Articles | Volume 19, issue 3
https://doi.org/10.5194/tc-19-1103-2025
© 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-19-1103-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
InSAR-derived seasonal subsidence reflects spatial soil moisture patterns in Arctic lowland permafrost regions
Barbara Widhalm
CORRESPONDING AUTHOR
b.geos, Industriestrasse 1, 2100 Korneuburg, Austria
Austrian Polar Research Institute, c/o Universität Wien, Vienna, Austria
Annett Bartsch
b.geos, Industriestrasse 1, 2100 Korneuburg, Austria
Austrian Polar Research Institute, c/o Universität Wien, Vienna, Austria
Tazio Strozzi
Gamma Remote Sensing, Gümligen, Switzerland
Nina Jones
Gamma Remote Sensing, Gümligen, Switzerland
Artem Khomutov
Earth Cryosphere Institute, Tyumen Scientific Centre SB RAS, Tyumen, Russia
Elena Babkina
Earth Cryosphere Institute, Tyumen Scientific Centre SB RAS, Tyumen, Russia
Marina Leibman
Earth Cryosphere Institute, Tyumen Scientific Centre SB RAS, Tyumen, Russia
Rustam Khairullin
b.geos, Industriestrasse 1, 2100 Korneuburg, Austria
Mathias Göckede
Max Planck Institute for Biogeochemistry, Jena, Germany
Helena Bergstedt
b.geos, Industriestrasse 1, 2100 Korneuburg, Austria
Austrian Polar Research Institute, c/o Universität Wien, Vienna, Austria
Clemens von Baeckmann
b.geos, Industriestrasse 1, 2100 Korneuburg, Austria
Austrian Polar Research Institute, c/o Universität Wien, Vienna, Austria
Xaver Muri
b.geos, Industriestrasse 1, 2100 Korneuburg, Austria
Austrian Polar Research Institute, c/o Universität Wien, Vienna, Austria
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Peter Stimmler, Mathias Goeckede, Bo Elberling, Susan Natali, Peter Kuhry, Nia Perron, Fabrice Lacroix, Gustaf Hugelius, Oliver Sonnentag, Jens Strauss, Christina Minions, Michael Sommer, and Jörg Schaller
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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
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Earth Syst. Sci. Data, 14, 5061–5091, https://doi.org/10.5194/essd-14-5061-2022, https://doi.org/10.5194/essd-14-5061-2022, 2022
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Permafrost thaw releases methane that can be emitted into the atmosphere or transported by Arctic rivers. Methane measurements are lacking in large Arctic river regions. In the Kolyma River (northeast Siberia), we measured dissolved methane to map its distribution with great spatial detail. The river’s edge and river junctions had the highest methane concentrations compared to other river areas. Microbial communities in the river showed that the river’s methane likely is from the adjacent land.
Aldo Bertone, Chloé Barboux, Xavier Bodin, Tobias Bolch, Francesco Brardinoni, Rafael Caduff, Hanne H. Christiansen, Margaret M. Darrow, Reynald Delaloye, Bernd Etzelmüller, Ole Humlum, Christophe Lambiel, Karianne S. Lilleøren, Volkmar Mair, Gabriel Pellegrinon, Line Rouyet, Lucas Ruiz, and Tazio Strozzi
The Cryosphere, 16, 2769–2792, https://doi.org/10.5194/tc-16-2769-2022, https://doi.org/10.5194/tc-16-2769-2022, 2022
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We present the guidelines developed by the IPA Action Group and within the ESA Permafrost CCI project to include InSAR-based kinematic information in rock glacier inventories. Nine operators applied these guidelines to 11 regions worldwide; more than 3600 rock glaciers are classified according to their kinematics. We test and demonstrate the feasibility of applying common rules to produce homogeneous kinematic inventories at global scale, useful for hydrological and climate change purposes.
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.
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.
Wolfgang Fischer, Christoph K. Thomas, Nikita Zimov, and Mathias Göckede
Biogeosciences, 19, 1611–1633, https://doi.org/10.5194/bg-19-1611-2022, https://doi.org/10.5194/bg-19-1611-2022, 2022
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Arctic permafrost ecosystems may release large amounts of carbon under warmer future climates and may therefore accelerate global climate change. Our study investigated how long-term grazing by large animals influenced ecosystem characteristics and carbon budgets at a Siberian permafrost site. Our results demonstrate that such management can contribute to stabilizing ecosystems to keep carbon in the ground, particularly through drying soils and reducing methane emissions.
Tazio Strozzi, Andreas Wiesmann, Andreas Kääb, Thomas Schellenberger, and Frank Paul
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-44, https://doi.org/10.5194/essd-2022-44, 2022
Revised manuscript not accepted
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Knowledge on surface velocity of glaciers and ice caps contributes to a better understanding of a wide range of processes related to glacier dynamics, mass change and response to climate. Based on the release of historical satellite radar data from various space agencies we compiled nearly complete mosaics of winter ice surface velocities for the 1990's over the Eastern Arctic. Compared to the present state, we observe a general increase of ice velocities along with a retreat of glacier fronts.
Martijn M. T. A. Pallandt, Jitendra Kumar, Marguerite Mauritz, Edward A. G. Schuur, Anna-Maria Virkkala, Gerardo Celis, Forrest M. Hoffman, and Mathias Göckede
Biogeosciences, 19, 559–583, https://doi.org/10.5194/bg-19-559-2022, https://doi.org/10.5194/bg-19-559-2022, 2022
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Thawing of Arctic permafrost soils could trigger the release of vast amounts of carbon to the atmosphere, thus enhancing climate change. Our study investigated how well the current network of eddy covariance sites to monitor greenhouse gas exchange at local scales captures pan-Arctic flux patterns. We identified large coverage gaps, e.g., in Siberia, but also demonstrated that a targeted addition of relatively few sites can significantly improve network performance.
Anna-Maria Virkkala, Susan M. Natali, Brendan M. Rogers, Jennifer D. Watts, Kathleen Savage, Sara June Connon, Marguerite Mauritz, Edward A. G. Schuur, Darcy Peter, Christina Minions, Julia Nojeim, Roisin Commane, Craig A. Emmerton, Mathias Goeckede, Manuel Helbig, David Holl, Hiroki Iwata, Hideki Kobayashi, Pasi Kolari, Efrén López-Blanco, Maija E. Marushchak, Mikhail Mastepanov, Lutz Merbold, Frans-Jan W. Parmentier, Matthias Peichl, Torsten Sachs, Oliver Sonnentag, Masahito Ueyama, Carolina Voigt, Mika Aurela, Julia Boike, Gerardo Celis, Namyi Chae, Torben R. Christensen, M. Syndonia Bret-Harte, Sigrid Dengel, Han Dolman, Colin W. Edgar, Bo Elberling, Eugenie Euskirchen, Achim Grelle, Juha Hatakka, Elyn Humphreys, Järvi Järveoja, Ayumi Kotani, Lars Kutzbach, Tuomas Laurila, Annalea Lohila, Ivan Mammarella, Yojiro Matsuura, Gesa Meyer, Mats B. Nilsson, Steven F. Oberbauer, Sang-Jong Park, Roman Petrov, Anatoly S. Prokushkin, Christopher Schulze, Vincent L. St. Louis, Eeva-Stiina Tuittila, Juha-Pekka Tuovinen, William Quinton, Andrej Varlagin, Donatella Zona, and Viacheslav I. Zyryanov
Earth Syst. Sci. Data, 14, 179–208, https://doi.org/10.5194/essd-14-179-2022, https://doi.org/10.5194/essd-14-179-2022, 2022
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The effects of climate warming on carbon cycling across the Arctic–boreal zone (ABZ) remain poorly understood due to the relatively limited distribution of ABZ flux sites. Fortunately, this flux network is constantly increasing, but new measurements are published in various platforms, making it challenging to understand the ABZ carbon cycle as a whole. Here, we compiled a new database of Arctic–boreal CO2 fluxes to help facilitate large-scale assessments of the ABZ carbon cycle.
Torben Windirsch, Guido Grosse, Mathias Ulrich, Bruce C. Forbes, Mathias Göckede, Juliane Wolter, Marc Macias-Fauria, Johan Olofsson, Nikita Zimov, and Jens Strauss
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-227, https://doi.org/10.5194/bg-2021-227, 2021
Revised manuscript not accepted
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With global warming, permafrost thaw and associated carbon release are of increasing importance. We examined how large herbivorous animals affect Arctic landscapes and how they might contribute to reduction of these emissions. We show that over a short timespan of roughly 25 years, these animals have already changed the vegetation and landscape. On pastures in a permafrost area in Siberia we found smaller thaw depth and higher carbon content than in surrounding non-pasture areas.
Kyle B. Delwiche, Sara Helen Knox, Avni Malhotra, Etienne Fluet-Chouinard, Gavin McNicol, Sarah Feron, Zutao Ouyang, Dario Papale, Carlo Trotta, Eleonora Canfora, You-Wei Cheah, Danielle Christianson, Ma. Carmelita R. Alberto, Pavel Alekseychik, Mika Aurela, Dennis Baldocchi, Sheel Bansal, David P. Billesbach, Gil Bohrer, Rosvel Bracho, Nina Buchmann, David I. Campbell, Gerardo Celis, Jiquan Chen, Weinan Chen, Housen Chu, Higo J. Dalmagro, Sigrid Dengel, Ankur R. Desai, Matteo Detto, Han Dolman, Elke Eichelmann, Eugenie Euskirchen, Daniela Famulari, Kathrin Fuchs, Mathias Goeckede, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Scott L. Graham, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, David Hollinger, Lukas Hörtnagl, Hiroki Iwata, Adrien Jacotot, Gerald Jurasinski, Minseok Kang, Kuno Kasak, John King, Janina Klatt, Franziska Koebsch, Ken W. Krauss, Derrick Y. F. Lai, Annalea Lohila, Ivan Mammarella, Luca Belelli Marchesini, Giovanni Manca, Jaclyn Hatala Matthes, Trofim Maximov, Lutz Merbold, Bhaskar Mitra, Timothy H. Morin, Eiko Nemitz, Mats B. Nilsson, Shuli Niu, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, William Riley, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Camilo Rey Sanchez, Edward A. Schuur, Karina V. R. Schäfer, Oliver Sonnentag, Jed P. Sparks, Ellen Stuart-Haëntjens, Cove Sturtevant, Ryan C. Sullivan, Daphne J. Szutu, Jonathan E. Thom, Margaret S. Torn, Eeva-Stiina Tuittila, Jessica Turner, Masahito Ueyama, Alex C. Valach, Rodrigo Vargas, Andrej Varlagin, Alma Vazquez-Lule, Joseph G. Verfaillie, Timo Vesala, George L. Vourlitis, Eric J. Ward, Christian Wille, Georg Wohlfahrt, Guan Xhuan Wong, Zhen Zhang, Donatella Zona, Lisamarie Windham-Myers, Benjamin Poulter, and Robert B. Jackson
Earth Syst. Sci. Data, 13, 3607–3689, https://doi.org/10.5194/essd-13-3607-2021, https://doi.org/10.5194/essd-13-3607-2021, 2021
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Methane is an important greenhouse gas, yet we lack knowledge about its global emissions and drivers. We present FLUXNET-CH4, a new global collection of methane measurements and a critical resource for the research community. We use FLUXNET-CH4 data to quantify the seasonality of methane emissions from freshwater wetlands, finding that methane seasonality varies strongly with latitude. Our new database and analysis will improve wetland model accuracy and inform greenhouse gas budgets.
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.
Alexander Savvichev, Igor Rusanov, Yury Dvornikov, Vitaly Kadnikov, Anna Kallistova, Elena Veslopolova, Antonina Chetverova, Marina Leibman, Pavel A. Sigalevich, Nikolay Pimenov, Nikolai Ravin, and Artem Khomutov
Biogeosciences, 18, 2791–2807, https://doi.org/10.5194/bg-18-2791-2021, https://doi.org/10.5194/bg-18-2791-2021, 2021
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Microbial processes of the methane cycle were studied in four lakes of the central part of the Yamal Peninsula in an area of continuous permafrost: two large, deep lakes and two small and shallow ones. It was found that only small, shallow lakes contributed significantly to the overall diffusive methane emissions from the water surface during the warm summer season. The water column of large, deep lakes on Yamal acted as a microbial filter preventing methane emissions into the atmosphere.
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.
Andreas Kääb, Tazio Strozzi, Tobias Bolch, Rafael Caduff, Håkon Trefall, Markus Stoffel, and Alexander Kokarev
The Cryosphere, 15, 927–949, https://doi.org/10.5194/tc-15-927-2021, https://doi.org/10.5194/tc-15-927-2021, 2021
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We present a map of rock glacier motion over parts of the northern Tien Shan and time series of surface speed for six of them over almost 70 years.
This is by far the most detailed investigation of this kind available for central Asia.
We detect a 2- to 4-fold increase in rock glacier motion between the 1950s and present, which we attribute to atmospheric warming.
Relative to the shrinking glaciers in the region, this implies increased importance of periglacial sediment transport.
Cited articles
Anders, K., Antonova, S., Beck, I., Boike, J., Höfle, B., Langer, M., Marsh, P., and Marx, S.: Multisensor ground-based measurements of the permafrost thaw subsidence in the Trail Valley Creek, NWT, Canada, 2015–2016, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.888566, 2018. a, b
Antonova, S., Sudhaus, H., Strozzi, T., Zwieback, S., Kääb, A., Heim, B., Langer, M., Bornemann, N., and Boike, J.: Thaw Subsidence of a Yedoma Landscape in Northern Siberia, Measured In Situ and Estimated from TerraSAR-X Interferometry, Remote Sens., 10, 494, https://doi.org/10.3390/rs10040494, 2018. a, b, c
Babkin, E. M., Khomutov, A. V., Dvornikov, Y. A., Khairullin, R. R., and Babkina, E. A.: Relief changes of the peat plateu with melting of polygonal-wedge ice in the northern part of the Pur-Taz interfluve, Regional Environmental Issues, 4, 115–119, https://doi.org/10.24411/1728-323X-2018-14115, 2018. a, b
Barrett, B. W., Dwyer, E., and Whelan, P.: Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques, Remote Sens., 1, 210–242, https://doi.org/10.3390/rs1030210, 2009. a
Bartalis, Z., Wagner, W., Naeimi, V., Hasenauer, S., Scipal, K., Bonekamp, H., Figa, J., and Anderson, C.: Initial soil moisture retrievals from the METOP-A Advanced Scatterometer (ASCAT), Geophys. Res. Lett., 34, L20401, https://doi.org/10.1029/2007GL031088, 2007. a
Bartholomé, E. and Belward, A. S.: GLC2000: a new approach to global land cover mapping from Earth observation data, Int. J. Remote Sens., 26, 1959–1977, https://doi.org/10.1080/01431160412331291297, 2005. a
Bartsch, A., Leibman, M., Strozzi, T., Khomutov, A., Widhalm, B., Babkina, E., Mullanurov, D., Ermokhina, K., Kroisleitner, C., and Bergstedt, H.: Seasonal Progression of Ground Displacement Identified with Satellite Radar Interferometry and the Impact of Unusually Warm Conditions on Permafrost at the Yamal Peninsula in 2016, Remote Sens., 11, 1865, https://doi.org/10.3390/rs11161865, 2019. a, b, c, d, e, f, g, h, i, j
Bartsch, A., Widhalm, B., Leibman, M., Ermokhina, K., Kumpula, T., Skarin, A., Wilcox, E. J., Jones, B. M., Frost, G. V., Höfler, A., and Pointner, G.: Feasibility of tundra vegetation height retrieval from Sentinel-1 and Sentinel-2 data, Remote Sens. Environ., 237, 111515, https://doi.org/10.1016/j.rse.2019.111515, 2020. a, b
Berardino, P., Fornaro, G., Lanari, R., and Sansosti, E.: A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms, IEEE T. Geosci. Remote Sens., 40, 2375–2383, https://doi.org/10.1109/TGRS.2002.803792, 2002. a
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology, Hydrol. Sci. B., 24, 43–69, https://doi.org/10.1080/02626667909491834, 1979. a
Boike, J., Miesner, F., Bornemann, N., Cable, W. L., and Grünberg, I.: Trail Valley Creek, NWT, Canada Soil Moisture and Temperature 2016 et seq, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.962726, 2023. a, b, c, d
Cheţan, M.-A., Dornik, A., Ardelean, F., Georgievski, G., Hagemann, S., Romanovsky, V. E., Onaca, A., and Drozdov, D. S.: 35 Years of Vegetation and Lake Dynamics in the Pechora Catchment, Russian European Arctic, Remote Sens, 12, 1863, https://doi.org/10.3390/rs12111863, 2020. a
Chen, J., Wu, Y., O'Connor, M., Cardenas, M. B., Schaefer, K., Michaelides, R., and Kling, G.: Active layer freeze-thaw and water storage dynamics in permafrost environments inferred from InSAR, Remote Sens. Environ., 248, 112007, https://doi.org/10.1016/j.rse.2020.112007, 2020. a
Chen, R. H., Michaelides, R. J., Zhao, Y., Huang, L., Wig, E., Sullivan, T. D., Parsekian, A. D., Zebker, H. A., Moghaddam, M., and Schaefer, K. M.: Permafrost Dynamics Observatory (PDO): 2. Joint Retrieval of Permafrost Active Layer Thickness and Soil Moisture From L-Band InSAR and P-Band PolSAR, Earth and Space Science, 10, e2022EA002453, https://doi.org/10.1029/2022EA002453, e2022EA002453 2022EA002453, 2023. a, b, c
Colliander, A., Jackson, T. J., Bindlish, R., Chan, S., Das, N., Kim, S. B., Cosh, M. H., Dunbar, R. S., Dang, L., Pashaian, L., Asanuma, J., Aida, K., Berg, A., Rowlandson, T., Bosch, D., Caldwell, T., Caylor, K., Goodrich, D., al Jassar, H., Lopez-Baeza, E., Martínez-Fernández, J., González-Zamora, A., Livingston, S., McNairn, H., Pacheco, A., Moghaddam, M., Montzka, C., Notarnicola, C., Niedrist, G., Pellarin, T., Prueger, J., Pulliainen, J., Rautiainen, K., Ramos, J., Seyfried, M., Starks, P., Su, Z., Zeng, Y., van der Velde, R., Thibeault, M., Dorigo, W., Vreugdenhil, M., Walker, J., Wu, X., Monerris, A., O'Neill, P., Entekhabi, D., Njoku, E., and Yueh, S.: Validation of SMAP surface soil moisture products with core validation sites, Remote Sens. Environ., 191, 215–231, https://doi.org/10.1016/j.rse.2017.01.021, 2017. a
Copernicus Data Space Ecosystem, European Space Agency: Sentinel-1 satellite data, [data set], https://dataspace.copernicus.eu (last access: 4 March 2025), 2023. a
Costantini, M.: A novel phase unwrapping method based on network programming, IEEE T. Geosci. Remote Sens., 36, 813–821, https://doi.org/10.1109/36.673674, 1998. a
Das, K. and Paul, P. K.: Present status of soil moisture estimation by microwave remote sensing, Cogent Geoscience, 1, 1084669, https://doi.org/10.1080/23312041.2015.1084669, 2015. a
De Zan, F. and Gomba, G.: Vegetation and soil moisture inversion from SAR closure phases: First experiments and results, Remote Sens. Environ., 217, 562–572, https://doi.org/10.1016/j.rse.2018.08.034, 2018. a
De Zan, F., Parizzi, A., Prats-Iraola, P., and López-Dekker, P.: A SAR Interferometric Model for Soil Moisture, IEEE T. Geosci. Remote Sens., 52, 418–425, https://doi.org/10.1109/TGRS.2013.2241069, 2014. a
Dini, B., Daout, S., Manconi, A., and Loew, S.: Classification of slope processes based on multitemporal DInSAR analyses in the Himalaya of NW Bhutan, Remote Sens. Environ., 233, 111408, https://doi.org/10.1016/j.rse.2019.111408, 2019. a
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sens. Environ., 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017. a, b, c
Dorigo, W., Preimesberger, W., Hahn, S., Van der Schalie, R., De Jeu, R., Kidd, R., Rodriguez-Fernandez, N., Hirschi, M., Stradiotti, P., Frederikse, T., Gruber, A., and Madelon, R.: ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 08.1 data collection, NERC EDS Centre for Environmental Data Analysis [data set], http://catalogue.ceda.ac.uk/uuid/ff890589c21f4033803aa550f52c980c/ (last access: 4 March 2025), 2023. a
Earth Resources Observation and Science (EROS) Center, Landsat 8-9 Operational Land Imager/Thermal Infrared Sensor Level-2, Collection 2, U.S. Geological Survey, [data set], https://doi.org/10.5066/P9OGBGM6, 2020. a
Engman, E. T.: Applications of microwave remote sensing of soil moisture for water resources and agriculture, Remote Sens. Environ., 35, 213–226, https://doi.org/10.1016/0034-4257(91)90013-V, 1991. a
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, P. IEEE, 98, 704–716, https://doi.org/10.1109/JPROC.2010.2043918, 2010. a
European Space Agency: Copernicus Global Digital Elevation Model, Open Topography [data set], https://doi.org/10.5069/G9028PQB, 2021. a
Farouki, O. T.: The thermal properties of soils in cold regions, Cold Reg. Sci. Technol., 5, 67–75, https://doi.org/10.1016/0165-232X(81)90041-0, 1981. a
Frappier, R., Lacelle, D., and Fraser, R.: Landscape Changes in the Tombstone Territorial Park region (central Yukon, Canada) from Multi-Level Remote Sensing Analysis, Arctic Science, 9, 838–855, https://doi.org/10.1139/AS-2022-0037, 2023. a
Fyodorov-Davydov, D. G., Sorokovikov, V. A., Ostroumov, V. E., Kholodov, A. L., Mitroshin, I. A., Mergelov, N. S., Davydov, S. P., Zimov, S. A., and Davydova, A. I.: Spatial and Temporal Observations of Seasonal Thaw in the Northern Kolyma Lowland, Polar Geography, 28, 308–325, https://doi.org/10.1080/789610208, 2004. a
Gao, B.-c.: NDWI –A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sens. Environ., 58, 257–266, https://doi.org/10.1016/S0034-4257(96)00067-3, 1996. a
Geological Survey of Canada: Surficial geology of Canada; Geological Survey of Canada, Canadian Geoscience Map 195 (preliminary, Surficial Data Model v. 2.0 conversion of Map1880A), scale 1:5000000, https://doi.org/10.4095/295462, 2014. a, b
Gisinger, C., Libert, L., Marinkovic, P., Krieger, L., Larsen, Y., Valentino, A., Breit, H., Balss, U., Suchandt, S., Nagler, T., Eineder, M., and Miranda, N.: The Extended Timing Annotation Dataset for Sentinel-1 – Product Description and First Evaluation Results, IEEE T. Geosci. Remote Sens., 60, 1–22, https://doi.org/10.1109/TGRS.2022.3194216, 2022. a
Göckede, M., Kittler, F., Kwon, M. J., Burjack, I., Heimann, M., Kolle, O., Zimov, N., and Zimov, S.: Shifted energy fluxes, increased Bowen ratios, and reduced thaw depths linked with drainage-induced changes in permafrost ecosystem structure, The Cryosphere, 11, 2975–2996, https://doi.org/10.5194/tc-11-2975-2017, 2017. a
Göckede, M., Kwon, M. J., Kittler, F., Heimann, M., Zimov, N., and Zimov, S.: Negative feedback processes following drainage slow down permafrost degradation, Glob. Change Biol., 25, 3254–3266, https://doi.org/10.1111/gcb.14744, 2019. a
Goldstein, R. M. and Werner, C. L.: Radar interferogram filtering for geophysical applications, Geophys. Res. Lett., 25, 4035–4038, https://doi.org/10.1029/1998GL900033, 1998. a
Grosse, G., Robinson, J., Bryant, R., Taylor, M., Harper, W., DeMasi, A., Kyker-Snowman, E., Veremeeva, A., Schirrmeister, L., and Harden, J.: Distribution of Late Pleistocene Ice-Rich Syngenetic Permafrost of the Yedoma Suite in East and Central Siberia, Russia, U.S. Geological Survey, Open-File Report 2013-1078, https://doi.org/10.3133/ofr20131078, 2013. a
Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W.: Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, 2019. a, b, c
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023. a
Högström, E. and Bartsch, A.: Impact of Backscatter Variations Over Water Bodies on Coarse-Scale Radar Retrieved Soil Moisture and the Potential of Correcting With Meteorological Data, IEEE T. Geosci. Remote Sens., 55, 3–13, https://doi.org/10.1109/TGRS.2016.2530845, 2017. a
Hu, Y., Liu, L., Larson, K. M., Schaefer, K. M., Zhang, J., and Yao, Y.: GPS Interferometric Reflectometry Reveals Cyclic Elevation Changes in Thaw and Freezing Seasons in a Permafrost Area (Barrow, Alaska), Geophys. Res. Lett., 45, 5581–5589, https://doi.org/10.1029/2018GL077960, 2018. a, b
Iijima, Y., Abe, T., Saito, H., Ulrich, M., Fedorov, A. N., Basharin, N. I., Gorokhov, A. N., and Makarov, V. S.: Thermokarst Landscape Development Detected by Multiple-Geospatial Data in Churapcha, Eastern Siberia, Front. Earth Sci., 9, 750298, https://doi.org/10.3389/feart.2021.750298, 2021. a
Jolivet, R., Grandin, R., Lasserre, C., Doin, M.-P., and Peltzer, G.: Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data, Geophys. Res. Lett., 38, L17311, https://doi.org/10.1029/2011GL048757, 2011. a
Jolivet, R., Agram, P. S., Lin, N. Y., Simons, M., Doin, M.-P., Peltzer, G., and Li, Z.: Improving InSAR geodesy using Global Atmospheric Models, J. Geophys. Res.-Sol. Ea., 119, 2324–2341, https://doi.org/10.1002/2013JB010588, 2014. a
Jones, B. M., Grosse, G., Arp, C. D., Jones, M. C., Walter Anthony, K. M., and Romanovsky, V. E.: Modern thermokarst lake dynamics in the continuous permafrost zone, northern Seward Peninsula, Alaska, J. Geophys. Res.-Biogeo., 116, G00M03, https://doi.org/10.1029/2011JG001666, 2011. a
Kerr, Y. H., Waldteufel, P., Richaume, P., Wigneron, J. P., Ferrazzoli, P., Mahmoodi, A., Al Bitar, A., Cabot, F., Gruhier, C., Juglea, S. E., Leroux, D., Mialon, A., and Delwart, S.: The SMOS Soil Moisture Retrieval Algorithm, IEEE T. Geosci. Remote Sens., 50, 1384–1403, https://doi.org/10.1109/TGRS.2012.2184548, 2012. a
Khairullin, R. R., Khomutov, A. V., Dvornikov, Y. A., Babkin, E. M., Babkina, E. A., and Soshchenko, D. D.: Analysis of peatland changes in the northeastern part of the Pur-Taz interfluve based on remote sensing and ground monitoring data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 16, 54–62, https://doi.org/10.21046/2070-7401-2019-16-4-54-62, 2019. a
Khitun, O. and Rebristaya, O.: Study of Plant Species Diversity in the West Siberian Arctic, Watson A., Aplet G., Hendee, J. (comps.) Personal, in: Societal and Ecological Values of the Wilderness, Proc. RMRS-P-4, Ogden, U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 42–48, 1998. a
Khomutov, A. V., Koroleva, E. S., Danko, M. M., and Khairullin, R. R.: Polygonal peatlands in the north of West Siberia: distribution and classification issues, in: Collection of reports of the Sixth Conference of Geocryologists of Russia “Monitoring in the permafrost zone”, 745–751, KDU, Dobrosvet, Lomonosov Moscow State University, https://doi.org/10.31453/kdu.ru.978-5-7913-1231-0-2022-1130, 2022. a
Kim, H., Crow, W., Li, X., Wagner, W., Hahn, S., and Lakshmi, V.: True global error maps for SMAP, SMOS, and ASCAT soil moisture data based on machine learning and triple collocation analysis, Remote Sens. Environ., 298, 113776, https://doi.org/10.1016/j.rse.2023.113776, 2023. a, b, c
Koroleva, E. S., Khairullin, R. R., Babkina, E. A., Slagoda, E. A., Khomutov, A. V., Melnikov, V. P., Babkin, E. M., and Tikhonravova, Y. V.: Vulnerability of the Ancient Peat Plateaus in Western Siberia, Dokl. Earth Sci., 491, 179–182, https://doi.org/10.1134/S1028334X20030095, 2021. a
Li, J., Wang, Q., Zhang, Y., Yang, S., and Gao, G.: An improved active layer thickness retrieval method over Qinghai-Tibet permafrost using InSAR technology: With emphasis on two-dimensional deformation and unfrozen water, Int. J. Appl. Earth Obs., 124, 103530, https://doi.org/10.1016/j.jag.2023.103530, 2023. a
Li, Z., Duan, M., Cao, Y., Mu, M., He, X., and Wei, J.: Mitigation of time-series InSAR turbulent atmospheric phase noise: A review, Geodesy and Geodynamics, 13, 93–103, https://doi.org/10.1016/j.geog.2021.12.002, 2022. a, b, c
Liljedahl, A., Boike, J., Daanen, R., Fedorov, A., Frost, G., Grosse, G., Hinzman, L., Iijima, Y., Jorgenson, J., Matveyeva, N., Necsoiu, M., Raynolds, M., Romanovsky, V., Schulla, J., Tape, K., Walker, D., Wilson, C., Yabuki, H., and Zona, D.: Pan-Arctic Ice-wedge Degradation in Warming Permafrost and its Influence on Tundra Hydrology, Nat. Geosci., 9, 312–318, https://doi.org/10.1038/ngeo2674, 2016. a, b
Liu, L.: Comment on eguspere-2024-2356, https://doi.org/10.5194/egusphere-2024-2356-RC2, 2024. a
Liu, L., Zhang, T., and Wahr, J.: InSAR measurements of surface deformation over permafrost on the North Slope of Alaska, J. Geophys. Res.-Earth, 115, F03023, https://doi.org/10.1029/2009JF001547, 2010. a, b
Liu, L., Schaefer, K., Zhang, T., and Wahr, J.: Estimating 1992–2000 average active layer thickness on the Alaskan North Slope from remotely sensed surface subsidence, J. Geophys. Res.-Earth, 117, F01005, https://doi.org/10.1029/2011JF002041, 2012. a, b, c
Liu, L., Schaefer, K. M., Chen, A. C., Gusmeroli, A., Zebker, H. A., and Zhang, T.: Remote sensing measurements of thermokarst subsidence using InSAR, J. Geophys. Res.-Earth, 120, 1935–1948, https://doi.org/10.1002/2015JF003599, 2015. a
Michaelides, R. and Zebker, H.: Feasibility of Retrieving Soil Moisture from InSAR Decorrelation Phase and Closure Phase, in: IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, 12–15, https://doi.org/10.1109/IGARSS39084.2020.9323833, 2020. a
Michaelides, R. J., Chen, R. H., Zhao, Y., Schaefer, K., Parsekian, A. D., Sullivan, T., Moghaddam, M., Zebker, H. A., Liu, L., Xu, X., and Chen, J.: Permafrost Dynamics Observatory—Part I: Postprocessing and Calibration Methods of UAVSAR L-Band InSAR Data for Seasonal Subsidence Estimation, Earth and Space Science, 8, e2020EA001630, https://doi.org/10.1029/2020EA001630, 2021. a, b, c
Miner, K. R., Malina, E., and Bartsch, A.: Permafrost carbon emissions in a changing Arctic, Nature Reviews Earth & Environment, 3, 55–67, https://doi.org/10.1038/s43017-021-00230-3, 2022. a
Mishra, U. and Riley, W. J.: Alaskan soil carbon stocks: spatial variability and dependence on environmental factors, Biogeosciences, 9, 3637–3645, https://doi.org/10.5194/bg-9-3637-2012, 2012. a
Moran, M. S., Peters-Lidard, C. D., Watts, J. M., and McElroy, S.: Estimating soil moisture at the watershed scale with satellite-based radar and land surface models, Can. J. Remote Sens., 30, 805–826, https://doi.org/10.5589/m04-043, 2004. a
Murray, K. D., Bekaert, D. P. S., and Lohman, R. B.: Tropospheric corrections for InSAR: Statistical assessments and applications to the Central United States and Mexico, Remote Sens. Environ., 232, 111326, https://doi.org/10.1016/j.rse.2019.111326, 2019. a, b
Muskett, R.: L-Band InSAR Penetration Depth Experiment, North Slope Alaska, Journal of Geoscience and Environment Protection, 5, 14–30, https://doi.org/10.4236/gep.2017.53002, 2017. a
Nitze, I., Grosse, G., Jones, B. M., Arp, C. D., Ulrich, M., Fedorov, A., and Veremeeva, A.: Landsat-Based Trend Analysis of Lake Dynamics across Northern Permafrost Regions, Remote Sens., 9, 640, https://doi.org/10.3390/rs9070640, 2017. a
Obu, J., Lantuit, H., Myers-Smith, I., Heim, B., Wolter, J., and Fritz, M.: Effect of Terrain Characteristics on Soil Organic Carbon and Total Nitrogen Stocks in Soils of Herschel Island, Western Canadian Arctic, Permafrost Periglac., 28, 92–107, https://doi.org/10.1002/ppp.1881, 2017. a
Obu, J., Westermann, S., Kääb, A., and Bartsch, A.: Ground Temperature Map, 2000-2016, Northern Hemisphere Permafrost, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.888600, 2018. a
Obu, J., Westermann, S., Bartsch, A., Berdnikov, N., Christiansen, H. H., Dashtseren, A., Delaloye, R., Elberling, B., Etzelmüller, B., Kholodov, A., Khomutov, A., Kääb, A., Leibman, M. O., Lewkowicz, A. G., Panda, S. K., Romanovsky, V., Way, R. G., Westergaard-Nielsen, A., Wu, T., Yamkhin, J., and Zou, D.: Northern Hemisphere permafrost map based on TTOP modelling for 2000–2016 at 1 km2 scale, Earth-Sci. Rev., 193, 299–316, https://doi.org/10.1016/j.earscirev.2019.04.023, 2019. a
Obu, J., Westermann, S., Barboux, C., Bartsch, A., Delaloye, R., Grosse, G., Heim, B., Hugelius, G., Irrgang, A., Kääb, A. M., Kroisleitner, C., Matthes, H., Nitze, I., Pellet, C., Seifert, F. M., Strozzi, T., Wegmüller, U., Wieczorek, M., and Wiesmann, A.: ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0, NERC EDS Centre for Environmental Data Analysis, https://doi.org/10.5285/67a3f8c8dc914ef99f7f08eb0d997e23, 2021. a
Parinussa, R. M., Holmes, T. R. H., Wanders, N., Dorigo, W. A., and de Jeu, R. A. M.: A Preliminary Study toward Consistent Soil Moisture from AMSR2, J. Hydrometeorol., 16, 932–947, https://doi.org/10.1175/JHM-D-13-0200.1, 2015. a
Pomeroy, J. W., Gray, D. M., Shook, K. R., Toth, B., Essery, R. L. H., Pietroniro, A., and Hedstrom, N.: An evaluation of snow accumulation and ablation processes for land surface modelling, Hydrol. Process., 12, 2339–2367, https://doi.org/10.1002/(SICI)1099-1085(199812)12:15<2339::AID-HYP800>3.0.CO;2-L, 1998. a
Porter, C., Howat, I., Noh, M.-J., Husby, E., Khuvis, S., Danish, E., Tomko, K., Gardiner, J., Negrete, A., Yadav, B., Klassen, J., Kelleher, C., Cloutier, M., Bakker, J., Enos, J., Arnold, G., Bauer, G., and Morin, P.: ArcticDEM – Strips, Version 4.1, Harvard Dataverse, V1 [data set], https://doi.org/10.7910/DVN/C98DVS, 2022. a
Preimesberger, W., Scanlon, T., Su, C.-H., Gruber, A., and Dorigo, W.: Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record, IEEE T. Geosci. Remote Sens., 59, 2845–2862, https://doi.org/10.1109/TGRS.2020.3012896, 2021. a, b, c
Riihimäki, H., Kemppinen, J., Kopecký, M., and Luoto, M.: Topographic Wetness Index as a Proxy for Soil Moisture: The Importance of Flow-Routing Algorithm and Grid Resolution, Water Resour. Res., 57, e2021WR029871, https://doi.org/10.1029/2021WR029871, 2021. a, b
Rouyet, L., Liu, L., Strand, S. M., Christiansen, H. H., Lauknes, T. R., and Larsen, Y.: Seasonal InSAR Displacements Documenting the Active Layer Freeze and Thaw Progression in Central-Western Spitsbergen, Svalbard, Remote Sens., 13, 2977, https://doi.org/10.3390/rs13152977, 2021. a
Sadri, S., Pan, M., Wada, Y., Vergopolan, N., Sheffield, J., Famiglietti, J. S., Kerr, Y., and Wood, E.: A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP, Remote Sens. Environ., 246, 111864, https://doi.org/10.1016/j.rse.2020.111864, 2020. a, b
Schaefer, K. M., Liu, L., Parsekian, A. D., Jafarov, E. E., Chen, A. C., jun Zhang, T., Gusmeroli, A., Panda, S. K., Zebker, H. A., and Schaefer, T.: Remotely Sensed Active Layer Thickness (ReSALT) at Barrow, Alaska Using Interferometric Synthetic Aperture Radar, Remote. Sens., 7, 3735–3759, 2015. a, b
Scheer, J., Caduff, R., How, P., Marcer, M., Strozzi, T., Bartsch, A., and Ingeman-Nielsen, T.: Thaw-Season InSAR Surface Displacements and Frost Susceptibility Mapping to Support Community-Scale Planning in Ilulissat, West Greenland, Remote Sens., 15, 3310, https://doi.org/10.3390/rs15133310, 2023. a, b
Shmelev, D., Veremeeva, A., Kraev, G., Kholodov, A., Spencer, R., Walker, W., and Rivkina, E.: Estimation and Sensitivity of Carbon Storage in Permafrost of North-Eastern Yakutia, Permafrost Periglac., 28, 379–390, https://doi.org/10.1002/ppp.1933, 2017. a
Short, N.: RADARSAT Constellation Mission: DInSAR potential in permafrost terrain, https://doi.org/10.4095/300078, 2017. a
Short, N. and Fraser, R.: Comparison of RADARSAT-2 and Sentinel-1 DInSAR displacements over upland ice-wedge polygonal terrain, Banks Island, Northwest Territories, Canada, Geomatics Canada, Open File 73, 22 p., https://doi.org/10.4095/331683, 2023. a
Short, N., LeBlanc, A.-M., Sladen, W., Oldenborger, G., Mathon-Dufour, V., and Brisco, B.: RADARSAT-2 D-InSAR for ground displacement in permafrost terrain, validation from Iqaluit Airport, Baffin Island, Canada, Remote Sens. Environ., 141, 40–51, https://doi.org/10.1016/j.rse.2013.10.016, 2014. a, b
Stolbovoi, V. and McCallum, I.: CD-ROM Land Resources of Russia, Laxenburg, Austria: International Institute for Applied Systems Analysis and the Russian Academy of Science, 2002. a
Streletskiy, D. A., Maslakov, A., Grosse, G., Shiklomanov, N. I., Farquharson, L., Zwieback, S., Iwahana, G., Bartsch, A., Liu, L., Strozzi, T., Lee, H., and Debolskiy, M. V.: Thawing permafrost is subsiding in the Northern Hemisphere – review and perspectives, Environ. Res. Lett., 20, 013006, https://doi.org/10.1088/1748-9326/ada2ff, 2025. a
Subin, Z., Koven, C., Riley, W., Torn, M., Lawrence, D., and Swenson, S.: Effects of Soil Moisture on the Responses of Soil Temperatures to Climate Change in Cold Regions, J. Climate, 26, 3139–3158, https://doi.org/10.1175/JCLI-D-12-00305.1, 2013. a
Sudmanns, M., Tiede, D., Augustin, H., and Lang, S.: Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass, Int. J. Digit. Earth, 13, 768–784, https://doi.org/10.1080/17538947.2019.1572799, 2020. a, b, c
Treat, C. C., Virkkala, A.-M., Burke, E., Bruhwiler, L., Chatterjee, A., Fisher, J. B., Hashemi, J., Parmentier, F.-J. W., Rogers, B. M., Westermann, S., Watts, J. D., Blanc-Betes, E., Fuchs, M., Kruse, S., Malhotra, A., Miner, K., Strauss, J., Armstrong, A., Epstein, H. E., Gay, B., Goeckede, M., Kalhori, A., Kou, D., Miller, C. E., Natali, S. M., Oh, Y., Shakil, S., Sonnentag, O., Varner, R. K., Zolkos, S., Schuur, E. A., and Hugelius, G.: Permafrost Carbon: Progress on Understanding Stocks and Fluxes Across Northern Terrestrial Ecosystems, J. Geophys. Res.-Biogeo., 129, e2023JG007638, https://doi.org/10.1029/2023JG007638, 2024. a
Treitz, P., Atkinson, D., Blaser, A., Bonney, M., Braybrook, C., Buckley, E., Collingwood, A., Edwards, R., van Ewijk, K., Freemantle, V., Gregory, F., Holloway, J., Hung, J., Lamoureux, S., Liu, N., Ljubicic, G., Robson, G., Rudy, A., Scott, N., Shang, C., and Wall, J.: Remote sensing of biogeophysical variables at the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut, Canada, Arctic Science, 10, 281–304, https://doi.org/10.1139/as-2023-0043, 2024. a
Ulma, T., Anjasmara, I. M., and Hayati, N.: Atmospheric phase delay correction of PS-InSAR to Monitor Land Subsidence in Surabaya, IOP C. Ser. Earth Env., 936, 012033, https://doi.org/10.1088/1755-1315/936/1/012033, 2021. a
Virkkala, A.-M., Aalto, J., Rogers, B. M., Tagesson, T., Treat, C. C., Natali, S. M., Watts, J. D., Potter, S., Lehtonen, A., Mauritz, M., Schuur, E. A. G., Kochendorfer, J., Zona, D., Oechel, W., Kobayashi, H., Humphreys, E., Goeckede, M., Iwata, H., Lafleur, P. M., Euskirchen, E. S., Bokhorst, S., Marushchak, M., Martikainen, P. J., Elberling, B., Voigt, C., Biasi, C., Sonnentag, O., Parmentier, F.-J. W., Ueyama, M., Celis, G., St.Louis, V. L., Emmerton, C. A., Peichl, M., Chi, J., Järveoja, J., Nilsson, M. B., Oberbauer, S. F., Torn, M. S., Park, S.-J., Dolman, H., Mammarella, I., Chae, N., Poyatos, R., López-Blanco, E., Christensen, T. R., Kwon, M. J., Sachs, T., Holl, D., and Luoto, M.: Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties, Glob. Change Biol., 27, 4040–4059, https://doi.org/10.1111/gcb.15659, 2021. a
Wagner, W., Bartalis, Z., Naeimi, V., Park, S.-E., Figa-Saldaña, J., and Bonekamp, H.: Status of the Metop ASCAT soil moisture product, in: 2010 IEEE International Geoscience and Remote Sensing Symposium, 276–279, https://doi.org/10.1109/IGARSS.2010.5653358, 2010. a
Walker, D. A., Epstein, H. E., Leibman, M. O., Moskalenko, N. G., Orekhov, P., Kuss, P., Matyshak, G. V., Kaarlejärvi, E., Forbes, B. C., Barbour, E. M., and Gobroski, K.: Data Report of the 2007 and 2008 Yamal Expeditions, Alaska Geobotany Center, Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, http://www.geobotany.uaf.edu/library/reports/WalkerDA2009_yamal_dr090401.pdf (last access: 4 March 2025), 2009. a, b
Wang, L. and Qu, J.: Satellite remote sensing applications for surface soil moisture monitoring: A review, Frontiers of Earth Science in China, 3, 237–247, https://doi.org/10.1007/s11707-009-0023-7, 2009. a
Wang, L., Zhao, L., Zhou, H., Liu, S., Du, E., Zou, D., Liu, G., Wang, C., and Li, Y.: Permafrost Ground Ice Melting and Deformation Time Series Revealed by Sentinel-1 InSAR in the Tanggula Mountain Region on the Tibetan Plateau, Remote Sens., 14, 811, https://doi.org/10.3390/rs14040811, 2022. a
Wang, Q., Yu, W., Xu, B., and Wei, G.: Assessing the Use of GACOS Products for SBAS-InSAR Deformation Monitoring: A Case in Southern California, Sensors, 19, 3894, https://doi.org/10.3390/s19183894, 2019. a
Widhalm, B., Bartsch, A., and Heim, B.: A novel approach for the characterization of tundra wetland regions with C-band SAR satellite data, Int. J. Remote Sens., 36, 5537–5556, https://doi.org/10.1080/01431161.2015.1101505, pMID: 27019539, 2015. a, b
Widhalm, B., Bartsch, A., Leibman, M., and Khomutov, A.: Active-layer thickness estimation from X-band SAR backscatter intensity, The Cryosphere, 11, 483–496, https://doi.org/10.5194/tc-11-483-2017, 2017a. a, b
Widhalm, B., Bartsch, A., Leibman, M., and Khomutov, A.: Active-layer thickness estimation from X-band SAR backscatter intensity, The Cryosphere, 11, 483–496, https://doi.org/10.5194/tc-11-483-2017, 2017b. a
Wig, E., Michaelides, R., and Zebker, H.: Fine-Resolution Measurement of Soil Moisture from Cumulative InSAR Closure Phase, IEEE T. Geosci. Remote, 62, 5212315, https://doi.org/10.36227/techrxiv.23929068, 2023. a, b
Wrona, E., Rowlandson, T. L., Nambiar, M., Berg, A. A., Colliander, A., and Marsh, P.: Validation of the Soil Moisture Active Passive (SMAP) satellite soil moisture retrieval in an Arctic tundra environment, Geophys. Res. Lett., 44, 4152–4158, https://doi.org/10.1002/2017GL072946, 2017. a, b
Xiao, R., Yu, C., Li, Z., and He, X.: Statistical assessment metrics for InSAR atmospheric correction: Applications to generic atmospheric correction online service for InSAR (GACOS) in Eastern China, Int. J. Appl. Earth Obs., 96, 102289, https://doi.org/10.1016/j.jag.2020.102289, 2021. a, b, c
Ye, N., Walker, J. P., Yeo, I.-Y., Jackson, T. J., Kerr, Y., Kim, E., Mcgrath, A., Popstefanija, I., Goodberlet, M., and Hills, J.: Toward P-Band Passive Microwave Sensing of Soil Moisture, IEEE Geosci. Remote S., 18, 504–508, https://doi.org/10.1109/LGRS.2020.2976204, 2021. a
Yu, C. and Li, Z.: Generic Atmospheric Correction Online Service for InSAR (GACOS) [data set], http://www.gacos.net/ (last access: 4 March 2025), 2024. a
Yu, C., Penna, N. T., and Li, Z.: Generation of real-time mode high-resolution water vapor fields from GPS observations, J. Geophys. Res.-Atmos., 122, 2008–2025, https://doi.org/10.1002/2016JD025753, 2017. a
Yu, C., Li, Z., Penna, N. T., and Crippa, P.: Generic Atmospheric Correction Model for Interferometric Synthetic Aperture Radar Observations, J. Geophys. Res.-Sol. Ea., 123, 9202–9222, https://doi.org/10.1029/2017JB015305, 2018. a, b
Zhang, D. and Zhou, G.: Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review, Sensors, 16, 1308, https://doi.org/10.3390/s16081308, 2016. a
Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021. a
Zwieback, S. and Berg, A. A.: Fine-Scale SAR Soil Moisture Estimation in the Subarctic Tundra, IEEE T. Geosci. Remote Sens., 57, 4898–4912, https://doi.org/10.1109/TGRS.2019.2893908, 2019. a
Zwieback, S. and Hajnsek, I.: Influence of Vegetation Growth on the Polarimetric Zero-Baseline DInSAR Phase Diversity – Implications for Deformation Studies, IEEE T. Geosci. Remote Sens., 54, 3070–3082, https://doi.org/10.1109/TGRS.2015.2511118, 2016. a
Zwieback, S., Hensley, S., and Hajnsek, I.: Assessment of soil moisture effects on L-band radar interferometry, Remote Sens. Environ., 164, 77–89, https://doi.org/10.1016/j.rse.2015.04.012, 2015. a
Zwieback, S., Hensley, S., and Hajnsek, I.: Soil Moisture Estimation Using Differential Radar Interferometry: Toward Separating Soil Moisture and Displacements, IEEE T. Geosci. Remote Sens., 55, 5069–5083, https://doi.org/10.1109/TGRS.2017.2702099, 2017. a
Zwieback, S., Westermann, S., Langer, M., Boike, J., Marsh, P., and Berg, A.: Improving Permafrost Modeling by Assimilating Remotely Sensed Soil Moisture, Water Resour. Res., 55, 1814–1832, https://doi.org/10.1029/2018WR023247, 2019. a
Short summary
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.
Mapping soil moisture in Arctic permafrost regions is crucial for various activities, but it is...