melt event across the transect and assess its impact on deep firn structure by quantifying changes to infiltration ice content and grain size. These results indicate that NIR-HSI firn core analysis is a robust technique that can document deep and long-lasting changes to the firn column from meltwater percolation while quickly and accurately providing detailed firn stratigraphy datasets necessary for firn research applications.more » « less
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<p><b> Introduction </b> <br> The National Science Foundations Center for Oldest Ice Exploration (<a href="https://www.coldex.org">NSF COLDEX</a>) is a Science and Technology Center working to extend the record of atmospheric gases, temperature and ice sheet history to greater than 1 million years. As part of this effort, NSF COLDEX has been searching for a site for a continuous ice core extending through the mid-Pleistocene transition. Two seasons of airborne survey were conducted from South Pole Station across the southern flank of Dome A. </p> <p><b> 2023-2024 Field Season </b> <br> In the 2023-2024 field season (CXA2), and using a BT-67 Basler, NSF COLDEX conducted 17 flights from South Pole Station toward the southern flank of Dome C. Three test flights were conducted from McMurdo Station. Instrumentation included the <a href="https://doi.org/10.18738/T8/J38CO5">60 MHz MARFA ice penetrating radar </a> from the University of Texas Institute for Geophysics, a <a href="https://doi.org/10.1109/IGARSS53475.2024.10640448">UHF ice penetrating radar </a> from the Center for Remote Sensing and Integrated Systems; an GT-2 Gravimeter, and LD-90 laser altimeter and an G-823 Magnetometer. </p> <p><b> Basal specularity content </b> <br> These basal specularity content were derived from comparing 1D and 2D focused MARFA data (<a href="http://doi.org/10.1109/TGRS.2007.897416">Peters et al., 2007</a>). By comparing bed echo strengths for different focusing apertures, and accounting for the ranges and angles involved, we can derive the "specularity content" of the bed echo, a proxy for small scale bed roughness and a good indicator for subglacial water pressure in regions of distributed subglacial water (<a href="https://doi.org/10.1109/LGRS.2014.2337878">Schroeder et al., 2014, IEEE GRSL </a>, <a href="https://doi.org/10.1016/j.epsl.2019.115961">Dow et al., 2019, EPSL </a>) and smooth deforming bed material (<a href="http://doi.org/10.1002/2014GL061645">Schroeder et al., 2014, GRL</a>, <a href="http://dx.doi/org/10.1098/rsta.2014.0297">Young et al., 2016, PTRS</a>. Specularity data are inherently noisy, so these products have been smoothed with a 1 km filter.</p>more » « less
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<p>NSF COLDEX performed two airborne campaigns from South Pole Station over the Southern Flank of Dome A and 2022-23 and 2023-24, searching for a potential site of a continuous ice core that could sample the mid-Pleistocene transition. Ice thickness data extracted from the MARFA radar system has allow for a new understanding of this region.</p> <p>Here we generate crustal scale maps of ice thickness, bed elevation, specularity content, subglacial RMS deviation and fractional basal ice thickness with 1 km sampling, and 10 km resolution. We include both masked and unmasked grids.</p> <p> The projection is in the SCAR standard ESPG:3031 polar stereographic projection with true scale at 71˚S.</p> <p>These geotiffs were generated using performed using GMT6.5 (<a href="https://doi.org/10.1029/2019GC008515">Wessel et al., 2019</a>) using the pygmt interface, by binning the raw data to 2.5 km cells, and using the <a href="https://github.com/sakov/nn-c"> nnbathy </a> program to apply natural neighbor interpolation to 1 km sampling. A 10 km Gaussian filter - representing typical lines spacings - was applied and then a mask was applied for all locations where the nearest data point was further than 8 km. </p> Ice thickness, bed elevation and RMS deviation @ 400 m length scale (<a href="http://dx.doi.org/10.1029/2000JE001429">roughness</a>) data includes the following datasets: <ul> <li> UTIG/CRESIS <a href="https://doi.org/10.18738/T8/J38CO5">NSF COLDEX Airborne MARFA data</a></li> <li> British Antarctic Survey <a href="https://doi.org/10.5285/0f6f5a45-d8af-4511-a264-b0b35ee34af6">AGAP-North</a></li> <li> LDEO <a href="https://doi.org/10.1594/IEDA/317765"> AGAP-South </a></li> <li> British Antarctic Survey <a href="https://doi.org/10.5270/esa-8ffoo3e">Polargap</a></li> <li> UTIG Support Office for Airborne Research <a href="https://doi.org/10.15784/601588">Pensacola-Pole Transect (PPT) </a></li> <li> NASA/CReSIS <a href="https://doi.org/10.5067/GDQ0CUCVTE2Q"> 2016 and 2018 Operation Ice Bridge </a> </li> <li> ICECAP/PRIC <a href="https://doi.org/10.15784/601437"> SPICECAP Titan Dome Survey </a> </ul> <p>Specularity content (<a href="https://doi.org/10.1109/LGRS.2014.2337878">Schroeder et al. 2014</a>) is compiled from <a href="https://doi.org/10.18738/T8/KHUT1U"> Young et al. 2025a </a> and <a href="https://doi.org/10.18738/T8/6T5JS6"> Young et al. 2025b</a>.</p> <p>Basal ice fractional thickness is complied from manual interpretation by Vega Gonzàlez, Yan and Singh. </p> <p>Code to generated these grids can be found at <a href="https://github.com/smudog/COLDEX_dichotomy_paper_2025"> at github.com </a></p>more » « less
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- https://doi.org/10.5194/egusphere-egu24-13878
