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.
Research article
| 
06 Jan 2022
Research article |
| 06 Jan 2022
| 06 Jan 2022
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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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
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Alex Mavrovic, Oliver Sonnentag, Juha Lemmetyinen, Carolina Voigt, Nick Rutter, Paul Mann, Jean-Daniel Sylvain, and Alexandre Roy
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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 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.
David M. W. Pritchard, Nathan Forsythe, Greg O'Donnell, Hayley J. Fowler, and Nick Rutter
The Cryosphere, 14, 1225–1244, https://doi.org/10.5194/tc-14-1225-2020, https://doi.org/10.5194/tc-14-1225-2020, 2020
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This study compares different snowpack model configurations applied in the western Himalaya. The results show how even sparse local observations can help to delineate climate input errors from model structure errors, which provides insights into model performance variation. The results also show how interactions between processes affect sensitivities to climate variability in different model configurations, with implications for model selection in climate change projections.
Markus Todt, Nick Rutter, Christopher G. Fletcher, and Leanne M. Wake
The Cryosphere, 13, 3077–3091, https://doi.org/10.5194/tc-13-3077-2019, https://doi.org/10.5194/tc-13-3077-2019, 2019
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Vegetation is often represented by a single layer in global land models. Studies have found deficient simulation of thermal radiation beneath forest canopies when represented by single-layer vegetation. This study corrects thermal radiation in forests for a global land model using single-layer vegetation in order to assess the effect of deficient thermal radiation on snow cover and snowmelt. Results indicate that single-layer vegetation causes snow in forests to be too cold and melt too late.
Nick Rutter, Melody J. Sandells, Chris Derksen, Joshua King, Peter Toose, Leanne Wake, Tom Watts, Richard Essery, Alexandre Roy, Alain Royer, Philip Marsh, Chris Larsen, and Matthew Sturm
The Cryosphere, 13, 3045–3059, https://doi.org/10.5194/tc-13-3045-2019, https://doi.org/10.5194/tc-13-3045-2019, 2019
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Impact of natural variability in Arctic tundra snow microstructural characteristics on the capacity to estimate snow water equivalent (SWE) from Ku-band radar was assessed. Median values of metrics quantifying snow microstructure adequately characterise differences between snowpack layers. Optimal estimates of SWE required microstructural values slightly less than the measured median but tolerated natural variability for accurate estimation of SWE in shallow snowpacks.
Michael Prince, Alexandre Roy, Ludovic Brucker, Alain Royer, Youngwook Kim, and Tianjie Zhao
Earth Syst. Sci. Data, 10, 2055–2067, https://doi.org/10.5194/essd-10-2055-2018, https://doi.org/10.5194/essd-10-2055-2018, 2018
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This paper presents the weekly polar-gridded Aquarius passive L-band surface freeze–thaw product (FT-AP) distributed on the EASE-Grid 2.0 with a resolution of 36 km. To evaluate the product, we compared it with the resampled 37 GHz FT Earth Science Data Record during the overlapping period between 2011 and 2014. The FT-AP ensures, with the SMAP mission that is still in operation, an L-band passive FT monitoring continuum with NASA’s space-borne radiometers, for a period beginning in August 2011.
Fanny Larue, Alain Royer, Danielle De Sève, Alexandre Roy, and Emmanuel Cosme
Hydrol. Earth Syst. Sci., 22, 5711–5734, https://doi.org/10.5194/hess-22-5711-2018, https://doi.org/10.5194/hess-22-5711-2018, 2018
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A data assimilation scheme was developed to improve snow water equivalent (SWE) simulations by updating meteorological forcings and snowpack states using passive microwave satellite observations. A chain of models was first calibrated to simulate satellite observations over northeastern Canada. The assimilation was then validated over 12 stations where daily SWE measurements were acquired during 4 winters (2012–2016). The overall SWE bias is reduced by 68 % compared to original SWE simulations.
Alex Mavrovic, Alexandre Roy, Alain Royer, Bilal Filali, François Boone, Christoforos Pappas, and Oliver Sonnentag
Geosci. Instrum. Method. Data Syst., 7, 195–208, https://doi.org/10.5194/gi-7-195-2018, https://doi.org/10.5194/gi-7-195-2018, 2018
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To improve microwave satellite and airborne observation products in forest environments, a precise and reliable estimation of the permittivity of trees is required. We developed a probe suitable to measure the permittivity of tree trunks at L band in the field. The system is easily transportable in the field, low energy consuming, operational at low temperatures and weatherproof. The permittivity of seven tree species in both frozen and thawed states was measured, showing important contrast.
Ron Kwok, Nathan T. Kurtz, Ludovic Brucker, Alvaro Ivanoff, Thomas Newman, Sinead L. Farrell, Joshua King, Stephen Howell, Melinda A. Webster, John Paden, Carl Leuschen, Joseph A. MacGregor, Jacqueline Richter-Menge, Jeremy Harbeck, and Mark Tschudi
The Cryosphere, 11, 2571–2593, https://doi.org/10.5194/tc-11-2571-2017, https://doi.org/10.5194/tc-11-2571-2017, 2017
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Since 2009, the ultra-wideband snow radar on Operation IceBridge has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Existing snow depth retrieval algorithms differ in the way the air–snow and snow–ice interfaces are detected and localized in the radar returns and in how the system limitations are addressed. Here, we assess five retrieval algorithms by comparisons with field measurements, ground-based campaigns, and analyzed fields of snow depth.
Sabrina Marx, Katharina Anders, Sofia Antonova, Inga Beck, Julia Boike, Philip Marsh, Moritz Langer, and Bernhard Höfle
Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2017-49, https://doi.org/10.5194/esurf-2017-49, 2017
Revised manuscript has not been submitted
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Global climate warming causes permafrost to warm and thaw, and, consequently, to release the carbon into the atmosphere. Terrestrial laser scanning is evaluated and current methods are extended in the context of monitoring subsidence in Arctic permafrost regions. The extracted information is important to gain a deeper understanding of permafrost-related subsidence processes and provides highly accurate ground-truth data which is necessary for further developing area-wide monitoring methods.
Yann Blanchard, Alain Royer, Norman T. O'Neill, David D. Turner, and Edwin W. Eloranta
Atmos. Meas. Tech., 10, 2129–2147, https://doi.org/10.5194/amt-10-2129-2017, https://doi.org/10.5194/amt-10-2129-2017, 2017
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Multiband thermal measurements of zenith sky radiance were used in a retrieval algorithm, to estimate cloud optical depth and effective particle diameter of thin ice clouds in the Canadian High Arctic. The retrieval technique was validated using a synergy lidar and radar data. Inversions were performed across three polar winters and results showed a significant correlation (R2 = 0.95) for cloud optical depth retrievals and an overall accuracy of 83 % for the classification of thin ice clouds.
Peter Toose, Alexandre Roy, Frederick Solheim, Chris Derksen, Tom Watts, Alain Royer, and Anne Walker
Geosci. Instrum. Method. Data Syst., 6, 39–51, https://doi.org/10.5194/gi-6-39-2017, https://doi.org/10.5194/gi-6-39-2017, 2017
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Radio-frequency interference (RFI) can significantly contaminate the measured radiometric signal of current spaceborne L-band passive microwave radiometers used for monitoring essential climate variables. A 385-channel hyperspectral L-band radiometer system was designed with the means to quantify the strength and type of RFI. The compact design makes it ideal for mounting on both surface and airborne platforms to be used for calibrating and validating measurement from spaceborne sensors.
Melody Sandells, Richard Essery, Nick Rutter, Leanne Wake, Leena Leppänen, and Juha Lemmetyinen
The Cryosphere, 11, 229–246, https://doi.org/10.5194/tc-11-229-2017, https://doi.org/10.5194/tc-11-229-2017, 2017
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This study looks at a wide range of options for simulating sensor signals for satellite monitoring of water stored as snow, though an ensemble of 1323 coupled snow evolution and microwave scattering models. The greatest improvements will be made with better computer simulations of how the snow microstructure changes, followed by how the microstructure scatters radiation at microwave frequencies. Snow compaction should also be considered in systems to monitor snow mass from space.
Xicai Pan, Daqing Yang, Yanping Li, Alan Barr, Warren Helgason, Masaki Hayashi, Philip Marsh, John Pomeroy, and Richard J. Janowicz
The Cryosphere, 10, 2347–2360, https://doi.org/10.5194/tc-10-2347-2016, https://doi.org/10.5194/tc-10-2347-2016, 2016
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This study demonstrates a robust procedure for accumulating precipitation gauge measurements and provides an analysis of bias corrections of precipitation measurements across experimental sites in different ecoclimatic regions of western Canada. It highlights the need for and importance of precipitation bias corrections at both research sites and operational networks for water balance assessment and the validation of global/regional climate–hydrology models.
Tom Watts, Nick Rutter, Peter Toose, Chris Derksen, Melody Sandells, and John Woodward
The Cryosphere, 10, 2069–2074, https://doi.org/10.5194/tc-10-2069-2016, https://doi.org/10.5194/tc-10-2069-2016, 2016
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Ice layers in snowpacks introduce uncertainty in satellite-derived estimates of snow water equivalent, have ecological impacts on plants and animals, and change the thermal and vapour transport properties of the snowpack. Here we present a new field method for measuring the density of ice layers. The method was used in the Arctic and mid-latitudes; the mean measured ice layer density was significantly higher than values typically used in the literature.
Stephen E. L. Howell, Frédéric Laliberté, Ron Kwok, Chris Derksen, and Joshua King
The Cryosphere, 10, 1463–1475, https://doi.org/10.5194/tc-10-1463-2016, https://doi.org/10.5194/tc-10-1463-2016, 2016
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The Canadian Ice Service record of observed landfast ice and snow thickness represents one of the longest in the Arctic that spans over 5 decades. We analyze this record to report on long-term trends and variability of ice and snow thickness within the Canadian Arctic Archipelago (CAA). Results indicate a thinning of ice at several sites in the CAA. State-of-the-art climate models still have difficultly capturing observed ice thickness values in the CAA and should be used with caution.
Alexandre Roy, Alain Royer, Olivier St-Jean-Rondeau, Benoit Montpetit, Ghislain Picard, Alex Mavrovic, Nicolas Marchand, and Alexandre Langlois
The Cryosphere, 10, 623–638, https://doi.org/10.5194/tc-10-623-2016, https://doi.org/10.5194/tc-10-623-2016, 2016
Martin Proksch, Nick Rutter, Charles Fierz, and Martin Schneebeli
The Cryosphere, 10, 371–384, https://doi.org/10.5194/tc-10-371-2016, https://doi.org/10.5194/tc-10-371-2016, 2016
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Density is a fundamental property of porous media such as snow. During the MicroSnow Davos 2014 workshop, different approaches (box-, wedge- and cylinder-type density cutters, micro-computed tomography) to measure snow density were applied in a controlled laboratory environment and in the field. In general, results suggest that snow densities measured by different methods agree within 9 %. However, the density profiles resolved by the measurement methods differed considerably.
C. Papasodoro, E. Berthier, A. Royer, C. Zdanowicz, and A. Langlois
The Cryosphere, 9, 1535–1550, https://doi.org/10.5194/tc-9-1535-2015, https://doi.org/10.5194/tc-9-1535-2015, 2015
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Located at the far south (~62.5° N) of the Canadian Arctic, Grinnell and Terra Nivea Ice Caps are good climate proxies in this scarce data region. Multiple data sets (in situ, airborne and spaceborne) reveal changes in area, elevation and mass over the past 62 years. Ice wastage sharply accelerated during the last decade for both ice caps, as illustrated by the strongly negative mass balance of Terra Nivea over 2007-2014 (-1.77 ± 0.36 m a-1 w.e.). Possible climatic drivers are also discussed.
G. Picard, A. Royer, L. Arnaud, and M. Fily
The Cryosphere, 8, 1105–1119, https://doi.org/10.5194/tc-8-1105-2014, https://doi.org/10.5194/tc-8-1105-2014, 2014
G. Picard, L. Brucker, A. Roy, F. Dupont, M. Fily, A. Royer, and C. Harlow
Geosci. Model Dev., 6, 1061–1078, https://doi.org/10.5194/gmd-6-1061-2013, https://doi.org/10.5194/gmd-6-1061-2013, 2013
A. Roy, A. Royer, B. Montpetit, P. A. Bartlett, and A. Langlois
The Cryosphere, 7, 961–975, https://doi.org/10.5194/tc-7-961-2013, https://doi.org/10.5194/tc-7-961-2013, 2013
Related subject area
Discipline: Snow | Subject: Remote Sensing
Brief communication: Not as dirty as they look, flawed airborne and satellite snow spectra
Evaluation of the Snow Climate Change Initiative (Snow CCI) snow-covered area product within a mountain snow water equivalent reanalysis
Mapping seasonal snow melting in Karakoram using SAR and topographic data
Do we still need reflectance? From radiance to snow properties in mountainous terrain: a case study with the EMIT imaging spectrometer
Temporal stability of a new 40-year daily AVHRR land surface temperature dataset for the pan-Arctic region
Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sites
Comparing High-Resolution Snow Mapping Approaches in Palsa Mires: UAS LiDAR vs. Machine Learning
Evaluating sensitivity of optical snow grain size retrievals to radiative transfer models, shape parameters, and inversion techniques
Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements
Radar Equivalent Snowpack: reducing the number of snow layers while retaining its microwave properties and bulk snow mass
Simulation of Arctic snow microwave emission in surface-sensitive atmosphere channels
Retrieval of snow and soil properties for forward radiative transfer modeling of airborne Ku-band SAR to estimate snow water equivalent: the Trail Valley Creek 2018/19 snow experiment
Evaluating L-band InSAR snow water equivalent retrievals with repeat ground-penetrating radar and terrestrial lidar surveys in northern Colorado
Reanalyzing the spatial representativeness of snow depth at automated monitoring stations using airborne lidar data
Contrasting patterns of change in snowline altitude across five Himalayan catchments
Tower-based C-band radar measurements of an alpine snowpack
Mapping surface hoar from near-infrared texture in a laboratory
Snow depth derived from Sentinel-1 compared to in-situ observations in northern Finland
Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign
Temperature-dominated spatiotemporal variability in snow phenology on the Tibetan Plateau from 2002 to 2022
Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method
Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17
Snow water equivalent retrieval over Idaho – Part 1: Using Sentinel-1 repeat-pass interferometry
Passive microwave remote-sensing-based high-resolution snow depth mapping for Western Himalayan zones using multifactor modeling approach
Retrieval of snow water equivalent from dual-frequency radar measurements: using time series to overcome the need for accurate a priori information
Snow accumulation, albedo and melt patterns following road construction on permafrost, Inuvik–Tuktoyaktuk Highway, Canada
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 1: Measurements, processing, and accuracy assessment
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions
Evaluating Snow Microwave Radiative Transfer (SMRT) model emissivities with 89 to 243 GHz observations of Arctic tundra snow
Evaluating the utility of active microwave observations as a snow mission concept using observing system simulation experiments
Evaluation of snow depth retrievals from ICESat-2 using airborne laser-scanning data
How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction?
Exploring the use of multi-source high-resolution satellite data for snow water equivalent reconstruction over mountainous catchments
Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR)
Snowmelt characterization from optical and synthetic-aperture radar observations in the La Joie Basin, British Columbia
Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar
Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends
Towards long-term records of rain-on-snow events across the Arctic from satellite data
Implementing spatially and temporally varying snow densities into the GlobSnow snow water equivalent retrieval
Evaluation of E3SM land model snow simulations over the western United States
Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets
Snow stratigraphy observations from Operation IceBridge surveys in Alaska using S and C band airborne ultra-wideband FMCW (frequency-modulated continuous wave) radar
Brief communication: A continuous formulation of microwave scattering from fresh snow to bubbly ice from first principles
Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing
Automated avalanche mapping from SPOT 6/7 satellite imagery with deep learning: results, evaluation, potential and limitations
Potential of X-band polarimetric synthetic aperture radar co-polar phase difference for arctic snow depth estimation
Snow water equivalent change mapping from slope-correlated synthetic aperture radar interferometry (InSAR) phase variations
Sentinel-1 time series for mapping snow cover depletion and timing of snowmelt in Arctic periglacial environments: case study from Zackenberg and Kobbefjord, Greenland
Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps
Mapping liquid water content in snow at the millimeter scale: an intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements
Edward H. Bair, Dar A. Roberts, David R. Thompson, Philip G. Brodrick, Brenton A. Wilder, Niklas Bohn, Christopher J. Crawford, Nimrod Carmon, Carrie M. Vuyovich, and Jeff Dozier
The Cryosphere, 19, 2315–2320, https://doi.org/10.5194/tc-19-2315-2025, https://doi.org/10.5194/tc-19-2315-2025, 2025
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Key to the success of future satellite missions is understanding snowmelt in our warming climate, as this has implications for nearly 2 billion people. An obstacle is that an artifact, called the hook, is often mistaken for soot or dust. Instead, it is caused by three amplifying effects: (1) background reflectance that is too dark, (2) an assumption of level terrain, and (3) differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions are presented.
Haorui Sun, Yiwen Fang, Steven A. Margulis, Colleen Mortimer, Lawrence Mudryk, and Chris Derksen
The Cryosphere, 19, 2017–2036, https://doi.org/10.5194/tc-19-2017-2025, https://doi.org/10.5194/tc-19-2017-2025, 2025
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The European Space Agency's Snow Climate Change Initiative (Snow CCI) developed a high-quality snow cover extent and snow water equivalent (SWE) climate data record. However, gaps exist in complex terrain due to challenges in using passive microwave sensing and in situ measurements. This study presents a methodology to fill the mountain SWE gap using Snow CCI snow cover fraction within a Bayesian SWE reanalysis framework, with potential applications in untested regions and with other sensors.
Shiyi Li, Lanqing Huang, Philipp Bernhard, and Irena Hajnsek
The Cryosphere, 19, 1621–1639, https://doi.org/10.5194/tc-19-1621-2025, https://doi.org/10.5194/tc-19-1621-2025, 2025
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This work presents an improved method for seasonal wet snow mapping in Karakoram using synthetic aperture radar (SAR) data and topographic data. This method enables robust wet snow classification in complex mountainous terrain. Large-scale wet snow maps were generated using the proposed method, covering three major water basins in Karakoram over 4 years (2017–2021). Crucial snow variables were further derived from the maps and provided valuable insights on regional snow melting dynamics.
Niklas Bohn, Edward H. Bair, Philip G. Brodrick, Nimrod Carmon, Robert O. Green, Thomas H. Painter, and David R. Thompson
The Cryosphere, 19, 1279–1302, https://doi.org/10.5194/tc-19-1279-2025, https://doi.org/10.5194/tc-19-1279-2025, 2025
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A new type of Earth-observing satellite is measuring reflected sunlight in all its colors. These measurements can be used to characterize snow properties, which give us important information about climate change. In our work, we emphasize the difficulties of obtaining these properties from rough mountainous regions and present a solution to the problem. Our research was inspired by the growing number of new satellite technologies and the increasing challenges associated with climate change.
Sonia Dupuis, Frank-Michael Göttsche, and Stefan Wunderle
The Cryosphere, 18, 6027–6059, https://doi.org/10.5194/tc-18-6027-2024, https://doi.org/10.5194/tc-18-6027-2024, 2024
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The Arctic has experienced pronounced warming the last few decades. This warming threatens ecosystems, vegetation dynamics, snow cover duration, and permafrost. Traditional monitoring methods like stations and climate models lack the detail needed. Land surface temperature (LST) data derived from satellites offer high spatial and temporal coverage, perfect for studying changes in the Arctic. In particular, LST information from AVHRR provides a 40-year record, valuable for analysing trends.
Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dunmire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall
The Cryosphere, 18, 5407–5430, https://doi.org/10.5194/tc-18-5407-2024, https://doi.org/10.5194/tc-18-5407-2024, 2024
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This study uses radar imagery from the Sentinel-1 satellite to derive snow depth from increases in the returning energy. These retrieved depths are then compared to nine lidar-derived snow depths across the western United State to assess the ability of this technique to be used to monitor global snow distributions. We also qualitatively compare the changes in underlying Sentinel-1 amplitudes against both the total lidar snow depths and nine automated snow monitoring stations.
Alexander Störmer, Timo Kumpula, Miguel Villoslada, Pasi Korpelainen, Henning Schumacher, and Benjamin Burkhard
EGUsphere, https://doi.org/10.5194/egusphere-2024-2862, https://doi.org/10.5194/egusphere-2024-2862, 2024
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Snow has a major impact on palsa development, yet understanding its distribution at small scale remains limited. We used LiDAR UAS and ground truth data in combination with machine learning to model snow distribution at three palsa sites. We identified extremes in snow depth corresponding to palsa topography, providing insights into the influence of snow distribution on their formation. The results demonstrate the applicability of machine learning for modeling snow distribution at a small scale.
James W. Dillon, Christopher P. Donahue, Evan N. Schehrer, and Kevin D. Hammonds
EGUsphere, https://doi.org/10.5194/egusphere-2024-3141, https://doi.org/10.5194/egusphere-2024-3141, 2024
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The optical grain size of snow controls albedo, playing a key role in Earth's energy balance. This parameter varies substantially in time and space, and thus accurate estimates are vital. Reflectance measurements can be used to map grain size, although results differ considerably depending on the algorithm and model used during retrieval. We perform a novel laboratory comparison to determine the optimal model, shape parameters, and retrieval algorithm for accurately estimating grain size.
Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn
The Cryosphere, 18, 5015–5029, https://doi.org/10.5194/tc-18-5015-2024, https://doi.org/10.5194/tc-18-5015-2024, 2024
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Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones is often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
Julien Meloche, Nicolas R. Leroux, Benoit Montpetit, Vincent Vionnet, and Chris Derksen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3169, https://doi.org/10.5194/egusphere-2024-3169, 2024
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Measuring the snow mass from radar measurements is possible with information on the 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 layer yielding richer information but at increased computational cost. Here, we show the capabilities of a new method to simplify a complex snowpack, while preserving the scattering behavior of the snowpack and conserving the mass.
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
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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.
Randall Bonnell, Daniel McGrath, Jack Tarricone, Hans-Peter Marshall, Ella Bump, Caroline Duncan, Stephanie Kampf, Yunling Lou, Alex Olsen-Mikitowicz, Megan Sears, Keith Williams, Lucas Zeller, and Yang Zheng
The Cryosphere, 18, 3765–3785, https://doi.org/10.5194/tc-18-3765-2024, https://doi.org/10.5194/tc-18-3765-2024, 2024
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Snow provides water for billions of people, but the amount of snow is difficult to detect remotely. During the 2020 and 2021 winters, a radar was flown over mountains in Colorado, USA, to measure the amount of snow on the ground, while our team collected ground observations to test the radar technique’s capabilities. The technique yielded accurate measurements of the snowpack that had good correlation with ground measurements, making it a promising application for the upcoming NISAR satellite.
Jordan N. Herbert, Mark S. Raleigh, and Eric E. Small
The Cryosphere, 18, 3495–3512, https://doi.org/10.5194/tc-18-3495-2024, https://doi.org/10.5194/tc-18-3495-2024, 2024
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Automated stations measure snow properties at a single point but are frequently used to validate data that represent much larger areas. We use lidar snow depth data to see how often the mean snow depth surrounding a snow station is within 10 cm of the snow station depth at different scales. We found snow stations overrepresent the area-mean snow depth in ~ 50 % of cases, but the direction of bias at a site is temporally consistent, suggesting a site could be calibrated to the surrounding area.
Orie Sasaki, Evan Stewart Miles, Francesca Pellicciotti, Akiko Sakai, and Koji Fujita
EGUsphere, https://doi.org/10.5194/egusphere-2024-2026, https://doi.org/10.5194/egusphere-2024-2026, 2024
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This study proposes a new method to detect snowline altitude (SLA) using the Google Earth Engine platform with high-resolution satellite imagery, applicable anywhere in the world. Applying this method to five glaciated watersheds in the Himalayas reveals regional consistencies and differences in snow dynamics. We also investigate the primary controls of these dynamics by analyzing climatic factors and topographic characteristics.
Isis Brangers, Hans-Peter Marshall, Gabrielle De Lannoy, Devon Dunmire, Christian Mätzler, and Hans Lievens
The Cryosphere, 18, 3177–3193, https://doi.org/10.5194/tc-18-3177-2024, https://doi.org/10.5194/tc-18-3177-2024, 2024
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To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains. The reflections were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The results demonstrate that C-band radar is sensitive to seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy and snow wetness may complicate satellite-based snow depth retrievals.
James Dillon, Christopher Donahue, Evan Schehrer, Karl Birkeland, and Kevin Hammonds
The Cryosphere, 18, 2557–2582, https://doi.org/10.5194/tc-18-2557-2024, https://doi.org/10.5194/tc-18-2557-2024, 2024
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Surface hoar crystals are snow grains that form when vapor deposits on a snow surface. They create a weak layer in the snowpack that can cause large avalanches to occur. Thus, determining when and where surface hoar forms is a lifesaving matter. Here, we developed a means of mapping surface hoar using remote-sensing technologies. We found that surface hoar displayed heightened texture, hence the variability of brightness. Using this, we created surface hoar maps with an accuracy upwards of 95 %.
Adriano Lemos and Aku Riihelä
EGUsphere, https://doi.org/10.5194/egusphere-2024-869, https://doi.org/10.5194/egusphere-2024-869, 2024
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Here we used satellite imagery to measure snow depth in northern Finland and compared to on-site weather stations from 2019–2022. We correlated snow depths and vegetation coverage, and found thicker snow over non-vegetated areas and frozen water bodies due to the satellite's sensitivity. Our estimates showed underestimated results of snow depth and need further investigation, but they highlight the potential in monitoring seasonal snow changes, particularly where direct measurements are lacking.
Steven J. Pestana, C. Chris Chickadel, and Jessica D. Lundquist
The Cryosphere, 18, 2257–2276, https://doi.org/10.5194/tc-18-2257-2024, https://doi.org/10.5194/tc-18-2257-2024, 2024
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We compared infrared images taken by GOES-R satellites of an area with snow and forests against surface temperature measurements taken on the ground, from an aircraft, and by another satellite. We found that GOES-R measured warmer temperatures than the other measurements, especially in areas with more forest and when the Sun was behind the satellite. From this work, we learned that the position of the Sun and surface features such as trees that can cast shadows impact GOES-R infrared images.
Jiahui Xu, Yao Tang, Linxin Dong, Shujie Wang, Bailang Yu, Jianping Wu, Zhaojun Zheng, and Yan Huang
The Cryosphere, 18, 1817–1834, https://doi.org/10.5194/tc-18-1817-2024, https://doi.org/10.5194/tc-18-1817-2024, 2024
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Understanding snow phenology (SP) and its possible feedback are important. We reveal spatiotemporal heterogeneous SP on the Tibetan Plateau (TP) and the mediating effects from meteorological, topographic, and environmental factors on it. The direct effects of meteorology on SP are much greater than the indirect effects. Topography indirectly effects SP, while vegetation directly effects SP. This study contributes to understanding past global warming and predicting future trends on the TP.
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
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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.
Siddharth Singh, Michael Durand, Edward Kim, and Ana P. Barros
The Cryosphere, 18, 747–773, https://doi.org/10.5194/tc-18-747-2024, https://doi.org/10.5194/tc-18-747-2024, 2024
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Seasonal snowfall accumulation plays a critical role in climate. The water stored in it is measured by the snow water equivalent (SWE), the amount of water released after completely melting. We demonstrate a Bayesian physical–statistical framework to estimate SWE from airborne X- and Ku-band synthetic aperture radar backscatter measurements constrained by physical snow hydrology and radar models. We explored spatial resolutions and vertical structures that agree well with ground observations.
Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, https://doi.org/10.5194/tc-18-559-2024, 2024
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The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
Dhiraj Kumar Singh, Srinivasarao Tanniru, Kamal Kant Singh, Harendra Singh Negi, and RAAJ Ramsankaran
The Cryosphere, 18, 451–474, https://doi.org/10.5194/tc-18-451-2024, https://doi.org/10.5194/tc-18-451-2024, 2024
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In situ techniques for snow depth (SD) measurement are not adequate to represent the spatiotemporal variability in SD in the Western Himalayan region. Therefore, this study focuses on the high-resolution mapping of daily snow depth in the Indian Western Himalayan region using passive microwave remote-sensing-based algorithms. Overall, the proposed multifactor SD models demonstrated substantial improvement compared to the operational products. However, there is a scope for further improvement.
Michael Durand, Joel T. Johnson, Jack Dechow, Leung Tsang, Firoz Borah, and Edward J. Kim
The Cryosphere, 18, 139–152, https://doi.org/10.5194/tc-18-139-2024, https://doi.org/10.5194/tc-18-139-2024, 2024
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Seasonal snow accumulates each winter, storing water to release later in the year and modulating both water and energy cycles, but the amount of seasonal snow is one of the most poorly measured components of the global water cycle. Satellite concepts to monitor snow accumulation have been proposed but not selected. This paper shows that snow accumulation can be measured using radar, and that (contrary to previous studies) does not require highly accurate information about snow microstructure.
Jennika Hammar, Inge Grünberg, Steven V. Kokelj, Jurjen van der Sluijs, and Julia Boike
The Cryosphere, 17, 5357–5372, https://doi.org/10.5194/tc-17-5357-2023, https://doi.org/10.5194/tc-17-5357-2023, 2023
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Roads on permafrost have significant environmental effects. This study assessed the Inuvik to Tuktoyaktuk Highway (ITH) in Canada and its impact on snow accumulation, albedo and snowmelt timing. Our findings revealed that snow accumulation increased by up to 36 m from the road, 12-day earlier snowmelt within 100 m due to reduced albedo, and altered snowmelt patterns in seemingly undisturbed areas. Remote sensing aids in understanding road impacts on permafrost.
Anssi Rauhala, Leo-Juhani Meriö, Anton Kuzmin, Pasi Korpelainen, Pertti Ala-aho, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4343–4362, https://doi.org/10.5194/tc-17-4343-2023, https://doi.org/10.5194/tc-17-4343-2023, 2023
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Snow conditions in the Northern Hemisphere are rapidly changing, and information on snow depth is important for decision-making. We present snow depth measurements using different drones throughout the winter at a subarctic site. Generally, all drones produced good estimates of snow depth in open areas. However, differences were observed in the accuracies produced by the different drones, and a reduction in accuracy was observed when moving from an open mire area to forest-covered areas.
Leo-Juhani Meriö, Anssi Rauhala, Pertti Ala-aho, Anton Kuzmin, Pasi Korpelainen, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4363–4380, https://doi.org/10.5194/tc-17-4363-2023, https://doi.org/10.5194/tc-17-4363-2023, 2023
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Information on seasonal snow cover is essential in understanding snow processes and operational forecasting. We study the spatiotemporal variability in snow depth and snow processes in a subarctic, boreal landscape using drones. We identified multiple theoretically known snow processes and interactions between snow and vegetation. The results highlight the applicability of the drones to be used for a detailed study of snow depth in multiple land cover types and snow–vegetation interactions.
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.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
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As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
César Deschamps-Berger, Simon Gascoin, David Shean, Hannah Besso, Ambroise Guiot, and Juan Ignacio López-Moreno
The Cryosphere, 17, 2779–2792, https://doi.org/10.5194/tc-17-2779-2023, https://doi.org/10.5194/tc-17-2779-2023, 2023
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The estimation of the snow depth in mountains is hard, despite the importance of the snowpack for human societies and ecosystems. We measured the snow depth in mountains by comparing the elevation of points measured with snow from the high-precision altimetric satellite ICESat-2 to the elevation without snow from various sources. Snow depths derived only from ICESat-2 were too sparse, but using external airborne/satellite products results in spatially richer and sufficiently precise snow depths.
Edward H. Bair, Jeff Dozier, Karl Rittger, Timbo Stillinger, William Kleiber, and Robert E. Davis
The Cryosphere, 17, 2629–2643, https://doi.org/10.5194/tc-17-2629-2023, https://doi.org/10.5194/tc-17-2629-2023, 2023
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To test the title question, three snow cover products were used in a snow model. Contrary to previous work, higher-spatial-resolution snow cover products only improved the model accuracy marginally. Conclusions are as follows: (1) snow cover and albedo from moderate-resolution sensors continue to provide accurate forcings and (2) finer spatial and temporal resolutions are the future for Earth observations, but existing moderate-resolution sensors still offer value.
Valentina Premier, Carlo Marin, Giacomo Bertoldi, Riccardo Barella, Claudia Notarnicola, and Lorenzo Bruzzone
The Cryosphere, 17, 2387–2407, https://doi.org/10.5194/tc-17-2387-2023, https://doi.org/10.5194/tc-17-2387-2023, 2023
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The large amount of information regularly acquired by satellites can provide important information about SWE. We explore the use of multi-source satellite data, in situ observations, and a degree-day model to reconstruct daily SWE at 25 m. The results show spatial patterns that are consistent with the topographical features as well as with a reference product. Being able to also reproduce interannual variability, the method has great potential for hydrological and ecological applications.
Jack Tarricone, Ryan W. Webb, Hans-Peter Marshall, Anne W. Nolin, and Franz J. Meyer
The Cryosphere, 17, 1997–2019, https://doi.org/10.5194/tc-17-1997-2023, https://doi.org/10.5194/tc-17-1997-2023, 2023
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Mountain snowmelt provides water for billions of people across the globe. Despite its importance, we cannot currently measure the amount of water in mountain snowpacks from satellites. In this research, we test the ability of an experimental snow remote sensing technique from an airplane in preparation for the same sensor being launched on a future NASA satellite. We found that the method worked better than expected for estimating important snowpack properties.
Sara E. Darychuk, Joseph M. Shea, Brian Menounos, Anna Chesnokova, Georg Jost, and Frank Weber
The Cryosphere, 17, 1457–1473, https://doi.org/10.5194/tc-17-1457-2023, https://doi.org/10.5194/tc-17-1457-2023, 2023
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We use synthetic-aperture radar (SAR) and optical observations to map snowmelt timing and duration on the watershed scale. We found that Sentinel-1 SAR time series can be used to approximate snowmelt onset over diverse terrain and land cover types, and we present a low-cost workflow for SAR processing over large, mountainous regions. Our approach provides spatially distributed observations of the snowpack necessary for model calibration and can be used to monitor snowmelt in ungauged basins.
Vasana Dharmadasa, Christophe Kinnard, and Michel Baraër
The Cryosphere, 17, 1225–1246, https://doi.org/10.5194/tc-17-1225-2023, https://doi.org/10.5194/tc-17-1225-2023, 2023
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This study highlights the successful usage of UAV lidar to monitor small-scale snow depth distribution. Our results show that underlying topography and wind redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. This emphasizes the importance of including and better representing these processes in physically based models for accurate snowpack estimates.
Ruben Urraca and Nadine Gobron
The Cryosphere, 17, 1023–1052, https://doi.org/10.5194/tc-17-1023-2023, https://doi.org/10.5194/tc-17-1023-2023, 2023
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We evaluate the fitness of some of the longest satellite (NOAA CDR, 1966–2020) and reanalysis (ERA5, 1950–2020; ERA5-Land, 1950–2020) products currently available to monitor the Northern Hemisphere snow cover trends using 527 stations as the reference. We found different artificial trends and stepwise discontinuities in all the products that hinder the accurate monitoring of snow trends, at least without bias correction. The study also provides updates on the snow cover trends during 1950–2020.
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
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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
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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.
Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung
The Cryosphere, 17, 673–697, https://doi.org/10.5194/tc-17-673-2023, https://doi.org/10.5194/tc-17-673-2023, 2023
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We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.
Timbo Stillinger, Karl Rittger, Mark S. Raleigh, Alex Michell, Robert E. Davis, and Edward H. Bair
The Cryosphere, 17, 567–590, https://doi.org/10.5194/tc-17-567-2023, https://doi.org/10.5194/tc-17-567-2023, 2023
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Understanding global snow cover is critical for comprehending climate change and its impacts on the lives of billions of people. Satellites are the best way to monitor global snow cover, yet snow varies at a finer spatial resolution than most satellite images. We assessed subpixel snow mapping methods across a spectrum of conditions using airborne lidar. Spectral-unmixing methods outperformed older operational methods and are ready to to advance snow cover mapping at the global scale.
Jilu Li, Fernando Rodriguez-Morales, Xavier Fettweis, Oluwanisola Ibikunle, Carl Leuschen, John Paden, Daniel Gomez-Garcia, and Emily Arnold
The Cryosphere, 17, 175–193, https://doi.org/10.5194/tc-17-175-2023, https://doi.org/10.5194/tc-17-175-2023, 2023
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Alaskan glaciers' loss of ice mass contributes significantly to ocean surface rise. It is important to know how deeply and how much snow accumulates on these glaciers to comprehend and analyze the glacial mass loss process. We reported the observed seasonal snow depth distribution from our radar data taken in Alaska in 2018 and 2021, developed a method to estimate the annual snow accumulation rate at Mt. Wrangell caldera, and identified transition zones from wet-snow zones to ablation zones.
Ghislain Picard, Henning Löwe, and Christian Mätzler
The Cryosphere, 16, 3861–3866, https://doi.org/10.5194/tc-16-3861-2022, https://doi.org/10.5194/tc-16-3861-2022, 2022
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Microwave satellite observations used to monitor the cryosphere require radiative transfer models for their interpretation. These models represent how microwaves are scattered by snow and ice. However no existing theory is suitable for all types of snow and ice found on Earth. We adapted a recently published generic scattering theory to snow and show how it may improve the representation of snows with intermediate densities (~500 kg/m3) and/or with coarse grains at high microwave frequencies.
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.
Elisabeth D. Hafner, Patrick Barton, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 16, 3517–3530, https://doi.org/10.5194/tc-16-3517-2022, https://doi.org/10.5194/tc-16-3517-2022, 2022
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Knowing where avalanches occur is very important information for several disciplines, for example avalanche warning, hazard zonation and risk management. Satellite imagery can provide such data systematically over large regions. In our work we propose a machine learning model to automate the time-consuming manual mapping. Additionally, we investigate expert agreement for manual avalanche mapping, showing that our network is equally as good as the experts in identifying avalanches.
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.
Jayson Eppler, Bernhard Rabus, and Peter Morse
The Cryosphere, 16, 1497–1521, https://doi.org/10.5194/tc-16-1497-2022, https://doi.org/10.5194/tc-16-1497-2022, 2022
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We introduce a new method for mapping changes in the snow water equivalent (SWE) of dry snow based on differences between time-repeated synthetic aperture radar (SAR) images. It correlates phase differences with variations in the topographic slope which allows the method to work without any "reference" targets within the imaged area and without having to numerically unwrap the spatial phase maps. This overcomes the key challenges faced in using SAR interferometry for SWE change mapping.
Sebastian Buchelt, Kirstine Skov, Kerstin Krøier Rasmussen, and Tobias Ullmann
The Cryosphere, 16, 625–646, https://doi.org/10.5194/tc-16-625-2022, https://doi.org/10.5194/tc-16-625-2022, 2022
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In this paper, we present a threshold and a derivative approach using Sentinel-1 synthetic aperture radar time series to capture the small-scale heterogeneity of snow cover (SC) and snowmelt. Thereby, we can identify start of runoff and end of SC as well as perennial snow and SC extent during melt with high spatiotemporal resolution. Hence, our approach could support monitoring of distribution patterns and hydrological cascading effects of SC from the catchment scale to pan-Arctic observations.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
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Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Christopher Donahue, S. McKenzie Skiles, and Kevin Hammonds
The Cryosphere, 16, 43–59, https://doi.org/10.5194/tc-16-43-2022, https://doi.org/10.5194/tc-16-43-2022, 2022
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The amount of water within a snowpack is important information for predicting snowmelt and wet-snow avalanches. From within a controlled laboratory, the optimal method for measuring liquid water content (LWC) at the snow surface or along a snow pit profile using near-infrared imagery was determined. As snow samples melted, multiple models to represent wet-snow reflectance were assessed against a more established LWC instrument. The best model represents snow as separate spheres of ice and water.
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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...
