Articles | Volume 17, issue 9
https://doi.org/10.5194/tc-17-4155-2023
© Author(s) 2023. 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-17-4155-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Brief communication: Identification of tundra topsoil frozen/thawed state from SMAP and GCOM-W1 radiometer measurements using the spectral gradient method
Konstantin Muzalevskiy
CORRESPONDING AUTHOR
Laboratory of Radiophysics of Remote Sensing, Kirensky Institute of
Physics, Federal Research Center, Krasnoyarsk Science Center of the Siberian
Branch of the Russian Academy of Sciences, Krasnoyarsk, Russia
Zdenek Ruzicka
Laboratory of Radiophysics of Remote Sensing, Kirensky Institute of
Physics, Federal Research Center, Krasnoyarsk Science Center of the Siberian
Branch of the Russian Academy of Sciences, Krasnoyarsk, Russia
Alexandre Roy
Département des Sciences de l'Environnement, Université du
Québec à Trois-Rivières (UQTR), Trois-Rivières, Centre
d'étude Nordique, Québec, Canada
Michael Loranty
Department of Geography, Colgate University, Hamilton, NY, USA
Alexander Vasiliev
Laboratory for Cartographic Modeling and Forecasting the State of
Permafrost Geosystems, Earth Cryosphere Institute, Tyumen Scientific Centre,
Siberian
Branch of the Russian Academy of Sciences, Tyumen, Russia
Related authors
Igor Savin, Valery Mironov, Konstantin Muzalevskiy, Sergey Fomin, Andrey Karavayskiy, Zdenek Ruzicka, and Yuriy Lukin
Earth Syst. Sci. Data, 12, 3481–3487, https://doi.org/10.5194/essd-12-3481-2020, https://doi.org/10.5194/essd-12-3481-2020, 2020
Short summary
Short summary
This article presents a dielectric database of organic Arctic soils. This database was created based on dielectric measurements of seven samples of organic soils collected in various parts of the Arctic tundra. The created database can serve not only as a source of experimental data for the development of new soil dielectric models for the Arctic tundra but also as a source of training data for artificial intelligence satellite algorithms of soil moisture retrievals based on neural networks.
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.
Anna C. Talucci, Michael M. Loranty, Jean E. Holloway, Brendan M. Rogers, Heather D. Alexander, Natalie Baillargeon, Jennifer L. Baltzer, Logan T. Berner, Amy Breen, Leya Brodt, Brian Buma, Jacqueline Dean, Clement J. F. Delcourt, Lucas R. Diaz, Catherine M. Dieleman, Thomas A. Douglas, Gerald V. Frost, Benjamin V. Gaglioti, Rebecca E. Hewitt, Teresa Hollingsworth, M. Torre Jorgenson, Mark J. Lara, Rachel A. Loehman, Michelle C. Mack, Kristen L. Manies, Christina Minions, Susan M. Natali, Jonathan A. O'Donnell, David Olefeldt, Alison K. Paulson, Adrian V. Rocha, Lisa B. Saperstein, Tatiana A. Shestakova, Seeta Sistla, Oleg Sizov, Andrey Soromotin, Merritt R. Turetsky, Sander Veraverbeke, and Michelle A. Walvoord
Earth Syst. Sci. Data, 17, 2887–2909, https://doi.org/10.5194/essd-17-2887-2025, https://doi.org/10.5194/essd-17-2887-2025, 2025
Short summary
Short summary
Wildfires have the potential to accelerate permafrost thaw and the associated feedbacks to climate change. We assembled a dataset of permafrost thaw depth measurements from burned and unburned sites contributed by researchers from across the northern high-latitude region. We estimated maximum thaw depth for each measurement, which addresses a key challenge: the ability to assess impacts of wildfire on maximum thaw depth when measurement timing varies.
Hesam Salmabadi, Renato Pardo Lara, Aaron Berg, Alex Mavrovic, Chelene Hanes, Benoit Montpetit, and Alexandre Roy
EGUsphere, https://doi.org/10.5194/egusphere-2025-620, https://doi.org/10.5194/egusphere-2025-620, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Our research introduces a framework for monitoring seasonally frozen ground that goes beyond simply checking whether soil temperature is above or below freezing. We found that soil often remains in a transitional state between frozen and unfrozen for as long as fully frozen periods – something traditional monitoring methods fail to capture. These findings enhance our understanding of seasonally frozen ground, its climate change impacts, and carbon release in cold regions.
Charlotte Crevier, Alexandre Langlois, Chris Derksen, and Alexandre Roy
EGUsphere, https://doi.org/10.5194/egusphere-2024-3580, https://doi.org/10.5194/egusphere-2024-3580, 2025
Short summary
Short summary
A multisensor C-Band SAR near-daily time series in an Arctic environment was developed to create a high-resolution freeze/thaw 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.
Lucas R. Diaz, Clement J. F. Delcourt, Moritz Langer, Michael M. Loranty, Brendan M. Rogers, Rebecca C. Scholten, Tatiana A. Shestakova, Anna C. Talucci, Jorien E. Vonk, Sonam Wangchuk, and Sander Veraverbeke
Earth Syst. Dynam., 15, 1459–1482, https://doi.org/10.5194/esd-15-1459-2024, https://doi.org/10.5194/esd-15-1459-2024, 2024
Short summary
Short summary
Our study in eastern Siberia investigated how fires affect permafrost thaw depth in larch forests. We found that fire induces deeper thaw, yet this process was mediated by topography and vegetation. By combining field and satellite data, we estimated summer thaw depth across an entire fire scar. This research provides insights into post-fire permafrost dynamics and the use of satellite data for mapping fire-induced permafrost thaw.
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.
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.
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.
Rafael Poyatos, Víctor Granda, Víctor Flo, Mark A. Adams, Balázs Adorján, David Aguadé, Marcos P. M. Aidar, Scott Allen, M. Susana Alvarado-Barrientos, Kristina J. Anderson-Teixeira, Luiza Maria Aparecido, M. Altaf Arain, Ismael Aranda, Heidi Asbjornsen, Robert Baxter, Eric Beamesderfer, Z. Carter Berry, Daniel Berveiller, Bethany Blakely, Johnny Boggs, Gil Bohrer, Paul V. Bolstad, Damien Bonal, Rosvel Bracho, Patricia Brito, Jason Brodeur, Fernando Casanoves, Jérôme Chave, Hui Chen, Cesar Cisneros, Kenneth Clark, Edoardo Cremonese, Hongzhong Dang, Jorge S. David, Teresa S. David, Nicolas Delpierre, Ankur R. Desai, Frederic C. Do, Michal Dohnal, Jean-Christophe Domec, Sebinasi Dzikiti, Colin Edgar, Rebekka Eichstaedt, Tarek S. El-Madany, Jan Elbers, Cleiton B. Eller, Eugénie S. Euskirchen, Brent Ewers, Patrick Fonti, Alicia Forner, David I. Forrester, Helber C. Freitas, Marta Galvagno, Omar Garcia-Tejera, Chandra Prasad Ghimire, Teresa E. Gimeno, John Grace, André Granier, Anne Griebel, Yan Guangyu, Mark B. Gush, Paul J. Hanson, Niles J. Hasselquist, Ingo Heinrich, Virginia Hernandez-Santana, Valentine Herrmann, Teemu Hölttä, Friso Holwerda, James Irvine, Supat Isarangkool Na Ayutthaya, Paul G. Jarvis, Hubert Jochheim, Carlos A. Joly, Julia Kaplick, Hyun Seok Kim, Leif Klemedtsson, Heather Kropp, Fredrik Lagergren, Patrick Lane, Petra Lang, Andrei Lapenas, Víctor Lechuga, Minsu Lee, Christoph Leuschner, Jean-Marc Limousin, Juan Carlos Linares, Maj-Lena Linderson, Anders Lindroth, Pilar Llorens, Álvaro López-Bernal, Michael M. Loranty, Dietmar Lüttschwager, Cate Macinnis-Ng, Isabelle Maréchaux, Timothy A. Martin, Ashley Matheny, Nate McDowell, Sean McMahon, Patrick Meir, Ilona Mészáros, Mirco Migliavacca, Patrick Mitchell, Meelis Mölder, Leonardo Montagnani, Georgianne W. Moore, Ryogo Nakada, Furong Niu, Rachael H. Nolan, Richard Norby, Kimberly Novick, Walter Oberhuber, Nikolaus Obojes, A. Christopher Oishi, Rafael S. Oliveira, Ram Oren, Jean-Marc Ourcival, Teemu Paljakka, Oscar Perez-Priego, Pablo L. Peri, Richard L. Peters, Sebastian Pfautsch, William T. Pockman, Yakir Preisler, Katherine Rascher, George Robinson, Humberto Rocha, Alain Rocheteau, Alexander Röll, Bruno H. P. Rosado, Lucy Rowland, Alexey V. Rubtsov, Santiago Sabaté, Yann Salmon, Roberto L. Salomón, Elisenda Sánchez-Costa, Karina V. R. Schäfer, Bernhard Schuldt, Alexandr Shashkin, Clément Stahl, Marko Stojanović, Juan Carlos Suárez, Ge Sun, Justyna Szatniewska, Fyodor Tatarinov, Miroslav Tesař, Frank M. Thomas, Pantana Tor-ngern, Josef Urban, Fernando Valladares, Christiaan van der Tol, Ilja van Meerveld, Andrej Varlagin, Holm Voigt, Jeffrey Warren, Christiane Werner, Willy Werner, Gerhard Wieser, Lisa Wingate, Stan Wullschleger, Koong Yi, Roman Zweifel, Kathy Steppe, Maurizio Mencuccini, and Jordi Martínez-Vilalta
Earth Syst. Sci. Data, 13, 2607–2649, https://doi.org/10.5194/essd-13-2607-2021, https://doi.org/10.5194/essd-13-2607-2021, 2021
Short summary
Short summary
Transpiration is a key component of global water balance, but it is poorly constrained from available observations. We present SAPFLUXNET, the first global database of tree-level transpiration from sap flow measurements, containing 202 datasets and covering a wide range of ecological conditions. SAPFLUXNET and its accompanying R software package
sapfluxnetrwill facilitate new data syntheses on the ecological factors driving water use and drought responses of trees and forests.
Alex Mavrovic, Renato Pardo Lara, Aaron Berg, François Demontoux, Alain Royer, and Alexandre Roy
Hydrol. Earth Syst. Sci., 25, 1117–1131, https://doi.org/10.5194/hess-25-1117-2021, https://doi.org/10.5194/hess-25-1117-2021, 2021
Short summary
Short summary
This paper presents a new probe that measures soil microwave permittivity in the frequency range of satellite L-band sensors. The probe capacities will allow for validation and calibration of the models used to estimate landscape physical properties from raw microwave satellite datasets. Our results show important discrepancies between model estimates and instrument measurements that will need to be addressed.
Nataniel M. Holtzman, Leander D. L. Anderegg, Simon Kraatz, Alex Mavrovic, Oliver Sonnentag, Christoforos Pappas, Michael H. Cosh, Alexandre Langlois, Tarendra Lakhankar, Derek Tesser, Nicholas Steiner, Andreas Colliander, Alexandre Roy, and Alexandra G. Konings
Biogeosciences, 18, 739–753, https://doi.org/10.5194/bg-18-739-2021, https://doi.org/10.5194/bg-18-739-2021, 2021
Short summary
Short summary
Microwave radiation coming from Earth's land surface is affected by both soil moisture and the water in plants that cover the soil. We measured such radiation with a sensor elevated above a forest canopy while repeatedly measuring the amount of water stored in trees at the same location. Changes in the microwave signal over time were closely related to tree water storage changes. Satellites with similar sensors could thus be used to monitor how trees in an entire region respond to drought.
Igor Savin, Valery Mironov, Konstantin Muzalevskiy, Sergey Fomin, Andrey Karavayskiy, Zdenek Ruzicka, and Yuriy Lukin
Earth Syst. Sci. Data, 12, 3481–3487, https://doi.org/10.5194/essd-12-3481-2020, https://doi.org/10.5194/essd-12-3481-2020, 2020
Short summary
Short summary
This article presents a dielectric database of organic Arctic soils. This database was created based on dielectric measurements of seven samples of organic soils collected in various parts of the Arctic tundra. The created database can serve not only as a source of experimental data for the development of new soil dielectric models for the Arctic tundra but also as a source of training data for artificial intelligence satellite algorithms of soil moisture retrievals based on neural networks.
Cited articles
Chang, A. T. C. and Shiue, J. C.: A comparative study of microwave radiometer observations over snowfields with radiative transfer model calculations, Remote Sens. Environ., 10, 215–229, https://doi.org/10.1016/0034-4257(80)90025-5, 1980.
Chaubell, J., Chan, S., Dunbar, R. S., Peng, J., and Yueh, S.: SMAP Enhanced L1C Radiometer Half-Orbit 9 km EASE-Grid Brightness Temperatures, Version 3, Boulder, Colorado, USA NASA National Snow and Ice Data Center [data set], https://doi.org/10.5067/XB8K63YM4U8O, 2020.
Derksen, C., Xu, X., Dunbar, R. S., Colliander, A., Kim, Y., Kimball, J. S.,
Black, T. A., Euskirchen, E., Langlois, A., Loranty, M.M., Marsh, P., Rautiainen, K., Roy, A., Royer, A., and Stephens, J.: Retrieving Landscape Freeze/Thaw State from Soil
Moisture Active Passive (SMAP) Radar and Radiometer Measurements, Remote
Sens. Environ., 194, 48–62, https://doi.org/10.1016/j.rse.2017.03.007, 2017.
De Roo, R. D. and Ulaby, F. T.: Bistatic specular scattering from rough
dielectric surfaces, IEEE T. Antenn. Propag., 42,
220–231, https://doi.org/10.1109/8.277216, 1994.
Dunbar, S., Xu, X., Colliander, A., Derksen, C., Kimball, J., and Kim, Y.:
Algorithm Theoretical Basis Document (ATBD), SMAP Level 3 Radiometer
Freeze/Thaw Data Products, JPL CIT: JPL D-56288, 33,
https://smap.jpl.nasa.gov/system/internal_resources/details/original/274_L3_FT_A_RevA_web.pdf (last access: 15 September 2023), 2016.
ESA: Land Cover CCI Product User Guide Version 2, Tech. Rep.,
http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: 15 September 2023), 2017.
Fernandez-Moran, R., Wigneron, J.-P., Lopez-Baeza, E., Al-Yaari, A., Coll-Pajaron, A., Mialon, A., Miernecki, M., Parrens, M., Salgado-Hernanz, P. M., Schwank, M., Wang, S., and Kerr, Y. H.:
Roughness and vegetation parameterizations at L-band for soil moisture
retrievals over a vineyard field, Remote Sens. Environ., 170,
269–279, https://doi.org/10.1016/j.rse.2015.09.006, 2015.
Fisher, R. A.: The use of multiple measurements in taxonomic problem, Ann.
Eugenic., 7, 179–188, https://doi.org/10.1111/j.1469-1809.1936.tb02137.x, 1936.
Gao, S., Li, Z., Chen, Q., Zhou, W., Lin, M., and Yin, X.: Inter-Sensor
Calibration between HY-2B and AMSR2 Passive Microwave Data in Land Surface
and First Result for Snow Water Equivalent Retrieval, Sensors, 19, 5023,
https://doi.org/10.3390/s19225023, 2019.
Harper, K. L., Lamarche, C., Hartley, A., Peylin, P., Ottlé, C., Bastrikov, V., San Martín, R., Bohnenstengel, S. I., Kirches, G., Boettcher, M., Shevchuk, R., Brockmann, C., and Defourny, P.: A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models, Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, 2023.
Hu, T., Zhao, T., Shi, J., Wu, S., Liu, D., Qin, H., and Zhao, K.:
High-Resolution Mapping of Freeze/Thaw Status in China via Fusion of MODIS
and AMSR2 Data, Remote Sens.-Basel, 9, 1339, https://doi.org/10.3390/rs9121339, 2017.
Hu, T., Zhao, T., Zhao, K., and Shi, J.: A continuous global record of
near-surface soil freeze/thaw status from AMSR-E and AMSR2 data,
Int. J. Remote Sens., 40, 6993–7016, https://doi.org/10.1080/01431161.2019.1597307, 2019.
Kilic, L., Prigent, C., Jimenez, C., and Donlon, C.: Technical note: A sensitivity analysis from 1 to 40 GHz for observing the Arctic Ocean with the Copernicus Imaging Microwave Radiometer, Ocean Sci., 17, 455–461, https://doi.org/10.5194/os-17-455-2021, 2021.
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, 4415216, https://doi.org/10.1109/TGRS.2022.3216324, 2022.
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.
Loranty, M. M. and Alexander, H. D.: Understory micrometorology across a larch
forest density gradient in northeastern Siberia 2014–2020, Arctic Data
Center [data set], https://doi.org/10.18739/A24B2X59C, 2021.
Maeda, T., Taniguchi, Y., and Imaoka, K.: GCOM-W1 AMSR2 Level 1R Product: Dataset of Brightness Temperature Modified Using the Antenna Pattern Matching Technique, IEEE T. Geosci. Remote, 54, 770–782, https://doi.org/10.1109/TGRS.2015.2465170, 2016.
Muzalevskiy, K. and Ruzicka, Z.: Detection of Soil Freeze/Thaw States in the
Arctic Region Based on Combined SMAP and AMSR-2 Radio Brightness
Observations, Int. J. Remote Sens., 41, 5046–5061,
https://doi.org/10.1080/01431161.2020.1724348, 2020.
Muzalevskiy, K., Ruzicka, Z., Roy, A., Loranty, M., and Vasiliev, A.:
Classification of the frozen/thawed surface state of Northern land areas
based on SMAP and GCOM-W1 brightness temperature observations at 1.4 GHz and
6.9 GHz, Remote Sens. Lett., 11, 1073–1081, https://doi.org/10.1080/2150704X.2021.1963497, 2021.
Muzalevskiy, K. V., Ruzicka, Z., Kosolapova, L. G., and Mironov, V. L.:
Temperature dependence of SMOS/MIRAS, GCOM-W1/AMSR2 brightness temperature
and ALOS/PALSAR radar backscattering at arctic test sites, Proceedings of
Progress in Electromagnetic Research Symposium (PIERS), Shanghai, 3578–3582,
https://doi.org/10.1109/PIERS.2016.7735375, 2016.
Permafrost Laboratory Geophysical Institute, the University of Alaska Fairbanks: Permafrost Laboratory, https://permafrost.gi.alaska.edu/sites_map, last access: 15 September 2023.
Piepmeier, J. R., Focardi, P., Horgan, K. A., Knuble, J., Ehsan, N., Lucey, J., Brambora, C., Brown, P. R., Hoffman, P. J., French, R. T., Mikhaylov, R. L., Kwack, E. Y., Slimko, E. M., Dawson, D. E., Hudson, D., Peng, J., Mohammed, P. N., De Amici, G., Freedman, A. P., Medeiros, J., Sacks, F., Estep, R., Spencer, M. W., Chen, C. W., Wheeler, K. B., Edelstein, W. N., O'Neill, P. E., and Njoku, E. G.: SMAP L-Band
Microwave Radiometer: Instrument Design and First Year on Orbit, IEEE T.
Geosci. Remote, 55, 1954–1966, https://doi.org/10.1109/tgrs.2016.2631978,
2017.
Rautiainen, K., Lemmetyinen, J., Schwank, M., Kontu, A., Ménard, C.,
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.
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.
Rodriguez-Alvarez, N., Camps, A., Vall-llossera, M., Bosch-Lluis, X.,
Monerris, A., Ramos-Perez, I., and Sanchez, N.: Land Geophysical Parameters
Retrieval Using the Interference Pattern GNSS-R Technique, IEEE T. Geosci. Remote, 49, 71–84, https://doi.org/10.1109/TGRS.2010.2049023, 2011.
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 J. Sel.
Top. Appl., 8, 4442–4459, https://doi.org/10.1109/JSTARS.2015.2476358, 2015.
Wang, J. R., O'Neill, P. E., Jackson, T. J., and Engman, E. T.: Multifrequency
Measurements of the Effects of Soil Moisture, Soil Texture, And Surface
Roughness, IEEE T. Geosci. Remote, 21,
44–51, https://doi.org/10.1109/TGRS.1983.350529, 1983.
Watanabe, M., Kadosaki, G., Kim, Y., Ishikawa, M., Kushida, K., Sawada, Y.,
Tadono, T., Fukuda, M., and Sato, M.: Analysis of the Sources of Variation in
L-band Backscatter From Terrains With Permafrost, IEEE T.
Geosci. Remote, 50, 44–54, https://doi.org/10.1109/TGRS.2011.2159843,
2012.
Wigneron J.-P., Chanzy, A., Kerr, Y., 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.
Zhao, T., Zhang, L., Jiang, L., Zhao, S., Chai, L., and Jin, R.: A new soil
freeze/thaw discriminant algorithm using AMSR-E passive microwave imagery,
Hydrol. Process., 25, 1704–1716, https://doi.org/10.1002/hyp.7930, 2011.
Zhao, T., Shi, J., Hu, T., Zhao, L., Zou, D., Wang, T., Ji, D., Li, R., and
Wang, P.: Estimation of high-resolution near surface freeze/thaw state by
the integration of microwave and thermal infrared remote sensing data on the
Tibetan Plateau, Earth Space Sci., 4, 472–484, https://doi.org/10.1002/2017EA000277, 2017.
Zuerndorfer, B. W., England, A. W., Dobson, M. C., and Ulaby, F. T.: Mapping
freeze/thaw boundaries with SMMR data, NASA Contractor Report 184991, 28,
https://ntrs.nasa.gov/citations/19890014590 (last access: 15 September 2023), 1989.
Zuerndorfer, B. W., England, A. W., Dobson, M. C., and Ulaby, F. T.: Mapping
freeze/thaw boundaries with SMMR data, Agr. Forest Meteorol.,
52, 199–225, 1990.
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.
A new all-weather method for determining the frozen/thawed (FT) state of soils in the Arctic...