Articles | Volume 19, issue 9
https://doi.org/10.5194/tc-19-3571-2025
© 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-19-3571-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Sep 2025
Research article |
| 09 Sep 2025
| 09 Sep 2025
Retrieving frozen ground surface temperature under the snowpack in the Arctic permafrost area from SMOS observations
Univ. Toulouse, CNES/IRD/CNRS/INRAe, CESBIO, Toulouse, France
Département des sciences de l'environnement, Université du Québec à Trois-Rivières, Trois-Rivières, Quebec, G9A 5H7, Canada
Centre d'études nordiques, Québec, Quebec, G1V 0A6, Canada
Arnaud Mialon
Univ. Toulouse, CNES/IRD/CNRS/INRAe, CESBIO, Toulouse, France
Alain Royer
Centre d'études nordiques, Québec, Quebec, G1V 0A6, Canada
Département de géomatique appliquée, Université de Sherbrooke, Sherbrooke, J1K 2R1, Canada
Mike Schwank
Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Gamma Remote Sensing Research and Consulting Ltd., Gümligen, Switzerland
Manu Holmberg
Finnish Meteorological Institute, Earth Observation Research Unit, Helsinki, Finland
Kimmo Rautiainen
Finnish Meteorological Institute, Earth Observation Research Unit, Helsinki, Finland
Simone Bircher-Adrot
MétéoSuisse, Payerne, Switzerland
Andreas Colliander
NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Yann Kerr
Univ. Toulouse, CNES/IRD/CNRS/INRAe, CESBIO, Toulouse, France
Alexandre Roy
Département des sciences de l'environnement, Université du Québec à Trois-Rivières, Trois-Rivières, Quebec, G9A 5H7, Canada
Centre d'études nordiques, Québec, Quebec, G1V 0A6, Canada
Related authors
No articles found.
Charlotte Crevier, Alexandre Langlois, Chris Derksen, and Alexandre Roy
The Cryosphere, 20, 3369–3385, https://doi.org/10.5194/tc-20-3369-2026, https://doi.org/10.5194/tc-20-3369-2026, 2026
Short summary
Short summary
A multisensor C-Band Synthetic Aperture Radar (SAR) near-daily time series in an Arctic environment was developed to create a high-resolution freeze/thaw (FT) algorithm with an accuracy of 96 %. The FT detection was highly correlated to near-surface state as measured by soil temperature. Small but significant FT date differences were identified for different Arctic ecotypes, showing the spatial variability of freeze/thaw process in Arctic environment.
Sara Hyvärinen, Maria K. Tenkanen, Aki Tsuruta, Anttoni Erkkilä, Kimmo Rautiainen, Hermanni Aaltonen, Motoki Sasakawa, and Tuula Aalto
Atmos. Chem. Phys., 26, 7555–7587, https://doi.org/10.5194/acp-26-7555-2026, https://doi.org/10.5194/acp-26-7555-2026, 2026
Short summary
Short summary
We analyzed melt period methane emissions from northern high-latitude wetlands using satellite thaw data and inverse modeling (2011–2021). Comparing region-based and grid-based approaches, we found that emissions varied with the length of the melt period, which depended on air temperature. We found spring melt period emissions ranged from 0.45 to 1.83 Tg depending on the approach, with no clear trend over the period. Our methods allow for seasonal methane monitoring across different scales.
Alex Gélinas, Benoît Montpetit, Julien Meloche, Peter Toose, Zeinab Akhavan, Wei Wang, Richard Kelly, Alexandre Langlois, and Alexandre Roy
EGUsphere, https://doi.org/10.5194/egusphere-2026-656, https://doi.org/10.5194/egusphere-2026-656, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
This research validates a method to link radar measurements to soil properties beneath snow in a temperate farming region. By using multiple radar sensors and computer models, we successfully decoupled the ground's interference from the snow signal. Our results show that radar captures unique information that traditional ground tools miss during freeze-thaw cycles. This work provides a vital framework for the future Canadian Terrestrial Snow Mass Mission to monitor water resources.
Mickaël Lalande, Alexandre Roy, Libo Wang, Diana Verseghy, Vincent Vionnet, Florent Dominé, and Christophe Kinnard
EGUsphere, https://doi.org/10.5194/egusphere-2026-492, https://doi.org/10.5194/egusphere-2026-492, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This study enhances a snow model for Arctic environments by improving the heat exchanges within the snowpack and at its interfaces, revising the compaction scheme, and adding consideration of blowing snow sublimation losses. Simulations at ten Arctic, mid-latitude, and Alpine sites show significant reductions in simulated soil and snow temperature biases and improved simulated snow depth and density, which are key features to improve simulated energy, water, and carbon budgets in the Arctic.
Zeinab Akhavan, Richard Kelly, Peter Toose, Aaron Thompson, Wei Wang, Benoit Montpetit, Alex Gélinas, and Alexandre Roy
EGUsphere, https://doi.org/10.5194/egusphere-2026-1065, https://doi.org/10.5194/egusphere-2026-1065, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Soils in cold regions freeze and thaw seasonally, affecting water flow, flooding, and carbon exchange, but these changes are hard to track beneath snow. We studied a Canadian agricultural field using high-resolution airborne radar and ground measurements through winter. Radar signals changed clearly as soils froze and thawed, with one polarization being most sensitive. Our results improve understanding of frozen soils and support future satellite monitoring.
Hesam Salmabadi, Renato Pardo Lara, Aaron Berg, Alex Mavrovic, Chelene Hanes, Benoit Montpetit, and Alexandre Roy
The Cryosphere, 20, 1635–1654, https://doi.org/10.5194/tc-20-1635-2026, https://doi.org/10.5194/tc-20-1635-2026, 2026
Short summary
Short summary
Current satellite monitoring often oversimplifies soil freezing by assuming it happens exactly at 0°C. We analyzed ground data across Canada and found that soil often stays in a partially frozen state for months, even when air temperatures are well below freezing, revealing a major gap in how we track seasonally frozen ground.
Rémi Madelon, K. Arthur Endsley, John S. Kimball, Gabriëlle J. M. De Lannoy, Oliver Sonnentag, Haley Alcock, Alex Mavrovic, Scott N. Williamson, Vincent Maire, Arnaud Mialon, and Alexandre Roy
EGUsphere, https://doi.org/10.5194/egusphere-2026-720, https://doi.org/10.5194/egusphere-2026-720, 2026
Short summary
Short summary
This study aims to improve estimates of carbon dioxide release and uptake in the North American Arctic and subarctic regions. Several modeling approaches were tested, showing that a better representation of sunlight and temperature effects on ecosystems leads to improved estimates. This work provides new perspectives to better assess whether these regions act as sources or sinks of greenhouse gases and how they may influence the climate system by amplifying or slowing global warming.
Andreas Colliander, Nicole-Jeanne Schlegel, Alamgir Hossan, Catherine Walker, and Joel Harper
EGUsphere, https://doi.org/10.5194/egusphere-2025-5953, https://doi.org/10.5194/egusphere-2025-5953, 2026
Short summary
Short summary
We studied how melting on an Antarctic ice shelf changes the snow and ice below the surface. By combining satellite microwave measurements with a detailed computer model, we show that meltwater can travel deeper and stay longer than models usually predict. This approach makes it possible to better track hidden melt and improve future estimates of ice loss and sea-level change.
Vincent Vionnet, Nicolas R. Leroux, Vincent Fortin, Maria Abrahamowicz, Georgina Woolley, Giulia Mazzotti, Manon Gaillard, Matthieu Lafaysse, Alain Royer, Florent Domine, Nathalie Gauthier, Nick Rutter, Chris Derksen, and Stéphane Bélair
Geosci. Model Dev., 18, 9119–9147, https://doi.org/10.5194/gmd-18-9119-2025, https://doi.org/10.5194/gmd-18-9119-2025, 2025
Short summary
Short summary
Snow microstructure controls snowpack properties, but most land surface models overlook this factor. To support future satellite missions, we created a new land surface model based on the Crocus scheme that simulates snow microstructure. Key improvements include better snow albedo representation, enhanced Arctic snow modeling, and improved forest module to capture Canada's diverse snow conditions. Results demonstrate improved simulations of snow density and melt across large regions of Canada.
Alamgir Hossan, Andreas Colliander, Nicole-Jeanne Schlegel, Joel Harper, Lauren Andrews, Jana Kolassa, Julie Z. Miller, and Richard Cullather
The Cryosphere, 19, 6077–6102, https://doi.org/10.5194/tc-19-6077-2025, https://doi.org/10.5194/tc-19-6077-2025, 2025
Short summary
Short summary
Microwave L-band radiometry offers a promising new tool for estimating the total surface-to-subsurface liquid water amount (LWA) in the snow and firn in polar ice sheets. An accurate modelling of wet snow effective permittivity is a key to this. Here, we evaluated the performance of ten commonly used microwave dielectric mixing models for estimating LWA in the percolation zone of the Greenland Ice Sheet to help an appropriate choice of dielectric mixing model for LWA retrieval algorithms.
Annett Bartsch, Rodrigue Tanguy, Helena Bergstedt, Clemens von Baeckmann, Hans Tømmervik, Marc Macias-Fauria, Juha Lemmetyinen, Kimmo Rautiainen, Chiara Gruber, and Bruce C. Forbes
The Cryosphere, 19, 4929–4967, https://doi.org/10.5194/tc-19-4929-2025, https://doi.org/10.5194/tc-19-4929-2025, 2025
Short summary
Short summary
We identified similarities between sea ice dynamics and conditions on land across the Arctic, above 60° N, for 2000–2019. Significant correlations were more common for snow water equivalent and permafrost ground temperature than for the vegetation parameters. Changes across all the different parameters could specifically be determined for eastern Siberia. The results provide a baseline for future studies on common drivers of essential climate variables and causative effects across the Arctic.
Anna-Maria Virkkala, Isabel Wargowsky, Judith Vogt, McKenzie A. Kuhn, Simran Madaan, Richard O'Keefe, Tiffany Windholz, Kyle A. Arndt, Brendan M. Rogers, Jennifer D. Watts, Kelcy Kent, Mathias Göckede, David Olefeldt, Gerard Rocher-Ros, Edward A. G. Schuur, David Bastviken, Kristoffer Aalstad, Kelly Aho, Joonatan Ala-Könni, Haley Alcock, Inge Althuizen, Christopher D. Arp, Jun Asanuma, Katrin Attermeyer, Mika Aurela, Sivakiruthika Balathandayuthabani, Alan Barr, Maialen Barret, Ochirbat Batkhishig, Christina Biasi, Mats P. Björkman, Andrew Black, Elena Blanc-Betes, Pascal Bodmer, Julia Boike, Abdullah Bolek, Frédéric Bouchard, Ingeborg Bussmann, Lea Cabrol, Eleonora Canfora, Sean Carey, Karel Castro-Morales, Namyi Chae, Andres Christen, Torben R. Christensen, Casper T. Christiansen, Housen Chu, Graham Clark, Francois Clayer, Patrick Crill, Christopher Cunada, Scott J. Davidson, Joshua F. Dean, Sigrid Dengel, Matteo Detto, Catherine Dieleman, Florent Domine, Egor Dyukarev, Colin Edgar, Bo Elberling, Craig A. Emmerton, Eugenie Euskirchen, Grant Falvo, Thomas Friborg, Michelle Garneau, Mariasilvia Giamberini, Mikhail V. Glagolev, Miquel A. Gonzalez-Meler, Gustaf Granath, Jón Guðmundsson, Konsta Happonen, Yoshinobu Harazono, Lorna Harris, Josh Hashemi, Nicholas Hasson, Janna Heerah, Liam Heffernan, Manuel Helbig, Warren Helgason, Michal Heliasz, Greg Henry, Geert Hensgens, Tetsuya Hiyama, Macall Hock, David Holl, Beth Holmes, Jutta Holst, Thomas Holst, Gabriel Hould-Gosselin, Elyn Humphreys, Jacqueline Hung, Jussi Huotari, Hiroki Ikawa, Danil V. Ilyasov, Mamoru Ishikawa, Go Iwahana, Hiroki Iwata, Marcin Antoni Jackowicz-Korczynski, Joachim Jansen, Järvi Järveoja, Vincent E. J. Jassey, Rasmus Jensen, Katharina Jentzsch, Robert G. Jespersen, Carl-Fredrik Johannesson, Chersity P. Jones, Anders Jonsson, Ji Young Jung, Sari Juutinen, Evan Kane, Jan Karlsson, Sergey Karsanaev, Kuno Kasak, Julia Kelly, Kasha Kempton, Marcus Klaus, George W. Kling, Natacha Kljun, Jacqueline Knutson, Hideki Kobayashi, John Kochendorfer, Kukka-Maaria Kohonen, Pasi Kolari, Mika Korkiakoski, Aino Korrensalo, Pirkko Kortelainen, Egle Koster, Kajar Koster, Ayumi Kotani, Praveena Krishnan, Juliya Kurbatova, Lars Kutzbach, Min Jung Kwon, Ethan D. Kyzivat, Jessica Lagroix, Theodore Langhorst, Elena Lapshina, Tuula Larmola, Klaus S. Larsen, Isabelle Laurion, Justin Ledman, Hanna Lee, A. Joshua Leffler, Lance Lesack, Anders Lindroth, David Lipson, Annalea Lohila, Efrén López-Blanco, Vincent L. St. Louis, Erik Lundin, Misha Luoto, Takashi Machimura, Marta Magnani, Avni Malhotra, Marja Maljanen, Ivan Mammarella, Elisa Männistö, Luca Belelli Marchesini, Phil Marsh, Pertti J. Martkainen, Maija E. Marushchak, Mikhail Mastepanov, Alex Mavrovic, Trofim Maximov, Christina Minions, Marco Montemayor, Tomoaki Morishita, Patrick Murphy, Daniel F. Nadeau, Erin Nicholls, Mats B. Nilsson, Anastasia Niyazova, Jenni Nordén, Koffi Dodji Noumonvi, Hannu Nykanen, Walter Oechel, Anne Ojala, Tomohiro Okadera, Sujan Pal, Alexey V. Panov, Tim Papakyriakou, Dario Papale, Sang-Jong Park, Frans-Jan W. Parmentier, Gilberto Pastorello, Mike Peacock, Matthias Peichl, Roman Petrov, Kyra St. Pierre, Norbert Pirk, Jessica Plein, Vilmantas Preskienis, Anatoly Prokushkin, Jukka Pumpanen, Hilary A. Rains, Niklas Rakos, Aleski Räsänen, Helena Rautakoski, Riika Rinnan, Janne Rinne, Adrian Rocha, Nigel Roulet, Alexandre Roy, Anna Rutgersson, Aleksandr F. Sabrekov, Torsten Sachs, Erik Sahlée, Alejandro Salazar, Henrique Oliveira Sawakuchi, Christopher Schulze, Roger Seco, Armando Sepulveda-Jauregui, Svetlana Serikova, Abbey Serrone, Hanna M. Silvennoinen, Sofie Sjogersten, June Skeeter, Jo Snöälv, Sebastian Sobek, Oliver Sonnentag, Emily H. Stanley, Maria Strack, Lena Strom, Patrick Sullivan, Ryan Sullivan, Anna Sytiuk, Torbern Tagesson, Pierre Taillardat, Julie Talbot, Suzanne E. Tank, Mario Tenuta, Irina Terenteva, Frederic Thalasso, Antoine Thiboult, Halldor Thorgeirsson, Fenix Garcia Tigreros, Margaret Torn, Amy Townsend-Small, Claire Treat, Alain Tremblay, Carlo Trotta, Eeva-Stiina Tuittila, Merritt Turetsky, Masahito Ueyama, Muhammad Umair, Aki Vähä, Lona van Delden, Maarten van Hardenbroek, Andrej Varlagin, Ruth K. Varner, Elena Veretennikova, Timo Vesala, Tarmo Virtanen, Carolina Voigt, Jorien E. Vonk, Robert Wagner, Katey Walter Anthony, Qinxue Wang, Masataka Watanabe, Hailey Webb, Jeffrey M. Welker, Andreas Westergaard-Nielsen, Sebastian Westermann, Jeffrey R. White, Christian Wille, Scott N. Williamson, Scott Zolkos, Donatella Zona, and Susan M. Natali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-585, https://doi.org/10.5194/essd-2025-585, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This dataset includes monthly measurements of carbon dioxide and methane exchange between land, water, and the atmosphere from over 1,000 sites in Arctic and boreal regions. It combines measurements from a variety of ecosystems, including wetlands, forests, tundra, lakes, and rivers, gathered by over 260 researchers from 1984–2024. This dataset can be used to improve and reduce uncertainty in carbon budgets in order to strengthen our understanding of climate feedbacks in a warming world.
Kimmo Rautiainen, Manu Holmberg, Juval Cohen, Arnaud Mialon, Mike Schwank, Juha Lemmetyinen, Antonio de la Fuente, and Yann Kerr
Earth Syst. Sci. Data, 17, 5337–5353, https://doi.org/10.5194/essd-17-5337-2025, https://doi.org/10.5194/essd-17-5337-2025, 2025
Short summary
Short summary
The SMOS (Soil Moisture and Ocean Salinity) Soil Freeze–Thaw State product uses satellite data to monitor seasonal soil freezing and thawing globally, with a focus on high-latitude regions. This is important for understanding greenhouse gas emissions, as frozen soil is associated with methane release. The product provides accurate data on key events such as the first day of soil freezing in autumn, helping scientists to study climate change, ecosystem dynamics, and its impact on our planet.
Renato Pardo Lara, Andreas Colliander, Erica Tetlock, Jarret Powers, Jaison Thomas Ambadan, and Aaron Berg
EGUsphere, https://doi.org/10.5194/egusphere-2025-3630, https://doi.org/10.5194/egusphere-2025-3630, 2025
Short summary
Short summary
Frozen ground affects ecosystems, infrastructure, and farming, yet detecting it worldwide remains a challenge. To improve soil freeze/thaw detection from space, we adapted a laboratory tool called the soil freezing curve for use with NASA’s Soil Moisture Active Passive (SMAP) satellite and temperature records. This new “surface freezing curve” method can identify thresholds tied to moisture phase changes, improving the accuracy and stability of freeze/thaw monitoring in agricultural regions.
Alamgir Hossan, Andreas Colliander, Baptiste Vandecrux, Nicole-Jeanne Schlegel, Joel Harper, Shawn Marshall, and Julie Z. Miller
The Cryosphere, 19, 4237–4258, https://doi.org/10.5194/tc-19-4237-2025, https://doi.org/10.5194/tc-19-4237-2025, 2025
Short summary
Short summary
We used L-band observations from the Soil Moisture Active Passive (SMAP) mission to quantify the surface and subsurface liquid water amounts (LWAs) in the percolation zone of the Greenland ice sheet. The algorithm is described, and the validation results are provided. The results demonstrate the potential for creating an LWA data product across the Greenland ice sheet (GrIS), which will advance our understanding of ice sheet physical processes for better projection of Greenland’s contribution to global sea level rise.
Ella Kivimäki, Maria Tenkanen, Tuula Aalto, Michael Buchwitz, Kari Luojus, Jouni Pulliainen, Kimmo Rautiainen, Oliver Schneising, Anu-Maija Sundström, Johanna Tamminen, Aki Tsuruta, and Hannakaisa Lindqvist
Biogeosciences, 22, 5193–5230, https://doi.org/10.5194/bg-22-5193-2025, https://doi.org/10.5194/bg-22-5193-2025, 2025
Short summary
Short summary
We study how environmental variables influencing natural methane fluxes explain the seasonal variability in satellite-observed methane in Northern Hemisphere high-latitude wetland areas. Using two atmospheric model set-ups, we assess consistency with satellite data. Methane loss through reaction with hydroxyl radicals and links with snow cover, temperature, and snowmelt had the strongest influence. Our study highlights the value of satellite observations for understanding large-scale wetland emissions.
Kerttu Kouki and Andreas Colliander
Hydrol. Earth Syst. Sci., 29, 4791–4810, https://doi.org/10.5194/hess-29-4791-2025, https://doi.org/10.5194/hess-29-4791-2025, 2025
Short summary
Short summary
Precipitation (P) and soil moisture (SM) are critical components of the climate system but poorly understood in the Arctic. Using NASA's SMAP satellite, we analyzed SM and P patterns in Finland. SM and P correlate strongly in summer and fall but weakly in spring due to snowmelt. While the area of P can be estimated from SM, estimating its intensity is more challenging. Water bodies complicate SM retrieval. The promising results suggest this method could be applied across the Arctic.
Zhimeng Zhang, Shannon Brown, and Andreas Colliander
The Cryosphere, 19, 4011–4026, https://doi.org/10.5194/tc-19-4011-2025, https://doi.org/10.5194/tc-19-4011-2025, 2025
Short summary
Short summary
Retrieving accurate water vapor and temperature profiles over land is challenging due to uncertainties in estimating surface emissions. To address this, we have developed an iterative method that combines atmospheric retrieval with surface emissions estimation. Using Advanced Technology Microwave Sounder (ATMS) data across various microwave frequencies, we successfully tracked atmospheric temperature and humidity changes. Testing against Radiosonde data showed our method is efficient and accurate, especially in detecting melting events.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Simon Boitard, Arnaud Mialon, Stéphane Mermoz, Nemesio J. Rodríguez-Fernández, Philippe Richaume, Julio César Salazar-Neira, Stéphane Tarot, and Yann H. Kerr
Earth Syst. Sci. Data, 17, 1101–1119, https://doi.org/10.5194/essd-17-1101-2025, https://doi.org/10.5194/essd-17-1101-2025, 2025
Short summary
Short summary
Aboveground biomass (AGB) is a critical component of the Earth's carbon cycle. The presented dataset aims to help monitor this essential climate variable with AGB time series from 2011 onward, derived with a carefully calibrated spatial relationship between the measurements of the Soil Moisture and Ocean Salinity (SMOS) mission and pre-existing AGB maps. The produced dataset has been extensively compared with other available AGB time series and can be used in AGB studies.
Min Huang, Gregory R. Carmichael, Kevin W. Bowman, Isabelle De Smedt, Andreas Colliander, Michael H. Cosh, Sujay V. Kumar, Alex B. Guenther, Scott J. Janz, Ryan M. Stauffer, Anne M. Thompson, Niko M. Fedkin, Robert J. Swap, John D. Bolten, and Alicia T. Joseph
Atmos. Chem. Phys., 25, 1449–1476, https://doi.org/10.5194/acp-25-1449-2025, https://doi.org/10.5194/acp-25-1449-2025, 2025
Short summary
Short summary
We use model simulations along with multiplatform, multidisciplinary observations and a range of analysis methods to estimate and understand the distributions, temporal changes, and impacts of reactive nitrogen and ozone over the most populous US region that has undergone significant environmental changes. Deposition, biogenic emissions, and extra-regional sources have been playing increasingly important roles in controlling pollutant budgets in this area as local anthropogenic emissions drop.
Annett Bartsch, Xaver Muri, Markus Hetzenecker, Kimmo Rautiainen, Helena Bergstedt, Jan Wuite, Thomas Nagler, and Dmitry Nicolsky
The Cryosphere, 19, 459–483, https://doi.org/10.5194/tc-19-459-2025, https://doi.org/10.5194/tc-19-459-2025, 2025
Short summary
Short summary
We developed a robust freeze–thaw detection approach, applying a constant threshold to Copernicus Sentinel-1 data that is suitable for tundra regions. All global, coarser-resolution products, tested with the resulting benchmarking dataset, are of value for freeze–thaw retrieval, although differences were found depending on the seasons, particularly during the spring and autumn transition.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
Short summary
Short summary
Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
Alex Mavrovic, Oliver Sonnentag, Juha Lemmetyinen, Carolina Voigt, Nick Rutter, Paul Mann, Jean-Daniel Sylvain, and Alexandre Roy
Biogeosciences, 20, 5087–5108, https://doi.org/10.5194/bg-20-5087-2023, https://doi.org/10.5194/bg-20-5087-2023, 2023
Short summary
Short summary
We present an analysis of soil CO2 emissions in boreal and tundra regions during the non-growing season. We show that when the soil is completely frozen, soil temperature is the main control on CO2 emissions. When the soil is around the freezing point, with a mix of liquid water and ice, the liquid water content is the main control on CO2 emissions. This study highlights that the vegetation–snow–soil interactions must be considered to understand soil CO2 emissions during the non-growing season.
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.
Alex Mavrovic, Oliver Sonnentag, Juha Lemmetyinen, Jennifer L. Baltzer, Christophe Kinnard, and Alexandre Roy
Biogeosciences, 20, 2941–2970, https://doi.org/10.5194/bg-20-2941-2023, https://doi.org/10.5194/bg-20-2941-2023, 2023
Short summary
Short summary
This review supports the integration of microwave spaceborne information into carbon cycle science for Arctic–boreal regions. The microwave data record spans multiple decades with frequent global observations of soil moisture and temperature, surface freeze–thaw cycles, vegetation water storage, snowpack properties, and land cover. This record holds substantial unexploited potential to better understand carbon cycle processes.
Bo Qu, Alexandre Roy, Joe R. Melton, Jennifer L. Baltzer, Youngryel Ryu, Matteo Detto, and Oliver Sonnentag
EGUsphere, https://doi.org/10.5194/egusphere-2023-1167, https://doi.org/10.5194/egusphere-2023-1167, 2023
Preprint archived
Short summary
Short summary
Accurately simulating photosynthesis and evapotranspiration challenges terrestrial biosphere models across North America’s boreal biome, in part due to uncertain representation of the maximum rate of photosynthetic carboxylation (Vcmax). This study used forest stand scale observations in an optimization framework to improve Vcmax values for representative vegetation types. Several stand characteristics well explained spatial Vcmax variability and were useful to improve boreal forest modelling.
Annett Bartsch, Helena Bergstedt, Georg Pointner, Xaver Muri, Kimmo Rautiainen, Leena Leppänen, Kyle Joly, Aleksandr Sokolov, Pavel Orekhov, Dorothee Ehrich, and Eeva Mariatta Soininen
The Cryosphere, 17, 889–915, https://doi.org/10.5194/tc-17-889-2023, https://doi.org/10.5194/tc-17-889-2023, 2023
Short summary
Short summary
Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. In extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record. Retrieval is most robust in the tundra biome, where records can be used to identify extremes and the results can be applied to impact studies at regional scale.
Emma Bousquet, Arnaud Mialon, Nemesio Rodriguez-Fernandez, Stéphane Mermoz, and Yann Kerr
Biogeosciences, 19, 3317–3336, https://doi.org/10.5194/bg-19-3317-2022, https://doi.org/10.5194/bg-19-3317-2022, 2022
Short summary
Short summary
Pre- and post-fire values of four climate variables and four vegetation variables were analysed at the global scale, in order to observe (i) the general fire likelihood factors and (ii) the vegetation recovery trends over various biomes. The main result of this study is that L-band vegetation optical depth (L-VOD) is the most impacted vegetation variable and takes the longest to recover over dense forests. L-VOD could then be useful for post-fire vegetation recovery studies.
Joëlle Voglimacci-Stephanopoli, Anna Wendleder, Hugues Lantuit, Alexandre Langlois, Samuel Stettner, Andreas Schmitt, Jean-Pierre Dedieu, Achim Roth, and Alain Royer
The Cryosphere, 16, 2163–2181, https://doi.org/10.5194/tc-16-2163-2022, https://doi.org/10.5194/tc-16-2163-2022, 2022
Short summary
Short summary
Changes in the state of the snowpack in the context of observed global warming must be considered to improve our understanding of the processes within the cryosphere. This study aims to characterize an arctic snowpack using the TerraSAR-X satellite. Using a high-spatial-resolution vegetation classification, we were able to quantify the variability in snow depth, as well as the topographic soil wetness index, which provided a better understanding of the electromagnetic wave–ground interaction.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
Short summary
Short summary
We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
Short summary
Short summary
Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Julien Meloche, Alexandre Langlois, Nick Rutter, Alain Royer, Josh King, Branden Walker, Philip Marsh, and Evan J. Wilcox
The Cryosphere, 16, 87–101, https://doi.org/10.5194/tc-16-87-2022, https://doi.org/10.5194/tc-16-87-2022, 2022
Short summary
Short summary
To estimate snow water equivalent from space, model predictions of the satellite measurement (brightness temperature in our case) have to be used. These models allow us to estimate snow properties from the brightness temperature by inverting the model. To improve SWE estimate, we proposed incorporating the variability of snow in these model as it has not been taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.
Chang-Hwan Park, Aaron Berg, Michael H. Cosh, Andreas Colliander, Andreas Behrendt, Hida Manns, Jinkyu Hong, Johan Lee, Runze Zhang, and Volker Wulfmeyer
Hydrol. Earth Syst. Sci., 25, 6407–6420, https://doi.org/10.5194/hess-25-6407-2021, https://doi.org/10.5194/hess-25-6407-2021, 2021
Short summary
Short summary
In this study, we proposed an inversion of the dielectric mixing model for a 50 Hz soil sensor for agricultural organic soil. This model can reflect the variability of soil organic matter (SOM) in wilting point and porosity, which play a critical role in improving the accuracy of SM estimation, using a dielectric-based soil sensor. The results of statistical analyses demonstrated a higher performance of the new model than the factory setting probe algorithm.
Alain Royer, Alexandre Roy, Sylvain Jutras, and Alexandre Langlois
The Cryosphere, 15, 5079–5098, https://doi.org/10.5194/tc-15-5079-2021, https://doi.org/10.5194/tc-15-5079-2021, 2021
Short summary
Short summary
Dense spatially distributed networks of autonomous instruments for continuously measuring the amount of snow on the ground are needed for operational water resource and flood management and the monitoring of northern climate change. Four new-generation non-invasive sensors are compared. A review of their advantages, drawbacks and accuracy is discussed. This performance analysis is intended to help researchers and decision-makers choose the one system that is best suited to their needs.
Cited articles
Aalto, J., Karjalainen, O., Hjort, J., and Luoto, M.: Statistical Forecasting of Current and Future Circum-Arctic Ground Temperatures and Active Layer Thickness, Geophys. Res. Lett., 45, 4889–4898, https://doi.org/10.1029/2018GL078007, 2018. a
Adams, W. and Lasenby, D.: The Roles of Snow, Lake Ice and Lake Water in the Distribution of Major Ions in the Ice Cover of a Lake, Ann. Glaciol., 7, 202–207, https://doi.org/10.3189/S0260305500006170, 1985. a, b
Aksoy, M. and Johnson, J. T.: A Study of SMOS RFI Over North America, IEEE Geosci. Remote S., 10, 515–519, https://doi.org/10.1109/LGRS.2012.2211993, 2013. a
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017. a
Ala-Aho, P., Autio, A., Bhattacharjee, J., Isokangas, E., Kujala, K., Marttila, H., Menberu, M., Meriö, L.-J., Postila, H., Rauhala, A., Ronkanen, A.-K., Rossi, P. M., Saari, M., Haghighi, A. T., and Kløve, B.: What Conditions Favor the Influence of Seasonally Frozen Ground on Hydrological Partitioning? A Systematic Review, Environ. Res. Lett., 16, 043008, https://doi.org/10.1088/1748-9326/abe82c, 2021. a
André, C., Ottlé, C., Royer, A., and Maignan, F.: Land Surface Temperature Retrieval over Circumpolar Arctic Using SSM/I–SSMIS and MODIS Data, Remote Sens. Environ., 162, 1–10, https://doi.org/10.1016/j.rse.2015.01.028, 2015. a
Bartlett, M. G., Chapman, D. S., and Harris, R. N.: Snow and the Ground Temperature Record of Climate Change, J. Geophys. Res.-Earth, 109, 2004JF000224, https://doi.org/10.1029/2004JF000224, 2004. a, b
Bartsch, A., Bergstedt, H., Pointner, G., Muri, X., Rautiainen, K., Leppänen, L., Joly, K., Sokolov, A., Orekhov, P., Ehrich, D., and Soininen, E. M.: Towards long-term records of rain-on-snow events across the Arctic from satellite data, The Cryosphere, 17, 889–915, https://doi.org/10.5194/tc-17-889-2023, 2023. a
Benninga, H.-J. F., Van Der Velde, R., and Su, Z.: Sentinel-1 Soil Moisture Content and Its Uncertainty over Sparsely Vegetated Fields, J. Hydrology X, 9, 100066, https://doi.org/10.1016/j.hydroa.2020.100066, 2020. a
Bircher, S., Demontoux, F., Razafindratsima, S., Zakharova, E., Drusch, M., Wigneron, J.-P., and Kerr, Y.: L-Band Relative Permittivity of Organic Soil Surface Layers – A New Dataset of Resonant Cavity Measurements and Model Evaluation, Remote Sens.-Basel, 8, 1024, https://doi.org/10.3390/rs8121024, 2016. a, b
Biskaborn, B. K., Smith, S. L., Noetzli, J., Matthes, H., Vieira, G., Streletskiy, D. A., Schoeneich, P., Romanovsky, V. E., Lewkowicz, A. G., Abramov, A., Allard, M., Boike, J., Cable, W. L., Christiansen, H. H., Delaloye, R., Diekmann, B., Drozdov, D., Etzelmüller, B., Grosse, G., Guglielmin, M., Ingeman-Nielsen, T., Isaksen, K., Ishikawa, M., Johansson, M., Johannsson, H., Joo, A., Kaverin, D., Kholodov, A., Konstantinov, P., Kröger, T., Lambiel, C., Lanckman, J.-P., Luo, D., Malkova, G., Meiklejohn, I., Moskalenko, N., Oliva, M., Phillips, M., Ramos, M., Sannel, A. B. K., Sergeev, D., Seybold, C., Skryabin, P., Vasiliev, A., Wu, Q., Yoshikawa, K., Zheleznyak, M., and Lantuit, H.: Permafrost Is Warming at a Global Scale, Nat. Commun., 10, 264, https://doi.org/10.1038/s41467-018-08240-4, 2019. a
Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., and Savoie, M. H.: EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets, ISPRS Int. Geo. Inf., 1, 32–45, https://doi.org/10.3390/ijgi1010032, 2012. a
Cao, B., Gruber, S., Zheng, D., and Li, X.: The ERA5-Land soil temperature bias in permafrost regions, The Cryosphere, 14, 2581–2595, https://doi.org/10.5194/tc-14-2581-2020, 2020. a
CATDS: CATDS-PDC L3TB – Daily Global Polarised Brightness Temperature Product from SMOS Satellite, L3TB [data set], CATDS, https://doi.org/10.12770/6294E08C-BAEC-4282-A251-33FEE22EC67F, 2024. a
Catherinot, J., Prigent, C., Maurer, R., Papa, F., Jiménez, C., Aires, F., and Rossow, W. B.: Evaluation of “All Weather” Microwave-Derived Land Surface Temperatures with in Situ CEOP Measurements: “ALL WEATHER” LAND SURFACE TEMPERATURE EVALUATION, J. Geophys. Res-Atmos., 116, https://doi.org/10.1029/2011JD016439, 2011. a
Chadburn, S. E., Burke, E. J., Cox, P. M., Friedlingstein, P., Hugelius, G., and Westermann, S.: An Observation-Based Constraint on Permafrost Loss as a Function of Global Warming, Nat. Clim. Change, 7, 340–344, https://doi.org/10.1038/nclimate3262, 2017. a
Chaubell, M. J., Yueh, S. H., Dunbar, R. S., Colliander, A., Chen, F., Chan, S. K., Entekhabi, D., Bindlish, R., O'Neill, P. E., Asanuma, J., Berg, A. A., Bosch, D. D., Caldwell, T., Cosh, M. H., Holifield Collins, C., Martinez-Fernandez, J., Seyfried, M., Starks, P. J., Su, Z., Thibeault, M., and Walker, J.: Improved SMAP Dual-Channel Algorithm for the Retrieval of Soil Moisture, IEEE T. Geosci. Remote, 58, 3894–3905, https://doi.org/10.1109/TGRS.2019.2959239, 2020. a
Choudhury, B. J., Schmugge, T. J., Chang, A., and Newton, R. W.: Effect of Surface Roughness on the Microwave Emission from Soils, J. Geophys. Res.-Oceans, 84, 5699–5706, https://doi.org/10.1029/JC084iC09p05699, 1979. a
Daganzo-Eusebio, E., Oliva, R., Kerr, Y. H., Nieto, S., Richaume, P., and Mecklenburg, S. M.: SMOS Radiometer in the 1400–1427-MHz Passive Band: Impact of the RFI Environment and Approach to Its Mitigation and Cancellation, IEEE T. Geosci. Remote, 51, 4999–5007, https://doi.org/10.1109/TGRS.2013.2259179, 2013. a
Defourny, P., Lamarche, C., Brockmann, C., Boettcher, M., Bon- temps, S., De Maet, T., Duveiller, G. L., Harper, K., Hartley A., Kirches, G., Moreau, I., Peylin, P., Ottlé, C., Radoux J., Van Bogaert, E, Ramoino, F., Albergel, C., and Arino, O.: Observed Annual Global Land-Use Change from 1992 to 2020 Three Times More Dynamic than Reported by Inventory-Based Statistics [Year 2015], https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: 4 September 2025), 2023. a, b, c
Derksen, C., Lemmetyinen, J., Toose, P., Silis, A., Pulliainen, J., and Sturm, M.: Physical Properties of Arctic versus Subarctic Snow: Implications for High Latitude Passive Microwave Snow Water Equivalent Retrievals, J. Geophys. Res.-Atmos., 119, 7254–7270, https://doi.org/10.1002/2013JD021264, 2014. a
Dobiński, W.: Permafrost Active Layer, Earth-Sci. Rev., 208, 103301, https://doi.org/10.1016/j.earscirev.2020.103301, 2020. a
Domine, F., Picard, G., Morin, S., Barrere, M., Madore, J.-B., and Langlois, A.: Major Issues in Simulating Some Arctic Snowpack Properties Using Current Detailed Snow Physics Models: Consequences for the Thermal Regime and Water Budget of Permafrost, J. Adv. Model. Earth Sy., 11, 34–44, https://doi.org/10.1029/2018MS001445, 2019. a
Domine, F., Fourteau, K., Picard, G., Lackner, G., Sarrazin, D., and Poirier, M.: Permafrost Cooled in Winter by Thermal Bridging through Snow-Covered Shrub Branches, Nat. Geosci., 15, 554–560, https://doi.org/10.1038/s41561-022-00979-2, 2022. a
Donlon, C., Galeazzi, C., Midthassel, R., Sallusti, M., Triggianese, M., Fiorelli, B., De Paris, G., Kornienko, A., and Khlystova, I.: The Copernicus Imaging Microwave Radiometer (CIMR): Mission Overview and Status, in: IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Pasadena, CA, USA, 16–21 July 2023, https://doi.org/10.1109/IGARSS52108.2023.10281934, 989–992, 2023. a
Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., Xaver, A., Annor, F., Ardö, J., Baldocchi, D., Bitelli, M., Blöschl, G., Bogena, H., Brocca, L., Calvet, J.-C., Camarero, J. J., Capello, G., Choi, M., Cosh, M. C., van de Giesen, N., Hajdu, I., Ikonen, J., Jensen, K. H., Kanniah, K. D., de Kat, I., Kirchengast, G., Kumar Rai, P., Kyrouac, J., Larson, K., Liu, S., Loew, A., Moghaddam, M., Martínez Fernández, J., Mattar Bader, C., Morbidelli, R., Musial, J. P., Osenga, E., Palecki, M. A., Pellarin, T., Petropoulos, G. P., Pfeil, I., Powers, J., Robock, A., Rüdiger, C., Rummel, U., Strobel, M., Su, Z., Sullivan, R., Tagesson, T., Varlagin, A., Vreugdenhil, M., Walker, J., Wen, J., Wenger, F., Wigneron, J. P., Woods, M., Yang, K., Zeng, Y., Zhang, X., Zreda, M., Dietrich, S., Gruber, A., van Oevelen, P., Wagner, W., Scipal, K., Drusch, M., and Sabia, R.: The International Soil Moisture Network: serving Earth system science for over a decade, Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, 2021. a
Druckenmiller, M. L. and Jeffries, M.: Arctic Report Card, http://www.Arctic.Noaa.Gov/Report-Card (last access: 4 September 2025), 2019. a
Duan, S.-B., Han, X.-J., Huang, C., Li, Z.-L., Wu, H., Qian, Y., Gao, M., and Leng, P.: Land Surface Temperature Retrieval from Passive Microwave Satellite Observations: State-of-the-Art and Future Directions, Remote Sens.-Basel, 12, 2573, https://doi.org/10.3390/rs12162573, 2020. a
Duguay, C. R. and Lafleur, P. M.: Determining Depth and Ice Thickness of Shallow Sub-Arctic Lakes Using Space-Borne Optical and SAR Data, Int. J. Remote Sens., 24, 475–489, https://doi.org/10.1080/01431160304992, 2003. a
Entekhabi, D., Reichle, R. H., Koster, R. D., and Crow, W. T.: Performance Metrics for Soil Moisture Retrievals and Application Requirements, J. Hydrometeorol., 11, 832–840, https://doi.org/10.1175/2010JHM1223.1, 2010. a
Escorihuela, M., Kerr, Y., De Rosnay, P., Wigneron, J.-P., Calvet, J.-C., and Lemaitre, F.: A Simple Model of the Bare Soil Microwave Emission at L-Band, IEEE T. Geosci. Remote, 45, 1978–1987, https://doi.org/10.1109/TGRS.2007.894935, 2007. a
Fily, M.: A Simple Retrieval Method for Land Surface Temperature and Fraction of Water Surface Determination from Satellite Microwave Brightness Temperatures in Sub-Arctic Areas, Remote Sens. Environ., 85, 328–338, https://doi.org/10.1016/S0034-4257(03)00011-7, 2003. a
Gélinas, A., Filali, B., Langlois, A., Kelly, R., Mavrovic, A., Demontoux, F., and Roy, A.: New Wideband Large Aperture Open-Ended Coaxial Microwave Probe for Soil Dielectric Characterization, IEEE T. Geosci. Remote, 63, 1–8, https://doi.org/10.1109/TGRS.2025.3539532, 2025. a
Gruber, A., De Lannoy, G., Albergel, C., Al-Yaari, A., Brocca, L., Calvet, J.-C., Colliander, A., Cosh, M., Crow, W., Dorigo, W., Draper, C., Hirschi, M., Kerr, Y., Konings, A., Lahoz, W., McColl, K., Montzka, C., Muñoz-Sabater, J., Peng, J., Reichle, R., Richaume, P., Rüdiger, C., Scanlon, T., Van Der Schalie, R., Wigneron, J.-P., and Wagner, W.: Validation Practices for Satellite Soil Moisture Retrievals: What Are (the) Errors?, Remote Sens. Environ., 244, 111806, https://doi.org/10.1016/j.rse.2020.111806, 2020. a, b
Grünberg, I., Wilcox, E. J., Zwieback, S., Marsh, P., and Boike, J.: Linking tundra vegetation, snow, soil temperature, and permafrost, Biogeosciences, 17, 4261–4279, https://doi.org/10.5194/bg-17-4261-2020, 2020. a
Guo, H., Zhu, W., Xiao, C., Zhao, C., and Chen, L.: High-Precision Estimation of Pan-Arctic Soil Surface Temperature from MODIS LST by Incorporating Multiple Environment Factors and Monthly-Based Modeling, Int. J. Appl. Earth Obs., 133, 104114, https://doi.org/10.1016/j.jag.2024.104114, 2024. a
Hallikainen, M., Ulaby, F., Dobson, M., El-rayes, M., and Wu, L.-k.: Microwave Dielectric Behavior of Wet Soil-Part 1: Empirical Models and Experimental Observations, IEEE T. Geosci. Remote, GE-23, 25–34, https://doi.org/10.1109/TGRS.1985.289497, 1985. a, b
Heginbottom, J., Brown, J., Ferrians, O., and Melnikov, E.: Circum-Arctic Map of Permafrost and Ground-Ice Conditions, Version 2, [Permafrost], Boulder, Colorado USA, National Snow and Ice Data Center, https://doi.org/10.7265/SKBG-KF16, 2002. a
Herrington, T. C., Fletcher, C. G., and Kropp, H.: Validation of pan-Arctic soil temperatures in modern reanalysis and data assimilation systems, The Cryosphere, 18, 1835–1861, https://doi.org/10.5194/tc-18-1835-2024, 2024. a
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), https://doi.org/10.24381/CDS.ADBB2D47, 2023. a, b
Holmberg, M., Lemmetyinen, J., Schwank, M., Kontu, A., Rautiainen, K., Merkouriadi, I., and Tamminen, J.: Retrieval of Ground, Snow, and Forest Parameters from Space Borne Passive L Band Observations. A Case Study over Sodankylä, Finland, Remote Sens. Environ., 306, 114143, https://doi.org/10.1016/j.rse.2024.114143, 2024. a, b, c, d, e
Jeffries, M. O., Morris, K., and Kozlenko, N.: Ice Characteristics and Processes, and Remote Sensing of Frozen Rivers and Lakes, in: Geophysical Monograph Series, edited by: Duguay, C. R. and Pietroniro, A., American Geophysical Union, Washington, DC, 63–90, https://doi.org/10.1029/163GM05, 2013. a
Jiménez-Muñoz, J. C., Sobrino, J. A., Skoković, D., Mattar, C., and Cristóbal, J.: Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data, IEEE Geosci. Remote S., 11, 1840–1843, https://doi.org/10.1109/LGRS.2014.2312032, 2014. a
Jones, L., Kimball, J., McDonald, K., Chan, S., Njoku, E., and Oechel, W.: Satellite Microwave Remote Sensing of Boreal and Arctic Soil Temperatures From AMSR-E, IEEE T. Geosci. Remote, 45, 2004–2018, https://doi.org/10.1109/TGRS.2007.898436, 2007. a, b
Kerr, Y., Al-Yaari, A., Rodriguez-Fernandez, N., Parrens, M., Molero, B., Leroux, D., Bircher, S., Mahmoodi, A., Mialon, A., Richaume, P., Delwart, S., Al Bitar, A., Pellarin, T., Bindlish, R., Jackson, T., Rüdiger, C., Waldteufel, P., Mecklenburg, S., and Wigneron, J.-P.: Overview of SMOS Performance in Terms of Global Soil Moisture Monitoring after Six Years in Operation, Remote Sens. Environ., 180, 40–63, https://doi.org/10.1016/j.rse.2016.02.042, 2016a. a
Kerr, Y., Reul, N., Martín-Neira, M., Drusch, M., Alvera-Azcarate, A., Wigneron, J.-P., and Mecklenburg, S.: ESA's Soil Moisture and Ocean Salinity Mission – Achievements and Applications after More than 6 Years in Orbit, Remote Sens. Environ., 180, 1–2, https://doi.org/10.1016/j.rse.2016.03.020, 2016b. a
Kerr, Y., Richaume, P., Waldteufel, P., Ferrazzoli, P., Wigneron, J. P., Schwank, M., and Rautiainen, K.: Algorithm Theoretical Basis Document (ATBD) for the SMOS Level 2 Soil Moisture Processor, Technical Report TN-ESL-SM-GS-0001-4b SM-ESL (CBSA), p. 145, https://earth.esa.int/eogateway/documents/20142/37627/SMOS-L2-SM-ATBD.pdf (last access: 4 September 2025), 2020. a, b, c, d, e, f, g
Kerr, Y. H., Waldteufel, P., Wigneron, J.-P., Delwart, S., Cabot, F., Boutin, J., Escorihuela, M.-J., Font, J., Reul, N., Gruhier, C., Juglea, S. E., Drinkwater, M. R., Hahne, A., Martín-Neira, M., and Mecklenburg, S.: The SMOS Mission: New Tool for Monitoring Key Elements Ofthe Global Water Cycle, P. IEEE, 98, 666–687, https://doi.org/10.1109/JPROC.2010.2043032, 2010. a, b
Köhn, J. and Royer, A.: Microwave Brightness Temperature as an Indicator of Near-Surface Air Temperature over Snow in Canadian Northern Regions, Int. J. Remote Sens., 33, 1126–1138, https://doi.org/10.1080/01431161.2010.550643, 2012. a
Konings, A. G., Piles, M., Rötzer, K., McColl, K. A., Chan, S. K., and Entekhabi, D.: Vegetation Optical Depth and Scattering Albedo Retrieval Using Time Series of Dual-Polarized L-band Radiometer Observations, Remote Sens. Environ., 172, 178–189, https://doi.org/10.1016/j.rse.2015.11.009, 2016. a
Kumawat, D., Olyaei, M., Gao, L., and Ebtehaj, A.: Passive Microwave Retrieval of Soil Moisture Below Snowpack at L-Band Using SMAP Observations, IEEE T. Geosci. Remote, 60, 1–16, https://doi.org/10.1109/TGRS.2022.3216324, 2022. a
Lawrence, H., Wigneron, J.-P., Demontoux, F., Mialon, A., and Kerr, Y. H.: Evaluating the Semiempirical $H$ – $Q$ Model Used to Calculate the L-Band Emissivity of a Rough Bare Soil, IEEE T. Geosci. Remote, 51, 4075–4084, https://doi.org/10.1109/TGRS.2012.2226995, 2013. a
Leavesley, G., David, O, Garen, D. C., Goodbody, A. G., Lea, J., Marron, T., Perkins, T., Strobel, M., and Tama, R.: A Modeling Framework for Improved Agricultural Water-Supply Forecasting, in: Joint Federal Interagency Hydrologic Modeling Conference,In Proceedings, Joint Federal Interagency Hydrologic Modeling Conference, Las Vegas, NV, 28 June–1 July 2010, 2010. a, b, c
Leduc-Leballeur, M., Picard, G., Macelloni, G., Mialon, A., and Kerr, Y. H.: Melt in Antarctica derived from Soil Moisture and Ocean Salinity (SMOS) observations at L band, The Cryosphere, 14, 539–548, https://doi.org/10.5194/tc-14-539-2020, 2020. a
Lembrechts, J. J., Van Den Hoogen, J., Aalto, J., Ashcroft, M. B., De Frenne, P., Kemppinen, J., Kopecký, M., Luoto, M., Maclean, I. M. D., Crowther, T. W., Bailey, J. J., Haesen, S., Klinges, D. H., Niittynen, P., Scheffers, B. R., Van Meerbeek, K., Aartsma, P., Abdalaze, O., Abedi, M., Aerts, R., Ahmadian, N., Ahrends, A., Alatalo, J. M., Alexander, J. M., Allonsius, C. N., Altman, J., Ammann, C., Andres, C., Andrews, C., Ardö, J., Arriga, N., Arzac, A., Aschero, V., Assis, R. L., Assmann, J. J., Bader, M. Y., Bahalkeh, K., Barančok, P., Barrio, I. C., Barros, A., Barthel, M., Basham, E. W., Bauters, M., Bazzichetto, M., Marchesini, L. B., Bell, M. C., Benavides, J. C., Benito Alonso, J. L., Berauer, B. J., Bjerke, J. W., Björk, R. G., Björkman, M. P., Björnsdóttir, K., Blonder, B., Boeckx, P., Boike, J., Bokhorst, S., Brum, B. N. S., Brůna, J., Buchmann, N., Buysse, P., Camargo, J. L., Campoe, O. C., Candan, O., Canessa, R., Cannone, N., Carbognani, M., Carnicer, J., Casanova-Katny, A., Cesarz, S., Chojnicki, B., Choler, P., Chown, S. L., Cifuentes, E. F., Čiliak, M., Contador, T., Convey, P., Cooper, E. J., Cremonese, E., Curasi, S. R., Curtis, R., Cutini, M., Dahlberg, C. J., Daskalova, G. N., De Pablo, M. A., Della Chiesa, S., Dengler, J., Deronde, B., Descombes, P., Di Cecco, V., Di Musciano, M., Dick, J., Dimarco, R. D., Dolezal, J., Dorrepaal, E., Dušek, J., Eisenhauer, N., Eklundh, L., Erickson, T. E., Erschbamer, B., Eugster, W., Ewers, R. M., Exton, D. A., Fanin, N., Fazlioglu, F., Feigenwinter, I., Fenu, G., Ferlian, O., Fernández Calzado, M. R., Fernández-Pascual, E., Finckh, M., Higgens, R. F., Forte, T. G. W., Freeman, E. C., Frei, E. R., Fuentes-Lillo, E., García, R. A., García, M. B., Géron, C., Gharun, M., Ghosn, D., Gigauri, K., Gobin, A., Goded, I., Goeckede, M., Gottschall, F., Goulding, K., Govaert, S., Graae, B. J., Greenwood, S., Greiser, C., Grelle, A., Guénard, B., Guglielmin, M., Guillemot, J., Haase, P., Haider, S., Halbritter, A. H., Hamid, M., Hammerle, A., Hampe, A., Haugum, S. V., Hederová, L., Heinesch, B., Helfter, C., Hepenstrick, D., Herberich, M., Herbst, M., Hermanutz, L., Hik, D. S., Hoffrén, R., Homeier, J., Hörtnagl, L., Høye, T. T., Hrbacek, F., Hylander, K., Iwata, H., Jackowicz-Korczynski, M. A., Jactel, H., Järveoja, J., Jastrzębowski, S., Jentsch, A., Jiménez, J. J., Jónsdóttir, I. S., Jucker, T., Jump, A. S., Juszczak, R., Kanka, R., Kašpar, V., Kazakis, G., Kelly, J., Khuroo, A. A., Klemedtsson, L., Klisz, M., Kljun, N., Knohl, A., Kobler, J., Kollár, J., Kotowska, M. M., Kovács, B., Kreyling, J., Lamprecht, A., Lang, S. I., Larson, C., Larson, K., Laska, K., Le Maire, G., Leihy, R. I., Lens, L., Liljebladh, B., Lohila, A., Lorite, J., Loubet, B., Lynn, J., Macek, M., Mackenzie, R., Magliulo, E., Maier, R., Malfasi, F., Máliš, F., Man, M., Manca, G., Manco, A., Manise, T., Manolaki, P., Marciniak, F., Matula, R., Mazzolari, A. C., Medinets, S., Medinets, V., Meeussen, C., Merinero, S., Mesquita, R. D. C. G., Meusburger, K., Meysman, F. J. R., Michaletz, S. T., Milbau, A., Moiseev, D., Moiseev, P., Mondoni, A., Monfries, R., Montagnani, L., Moriana-Armendariz, M., Morra Di Cella, U., Mörsdorf, M., Mosedale, J. R., Muffler, L., Muñoz-Rojas, M., Myers, J. A., Myers-Smith, I. H., Nagy, L., Nardino, M., Naujokaitis-Lewis, I., Newling, E., Nicklas, L., Niedrist, G., Niessner, A., Nilsson, M. B., Normand, S., Nosetto, M. D., Nouvellon, Y., Nuñez, M. A., Ogaya, R., Ogée, J., Okello, J., Olejnik, J., Olesen, J. E., Opedal, Ø. H., Orsenigo, S., Palaj, A., Pampuch, T., Panov, A. V., Pärtel, M., Pastor, A., Pauchard, A., Pauli, H., Pavelka, M., Pearse, W. D., Peichl, M., Pellissier, L., Penczykowski, R. M., Penuelas, J., Petit Bon, M., Petraglia, A., Phartyal, S. S., Phoenix, G. K., Pio, C., Pitacco, A., Pitteloud, C., Plichta, R., Porro, F., Portillo-Estrada, M., Poulenard, J., Poyatos, R., Prokushkin, A. S., Puchalka, R., Puşcaş, M., Radujković, D., Randall, K., Ratier Backes, A., Remmele, S., Remmers, W., Renault, D., Risch, A. C., Rixen, C., Robinson, S. A., Robroek, B. J. M., Rocha, A. V., Rossi, C., Rossi, G., Roupsard, O., Rubtsov, A. V., Saccone, P., Sagot, C., Sallo Bravo, J., Santos, C. C., Sarneel, J. M., Scharnweber, T., Schmeddes, J., Schmidt, M., Scholten, T., Schuchardt, M., Schwartz, N., Scott, T., Seeber, J., Segalin De Andrade, A. C., Seipel, T., Semenchuk, P., Senior, R. A., Serra-Diaz, J. M., Sewerniak, P., Shekhar, A., Sidenko, N. V., Siebicke, L., Siegwart Collier, L., Simpson, E., Siqueira, D. P., Sitková, Z., Six, J., Smiljanic, M., Smith, S. W., Smith-Tripp, S., Somers, B., Sørensen, M. V., Souza, J. J. L. L., Souza, B. I., Souza Dias, A., Spasojevic, M. J., Speed, J. D. M., Spicher, F., Stanisci, A., Steinbauer, K., Steinbrecher, R., Steinwandter, M., Stemkovski, M., Stephan, J. G., Stiegler, C., Stoll, S., Svátek, M., Svoboda, M., Tagesson, T., Tanentzap, A. J., Tanneberger, F., Theurillat, J.-P., Thomas, H. J. D., Thomas, A. D., Tielbörger, K., Tomaselli, M., Treier, U. A., Trouillier, M., Turtureanu, P. D., Tutton, R., Tyystjärvi, V. A., Ueyama, M., Ujházy, K., Ujházyová, M., Uogintas, D., Urban, A. V., Urban, J., Urbaniak, M., Ursu, T.-M., Vaccari, F. P., Van De Vondel, S., Van Den Brink, L., Van Geel, M., Vandvik, V., Vangansbeke, P., Varlagin, A., Veen, G. F., Veenendaal, E., Venn, S. E., Verbeeck, H., Verbrugggen, E., Verheijen, F. G. A., Villar, L., Vitale, L., Vittoz, P., Vives-Ingla, M., Von Oppen, J., Walz, J., Wang, R., Wang, Y., Way, R. G., Wedegärtner, R. E. M., Weigel, R., Wild, J., Wilkinson, M., Wilmking, M., Wingate, L., Winkler, M., Wipf, S., Wohlfahrt, G., Xenakis, G., Yang, Y., Yu, Z., Yu, K., Zellweger, F., Zhang, J., Zhang, Z., Zhao, P., Ziemblińska, K., Zimmermann, R., Zong, S., Zyryanov, V. I., Nijs, I., and Lenoir, J.: Global Maps of Soil Temperature, Glob. Change Biol., 28, 3110–3144, https://doi.org/10.1111/gcb.16060, 2022. a, b
Lemmetyinen, J., Kontu, A., Kärnä, J.-P., Vehviläinen, J., Takala, M., and Pulliainen, J.: Correcting for the Influence of Frozen Lakes in Satellite Microwave Radiometer Observations through Application of a Microwave Emission Model, Remote Sens. Environ., 115, 3695–3706, https://doi.org/10.1016/j.rse.2011.09.008, 2011. a
Lemmetyinen, J., Schwank, M., Rautiainen, K., Kontu, A., Parkkinen, T., Mätzler, C., Wiesmann, A., Wegmüller, U., Derksen, C., Toose, P., Roy, A., and Pulliainen, J.: Snow Density and Ground Permittivity Retrieved from L-band Radiometry: Application to Experimental Data, Remote Sens. Environ., 180, 377–391, https://doi.org/10.1016/j.rse.2016.02.002, 2016. a, b, c
Liebe, H. J., Hufford, G. A., and Manabe, T.: A Model for the Complex Permittivity of Water at Frequencies below 1 THz, Int. J. Infrared Milli., 12, 659–675, https://doi.org/10.1007/BF01008897, 1991. a
Liston, G. E., Mcfadden, J. P., Sturm, M., and Pielke, R. A.: Modelled Changes in Arctic Tundra Snow, Energy and Moisture Fluxes Due to Increased Shrubs, Glob. Change Biol., 8, 17–32, https://doi.org/10.1046/j.1354-1013.2001.00416.x, 2002. a
Macelloni, G., Brogioni, M., Leduc-Leballeur, M., Montomoli, F., Bartsch, A., Mialon, A., Ritz, C., Soteras, J. C., Stammer, D., Picard, G., De Carolis, G., Boutin, J., Johnson, J. T., Nicholls, K. W., Jezek, K., Rautiainen, K., Kaleschke, L., Bertino, L., Tsang, L., Van Den Broeke, M., Skou, N., and Tietsche, S.: Cryorad: A Low Frequency Wideband Radiometer Mission for the Study of the Cryosphere, in: IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Valencia, 22–27 July 2018, 1998–2000, https://doi.org/10.1109/IGARSS.2018.8519172, 2018. a
Marchand, N., Royer, A., Krinner, G., Roy, A., Langlois, A., and Vargel, C.: Snow-Covered Soil Temperature Retrieval in Canadian Arctic Permafrost Areas, Using a Land Surface Scheme Informed with Satellite Remote Sensing Data, Remote Sens.-Basel, 10, 1703, https://doi.org/10.3390/rs10111703, 2018. a
Mätzler, C. (Ed.): Thermal Microwave Radiation: Applications for Remote Sensing, no. 52 in IET Electromagnetic Waves Series, IET, London, https://doi.org/10.1049/PBEW052E, 2006. a, b
Mätzler, C. and Wiesmann, A.: Documentation for MEMLS, Version 3 `Microwave Emission Model of Layered Snowpacks', Tech. rep., Institute of Applied Physics (IAP) at the University of Bern, https://github.com/akasurak/memls_TVC/blob/master/MEMLS3.pdf (last access: 4 September 2025), 2012. a
Mavrovic, A., Sonnentag, O., Lemmetyinen, J., Voigt, C., Rutter, N., Mann, P., Sylvain, J.-D., and Roy, A.: Environmental controls of winter soil carbon dioxide fluxes in boreal and tundra environments, Biogeosciences, 20, 5087–5108, https://doi.org/10.5194/bg-20-5087-2023, 2023. a, b
Mialon, A., Royer, A., Fily, M., and Picard, G.: Daily Microwave-Derived Surface Temperature over Canada/Alaska, J. Appl. Meteorol. Clim., 46, 591–604, https://doi.org/10.1175/JAM2485.1, 2007. a
Mialon, A., Coret, L., Kerr, Y., Secherre, F., and Wigneron, J.-P.: Flagging the Topographic Impact on the SMOS Signal, IEEE T. Geosci. Remote, 46, 689–694, https://doi.org/10.1109/TGRS.2007.914788, 2008. a
Mialon, A., Rodríguez-Fernández, N. J., Santoro, M., Saatchi, S., Mermoz, S., Bousquet, E., and Kerr, Y. H.: Evaluation of the Sensitivity of SMOS L-VOD to Forest Above-Ground Biomass at Global Scale, Remote Sens.-Basel, 12, 1450, https://doi.org/10.3390/rs12091450, 2020. a
Mironov, V., Kosolapova, L., and Fomin, S.: Physically and Mineralogically Based Spectroscopic Dielectric Model for Moist Soils, IEEE T. Geosci. Remote, 47, 2059–2070, https://doi.org/10.1109/TGRS.2008.2011631, 2009. a, b, c
Mironov, V. L., Kosolapova, L. G., Savin, I. V., and Muzalevskiy, K. V.: Temperature Dependent Dielectric Model at 1.4 GHz for a Tundra Organic-Rich Soil Thawed and Frozen, in: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, Milan, Italy, 26-31 July 2015, 2016–2019, https://doi.org/10.1109/IGARSS.2015.7326194, 2015. a, b, c, d
Murfitt, J., Duguay, C., Picard, G., and Gunn, G.: Forward Modelling of Synthetic Aperture Radar Backscatter from Lake Ice over Canadian Subarctic Lakes, Remote Sens. Environ., 286, 113424, https://doi.org/10.1016/j.rse.2022.113424, 2023. a, b
Natali, S. M., Watts, J. D., Rogers, B. M., Potter, S., Ludwig, S. M., Selbmann, A.-K., Sullivan, P. F., Abbott, B. W., Arndt, K. A., Birch, L., Björkman, M. P., Bloom, A. A., Celis, G., Christensen, T. R., Christiansen, C. T., Commane, R., Cooper, E. J., Crill, P., Czimczik, C., Davydov, S., Du, J., Egan, J. E., Elberling, B., Euskirchen, E. S., Friborg, T., Genet, H., Göckede, M., Goodrich, J. P., Grogan, P., Helbig, M., Jafarov, E. E., Jastrow, J. D., Kalhori, A. A. M., Kim, Y., Kimball, J. S., Kutzbach, L., Lara, M. J., Larsen, K. S., Lee, B.-Y., Liu, Z., Loranty, M. M., Lund, M., Lupascu, M., Madani, N., Malhotra, A., Matamala, R., McFarland, J., McGuire, A. D., Michelsen, A., Minions, C., Oechel, W. C., Olefeldt, D., Parmentier, F.-J. W., Pirk, N., Poulter, B., Quinton, W., Rezanezhad, F., Risk, D., Sachs, T., Schaefer, K., Schmidt, N. M., Schuur, E. A. G., Semenchuk, P. R., Shaver, G., Sonnentag, O., Starr, G., Treat, C. C., Waldrop, M. P., Wang, Y., Welker, J., Wille, C., Xu, X., Zhang, Z., Zhuang, Q., and Zona, D.: Large Loss of CO2 in Winter Observed across the Northern Permafrost Region, Nat. Clim. Change, 9, 852–857, https://doi.org/10.1038/s41558-019-0592-8, 2019. a
Oechel, W., Verfaillie, J., Vourlitis, G., and Zulueta, R.: CARVE: L1 In-situ Carbon and CH4 Flux and Meteorology at EC Towers, Alaska, 2011–2015, ORNL Distributed Active Archive Center, https://doi.org/10.3334/ORNLDAAC/1424, 2016. a, b
Ortet, J., Mialon, A., Kerr, Y., Royer, A., Berg, A., Boike, J., Humphreys, E., Gibon, F., Richaume, P., Bircher-Adrot, S., Gorrab, A., and Roy, A.: Evaluating Soil Moisture Retrieval in Arctic and Sub-Arctic Environments Using Passive Microwave Satellite Data, Int. J. Digit. Earth, 17, 2385079, https://doi.org/10.1080/17538947.2024.2385079, 2024. a
Oveisy, A., Boegman, L., and Imberger, J.: Three-dimensional Simulation of Lake and Ice Dynamics during Winter, Limnol. Oceanogr., 57, 43–57, https://doi.org/10.4319/lo.2012.57.1.0043, 2012. a
Pardo Lara, R., Berg, A. A., Warland, J., and Tetlock, E.: In Situ Estimates of Freezing/Melting Point Depression in Agricultural Soils Using Permittivity and Temperature Measurements, Water Resour. Res., 56, e2019WR026020, https://doi.org/10.1029/2019WR026020, 2020. a
Park, C.-H., Behrendt, A., LeDrew, E., and Wulfmeyer, V.: New Approach for Calculating the Effective Dielectric Constant of the Moist Soil for Microwaves, Remote Sens.-Basel, 9, 732, https://doi.org/10.3390/rs9070732, 2017. a, b, c
Park, C.-H., Montzka, C., Jagdhuber, T., Jonard, F., De Lannoy, G., Hong, J., Jackson, T. J., and Wulfmeyer, V.: A Dielectric Mixing Model Accounting for Soil Organic Matter, Vadose Zone J., 18, 190036, https://doi.org/10.2136/vzj2019.04.0036, 2019. a, b
Parrens, M., Wigneron, J.-P., Richaume, P., Al Bitar, A., Mialon, A., Fernandez-Moran, R., Al-Yaari, A., O'Neill, P., and Kerr, Y.: Considering Combined or Separated Roughness and Vegetation Effects in Soil Moisture Retrievals, Int. J. Appl. Earth Obs., 55, 73–86, https://doi.org/10.1016/j.jag.2016.11.001, 2017. a
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, 2021. a, b
Preethi, K., Li, X., Fernandez-Moran, R., Liu, X., Xing, Z., Frappart, F., Piles, M., Lanka, K., and Wigneron, J.-P.: A New Calibration of Soil Roughness Effects in the SMOS-IC Algorithm for Soil Moisture and VOD Retrievals, in: IGARSS 2024 – 2024 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Athens, Greece, 7–12 July 2024, 6701–6704, https://doi.org/10.1109/IGARSS53475.2024.10642708, 2024. a
Rautiainen, K., Lemmetyinen, J., Pulliainen, J., Vehvilainen, J., Drusch, M., Kontu, A., Kainulainen, J., and Seppanen, J.: L-Band Radiometer Observations of Soil Processes in Boreal and Subarctic Environments, IEEE T. Geosci. Remote, 50, 1483–1497, https://doi.org/10.1109/TGRS.2011.2167755, 2012. a, b
Rautiainen, K., Lemmetyinen, J., Schwank, M., Kontu, A., Ménard, C. B., Mätzler, C., Drusch, M., Wiesmann, A., Ikonen, J., and Pulliainen, J.: Detection of Soil Freezing from L-band Passive Microwave Observations, Remote Sens. Environ., 147, 206–218, https://doi.org/10.1016/j.rse.2014.03.007, 2014. a
Rautiainen, K., Parkkinen, T., Lemmetyinen, J., Schwank, M., Wiesmann, A., Ikonen, J., Derksen, C., Davydov, S., Davydova, A., Boike, J., Langer, M., Drusch, M., and Pulliainen, J.: SMOS Prototype Algorithm for Detecting Autumn Soil Freezing, Remote Sens. Environ., 180, 346–360, https://doi.org/10.1016/j.rse.2016.01.012, 2016. a, b
Rouse, W. R., Douglas, M. S. V., Hecky, R. E., Hershey, A. E., Kling, G. W., Lesack, L., Marsh, P., Mcdonald, M., Nicholson, B. J., Roulet, N. T., and Smol, J. P.: EFFECTS OF CLIMATE CHANGE ON THE FRESHWATERS OF ARCTIC AND SUBARCTIC NORTH AMERICA, Hydrol. Process., 11, 873–902, https://doi.org/10.1002/(SICI)1099-1085(19970630)11:8<873::AID-HYP510>3.0.CO;2-6, 1997. a
Roy, A., Royer, A., Derksen, C., Brucker, L., Langlois, A., Mialon, A., and Kerr, Y. H.: Evaluation of Spaceborne L-Band Radiometer Measurements for Terrestrial Freeze/Thaw Retrievals in Canada, IEEE J. Sel. Top. Appl., 8, 4442–4459, https://doi.org/10.1109/JSTARS.2015.2476358, 2015. a
Roy, A., Toose, P., Williamson, M., Rowlandson, T., Derksen, C., Royer, A., Berg, A. A., Lemmetyinen, J., and Arnold, L.: Response of L-Band Brightness Temperatures to Freeze/Thaw and Snow Dynamics in a Prairie Environment from Ground-Based Radiometer Measurements, Remote Sens. Environ., 191, 67–80, https://doi.org/10.1016/j.rse.2017.01.017, 2017. a, b, c, d
Roy, A., Leduc-Leballeur, M., Picard, G., Royer, A., Toose, P., Derksen, C., Lemmetyinen, J., Berg, A., Rowlandson, T., and Schwank, M.: Modelling the L-Band Snow-Covered Surface Emission in a Winter Canadian Prairie Environment, Remote Sens.-Basel, 10, 1451, https://doi.org/10.3390/rs10091451, 2018. a
Royer, A., Domine, F., Roy, A., Langlois, A., Marchand, N., and Davesne, G.: New Northern Snowpack Classification Linked to Vegetation Cover on a Latitudinal Mega-Transect across Northeastern Canada, Écoscience, 28, 225–242, https://doi.org/10.1080/11956860.2021.1898775, 2021a. a
Royer, A., Picard, G., Vargel, C., Langlois, A., Gouttevin, I., and Dumont, M.: Improved Simulation of Arctic Circumpolar Land Area Snow Properties and Soil Temperatures, Front. Earth Sci., 9, 685140, https://doi.org/10.3389/feart.2021.685140, 2021b. a
Schaefer, G. L., Cosh, M. H., and Jackson, T. J.: The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN), J. Atmos. Ocean. Tech., 24, 2073–2077, https://doi.org/10.1175/2007JTECHA930.1, 2007. a, b
Schmugge, T. J.: Remote Sensing of Soil Moisture: Recent Advances, IEEE T. Geosci. Remote, GE-21, 336–344, https://doi.org/10.1109/TGRS.1983.350563, 1983. a
Schuur, E. A. G., McGuire, A. D., Schädel, C., Grosse, G., Harden, J. W., Hayes, D. J., Hugelius, G., Koven, C. D., Kuhry, P., Lawrence, D. M., Natali, S. M., Olefeldt, D., Romanovsky, V. E., Schaefer, K., Turetsky, M. R., Treat, C. C., and Vonk, J. E.: Climate Change and the Permafrost Carbon Feedback, Nature, 520, 171–179, https://doi.org/10.1038/nature14338, 2015. a
Schwank, M., Stahli, M., Wydler, H., Leuenberger, J., Matzler, C., and Fluhler, H.: Microwave L-band Emission of Freezing Soil, IEEE T. Geosci. Remote, 42, 1252–1261, https://doi.org/10.1109/TGRS.2004.825592, 2004. a
Schwank, M., Rautiainen, K., Mätzler, C., Stähli, M., Lemmetyinen, J., Pulliainen, J., Vehviläinen, J., Kontu, A., Ikonen, J., Ménard, C. B., Drusch, M., Wiesmann, A., and Wegmüller, U.: Model for Microwave Emission of a Snow-Covered Ground with Focus on L Band, Remote Sens. Environ., 154, 180–191, https://doi.org/10.1016/j.rse.2014.08.029, 2014. a, b, c, d
Schwank, M., Matzler, C., Wiesmann, A., Wegmuller, U., Pulliainen, J., Lemmetyinen, J., Rautiainen, K., Derksen, C., Toose, P., and Drusch, M.: Snow Density and Ground Permittivity Retrieved from L-Band Radiometry: A Synthetic Analysis, IEEE J. Sel. Top. Appl., 8, 3833–3845, https://doi.org/10.1109/JSTARS.2015.2422998, 2015. a, b, c, d
Schwank, M., Kontu, A., Mialon, A., Naderpour, R., Houtz, D., Lemmetyinen, J., Rautiainen, K., Li, Q., Richaume, P., Kerr, Y., and Mätzler, C.: Temperature Effects on L-band Vegetation Optical Depth of a Boreal Forest, Remote Sens. Environ., 263, 112542, https://doi.org/10.1016/j.rse.2021.112542, 2021. a
Shiklomanov, N. I.: Non-Climatic Factors and Long-Term, Continental-Scale Changes in Seasonally Frozen Ground, Environ. Res. Lett., 7, 011003, https://doi.org/10.1088/1748-9326/7/1/011003, 2012. a
Sturm, M., Holmgren, J., and Liston, G. E.: A seasonal snow cover classification system for local to global applications, J. Climate, 8, 1261–128, https://doi.org/10.1175/1520-0442(1995)008%3C1261:ASSCCS%3E2.0.CO;2, 1995. a
Turetsky, M. R., Abbott, B. W., Jones, M. C., Anthony, K. W., Olefeldt, D., Schuur, E. A. G., 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
Ulaby, F., Allen, C., Eger, G., and Kanemasu, E.: Relating the Microwave Backscattering Coefficient to Leaf Area Index, Remote Sens. Environ., 14, 113–133, https://doi.org/10.1016/0034-4257(84)90010-5, 1984. a
Urban, F.: Data Release Associated with Data Series – DOI/GTN-P Climate and Active-Layer Data Acquired in the National Petroleum Reserve-Alaska and the Arctic National Wildlife Refuge, 1998–2019 (Ver. 3.0, March 2021), U.S. Geological Survey data release, https://doi.org/10.5066/F7VX0FGB, 2017. a, b
Wang, J. R. and Choudhury, B. J.: Remote Sensing of Soil Moisture Content, over Bare Field at 1.4 GHz Frequency, J. Geophys. Res., 86, 5277, https://doi.org/10.1029/JC086iC06p05277, 1981. a, b
Wang, Z., Kim, Y., Seo, H., Um, M.-J., and Mao, J.: Permafrost Response to Vegetation Greenness Variation in the Arctic Tundra through Positive Feedback in Surface Air Temperature and Snow Cover, Environ. Res. Lett., 14, 044024, https://doi.org/10.1088/1748-9326/ab0839, 2019. a
Westermann, S., Langer, M., and Boike, J.: Systematic Bias of Average Winter-Time Land Surface Temperatures Inferred from MODIS at a Site on Svalbard, Norway, Remote Sens. Environ., 118, 162–167, https://doi.org/10.1016/j.rse.2011.10.025, 2012. a
Westermann, S., Østby, T. I., Gisnås, K., Schuler, T. V., and Etzelmüller, B.: A ground temperature map of the North Atlantic permafrost region based on remote sensing and reanalysis data, The Cryosphere, 9, 1303–1319, https://doi.org/10.5194/tc-9-1303-2015, 2015. a
Wiesmann, A. and Mätzler, C.: Microwave Emission Model of Layered Snowpacks, Remote Sens. Environ., 70, 307–316, https://doi.org/10.1016/S0034-4257(99)00046-2, 1999. a
Wigneron, J.-P., Chanzy, A., Kerr, Y. H., Lawrence, H., Shi, J., Escorihuela, M. J., Mironov, V., Mialon, A., Demontoux, F., De Rosnay, P., and Saleh-Contell, K.: Evaluating an Improved Parameterization of the Soil Emission in L-MEB, IEEE T. Geosci. Remote, 49, 1177–1189, https://doi.org/10.1109/TGRS.2010.2075935, 2011. a
Zhang, L., Zhao, T., Jiang, L., and Zhao, S.: Estimate of Phase Transition Water Content in Freeze–Thaw Process Using Microwave Radiometer, IEEE T. Geosci. Remote, 48, 4248–4255, https://doi.org/10.1109/TGRS.2010.2051158, 2010. a
Zhang, T.: Influence of the Seasonal Snow Cover on the Ground Thermal Regime: An Overview, Rev. Geophys., 43, 2004RG000157, https://doi.org/10.1029/2004RG000157, 2005. a
Short summary
We propose a new method to determine the ground surface temperature under the snowpack in the Arctic area from satellite observations. The obtained ground temperature time series were evaluated over 21 reference sites in Northern Alaska and compared with ground temperatures obtained with global models. The method is extremely promising for monitoring ground temperature below the snowpack and studying the spatio-temporal variability thanks to 15 years of observations over the whole Arctic area.
We propose a new method to determine the ground surface temperature under the snowpack in the...
