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.
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
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.
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
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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, Karl Rittger, Timbo Stillinger, William Kleiber, and Robert E. Davis
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-230, https://doi.org/10.5194/tc-2022-230, 2022
Revised manuscript under review for TC
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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: 1) snow cover and snow albedo from moderate resolution sensors continue to provide accurate forcings for snow models; and 2) finer spatial and temporal resolution through sensor design, fusion techniques, and satellite constellations are the future for Earth observations.
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
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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.
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, Karl Rittger, Timbo Stillinger, William Kleiber, and Robert E. Davis
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-230, https://doi.org/10.5194/tc-2022-230, 2022
Revised manuscript under review for TC
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: 1) snow cover and snow albedo from moderate resolution sensors continue to provide accurate forcings for snow models; and 2) finer spatial and temporal resolution through sensor design, fusion techniques, and satellite constellations are the future for Earth observations.
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
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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
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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
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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
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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
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
Exploring the Use of Multi-source High-Resolution Satellite Data for Snow Water Equivalent Reconstruction over Mountainous Catchments
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
The retrieval of snow properties from SLSTR Sentinel-3 – Part 2: Results and validation
Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning
Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States
Mapping avalanches with satellites – evaluation of performance and completeness
Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America
Snow depth time series retrieval by time-lapse photography: Finnish and Italian case studies
Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping
Simulating optical top-of-atmosphere radiance satellite images over snow-covered rugged terrain
Parameterizing anisotropic reflectance of snow surfaces from airborne digital camera observations in Antarctica
Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data
Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques
Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions
Comparison of modeled snow properties in Afghanistan, Pakistan, and Tajikistan
Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals
Regional influence of ocean–atmosphere teleconnections on the timing and duration of MODIS-derived snow cover in British Columbia, Canada
Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network
Suitability analysis of ski areas in China: an integrated study based on natural and socioeconomic conditions
Estimating snow depth over Arctic sea ice from calibrated dual-frequency radar freeboards
Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos
Repeat mapping of snow depth across an alpine catchment with RPAS photogrammetry
On the reflectance spectroscopy of snow
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
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
Short summary
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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.
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.
Valentina Premier, Carlo Marin, Giacomo Bertoldi, Riccardo Barella, Claudia Notarnicola, and Lorenzo Bruzzone
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-146, https://doi.org/10.5194/tc-2022-146, 2022
Revised manuscript accepted for TC
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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 data, in-situ observations and a degree-day melting model to reconstruct daily SWE at 25 m. The results show spatial patterns that are consistent with the geomorphological features as well as with a reference product. Being able to also reproduce inter-annual variability, the method has great potentiality for hydrological and ecological applications.
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.
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
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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
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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.
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
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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
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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
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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
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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
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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.
Linlu Mei, Vladimir Rozanov, Evelyn Jäkel, Xiao Cheng, Marco Vountas, and John P. Burrows
The Cryosphere, 15, 2781–2802, https://doi.org/10.5194/tc-15-2781-2021, https://doi.org/10.5194/tc-15-2781-2021, 2021
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This paper presents a new snow property retrieval algorithm from satellite observations. This is Part 2 of two companion papers and shows the results and validation. The paper performs the new retrieval algorithm on the Sea and Land
Surface Temperature Radiometer (SLSTR) instrument and compares the retrieved snow properties with ground-based measurements, aircraft measurements and other satellite products.
Ahmad Hojatimalekshah, Zachary Uhlmann, Nancy F. Glenn, Christopher A. Hiemstra, Christopher J. Tennant, Jake D. Graham, Lucas Spaete, Arthur Gelvin, Hans-Peter Marshall, James P. McNamara, and Josh Enterkine
The Cryosphere, 15, 2187–2209, https://doi.org/10.5194/tc-15-2187-2021, https://doi.org/10.5194/tc-15-2187-2021, 2021
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We describe the relationships between snow depth, vegetation canopy, and local-scale processes during the snow accumulation period using terrestrial laser scanning (TLS). In addition to topography and wind, our findings suggest the importance of fine-scale tree structure, species type, and distributions on snow depth. Snow depth increases from the canopy edge toward the open areas, but wind and topographic controls may affect this trend. TLS data are complementary to wide-area lidar surveys.
Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, Elizabeth A. Burakowski, Christina Herrick, and Eunsang Cho
The Cryosphere, 15, 1485–1500, https://doi.org/10.5194/tc-15-1485-2021, https://doi.org/10.5194/tc-15-1485-2021, 2021
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This pilot study describes a proof of concept for using lidar on an unpiloted aerial vehicle to map shallow snowpack (< 20 cm) depth in open terrain and forests. The 1 m2 resolution snow depth map, generated by subtracting snow-off from snow-on lidar-derived digital terrain models, consistently had 0.5 to 1 cm precision in the field, with a considerable reduction in accuracy in the forest. Performance depends on the point cloud density and the ground surface variability and vegetation.
Elisabeth D. Hafner, Frank Techel, Silvan Leinss, and Yves Bühler
The Cryosphere, 15, 983–1004, https://doi.org/10.5194/tc-15-983-2021, https://doi.org/10.5194/tc-15-983-2021, 2021
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Satellites prove to be very valuable for documentation of large-scale avalanche periods. To test reliability and completeness, which has not been satisfactorily verified before, we attempt a full validation of avalanches mapped from two optical sensors and one radar sensor. Our results demonstrate the reliability of high-spatial-resolution optical data for avalanche mapping, the suitability of radar for mapping of larger avalanches and the unsuitability of medium-spatial-resolution optical data.
Xiongxin Xiao, Shunlin Liang, Tao He, Daiqiang Wu, Congyuan Pei, and Jianya Gong
The Cryosphere, 15, 835–861, https://doi.org/10.5194/tc-15-835-2021, https://doi.org/10.5194/tc-15-835-2021, 2021
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Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. Due to the fact that observations from optical satellite sensors are affected by clouds, this study attempts to capture dynamic characteristics of snow cover at a fine spatiotemporal resolution (daily; 6.25 km) accurately by using passive microwave data. We demonstrate the potential to use the passive microwave and the MODIS data to map the fractional snow cover area.
Marco Bongio, Ali Nadir Arslan, Cemal Melih Tanis, and Carlo De Michele
The Cryosphere, 15, 369–387, https://doi.org/10.5194/tc-15-369-2021, https://doi.org/10.5194/tc-15-369-2021, 2021
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The capability of time-lapse photography to retrieve snow depth time series was tested. We demonstrated that this method can be efficiently used in three different case studies: two in the Italian Alps and one in a forested region of Finland, with an accuracy comparable to the most common methods such as ultrasonic sensors or manual measurements. We hope that this simple method based only on a camera and a graduated stake can enable snow depth measurements in dangerous and inaccessible sites.
Lucie A. Eberhard, Pascal Sirguey, Aubrey Miller, Mauro Marty, Konrad Schindler, Andreas Stoffel, and Yves Bühler
The Cryosphere, 15, 69–94, https://doi.org/10.5194/tc-15-69-2021, https://doi.org/10.5194/tc-15-69-2021, 2021
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In spring 2018 in the alpine Dischma valley (Switzerland), we tested different industrial photogrammetric platforms for snow depth mapping. These platforms were high-resolution satellites, an airplane, unmanned aerial systems and a terrestrial system. Therefore, this study gives a general overview of the accuracy and precision of the different photogrammetric platforms available in space and on earth and their use for snow depth mapping.
Maxim Lamare, Marie Dumont, Ghislain Picard, Fanny Larue, François Tuzet, Clément Delcourt, and Laurent Arnaud
The Cryosphere, 14, 3995–4020, https://doi.org/10.5194/tc-14-3995-2020, https://doi.org/10.5194/tc-14-3995-2020, 2020
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Terrain features found in mountainous regions introduce large errors into the calculation of the physical properties of snow using optical satellite images. We present a new model performing rapid calculations of solar radiation over snow-covered rugged terrain that we tested over a site in the French Alps. The results of the study show that all the interactions between sunlight and the terrain should be accounted for over snow-covered surfaces to correctly estimate snow properties from space.
Tim Carlsen, Gerit Birnbaum, André Ehrlich, Veit Helm, Evelyn Jäkel, Michael Schäfer, and Manfred Wendisch
The Cryosphere, 14, 3959–3978, https://doi.org/10.5194/tc-14-3959-2020, https://doi.org/10.5194/tc-14-3959-2020, 2020
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The angular reflection of solar radiation by snow surfaces is particularly anisotropic and highly variable. We measured the angular reflection from an aircraft using a digital camera in Antarctica in 2013/14 and studied its variability: the anisotropy increases with a lower Sun but decreases for rougher surfaces and larger snow grains. The applied methodology allows for a direct comparison with satellite observations, which generally underestimated the anisotropy measured within this study.
César Deschamps-Berger, Simon Gascoin, Etienne Berthier, Jeffrey Deems, Ethan Gutmann, Amaury Dehecq, David Shean, and Marie Dumont
The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, https://doi.org/10.5194/tc-14-2925-2020, 2020
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We evaluate a recent method to map snow depth based on satellite photogrammetry. We compare it with accurate airborne laser-scanning measurements in the Sierra Nevada, USA. We find that satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountains.
Phillip Harder, John W. Pomeroy, and Warren D. Helgason
The Cryosphere, 14, 1919–1935, https://doi.org/10.5194/tc-14-1919-2020, https://doi.org/10.5194/tc-14-1919-2020, 2020
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Unmanned-aerial-vehicle-based (UAV) structure-from-motion (SfM) techniques have the ability to map snow depths in open areas. Here UAV lidar and SfM are compared to map sub-canopy snowpacks. Snow depth accuracy was assessed with data from sites in western Canada collected in 2019. It is demonstrated that UAV lidar can measure the sub-canopy snow depth at a high accuracy, while UAV-SfM cannot. UAV lidar promises to quantify snow–vegetation interactions at unprecedented accuracy and resolution.
Carlo Marin, Giacomo Bertoldi, Valentina Premier, Mattia Callegari, Christian Brida, Kerstin Hürkamp, Jochen Tschiersch, Marc Zebisch, and Claudia Notarnicola
The Cryosphere, 14, 935–956, https://doi.org/10.5194/tc-14-935-2020, https://doi.org/10.5194/tc-14-935-2020, 2020
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In this paper, we use for the first time the synthetic aperture radar (SAR) time series acquired by Sentinel-1 to monitor snowmelt dynamics in alpine regions. We found that the multitemporal SAR allows the identification of the three phases that characterize the melting process, i.e., moistening, ripening and runoff, in a spatial distributed way. We believe that the presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.
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
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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.
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.
Alexandre R. Bevington, Hunter E. Gleason, Vanessa N. Foord, William C. Floyd, and Hardy P. Griesbauer
The Cryosphere, 13, 2693–2712, https://doi.org/10.5194/tc-13-2693-2019, https://doi.org/10.5194/tc-13-2693-2019, 2019
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We investigate the influence of ocean–atmosphere teleconnections on the start, end, and duration of snow cover in British Columbia, Canada. We do this using daily satellite imagery from 2002 to 2018 and assess the accuracy of our methods using reported snow cover at 60 weather stations. We found that there are very strong relationships that vary by region and elevation. This improves our understanding of snow cover distribution and could be used to predict snow cover from ocean–climate indices.
Anne Braakmann-Folgmann and Craig Donlon
The Cryosphere, 13, 2421–2438, https://doi.org/10.5194/tc-13-2421-2019, https://doi.org/10.5194/tc-13-2421-2019, 2019
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Snow on sea ice is a fundamental climate variable. We propose a novel approach to estimate snow depth on sea ice from satellite microwave radiometer measurements at several frequencies using neural networks (NNs). We evaluate our results with airborne snow depth measurements and compare them to three other established snow depth algorithms. We show that our NN results agree better with the airborne data than the other algorithms. This is also advantageous for sea ice thickness calculation.
Jie Deng, Tao Che, Cunde Xiao, Shijin Wang, Liyun Dai, and Akynbekkyzy Meerzhan
The Cryosphere, 13, 2149–2167, https://doi.org/10.5194/tc-13-2149-2019, https://doi.org/10.5194/tc-13-2149-2019, 2019
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The Chinese ski industry is rapidly booming driven by enormous market demand and government support with the coming 2022 Beijing Winter Olympics. We evaluate the locational suitability of ski areas in China by integrating the natural and socioeconomic conditions. Corresponding development strategies for decision-makers are proposed based on the multi-criteria metrics, which will be extended to incorporate potential influences from future climate change and socioeconomic development.
Isobel R. Lawrence, Michel C. Tsamados, Julienne C. Stroeve, Thomas W. K. Armitage, and Andy L. Ridout
The Cryosphere, 12, 3551–3564, https://doi.org/10.5194/tc-12-3551-2018, https://doi.org/10.5194/tc-12-3551-2018, 2018
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In this paper we estimate the thickness of snow cover on Arctic sea ice from space. We use data from two radar altimeter satellites, AltiKa and CryoSat-2, that have been operating synchronously since 2013. We produce maps of monthly average snow depth for the four growth seasons (October to April): 2012–2013, 2013–2014, 2014–2015, and 2015–2016. Snow depth estimates are essential for the accurate retrieval of sea ice thickness from satellite altimetry.
Richard Fernandes, Christian Prevost, Francis Canisius, Sylvain G. Leblanc, Matt Maloley, Sarah Oakes, Kiyomi Holman, and Anders Knudby
The Cryosphere, 12, 3535–3550, https://doi.org/10.5194/tc-12-3535-2018, https://doi.org/10.5194/tc-12-3535-2018, 2018
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The use of lightweight UAV-based surveys of surface elevation to map snow depth and weekly snow depth change was evaluated over five study areas spanning a range of topography and vegetation cover. Snow depth was estimated with an accuracy of better than 10 cm in the vertical and 3 cm in the horizontal. Vegetation in the snow-free elevation map was a major source of error. As a result, the snow depth change between two dates with snow cover was estimated with an accuracy of better than 4 cm.
Todd A. N. Redpath, Pascal Sirguey, and Nicolas J. Cullen
The Cryosphere, 12, 3477–3497, https://doi.org/10.5194/tc-12-3477-2018, https://doi.org/10.5194/tc-12-3477-2018, 2018
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A remotely piloted aircraft system (RPAS) is evaluated for mapping seasonal snow depth across an alpine basin. RPAS photogrammetry performs well at providing maps of snow depth at high spatial resolution, outperforming field measurements for resolving spatial variability. Uncertainty and error analysis reveal limitations and potential pitfalls of photogrammetric surface-change analysis. Ultimately, RPAS can be a useful tool for understanding snow processes and improving snow modelling efforts.
Alexander Kokhanovsky, Maxim Lamare, Biagio Di Mauro, Ghislain Picard, Laurent Arnaud, Marie Dumont, François Tuzet, Carsten Brockmann, and Jason E. Box
The Cryosphere, 12, 2371–2382, https://doi.org/10.5194/tc-12-2371-2018, https://doi.org/10.5194/tc-12-2371-2018, 2018
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This work presents a new technique with which to derive the snow microphysical and optical properties from snow spectral reflectance measurements. The technique is robust and easy to use, and it does not require the extraction of snow samples from a given snowpack. It can be used in processing satellite imagery over extended fresh dry, wet and polluted snowfields.
Stefan Härer, Matthias Bernhardt, Matthias Siebers, and Karsten Schulz
The Cryosphere, 12, 1629–1642, https://doi.org/10.5194/tc-12-1629-2018, https://doi.org/10.5194/tc-12-1629-2018, 2018
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The paper presents an approach which can be used to process satellite-based snow cover maps with a higher-than-today accuracy at the local scale. Many of the current satellite-based snow maps are using the NDSI with a threshold as a tool for deciding if there is snow on the ground or not. The presented study has shown that, firstly, using the standard threshold of 0.4 can result in significant derivations at the local scale and that, secondly, the deviations become smaller for coarser scales.
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...