Articles | Volume 17, issue 10
https://doi.org/10.5194/essd-17-5337-2025
© 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/essd-17-5337-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
15 Oct 2025
Data description paper |
| 15 Oct 2025
| 15 Oct 2025
An operational SMOS soil freeze–thaw product
Finnish Meteorological Institute, Erik Palménin aukio 1, 00560 Helsinki, Finland
Manu Holmberg
Finnish Meteorological Institute, Erik Palménin aukio 1, 00560 Helsinki, Finland
Juval Cohen
Finnish Meteorological Institute, Erik Palménin aukio 1, 00560 Helsinki, Finland
Arnaud Mialon
CESBIO, Université Toulouse 3, CNES/CNRS/IRD/INRAe/UPS, 18 Avenue Edouard Belin, 31401 Toulouse CEDEX 9, France
Mike Schwank
GAMMA Remote Sensing Research and Consulting AG, Worbstr. 225, 3073 Gümligen, Switzerland
Swiss Federal Institute WSL, Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
Juha Lemmetyinen
Finnish Meteorological Institute, Erik Palménin aukio 1, 00560 Helsinki, Finland
Antonio de la Fuente
ESA – ESRIN, Largo Galileo Galilei 1, 00044, Frascati, Italy
Yann Kerr
CESBIO, Université Toulouse 3, CNES/CNRS/IRD/INRAe/UPS, 18 Avenue Edouard Belin, 31401 Toulouse CEDEX 9, France
Related authors
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.
Juliette Ortet, Arnaud Mialon, Alain Royer, Mike Schwank, Manu Holmberg, Kimmo Rautiainen, Simone Bircher-Adrot, Andreas Colliander, Yann Kerr, and Alexandre Roy
The Cryosphere, 19, 3571–3598, https://doi.org/10.5194/tc-19-3571-2025, https://doi.org/10.5194/tc-19-3571-2025, 2025
Short summary
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.
Sara Hyvärinen, Maria Katariina Tenkanen, Aki Tsuruta, Anttoni Erkkilä, Kimmo Rautiainen, Hermanni Aaltonen, Motoki Sasakawa, and Tuula Aalto
EGUsphere, https://doi.org/10.5194/egusphere-2025-2794, https://doi.org/10.5194/egusphere-2025-2794, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We analyzed spring methane emissions from northern high-latitude wetlands using satellite thaw data and inverse modeling (2011–2021). Comparing region-based and grid-based approaches, we found that emissions varied with the length of the melting season, which depended on air temperature. We found spring melting season emissions ranged from 0.45 Tg to 1.83 Tg depending on the approach, with no clear trend over the period. Our methods allow for seasonal methane monitoring across different scales.
Annett Bartsch, Rodrigue Tanguy, Helena Bergstedt, Clemens von Baeckmann, Hans Tømmervik, Marc Macias-Fauria, Juha Lemmentiynen, Kimmo Rautiainen, Chiara Gruber, and Bruce C. Forbes
EGUsphere, https://doi.org/10.5194/egusphere-2025-1358, https://doi.org/10.5194/egusphere-2025-1358, 2025
Short summary
Short summary
We identified similarities between sea ice dynamics and conditions on land across the Arctic. Significant correlations north of 60°N was more common for snow water equivalent and permafrost ground temperature than for the vegetation parameters. Changes across all the different parameters could be specifically determined for Eastern Siberia. The results provide a baseline for future studies on common drivers of essential climate parameters and causative effects across the Arctic.
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.
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.
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.
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.
Juliette Ortet, Arnaud Mialon, Alain Royer, Mike Schwank, Manu Holmberg, Kimmo Rautiainen, Simone Bircher-Adrot, Andreas Colliander, Yann Kerr, and Alexandre Roy
The Cryosphere, 19, 3571–3598, https://doi.org/10.5194/tc-19-3571-2025, https://doi.org/10.5194/tc-19-3571-2025, 2025
Short summary
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.
Sara Hyvärinen, Maria Katariina Tenkanen, Aki Tsuruta, Anttoni Erkkilä, Kimmo Rautiainen, Hermanni Aaltonen, Motoki Sasakawa, and Tuula Aalto
EGUsphere, https://doi.org/10.5194/egusphere-2025-2794, https://doi.org/10.5194/egusphere-2025-2794, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We analyzed spring methane emissions from northern high-latitude wetlands using satellite thaw data and inverse modeling (2011–2021). Comparing region-based and grid-based approaches, we found that emissions varied with the length of the melting season, which depended on air temperature. We found spring melting season emissions ranged from 0.45 Tg to 1.83 Tg depending on the approach, with no clear trend over the period. Our methods allow for seasonal methane monitoring across different scales.
Annett Bartsch, Rodrigue Tanguy, Helena Bergstedt, Clemens von Baeckmann, Hans Tømmervik, Marc Macias-Fauria, Juha Lemmentiynen, Kimmo Rautiainen, Chiara Gruber, and Bruce C. Forbes
EGUsphere, https://doi.org/10.5194/egusphere-2025-1358, https://doi.org/10.5194/egusphere-2025-1358, 2025
Short summary
Short summary
We identified similarities between sea ice dynamics and conditions on land across the Arctic. Significant correlations north of 60°N was more common for snow water equivalent and permafrost ground temperature than for the vegetation parameters. Changes across all the different parameters could be specifically determined for Eastern Siberia. The results provide a baseline for future studies on common drivers of essential climate parameters and causative effects across the Arctic.
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.
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.
David Brodylo, Lauren V. Bosche, Ryan R. Busby, Elias J. Deeb, Thomas A. Douglas, and Juha Lemmetyinen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3936, https://doi.org/10.5194/egusphere-2024-3936, 2025
Short summary
Short summary
We combined field-based snow depth and snow water equivalent (SWE) measurements, remote sensing data, and machine learning to estimate snow depth and SWE over a 10 km2 local scale area in Sodankylä, Finland. Associations were found for snow depth and SWE with carbon- and mineral-based forest surface soils, alongside dry and wet peatbogs. This approach to upscale field-based snow depth and SWE measurements to a local scale can be used in regions that regularly experience snowfall.
Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi
The Cryosphere, 18, 1561–1578, https://doi.org/10.5194/tc-18-1561-2024, https://doi.org/10.5194/tc-18-1561-2024, 2024
Short summary
Short summary
We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
Justin Murfitt, Claude Duguay, Ghislain Picard, and Juha Lemmetyinen
The Cryosphere, 18, 869–888, https://doi.org/10.5194/tc-18-869-2024, https://doi.org/10.5194/tc-18-869-2024, 2024
Short summary
Short summary
This research focuses on the interaction between microwave signals and lake ice under wet conditions. Field data collected for Lake Oulujärvi in Finland were used to model backscatter under different conditions. The results of the modelling likely indicate that a combination of increased water content and roughness of different interfaces caused backscatter to increase. These results could help to identify areas where lake ice is unsafe for winter transportation.
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.
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.
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.
Pinja Venäläinen, Kari Luojus, Colleen Mortimer, Juha Lemmetyinen, Jouni Pulliainen, Matias Takala, Mikko Moisander, and Lina Zschenderlein
The Cryosphere, 17, 719–736, https://doi.org/10.5194/tc-17-719-2023, https://doi.org/10.5194/tc-17-719-2023, 2023
Short summary
Short summary
Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
Short summary
Short summary
Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
Short summary
Short summary
The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
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.
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.
Bin Cheng, Yubing Cheng, Timo Vihma, Anna Kontu, Fei Zheng, Juha Lemmetyinen, Yubao Qiu, and Jouni Pulliainen
Earth Syst. Sci. Data, 13, 3967–3978, https://doi.org/10.5194/essd-13-3967-2021, https://doi.org/10.5194/essd-13-3967-2021, 2021
Short summary
Short summary
Climate change strongly impacts the Arctic, with clear signs of higher air temperature and more precipitation. A sustainable observation programme has been carried out in Lake Orajärvi in Sodankylä, Finland. The high-quality air–snow–ice–water temperature profiles have been measured every winter since 2009. The data can be used to investigate the lake ice surface heat balance and the role of snow in lake ice mass balance and parameterization of snow-to-ice transformation in snow/ice models.
Pinja Venäläinen, Kari Luojus, Juha Lemmetyinen, Jouni Pulliainen, Mikko Moisander, and Matias Takala
The Cryosphere, 15, 2969–2981, https://doi.org/10.5194/tc-15-2969-2021, https://doi.org/10.5194/tc-15-2969-2021, 2021
Short summary
Short summary
Information about snow water equivalent (SWE) is needed in many applications, including climate model evaluation and forecasting fresh water availability. Space-borne radiometer observations combined with ground snow depth measurements can be used to make global estimates of SWE. In this study, we investigate the possibility of using sparse snow density measurement in satellite-based SWE retrieval and show that using the snow density information in post-processing improves SWE estimations.
Cited articles
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
Arndt, K., Oechel, W., Goodrich, J., Bailey, B., Kalhori, A., Hashemi, J., Sweeney, C., and Zona, D.: Sensitivity of Methane Emissions to Later Soil Freezing in Arctic Tundra Ecosystems, Journal of Geophysical Research: Biogeosciences, 124, 2595 – 2609, https://doi.org/10.1029/2019JG005242, 2019. a
Bell, J. E., Palecki, M. A., Baker, C. B., Collins, W. G., Lawrimore, J. H., Leeper, R. D., Hall, M. E., Kochendorfer, J., Meyers, T. P., Wilson, T., and Diamond, H. J.: U.S. Climate Reference Network Soil Moisture and Temperature Observations, J. Hydrometeorol., 14, 977–988, https://doi.org/10.1175/JHM-D-12-0146.1, 2013. a
Boswell, E. P., Thompson, A. M., Balster, N. J., and Bajcz, A. W.: Novel determination of effective freeze–thaw cycles as drivers of ecosystem change, Journal of Environmental Quality, 49, 314–323, https://doi.org/10.1002/jeq2.20053, 2020. 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 International Journal of Geo-Information, 1, 32–45, https://doi.org/10.3390/ijgi1010032, 2012. a
CATDS: CATDS-PDC L3TB – Global polarised brightness temperature product from SMOS satellite, CATDS (CNES, IFREMER, CESBIO) [data set], https://doi.org/10.12770/6294e08c-baec-4282-a251-33fee22ec67f, 2024. a, b
Cohen, J., Rautiainen, K., Lemmetyinen, J., Smolander, T., Vehviläinen, J., and Pulliainen, J.: Sentinel-1 based soil freeze/thaw estimation in boreal forest environments, Remote Sensing of Environment, 254, 112267, https://doi.org/10.1016/j.rse.2020.112267, 2021. a
Derksen, C., Xu, X., Dunbar, R. S., Colliander, A., Kim, Y., Kimball, J. S., Black, T. A., Euskirchen, E., Langlois, A., Loranty, M. M., Marsh, P., Rautiainen, K., Roy, A., Royer, A., and Stephens, J.: Retrieving landscape freeze/thaw state from Soil Moisture Active Passive (SNAP) radar and radiometer measurements, Remote Sensing of Environment, 194, 48–62, https://doi.org/10.1016/j.rse.2017.03.007, 2017. 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
Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A., Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., Robock, A., and Jackson, T.: The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, Hydrol. Earth Syst. Sci., 15, 1675–1698, https://doi.org/10.5194/hess-15-1675-2011, 2011. a
Erkkilä, A., Tenkanen, M., Tsuruta, A., Rautiainen, K., and Aalto, T.: Environmental and Seasonal Variability of High Latitude Methane Emissions Based on Earth Observation Data and Atmospheric Inverse Modelling, Remote Sensing, 15, https://doi.org/10.3390/rs15245719, 2023. a
ESA: ESA Land Cover CCI Product User Guide Version 2 Tech. Rep., European Space Agency, https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: 2 October 2025), 2017. a
ESA: SMOS Soil Freeze and Thaw State, Version 300, European Space Agency [data set], https://doi.org/10.57780/sm1-fbf89e0, 2023. a, b, c, d
Frauenfeld, O. W. and Zhang, T.: An observational 71-year history of seasonally frozen ground changes in the Eurasian high latitudes, Environmental Research Letters, 6, https://doi.org/10.1088/1748-9326/6/4/044024, 2011. a
Gregow, H., Jylha, R., Ruuhela, A., Saku, A., and Laaksonen, T.: Lumettoman maan routaolojen mallintaminen ja ennustettavuus muuttuvassa ilmastossa (Modelling and predictability of soil frost depths of snow-free ground under climate-model projections), Report, Finnish Meteorological Institute, https://core.ac.uk/download/pdf/14922313.pdf (last access: 2 October 2025), 2011. a
Hayashi, M.: The Cold Vadose Zone: Hydrological and Ecological Significance of Frozen‐Soil Processes, Vadose Zone Journal, 12, https://doi.org/10.2136/vzj2013.03.0064, 2013. a
Helfrich, S., Li, M., Kongoli, C., Nagdimunov, L., and Rodriguez, E.: IMS Algorithm Theoretical Basis Document Version 2.5, Tech. rep., NOAA National Ice Center, https://nsidc.org/media/4619 (last access: 2 October 2025), 2019. a
Houtz, D., Mätzler, C., Naderpour, R., Schwank, M., and Steffen, K.: Quantifying Surface Melt and Liquid Water on the Greenland Ice Sheet using L-band Radiometry, Remote Sensing of Environment, 256, 112341, https://doi.org/10.1016/j.rse.2021.112341, 2021. a
Ikonen, J., Vehvilainen, J., Rautiainen, K., Smolander, T., Lemmetyinen, J., Bircher, S., and Pulliainen, J.: The Sodankyla in situ soil moisture observation network: an example application of ESA CCI soil moisture product evaluation, Geoscientific Instrumentation Methods And Data Systems, 5, 95–108, https://doi.org/10.5194/gi-5-95-2016, 2016. a, b
Ikonen, J., Smolander, T., Rautiainen, K., Cohen, J., Lemmetyinen, J., Salminen, M., and Pulliainen, J.: Spatially Distributed Evaluation of ESA CCI Soil Moisture Products in a Northern Boreal Forest Environment, Geosciences, 8, https://doi.org/10.3390/geosciences8020051, 2018. a
Jackson, T. J., Hsu, A. Y., van de Griend, A., and Eagleman, J. R: Skylab L-band microwave radiometer observations of soil moisture revisited, International Journal of Remote Sensing, 25, 2585–2606, https://doi.org/10.1080/01431160310001647723, 2004. a
Johnston, C. E., Ewing, S., Harden, J., Varner, R., Wickland, K., Koch, J., Fuller, C., Manies, K., and Jorgenson, M.: Effect of permafrost thaw on CO2 and CH4 exchange in a western Alaska peatland chronosequence, Environmental Research Letters, 9, https://doi.org/10.1088/1748-9326/9/8/085004, 2014. a
Kalman, R. E.: A New Approach to Linear Filtering and Prediction Problems, Transactions of the ASME – Journal of Basic Engineering, 82, 35–45, 1960. a
Kerr, Y., Waldteufel, P., Wigneron, J., Delwart, S., Cabot, F., Boutin, J., Escorihuela, M., Font, J., Reul, N., Gruhier, C., Juglea, S., Drinkwater, M., Hahne, A., Martín-Neira, M., and Mecklenburg, S.: The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle, Proceedings of the IEEE, 98, 666–687, https://doi.org/10.1109/JPROC.2010.2043032, 2010. a, b, c
Kim, Y., Kimball, J. S., Glassy, J., and Du, J.: An extended global Earth system data record on daily landscape freeze–thaw status determined from satellite passive microwave remote sensing, Earth Syst. Sci. Data, 9, 133–147, https://doi.org/10.5194/essd-9-133-2017, 2017. a
Knoblauch, C., Beer, C., Liebner, S., Grigoriev, M., and Pfeiffer, E.: Methane production as key to the greenhouse gas budget of thawing permafrost, Nature Climate Change, 8, 309–312, https://doi.org/10.1038/s41558-018-0095-z, 2017. a
Kreyling, J., Beierkuhnlein, C., Pritsch, K., Schloter, M., and Jentsch, A.: Recurrent soil freeze-thaw cycles enhance grassland productivity, The New phytologist, 177, 938–45, https://doi.org/10.1111/J.1469-8137.2007.02309.X, 2008. a
Krogstad, K., Gharasoo, M., Jensen, G., Hug, L., Rudolph, D., Cappellen, P. V., and Rezanezhad, F.: Nitrogen Leaching From Agricultural Soils Under Imposed Freeze-Thaw Cycles: A Column Study With and Without Fertilizer Amendment, Frontiers in Environmental Science, 10, https://doi.org/10.3389/fenvs.2022.915329, 2022. a
Leavesley, G., David, O., Garen, D., Lea, J., Marron, J., Pagano, T., Perkins, T., and Strobel, M.: A modeling framework for improved agricultural water supply forecasting, in: AGU Fall Meeting Abstracts, 1, 0497, 2008. 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 Sensing of Environment, 180, 377–391, https://doi.org/10.1016/j.rse.2016.02.002, 2016. a
McMullan, K. D., Brown, M. A., Martin-Neira, M., Rits, W., Ekholm, S., Marti, J., and Lemanczyk, J.: SMOS: The Payload, IEEE Transactions on Geoscience and Remote Sensing, 46, 594–605, https://doi.org/10.1109/TGRS.2007.914809, 2008. a
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a, b
Mätzler, C., Ellison, W., Thomas, B., Sihvola, A., and Schwank, M.: Dielectric Properties of Natural Media, in: Thermal Microwave Radiation: Applications for Remote Sensing, edited by: Mätzler, C., The Institution of Engineering and Technology, 427–539, https://doi.org/10.1049/PBEW052E, 2006. a
Naderpour, R., Schwank, M., Mätzler, C., Lemmetyinen, J., and Steffen, K.: Snow Density and Ground Permittivity Retrieved From L-Band Radiometry: A Retrieval Sensitivity Analysis, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 3148–3161, https://doi.org/10.1109/JSTARS.2017.2669336, 2017. a
Nikrad, M. P., Kerkhof, L. J., and Häggblom, M. M.: The subzero microbiome: microbial activity in frozen and thawing soils, FEMS Microbiology Ecology, 92, fiw081, https://doi.org/10.1093/femsec/fiw081, 2016. a
Ojo, E. R., Bullock, P. R., L'Heureux, J., Powers, J., McNairn, H., and Pacheco, A.: Calibration and Evaluation of a Frequency Domain Reflectometry Sensor for Real-Time Soil Moisture Monitoring, Vadose Zone Journal, 14, https://doi.org/10.2136/vzj2014.08.0114, 2015. a
Oliva, R., Daganzo, E., Kerr, Y. H., Mecklenburg, S., Nieto, S., Richaume, P., and Gruhier, C.: SMOS Radio Frequency Interference Scenario: Status and Actions Taken to Improve the RFI Environment in the 1400–1427-MHz Passive Band, IEEE Transactions on Geoscience and Remote Sensing, 50, 1427–1439, https://doi.org/10.1109/TGRS.2012.2182775, 2012. a
Oliva, R., Daganzo, E., Richaume, P., Kerr, Y., Cabot, F., Soldo, Y., Anterrieu, E., Reul, N., Gutierrez, A., Barbosa, J., and Lopes, G.: Status of Radio Frequency Interference (RFI) in the 1400–1427 MHz passive band based on six years of SMOS mission, Remote Sensing of Environment, 180, 64–75, https://doi.org/10.1016/j.rse.2016.01.013, 2016. a, b
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 Resources Research, 56, e2019WR026020, https://doi.org/10.1029/2019WR026020, 2020. a
Pellarin, T., Mialon, A., Biron, R., Coulaud, C., Gibon, F., Kerr, Y., Lafaysse, M., Mercier, B., Morin, S., Redor, I., Schwank, M., and Völksch, I.: Three years of L-band brightness temperature measurements in a mountainous area: Topography, vegetation and snowmelt issues, Remote Sensing of Environment, 180, 85–98, https://doi.org/10.1016/j.rse.2016.02.047, 2016. a
Peng, X., Frauenfeld, O. W., Cao, B., Wang, K., Wang, H., Su, H., Huang, Z., Yue, D., and Zhang, T.: Response of changes in seasonal soil freeze/thaw state to climate change from 1950 to 2010 across china, Journal of Geophysical Research-Earth Surface, 121, 1984–2000, https://doi.org/10.1002/2016JF003876, 2016. a
Piepmeier, J. R., Johnson, J. T., Mohammed, P. N., Bradley, D., Ruf, C., Aksoy, M., Garcia, R., Hudson, D., Miles, L., and Wong, M.: Radio-Frequency Interference Mitigation for the Soil Moisture Active Passive Microwave Radiometer, IEEE Transactions on Geoscience and Remote Sensing, 52, 761–775, https://doi.org/10.1109/TGRS.2013.2281266, 2014. a
Rautiainen, K. and Holmberg, M.: ReadMe-first Technical Note, Tech. Rep., Finnish Meteorological Institute (FMI), https://earth.esa.int/eogateway/documents/20142/37627/SMOS-FTS-Read-me-first.pdf (last access: 2 October 2025), 2023. a
Rautiainen, K., Lemmetyinen, J., Schwank, M., Kontu, A., Menard, C. B., Maetzler, C., Drusch, M., Wiesmann, A., Ikonen, J., and Pulliainen, J.: Detection of soil freezing from L-band passive microwave observations, Remote Sensing Of Environment, 147, 206–218, https://doi.org/10.1016/j.rse.2014.03.007, 2014. a, b
Rautiainen, K., Parkkinen, T., Lemmetyinen, J., Schwank, M., Wiesmann, A., Ikonen, J., Derksen, C., Davydov, S., Davydova, A., Boike, J., Langer, M., Drusch, M., and Pulliainen, J.: SMOS prototype algorithm for detecting autumn soil freezing, Remote Sensing Of Environment, 180, 346–360, https://doi.org/10.1016/j.rse.2016.01.012, 2016. a, b, c, d
Roy, A., Royer, A., Derksen, C., Brucker, L., Langlois, A., Mialon, A., and Kerr, Y.: Evaluation of Spaceborne L-Band Radiometer Measurements for Terrestrial Freeze/Thaw Retrievals in Canada, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 4442–4459, https://doi.org/10.1109/JSTARS.2015.2476358, 2015. a
Schaefer, G. L., Cosh, M. H., and Jackson, T. J.: The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN), Journal of Atmospheric and Oceanic Technology, 24, 2073–2077, https://doi.org/10.1175/2007JTECHA930.1, 2007. a
Schwank, M., Mätzler, C., Wiesmann, A., Wegmüller, 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 Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 3833–3845, https://doi.org/10.1109/JSTARS.2015.2422998, 2015. a
Shiklomanov, N.: Non-climatic factors and long-term, continental-scale changes in seasonally frozen ground, Environmental Research Letters, 7, 011003, https://doi.org/10.1088/1748-9326/7/1/011003, 2012. a
Sokratov, S. and Barry, R.: Intraseasonal variation in the thermoinsulation effect of snow cover on soil temperatures and energy balance, Journal of Geophysical Research, 107, https://doi.org/10.1029/2001JD000489, 2002. a
Song, Y., Zou, Y., Wang, G., and Yu, X.: Altered soil carbon and nitrogen cycles due to the freeze-thaw effect: A meta-analysis, Soil Biology & Biochemistry, 109, 35–49, https://doi.org/10.1016/J.SOILBIO.2017.01.020, 2017. a
Tenkanen, M., Tsuruta, A., Rautiainen, K., Kangasaho, V., Ellul, R., and Aalto, T.: Utilizing Earth Observations of Soil Freeze/Thaw Data and Atmospheric Concentrations to Estimate Cold Season Methane Emissions in the Northern High Latitudes, Remote Sensing, 13, https://doi.org/10.3390/rs13245059, 2021. a, b
U.S. National Ice Center: IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions, Version 1 [4 km resolution, 1.1.2010–2.10.2025], NSIDC: National Snow and Ice Data Center [data set], https://doi.org/10.7265/N52R3PMC, 2008. a, b
Wagner-Riddle, C., Congreves, K. A., Abalos, D., Berg, A. A., Brown, S. E., Ambadan, J. T., Gao, X., and Tenuta, M.: Globally important nitrous oxide emissions from croplands induced by freeze-thaw cycles, Nature Geoscience, 10, https://doi.org/10.1038/NGEO2907, 2017. a
Yang, K. and Wang, C.: Water storage effect of soil freeze-thaw process and its impacts on soil hydro-thermal regime variations, Agricultural and Forest Meteorology, https://doi.org/10.1016/J.AGRFORMET.2018.11.011, 2019. a
Zhang, T., Barry, R. G., Knowles, K., Ling, F., and Armstrong, R. L.: Distribution of Seasonally and Perennially Frozen Ground in the Northern Hemisphere, in: Proceedings of the 8th International Conference on Permafrost, edited by: Phillips, M., Springman, S. M., and Arenson, L. U., A. A. Balkema, Zurich, Switzerland, 1289–1294, ISBN 90-5809-582-7, 2003. a
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.
The SMOS (Soil Moisture and Ocean Salinity) Soil Freeze–Thaw State product uses satellite data...
Altmetrics
Final-revised paper
Preprint