Articles | Volume 19, issue 1
https://doi.org/10.5194/tc-19-459-2025
© Author(s) 2025. 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-19-459-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jan 2025
Research article |
| 29 Jan 2025
| 29 Jan 2025
Benchmarking passive-microwave-satellite-derived freeze–thaw datasets
b.geos, Industriestrasse 1, 2100 Korneuburg, Austria
Xaver Muri
b.geos, Industriestrasse 1, 2100 Korneuburg, Austria
Markus Hetzenecker
ENVEO, Innsbruck, Austria
Kimmo Rautiainen
FMI, Helsinki, Finland
Helena Bergstedt
b.geos, Industriestrasse 1, 2100 Korneuburg, Austria
Jan Wuite
ENVEO, Innsbruck, Austria
Thomas Nagler
ENVEO, Innsbruck, Austria
Dmitry Nicolsky
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, 99775, AK, USA
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The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
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The Cryosphere, 16, 2505–2526, https://doi.org/10.5194/tc-16-2505-2022, https://doi.org/10.5194/tc-16-2505-2022, 2022
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Glacier surges are widespread in the Karakoram and have been intensely studied using satellite data and DEMs. We use time series of such datasets to study three glacier surges in the same region of the Karakoram. We found strongly contrasting advance rates and flow velocities, maximum velocities of 30 m d−1, and a change in the surge mechanism during a surge. A sensor comparison revealed good agreement, but steep terrain and the two smaller glaciers caused limitations for some of them.
Ludivine Libert, Jan Wuite, and Thomas Nagler
The Cryosphere, 16, 1523–1542, https://doi.org/10.5194/tc-16-1523-2022, https://doi.org/10.5194/tc-16-1523-2022, 2022
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Open fractures are important to monitor because they weaken the ice shelf structure. We propose a novel approach using synthetic aperture radar (SAR) interferometry for automatic delineation of ice shelf cracks. The method is applied to Sentinel-1 images of Brunt Ice Shelf, Antarctica, and the propagation of the North Rift, which led to iceberg calving in February 2021, is traced. It is also shown that SAR interferometry is more sensitive to rifting than SAR backscatter and optical imagery.
Noriaki Ohara, Benjamin M. Jones, Andrew D. Parsekian, Kenneth M. Hinkel, Katsu Yamatani, Mikhail Kanevskiy, Rodrigo C. Rangel, Amy L. Breen, and Helena Bergstedt
The Cryosphere, 16, 1247–1264, https://doi.org/10.5194/tc-16-1247-2022, https://doi.org/10.5194/tc-16-1247-2022, 2022
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New variational principle suggests that a semi-ellipsoid talik shape (3D Stefan equation) is optimum for incoming energy. However, the lake bathymetry tends to be less ellipsoidal due to the ice-rich layers near the surface. Wind wave erosion is likely responsible for the elongation of lakes, while thaw subsidence slows the wave effect and stabilizes the thermokarst lakes. The derived 3D Stefan equation was compared to the field-observed talik thickness data using geophysical methods.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
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We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Helmut Rott, Stefan Scheiblauer, Jan Wuite, Lukas Krieger, Dana Floricioiu, Paola Rizzoli, Ludivine Libert, and Thomas Nagler
The Cryosphere, 15, 4399–4419, https://doi.org/10.5194/tc-15-4399-2021, https://doi.org/10.5194/tc-15-4399-2021, 2021
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We studied relations between interferometric synthetic aperture radar (InSAR) signals and snow–firn properties and tested procedures for correcting the penetration bias of InSAR digital elevation models at Union Glacier, Antarctica. The work is based on SAR data of the TanDEM-X mission, topographic data from optical sensors and field measurements. We provide new insights on radar signal interactions with polar snow and show the performance of penetration bias retrievals using InSAR coherence.
Zhen Zhang, Etienne Fluet-Chouinard, Katherine Jensen, Kyle McDonald, Gustaf Hugelius, Thomas Gumbricht, Mark Carroll, Catherine Prigent, Annett Bartsch, and Benjamin Poulter
Earth Syst. Sci. Data, 13, 2001–2023, https://doi.org/10.5194/essd-13-2001-2021, https://doi.org/10.5194/essd-13-2001-2021, 2021
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The spatiotemporal distribution of wetlands is one of the important and yet uncertain factors determining the time and locations of methane fluxes. The Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset describes the global data product used to quantify the areal dynamics of natural wetlands and how global wetlands are changing in response to climate.
Georg Pointner, Annett Bartsch, Yury A. Dvornikov, and Alexei V. Kouraev
The Cryosphere, 15, 1907–1929, https://doi.org/10.5194/tc-15-1907-2021, https://doi.org/10.5194/tc-15-1907-2021, 2021
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This study presents strong new indications that regions of anomalously low backscatter in C-band synthetic aperture radar (SAR) imagery of ice of Lake Neyto in northwestern Siberia are related to strong emissions of natural gas. Spatio-temporal dynamics and potential scattering and formation mechanisms are assessed. It is suggested that exploiting the spatial and temporal properties of Sentinel-1 SAR data may be beneficial for the identification of similar phenomena in other Arctic lakes.
Cited articles
Aalto, T., Lindqvist, H., Tsuruta, A., Tenkanen, M., Karppinen, T., Kivimäki, E., Smolander, T., Kangasaho, V., and Rautiainen, K.: ESA MethEO Final Report, Tech. rep., FMI, https://eo4society.esa.int/wp-content/uploads/2021/02/MethEO_Final-Report_v1_0.pdf (last access: 21 January 2025), 2020. a
Bartsch, A., Pointner, G., Bergstedt, H., Widhalm, B., Wendleder, A., and Roth, A.: Utility of Polarizations Available from Sentinel-1 for Tundra Mapping, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, IEEE, 11–16 July 2021, Brussels, 1452–1455, https://doi.org/10.1109/igarss47720.2021.9553993, 2021a. a
Bartsch, A., Pointner, G., Nitze, I., Efimova, A., Jakober, D., Ley, S., Högström, E., Grosse, G., and Schweitzer, P.: Expanding infrastructure and growing anthropogenic impacts along Arctic coasts, Environ. Res. Lett., 16, 115013, https://doi.org/10.1088/1748-9326/ac3176, 2021b. a
Bartsch, A., Bergstedt, H., Pointner, G., Muri, X., and Rautiainen, K.: Circumpolar mid-winter thaw and refreeze based on fusion of Metop ASCAT and SMOS, 2011/2012–2021/2022, Zenodo [data set], https://doi.org/10.5281/ZENODO.7575927, 2023a. a, b
Bartsch, A., Bergstedt, H., Pointner, G., Muri, X., Rautiainen, K., Leppänen, L., Joly, K., Sokolov, A., Orekhov, P., Ehrich, D., and Soininen, E. M.: Towards long-term records of rain-on-snow events across the Arctic from satellite data, The Cryosphere, 17, 889–915, https://doi.org/10.5194/tc-17-889-2023, 2023b. a, b, c, d, e, f
Bartsch, A., Efimova, A., Widhalm, B., Muri, X., von Baeckmann, C., Bergstedt, H., Ermokhina, K., Hugelius, G., Heim, B., and Leibmann, M.: Circumpolar Landcover Units, Zenodo [data set], https://doi.org/10.5281/ZENODO.8399017, 2023c. a
Bartsch, A., Efimova, A., Widhalm, B., Muri, X., von Baeckmann, C., Bergstedt, H., Ermokhina, K., Hugelius, G., Heim, B., and Leibman, M.: Circumarctic land cover diversity considering wetness gradients, Hydrol. Earth Syst. Sci., 28, 2421–2481, https://doi.org/10.5194/hess-28-2421-2024, 2024. a
Bergstedt, H. and Bartsch, A.: Surface State across Scales; Temporal and SpatialPatterns in Land Surface Freeze/Thaw Dynamics, Geosciences, 7, 65, https://doi.org/10.3390/geosciences7030065, 2017. a, b
Bergstedt, H. and Bartsch, A.: Near surface ground temperature, soil moisture and snow depth measurements in the Kaldoaivi Wilderness Area, for 2016–2018, Pangaea [data set], https://doi.org/10.1594/PANGAEA.912482, 2020. a
Bergstedt, H., Zwieback, S., Bartsch, A., and Leibman, M.: Dependence of C-Band Backscatter on Ground Temperature, Air Temperature and Snow Depth in Arctic Permafrost Regions, Remote Sens.-Basel, 10, 142, https://doi.org/10.3390/rs10010142, 2018. a
Bergstedt, H., Bartsch, A., Duguay, C. R., and Jones, B. M.: Influence of surface water on coarse resolution C-band backscatter: Implications for freeze/thaw retrieval from scatterometer data, Remote Sens. Environ., 247, 111911, https://doi.org/10.1016/j.rse.2020.111911, 2020a. a
Bergstedt, H., Bartsch, A., Neureiter, A., Hofler, A., Widhalm, B., Pepin, N., and Hjort, J.: Deriving a Frozen Area Fraction From Metop ASCAT Backscatter Based on Sentinel-1, IEEE T. Geosci. Remote, 58, 6008–6019, https://doi.org/10.1109/tgrs.2020.2967364, 2020b. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r
Biskaborn, B. K., Smith, S. L., Noetzli, J., Matthes, H., Vieira, G., Streletskiy, D. A., Schoeneich, P., Romanovsky, V. E., Lewkowicz, A. G., Abramov, A., Allard, M., Boike, J., Cable, W. L., Christiansen, H. H., Delaloye, R., Diekmann, B., Drozdov, D., Etzelmüller, B., Grosse, G., Guglielmin, M., Ingeman-Nielsen, T., Isaksen, K., Ishikawa, M., Johansson, M., Johannsson, H., Joo, A., Kaverin, D., Kholodov, A., Konstantinov, P., Krüger, T., Lambiel, C., Lanckman, J.-P., Luo, D., Malkova, G., Meiklejohn, I., Moskalenko, N., Oliva, M., Phillips, M., Ramos, M., Sannel, A. B. K., Sergeev, D., Seybold, C., Skryabin, P., Vasiliev, A., Wu, Q., Yoshikawa, K., Zheleznyak, M., and Lantuit, H.: Permafrost is warming at a global scale, Nat. Commun., 10, 264, https://doi.org/10.1038/s41467-018-08240-4, 2019. a
Böttcher, K., Rautiainen, K., Aurela, M., Kolari, P., Mäkelä, A., Arslan, A. N., Black, T. A., and Koponen, S.: Proxy Indicators for Mapping the End of the Vegetation Active Period in Boreal Forests Inferred from Satellite-Observed Soil Freeze and ERA-Interim Reanalysis Air Temperature, PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 86, 169–185, https://doi.org/10.1007/s41064-018-0059-y, 2018. a, b, c
Chen, X., Liu, L., and Bartsch, A.: Detecting soil freeze/thaw onsets in Alaska using SMAP and ASCAT data, Remote Sens. Environ., 220, 59–70, https://doi.org/10.1016/j.rse.2018.10.010, 2019. a
Chen, Y., Li, S., Wang, L., Mittermeier, M., Bernier, M., and Ludwig, R.: Retrieving freeze-thaw states using deep learning with remote sensing data in permafrost landscapes, Int. J. Appl. Earth Obs., 126, 103616, https://doi.org/10.1016/j.jag.2023.103616, 2024. a, b
Cohen, J., Rautiainen, K., Lemmetyinen, J., Smolander, T., Vehviläinen, J., and Pulliainen, J.: Sentinel-1 based soil freeze/thaw estimation in boreal forest environments, Remote Sens. Environ., 254, 112267, https://doi.org/10.1016/j.rse.2020.112267, 2021. a, b, c, d
Copernicus: Copernicus Open Access Hub, Copernicus [data set], https://scihub.copernicus.eu (last access: 21 January 2025), 2024. a
Das, A., Kumar, R., and Rosen, P.: Nisar Mission Overview and Updates on ISRO Science Plan, 2021 IEEE International India Geoscience and Remote Sensing Symposium (InGARSS), 6–10 December 2021, Ahmedabad, India, 2021, pp. 269-272, https://doi.org/10.1109/ingarss51564.2021.9791979, 2021. a
Derksen, C., Xu, X., Dunbar, R. S., Colliander, A., Kim, Y., Kimball, J. S., Black, T. A., Euskirchen, E., Langlois, A., Loranty, M. M., Marsh, P., Rautiainen, K., Roy, A., Royer, A., and Stephens, J.: Retrieving landscape freeze/thaw state from Soil Moisture Active Passive (SMAP) radar and radiometer measurements, Remote Sens. Environ., 194, 48–62, https://doi.org/10.1016/j.rse.2017.03.007, 2017. a, b, c
Dodd, E., Ermida, S., Jimenez, C., Martin, M., and Ghent, D.: Data Access Requirements Document: WP1.3-LST-CCI-D1.3, Tech. rep., Consortium CCI LST, https://admin.climate.esa.int/media/documents/LST-CCI-D1.3-DARD_-_i2r0_-_Data_Access_Requirements_Document.pdf (last access: 21 January 2025), 2021. a
Entekhabi, D., Yueh, S., O'Neill, P. E., Kellogg, K. H., Allen, A., Bindlish, R., Brown, M., Chan, S., Colliander, A., Crow, W. T., Das, N., De Lannoy, G., Dunbar, R. S., Edelstein, W. N., Entin, J. K., Escobar, V., Goodman, S. D., Jackson, T. J., Jai, B., Johnson, J., Kim, E., Kim, S., Kimball, J., Koster, R. D., Leon, A., McDonald, K. C., Moghaddam, M., Mohammed, P., Moran, S., Njoku, E. G., Piepmeier, J. R., Reichle, R., Rogez, F., Shi, J., Spencer, M. W., Thurman, S. W., Tsang, L., Van Zyl, J., Weiss, B., and West, R.: SMAP Handbook-Soil Moisture Active Passive: Mapping Soil Moisture and Freeze/Thaw from Space, https://smap.jpl.nasa.gov/files/smap2/SMAP_handbook_web.pdf (last access: 21 January 2025), 2014. a, b
Erkkilä, A., Tenkanen, M., Tsuruta, A., Rautiainen, K., and Aalto, T.: Environmental and Seasonal Variability of High Latitude Methane Emissions Based on Earth Observation Data and Atmospheric Inverse Modelling, Remote Sens.-Basel, 15, 5719, https://doi.org/10.3390/rs15245719, 2023. a
Holmberg, M., Lemmetyinen, J., Schwank, M., Kontu, A., Rautiainen, K., Merkouriadi, I., and Tamminen, J.: Retrieval of ground, snow, and forest parameters from space borne passive L band observations. A case study over Sodankylä, Finland, Remote Sens. Environ., 306, 114143, https://doi.org/10.1016/j.rse.2024.114143, 2024. a
Johnston, J., Maggioni, V., and Houser, P.: Comparing global passive microwave freeze/thaw records: Investigating differences between Ka- and L-band products, Remote Sens. Environ., 247, 111936, https://doi.org/10.1016/j.rse.2020.111936, 2020. a, b
Jorgenson, M., Yoshikawa, K., Kanevskiy, M., Shur, Y., Romanovsky, V., Marchenko, S., Grosse, G., Brown, J., and Jones, B.: Permafrost characteristics of Alaska, in: Proceedings of the 9th international conference on permafrost, 29 June–3 July 2008, Fairbanks, Extended abstracts volume, 121–122, University of Alaska Fairbanks, https://www.scribd.com/document/438031132/09th-International-Conference-on-Permafrost-Extended-Abstracts-pdf (last access: 21 January 2025), 2008. a
Kim, Y., Kimball, J. S., Zhang, K., and McDonald, K. C.: Satellite detection of increasing Northern Hemisphere non-frozen seasons from 1979 to 2008: Implications for regional vegetation growth, Remote Sens. Environ., 121, 472–487, https://doi.org/10.1016/j.rse.2012.02.014, 2012. a
Kim, Y., Kimball, J. S., Glassy, J., and McDonald, K. C.: MEaSUREs Global Record of Daily Landscape Freeze/Thaw Status, Version 3, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/measures/cryosphere/nsidc-0477.003, 2014. a, b, c, d, e, f
Kim, Y., Kimball, J. S., Glassy, J., and Du, J.: An extended global Earth system data record on daily landscape freeze–thaw status determined from satellite passive microwave remote sensing, Earth Syst. Sci. Data, 9, 133–147, https://doi.org/10.5194/essd-9-133-2017, 2017. a, b
Kim, Y., Kimball, J. S., Du, J., Schaaf, C. L. B., and Kirchner, P. B.: Quantifying the effects of freeze-thaw transitions and snowpack melt on land surface albedo and energy exchange over Alaska and Western Canada, Environ. Res. Lett., 13, 075009, https://doi.org/10.1088/1748-9326/aacf72, 2018. a
Kim, Y., Kimball, J. S., Xu, X., Dunbar, R. S., Colliander, A., and Derksen, C.: Global Assessment of the SMAP Freeze/Thaw Data Record and Regional Applications for Detecting Spring Onset and Frost Events, Remote Sens.-Basel, 11, 1317, https://doi.org/10.3390/rs11111317, 2019. a
Kim, Y., Kimball, J. S., Parazoo, N., and Kirchner, P.: Diagnosing Environmental Controls on Vegetation Greening and Browning Trends Over Alaska and Northwest Canada Using Complementary Satellite Observations, Springer International Publishing, 583–613, https://doi.org/10.1007/978-3-030-50930-9_20, 2020. a
Kim, Y., Kimball, J., Glassy, J., and McDonald, K.: MEaSUREs Northern Hemisphere Polar EASE-Grid 2.0 Daily 6 km Land Freeze/Thaw Status from AMSR-E and AMSR2, Version 2, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/BDY2V548E07C, 2021. a, b
Kouki, K., Anttila, K., Manninen, T., Luojus, K., Wang, L., and Riihelä, A.: Intercomparison of Snow Melt Onset Date Estimates From Optical and Microwave Satellite Instruments Over the Northern Hemisphere for the Period 1982–2015, J. Geophys. Res.-Atmos., 124, 11205–11219, https://doi.org/10.1029/2018jd030197, 2019. a, b
Kraatz, S., Jacobs, J., Schroder, R., Cho, E., Cosh, M., Seyfried, M., Prueger, J., and Livingston, S.: Evaluation of SMAP Freeze/Thaw Retrieval Accuracy at Core Validation Sites in the Contiguous United States, Remote Sens.-Basel, 10, 1483, https://doi.org/10.3390/rs10091483, 2018. a, b
Naeimi, V., Paulik, C., Bartsch, A., Wagner, W., Kidd, R., Boike, J., and Elger, K.: ASCAT Surface State Flag (SSF): Extracting Information on Surface Freeze/Thaw Conditions from Backscatter Data Using an Empirical Threshold-Analysis Algorithm, IEEE T. Geosci. Remote, 50, 2566–2582, https://doi.org/10.1109/TGRS.2011.2177667, 2012. a, b, c, d, e, f, g, h, i, j, k, l
National Research Council: Opportunities to Use Remote Sensing in Understanding Permafrost and Related Ecological Characteristics: Report of a Workshop, National Academies Press, Washington, DC, https://doi.org/10.17226/18711, 2014. a
Outcalt, S. I., Nelson, F. E., and Hinkel, K. M.: The zero-curtain effect: Heat and mass transfer across an isothermal region in freezing soil, Water Resour. Res., 26, 1509–1516, https://doi.org/10.1029/wr026i007p01509, 1990. a
Park, H., Kim, Y., and Kimball, J.: Widespread permafrost vulnerability and soil active layer increases over the high northern latitudes inferred from satellite remote sensing and process model assessments, Remote Sens. Environ., 175, 349–358, https://doi.org/10.1016/j.rse.2015.12.046, 2016a. a, b
Park, S.-E., Bartsch, A., Sabel, D., Wagner, W., Naeimi, V., and Yamaguchi, Y.: Monitoring Freeze/Thaw Cycles Using ENVISAT ASAR Global Mode, Remote Sens. Environ., 115, 3457–3467, https://doi.org/10.1016/j.rse.2011.08.009, 2011. a
Park, T., Ganguly, S., Tommervik, H., Euskirchen, E. S., Hogda, K.-A., Karlsen, S. R., Brovkin, V., Nemani, R. R., and Myneni, R. B.: Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data, Environ. Res. Lett., 11, 084001, https://doi.org/10.1088/1748-9326/11/8/084001, 2016b. a, b
Paulik, C., Melzer, T., Hahn, S., Bartsch, A., Heim, B., Elger, K., and Wagner, W.: Circumpolar surface soil moisture and freeze/thaw surface status remote sensing products (Version 2) with links to geotiff images and NetCDF files (2007-01 to 2010-09), Pangaea [data set], https://doi.org/10.1594/PANGAEA.775959, 2012. a
Rautiainen, K. and Holmberg, M.: SMOS Freeze and Thaw Processing and Dissemination Service – Algorithm Theoretical Baseline Document, Tech. rep., Finnish Meteorological Institute, https://earth.esa.int/documents/d/earth-online/smos-soil-freeze-and-thaw-state-atbd (last access: 21 January 2025), 2023. a, b
Rautiainen, K., Parkkinen, T., Lemmetyinen, J., Schwank, M., Wiesmann, A., Ikonen, J., Derksen, C., Davydov, S., Davydova, A., Boike, J., Langer, M., Drusch, M., and Pulliainen, J.: SMOS prototype algorithm for detecting autumn soil freezing, Remote Sens. Environ., 180, 346–360, https://doi.org/10.1016/j.rse.2016.01.012, 2016. a, b, c, d, e
Rixen, C., Hoye, T. T., Macek, P., Aerts, R., Alatalo, J. M., Anderson, J. T., Arnold, P. A., Barrio, I. C., Bjerke, J. W., Bjorkman, M. P., Blok, D., Blume-Werry, G., Boike, J., Bokhorst, S., Carbognani, M., Christiansen, C. T., Convey, P., Cooper, E. J., Cornelissen, J. H. C., Coulson, S. J., Dorrepaal, E., Elberling, B., Elmendorf, S. C., Elphinstone, C., Forte, T. G., Frei, E. R., Geange, S. R., Gehrmann, F., Gibson, C., Grogan, P., Halbritter, A. H., Harte, J., Henry, G. H., Inouye, D. W., Irwin, R. E., Jespersen, G., Jonsdottir, I. S., Jung, J. Y., Klinges, D. H., Kudo, G., Lamsa, J., Lee, H., Lembrechts, J. J., Lett, S., Lynn, J. S., Mann, H. M., Mastepanov, M., Morse, J., Myers-Smith, I. H., Olofsson, J., Paavola, R., Petraglia, A., Phoenix, G. K., Semenchuk, P., Siewert, M. B., Slatyer, R., Spasojevic, M. J., Suding, K., Sullivan, P., Thompson, K. L., Vaisanen, M., Vandvik, V., Venn, S., Walz, J., Way, R., Welker, J. M., Wipf, S., and Zong, S.: Winters are changing: snow effects on Arctic and alpine tundra ecosystems, Arctic Science, 8, 572–608, https://doi.org/10.1139/as-2020-0058, 2022. a
Romanovsky, V., Kholodov, A., Hasson, N., Nicolsky, D., and Wright, T.: Thermal State of Permafrost in North America – continuously observed ground temperatures, 2018–2019, Arctic Data Centre [data set], https://doi.org/10.18739/A2W37KW4J, 2020. a, b, c
Romanovsky, V., Kholodov, A., Nicolsky, D., and Wright, T.: Thermal State of Permafrost in North America – continuously observed ground temperatures, 2019–2020, Arctic Data Centre [data set], https://doi.org/10.18739/A29G5GF47, 2021. a, b, c
Romanovsky, V., Kholodov, A., Nicolsky, D., and Wright, T.: Thermal State of Permafrost in North America – continuously observed ground temperatures, 2020–2021, Arctic Data Centre [data set], https://doi.org/10.18739/A2F47GV9K, 2022. a, b, c
Rosen, P. A. and Kumar, R.: NASA-ISRO SAR (NISAR) Mission Status, in: 2021 IEEE Radar Conference (RadarConf21), 7–14 May 2021, Atlanta, GA, USA 1–6, IEEE, https://doi.org/10.1109/radarconf2147009.2021.9455211, 2021. a
Small, D.: Flattening Gamma: Radiometric Terrain Correction for SAR Imagery, IEEE T. Geosci. Remote, 49, 3081–3093, https://doi.org/10.1109/tgrs.2011.2120616, 2011. a
Smith, A., Jahn, A., Burgard, C., and Notz, D.: Improving model-satellite comparisons of sea ice melt onset with a satellite simulator, The Cryosphere, 16, 3235–3248, https://doi.org/10.5194/tc-16-3235-2022, 2022. a
Tenkanen, M., Tsuruta, A., Rautiainen, K., Kangasaho, V., Ellul, R., and Aalto, T.: Utilizing Earth Observations of Soil Freeze/Thaw Data and Atmospheric Concentrations to Estimate Cold Season Methane Emissions in the Northern High Latitudes, Remote Sens.-Basel, 13, 5059, https://doi.org/10.3390/rs13245059, 2021. a
Trofaier, A. M., Westermann, S., and Bartsch, A.: Progress in space-borne studies of permafrost for climate science: Towards a multi-ECV approach, Remote Sens. Environ., 203, 55–70, https://doi.org/10.1016/j.rse.2017.05.021, 2017. a, b, c
Wang, C., Yang, N., Zhao, T., Xue, H., Peng, Z., Zheng, J., Pan, J., Yao, P., Gao, X., Yan, H., Song, P., Liou, Y.-A., and Shi, J.: All-Season Liquid Soil Moisture Retrieval From SMAP, IEEE J. Sel. Top. Appl., 17, 8258–8270, https://doi.org/10.1109/jstars.2024.3382315, 2024. a
Westermann, S., Boike, J., Langer, M., Schuler, T. V., and Etzelmüller, B.: Modeling the impact of wintertime rain events on the thermal regime of permafrost, The Cryosphere, 5, 945–959, https://doi.org/10.5194/tc-5-945-2011, 2011. a, b
Widhalm, B., Bartsch, A., and Goler, R.: Simplified Normalization of C-Band Synthetic Aperture Radar Data for Terrestrial Applications in High Latitude Environments, Remote Sens.-Basel, 10, 551, https://doi.org/10.3390/rs10040551, 2018. a, b
Xu, X., Dunbar, R., Derksen, C., Colliander, A., Kim, Y., and Kimball, J.: SMAP L3 Radiometer Global and Northern Hemisphere Daily 36 km EASE-Grid Freeze/Thaw State, Version 4, Arctic Data Centre [data set], https://doi.org/10.5067/LQQ5I3QVGFTU, 2023. a, b, c, d
Zhang, C., Douglas, T. A., and Anderson, J. E.: Modeling and mapping permafrost active layer thickness using field measurements and remote sensing techniques, Int. J. Appl. Earth Obs., 102, 102455, https://doi.org/10.1016/j.jag.2021.102455, 2021. a
Zhong, W., Yuan, Q., Liu, T., and Yue, L.: Freeze/thaw onset detection combining SMAP and ASCAT data over Alaska: A machine learning approach, J. Hydrol., 605, 127354, https://doi.org/10.1016/j.jhydrol.2021.127354, 2022. a
Short summary
We developed a robust freeze–thaw detection approach, applying a constant threshold to Copernicus Sentinel-1 data that is suitable for tundra regions. All global, coarser-resolution products, tested with the resulting benchmarking dataset, are of value for freeze–thaw retrieval, although differences were found depending on the seasons, particularly during the spring and autumn transition.
We developed a robust freeze–thaw detection approach, applying a constant threshold to...
