Articles | Volume 17, issue 2
https://doi.org/10.5194/tc-17-567-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-567-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Feb 2023
Research article |
| 08 Feb 2023
| 08 Feb 2023
Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets
Earth Research Institute, University of California at Santa Barbara,
Santa Barbara, CA 93106, USA
Karl Rittger
Earth Research Institute, University of California at Santa Barbara,
Santa Barbara, CA 93106, USA
Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO 80309, USA
Mark S. Raleigh
College of Earth, Ocean, and Atmospheric Sciences, Oregon State
University, Corvallis, OR 97331, USA
Alex Michell
Earth Research Institute, University of California at Santa Barbara,
Santa Barbara, CA 93106, USA
Robert E. Davis
Cold Regions Research and Engineering Laboratory, Hanover, NH 03755, USA
Edward H. Bair
Earth Research Institute, University of California at Santa Barbara,
Santa Barbara, CA 93106, USA
Related authors
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
Short summary
Short summary
Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Edward H. Bair, Jeff Dozier, Charles Stern, Adam LeWinter, Karl Rittger, Alexandria Savagian, Timbo Stillinger, and Robert E. Davis
The Cryosphere, 16, 1765–1778, https://doi.org/10.5194/tc-16-1765-2022, https://doi.org/10.5194/tc-16-1765-2022, 2022
Short summary
Short summary
Understanding how snow and ice reflect solar radiation (albedo) is important for global climate. Using high-resolution topography, darkening from surface roughness (apparent albedo) is separated from darkening by the composition of the snow (intrinsic albedo). Intrinsic albedo is usually greater than apparent albedo, especially during melt. Such high-resolution topography is often not available; thus the use of a shade component when modeling mixtures is advised.
Bareera N. Mirza, Eric E. Small, and Mark S. Raleigh
EGUsphere, https://doi.org/10.5194/egusphere-2025-978, https://doi.org/10.5194/egusphere-2025-978, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Measuring snow depth in mountains is essential for water management, but current satellite methods have limitations. This study evaluates snow depth estimates from the Sentinel-1 radar satellite, revealing significant spatial errors, particularly during snowmelt. Combining it with other satellite data did not improve accuracy, emphasizing the need for improved techniques to advance global snow mapping for better water resource predictions
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
Short summary
Short summary
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.
Max Berkelhammer, Gerald F. M. Page, Frank Zurek, Christopher Still, Mariah S. Carbone, William Talavera, Laura Hildebrand, James Byron, Kyle Inthabandith, Angellica Kucinski, Melissa Carlson, Kelsey Foss, Wendy Brown, Rosemary W. H. Carroll, Austin Simonpietri, Marshall Worsham, Ian Breckheimer, Anna Ryken, Reed Maxwell, David Gochis, Mark S. Raleigh, Eric Small, and Kenneth H. Williams
Hydrol. Earth Syst. Sci., 29, 701–718, https://doi.org/10.5194/hess-29-701-2025, https://doi.org/10.5194/hess-29-701-2025, 2025
Short summary
Short summary
Warming in montane systems is affecting the snowmelt input amount. At the global scale, this will impact subalpine forests that rely on spring snowmelt to support their water demands. We use a network of sensors across a hillslope in the Upper Colorado Basin to show that the changing spring snowpack has a more pronounced impact on dense forest stands, while open stands show a higher reliance on summer rain and are less sensitive to significant changes in snow.
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
Short summary
Short summary
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.
Edward H. Bair, Dar A. Roberts, David R. Thompson, Philip G. Brodrick, Brenton A. Wilder, Niklas Bohn, Chris J. Crawford, Nimrod Carmon, Carrie M. Vuyovich, and Jeff Dozier
EGUsphere, https://doi.org/10.5194/egusphere-2024-1681, https://doi.org/10.5194/egusphere-2024-1681, 2024
Short summary
Short summary
Key to the success of future satellite missions is understanding snowmelt in our warming climate, having 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 3 amplifying effects: 1) a background reflectance that is too dark; 2) level terrain assumptions; 3) and differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions are presented.
Tate G. Meehan, Ahmad Hojatimalekshah, Hans-Peter Marshall, Elias J. Deeb, Shad O'Neel, Daniel McGrath, Ryan W. Webb, Randall Bonnell, Mark S. Raleigh, Christopher Hiemstra, and Kelly Elder
The Cryosphere, 18, 3253–3276, https://doi.org/10.5194/tc-18-3253-2024, https://doi.org/10.5194/tc-18-3253-2024, 2024
Short summary
Short summary
Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. We combined high-spatial-resolution snow depth information with ground-based radar measurements to solve for snow density. Extrapolated density estimates over our study area resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation and were utilized in the calculation of SWE.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
Short summary
Short summary
Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Edward H. Bair, Jeff Dozier, Charles Stern, Adam LeWinter, Karl Rittger, Alexandria Savagian, Timbo Stillinger, and Robert E. Davis
The Cryosphere, 16, 1765–1778, https://doi.org/10.5194/tc-16-1765-2022, https://doi.org/10.5194/tc-16-1765-2022, 2022
Short summary
Short summary
Understanding how snow and ice reflect solar radiation (albedo) is important for global climate. Using high-resolution topography, darkening from surface roughness (apparent albedo) is separated from darkening by the composition of the snow (intrinsic albedo). Intrinsic albedo is usually greater than apparent albedo, especially during melt. Such high-resolution topography is often not available; thus the use of a shade component when modeling mixtures is advised.
Edward H. Bair, Karl Rittger, Jawairia A. Ahmad, and Doug Chabot
The Cryosphere, 14, 331–347, https://doi.org/10.5194/tc-14-331-2020, https://doi.org/10.5194/tc-14-331-2020, 2020
Short summary
Short summary
Ice and snowmelt feed the Indus River and Amu Darya, but validation of estimates from satellite sensors has been a problem until recently, when we were given daily snow depth measurements from these basins. Using these measurements, estimates of snow on the ground were created and compared with models. Estimates of water equivalent in the snowpack were mostly in agreement. Stratigraphy was also modeled and showed 1 year with a relatively stable snowpack but another with multiple weak layers.
Chandan Sarangi, Yun Qian, Karl Rittger, Kathryn J. Bormann, Ying Liu, Hailong Wang, Hui Wan, Guangxing Lin, and Thomas H. Painter
Atmos. Chem. Phys., 19, 7105–7128, https://doi.org/10.5194/acp-19-7105-2019, https://doi.org/10.5194/acp-19-7105-2019, 2019
Short summary
Short summary
Radiative forcing induced by deposition of light-absorbing particles (LAPs) on snow is an important surface forcing. Here, we have used high-resolution WRF-Chem (coupled with online snow–LAP–radiation model) simulations for 2013–2014 to estimate the spatial variation in LAP-induced snow albedo darkening effect in high-mountain Asia. Significant improvement in simulated LAP–snow properties with use of a higher spatial resolution for the same model configuration is illustrated over this region.
Edward H. Bair, Andre Abreu Calfa, Karl Rittger, and Jeff Dozier
The Cryosphere, 12, 1579–1594, https://doi.org/10.5194/tc-12-1579-2018, https://doi.org/10.5194/tc-12-1579-2018, 2018
Short summary
Short summary
In Afghanistan, almost no snow measurements exist. Operational estimates use measurements from satellites, but all have limitations. We have developed a satellite-based technique called reconstruction that accurately estimates the snowpack retrospectively. To solve the problem of estimating today's snowpack, we used machine learning, trained on our reconstructed snow estimates, using predictors that are available today. Our results show low errors, demonstrating the utility of this approach.
Edward H. Bair, Robert E. Davis, and Jeff Dozier
Earth Syst. Sci. Data, 10, 549–563, https://doi.org/10.5194/essd-10-549-2018, https://doi.org/10.5194/essd-10-549-2018, 2018
Short summary
Short summary
The mass and energy balance of the snowpack govern its evolution. Here, we present a fully filtered and model-ready dataset containing a continuous hourly record of selected measurements from three sites on Mammoth Mountain, CA USA. These measurements can be used to run a variety of snow models and complement a previously published dataset. In addition to the hand-weighed snow water equivalent, novel measurements include hourly snow albedo corrected for terrain and other measurement biases.
E. H. Bair, R. Simenhois, A. van Herwijnen, and K. Birkeland
The Cryosphere, 8, 1407–1418, https://doi.org/10.5194/tc-8-1407-2014, https://doi.org/10.5194/tc-8-1407-2014, 2014
Related subject area
Discipline: Snow | Subject: Remote Sensing
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
Evaluation of the Snow CCI Snow Covered Area Product within a Mountain Snow Water Equivalent Reanalysis
Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements
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
Brief communication: Not as dirty as they look, flawed airborne and satellite snow spectra
Tower-based C-band radar measurements of an alpine snowpack
Mapping surface hoar from near-infrared texture in a laboratory
Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign
Mapping Seasonal Snow Melting in Karakoram Using SAR and Topographic Data
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
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
Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals
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
Brief communication: Evaluation of the snow cover detection in the Copernicus High Resolution Snow & Ice Monitoring Service
Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas
Deriving Arctic 2 m air temperatures over snow and ice from satellite surface temperature measurements
Impact of dynamic snow density on GlobSnow snow water equivalent retrieval accuracy
The retrieval of snow properties from SLSTR Sentinel-3 – Part 1: Method description and sensitivity study
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Haorui Sun, Yiwen Fang, Steven Margulis, Colleen Mortimer, Lawrence Mudryk, and Chris Derksen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3213, https://doi.org/10.5194/egusphere-2024-3213, 2024
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
Short summary
Short summary
Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
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
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Edward H. Bair, Dar A. Roberts, David R. Thompson, Philip G. Brodrick, Brenton A. Wilder, Niklas Bohn, Chris J. Crawford, Nimrod Carmon, Carrie M. Vuyovich, and Jeff Dozier
EGUsphere, https://doi.org/10.5194/egusphere-2024-1681, https://doi.org/10.5194/egusphere-2024-1681, 2024
Short summary
Short summary
Key to the success of future satellite missions is understanding snowmelt in our warming climate, having 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 3 amplifying effects: 1) a background reflectance that is too dark; 2) level terrain assumptions; 3) and differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions are presented.
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
Short summary
Short summary
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
Short summary
Short summary
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 %.
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
Short summary
Short summary
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.
Shiyi Li, Lanqing Huang, Philipp Bernhard, and Irena Hajnsek
EGUsphere, https://doi.org/10.5194/egusphere-2024-942, https://doi.org/10.5194/egusphere-2024-942, 2024
Short summary
Short summary
This work presented an improved method for seasonal wet snow mapping in Karakoram. SAR and topographic data were effectively integrated for robust wet snow classification in complex mountainous terrain. Applying the method to large scale Sentinel-1 imagery, we have generated wet snow maps covering the three major water basins in Karakraom over four years (2017–2021). Critical snow variables were further derived from the maps and provided valuable insights on regional snow melting dynamics.
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
Short summary
Short summary
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
Short summary
Short summary
We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. In extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record. Retrieval is most robust in the tundra biome, where records can be used to identify extremes and the results can be applied to impact studies at regional scale.
Pinja Venäläinen, Kari Luojus, Colleen Mortimer, Juha Lemmetyinen, Jouni Pulliainen, Matias Takala, Mikko Moisander, and Lina Zschenderlein
The Cryosphere, 17, 719–736, https://doi.org/10.5194/tc-17-719-2023, https://doi.org/10.5194/tc-17-719-2023, 2023
Short summary
Short summary
Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
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
Short summary
Short summary
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
Short summary
Short summary
Changes in the state of the snowpack in the context of observed global warming must be considered to improve our understanding of the processes within the cryosphere. This study aims to characterize an arctic snowpack using the TerraSAR-X satellite. Using a high-spatial-resolution vegetation classification, we were able to quantify the variability in snow depth, as well as the topographic soil wetness index, which provided a better understanding of the electromagnetic wave–ground interaction.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Julien Meloche, Alexandre Langlois, Nick Rutter, Alain Royer, Josh King, Branden Walker, Philip Marsh, and Evan J. Wilcox
The Cryosphere, 16, 87–101, https://doi.org/10.5194/tc-16-87-2022, https://doi.org/10.5194/tc-16-87-2022, 2022
Short summary
Short summary
To estimate snow water equivalent from space, model predictions of the satellite measurement (brightness temperature in our case) have to be used. These models allow us to estimate snow properties from the brightness temperature by inverting the model. To improve SWE estimate, we proposed incorporating the variability of snow in these model as it has not been taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.
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
Short summary
Short summary
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.
Zacharie Barrou Dumont, Simon Gascoin, Olivier Hagolle, Michaël Ablain, Rémi Jugier, Germain Salgues, Florence Marti, Aurore Dupuis, Marie Dumont, and Samuel Morin
The Cryosphere, 15, 4975–4980, https://doi.org/10.5194/tc-15-4975-2021, https://doi.org/10.5194/tc-15-4975-2021, 2021
Short summary
Short summary
Since 2020, the Copernicus High Resolution Snow & Ice Monitoring Service has distributed snow cover maps at 20 m resolution over Europe in near-real time. These products are derived from the Sentinel-2 Earth observation mission, with a revisit time of 5 d or less (cloud-permitting). Here we show the good accuracy of the snow detection over a wide range of regions in Europe, except in dense forest regions where the snow cover is hidden by the trees.
Xiaodan Wu, Kathrin Naegeli, Valentina Premier, Carlo Marin, Dujuan Ma, Jingping Wang, and Stefan Wunderle
The Cryosphere, 15, 4261–4279, https://doi.org/10.5194/tc-15-4261-2021, https://doi.org/10.5194/tc-15-4261-2021, 2021
Short summary
Short summary
We performed a comprehensive accuracy assessment of an Advanced Very High Resolution Radiometer global area coverage snow-cover extent time series dataset for the Hindu Kush Himalayan (HKH) region. The sensor-to-sensor consistency, the accuracy related to snow depth, elevations, land-cover types, slope, and aspects, and topographical variability were also explored. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006.
Pia Nielsen-Englyst, Jacob L. Høyer, Kristine S. Madsen, Rasmus T. Tonboe, Gorm Dybkjær, and Sotirios Skarpalezos
The Cryosphere, 15, 3035–3057, https://doi.org/10.5194/tc-15-3035-2021, https://doi.org/10.5194/tc-15-3035-2021, 2021
Short summary
Short summary
The Arctic region is responding heavily to climate change, and yet, the air temperature of Arctic ice-covered areas is heavily under-sampled when it comes to in situ measurements. This paper presents a method for estimating daily mean 2 m air temperatures (T2m) in the Arctic from satellite observations of skin temperature, providing spatially detailed observations of the Arctic. The satellite-derived T2m product covers clear-sky snow and ice surfaces in the Arctic for the period 2000–2009.
Pinja Venäläinen, Kari Luojus, Juha Lemmetyinen, Jouni Pulliainen, Mikko Moisander, and Matias Takala
The Cryosphere, 15, 2969–2981, https://doi.org/10.5194/tc-15-2969-2021, https://doi.org/10.5194/tc-15-2969-2021, 2021
Short summary
Short summary
Information about snow water equivalent (SWE) is needed in many applications, including climate model evaluation and forecasting fresh water availability. Space-borne radiometer observations combined with ground snow depth measurements can be used to make global estimates of SWE. In this study, we investigate the possibility of using sparse snow density measurement in satellite-based SWE retrieval and show that using the snow density information in post-processing improves SWE estimations.
Linlu Mei, Vladimir Rozanov, Christine Pohl, Marco Vountas, and John P. Burrows
The Cryosphere, 15, 2757–2780, https://doi.org/10.5194/tc-15-2757-2021, https://doi.org/10.5194/tc-15-2757-2021, 2021
Short summary
Short summary
This paper presents a new snow property retrieval algorithm from satellite observations. This is Part 1 of two companion papers and shows the method description and sensitivity study. The paper investigates the major factors, including the assumptions of snow optical properties, snow particle distribution and atmospheric conditions (cloud and aerosol), impacting snow property retrievals from satellite observation.
Cited articles
Aalstad, K., Westermann, S., and Bertino, L.: Evaluating satellite
retrieved fractional snow-covered area at a high-Arctic site using
terrestrial photography, Remote Sens. Environ., 239, 111618, https://doi.org/10.1016/j.rse.2019.111618, 2020.
Adams, J. B., Smith, M. O., and Johnson, P. E.: Spectral Mixture Modeling –
a New Analysis of Rock and Soil Types at the Viking Lander-1 Site, J. Geophys.
Res.-Sol. Ea., 91, 8098–8112, https://doi.org/10.1029/JB091iB08p08098, 1986.
Armstrong, R. L., Rittger, K., Brodzik, M. J., Racoviteanu, A., Barrett, A. P.,Singh Khalsa, S.-J., Raup, B., Hill, A. F., Khan, A. L., Wilson, A. M., Kayastha, R. B., Fetterer, F., and Armstrong, B.: Runoff from
glacier ice and seasonal snow in High Asia: separating melt water sources in
river flow, Reg. Environ. Change, 19, 1249–1261, https://doi.org/10.1007/s10113-018-1429-0,
2018.
Ault, T. W., Czajkowski, K. P., Benko, T., Coss, J., Struble, J., Spongberg,
A., Templin, M., and Gross, C.: Validation of the MODIS snow product and
cloud mask using student and NWS cooperative station observations in the
Lower Great Lakes Region, Remote Sens. Environ., 105, 341–353, https://doi.org/10.1016/j.rse.2006.07.004, 2006.
Bair, E. and Stillinger, T.: SPIReS: Western USA snow cover and snow surface properties, water years 2001–2021, UCSB [data set], https://doi.org/10.21424/R4H05T, 2022.
Bair, E. H., Rittger, K., Davis, R. E., Painter, T. H., and Dozier, J.:
Validating reconstruction of snow water equivalent in California's Sierra
Nevada using measurements from the NASA Airborne Snow Observatory, Water
Resour. Res., 52, 8437–8460, https://doi.org/10.1002/2016wr018704, 2016.
Bair, E. H., Rittger, K., Skiles, S. M., and Dozier, J.: An Examination of
Snow Albedo Estimates From MODIS and Their Impact on Snow Water Equivalent
Reconstruction, Water Resour. Res., 55, 7826–7842, https://doi.org/10.1029/2019wr024810, 2019.
Bair, E. H., Stillinger, T., and Dozier, J.: Snow Property Inversion From
Remote Sensing (SPIReS): A Generalized Multispectral Unmixing Approach With
Examples From MODIS and Landsat 8 OLI, Ieee T. Geosci. Remote, 59, 7270–7284, https://doi.org/10.1109/TGRS.2020.3040328, 2021a.
Bair, E. H., Stillinger, T., Rittger, K., and Skiles, S. M.: COVID-19
Lockdowns Show Reduced Pollution on Snow and Ice in the Indus River Basin,
P. Natl. Acad. Sci. USA, 118, e2101174118, https://doi.org/10.1073/pnas.2101174118, 2021b.
Bair, E. H., Dozier, J., Stern, C., LeWinter, A., Rittger, K., Savagian, A., Stillinger, T., and Davis, R. E.: Divergence of apparent and intrinsic snow albedo over a season at a sub-alpine site with implications for remote sensing, The Cryosphere, 16, 1765–1778, https://doi.org/10.5194/tc-16-1765-2022, 2022.
Baldridge, A. M., Hook, S. J., Grove, C. I., and Rivera, G.: The ASTER
spectral library version 2.0, Remote Sens. Environ., 113, 711–715, https://doi.org/10.1016/j.rse.2008.11.007, 2009.
Bormann, K. J., Brown, R. D., Derksen, C., and Painter, T. H.: Estimating
snow-cover trends from space, Nat. Clim. Change, 8, 923–927, https://doi.org/10.1038/s41558-018-0318-3, 2018.
Branham, R. L. (Ed.): Scientific Data Analysis: An
Introduction to Overdetermined Systems, Springer New York, New York City,
NY, 1990.
Campagnolo, M. L. and Montaño, E. L.: Estimation of Effective Resolution
for Daily MODIS Gridded Surface Reflectance Products, Ieee T. Geosci. Remote,
52, 5622–5632, https://doi.org/10.1109/TGRS.2013.2291496, 2014.
Cao, Q., Painter, T. H., Currier, W. R., Lundquist, J. D., and Lettenmaier,
D. P.: Estimation of Precipitation over the OLYMPEX Domain during Winter
2015/16, J. Hydrometeorol., 19, 143–160, https://doi.org/10.1175/JHM-D-17-0076.1, 2018.
Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B.,
Cullen, N. J., Kerr, T., Hreinsson, E. O., and Woods, R. A.: Representing
spatial variability of snow water equivalent in hydrologic and land-surface
models: A review, Water Resour. Res., 47, W07539, https://doi.org/10.1029/2011wr010745,
2011.
Coulston, J. W., Moisen, G. G., Wilson, B. T., Finco, M. V., Cohen, W. B., Brewer, C. K., Modeling percent tree canopy cover – A pilot study: Photogramm. Eng. Remote Sens., 78, 715–727, https://doi.org/10.14358/PERS.78.7.715, 2012 (data available at: https://s3-us-west-2.amazonaws.com/mrlc/nlcd_2016_treecanopy_2019_08_31.zip, last access: 20 June 2021).
Currier, W. R., Pflug, J., Mazzotti, G., Jonas, T., Deems, J. S., and
Bormann, K. J.: Comparing aerial lidar observations with terrestrial lidar
and snow-probe transects from NASA's 2017 SnowEx campaign, Water Resour. Res.,
55, 6285–6294, https://doi.org/10.1029/2018WR024533, 2019.
Deems, J. S., Painter, T. H., Barsugli, J. J., Belnap, J., and Udall, B.: Combined impacts of current and future dust deposition and regional warming on Colorado River Basin snow dynamics and hydrology, Hydrol. Earth Syst. Sci., 17, 4401–4413, https://doi.org/10.5194/hess-17-4401-2013, 2013a.
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow
depth: a review, J. Glaciol., 59, 467–479, https://doi.org/10.3189/2013JoG12J154, 2013b.
Dickerson-Lange, S. E., Vano, J. A., Gersonde, R., and Lundquist, J. D.:
Ranking Forest Effects on Snow Storage: A Decision Tool for Forest
Management, Water Resour. Res., 57, e2020WR027926, https://doi.org/10.1029/2020WR027926,
2021.
Dozier, J.: Spectral Signature of Alpine Snow Cover from the Landsat
Thematic Mapper, Remote Sens. Environ., 28, 9, https://doi.org/10.1016/0034-4257(89)90101-6, 1989.
Dozier, J., Schneider, S. R., and Mcginnis, D. F.: Effect of Grain-Size and
Snowpack Water Equivalence on Visible and near-Infrared
Satellite-Observations of Snow, Water Resour. Res., 17, 1213–1221, https://doi.org/10.1029/WR017i004p01213, 1981.
Dozier, J., Painter, T. H., Rittger, K., and Frew, J. E.: Time-space
continuity of daily maps of fractional snow cover and albedo from MODIS, Adv.
Water Resour., 31, 1515–1526, https://doi.org/10.1016/j.advwatres.2008.08.011, 2008.
Feng, S. and Hu, Q.: Changes in winter snowfall/precipitation ratio in the
contiguous United States, J. Geophys. Res.-Atmos., 112, D15109, https://doi.org/10.1029/2007JD008397, 2007.
Guan, B., Molotch, N. P., Waliser, D. E., Jepsen, S. M., Painter, T. H., and
Dozier, J.: Snow water equivalent in the Sierra Nevada: blending snow sensor
observations with snowmelt model simulations, Water Resour. Res., 49,
5029–5046, https://doi.org/10.1002/wrcr.20387, 2013.
Hall, D. K. and Riggs, G. A.: Accuracy assessment of the MODIS snow
products, Hydrol. Process., 21, 1534–1547, https://doi.org/10.1002/hyp.6715, 2007.
Hall, D. K. and Riggs, G. A.: MODIS/Terra CGF Snow Cover Daily L3 Global 500m SIN Grid, Version 61, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data Set], https://doi.org/10.5067/MODIS/MOD10A1F.061, 2020.
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr,
K. J.: MODIS snow-cover products, Remote Sens. Environ., 83, 181–194, https://doi.org/10.1016/S0034-4257(02)00095-0, 2002.
Hall, D. K., Riggs, G. A., Foster, J. L., and Kumar, S. V.: Development and
evaluation of a cloud-gap-filled MODIS daily snow-cover product, Remote Sens.
Environ., 114, 496–503, https://doi.org/10.1016/j.rse.2009.10.007, 2010.
Hall, D. K., Riggs, G. A., DiGirolamo, N. E., and Román, M. O.: Evaluation of MODIS and VIIRS cloud-gap-filled snow-cover products for production of an Earth science data record, Hydrol. Earth Syst. Sci., 23, 5227–5241, https://doi.org/10.5194/hess-23-5227-2019, 2019.
Hansen, J. and Nazarenko, L.: Soot climate forcing via snow and ice albedos,
P. Natl. Acad. Sci USA., 101, 423–428, https://doi.org/10.1073/pnas.2237157100, 2004.
Härer, S., Bernhardt, M., Siebers, M., and Schulz, K.: On the need for a time- and location-dependent estimation of the NDSI threshold value for reducing existing uncertainties in snow cover maps at different scales, The Cryosphere, 12, 1629–1642, https://doi.org/10.5194/tc-12-1629-2018, 2018.
Homer, C., Huang, C. Q., Yang, L. M., Wylie, B., and Coan, M.: Development
of a 2001 National Land-Cover Database for the United States, Photogramm. Eng.
Rem. S., 70, 829–840, https://doi.org/10.14358/Pers.70.7.829, 2004.
Immerzeel, W. W., van Beek, L. P. H., and Bierkens, M. F. P.: Climate Change
Will Affect the Asian Water Towers, Science, 328, 1382–1385, https://doi.org/10.1126/science.1183188, 2010.
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch,
T., Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M.,
Fernandez, A., Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink, P.
D. A., Kulkarni, A. V., Mayewski, P. A., Nepal, S., Pacheco, P., Painter, T.
H., Pellicciotti, F., Rajaram, H., Rupper, S., Sinisalo, A., Shrestha, A.
B., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and Baillie, J. E. M.:
Importance and vulnerability of the world's water towers, Nature, 577,
364, https://doi.org/10.1038/s41586-019-1822-y, 2020.
Justice, C. O., Roman, M. O., Csiszar, I., Vermote, E. F., Wolfe, R. E.,
Hook, S. J., Friedl, M., Wang, Z. S., Schaaf, C. B., Miura, T., Tschudi, M.,
Riggs, G., Hall, D. K., Lyapustin, A. I., Devadiga, S., Davidson, C., and
Masuoka, E. J.: Land and cryosphere products from Suomi NPP VIIRS: Overview
and status, J. Geophys. Res.-Atmos., 118, 9753–9765, https://doi.org/10.1002/jgrd.50771, 2013.
Klein, A. G. and Barnett, A. C.: Validation of daily MODIS snow cover maps
of the Upper Rio Grande River Basin for the 2000–2001 snow year, Remote Sens.
Environ., 86, 162–176, https://doi.org/10.1016/S0034-4257(03)00097-X, 2003.
Klein, A. G., Hall, D. K., and Riggs, G. A.: Improving snow cover mapping in
forests through the use of a canopy reflectance model, Hydrol. Process., 12,
1723–1744, https://doi.org/10.1002/(Sici)1099-1085(199808/09)12:10/11<1723::Aid-Hyp691>3.0.Co;2-2, 1998.
Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., and
Wood, E. F.: Inroads of remote sensing into hydrologic science during the
WRR era, Water Resour. Res., 51, 7309–7342, https://doi.org/10.1002/2015wr017616, 2015.
Liston, G. E.: Representing subgrid snow cover heterogeneities in regional
and global models, J. Climate, 17, 1381–1397, https://doi.org/10.1175/1520-0442(2004)017<1381:Rsschi>2.0.Co;2, 2004.
Liu, J., Woodcock, C. E., Melloh, R. A., Davis, R. E., McKenzie, C., and
Painter, T. H.: Modeling the view angle dependence of gap fractions in
forest canopies: Implications for mapping fractional snow cover using
optical remote sensing, J. Hydrometeorol., 9, 1005–1019, https://doi.org/10.1175/2008JHM866.1, 2008.
Liu, J. C., Melloh, R. A., Woodcock, C. E., Davis, R. E., and Ochs, E. S.:
The effect of viewing geometry and topography on viewable gap fractions
through forest canopies, Hydrol. Process., 18, 3595–3607, https://doi.org/10.1002/hyp.5802,
2004.
Lundquist, J. D., Chickadel, C., Cristea, N., Currier, W. R., Henn, B.,
Keenan, E., and Dozier, J.: Separating snow and forest temperatures with
thermal infrared remote sensing, Remote Sens. Environ., 209, 764–779, https://doi.org/10.1016/j.rse.2018.03.001, 2018.
Mankin, J. S., Viviroli, D., Singh, D., Hoekstra, A. Y., and Diffenbaugh, N.
S.: The potential for snow to supply human water demand in the present and
future, Environ. Res. Lett., 10, 114016, https://doi.org/10.1088/1748-9326/10/11/114016,
2015.
Masson, T., Dumont, M., Dalla Mura, M., Sirguey, P., Gascoin, S., Dedieu, J.
P., and Chanussot, J.: An Assessment of Existing Methodologies to Retrieve
Snow Cover Fraction from MODIS Data, Remote Sens.-Basel, 10, 619, https://doi.org/10.3390/rs10040619, 2018.
Maurer, E. P., Rhoads, J. D., Dubayah, R. O., and Lettenmaier, D. P.:
Evaluation of the snow-covered area data product from MODIS, Hydrol. Process.,
17, 59–71, https://doi.org/10.1002/hyp.1193, 2003.
Meerdink, S. K., Hook, S. J., Roberts, D. A., and Abbott, E. A.: The
ECOSTRESS spectral library version 1.0, Remote Sens. Environ., 230,
111196, https://doi.org/10.1016/j.rse.2019.05.015, 2019.
Micheletty, P., Perrot, D., Day, G., and Rittger, K.: Assimilation of
Ground and Satellite Snow Observations in a Distributed Hydrologic Model for
Water Supply Forecasting, J. Am. Water Resour.
A., 58, 1030–1048, https://doi.org/10.1111/1752-1688.12975, 2021.
Micheletty, P. D., Kinoshita, A. M., and Hogue, T. S.: Application of MODIS snow cover products: wildfire impacts on snow and melt in the Sierra Nevada, Hydrol. Earth Syst. Sci., 18, 4601–4615, https://doi.org/10.5194/hess-18-4601-2014, 2014.
Minder, J. R., Letcher, T. W., and Skiles, S. M.: An evaluation of
high-resolution regional climate model simulations of snow cover and albedo
over the Rocky Mountains, with implications for the simulated snow-albedo
feedback, J. Geophys. Res.-Atmos., 121, 9069–9088, https://doi.org/10.1002/2016jd024995, 2016.
Molotch, N. P., Painter, T. H., Bales, R. C., and Dozier, J.: Incorporating
remotely-sensed snow albedo into a spatially-distributed snowmelt model,
Geophys. Res. Lett., 31, L03501, https://doi.org/10.1029/2003gl019063, 2004.
Morsdorf, F., Kötz, B., Meier, E., Itten, K. I., and Allgöwer, B.: Estimation of LAI and fractional cover from small
footprint airborne laser scanning data based on gap fraction, Remote Sens.
Environ., 104.1, 50–61, https://doi.org/10.1016/j.rse.2006.04.019, 2006.
Nolin, A. W.: Recent advances in remote sensing of seasonal snow, J. Glaciol.,
56, 1141–1150, https://doi.org/10.3189/002214311796406077, 2010.
Nolin, A. W. and Dozier, J.: Estimating Snow Grain-Size Using Aviris Data,
Remote Sens. Environ., 44, 231–238, https://doi.org/10.1016/0034-4257(93)90018-S, 1993.
Nolin, A. W., Dozier, J., and Mertes, L. A. K.: Mapping Alpine Snow Using a
Spectral Mixture Modeling Technique, Ann. Glaciol., 17, 121–124, https://doi.org/10.3189/S0260305500012702, 1993.
Nolin, A. W., Sproles, E. A., Rupp, D. E., Crumley, R. L., Webb, M. J.,
Palomaki, R. T., and Mar, E.: New snow metrics for a warming world, Hydrol.
Process., 35, e14262, https://doi.org/10.1002/hyp.14262, 2021.
Oaida, C. M., Reager, J. T., Andreadis, K. M., David, C. H., Levoe, S. R.,
Painter, T. H., Bormann, K. J., Trangsrud, A. R., Girotto, M., and
Famiglietti, J. S.: A High-Resolution Data Assimilation Framework for Snow
Water Equivalent Estimation across the Western United States and Validation
with the Airborne Snow Observatory, J Hydrometeorol., 20, 357–378, https://doi.org/10.1175/JHM-D-18-0009.1, 2019.
Painter, T. H., Roberts, D. A., Green, R. O., and Dozier, J.: The effect of
grain size on spectral mixture analysis of snow-covered area from AVIRIS
data, Remote Sens. Environ., 65, 320–332, https://doi.org/10.1016/S0034-4257(98)00041-8, 1998.
Painter, T. H., Dozier, J., Roberts, D. A., Davis, R. E., and Green, R. O.:
Retrieval of subpixel snow-covered area and grain size from imaging
spectrometer data, Remote Sens. Environ., 85, 64–77, https://doi.org/10.1016/S0034-4257(02)00187-6, 2003.
Painter, T. H., Rittger, K., McKenzie, C., Slaughter, P., Davis, R. E., and
Dozier, J.: Retrieval of subpixel snow covered area, grain size, and albedo
from MODIS, Remote Sens. Environ., 113, 868–879, https://doi.org/10.1016/j.rse.2009.01.001,
2009.
Painter, T. H., Bryant, A. C., and Skiles, S. M.: Radiative forcing by light
absorbing impurities in snow from MODIS surface reflectance data, Geophys.
Res. Lett., 39, L17502, https://doi.org/10.1029/2012gl052457, 2012.
Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J.
S., Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., Mattmann,
C., McGurk, B., Ramirez, P., Richardson, M., Skiles, S. M., Seidel, F. C.,
and Winstral, A.: The Airborne Snow Observatory: Fusion of scanning lidar,
imaging spectrometer, and physically-based modeling for mapping snow water
equivalent and snow albedo, Remote Sens. Environ., 184, 139–152, https://doi.org/10.1016/j.rse.2016.06.018, 2016 (data available at: https://nsidc.org/data/aso, last access: 26 March 2021; https://data.airbornesnowobservatories.com/, last access: 12 February 2012).
Raleigh, M. S., Rittger, K., Moore, C. E., Henn, B., Lutz, J. A., and
Lundquist, J. D.: Ground-based testing of MODIS fractional snow cover in
subalpine meadows and forests of the Sierra Nevada, Remote Sens. Environ.,
128, 44–57, https://doi.org/10.1016/j.rse.2012.09.016, 2013.
Riggs, G., Hall, D. K., and Román, M. O.: VIIRS/NPP CGF Snow Cover Daily L3 Global 375m SIN Grid, Version 1, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data Set], https://doi.org/10.5067/VIIRS/VNP10A1F.001, 2019.
Rittger, K.: Snow cover from spectral mixture analysis algorithm SCAG: OLI and MODIS (v2023.beta), Zenodo [data set], https://doi.org/10.5281/zenodo.7510861, 2023.
Rittger, K. and Raleigh, M. S.: Snow Today, https://nsidc.org/snow-today (last access: 29 June 2022), 24 February 2020.
Rittger, K., Painter, T. H., and Dozier, J.: Assessment of methods for
mapping snow cover from MODIS, Adv. Water Resour., 51, 367–380, https://doi.org/10.1016/j.advwatres.2012.03.002, 2013.
Rittger, K., Bair, E. H., Kahl, A., and Dozier, J.: Spatial estimates of
snow water equivalent from reconstruction, Adv. Water Resour., 94, 345–363, https://doi.org/10.1016/j.advwatres.2016.05.015, 2016.
Rittger, K., Raleigh, M. S., Dozier, J., Hill, A. F., Lutz, J. A., and
Painter, T. H.: Canopy Adjustment and Improved Cloud Detection for Remotely
Sensed Snow Cover Mapping, Water Resour. Res., 56, e2019WR024914, https://doi.org/10.1029/2019WR024914, 2020.
Rittger, K., Bormann, K. J., Bair, E. H., Dozier, J., and Painter, T. H.:
Evaluation of VIIRS and MODIS Snow Cover Fraction in High-Mountain Asia
Using Landsat 8 OLI, Front. Remote Sens., 2, https://doi.org/10.3389/frsen.2021.647154, 2021a.
Rittger, K., Krock, M., Kleiber, W., Bair, E. H., Brodzik, M. J.,
Stephenson, T. R., Rajagopalan, B., Bormann, K. J., and Painter, T. H.:
Multi-sensor fusion using random forests for daily fractional snow cover at
30 m, Remote Sens. Environ., 264, 112608, https://doi.org/10.1016/j.rse.2021.112608, 2021b.
Roberts, D. A., Gardner, M., Church, R., Ustin, S., Scheer, G., and Green,
R. O.: Mapping chaparral in the Santa Monica Mountains using multiple
endmember spectral mixture models, Remote Sens. Environ., 65, 267–279, https://doi.org/10.1016/S0034-4257(98)00037-6, 1998.
Romanov, P., Tarpley, D., Gutman, G., and Carroll, T.: Mapping and
monitoring of the snow cover fraction over North America, J.
Geophys. Res., 108, 8619, https://doi.org/10.1029/2002JD003142, 2003.
Rosenthal, W. and Dozier, J.: Automated Mapping of Montane Snow Cover at
Subpixel Resolution From the Landsat Thematic Mapper, Water Resour. Res., 115–130, https://doi.org/10.1029/95WR02718, 1996.
Safa, H., Krogh, S. A., Greenberg, J., Kostadinov, T. S., and Harpold, A.
A.: Unraveling the Controls on Snow Disappearance in Montane Conifer Forests
Using Multi-Site Lidar, Water Resour. Res., 57, e2020WR027522, https://doi.org/10.1029/2020WR027522, 2021.
Salomonson, V. V. and Appel, I.: Estimating fractional snow cover from MODIS
using the normalized difference snow index, Remote Sens. Environ., 89,
351–360, https://doi.org/10.1016/j.rse.2003.10.016, 2004.
Salomonson, V. V. and Appel, I.: Development of the Aqua MODIS NDSI
fractional snow cover algorithm and validation results, Ieee T. Geosci.
Remote, 44, 1747–1756, https://doi.org/10.1109/Tgrs.2006.876029, 2006.
Selkowitz, D. J., Forster, R. R., and Caldwell, M. K.: Prevalence of Pure
Versus Mixed Snow Cover Pixels across Spatial Resolutions in Alpine
Environments, Remote Sens.-Basel, 6, 12478–12508, https://doi.org/10.3390/rs61212478, 2014.
Selkowitz, D. J., Painter, T. H., Rittger, K. E., Schmidt, G., and Forster,
R.: The USGS Landsat Snow Covered Area Products: Methods and Preliminary
Validation, in: Automated Approaches for Snow and Ice Cover Monitoring Using
Optical Remote Sensing, edited by: Selkowitz, D. J., The University of Utah,
Salt Lake City, Utah, 76–119, 2017.
Serquet, G., Marty, C., Dulex, J.-P., and Rebetez, M.: Seasonal trends and
temperature dependence of the snowfall/precipitation-day ratio in
Switzerland, Geophys Res Lett, 38, L07703, https://doi.org/10.1029/2011GL046976, 2011.
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy
height globally with spaceborne lidar, J. Biophys. Res., 116, G04021, https://doi.org/10.1029/2011JG001708, 2011.
Simic, A., Fernandes, R., Brown, R., Romanov, P., Park, W., Hall, D. K., and
Ca, A. S. N. G.: Validation of MODIS, VEGETATION, and GOES plus SSM/I snow
cover products over Canada based on surface snow depth observations, Hydrol.
Process., 836–838, https://doi.org/10.1002/hyp.5509, 2004.
Sirguey, P., Mathieu, R., and Arnaud, Y.: Subpixel monitoring of the
seasonal snow cover with MODIS at 250 m spatial resolution in the southern
alps of New Zealand: Methodology and accuracy assessment, Remote Sens.
Environ., 113, 160–181, https://doi.org/10.1016/j.rse.2008.09.008, 2009.
Skiles, S. M., Painter, T. H., Deems, J. S., Bryant, A. C., and Landry, C.
C.: Dust radiative forcing in snow of the Upper Colorado River Basin: 2.
Interannual variability in radiative forcing and snowmelt rates, Water
Resour. Res., 48, W07522, https://doi.org/10.1029/2012wr011986, 2012.
Stewart, I. T., Cayan, D. R., and Dettinger, M. D.: Changes toward earlier
streamflow timing across western North America, J. Climate, 18, 1136–1155, https://doi.org/10.1175/Jcli3321.1, 2005.
Stillinger, T. and Bair, E.: SPIReS: Landsat 8 snow cover and snow surface properties co-incident with 3 m LiDAR from the Airborne Snow Observatory, UCSB [data set], https://doi.org/10.21424/R4C62H, 2022.
Stillinger, T., Roberts, D. A., Collar, N. M., and Dozier, J.: Cloud Masking
for Landsat 8 and MODIS Terra Over Snow-Covered Terrain: Error Analysis and
Spectral Similarity Between Snow and Cloud, Water Resour. Res., 55, 6169–6184, https://doi.org/10.1029/2019wr024932, 2019.
Sturm, M. and Liston, G. E.: Revisiting the Global Seasonal Snow
Classification: An Updated Dataset for Earth System Applications, J.
Hydrometeorol., 22, 2917–2938, https://doi.org/10.1175/Jhm-D-21-0070.1, 2021.
Tong, R., Parajka, J., Komma, J., and Bloschl, G.: Mapping snow cover from
daily Collection 6 MODIS products over Austria, J. Hydrol., 590, 125548, https://doi.org/10.1016/j.jhydrol.2020.125548, 2020.
Vikhamar, D. and Solberg, R.: Snow-cover mapping in forests by constrained
linear spectral unmixing of MODIS data, Remote Sens. Environ., 88, 309–323, https://doi.org/10.1016/j.rse.2003.06.004, 2003.
Warren, S. G.: Optical-Properties of Snow, Rev. Geophys., 20, 67–89, https://doi.org/10.1029/RG020i001p00067, 1982.
Wickham, J., Stehman, S. V., Sorenson, D. G., Gass, L., and Dewitz, J. A.:
Thematic accuracy assessment of the NLCD 2016 land cover for the
conterminous United States, Remote Sens. Environ., 257, 112357, https://doi.org/10.1016/j.rse.2021.112357, 2021.
Xin, Q., Woodcock, C. E., Liu, J., Tan, B., Melloh, R. A., and Davis, R. E.:
View angle effects on MODIS snow mapping in forests, Remote Sens. Environ.,
118, 50–59, https://doi.org/10.1016/j.rse.2011.10.029, 2012.
Zemp, M., Frey, H., Gartner-Roer, I., Nussbaumer, S. U., Hoelzle, M., Paul,
F., Haeberli, W., Denzinger, F., Ahlstrom, A. P., Anderson, B., Bajracharya,
S., Baroni, C., Braun, L. N., Caceres, B. E., Casassa, G., Cobos, G.,
Davila, L. R., Granados, H. D., Demuth, M. N., Espizua, L., Fischer, A.,
Fujita, K., Gadek, B., Ghazanfar, A., Hagen, J. O., Holmlund, P., Karimi,
N., Li, Z. Q., Pelto, M., Pitte, P., Popovnin, V. V., Portocarrero, C. A.,
Prinz, R., Sangewar, C. V., Severskiy, I., Sigurosson, O., Soruco, A.,
Usubaliev, R., Vincent, C., and Correspondents, W. N.: Historically
unprecedented global glacier decline in the early 21st century, J. Glaciol.,
61, 745, https://doi.org/10.3189/2015JoG15J017, 2015.
Zhao, F., Strahler, A. H., Schaaf, C. L., Yao, T., Yang, X., Wang, Z., and
Schull, M. A.: Measuring gap fraction, element clumping index and LAI in
Sierra Forest stands using a full-waveform ground-based lidar, Remote Sens.
Environ., 125, 73–79, https://doi.org/10.1016/j.rse.2012.07.007, 2012.
Zheng, Z., Kirchner, P. B., and Bales, R. C.: Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data, The Cryosphere, 10, 257–269, https://doi.org/10.5194/tc-10-257-2016, 2016.
Short summary
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.
Understanding global snow cover is critical for comprehending climate change and its impacts on...
