Articles | Volume 16, issue 1
https://doi.org/10.5194/tc-16-87-2022
© Author(s) 2022. 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-16-87-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals
Julien Meloche
CORRESPONDING AUTHOR
Centre d'Applications et de Recherche en Télédétection,
Université de Sherbrooke, Sherbrooke, J1K 2R1, Canada
Centre d'études Nordiques, Université Laval, Québec, G1V
0A6, Canada
Alexandre Langlois
Centre d'Applications et de Recherche en Télédétection,
Université de Sherbrooke, Sherbrooke, J1K 2R1, Canada
Centre d'études Nordiques, Université Laval, Québec, G1V
0A6, Canada
Nick Rutter
Department of Geography and Environmental Sciences, Northumbria
University, Newcastle upon Tyne, NE1 8ST, UK
Alain Royer
Centre d'Applications et de Recherche en Télédétection,
Université de Sherbrooke, Sherbrooke, J1K 2R1, Canada
Centre d'études Nordiques, Université Laval, Québec, G1V
0A6, Canada
Josh King
Environment and Climate Change Canada, Climate Research Division,
Toronto, M3H 5T4, Canada
Branden Walker
Cold Regions Research Centre, Wilfrid Laurier University, Waterloo,
N2L 3C5, Canada
Philip Marsh
Cold Regions Research Centre, Wilfrid Laurier University, Waterloo,
N2L 3C5, Canada
Evan J. Wilcox
Cold Regions Research Centre, Wilfrid Laurier University, Waterloo,
N2L 3C5, Canada
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Julien Meloche, Nicolas R. Leroux, Benoit Montpetit, Vincent Vionnet, and Chris Derksen
The Cryosphere, 19, 2949–2962, https://doi.org/10.5194/tc-19-2949-2025, https://doi.org/10.5194/tc-19-2949-2025, 2025
Short summary
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Measuring snow mass from radar measurements is possible with information on snow and a radar model to link the measurements to snow. A key variable in a retrieval is the number of snow layers, with more layers yielding richer information but at increased computational cost. Here, we show the capabilities of a new method for simplifying a complex snowpack while preserving the scattering behavior of the snowpack and conserving its mass.
Benoit Montpetit, Julien Meloche, Vincent Vionnet, Chris Derksen, Georgina Wooley, Nicolas R. Leroux, Paul Siqueira, J. Max Adams, and Mike Brady
EGUsphere, https://doi.org/10.5194/egusphere-2025-2317, https://doi.org/10.5194/egusphere-2025-2317, 2025
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This paper presents the workflow to retrieve snow water equivalent from radar measurements for the future Canadian radar satellite mission, TSMM. The workflow is validated by using airborne radar data collected at Trail Valley Creek, Canada, during winter 2018–19. We detail important considerations to have in the context of an Earth Observation mission over a vast region such as Canada. The results show that it is possible to achieve the desired accuracy for TSMM, over an Arctic environment.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Julien Meloche, Benoit Montpetit, Nicolas R. Leroux, Richard Essery, Gabriel Hould Gosselin, and Philip Marsh
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This preprint is open for discussion and under review for The Cryosphere (TC).
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The impact of uncertainties in the simulation of snow density and SSA by the snow model Crocus (embedded within the Soil, Vegetation and Snow version 2 land surface model) on the simulation of snow backscatter (13.5 GHz) using the Snow Microwave Radiative Transfer model were quantified. The simulation of SSA was found to be a key model uncertainty. Underestimated SSA values lead to high errors in the simulation of snow backscatter, reduced by implementing a minimum SSA value (8.7 m2 kg-1).
Benoit Montpetit, Joshua King, Julien Meloche, Chris Derksen, Paul Siqueira, J. Max Adam, Peter Toose, Mike Brady, Anna Wendleder, Vincent Vionnet, and Nicolas R. Leroux
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 2024
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This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
Julien Meloche, Melody Sandells, Henning Löwe, Nick Rutter, Richard Essery, Ghislain Picard, Randall K. Scharien, Alexandre Langlois, Matthias Jaggi, Josh King, Peter Toose, Jérôme Bouffard, Alessandro Di Bella, and Michele Scagliola
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Sea ice thickness is essential for climate studies. Radar altimetry has provided sea ice thickness measurement, but uncertainty arises from interaction of the signal with the snow cover. Therefore, modelling the signal interaction with the snow is necessary to improve retrieval. A radar model was used to simulate the radar signal from the snow-covered sea ice. This work paved the way to improved physical algorithm to retrieve snow depth and sea ice thickness for radar altimeter missions.
Julien Meloche, Nicolas R. Leroux, Benoit Montpetit, Vincent Vionnet, and Chris Derksen
The Cryosphere, 19, 2949–2962, https://doi.org/10.5194/tc-19-2949-2025, https://doi.org/10.5194/tc-19-2949-2025, 2025
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Measuring snow mass from radar measurements is possible with information on snow and a radar model to link the measurements to snow. A key variable in a retrieval is the number of snow layers, with more layers yielding richer information but at increased computational cost. Here, we show the capabilities of a new method for simplifying a complex snowpack while preserving the scattering behavior of the snowpack and conserving its mass.
Vincent Vionnet, Nicolas Romain 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
EGUsphere, https://doi.org/10.5194/egusphere-2025-3396, https://doi.org/10.5194/egusphere-2025-3396, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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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.
Richard Essery, Giulia Mazzotti, Sarah Barr, Tobias Jonas, Tristan Quaife, and Nick Rutter
Geosci. Model Dev., 18, 3583–3605, https://doi.org/10.5194/gmd-18-3583-2025, https://doi.org/10.5194/gmd-18-3583-2025, 2025
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How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.
Benoit Montpetit, Julien Meloche, Vincent Vionnet, Chris Derksen, Georgina Wooley, Nicolas R. Leroux, Paul Siqueira, J. Max Adams, and Mike Brady
EGUsphere, https://doi.org/10.5194/egusphere-2025-2317, https://doi.org/10.5194/egusphere-2025-2317, 2025
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This paper presents the workflow to retrieve snow water equivalent from radar measurements for the future Canadian radar satellite mission, TSMM. The workflow is validated by using airborne radar data collected at Trail Valley Creek, Canada, during winter 2018–19. We detail important considerations to have in the context of an Earth Observation mission over a vast region such as Canada. The results show that it is possible to achieve the desired accuracy for TSMM, over an Arctic environment.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Julien Meloche, Benoit Montpetit, Nicolas R. Leroux, Richard Essery, Gabriel Hould Gosselin, and Philip Marsh
EGUsphere, https://doi.org/10.5194/egusphere-2025-1498, https://doi.org/10.5194/egusphere-2025-1498, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
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The impact of uncertainties in the simulation of snow density and SSA by the snow model Crocus (embedded within the Soil, Vegetation and Snow version 2 land surface model) on the simulation of snow backscatter (13.5 GHz) using the Snow Microwave Radiative Transfer model were quantified. The simulation of SSA was found to be a key model uncertainty. Underestimated SSA values lead to high errors in the simulation of snow backscatter, reduced by implementing a minimum SSA value (8.7 m2 kg-1).
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The Cryosphere, 19, 1539–1558, https://doi.org/10.5194/tc-19-1539-2025, https://doi.org/10.5194/tc-19-1539-2025, 2025
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Models often underestimate the role of snow cover in permafrost regions, leading to soil temperatures and permafrost dynamics inaccuracies. Through the use of a snow thermal conductivity scheme better adapted to this region, we mitigated soil temperature biases and permafrost extent overestimation within a land surface model. Our study sheds light on the importance of refining snow-related processes in models to enhance our understanding of permafrost dynamics in the context of climate change.
Charlotte Crevier, Alexandre Langlois, Chris Derksen, and Alexandre Roy
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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.
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EGUsphere, https://doi.org/10.5194/egusphere-2024-3963, https://doi.org/10.5194/egusphere-2024-3963, 2025
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We propose a new method to determine the ground surface temperature under the snowpack in the Arctic area from satellite observations. The obtained ground temperatures time series were evaluated over 21 reference sites in Northern Alaska and compared with ground temperatures obtained with global models. The method is excessively promising for monitoring ground temperature below the snowpack and studying the spatiotemporal variability thanks to 15 years of observations over the whole Arctic area.
Georgina J. Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamond Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
The Cryosphere, 18, 5685–5711, https://doi.org/10.5194/tc-18-5685-2024, https://doi.org/10.5194/tc-18-5685-2024, 2024
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Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of the snow model Crocus (embedded within the Soil, Vegetation, and Snow version 2 land surface model) and evaluated at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and specific surface area featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift, and increase viscosity in basal layers.
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
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Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
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
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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.
Benoit Montpetit, Joshua King, Julien Meloche, Chris Derksen, Paul Siqueira, J. Max Adam, Peter Toose, Mike Brady, Anna Wendleder, Vincent Vionnet, and Nicolas R. Leroux
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 2024
Short summary
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This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
Johnny Rutherford, Nick Rutter, Leanne Wake, and Alex Cannon
EGUsphere, https://doi.org/10.5194/egusphere-2024-2445, https://doi.org/10.5194/egusphere-2024-2445, 2024
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The Arctic winter is vulnerable to climate warming and ~1700 Gt of carbon stored in high latitude permafrost ecosystems is at risk of degradation in the future due to enhanced microbial activity. Poorly represented cold season processes, such as the simulation of snow thermal conductivity in Land Surface Models (LSMs), causes uncertainty in projected carbon emission simulations. Improved snow conductivity parameterization in CLM5.0 significantly increases predicted winter CO2 emissions to 2100.
Paul Billecocq, Alexandre Langlois, and Benoit Montpetit
The Cryosphere, 18, 2765–2782, https://doi.org/10.5194/tc-18-2765-2024, https://doi.org/10.5194/tc-18-2765-2024, 2024
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Snow covers a vast part of the globe, making snow water equivalent (SWE) crucial for climate science and hydrology. SWE can be inversed from satellite data, but the snow's complex structure highly affects the signal, and thus an educated first guess is mandatory. In this study, a subgridding framework was developed to model snow at the local scale from model weather data. The framework enhanced snow parameter modeling, paving the way for SWE inversion algorithms from satellite data.
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
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NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 120 000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. This paper summarizes those results and provides links to details on ~ 80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Julien Meloche, Melody Sandells, Henning Löwe, Nick Rutter, Richard Essery, Ghislain Picard, Randall K. Scharien, Alexandre Langlois, Matthias Jaggi, Josh King, Peter Toose, Jérôme Bouffard, Alessandro Di Bella, and Michele Scagliola
EGUsphere, https://doi.org/10.5194/egusphere-2024-1583, https://doi.org/10.5194/egusphere-2024-1583, 2024
Preprint archived
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Sea ice thickness is essential for climate studies. Radar altimetry has provided sea ice thickness measurement, but uncertainty arises from interaction of the signal with the snow cover. Therefore, modelling the signal interaction with the snow is necessary to improve retrieval. A radar model was used to simulate the radar signal from the snow-covered sea ice. This work paved the way to improved physical algorithm to retrieve snow depth and sea ice thickness for radar altimeter missions.
Francis Meloche, Francis Gauthier, and Alexandre Langlois
The Cryosphere, 18, 1359–1380, https://doi.org/10.5194/tc-18-1359-2024, https://doi.org/10.5194/tc-18-1359-2024, 2024
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Snow avalanches are a dangerous natural hazard. Backcountry recreationists and avalanche practitioners try to predict avalanche hazard based on the stability of snow cover. However, snow cover is variable in space, and snow stability observations can vary within several meters. We measure the snow stability several times on a small slope to create high-resolution maps of snow cover stability. These results help us to understand the snow variation for scientists and practitioners.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Oliver Sonnentag, Gabriel Hould Gosselin, Melody Sandells, Chris Derksen, Branden Walker, Gesa Meyer, Richard Essery, Richard Kelly, Phillip Marsh, Julia Boike, and Matteo Detto
Biogeosciences, 21, 825–841, https://doi.org/10.5194/bg-21-825-2024, https://doi.org/10.5194/bg-21-825-2024, 2024
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We undertake a sensitivity study of three different parameters on the simulation of net ecosystem exchange (NEE) during the snow-covered non-growing season at an Arctic tundra site. Simulations are compared to eddy covariance measurements, with near-zero NEE simulated despite observed CO2 release. We then consider how to parameterise the model better in Arctic tundra environments on both sub-seasonal timescales and cumulatively throughout the snow-covered non-growing season.
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
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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.
Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
Earth Syst. Sci. Data, 15, 5121–5133, https://doi.org/10.5194/essd-15-5121-2023, https://doi.org/10.5194/essd-15-5121-2023, 2023
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Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Kirsty Wivell, Stuart Fox, Melody Sandells, Chawn Harlow, Richard Essery, and Nick Rutter
The Cryosphere, 17, 4325–4341, https://doi.org/10.5194/tc-17-4325-2023, https://doi.org/10.5194/tc-17-4325-2023, 2023
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Satellite microwave observations improve weather forecasts, but to use these observations in the Arctic, snow emission must be known. This study uses airborne and in situ snow observations to validate emissivity simulations for two- and three-layer snowpacks at key frequencies for weather prediction. We assess the impact of thickness, grain size and density in key snow layers, which will help inform development of physical snow models that provide snow profile input to emissivity simulations.
Evan J. Wilcox, Brent B. Wolfe, and Philip Marsh
Hydrol. Earth Syst. Sci., 27, 2173–2188, https://doi.org/10.5194/hess-27-2173-2023, https://doi.org/10.5194/hess-27-2173-2023, 2023
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The Arctic is warming quickly and influencing lake water balances. We used water isotope concentrations taken from samples of 25 lakes in the Canadian Arctic and estimated the average ratio of evaporation to inflow (E / I) for each lake. The ratio of watershed area (the area that flows into the lake) to lake area (WA / LA) strongly predicted E / I, as lakes with relatively smaller watersheds received less inflow. The WA / LA could be used to predict the vulnerability of Arctic lakes to future change.
Evan J. Wilcox, Brent B. Wolfe, and Philip Marsh
Hydrol. Earth Syst. Sci., 26, 6185–6205, https://doi.org/10.5194/hess-26-6185-2022, https://doi.org/10.5194/hess-26-6185-2022, 2022
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We estimated how much of the water flowing into lakes during snowmelt replaced the pre-snowmelt lake water. Our data show that, as lake depth increases, the amount of water mixed into lakes decreased, because vertical mixing is reduced as lake depth increases. Our data also show that the water mixing into lakes is not solely snow-sourced but is a mixture of snowmelt and soil water. These results are relevant for lake biogeochemistry given the unique properties of snowmelt runoff.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Melody Sandells, Chris Derksen, Branden Walker, Gabriel Hould Gosselin, Oliver Sonnentag, Richard Essery, Richard Kelly, Phillip Marsh, Joshua King, and Julia Boike
The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, https://doi.org/10.5194/tc-16-4201-2022, 2022
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Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.
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
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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
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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.
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
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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.
Anton Jitnikovitch, Philip Marsh, Branden Walker, and Darin Desilets
The Cryosphere, 15, 5227–5239, https://doi.org/10.5194/tc-15-5227-2021, https://doi.org/10.5194/tc-15-5227-2021, 2021
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Conventional methods used to measure snow have many limitations which hinder our ability to document annual cycles, test predictive models, or analyze the impact of climate change. A modern snow measurement method using in situ cosmic ray neutron sensors demonstrates the capability of continuously measuring spatially variable snowpacks with considerable accuracy. These sensors can provide important data for testing models, validating remote sensing, and water resource management applications.
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
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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.
Chris M. DeBeer, Howard S. Wheater, John W. Pomeroy, Alan G. Barr, Jennifer L. Baltzer, Jill F. Johnstone, Merritt R. Turetsky, Ronald E. Stewart, Masaki Hayashi, Garth van der Kamp, Shawn Marshall, Elizabeth Campbell, Philip Marsh, Sean K. Carey, William L. Quinton, Yanping Li, Saman Razavi, Aaron Berg, Jeffrey J. McDonnell, Christopher Spence, Warren D. Helgason, Andrew M. Ireson, T. Andrew Black, Mohamed Elshamy, Fuad Yassin, Bruce Davison, Allan Howard, Julie M. Thériault, Kevin Shook, Michael N. Demuth, and Alain Pietroniro
Hydrol. Earth Syst. Sci., 25, 1849–1882, https://doi.org/10.5194/hess-25-1849-2021, https://doi.org/10.5194/hess-25-1849-2021, 2021
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This article examines future changes in land cover and hydrological cycling across the interior of western Canada under climate conditions projected for the 21st century. Key insights into the mechanisms and interactions of Earth system and hydrological process responses are presented, and this understanding is used together with model application to provide a synthesis of future change. This has allowed more scientifically informed projections than have hitherto been available.
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
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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
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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.
Joshua King, Stephen Howell, Mike Brady, Peter Toose, Chris Derksen, Christian Haas, and Justin Beckers
The Cryosphere, 14, 4323–4339, https://doi.org/10.5194/tc-14-4323-2020, https://doi.org/10.5194/tc-14-4323-2020, 2020
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Physical measurements of snow on sea ice are sparse, making it difficulty to evaluate satellite estimates or model representations. Here, we introduce new measurements of snow properties on sea ice to better understand variability at distances less than 200 m. Our work shows that similarities in the snow structure are found at longer distances on younger ice than older ice.
Paul Donchenko, Joshua King, and Richard Kelly
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-283, https://doi.org/10.5194/tc-2020-283, 2020
Publication in TC not foreseen
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Estimating Arctic sea ice surface elevation from the CryoSat-2 instrument may not fully compensate for the incomplete penetration of radar through the snow cover and overestimate the ice thickness. This study investigates the accuracy of the ice surface measurement and how it is affected by the properties snow and ice properties. It was found that deep or salty snow, and rough ice can make the surface appear higher, but including these properties in the calculation may improve the estimate.
Inge Grünberg, Evan J. Wilcox, Simon Zwieback, Philip Marsh, and Julia Boike
Biogeosciences, 17, 4261–4279, https://doi.org/10.5194/bg-17-4261-2020, https://doi.org/10.5194/bg-17-4261-2020, 2020
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Based on topsoil temperature data for different vegetation types at a low Arctic tundra site, we found large small-scale variability. Winter temperatures were strongly influenced by vegetation through its effects on snow. Summer temperatures were similar below most vegetation types and not consistently related to late summer permafrost thaw depth. Given that vegetation type defines the relationship between winter and summer soil temperature and thaw depth, it controls permafrost vulnerability.
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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.
To estimate snow water equivalent from space, model predictions of the satellite measurement...