Articles | Volume 18, issue 8
https://doi.org/10.5194/tc-18-3723-2024
© Author(s) 2024. 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-18-3723-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Aug 2024
Research article |
| 20 Aug 2024
| 20 Aug 2024
Toward long-term monitoring of regional permafrost thaw with satellite interferometric synthetic aperture radar
School of Geosciences, University of South Florida, Tampa, FL, USA
Franz J. Meyer
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA
Timothy H. Dixon
School of Geosciences, University of South Florida, Tampa, FL, USA
Related authors
No articles found.
Kel N. Markert, Hyongki Lee, Gustavious P. Williams, E. James Nelson, Daniel P. Ames, Robert E. Griffin, and Franz J. Meyer
EGUsphere, https://doi.org/10.5194/egusphere-2024-3491, https://doi.org/10.5194/egusphere-2024-3491, 2024
Short summary
Short summary
Flooding is a major problem and predicting it accurately over large areas is tough. This study tested a new approach to forecast floods across a large region in the United States. By dividing the area into smaller areas to develop the prediction models and then combining, the method successfully simulated surface water extent for both high and low flow periods. The results were more accurate than existing approaches with similar methods which can improve flood forecasting for larger areas.
Jack Tarricone, Ryan W. Webb, Hans-Peter Marshall, Anne W. Nolin, and Franz J. Meyer
The Cryosphere, 17, 1997–2019, https://doi.org/10.5194/tc-17-1997-2023, https://doi.org/10.5194/tc-17-1997-2023, 2023
Short summary
Short summary
Mountain snowmelt provides water for billions of people across the globe. Despite its importance, we cannot currently measure the amount of water in mountain snowpacks from satellites. In this research, we test the ability of an experimental snow remote sensing technique from an airplane in preparation for the same sensor being launched on a future NASA satellite. We found that the method worked better than expected for estimating important snowpack properties.
Simon Zwieback and Franz J. Meyer
The Cryosphere, 15, 2041–2055, https://doi.org/10.5194/tc-15-2041-2021, https://doi.org/10.5194/tc-15-2041-2021, 2021
Short summary
Short summary
Thawing of ice-rich permafrost leads to subsidence and slumping, which can compromise Arctic infrastructure. However, we lack fine-scale maps of permafrost ground ice, chiefly because it is usually invisible at the surface. We show that subsidence at the end of summer serves as a
fingerprintwith which near-surface permafrost ground ice can be identified. As this can be done with satellite data, this method may help improve ground ice maps and thus sustainably steward the Arctic.
Dyre O. Dammann, Leif E. B. Eriksson, Son V. Nghiem, Erin C. Pettit, Nathan T. Kurtz, John G. Sonntag, Thomas E. Busche, Franz J. Meyer, and Andrew R. Mahoney
The Cryosphere, 13, 1861–1875, https://doi.org/10.5194/tc-13-1861-2019, https://doi.org/10.5194/tc-13-1861-2019, 2019
Short summary
Short summary
We validate TanDEM-X interferometry as a tool for deriving iceberg subaerial morphology using Operation IceBridge data. This approach enables a volumetric classification of icebergs, according to volume relevant to iceberg drift and decay, freshwater contribution, and potential impact on structures. We find iceberg volumes to generally match within 7 %. These results suggest that TanDEM-X could pave the way for future interferometric systems of scientific and operational iceberg classification.
Dyre Oliver Dammann, Leif E. B. Eriksson, Joshua M. Jones, Andrew R. Mahoney, Roland Romeiser, Franz J. Meyer, Hajo Eicken, and Yasushi Fukamachi
The Cryosphere, 13, 1395–1408, https://doi.org/10.5194/tc-13-1395-2019, https://doi.org/10.5194/tc-13-1395-2019, 2019
Short summary
Short summary
We evaluate single-pass synthetic aperture radar interferometry (InSAR) as a tool to assess sea ice drift and deformation. Initial validation shows that TanDEM-X phase-derived drift speed corresponds well with ground-based radar-derived motion. We further show that InSAR enables the identification of potentially important short-lived dynamic processes otherwise difficult to observe, with possible implication for engineering and sea ice modeling.
Dyre O. Dammann, Leif E. B. Eriksson, Andrew R. Mahoney, Hajo Eicken, and Franz J. Meyer
The Cryosphere, 13, 557–577, https://doi.org/10.5194/tc-13-557-2019, https://doi.org/10.5194/tc-13-557-2019, 2019
Short summary
Short summary
We present an approach for mapping bottomfast sea ice and landfast sea ice stability using Synthetic Aperture Radar Interferometry. This is the first comprehensive assessment of Arctic bottomfast sea ice extent with implications for subsea permafrost and marine habitats. Our pan-Arctic analysis also provides a new understanding of sea ice dynamics in five marginal seas of the Arctic Ocean relevant for strategic planning and tactical decision-making for different uses of coastal ice.
Surui Xie, Timothy H. Dixon, Denis Voytenko, Fanghui Deng, and David M. Holland
The Cryosphere, 12, 1387–1400, https://doi.org/10.5194/tc-12-1387-2018, https://doi.org/10.5194/tc-12-1387-2018, 2018
Short summary
Short summary
Time-varying velocity and topography of the terminus of Jakobshavn Isbræ were observed with a terrestrial radar interferometer in three summer campaigns (2012, 2015, 2016). Surface elevation and tidal responses of ice speed suggest a narrow floating zone in early summer, while in late summer the entire glacier is likely grounded. We hypothesize that Jakobshavn Isbræ advances a few km in winter to form a floating zone but loses this floating portion in the subsequent summer through calving.
P. R. Lindgren, G. Grosse, K. M. Walter Anthony, and F. J. Meyer
Biogeosciences, 13, 27–44, https://doi.org/10.5194/bg-13-27-2016, https://doi.org/10.5194/bg-13-27-2016, 2016
Short summary
Short summary
We mapped and characterized methane ebullition bubbles trapped in lake ice, and estimated whole-lake methane emission using high-resolution aerial images of a lake acquired following freeze-up. We identified the location and relative sizes of high- and low-flux seepage zones within the lake. A large number of seeps showed spatiotemporal stability over our study period. Our approach is applicable to other regions to improve the estimation of methane emission from lakes at the regional scale.
F. Alshawaf, B. Fersch, S. Hinz, H. Kunstmann, M. Mayer, and F. J. Meyer
Hydrol. Earth Syst. Sci., 19, 4747–4764, https://doi.org/10.5194/hess-19-4747-2015, https://doi.org/10.5194/hess-19-4747-2015, 2015
Short summary
Short summary
This work aims at deriving high spatially resolved maps of atmospheric water vapor by the fusion data from Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite Systems (GNSS), and the Weather Research and Forecasting (WRF) model. The data fusion approach exploits the redundant and complementary spatial properties of all data sets to provide more accurate and high-resolution maps of water vapor. The comparison with maps from MERIS shows rms values of less than 1 mm.
Related subject area
Discipline: Frozen ground | Subject: Remote Sensing
Land cover succession for recently drained lakes in permafrost on the Yamal Peninsula, Western Siberia
Multitemporal UAV LiDAR detects seasonal heave and subsidence on palsas
Allometric scaling of retrogressive thaw slumps
Brief communication: Identification of tundra topsoil frozen/thawed state from SMAP and GCOM-W1 radiometer measurements using the spectral gradient method
Bedfast and floating-ice dynamics of thermokarst lakes using a temporal deep-learning mapping approach: case study of the Old Crow Flats, Yukon, Canada
Contribution of ground ice melting to the expansion of Selin Co (lake) on the Tibetan Plateau
Incorporating InSAR kinematics into rock glacier inventories: insights from 11 regions worldwide
Assessing volumetric change distributions and scaling relations of retrogressive thaw slumps across the Arctic
Top-of-permafrost ground ice indicated by remotely sensed late-season subsidence
Inventory and changes of rock glacier creep speeds in Ile Alatau and Kungöy Ala-Too, northern Tien Shan, since the 1950s
The catastrophic thermokarst lake drainage events of 2018 in northwestern Alaska: fast-forward into the future
Global Positioning System interferometric reflectometry (GPS-IR) measurements of ground surface elevation changes in permafrost areas in northern Canada
InSAR time series analysis of seasonal surface displacement dynamics on the Tibetan Plateau
Rapid retreat of permafrost coastline observed with aerial drone photogrammetry
Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models
Sensitivity of active-layer freezing process to snow cover in Arctic Alaska
An estimate of ice wedge volume for a High Arctic polar desert environment, Fosheim Peninsula, Ellesmere Island
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.
Cas Renette, Mats Olvmo, Sofia Thorsson, Björn Holmer, and Heather Reese
EGUsphere, https://doi.org/10.5194/egusphere-2024-141, https://doi.org/10.5194/egusphere-2024-141, 2024
Short summary
Short summary
We used a drone to monitor seasonal changes in the height of subarctic permafrost mounds (palsas). With five drone flights in one year, we found a seasonal fluctuation of ca. 15 cm as result of freeze/thaw cycles. On one mound, a large area sank down between each flight as a result of permafrost thaw. The approach of using repeated high-resolution scans from such drone is unique for such environments and highlights its effectiveness in capturing the subtle dynamics of permafrost landscapes.
Jurjen van der Sluijs, Steven V. Kokelj, and Jon F. Tunnicliffe
The Cryosphere, 17, 4511–4533, https://doi.org/10.5194/tc-17-4511-2023, https://doi.org/10.5194/tc-17-4511-2023, 2023
Short summary
Short summary
There is an urgent need to obtain size and erosion estimates of climate-driven landslides, such as retrogressive thaw slumps. We evaluated surface interpolation techniques to estimate slump erosional volumes and developed a new inventory method by which the size and activity of these landslides are tracked through time. Models between slump area and volume reveal non-linear intensification, whereby model coefficients improve our understanding of how permafrost landscapes may evolve over time.
Konstantin Muzalevskiy, Zdenek Ruzicka, Alexandre Roy, Michael Loranty, and Alexander Vasiliev
The Cryosphere, 17, 4155–4164, https://doi.org/10.5194/tc-17-4155-2023, https://doi.org/10.5194/tc-17-4155-2023, 2023
Short summary
Short summary
A new all-weather method for determining the frozen/thawed (FT) state of soils in the Arctic region based on satellite data was proposed. The method is based on multifrequency measurement of brightness temperatures by the SMAP and GCOM-W1/AMSR2 satellites. The created method was tested at sites in Canada, Finland, Russia, and the USA, based on climatic weather station data. The proposed method identifies the FT state of Arctic soils with better accuracy than existing methods.
Maria Shaposhnikova, Claude Duguay, and Pascale Roy-Léveillée
The Cryosphere, 17, 1697–1721, https://doi.org/10.5194/tc-17-1697-2023, https://doi.org/10.5194/tc-17-1697-2023, 2023
Short summary
Short summary
We explore lake ice in the Old Crow Flats, Yukon, Canada, using a novel approach that employs radar imagery and deep learning. Results indicate an 11 % increase in the fraction of lake ice that grounds between 1992/1993 and 2020/2021. We believe this is caused by widespread lake drainage and fluctuations in water level and snow depth. This transition is likely to have implications for permafrost beneath the lakes, with a potential impact on methane ebullition and the regional carbon budget.
Lingxiao Wang, Lin Zhao, Huayun Zhou, Shibo Liu, Erji Du, Defu Zou, Guangyue Liu, Yao Xiao, Guojie Hu, Chong Wang, Zhe Sun, Zhibin Li, Yongping Qiao, Tonghua Wu, Chengye Li, and Xubing Li
The Cryosphere, 16, 2745–2767, https://doi.org/10.5194/tc-16-2745-2022, https://doi.org/10.5194/tc-16-2745-2022, 2022
Short summary
Short summary
Selin Co has exhibited the greatest increase in water storage among all the lakes on the Tibetan Plateau in the past decades. This study presents the first attempt to quantify the water contribution of ground ice melting to the expansion of Selin Co by evaluating the ground surface deformation since terrain surface settlement provides a
windowto detect the subsurface ground ice melting. Results reveal that ground ice meltwater contributed ~ 12 % of the lake volume increase during 2017–2020.
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
Short summary
Short summary
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.
Philipp Bernhard, Simon Zwieback, Nora Bergner, and Irena Hajnsek
The Cryosphere, 16, 1–15, https://doi.org/10.5194/tc-16-1-2022, https://doi.org/10.5194/tc-16-1-2022, 2022
Short summary
Short summary
We present an investigation of retrogressive thaw slumps in 10 study sites across the Arctic. These slumps have major impacts on hydrology and ecosystems and can also reinforce climate change by the mobilization of carbon. Using time series of digital elevation models, we found that thaw slump change rates follow a specific type of distribution that is known from landslides in more temperate landscapes and that the 2D area change is strongly related to the 3D volumetric change.
Simon Zwieback and Franz J. Meyer
The Cryosphere, 15, 2041–2055, https://doi.org/10.5194/tc-15-2041-2021, https://doi.org/10.5194/tc-15-2041-2021, 2021
Short summary
Short summary
Thawing of ice-rich permafrost leads to subsidence and slumping, which can compromise Arctic infrastructure. However, we lack fine-scale maps of permafrost ground ice, chiefly because it is usually invisible at the surface. We show that subsidence at the end of summer serves as a
fingerprintwith which near-surface permafrost ground ice can be identified. As this can be done with satellite data, this method may help improve ground ice maps and thus sustainably steward the Arctic.
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
Short summary
Short summary
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.
Ingmar Nitze, Sarah W. Cooley, Claude R. Duguay, Benjamin M. Jones, and Guido Grosse
The Cryosphere, 14, 4279–4297, https://doi.org/10.5194/tc-14-4279-2020, https://doi.org/10.5194/tc-14-4279-2020, 2020
Short summary
Short summary
In summer 2018, northwestern Alaska was affected by widespread lake drainage which strongly exceeded previous observations. We analyzed the spatial and temporal patterns with remote sensing observations, weather data and lake-ice simulations. The preceding fall and winter season was the second warmest and wettest on record, causing the destabilization of permafrost and elevated water levels which likely led to widespread and rapid lake drainage during or right after ice breakup.
Jiahua Zhang, Lin Liu, and Yufeng Hu
The Cryosphere, 14, 1875–1888, https://doi.org/10.5194/tc-14-1875-2020, https://doi.org/10.5194/tc-14-1875-2020, 2020
Short summary
Short summary
Ground surface in permafrost areas undergoes uplift and subsides seasonally due to freezing–thawing active layer. Surface elevation change serves as an indicator of frozen-ground dynamics. In this study, we identify 12 GPS stations across the Canadian Arctic, which are useful for measuring elevation changes by using reflected GPS signals. Measurements span from several years to over a decade and at daily intervals and help to reveal frozen ground dynamics at various temporal and spatial scales.
Eike Reinosch, Johannes Buckel, Jie Dong, Markus Gerke, Jussi Baade, and Björn Riedel
The Cryosphere, 14, 1633–1650, https://doi.org/10.5194/tc-14-1633-2020, https://doi.org/10.5194/tc-14-1633-2020, 2020
Short summary
Short summary
In this research we present the results of our satellite analysis of a permafrost landscape and periglacial landforms in mountainous regions on the Tibetan Plateau. We study seasonal and multiannual surface displacement processes, such as the freezing and thawing of the ground, seasonally accelerated sliding on steep slopes, and continuous permafrost creep. This study is the first step of our goal to create an inventory of actively moving landforms within the Nyainqêntanglha range.
Andrew M. Cunliffe, George Tanski, Boris Radosavljevic, William F. Palmer, Torsten Sachs, Hugues Lantuit, Jeffrey T. Kerby, and Isla H. Myers-Smith
The Cryosphere, 13, 1513–1528, https://doi.org/10.5194/tc-13-1513-2019, https://doi.org/10.5194/tc-13-1513-2019, 2019
Short summary
Short summary
Episodic changes of permafrost coastlines are poorly understood in the Arctic. By using drones, satellite images, and historic photos we surveyed a permafrost coastline on Qikiqtaruk – Herschel Island. We observed short-term coastline retreat of 14.5 m per year (2016–2017), exceeding long-term average rates of 2.2 m per year (1952–2017). Our study highlights the value of these tools to assess understudied episodic changes of eroding permafrost coastlines in the context of a warming Arctic.
Charles J. Abolt, Michael H. Young, Adam L. Atchley, and Cathy J. Wilson
The Cryosphere, 13, 237–245, https://doi.org/10.5194/tc-13-237-2019, https://doi.org/10.5194/tc-13-237-2019, 2019
Short summary
Short summary
We present a workflow that uses a machine-learning algorithm known as a convolutional neural network (CNN) to rapidly delineate ice wedge polygons in high-resolution topographic datasets. Our workflow permits thorough assessments of polygonal microtopography at the kilometer scale or greater, which can improve understanding of landscape hydrology and carbon budgets. We demonstrate that a single CNN can be trained to delineate polygons with high accuracy in diverse tundra settings.
Yonghong Yi, John S. Kimball, Richard H. Chen, Mahta Moghaddam, and Charles E. Miller
The Cryosphere, 13, 197–218, https://doi.org/10.5194/tc-13-197-2019, https://doi.org/10.5194/tc-13-197-2019, 2019
Short summary
Short summary
To better understand active-layer freezing process and its climate sensitivity, we developed a new 1 km snow data set for permafrost modeling and used the model simulations with multiple new in situ and P-band radar data sets to characterize the soil freeze onset and duration of zero curtain in Arctic Alaska. Results show that zero curtains of upper soils are primarily affected by early snow cover accumulation, while zero curtains of deeper soils are more closely related to maximum thaw depth.
Claire Bernard-Grand'Maison and Wayne Pollard
The Cryosphere, 12, 3589–3604, https://doi.org/10.5194/tc-12-3589-2018, https://doi.org/10.5194/tc-12-3589-2018, 2018
Short summary
Short summary
This study provides a first approximation of the volume of ice in ice wedges, a ground-ice feature in permafrost for a High Arctic polar desert region. We demonstrate that Geographical Information System analyses can be used on satellite images to estimate ice wedge volume. We estimate that 3.81 % of the top 5.9 m of permafrost could be ice-wedge ice on the Fosheim Peninsula. In response to climate change, melting ice wedges will result in widespread terrain disturbance in this region.
Cited articles
Alaska Satellite Facility: ASF Data Search, NASA [data set], https://search.asf.alaska.edu/, last access: 10 June 2024. a
Bakian-Dogaheh, K., Chen, R., Moghaddam, M., Yi, Y., and Tabatabaeenejad, A.: ABoVE: Active layer soil characterization of permafrost sites, northern Alaska, 2018, ORNL DAAC [data set], https://doi.org/10.3334/ORNLDAAC/1759, 2020. a
Bakian-Dogaheh, K., Chen, R. H., Yi, Y., Kimball, J. S., Moghaddam, M., and Tabatabaeenejad, A.: A model to characterize soil moisture and organic matter profiles in the permafrost active layer in support of radar remote sensing in Alaskan Arctic tundra, Environ. Res. Lett., 17, 025011, https://doi.org/10.1088/1748-9326/ac4e37, 2022. a, b
Bakian-Dogaheh, K., Chen, R., Yi, Y., Sullivan, T., Michaelides, R., Parsekian, A., Schaefer, K., Tabatabaeenejad, A., Kimball, J., and Moghaddam, M.: Soil Matric Potential, Dielectric, and Physical Properties, Arctic Alaska, 2018, ORNL DAAC [data set], https://doi.org/10.3334/ORNLDAAC/2149, 2023. 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
Bekaert, D., Hamlington, B., Buzzanga, B., and Jones, C.: Spaceborne synthetic aperture radar survey of subsidence in Hampton Roads, Virginia (USA), Sci. Rep., 7, 14752, https://doi.org/10.1038/s41598-017-15309-5, 2017. a
Bernhard, P., Zwieback, S., Leinss, S., and Hajnsek, I.: Mapping retrogressive thaw slumps using single-pass TanDEM-X observations, IEEE J. Sel. Top. Appl. Earth Obs., 13, 3263–3280, 2020. a
Brown, J., Hinkel, K. M., and Nelson, F.: The circumpolar active layer monitoring (CALM) program: research designs and initial results, Polar Geogr., 24, 166–258, https://doi.org/10.1080/10889370009377698, 2000 (data available at: https://www2.gwu.edu/~calm/, last access: 29 August 2023). a, b
Bürgmann, R., Rosen, P. A., and Fielding, E. J.: Synthetic aperture radar interferometry to measure Earth's surface topography and its deformation, Annu. Rev. Earth Planet. Sci., 28, 169–209, https://doi.org/10.1146/annurev.earth.28.1.169, 2000. a
Castellazzi, P., Martel, R., Galloway, D. L., Longuevergne, L., and Rivera, A.: Assessing groundwater depletion and dynamics using GRACE and InSAR: Potential and limitations, Groundwater, 54, 768–780, https://doi.org/10.1111/gwat.12453, 2016. a
Chen, C. W. and Zebker, H. A.: Phase unwrapping for large SAR interferograms: Statistical segmentation and generalized network models, IEEE T. Geosci. Remote, 40, 1709–1719, https://doi.org/10.1109/TGRS.2002.802453, 2002. a
Chen, J., Günther, F., Grosse, G., Liu, L., and Lin, H.: Sentinel-1 InSAR measurements of elevation changes over Yedoma uplands on Sobo-Sise island, Lena Delta, Remote Sens., 10, 1152, https://doi.org/10.3390/rs10071152, 2018. 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, b, c
Chen, R., Michaelides, R., Chen, J., Chen, A., Clayton, L., Bakian-Dogaheh, K., Huang, L., Jafarov, E., Liu, L., Moghaddam, M., Parsekian, A. D., Sullivan, T. D., Tabatabaeenejad, A., Wig, E., Zebker, H. A., and Zhao, Y.: ABoVE: Active Layer Thickness from Airborne L-and P-band SAR, Alaska, 2017, Ver. 3, ORNL DAAC [data set], https://doi.org/10.3334/ORNLDAAC/2004, 2022. 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 Space Sci., 10, e2022EA002453, https://doi.org/10.1029/2020EA001630, 2023. a, b
Clayton, L. K., Schaefer, K., Battaglia, M. J., Bourgeau-Chavez, L., Chen, J., Chen, R. H., Chen, A., Bakian-Dogaheh, K., Grelik, S., Jafarov, E., Liu, L., Michaelides, R. J., Moghaddam, M., Parsekian, A. D., Rocha, A. V., Schaefer, S. R., Sullivan, T., Tabatabaeenejad, A., Wang, K., Wilson, C. J., Zebker, H. A., Zhang, T., and Zhao, Y.: Active layer thickness as a function of soil water content, Environ. Res. Lett., 16, 055028, https://doi.org/10.1088/1748-9326/abfa4c, 2021. a
Dammann, D. O., Eriksson, L. E. B., Mahoney, A. R., Eicken, H., and Meyer, F. J.: Mapping pan-Arctic landfast sea ice stability using Sentinel-1 interferometry, The Cryosphere, 13, 557–577, https://doi.org/10.5194/tc-13-557-2019, 2019. a
Daout, S., Doin, M.-P., Peltzer, G., Socquet, A., and Lasserre, C.: Large-scale InSAR monitoring of permafrost freeze-thaw cycles on the Tibetan Plateau, Geophys. Res. Lett., 44, 901–909, https://doi.org/10.1002/2016GL070781, 2017. a
Deng, F., Dixon, T. H., and Xie, S.: Surface deformation and induced seismicity due to fluid injection and oil and gas extraction in western Texas, J. Geophys. Res.-Sol. Ea., 125, e2019JB018962, https://doi.org/10.1029/2019JB018962, 2020. a
Ding, X.-L., Li, Z.-W., Zhu, J.-J., Feng, G.-C., and Long, J.-P.: Atmospheric effects on InSAR measurements and their mitigation, Sensors, 8, 5426–5448, https://doi.org/10.3390/s8095426, 2008. a
Dobinski, W.: Permafrost, Earth-Sci. Rev., 108, 158–169, https://doi.org/10.1016/j.earscirev.2011.06.007, 2011. a
Doin, M.-P., Lasserre, C., Peltzer, G., Cavalié, O., and Doubre, C.: Corrections of stratified tropospheric delays in SAR interferometry: Validation with global atmospheric models, J. Appl. Geophys., 69, 35–50, https://doi.org/10.1016/j.jappgeo.2009.03.010, 2009. a
European Space Agency: Copernicus Global Digital Elevation Model, OpenTopography [data set], https://doi.org/10.5069/G9028PQB, 2021. a
Fattahi, H., Agram, P., and Simons, M.: A network-based enhanced spectral diversity approach for TOPS time-series analysis, IEEE T. Geosci. Remote, 55, 777–786, https://doi.org/10.1109/TGRS.2016.2614925, 2016. 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
Grapenthin, R., Cheng, Y., Angarita, M., Tan, D., Meyer, F. J., Fee, D., and Wech, A.: Return from Dormancy: Rapid inflation and seismic unrest driven by transcrustal magma transfer at Mt. Edgecumbe (L’úx Shaa) Volcano, Alaska, Geophys. Res. Lett., 49, e2022GL099464, https://doi.org/10.1029/2022GL099464, 2022. a
Grigal, D., Brovold, S., Nord, W., and Ohmann, L.: Bulk density of surface soils and peat in the north central United States, Can. J. Soil Sci., 69, 895–900, https://doi.org/10.4141/cjss89-092, 1989. a
Hanssen, R. F.: Radar interferometry: data interpretation and error analysis, vol. 2, Springer Science & Business Media, https://doi.org/10.1007/0-306-47633-9, 2001. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020 (data available at: https://cds.climate.copernicus.eu, last access: 24 June 2024). a, b
Hinkel, K. and Nelson, F.: Spatial and temporal patterns of active layer thickness at Circumpolar Active Layer Monitoring (CALM) sites in northern Alaska, 1995–2000, J. Geophys. Res.-Atmos., 108, D2, https://doi.org/10.1029/2001JD000927, 2003. a
Hogenson, K., Kristenson, H., Kennedy, J., Johnston, A., Rine, J., Logan, T., Zhu, J., Williams, F., Herrmann, J., Smale, J., and Meyer, F.: Hybrid Pluggable Processing Pipeline (HyP3): A cloud-native infrastructure for generic processing of SAR data, Zenodo [computer software], https://doi.org/10.5281/zenodo.4646138, 2020. a, b
Honglei, Y., Qiao, J., Jianfeng, H., Ki-Yeob, K., and Junhuan, P.: InSAR measurements of surface deformation over permafrost on Fenghuoshan Mountains section, Qinghai-Tibet Plateau, J. Syst. Eng. Electr., 32, 1284–1303, https://doi.org/10.23919/JSEE.2021.000109, 2021. a
Hossain, M., Chen, W., and Zhang, Y.: Bulk density of mineral and organic soils in the Canada's arctic and sub-arctic, Information processing in agriculture, 2, 183–190, https://doi.org/10.1016/j.inpa.2015.09.001, 2015. 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
Iwahana, G., Uchida, M., Liu, L., Gong, W., Meyer, F. J., Guritz, R., Yamanokuchi, T., and Hinzman, L.: InSAR detection and field evidence for thermokarst after a tundra wildfire, using ALOS-PALSAR, Remote Sens., 8, 218, https://doi.org/10.3390/rs8030218, 2016. a
Jackson, R., Mooney, H., and Schulze, E.-D.: Global distribution of fine root biomass in terrestrial ecosystems, ORNL DAAC [data set], https://doi.org/10.3334/ORNLDAAC/658, 2003. a
Johnson, K. D., Harden, J., McGuire, A. D., Bliss, N. B., Bockheim, J. G., Clark, M., Nettleton-Hollingsworth, T., Jorgenson, M. T., Kane, E. S., Mack, M., O'Donnell, J., Ping, C.-L., Schuur, E. A. G., Turetsky, M. R., and Valentine, D. W.: Soil carbon distribution in Alaska in relation to soil-forming factors, Geoderma, 167, 71–84, https://doi.org/10.1016/j.geoderma.2011.10.006, 2011. a, b, c
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, 17, 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
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 ninth international conference on permafrost, 3, 121–122, University of Alaska Fairbanks, 2008. a
Liu, L. and Larson, K. M.: Decadal changes of surface elevation over permafrost area estimated using reflected GPS signals, The Cryosphere, 12, 477–489, https://doi.org/10.5194/tc-12-477-2018, 2018. 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 Surf., 115, F3, https://doi.org/10.1029/2009JF001547, 2010. a, b, c, d
Liu, L., Jafarov, E. E., Schaefer, K. M., Jones, B. M., Zebker, H. A., Williams, C. A., Rogan, J., and Zhang, T.: InSAR detects increase in surface subsidence caused by an Arctic tundra fire, Geophys. Res. Lett., 41, 3906–3913, https://doi.org/10.1002/2014GL060533, 2014. a
Liu, L., Schaefer, K., Chen, A., Gusmeroli, A., Zebker, H., and Zhang, T.: Remote sensing measurements of thermokarst subsidence using InSAR, J. Geophys. Res.-Earth Surf., 120, 1935–1948, https://doi.org/10.1002/2015JF003599, 2015. a, b
Martino, A. J., Neumann, T. A., Kurtz, N. T., and McLennan, D.: ICESat-2 mission overview and early performance, in: Sensors, systems, and next-generation satellites XXIII, vol. 11151, 68–77, SPIE, 2019. a
Menne, M. J., Durre, I., Korzeniewski, B., McNeill, S., Thomas, K., Yin, X., Anthony, S., Ray, R., Vose, R. S., Gleason, B. E., and Houston, T. G.: Global Historical Climatology Network – Daily (GHCN-Daily), Version 3, NOAA National Climatic Data Center [data set], https://doi.org/10.7289/V5D21VHZ, 2012a. a
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An Overview of the Global Historical Climatology Network-Daily Database, J. Atmos. Ocean. Tech., 29, 897–910, https://doi.org/10.1175/JTECH-D-11-00103.1, 2012b. a
Meyer, F., Kampes, B., Bamler, R., and Fischer, J.: Methods for atmospheric correction in InSAR data, in: Fringe 2005 Workshop, vol. 610, 2006. a
Meyer, F. J.: Performance requirements for ionospheric correction of low-frequency SAR data, IEEE T. Geosci. Remote, 49, 3694–3702, https://doi.org/10.1109/TGRS.2011.2146786, 2011. a
Michaelides, R. J., Schaefer, K., Zebker, H. A., Parsekian, A., Liu, L., Chen, J., Natali, S., Ludwig, S., and Schaefer, S. R.: Inference of the impact of wildfire on permafrost and active layer thickness in a discontinuous permafrost region using the remotely sensed active layer thickness (ReSALT) algorithm, Environ. Res. Lett., 14, 035007, https://doi.org/10.1088/1748-9326/aaf932, 2019. a, b, c
Michaelides, R., Bryant, M., Siegfried, M., and Borsa, A.: Quantifying Surface-Height Change Over a Periglacial Environment With ICESat-2 Laser Altimetry, Earth Space Sci., 8, e2020EA001538, https://doi.org/10.1029/2020EA001538, 2021a. 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 Space Sci., 8, e2020EA001630, https://doi.org/10.1029/2020EA001630, 2021b. a, b
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, b, c
NASA LDAS: GLDAS Soil Land Surface, NASA [data set], https://ldas.gsfc.nasa.gov/gldas/soils (last access: 20 July 2023), 2024. a
Nelson, F., Shiklomanov, N., Mueller, G., Hinkel, K., Walker, D., and Bockheim, J.: Estimating active-layer thickness over a large region: Kuparuk River basin, Alaska, USA, Arct. Alp. Res., 29, 367–378, https://doi.org/10.2307/1551985, 1997. a, b
Nelson, F., Hinkel, K., Shiklomanov, N., Mueller, G., Miller, L., and Walker, D.: Active-layer thickness in north central Alaska: Systematic sampling, scale, and spatial autocorrelation, J. Geophys. Res.-Atmos., 103, 28963–28973, 1998. a
Nelson, F., Shiklomanov, N., and Mueller, G.: Variability of active-layer thickness at multiple spatial scales, north-central Alaska, USA, Arct. Antarct. Alp. Res., 31, 179–186, 1999. a
Neuenschwander, A. and Pitts, K.: The ATL08 land and vegetation product for the ICESat-2 Mission, Remote Sens. Environ., 221, 247–259, https://doi.org/10.1016/j.rse.2018.11.005, 2019. a, b, c
Neuenschwander, A., Pitts, K., Jelley, B., Robbins, J., Markel, J., Popescu, S., Nelson, R., Harding, D., Pederson, D., Klotz, B., and Sheridan, R.: Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) algorithm theoretical basis document (ATBD) for land-vegetation along-track products (ATL08), National Aeronautics and Space Administration: Washington, DC, USA, https://icesat-2.gsfc.nasa.gov/sites/default/files/page_files/ICESat2_ATL08_ATBD_r004.pdf (last access: 15 September 2023), 2019. a, b
Neuenschwander, A. L. and Magruder, L. A.: Canopy and terrain height retrievals with ICESat-2: A first look, Remote Sens., 11, 1721, https://doi.org/10.3390/rs11141721, 2019. a, b
Neuenschwander, A. L., Pitts, K., Jelley, B., Robbins, J., Klotz, B., Popescu, S. C., Nelson, R. F., Harding, D., Pederson, D., and Sheridan, R.: ATLAS/ICESat-2 L3A Land and Vegetation Height, Version 3, Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/ATLAS/ATL08.005, 2021. a, b
O'Connor, M. T., Cardenas, M. B., Ferencz, S. B., Wu, Y., Neilson, B. T., Chen, J., and Kling, G. W.: Empirical models for predicting water and heat flow properties of permafrost soils, Geophys. Res. Lett., 47, e2020GL087646, https://doi.org/10.1029/2020GL087646, 2020. a
OpenScience Lab: JupyterHub computing environment, OpenScience Lab [code], https://opensciencelab.asf.alaska.edu, last access: 20 May 2024. a
OpenTopography: Global Multi-Resolution Topography (GMRT) Data Synthesis, OpenTopography [data set], https://doi.org/10.5069/G9BG2M6R, 2016. a
Peddle, D. R. and Franklin, S. E.: Classification of permafrost active layer depth from remotely sensed and topographic evidence, Remote Sens. Environ., 44, 67–80, https://doi.org/10.1016/0034-4257(93)90103-5, 1993. a
Ping, C. L., Jastrow, J. D., Jorgenson, M. T., Michaelson, G. J., and Shur, Y. L.: Permafrost soils and carbon cycling, SOIL, 1, 147–171, https://doi.org/10.5194/soil-1-147-2015, 2015. a
Poland, M. P. and Zebker, H. A.: Volcano geodesy using InSAR in 2020: the past and next decades, B. Volcanol., 84, 27, https://doi.org/10.1007/s00445-022-01531-1, 2022. a
Riseborough, D.: Thawing and freezing indices in the active layer, in: Proceedings of the 8th International Conference on Permafrost, vol. 2, 953–958, AA Balkema, Rotterdam, 2003. a
Rodell, M., Houser, P., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The global land data assimilation system, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004. a
Rykhus, R. P. and Lu, Z.: InSAR detects possible thaw settlement in the Alaskan Arctic Coastal Plain, Can. J. Remote Sens., 34, 100–112, https://doi.org/10.5589/m08-018, 2008. a
Sadeghi Chorsi, T.: Activer Layer Thickness Estimation using InSAR, Meteorologiical data and Soil parameters, Zenodo [code], https://doi.org/10.5281/zenodo.10023340, 2023. a
Sadeghi Chorsi, T., Braunmiller, J., Deng, F., and Dixon, T. H.: Afterslip from the 2020 M 6.5 Monte Cristo Range, Nevada Earthquake, Geophys. Res. Lett., 49, e2022GL099952, https://doi.org/10.1029/2022GL099952, 2022a. a
Sadeghi Chorsi, T., Braunmiller, J., Deng, F., Mueller, N., Kerstetter, S., Stern, R. J., and Dixon, T. H.: The May 15, 2020 M 6.5 Monte Cristo Range, Nevada, earthquake: eyes in the sky, boots on the ground, and a chance for students to learn, Int. Geol. Rev., 64, 2683–2702, https://doi.org/10.1080/00206814.2021.2000507, 2022b. a
Schaefer, K., Zhang, T., Slater, A. G., Lu, L., Etringer, A., and Baker, I.: Improving simulated soil temperatures and soil freeze/thaw at high-latitude regions in the Simple Biosphere/Carnegie-Ames-Stanford Approach model, J. Geophys. Res.-Earth Surf., 114, F2, https://doi.org/10.1029/2008JF001125, 2009. a, b
Schaefer, K., Liu, L., Parsekian, A., Jafarov, E., Chen, A., Zhang, T., Gusmeroli, A., Panda, S., 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, https://doi.org/10.3390/rs70403735, 2015. a, b, c, d, e, f, g, h, i
Singhroy, V., Alasset, P.-J., Couture, R., and Poncos, V.: InSAR monitoring of landslides on permafrost terrain in Canada, in: 2007 IEEE International Geoscience and Remote Sensing Symposium, 2451–2454, IEEE, https://doi.org/10.1109/IGARSS.2007.4423338, 2007. a
Smith, B., Fricker, H. A., Holschuh, N., Gardner, A. S., Adusumilli, S., Brunt, K. M., Csatho, B., Harbeck, K., Huth, A., Neumann, T., Nilsson, J., and Siegfried, M. R.: Land ice height-retrieval algorithm for NASA's ICESat-2 photon-counting laser altimeter, Remote Sens. Environ., 233, 111352, https://doi.org/10.1016/j.rse.2019.111352, 2019. a
Staff, S. S.: Soil Taxonomy: A Basic System of Soil Classification for Making and Interpreting Soil Surveys, Agric. Handb, 436, https://www.nrcs.usda.gov/sites/default/files/2022-06/Soil Taxonomy.pdf (last access: 25 November 2023), 1999. a
Strozzi, T., Antonova, S., Günther, F., Mätzler, E., Vieira, G., Wegmüller, U., Westermann, S., and Bartsch, A.: Sentinel-1 SAR interferometry for surface deformation monitoring in low-land permafrost areas, Remote Sens., 10, 1360, https://doi.org/10.3390/rs10091360, 2018. a, b, c
Strozzi, T., Caduff, R., Jones, N., Barboux, C., Delaloye, R., Bodin, X., Kääb, A., Mätzler, E., and Schrott, L.: Monitoring rock glacier kinematics with satellite synthetic aperture radar, Remote Sens., 12, 559, https://doi.org/10.3390/rs12030559, 2020. a
Turetsky, M. R., Abbott, B. W., Jones, M. C., Anthony, K. W., Olefeldt, D., Schuur, E. A., Grosse, G., Kuhry, P., Hugelius, G., Koven, C., Lawrence, D. M., Gibson, C., Sannel, A. B. K., and McGuire, A. D.: Carbon release through abrupt permafrost thaw, Nat. Geosci., 13, 138–143, https://doi.org/10.1038/s41561-019-0526-0, 2020. a
Vasco, D. W., Dixon, T. H., Ferretti, A., and Samsonov, S. V.: Monitoring the fate of injected CO2 using geodetic techniques, The Leading Edge, 39, 29–37, https://doi.org/10.1190/tle39010029.1, 2020. a
Wang, L., Marzahn, P., Bernier, M., and Ludwig, R.: Sentinel-1 InSAR measurements of deformation over discontinuous permafrost terrain, Northern Quebec, Canada, Remote Sens. Environ., 248, 111965, https://doi.org/10.1016/j.rse.2020.111965, 2020. a, b
Yang, Q., Zhao, W., Dixon, T. H., Amelung, F., Han, W. S., and Li, P.: InSAR monitoring of ground deformation due to CO2 injection at an enhanced oil recovery site, West Texas, Int. J. Greenh. Gas C., 41, 20–28, https://doi.org/10.1016/j.ijggc.2015.06.016, 2015. a
Yunjun, Z., Fattahi, H., and Amelung, F.: Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction, Comput. Geosci., 133, 104331, https://doi.org/10.1016/j.cageo.2019.104331, 2019 (code available at: https://github.com/insarlab/MintPy, last access: 24 June 2024). a, b
Zhang, X., Jones, C. E., Oliver-Cabrera, T., Simard, M., and Fagherazzi, S.: Using rapid repeat SAR interferometry to improve hydrodynamic models of flood propagation in coastal wetlands, Adv. Water Resour., 159, 104088, https://doi.org/10.1016/j.advwatres.2021.104088, 2022. a
Zwieback, S., Kokelj, S. V., Günther, F., Boike, J., Grosse, G., and Hajnsek, I.: Sub-seasonal thaw slump mass wasting is not consistently energy limited at the landscape scale, The Cryosphere, 12, 549–564, https://doi.org/10.5194/tc-12-549-2018, 2018. a
Zwieback, S., Iwahana, G., Sakhalkar, S., Biessel, R., Taylor, S., and Meyer, F.: Excess ground ice profiles in continuous permafrost mapped from InSAR subsidence, Water Resour. Res., 60, e2023WR035331, https://doi.org/10.1029/2023WR035331, 2024. a, b
Short summary
The active layer thaws and freezes seasonally. The annual freeze–thaw cycle of the active layer causes significant surface height changes due to the volume difference between ice and liquid water. We estimate the subsidence rate and active-layer thickness (ALT) for part of northern Alaska for summer 2017 to 2022 using interferometric synthetic aperture radar and lidar. ALT estimates range from ~20 cm to larger than 150 cm in area. Subsidence rate varies between close points (2–18 mm per month).
The active layer thaws and freezes seasonally. The annual freeze–thaw cycle of the active layer...
