Articles | Volume 20, issue 4
https://doi.org/10.5194/tc-20-2237-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-20-2237-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Apr 2026
Research article |
| 20 Apr 2026
| 20 Apr 2026
Modelling L-band microwave brightness temperature time series for firn aquifers
Haokui Xu
Department of Electrical Engineering and Computer Sciences, University of Michigan, Ann Arbor, MI 48105, USA
Department of Electrical Engineering and Computer Sciences, University of Michigan, Ann Arbor, MI 48105, USA
Julie Miller
Earth Science and Observation Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80309, USA
Brooke C. Medley
Earth Science Divisions, NASA Goddard Space Flight Center, Green Belt, MD 20771, USA
Joel T. Johnson
ElectroScience Lab, The Ohio State University, Columbus, OH 43210, USA
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Cited articles
van den Akker, T., van Pelt, W., Petterson, R., and Pohjola, V. A.: Long-term development of a perennial firn aquifer on the Lomonosovfonna ice cap, Svalbard, The Cryosphere, 19, 1513–1525, https://doi.org/10.5194/tc-19-1513-2025, 2025.
Baker, I.: Microstructural characterization of snow, firn and ice, Philos. T. R. Soc. A., 377, 20180162, https://doi.org/10.1098/rsta.2018.0162, 2019.
Bamber, J. L., Westaway, R. M., Marzeion, B., and Wouters, B.: The land ice contribution to sea level during the satellite era, Environ. Res. Lett., 13, 063008, https://doi.org/10.1088/1748-9326/aac2f0, 2018.
Brangers, I., Lievens, H., Miège, C., Demuzere, M., Brucker, L., and De Lannoy, G. J. M.: Sentinel-1 Detects Firn Aquifers in the Greenland Ice Sheet, Geophys. Res. Lett., 47, e2019GL085192, https://doi.org/10.1029/2019GL085192, 2020.
Bringer, A., Miller, J., Johnson, J. T., and Jezek, K. C.: Radiative transfer modeling of the brightness temperature signatures of firn aquifers, in: AGU Fall Meeting Abstracts, C11A-0899, https://ui.adsabs.harvard.edu/abs/2017AGUFM.C11A0899B/abstract (last access: 1 April 2026), 2017.
Christianson, K., Kohler, J., Alley, R. B., Nuth, C., and Van Pelt, W. J. J.: Dynamic perennial firn aquifer on an Arctic glacier, Geophys. Res. Lett., 42, 1418–1426, https://doi.org/10.1002/2014GL062806, 2015.
Chu, W., Schroeder, D. M., and Siegfried, M. R.: Retrieval of Englacial Firn Aquifer Thickness From Ice-Penetrating Radar Sounding in Southeastern Greenland, Geophys. Res. Lett., 45, https://doi.org/10.1029/2018GL079751, 2018.
CReSIS: L1B accumulation radar product, https://data.cresis.ku.edu/data/accum/ (last access: 1 April 2026), 2024.
Ding, K.-H., Xu, X., and Tsang, L.: Electromagnetic Scattering by Bicontinuous Random Microstructures With Discrete Permittivities, IEEE T. Geosci. Remote, 48, 3139–3151, https://doi.org/10.1109/TGRS.2010.2043953, 2010.
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J., Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C., Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman, S. W., Tsang, L., and Van Zyl, J.: The Soil Moisture Active Passive (SMAP) Mission, Proc. IEEE, 98, 704–716, https://doi.org/10.1109/JPROC.2010.2043918, 2010.
Forster, R. R., Box, J. E., Van Den Broeke, M. R., Miège, C., Burgess, E. W., Van Angelen, J. H., Lenaerts, J. T. M., Koenig, L. S., Paden, J., Lewis, C., Gogineni, S. P., Leuschen, C., and McConnell, J. R.: Extensive liquid meltwater storage in firn within the Greenland ice sheet, Nat. Geosci., 7, 95–98, https://doi.org/10.1038/ngeo2043, 2014.
Huang, Z., Xu, H., Borah, F., Tsang, L., Medley, B., Johnson, J. T., and De Roo, R. D.: L-Band Full-Wave Simulations of the Effective Permittivity of Bi/Tri-Continuous Media With Applications to Firn Aquifer in Polar Regions and Terrestrial Wet Snow, IEEE T. Geosci. Remote, 62, 1–12, https://doi.org/10.1109/TGRS.2024.3455236, 2024.
Haokui, X.: Jokerleonxv/firn-aquifer: Firn aquifer simulation code v1.0 (firn_aquifer), Zenodo [code], https://doi.org/10.5281/zenodo.19369396, 2026.
Koenig, L. S., Miège, C., Forster, R. R., and Brucker, L.: Initial in situ measurements of perennial meltwater storage in the Greenland firn aquifer, Geophys. Res. Lett., 41, 81–85, https://doi.org/10.1002/2013GL058083, 2014.
Lenaerts, J. T. M., Medley, B., van den Broeke, M. R., and Wouters, B.: Observing and Modeling Ice-Sheet Surface Mass Balance, Rev. Geophys., 57, 376–420, https://doi.org/10.1029/2018RG000622, 2019.
Long, D. and Ulaby, F.: Microwave Radar and Radiometric Remote Sensing, Artech, Ann. Arbor., ISBN: 978-0-472-11935-6, 2015.
Long, D. G., Brodzik, M. J., and Hardman, M. A.: Enhanced-Resolution SMAP Brightness Temperature Image Products, IEEE Trans. Geosci. Remote Sens., 57, 4151–4163, https://doi.org/10.1109/TGRS.2018.2889427, 2019.
Mankoff, K. D., Fettweis, X., Langen, P. L., Stendel, M., Kjeldsen, K. K., Karlsson, N. B., Noël, B., van den Broeke, M. R., Solgaard, A., Colgan, W., Box, J. E., Simonsen, S. B., King, M. D., Ahlstrøm, A. P., Andersen, S. B., and Fausto, R. S.: Greenland ice sheet mass balance from 1840 through next week, Earth Syst. Sci. Data, 13, 5001–5025, https://doi.org/10.5194/essd-13-5001-2021, 2021.
Matzler, C.: Microwave permittivity of dry snow, IEEE Trans. Geosci. Remote Sens., 34, 573–581, https://doi.org/10.1109/36.485133, 1996.
Miège, C., Forster, R. R., Brucker, L., Koenig, L. S., Solomon, D. K., Paden, J. D., Box, J. E., Burgess, E. W., Miller, J. Z., McNerney, L., Brautigam, N., Fausto, R. S., and Gogineni, S.: Spatial extent and temporal variability of Greenland firn aquifers detected by ground and airborne radars, J. Geophys. Res.-Earth, 121, 2381–2398, https://doi.org/10.1002/2016JF003869, 2016.
Miège, C., Forster, R., Brucker, L., Koenig, L., Miller, O., Solomon, K., and Schmerr, N.: Firn temperatures (2013–2017) and water-level changes (2015–2017) collected at three locations in a firn-aquifer region of the southeastern part of the Greenland Ice Sheet, Arctic Data Center [data set], https://doi.org/10.18739/A2R785P5W, 2020a.
Miège, C., Forster, R., Koenig, L., Miller, J., Miller, O., Montgomery, L., et al.: Density, hydrology and geophysical measurements from the Wilkins Ice Shelf firn aquifer, U.S. Antarctic Program (USAP) Data Center [data set], https://doi.org/10.15784/601390, 2020b.
Miller, J. Z., Long, D. G., Jezek, K. C., Johnson, J. T., Brodzik, M. J., Shuman, C. A., Koenig, L. S., and Scambos, T. A.: Brief communication: Mapping Greenland's perennial firn aquifers using enhanced-resolution L-band brightness temperature image time series, The Cryosphere, 14, 2809–2817, https://doi.org/10.5194/tc-14-2809-2020, 2020a.
Miller, J. Z., Culberg, R., Long, D. G., Shuman, C. A., Schroeder, D. M., and Brodzik, M. J.: An empirical algorithm to map perennial firn aquifers and ice slabs within the Greenland Ice Sheet using satellite L-band microwave radiometry, The Cryosphere, 16, 103–125, https://doi.org/10.5194/tc-16-103-2022, 2022.
Miller, O., Solomon, D. K., Miège, C., Koenig, L., Forster, R., Schmerr, N., Ligtenberg, S. R. M., and Montgomery, L.: Direct Evidence of Meltwater Flow Within a Firn Aquifer in Southeast Greenland, Geophys. Res. Lett., 45, 207–215, https://doi.org/10.1002/2017GL075707, 2018.
Miller, O., Solomon, D. K., Miège, C., Koenig, L., Forster, R., Schmerr, N., Ligtenberg, S. R. M., Legchenko, A., Voss, C. I., Montgomery, L., and McConnell, J. R.: Hydrology of a Perennial Firn Aquifer in Southeast Greenland: An Overview Driven by Field Data, Water Resour. Res., 56, e2019WR026348, https://doi.org/10.1029/2019WR026348, 2020b.
Miller, O. L., Solomon, D. K., Miège, C., Koenig, L. S., Forster, R. R., Montgomery, L. N., Schmerr, N., Ligtenberg, S. R. M., Legchenko, A., and Brucker, L.: Hydraulic Conductivity of a Firn Aquifer in Southeast Greenland, Front. Earth Sci., 5, 38, https://doi.org/10.3389/feart.2017.00038, 2017.
Montgomery, L., Koenig, L., and Alexander, P.: The SUMup dataset: compiled measurements of surface mass balance components over ice sheets and sea ice with analysis over Greenland, Earth Syst. Sci. Data, 10, 1959–1985, https://doi.org/10.5194/essd-10-1959-2018, 2018.
Montgomery, L., Miège, C., Miller, J., Scambos, T. A., Wallin, B., Miller, O., Solomon, D. K., Forster, R., and Koenig, L.: Hydrologic Properties of a Highly Permeable Firn Aquifer in the Wilkins Ice Shelf, Antarctica, Geophys. Res. Lett., 47, e2020GL089552, https://doi.org/10.1029/2020GL089552, 2020.
Nghiem, S. V., Hall, D. K., Mote, T. L., Tedesco, M., Albert, M. R., Keegan, K., Shuman, C. A., DiGirolamo, N. E., and Neumann, G.: The extreme melt across the Greenland ice sheet in 2012, Geophys. Res. Lett., 39, 2012GL053611, https://doi.org/10.1029/2012GL053611, 2012.
Shepherd, A., Ivins, E. R., A, G., Barletta, V. R., Bentley, M. J., Bettadpur, S., Briggs, K. H., Bromwich, D. H., Forsberg, R., Galin, N., Horwath, M., Jacobs, S., Joughin, I., King, M. A., Lenaerts, J. T. M., Li, J., Ligtenberg, S. R. M., Luckman, A., Luthcke, S. B., McMillan, M., Meister, R., Milne, G., Mouginot, J., Muir, A., Nicolas, J. P., Paden, J., Payne, A. J., Pritchard, H., Rignot, E., Rott, H., Sørensen, L. S., Scambos, T. A., Scheuchl, B., Schrama, E. J. O., Smith, B., Sundal, A. V., Van Angelen, J. H., Van De Berg, W. J., Van Den Broeke, M. R., Vaughan, D. G., Velicogna, I., Wahr, J., Whitehouse, P. L., Wingham, D. J., Yi, D., Young, D., and Zwally, H. J.: A Reconciled Estimate of Ice-Sheet Mass Balance, Science, 338, 1183–1189, https://doi.org/10.1126/science.1228102, 2012.
Sihvola, A. H.: Electromagnetic Mixing Formulas and Applications, Institution of Electrical Engineers, ISBN: 978-0-85296-772-0, 1999.
Slater, T., Lawrence, I. R., Otosaka, I. N., Shepherd, A., Gourmelen, N., Jakob, L., Tepes, P., Gilbert, L., and Nienow, P.: Review article: Earth's ice imbalance, The Cryosphere, 15, 233–246, https://doi.org/10.5194/tc-15-233-2021, 2021.
Stevens, M., Vo, H., emmakahle, and Jboat: UWGlaciology/CommunityFirnModel: Version 1.1.6, Zenodo [code], https://doi.org/10.5281/zenodo.5719748, 2021.
Tiuri, M., Sihvola, A., Nyfors, E., and Hallikaiken, M.: The complex dielectric constant of snow at microwave frequencies, IEEE J. Ocean. Eng., 9, 377–382, https://doi.org/10.1109/JOE.1984.1145645, 1984.
Tsang, L., Kong, J. A., and Ding, K. H.: Scattering of Electromagnetic Waves: Theories and Applications, Wiley, ISBN: 9780471387992, 2004.
Xu, H.: Electromagnetic Modelling for the Active and Passive Remote Sensing of Polar Ice Sheet and Signal of Opportunity (SoOp) Land Observation., University of Michigan, Ann Arbor, 141 pp., PhD Thesis, ISBN: 9798382738222, 2024.
Xu, H., Medley, B., Tsang, L., Johnson, J. T., Jezek, K. C., Brogioni, M., and Kaleschke, L.: Polar firn properties in Greenland and Antarctica and related effects on microwave brightness temperatures, The Cryosphere, 17, 2793–2809, https://doi.org/10.5194/tc-17-2793-2023, 2023.
Short summary
This paper provides a physical model to analyze the brightness temperature time series over the firn aquifer in Greenland and Antarctica. The model can match the Vertical polarized and Horizontal polarized brightness temperature time series well. This model provides a potential to study the aquifer water table depth and liquid water content with radiometry.
This paper provides a physical model to analyze the brightness temperature time series over the...
