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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We identified similarities between sea ice dynamics and conditions on land across the Arctic, above 60° N, for 2000–2019. Significant correlations were more common for snow water equivalent and permafrost ground temperature than for the vegetation parameters. Changes across all the different parameters could specifically be determined for eastern Siberia. The results provide a baseline for future studies on common drivers of essential climate variables and causative effects across the Arctic.
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Ice cover on small water bodies is highly sensitive to climate change and influences ecosystems, water, and the carbon cycle. We produced a satellite-based ice fraction dataset for small water bodies on the Arctic Coastal Plain from 2017 to 2023. The dataset captures freeze-up and break-up timing and reveals spatial variability. It will support studies of climate–ice interactions and improve models of water and carbon processes.
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We measured over 13,000 methane fluxes at a site in the Canadian Arctic and linked them with drone and free satellite images. We tested four machine-learning methods and two map scales. Metre-scale maps captured small wet and dry features that strongly affect methane release, while coarser maps blurred them. Different models shifted the monthly methane estimate. This helps choose the right data and tools to map methane, design monitoring networks, and check climate models.
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Manuscript not accepted for further review
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Arctic warming is causing permafrost to thaw, affecting ecosystems and climate. Since land cover, especially vegetation, shapes how permafrost responds, accurate maps are crucial. Using machine learning, we combined existing global and regional datasets to create a hybrid detailed 1-km map of Arctic-Boreal land cover, improving the representation of forests, shrubs, and wetlands across the circumpolar.
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Mapping soil moisture in Arctic permafrost regions is crucial for various activities, but it is challenging with typical satellite methods due to the landscape's diversity. Seasonal freezing and thawing cause the ground to periodically rise and subside. Our research demonstrates that this seasonal ground settlement, measured with Sentinel-1 satellite data, is larger in areas with wetter soils. This method helps to monitor permafrost degradation.
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The Cryosphere, 19, 4929–4967, https://doi.org/10.5194/tc-19-4929-2025, https://doi.org/10.5194/tc-19-4929-2025, 2025
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We identified similarities between sea ice dynamics and conditions on land across the Arctic, above 60° N, for 2000–2019. Significant correlations were more common for snow water equivalent and permafrost ground temperature than for the vegetation parameters. Changes across all the different parameters could specifically be determined for eastern Siberia. The results provide a baseline for future studies on common drivers of essential climate variables and causative effects across the Arctic.
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Preprint under review for ESSD
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Ice cover on small water bodies is highly sensitive to climate change and influences ecosystems, water, and the carbon cycle. We produced a satellite-based ice fraction dataset for small water bodies on the Arctic Coastal Plain from 2017 to 2023. The dataset captures freeze-up and break-up timing and reveals spatial variability. It will support studies of climate–ice interactions and improve models of water and carbon processes.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-3968, https://doi.org/10.5194/egusphere-2025-3968, 2025
Short summary
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We measured over 13,000 methane fluxes at a site in the Canadian Arctic and linked them with drone and free satellite images. We tested four machine-learning methods and two map scales. Metre-scale maps captured small wet and dry features that strongly affect methane release, while coarser maps blurred them. Different models shifted the monthly methane estimate. This helps choose the right data and tools to map methane, design monitoring networks, and check climate models.
Anna Puggaard, Nicolaj Hansen, Ruth Mottram, Thomas Nagler, Stefan Scheiblauer, Sebastian B. Simonsen, Louise S. Sørensen, Jan Wuite, and Anne M. Solgaard
The Cryosphere, 19, 2963–2981, https://doi.org/10.5194/tc-19-2963-2025, https://doi.org/10.5194/tc-19-2963-2025, 2025
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Regional climate models are currently the only source for assessing the melt volume of the Greenland Ice Sheet on a global scale. This study compares the modeled melt volume with observations from weather stations and melt extent observed from the Advanced SCATterometer (ASCAT) to assess the performance of the models. It highlights the importance of critically evaluating model outputs with high-quality satellite measurements to improve the understanding of variability among models.
Sara Hyvärinen, Maria Katariina Tenkanen, Aki Tsuruta, Anttoni Erkkilä, Kimmo Rautiainen, Hermanni Aaltonen, Motoki Sasakawa, and Tuula Aalto
EGUsphere, https://doi.org/10.5194/egusphere-2025-2794, https://doi.org/10.5194/egusphere-2025-2794, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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We analyzed spring methane emissions from northern high-latitude wetlands using satellite thaw data and inverse modeling (2011–2021). Comparing region-based and grid-based approaches, we found that emissions varied with the length of the melting season, which depended on air temperature. We found spring melting season emissions ranged from 0.45 Tg to 1.83 Tg depending on the approach, with no clear trend over the period. Our methods allow for seasonal methane monitoring across different scales.
Constanze Reinken, Victor Brovkin, Philipp de Vrese, Ingmar Nitze, Helena Bergstedt, and Guido Grosse
EGUsphere, https://doi.org/10.5194/egusphere-2025-1817, https://doi.org/10.5194/egusphere-2025-1817, 2025
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Thermokarst lakes are dynamic features of ice-rich permafrost landscapes, altering energy, water and carbon cycles, but have so far mostly been modeled on site-level scale. A deterministic modelling approach would be challenging on larger scales due to the lack of extensive high-resolution data of sub-surface conditions. We therefore develop a conceptual stochastic model of thermokarst lake dynamics that treats the involved processes as probabilistic.
Qing Ying, Benjamin Poulter, Jennifer D. Watts, Kyle A. Arndt, Anna-Maria Virkkala, Lori Bruhwiler, Youmi Oh, Brendan M. Rogers, Susan M. Natali, Hilary Sullivan, Amanda Armstrong, Eric J. Ward, Luke D. Schiferl, Clayton D. Elder, Olli Peltola, Annett Bartsch, Ankur R. Desai, Eugénie Euskirchen, Mathias Göckede, Bernhard Lehner, Mats B. Nilsson, Matthias Peichl, Oliver Sonnentag, Eeva-Stiina Tuittila, Torsten Sachs, Aram Kalhori, Masahito Ueyama, and Zhen Zhang
Earth Syst. Sci. Data, 17, 2507–2534, https://doi.org/10.5194/essd-17-2507-2025, https://doi.org/10.5194/essd-17-2507-2025, 2025
Short summary
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We present daily methane (CH4) fluxes of northern wetlands at 10 km resolution during 2016–2022 (WetCH4) derived from a novel machine learning framework. We estimated an average annual CH4 emission of 22.8 ± 2.4 Tg CH4 yr−1 (15.7–51.6 Tg CH4 yr−1). Emissions were intensified in 2016, 2020, and 2022, with the largest interannual variation coming from Western Siberia. Continued, all-season tower observations and improved soil moisture products are needed for future improvement of CH4 upscaling.
Valeria Briones, Hélène Genet, Elchin E. Jafarov, Brendan M. Rogers, Jennifer D. Watts, Anna-Maria Virkkala, Annett Bartsch, Benjamin C. Maglio, Joshua Rady, and Susan M. Natali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-226, https://doi.org/10.5194/essd-2025-226, 2025
Manuscript not accepted for further review
Short summary
Short summary
Arctic warming is causing permafrost to thaw, affecting ecosystems and climate. Since land cover, especially vegetation, shapes how permafrost responds, accurate maps are crucial. Using machine learning, we combined existing global and regional datasets to create a hybrid detailed 1-km map of Arctic-Boreal land cover, improving the representation of forests, shrubs, and wetlands across the circumpolar.
Julia Wagner, Juliane Wolter, Justine Ramage, Victoria Martin, Andreas Richter, Niek Jesse Speetjens, Jorien E. Vonk, Rachele Lodi, Annett Bartsch, Michael Fritz, Hugues Lantuit, and Gustaf Hugelius
EGUsphere, https://doi.org/10.5194/egusphere-2025-1052, https://doi.org/10.5194/egusphere-2025-1052, 2025
Short summary
Short summary
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Barbara Widhalm, Annett Bartsch, Tazio Strozzi, Nina Jones, Artem Khomutov, Elena Babkina, Marina Leibman, Rustam Khairullin, Mathias Göckede, Helena Bergstedt, Clemens von Baeckmann, and Xaver Muri
The Cryosphere, 19, 1103–1133, https://doi.org/10.5194/tc-19-1103-2025, https://doi.org/10.5194/tc-19-1103-2025, 2025
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Short summary
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Richard Parsons, Sainan Sun, G. Hilmar Gudmundsson, Jan Wuite, and Thomas Nagler
The Cryosphere, 18, 5789–5801, https://doi.org/10.5194/tc-18-5789-2024, https://doi.org/10.5194/tc-18-5789-2024, 2024
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In 2022, multi-year landfast sea ice in Antarctica's Larsen B embayment disintegrated, after which time an increase in the rate at which Crane Glacier discharged ice into the ocean was observed. As the fast ice was joined to the glacier terminus, it could provide resistance against the glacier's flow, slowing down the rate of ice discharge. We used numerical modelling to quantify this resistive stress and found that the fast ice provided significant support to Crane prior to its disintegration.
Clemens von Baeckmann, Annett Bartsch, Helena Bergstedt, Aleksandra Efimova, Barbara Widhalm, Dorothee Ehrich, Timo Kumpula, Alexander Sokolov, and Svetlana Abdulmanova
The Cryosphere, 18, 4703–4722, https://doi.org/10.5194/tc-18-4703-2024, https://doi.org/10.5194/tc-18-4703-2024, 2024
Short summary
Short summary
Lakes are common features in Arctic permafrost areas. Land cover change following their drainage needs to be monitored since it has implications for ecology and the carbon cycle. Satellite data are key in this context. We compared a common vegetation index approach with a novel land-cover-monitoring scheme. Land cover information provides specific information on wetland features. We also showed that the bioclimatic gradients play a significant role after drainage within the first 10 years.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
Short summary
Short summary
Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
Annett Bartsch, Aleksandra Efimova, Barbara Widhalm, Xaver Muri, Clemens von Baeckmann, Helena Bergstedt, Ksenia Ermokhina, Gustaf Hugelius, Birgit Heim, and Marina Leibman
Hydrol. Earth Syst. Sci., 28, 2421–2481, https://doi.org/10.5194/hess-28-2421-2024, https://doi.org/10.5194/hess-28-2421-2024, 2024
Short summary
Short summary
Wetness gradients and landcover diversity for the entire Arctic tundra have been assessed using a novel satellite-data-based map. Patterns of lakes, wetlands, general soil moisture conditions and vegetation physiognomy are represented at 10 m. About 40 % of the area north of the treeline falls into three units of dry types, with limited shrub growth. Wetter regions have higher landcover diversity than drier regions.
Annett Bartsch, Helena Bergstedt, Georg Pointner, Xaver Muri, Kimmo Rautiainen, Leena Leppänen, Kyle Joly, Aleksandr Sokolov, Pavel Orekhov, Dorothee Ehrich, and Eeva Mariatta Soininen
The Cryosphere, 17, 889–915, https://doi.org/10.5194/tc-17-889-2023, https://doi.org/10.5194/tc-17-889-2023, 2023
Short summary
Short summary
Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. In extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record. Retrieval is most robust in the tundra biome, where records can be used to identify extremes and the results can be applied to impact studies at regional scale.
Karla Boxall, Frazer D. W. Christie, Ian C. Willis, Jan Wuite, and Thomas Nagler
The Cryosphere, 16, 3907–3932, https://doi.org/10.5194/tc-16-3907-2022, https://doi.org/10.5194/tc-16-3907-2022, 2022
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Using high-spatial- and high-temporal-resolution satellite imagery, we provide the first evidence for seasonal flow variability of land ice draining to George VI Ice Shelf (GVIIS), Antarctica. Ultimately, our findings imply that other glaciers in Antarctica may be susceptible to – and/or currently undergoing – similar ice-flow seasonality, including at the highly vulnerable and rapidly retreating Pine Island and Thwaites glaciers.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
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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.
Frank Paul, Livia Piermattei, Désirée Treichler, Lin Gilbert, Luc Girod, Andreas Kääb, Ludivine Libert, Thomas Nagler, Tazio Strozzi, and Jan Wuite
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
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
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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...
