Articles | Volume 19, issue 3
https://doi.org/10.5194/tc-19-1279-2025
© Author(s) 2025. 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-19-1279-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Mar 2025
Research article |
| 17 Mar 2025
| 17 Mar 2025
Do we still need reflectance? From radiance to snow properties in mountainous terrain: a case study with the EMIT imaging spectrometer
Niklas Bohn
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Edward H. Bair
Civil Group, Leidos, Inc., Reston, VA, USA
Philip G. Brodrick
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Nimrod Carmon
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Robert O. Green
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Thomas H. Painter
Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, CA, USA
David R. Thompson
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
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Key to the success of future satellite missions is understanding snowmelt in our warming climate, as this has implications for nearly 2 billion people. An obstacle is that an artifact, called the hook, is often mistaken for soot or dust. Instead, it is caused by three amplifying effects: (1) background reflectance that is too dark, (2) an assumption of level terrain, and (3) differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions are presented.
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Key to the success of future satellite missions is understanding snowmelt in our warming climate, as this has implications for nearly 2 billion people. An obstacle is that an artifact, called the hook, is often mistaken for soot or dust. Instead, it is caused by three amplifying effects: (1) background reflectance that is too dark, (2) an assumption of level terrain, and (3) differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions are presented.
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The Cryosphere, 17, 567–590, https://doi.org/10.5194/tc-17-567-2023, https://doi.org/10.5194/tc-17-567-2023, 2023
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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.
Siraput Jongaramrungruang, Christian Frankenberg, Georgios Matheou, Andrew K. Thorpe, David R. Thompson, Le Kuai, and Riley M. Duren
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This paper demonstrates the use of high-resolution 2-D plume imagery from airborne remote sensing retrievals to quantify methane point-source emissions. It shows significant improvements on the flux estimates without the need for direct wind speed measurements. This paves the way for enhanced flux estimates in future field campaign and space-based observations to better understand the magnitude and distribution of various point sources of methane.
Daniel H. Cusworth, Daniel J. Jacob, Daniel J. Varon, Christopher Chan Miller, Xiong Liu, Kelly Chance, Andrew K. Thorpe, Riley M. Duren, Charles E. Miller, David R. Thompson, Christian Frankenberg, Luis Guanter, and Cynthia A. Randles
Atmos. Meas. Tech., 12, 5655–5668, https://doi.org/10.5194/amt-12-5655-2019, https://doi.org/10.5194/amt-12-5655-2019, 2019
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We examine the potential for global detection of methane plumes from individual point sources with the new generation of spaceborne imaging spectrometers scheduled for launch in 2019–2025. We perform methane retrievals on simulated scenes with varying surfaces and atmospheric methane concentrations. Our results suggest that imaging spectrometers in space could play a transformative role in the future for quantifying methane emissions from point sources on a global scale.
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.
Brian D. Bue, David R. Thompson, Shubhankar Deshpande, Michael Eastwood, Robert O. Green, Vijay Natraj, Terry Mullen, and Mario Parente
Atmos. Meas. Tech., 12, 2567–2578, https://doi.org/10.5194/amt-12-2567-2019, https://doi.org/10.5194/amt-12-2567-2019, 2019
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Imaging spectrometers provide valuable remote measurements of Earth's surface and atmosphere. These measurements rely on computationally expensive radiative transfer models (RTMs). Spectrometers produce too much data to process with RTMs directly, requiring approximations that trade accuracy for speed. We demonstrate that neural networks can quickly emulate RTM calculations more accurately than current approaches, enabling the application of more sophisticated RTMs than current methods permit.
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.
David R. Thompson, Brian H. Kahn, Robert O. Green, Steve A. Chien, Elizabeth M. Middleton, and Daniel Q. Tran
Atmos. Meas. Tech., 11, 1019–1030, https://doi.org/10.5194/amt-11-1019-2018, https://doi.org/10.5194/amt-11-1019-2018, 2018
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The distribution of ice and liquid particles in clouds (i.e., their thermodynamic phase) has a large impact on Earth's climate. We report a global high spatial resolution survey of cloud phase based on a decade of data from the Hyperion orbital imaging spectrometer. Seasonal and latitudinal trends corroborate observations by the Atmospheric Infrared Sounder (AIRS). Most variance observed at climate model grid scales of 100 km is explained by spatial structure at finer spatial resolutions.
Andrew K. Thorpe, Christian Frankenberg, David R. Thompson, Riley M. Duren, Andrew D. Aubrey, Brian D. Bue, Robert O. Green, Konstantin Gerilowski, Thomas Krings, Jakob Borchardt, Eric A. Kort, Colm Sweeney, Stephen Conley, Dar A. Roberts, and Philip E. Dennison
Atmos. Meas. Tech., 10, 3833–3850, https://doi.org/10.5194/amt-10-3833-2017, https://doi.org/10.5194/amt-10-3833-2017, 2017
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At local scales emissions of methane (CH4) and carbon dioxide (CO2) are highly uncertain. The AVIRIS-NG imaging spectrometer maps large regions and generates high-spatial-resolution CH4 and CO2 concentration maps from anthropogenic and natural sources. Examples include CH4 from a processing plant, tank, pipeline leak, seep, mine vent shafts, and CO2 from power plants. This demonstrates a greenhouse gas monitoring capability that targets the two dominant anthropogenic climate-forcing agents.
Sven Krautwurst, Konstantin Gerilowski, Haflidi H. Jonsson, David R. Thompson, Richard W. Kolyer, Laura T. Iraci, Andrew K. Thorpe, Markus Horstjann, Michael Eastwood, Ira Leifer, Samuel A. Vigil, Thomas Krings, Jakob Borchardt, Michael Buchwitz, Matthew M. Fladeland, John P. Burrows, and Heinrich Bovensmann
Atmos. Meas. Tech., 10, 3429–3452, https://doi.org/10.5194/amt-10-3429-2017, https://doi.org/10.5194/amt-10-3429-2017, 2017
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This study investigates a subset of data collected during the CO2 and Methane EXperiment (COMEX) in 2014. It focuses on airborne measurements to quantify the emissions from landfills in the Los Angeles Basin. Airborne remote sensing data have been used to estimate the emission rate of one particular landfill on four different days. The results have been compared to airborne in situ measurements. Airborne imaging spectroscopy has been used to identify emission hotspots across the landfill.
Felix C. Seidel, Karl Rittger, S. McKenzie Skiles, Noah P. Molotch, and Thomas H. Painter
The Cryosphere, 10, 1229–1244, https://doi.org/10.5194/tc-10-1229-2016, https://doi.org/10.5194/tc-10-1229-2016, 2016
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Quantifying the snow albedo effect is an important step to predict water availability as well as changes in climate and sea level. We use imaging spectroscopy to determine optical properties of mountain snow. We find an inverse relationship between snow albedo and grain size as well as between elevation and grain size. Under strong melt conditions, however, we show that the optical-equivalent snow grain size increases slower than expected at lower elevations and we explain possible reasons.
D. R. Thompson, I. Leifer, H. Bovensmann, M. Eastwood, M. Fladeland, C. Frankenberg, K. Gerilowski, R. O. Green, S. Kratwurst, T. Krings, B. Luna, and A. K. Thorpe
Atmos. Meas. Tech., 8, 4383–4397, https://doi.org/10.5194/amt-8-4383-2015, https://doi.org/10.5194/amt-8-4383-2015, 2015
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We discuss principles for real-time infrared spectral signature detection and measurement, and report performance onboard the NASA Airborne Visible Infrared Spectrometer - Next Generation (AVIRIS-NG). We describe a case study of the NASA/ESA CO2 and MEthane eXperiment (COMEX), a multi-platform campaign to measure CH4 plumes released from anthropogenic sources including oil and gas infrastructure. AVIRIS-NG successfully detected CH4 plumes in concert with other in situ and remote instruments.
S. Kaspari, T. H. Painter, M. Gysel, S. M. Skiles, and M. Schwikowski
Atmos. Chem. Phys., 14, 8089–8103, https://doi.org/10.5194/acp-14-8089-2014, https://doi.org/10.5194/acp-14-8089-2014, 2014
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
J. S. Deems, T. H. Painter, J. J. Barsugli, J. Belnap, and B. Udall
Hydrol. Earth Syst. Sci., 17, 4401–4413, https://doi.org/10.5194/hess-17-4401-2013, https://doi.org/10.5194/hess-17-4401-2013, 2013
E. A. Sproles, A. W. Nolin, K. Rittger, and T. H. Painter
Hydrol. Earth Syst. Sci., 17, 2581–2597, https://doi.org/10.5194/hess-17-2581-2013, https://doi.org/10.5194/hess-17-2581-2013, 2013
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Evaluation of E3SM land model snow simulations over the western United States
Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets
Snow stratigraphy observations from Operation IceBridge surveys in Alaska using S and C band airborne ultra-wideband FMCW (frequency-modulated continuous wave) radar
Brief communication: A continuous formulation of microwave scattering from fresh snow to bubbly ice from first principles
Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing
Automated avalanche mapping from SPOT 6/7 satellite imagery with deep learning: results, evaluation, potential and limitations
Potential of X-band polarimetric synthetic aperture radar co-polar phase difference for arctic snow depth estimation
Snow water equivalent change mapping from slope-correlated synthetic aperture radar interferometry (InSAR) phase variations
Sentinel-1 time series for mapping snow cover depletion and timing of snowmelt in Arctic periglacial environments: case study from Zackenberg and Kobbefjord, Greenland
Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps
Edward H. Bair, Dar A. Roberts, David R. Thompson, Philip G. Brodrick, Brenton A. Wilder, Niklas Bohn, Christopher J. Crawford, Nimrod Carmon, Carrie M. Vuyovich, and Jeff Dozier
The Cryosphere, 19, 2315–2320, https://doi.org/10.5194/tc-19-2315-2025, https://doi.org/10.5194/tc-19-2315-2025, 2025
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Key to the success of future satellite missions is understanding snowmelt in our warming climate, as this has implications for nearly 2 billion people. An obstacle is that an artifact, called the hook, is often mistaken for soot or dust. Instead, it is caused by three amplifying effects: (1) background reflectance that is too dark, (2) an assumption of level terrain, and (3) differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions are presented.
Haorui Sun, Yiwen Fang, Steven A. Margulis, Colleen Mortimer, Lawrence Mudryk, and Chris Derksen
The Cryosphere, 19, 2017–2036, https://doi.org/10.5194/tc-19-2017-2025, https://doi.org/10.5194/tc-19-2017-2025, 2025
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The European Space Agency's Snow Climate Change Initiative (Snow CCI) developed a high-quality snow cover extent and snow water equivalent (SWE) climate data record. However, gaps exist in complex terrain due to challenges in using passive microwave sensing and in situ measurements. This study presents a methodology to fill the mountain SWE gap using Snow CCI snow cover fraction within a Bayesian SWE reanalysis framework, with potential applications in untested regions and with other sensors.
Shiyi Li, Lanqing Huang, Philipp Bernhard, and Irena Hajnsek
The Cryosphere, 19, 1621–1639, https://doi.org/10.5194/tc-19-1621-2025, https://doi.org/10.5194/tc-19-1621-2025, 2025
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This work presents an improved method for seasonal wet snow mapping in Karakoram using synthetic aperture radar (SAR) data and topographic data. This method enables robust wet snow classification in complex mountainous terrain. Large-scale wet snow maps were generated using the proposed method, covering three major water basins in Karakoram over 4 years (2017–2021). Crucial snow variables were further derived from the maps and provided valuable insights on regional snow melting dynamics.
Andreas J. Dietz and Sebastian Roessler
EGUsphere, https://doi.org/10.5194/egusphere-2025-382, https://doi.org/10.5194/egusphere-2025-382, 2025
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The "Global SnowPack" product of the German Aerospace Center (DLR) contains binary information about the presence or absence of snow on a global scale since the year 2000. Now incorporating new input datasets, it was possible to increase the spatial resolution to 370 m. The detailed accuracy assessment proves the feasibility of the applied methods to remove data gaps caused by clouds and polar darkness.
Sonia Dupuis, Frank-Michael Göttsche, and Stefan Wunderle
The Cryosphere, 18, 6027–6059, https://doi.org/10.5194/tc-18-6027-2024, https://doi.org/10.5194/tc-18-6027-2024, 2024
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The Arctic has experienced pronounced warming the last few decades. This warming threatens ecosystems, vegetation dynamics, snow cover duration, and permafrost. Traditional monitoring methods like stations and climate models lack the detail needed. Land surface temperature (LST) data derived from satellites offer high spatial and temporal coverage, perfect for studying changes in the Arctic. In particular, LST information from AVHRR provides a 40-year record, valuable for analysing trends.
Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dunmire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall
The Cryosphere, 18, 5407–5430, https://doi.org/10.5194/tc-18-5407-2024, https://doi.org/10.5194/tc-18-5407-2024, 2024
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This study uses radar imagery from the Sentinel-1 satellite to derive snow depth from increases in the returning energy. These retrieved depths are then compared to nine lidar-derived snow depths across the western United State to assess the ability of this technique to be used to monitor global snow distributions. We also qualitatively compare the changes in underlying Sentinel-1 amplitudes against both the total lidar snow depths and nine automated snow monitoring stations.
Alexander Störmer, Timo Kumpula, Miguel Villoslada, Pasi Korpelainen, Henning Schumacher, and Benjamin Burkhard
EGUsphere, https://doi.org/10.5194/egusphere-2024-2862, https://doi.org/10.5194/egusphere-2024-2862, 2024
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Snow has a major impact on palsa development, yet understanding its distribution at small scale remains limited. We used LiDAR UAS and ground truth data in combination with machine learning to model snow distribution at three palsa sites. We identified extremes in snow depth corresponding to palsa topography, providing insights into the influence of snow distribution on their formation. The results demonstrate the applicability of machine learning for modeling snow distribution at a small scale.
James W. Dillon, Christopher P. Donahue, Evan N. Schehrer, and Kevin D. Hammonds
EGUsphere, https://doi.org/10.5194/egusphere-2024-3141, https://doi.org/10.5194/egusphere-2024-3141, 2024
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The optical grain size of snow controls albedo, playing a key role in Earth's energy balance. This parameter varies substantially in time and space, and thus accurate estimates are vital. Reflectance measurements can be used to map grain size, although results differ considerably depending on the algorithm and model used during retrieval. We perform a novel laboratory comparison to determine the optimal model, shape parameters, and retrieval algorithm for accurately estimating grain size.
Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn
The Cryosphere, 18, 5015–5029, https://doi.org/10.5194/tc-18-5015-2024, https://doi.org/10.5194/tc-18-5015-2024, 2024
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Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones is often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
Julien Meloche, Nicolas R. Leroux, Benoit Montpetit, Vincent Vionnet, and Chris Derksen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3169, https://doi.org/10.5194/egusphere-2024-3169, 2024
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Measuring the snow mass from radar measurements is possible with information on the snow and a radar model to link the measurements to snow. A key variable in a retrieval is the number of snow layers, with more layer yielding richer information but at increased computational cost. Here, we show the capabilities of a new method to simplify a complex snowpack, while preserving the scattering behavior of the snowpack and conserving the mass.
Filippo Emilio Scarsi, Alessandro Battaglia, Maximilian Maahn, and Stef Lhermitte
EGUsphere, https://doi.org/10.5194/egusphere-2024-1917, https://doi.org/10.5194/egusphere-2024-1917, 2024
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Snowfall measurements at high latitudes are crucial for estimating ice sheet mass balance. Spaceborne radar and radiometer missions help estimate snowfall but face uncertainties. This work evaluates uncertainties in snowfall estimates from a fixed near-nadir radar (CloudSat-like) and a conically scanning radar (WIVERN-like), determining that WIVERN will provide much better estimates than CloudSat, and at much smaller spatial and temporal scales.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
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Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
Benoit Montpetit, Joshua King, Julien Meloche, Chris Derksen, Paul Siqueira, J. Max Adam, Peter Toose, Mike Brady, Anna Wendleder, Vincent Vionnet, and Nicolas R. Leroux
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 2024
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This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
Randall Bonnell, Daniel McGrath, Jack Tarricone, Hans-Peter Marshall, Ella Bump, Caroline Duncan, Stephanie Kampf, Yunling Lou, Alex Olsen-Mikitowicz, Megan Sears, Keith Williams, Lucas Zeller, and Yang Zheng
The Cryosphere, 18, 3765–3785, https://doi.org/10.5194/tc-18-3765-2024, https://doi.org/10.5194/tc-18-3765-2024, 2024
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Snow provides water for billions of people, but the amount of snow is difficult to detect remotely. During the 2020 and 2021 winters, a radar was flown over mountains in Colorado, USA, to measure the amount of snow on the ground, while our team collected ground observations to test the radar technique’s capabilities. The technique yielded accurate measurements of the snowpack that had good correlation with ground measurements, making it a promising application for the upcoming NISAR satellite.
Jordan N. Herbert, Mark S. Raleigh, and Eric E. Small
The Cryosphere, 18, 3495–3512, https://doi.org/10.5194/tc-18-3495-2024, https://doi.org/10.5194/tc-18-3495-2024, 2024
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Automated stations measure snow properties at a single point but are frequently used to validate data that represent much larger areas. We use lidar snow depth data to see how often the mean snow depth surrounding a snow station is within 10 cm of the snow station depth at different scales. We found snow stations overrepresent the area-mean snow depth in ~ 50 % of cases, but the direction of bias at a site is temporally consistent, suggesting a site could be calibrated to the surrounding area.
Orie Sasaki, Evan Stewart Miles, Francesca Pellicciotti, Akiko Sakai, and Koji Fujita
EGUsphere, https://doi.org/10.5194/egusphere-2024-2026, https://doi.org/10.5194/egusphere-2024-2026, 2024
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This study proposes a new method to detect snowline altitude (SLA) using the Google Earth Engine platform with high-resolution satellite imagery, applicable anywhere in the world. Applying this method to five glaciated watersheds in the Himalayas reveals regional consistencies and differences in snow dynamics. We also investigate the primary controls of these dynamics by analyzing climatic factors and topographic characteristics.
Isis Brangers, Hans-Peter Marshall, Gabrielle De Lannoy, Devon Dunmire, Christian Mätzler, and Hans Lievens
The Cryosphere, 18, 3177–3193, https://doi.org/10.5194/tc-18-3177-2024, https://doi.org/10.5194/tc-18-3177-2024, 2024
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To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains. The reflections were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The results demonstrate that C-band radar is sensitive to seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy and snow wetness may complicate satellite-based snow depth retrievals.
James Dillon, Christopher Donahue, Evan Schehrer, Karl Birkeland, and Kevin Hammonds
The Cryosphere, 18, 2557–2582, https://doi.org/10.5194/tc-18-2557-2024, https://doi.org/10.5194/tc-18-2557-2024, 2024
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Surface hoar crystals are snow grains that form when vapor deposits on a snow surface. They create a weak layer in the snowpack that can cause large avalanches to occur. Thus, determining when and where surface hoar forms is a lifesaving matter. Here, we developed a means of mapping surface hoar using remote-sensing technologies. We found that surface hoar displayed heightened texture, hence the variability of brightness. Using this, we created surface hoar maps with an accuracy upwards of 95 %.
Adriano Lemos and Aku Riihelä
EGUsphere, https://doi.org/10.5194/egusphere-2024-869, https://doi.org/10.5194/egusphere-2024-869, 2024
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Here we used satellite imagery to measure snow depth in northern Finland and compared to on-site weather stations from 2019–2022. We correlated snow depths and vegetation coverage, and found thicker snow over non-vegetated areas and frozen water bodies due to the satellite's sensitivity. Our estimates showed underestimated results of snow depth and need further investigation, but they highlight the potential in monitoring seasonal snow changes, particularly where direct measurements are lacking.
Steven J. Pestana, C. Chris Chickadel, and Jessica D. Lundquist
The Cryosphere, 18, 2257–2276, https://doi.org/10.5194/tc-18-2257-2024, https://doi.org/10.5194/tc-18-2257-2024, 2024
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We compared infrared images taken by GOES-R satellites of an area with snow and forests against surface temperature measurements taken on the ground, from an aircraft, and by another satellite. We found that GOES-R measured warmer temperatures than the other measurements, especially in areas with more forest and when the Sun was behind the satellite. From this work, we learned that the position of the Sun and surface features such as trees that can cast shadows impact GOES-R infrared images.
Jiahui Xu, Yao Tang, Linxin Dong, Shujie Wang, Bailang Yu, Jianping Wu, Zhaojun Zheng, and Yan Huang
The Cryosphere, 18, 1817–1834, https://doi.org/10.5194/tc-18-1817-2024, https://doi.org/10.5194/tc-18-1817-2024, 2024
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Understanding snow phenology (SP) and its possible feedback are important. We reveal spatiotemporal heterogeneous SP on the Tibetan Plateau (TP) and the mediating effects from meteorological, topographic, and environmental factors on it. The direct effects of meteorology on SP are much greater than the indirect effects. Topography indirectly effects SP, while vegetation directly effects SP. This study contributes to understanding past global warming and predicting future trends on the TP.
Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi
The Cryosphere, 18, 1561–1578, https://doi.org/10.5194/tc-18-1561-2024, https://doi.org/10.5194/tc-18-1561-2024, 2024
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We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
Siddharth Singh, Michael Durand, Edward Kim, and Ana P. Barros
The Cryosphere, 18, 747–773, https://doi.org/10.5194/tc-18-747-2024, https://doi.org/10.5194/tc-18-747-2024, 2024
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Seasonal snowfall accumulation plays a critical role in climate. The water stored in it is measured by the snow water equivalent (SWE), the amount of water released after completely melting. We demonstrate a Bayesian physical–statistical framework to estimate SWE from airborne X- and Ku-band synthetic aperture radar backscatter measurements constrained by physical snow hydrology and radar models. We explored spatial resolutions and vertical structures that agree well with ground observations.
Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, https://doi.org/10.5194/tc-18-559-2024, 2024
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The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
Dhiraj Kumar Singh, Srinivasarao Tanniru, Kamal Kant Singh, Harendra Singh Negi, and RAAJ Ramsankaran
The Cryosphere, 18, 451–474, https://doi.org/10.5194/tc-18-451-2024, https://doi.org/10.5194/tc-18-451-2024, 2024
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In situ techniques for snow depth (SD) measurement are not adequate to represent the spatiotemporal variability in SD in the Western Himalayan region. Therefore, this study focuses on the high-resolution mapping of daily snow depth in the Indian Western Himalayan region using passive microwave remote-sensing-based algorithms. Overall, the proposed multifactor SD models demonstrated substantial improvement compared to the operational products. However, there is a scope for further improvement.
Michael Durand, Joel T. Johnson, Jack Dechow, Leung Tsang, Firoz Borah, and Edward J. Kim
The Cryosphere, 18, 139–152, https://doi.org/10.5194/tc-18-139-2024, https://doi.org/10.5194/tc-18-139-2024, 2024
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Seasonal snow accumulates each winter, storing water to release later in the year and modulating both water and energy cycles, but the amount of seasonal snow is one of the most poorly measured components of the global water cycle. Satellite concepts to monitor snow accumulation have been proposed but not selected. This paper shows that snow accumulation can be measured using radar, and that (contrary to previous studies) does not require highly accurate information about snow microstructure.
Jennika Hammar, Inge Grünberg, Steven V. Kokelj, Jurjen van der Sluijs, and Julia Boike
The Cryosphere, 17, 5357–5372, https://doi.org/10.5194/tc-17-5357-2023, https://doi.org/10.5194/tc-17-5357-2023, 2023
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Roads on permafrost have significant environmental effects. This study assessed the Inuvik to Tuktoyaktuk Highway (ITH) in Canada and its impact on snow accumulation, albedo and snowmelt timing. Our findings revealed that snow accumulation increased by up to 36 m from the road, 12-day earlier snowmelt within 100 m due to reduced albedo, and altered snowmelt patterns in seemingly undisturbed areas. Remote sensing aids in understanding road impacts on permafrost.
Anssi Rauhala, Leo-Juhani Meriö, Anton Kuzmin, Pasi Korpelainen, Pertti Ala-aho, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4343–4362, https://doi.org/10.5194/tc-17-4343-2023, https://doi.org/10.5194/tc-17-4343-2023, 2023
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Snow conditions in the Northern Hemisphere are rapidly changing, and information on snow depth is important for decision-making. We present snow depth measurements using different drones throughout the winter at a subarctic site. Generally, all drones produced good estimates of snow depth in open areas. However, differences were observed in the accuracies produced by the different drones, and a reduction in accuracy was observed when moving from an open mire area to forest-covered areas.
Leo-Juhani Meriö, Anssi Rauhala, Pertti Ala-aho, Anton Kuzmin, Pasi Korpelainen, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4363–4380, https://doi.org/10.5194/tc-17-4363-2023, https://doi.org/10.5194/tc-17-4363-2023, 2023
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Information on seasonal snow cover is essential in understanding snow processes and operational forecasting. We study the spatiotemporal variability in snow depth and snow processes in a subarctic, boreal landscape using drones. We identified multiple theoretically known snow processes and interactions between snow and vegetation. The results highlight the applicability of the drones to be used for a detailed study of snow depth in multiple land cover types and snow–vegetation interactions.
Kirsty Wivell, Stuart Fox, Melody Sandells, Chawn Harlow, Richard Essery, and Nick Rutter
The Cryosphere, 17, 4325–4341, https://doi.org/10.5194/tc-17-4325-2023, https://doi.org/10.5194/tc-17-4325-2023, 2023
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Satellite microwave observations improve weather forecasts, but to use these observations in the Arctic, snow emission must be known. This study uses airborne and in situ snow observations to validate emissivity simulations for two- and three-layer snowpacks at key frequencies for weather prediction. We assess the impact of thickness, grain size and density in key snow layers, which will help inform development of physical snow models that provide snow profile input to emissivity simulations.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
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As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
César Deschamps-Berger, Simon Gascoin, David Shean, Hannah Besso, Ambroise Guiot, and Juan Ignacio López-Moreno
The Cryosphere, 17, 2779–2792, https://doi.org/10.5194/tc-17-2779-2023, https://doi.org/10.5194/tc-17-2779-2023, 2023
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The estimation of the snow depth in mountains is hard, despite the importance of the snowpack for human societies and ecosystems. We measured the snow depth in mountains by comparing the elevation of points measured with snow from the high-precision altimetric satellite ICESat-2 to the elevation without snow from various sources. Snow depths derived only from ICESat-2 were too sparse, but using external airborne/satellite products results in spatially richer and sufficiently precise snow depths.
Edward H. Bair, Jeff Dozier, Karl Rittger, Timbo Stillinger, William Kleiber, and Robert E. Davis
The Cryosphere, 17, 2629–2643, https://doi.org/10.5194/tc-17-2629-2023, https://doi.org/10.5194/tc-17-2629-2023, 2023
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To test the title question, three snow cover products were used in a snow model. Contrary to previous work, higher-spatial-resolution snow cover products only improved the model accuracy marginally. Conclusions are as follows: (1) snow cover and albedo from moderate-resolution sensors continue to provide accurate forcings and (2) finer spatial and temporal resolutions are the future for Earth observations, but existing moderate-resolution sensors still offer value.
Valentina Premier, Carlo Marin, Giacomo Bertoldi, Riccardo Barella, Claudia Notarnicola, and Lorenzo Bruzzone
The Cryosphere, 17, 2387–2407, https://doi.org/10.5194/tc-17-2387-2023, https://doi.org/10.5194/tc-17-2387-2023, 2023
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The large amount of information regularly acquired by satellites can provide important information about SWE. We explore the use of multi-source satellite data, in situ observations, and a degree-day model to reconstruct daily SWE at 25 m. The results show spatial patterns that are consistent with the topographical features as well as with a reference product. Being able to also reproduce interannual variability, the method has great potential for hydrological and ecological applications.
Jack Tarricone, Ryan W. Webb, Hans-Peter Marshall, Anne W. Nolin, and Franz J. Meyer
The Cryosphere, 17, 1997–2019, https://doi.org/10.5194/tc-17-1997-2023, https://doi.org/10.5194/tc-17-1997-2023, 2023
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Mountain snowmelt provides water for billions of people across the globe. Despite its importance, we cannot currently measure the amount of water in mountain snowpacks from satellites. In this research, we test the ability of an experimental snow remote sensing technique from an airplane in preparation for the same sensor being launched on a future NASA satellite. We found that the method worked better than expected for estimating important snowpack properties.
Sara E. Darychuk, Joseph M. Shea, Brian Menounos, Anna Chesnokova, Georg Jost, and Frank Weber
The Cryosphere, 17, 1457–1473, https://doi.org/10.5194/tc-17-1457-2023, https://doi.org/10.5194/tc-17-1457-2023, 2023
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We use synthetic-aperture radar (SAR) and optical observations to map snowmelt timing and duration on the watershed scale. We found that Sentinel-1 SAR time series can be used to approximate snowmelt onset over diverse terrain and land cover types, and we present a low-cost workflow for SAR processing over large, mountainous regions. Our approach provides spatially distributed observations of the snowpack necessary for model calibration and can be used to monitor snowmelt in ungauged basins.
Vasana Dharmadasa, Christophe Kinnard, and Michel Baraër
The Cryosphere, 17, 1225–1246, https://doi.org/10.5194/tc-17-1225-2023, https://doi.org/10.5194/tc-17-1225-2023, 2023
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This study highlights the successful usage of UAV lidar to monitor small-scale snow depth distribution. Our results show that underlying topography and wind redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. This emphasizes the importance of including and better representing these processes in physically based models for accurate snowpack estimates.
Ruben Urraca and Nadine Gobron
The Cryosphere, 17, 1023–1052, https://doi.org/10.5194/tc-17-1023-2023, https://doi.org/10.5194/tc-17-1023-2023, 2023
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We evaluate the fitness of some of the longest satellite (NOAA CDR, 1966–2020) and reanalysis (ERA5, 1950–2020; ERA5-Land, 1950–2020) products currently available to monitor the Northern Hemisphere snow cover trends using 527 stations as the reference. We found different artificial trends and stepwise discontinuities in all the products that hinder the accurate monitoring of snow trends, at least without bias correction. The study also provides updates on the snow cover trends during 1950–2020.
Annett Bartsch, Helena Bergstedt, Georg Pointner, Xaver Muri, Kimmo Rautiainen, Leena Leppänen, Kyle Joly, Aleksandr Sokolov, Pavel Orekhov, Dorothee Ehrich, and Eeva Mariatta Soininen
The Cryosphere, 17, 889–915, https://doi.org/10.5194/tc-17-889-2023, https://doi.org/10.5194/tc-17-889-2023, 2023
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Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. In extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record. Retrieval is most robust in the tundra biome, where records can be used to identify extremes and the results can be applied to impact studies at regional scale.
Pinja Venäläinen, Kari Luojus, Colleen Mortimer, Juha Lemmetyinen, Jouni Pulliainen, Matias Takala, Mikko Moisander, and Lina Zschenderlein
The Cryosphere, 17, 719–736, https://doi.org/10.5194/tc-17-719-2023, https://doi.org/10.5194/tc-17-719-2023, 2023
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Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.
Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung
The Cryosphere, 17, 673–697, https://doi.org/10.5194/tc-17-673-2023, https://doi.org/10.5194/tc-17-673-2023, 2023
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We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.
Timbo Stillinger, Karl Rittger, Mark S. Raleigh, Alex Michell, Robert E. Davis, and Edward H. Bair
The Cryosphere, 17, 567–590, https://doi.org/10.5194/tc-17-567-2023, https://doi.org/10.5194/tc-17-567-2023, 2023
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Understanding global snow cover is critical for comprehending climate change and its impacts on the lives of billions of people. Satellites are the best way to monitor global snow cover, yet snow varies at a finer spatial resolution than most satellite images. We assessed subpixel snow mapping methods across a spectrum of conditions using airborne lidar. Spectral-unmixing methods outperformed older operational methods and are ready to to advance snow cover mapping at the global scale.
Jilu Li, Fernando Rodriguez-Morales, Xavier Fettweis, Oluwanisola Ibikunle, Carl Leuschen, John Paden, Daniel Gomez-Garcia, and Emily Arnold
The Cryosphere, 17, 175–193, https://doi.org/10.5194/tc-17-175-2023, https://doi.org/10.5194/tc-17-175-2023, 2023
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Alaskan glaciers' loss of ice mass contributes significantly to ocean surface rise. It is important to know how deeply and how much snow accumulates on these glaciers to comprehend and analyze the glacial mass loss process. We reported the observed seasonal snow depth distribution from our radar data taken in Alaska in 2018 and 2021, developed a method to estimate the annual snow accumulation rate at Mt. Wrangell caldera, and identified transition zones from wet-snow zones to ablation zones.
Ghislain Picard, Henning Löwe, and Christian Mätzler
The Cryosphere, 16, 3861–3866, https://doi.org/10.5194/tc-16-3861-2022, https://doi.org/10.5194/tc-16-3861-2022, 2022
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Microwave satellite observations used to monitor the cryosphere require radiative transfer models for their interpretation. These models represent how microwaves are scattered by snow and ice. However no existing theory is suitable for all types of snow and ice found on Earth. We adapted a recently published generic scattering theory to snow and show how it may improve the representation of snows with intermediate densities (~500 kg/m3) and/or with coarse grains at high microwave frequencies.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
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Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Elisabeth D. Hafner, Patrick Barton, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 16, 3517–3530, https://doi.org/10.5194/tc-16-3517-2022, https://doi.org/10.5194/tc-16-3517-2022, 2022
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Knowing where avalanches occur is very important information for several disciplines, for example avalanche warning, hazard zonation and risk management. Satellite imagery can provide such data systematically over large regions. In our work we propose a machine learning model to automate the time-consuming manual mapping. Additionally, we investigate expert agreement for manual avalanche mapping, showing that our network is equally as good as the experts in identifying avalanches.
Joëlle Voglimacci-Stephanopoli, Anna Wendleder, Hugues Lantuit, Alexandre Langlois, Samuel Stettner, Andreas Schmitt, Jean-Pierre Dedieu, Achim Roth, and Alain Royer
The Cryosphere, 16, 2163–2181, https://doi.org/10.5194/tc-16-2163-2022, https://doi.org/10.5194/tc-16-2163-2022, 2022
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Changes in the state of the snowpack in the context of observed global warming must be considered to improve our understanding of the processes within the cryosphere. This study aims to characterize an arctic snowpack using the TerraSAR-X satellite. Using a high-spatial-resolution vegetation classification, we were able to quantify the variability in snow depth, as well as the topographic soil wetness index, which provided a better understanding of the electromagnetic wave–ground interaction.
Jayson Eppler, Bernhard Rabus, and Peter Morse
The Cryosphere, 16, 1497–1521, https://doi.org/10.5194/tc-16-1497-2022, https://doi.org/10.5194/tc-16-1497-2022, 2022
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We introduce a new method for mapping changes in the snow water equivalent (SWE) of dry snow based on differences between time-repeated synthetic aperture radar (SAR) images. It correlates phase differences with variations in the topographic slope which allows the method to work without any "reference" targets within the imaged area and without having to numerically unwrap the spatial phase maps. This overcomes the key challenges faced in using SAR interferometry for SWE change mapping.
Sebastian Buchelt, Kirstine Skov, Kerstin Krøier Rasmussen, and Tobias Ullmann
The Cryosphere, 16, 625–646, https://doi.org/10.5194/tc-16-625-2022, https://doi.org/10.5194/tc-16-625-2022, 2022
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In this paper, we present a threshold and a derivative approach using Sentinel-1 synthetic aperture radar time series to capture the small-scale heterogeneity of snow cover (SC) and snowmelt. Thereby, we can identify start of runoff and end of SC as well as perennial snow and SC extent during melt with high spatiotemporal resolution. Hence, our approach could support monitoring of distribution patterns and hydrological cascading effects of SC from the catchment scale to pan-Arctic observations.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
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Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Cited articles
Aoki, T., Fukabori, M., Hachikubo, A., Tachibana, Y., and Nishio, F.: Effects of snow physical parameters on spectral albedo and bidirectional reflectance of snow surface, J. Geophys. Res.-Atmos., 105, 10219–10236, https://doi.org/10.1029/2003JD003506, 2000. a
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, 2021. a
Bair, E. H., Roberts, D. A., Thompson, D. R., Brodrick, P. G., Wilder, B. A., Bohn, N., Crawford, C. J., Carmon, N., Vuyovich, C. M., and Dozier, J.: Brief communication: Not as dirty as they look, flawed airborne and satellite snow spectra, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-1681, 2024. a, b
Balkanski, Y., Schulz, M., Claquin, T., and Guibert, S.: Reevaluation of Mineral aerosol radiative forcings suggests a better agreement with satellite and AERONET data, Atmos. Chem. Phys., 7, 81–95, https://doi.org/10.5194/acp-7-81-2007, 2007. a
Berk, A. and Hawes, F.: Validation of MODTRAN6 and its line-by-line algorithm, J. Quant. Spectrosc. Ra., 203, 542–556, https://doi.org/10.1016/j.jqsrt.2017.03.004, 2017. a
Bohn, N., Painter, T. H., Thompson, D. R., Carmon, N., Susiluoto, J., Turmon, M. J., Helmlinger, M. C., Green, R. O., Cook, J. M., and Guanter, L.: Optimal estimation of snow and ice surface parameters from imaging spectroscopy measurements, Remote Sens. Environ., 264, 112613, https://doi.org/10.1016/j.rse.2021.112613, 2021. a, b, c, d, e
Bohn, N., Mauro, B. D., Colombo, R., Thompson, D. R., Susiluoto, J., Carmon, N., Turmon, M. J., and Guanter, L.: Glacier ice surface properties in South-West Greenland ice sheet: first estimates from PRISMA imaging spectroscopy data, J. Geophys. Res.-Biogeo., 127, e2021JG006718, https://doi.org/10.1029/2021JG006718, 2022. a, b, c, d, e
Bohn, N., Bair, E. H., Brodrick, P. G., Carmon, N., Green, R. O., Painter, T. H., and Thompson, D. R.: Estimating dust on snow – application of a coupled atmosphere-surface model to spaceborne emit imaging spectrometer data, in: IGARSS 2023–2023 IEEE International Geoscience and Remote Sensing Symposium, 16–21 July 2023, Pasadena, CA, USA, 685–688, https://doi.org/10.1109/IGARSS52108.2023.10283084, 2023. a, b, c, d, e, f
Brodrick, P. G., Thompson, D. R., Bohn, N., Carmon, N., Eckert, R.,Montgomery, J., Wurster, K., Vaughn, N., and Greenberg, E.: isofit (v3.4.0), Zenodo [code], https://doi.org/10.5281/zenodo.14907697, 2025. a
Carmon, N., Berk, A., Bohn, N., Brodrick, P. G., Kalashnikova, O., Nguyen, H., Thompson, D. R., and Turmon, M.: Unified topographic and atmospheric correction for remote imaging spectroscopy, Frontiers in Remote Sensing, 3, 916155, https://doi.org/10.3389/frsen.2022.916155, 2022. a, b, c
Carmon, N., Berk, A., Bohn, N., Brodrick, P. G., Dozier, J., Johnson, M., Miller, C. E., Thompson, D. R., Turmon, M., Bachmann, C. M., Green, R. O., Eckert, R., Liggett, E., Nguyen, H., Ochoa, F., Okin, G. S., Samuels, R., Schimel, D., Song, J. J., and Susiluoto, J.: Shape from spectra, Remote Sens. Environ., 288, 113497, https://doi.org/10.1016/j.rse.2023.113497, 2023. a, b, c, d, e
Cawse-Nicholson, K., Townsend, P. A., Schimel, D., Assiri, A. M., Blake, P. L., Buongiorno, M. F., Campbell, P., Carmon, N., Casey, K. A., Correa-Pabón, R. E., Dahlin, K. M., Dashti, H., Dennison, P. E., Dierssen, H., Erickson, A., Fisher, J. B., Frouin, R., Gatebe, C. K., Gholizadeh, H., Gierach, M., Glenn, N. F., Goodman, J. A., Griffith, D. M., Guild, L., Hakkenberg, C. R., Hochberg, E. J., Holmes, T. R., Hu, C., Huemmrich, G. H. K. F., Kudela, R. M., Kokaly, R. F., Lee, C. M., Martin, R., Miller, C. E., Moses, W. J., Muller-Karger, F. E., Ortiz, J. D., Otis, D. B., Pahlevan, N., Painter, T. H., Pavlick, R., Poulter, B., Qi, Y., Realmuto, V. J., Roberts, D., Schaepman, M. E., Schneider, F. D., Schwandner, F. M., Serbin, S. P., Shiklomanov, A. N., Stavros, N., Thompson, D. R., Torres-Perez, J. L., Turpie, K. R., Tzortziou, M., Ustin, S., Yu, Q., Yusup, Y., Zhang, Q., and the SBG Algorithms Working Group: NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms, Remote Sens. Environ., 257, 112349, https://doi.org/10.1016/j.rse.2021.112349, 2021. a
Chevrollier, L.-A., Cook, J. M., Halbach, L., Jakobsen, H., Benning, L. G., Anesio, A. M., and Tranter, M.: Light absorption and albedo reduction by pigmented microalgae on snow and ice, J. Glaciol., 69, 1–9, https://doi.org/10.1016/j.rse.2023.113497, 2022. a, b, c
Clarke, A. D. and Noone, K. J.: Soot in the Artic snowpack: A cause for perturbations in radiative transfer, Atmos. Environ., 19, 2045–2053, https://doi.org/10.1016/0004-6981(85)90113-1, 1985. a
Connelly, D. S., Thompson, D. R., Mahowald, N. M., Li, L., Carmon, N., Okin, G. S., and Green, R. O.: The EMIT mission information yield for mineral dust radiative forcing, Remote Sens. Environ., 258, 112380, https://doi.org/10.1017/jog.2022.64, 2021. a, b
Cook, J. M., Hodson, A. J., Gardner, A. S., Flanner, M., Tedstone, A. J., Williamson, C., Irvine-Fynn, T. D. L., Nilsson, J., Bryant, R., and Tranter, M.: Quantifying bioalbedo: a new physically based model and discussion of empirical methods for characterising biological influence on ice and snow albedo, The Cryosphere, 11, 2611–2632, https://doi.org/10.5194/tc-11-2611-2017, 2017. a
Cook, J. M., Tedstone, A. J., Williamson, C., McCutcheon, J., Hodson, A. J., Dayal, A., Skiles, M., Hofer, S., Bryant, R., McAree, O., McGonigle, A., Ryan, J., Anesio, A. M., Irvine-Fynn, T. D. L., Hubbard, A., Hanna, E., Flanner, M., Mayanna, S., Benning, L. G., van As, D., Yallop, M., McQuaid, J. B., Gribbin, T., and Tranter, M.: Glacier algae accelerate melt rates on the south-western Greenland Ice Sheet, The Cryosphere, 14, 309–330, https://doi.org/10.5194/tc-14-309-2020, 2020. a, b
Cordero, R. R., Sepúlveda, E., Feron, S., Wang, C., Damiani, A., Fernandoy, F., Neshyba, S., Rowe, P. M., Asencio, V., Carrasco, J., Alfonso, J. A., MacDonell, S., Seckmeyer, G., Carrera, J. M., Jorquera, J., Llanillo, P., Dana, J., Khan, A. L., and Casassa, G.: Black carbon in the Southern Andean snowpack, Environ. Res. Lett., 17, 044042, https://doi.org/10.1088/1748-9326/ac5df0, 2022. a
Cressie, N.: Mission CO2ntrol: a statistical scientist's role in remote sensing of atmospheric carbon dioxide, J. Am. Stat. Assoc., 113, 152–168, https://doi.org/10.1080/01621459.2017.1419136, 2018. a
Dang, C., Fu, Q., and Warren, S. G.: Effect of snow grain shape on snow albedo, J. Atmos. Sci., 73, 3573–3583, https://doi.org/10.1175/JAS-D-15-0276.1, 2016. a
Demasy, C., Boye, M., Lai, B., Burckel, P., Feng, Y., Losno, R., Borensztajn, S., and Besson, P.: Iron dissolution from Patagonian dust in the Southern Ocean: under present and future conditions, Frontiers in Marine Science, 11, 1363088, https://doi.org/10.3389/fmars.2024.1363088, 2024. a
Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., Journet, E., Nowak, S., Andreae, M. O., Kandler, K., Saeed, T., Piketh, S., Seibert, D., Williams, E., and Doussin, J.-F.: Complex refractive indices and single-scattering albedo of global dust aerosols in the shortwave spectrum and relationship to size and iron content, Atmos. Chem. Phys., 19, 15503–15531, https://doi.org/10.5194/acp-19-15503-2019, 2019. a
Di Mauro, B.: A darker cryosphere in a warming world, Nat. Clim. Change, 10, 979–980, https://doi.org/10.1038/s41558-020-00911-9, 2020. a, b
Di Mauro, B., Fava, F., Ferrero, L., Garzonio, R., Baccolo, G., Delmonte, B., and Colombo, R.: Mineral dust impact on snow radiative properties in the European Alps combining ground, UAV, and satellite observations, J. Geophys. Res.-Atmos., 120, 6080–6097, https://doi.org/10.1002/2015JD023287, 2015. a, b, c
Di Mauro, B., Baccolo, G., Garzonio, R., Giardino, C., Massabò, D., Piazzalunga, A., Rossini, M., and Colombo, R.: Impact of impurities and cryoconite on the optical properties of the Morteratsch Glacier (Swiss Alps), The Cryosphere, 11, 2393–2409, https://doi.org/10.5194/tc-11-2393-2017, 2017. a
Di Mauro, B., Garzonio, R., Baccolo, G., Franzetti, A., Pittino, F., Leoni, B., Remias, D., Colombo, R., and Rossini, M.: Glacier algae foster ice-albedo feedback in the European Alps, Sci. Rep.-UK, 10, 4739, https://doi.org/10.1038/s41598-020-61762-0, 2020. a
Donahue, C. P., Menounos, B., Viner, N., Skiles, S. M., Beffort, S., Denouden, T., Arriola, S. G., White, R., and Heathfield, D.: Bridging the gap between airborne and spaceborne imaging spectroscopy for mountain glacier surface property retrievals, Remote Sens. Environ., 299, 113849, https://doi.org/10.1016/j.rse.2023.113849, 2023. a
Dozier, J.: Spectral signature of alpine snow cover from the Landsat Thematic Mapper, Remote Sens. Environ., 28, 9–22, https://doi.org/10.1016/0034-4257(89)90101-6, 1989. a, b
Dozier, J., Bair, E. H., Baskaran, L., Brodrick, P. G., Carmon, N., Kokaly, R. F., Miller, C. E., Miner, K. R., Painter, T. H., and Thompson, D. R.: Error and uncertainty degrade topographic corrections of remotely sensed data, J. Geophys. Res.-Biogeo., 127, S25–S37, https://doi.org/10.1029/2022JG007147, 2022. a
Dumont, M., Brun, E., Picard, G., Michou, M., Libois, Q., Petit, J.-R., Geyer, M., Morin, S., and Josse, B.: Contribution of light-absorbing impurities in snow to Greenland's darkening since 2009, Nat. Geosci., 7, 509–512, https://doi.org/10.1038/ngeo2180, 2014. a, b
Dumont, M., Flin, F., Malinka, A., Brissaud, O., Hagenmuller, P., Lapalus, P., Lesaffre, B., Dufour, A., Calonne, N., Rolland du Roscoat, S., and Ando, E.: Experimental and model-based investigation of the links between snow bidirectional reflectance and snow microstructure, The Cryosphere, 15, 3921–3948, https://doi.org/10.5194/tc-15-3921-2021, 2021. a
Figueroa-Villanueva, L., Castro, L., Bolaño-Ortiz, T. R., Flores, R. P., Pacheco-Ferrada, D., and Cereceda-Balic, F.: Changes in snow surface albedo and radiative forcing in the Chilean Central Andes measured by in situ and remote sensing data, Water-Sui., 15, 3198, https://doi.org/10.3390/w15183198, 2023. a
Flanner, M. G. and Zender, C. S.: Snowpack radiative heating: influence on Tibetan Plateau climate, Geophys. Res. Lett., 32, L06501, https://doi.org/10.1029/2004GL022076, 2005. a
Flanner, M. G. and Zender, C. S.: Linking snowpack microphysics and albedo evolution, J. Geophys. Res., 111, D12208, https://doi.org/10.1029/2005JD006834, 2006. a
Flanner, M. G., Zender, C. S., Randerson, J. T., and Rasch, P. J.: Present-day climate forcing and response from black carbon in snow, J. Geophys. Res., 112, D11202, https://doi.org/10.1029/2006JD008003, 2007. a, b, c
Flanner, M. G., Zender, C. S., Hess, P. G., Mahowald, N. M., Painter, T. H., Ramanathan, V., and Rasch, P. J.: Springtime warming and reduced snow cover from carbonaceous particles, Atmos. Chem. Phys., 9, 2481–2497, https://doi.org/10.5194/acp-9-2481-2009, 2009. a
Gardner, A. S. and Sharp, M. J.: A review of snow and ice albedo and the development of a new physically based broadband albedo parameterization, J. Geophys. Res., 115, F01009, https://doi.org/10.1029/2009JF001444, 2010. a
Gasso, S. and Torres, O.: Temporal characterization of dust activity in the Central Patagonia Desert (Years 1964–2017), J. Geophys. Res.-Atmos., 124, 3417–3434, https://doi.org/10.1029/2018JD030209, 2019. a
Gautam, R., Hsu, N. C., Lau, W. K.-M., and Yasunari, T. J.: Satellite observations of desert dust-induced Himalayan snow darkening, Geophys. Res. Lett., 40, 988–993, https://doi.org/10.1002/grl.50226, 2013. a
Gonçalves Ageitos, M., Obiso, V., Miller, R. L., Jorba, O., Klose, M., Dawson, M., Balkanski, Y., Perlwitz, J., Basart, S., Di Tomaso, E., Escribano, J., Macchia, F., Montané, G., Mahowald, N. M., Green, R. O., Thompson, D. R., and Pérez García-Pando, C.: Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts, Atmos. Chem. Phys., 23, 8623–8657, https://doi.org/10.5194/acp-23-8623-2023, 2023. a
Govaerts, Y. M., Wagner, S., Lattanzio, A., and Watts, P.: Joint retrieval of surface reflectance and aerosol optical depth from MSG/SEVIRI observations with an optimal estimation approach: 1. Theory, J. Geophys. Res.-Atmos., 115, D02203, https://doi.org/10.1029/2009JD011779, 2010. a
Green, R.: EMIT L1B At-Sensor Calibrated Radiance and Geolocation Data 60 m V001, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/EMIT/EMITL1BRAD.001, 2022. a
Green, R. O., Dozier, J., Roberts, D. A., and Painter, T. H.: Spectral snow-reflectance models for grain-size and liquid-water fraction in melting snow for the solar-reflected spectrum, Ann. Glaciol., 34, 71–73, https://doi.org/10.3189/172756402781817987, 2002. a
Green, R. O., Mahowald, N., Ung, C., Thompson, D. R., Bator, L., Bennet, M., Bernas, M., Blackway, N., Bradley, C., Cha, J., Clark, P., Clark, R., Cloud, D., Diaz, E., Ben Dor, E., Duren, R., Eastwood, M., Ehlmann, B. L., Fuentes, L., Ginoux, P., Gross, J., He, Y., Kalashnikova, O., Kert, W., Keymeulen, D., Klimesh, M., Ku, D., Kwong-Fu, H., Liggett, E., Li, L., Lundeen, S., Makowski, M. D., Mazer, A., Miller, R., Mouroulis, P., Oaida, B., Okin, G. S., Ortega, A., Oyake, A., Nguyen, H., Pace, T., Painter, T. H., Pempejian, J., Garcia-Pando, C. P., Pham, T., Phillips, B., Pollock, R., Purcell, R., Realmuto, V., Schoolcraft, J., Sen, A., Shin, S., Shaw, L., Soriano, M., Swayze, G., Thingvold, E., Vaid, A., and Zan, J.: The earth surface mineral dust source investigation: an earth science imaging spectroscopy mission, in: 2020 IEEE Aerospace Conference, 7–14 March 2020, Big Sky, Montana, USA, 1–15, https://doi.org/10.1109/AERO47225.2020.9172731, 2020. a, b
Grenfell, T. C. and Warren, S. G.: Representation of a nonspherical ice particle by a collection of independent spheres for scattering and absorption of radiation, J. Geophys. Res.-Atmos., 104, 31679–31709, https://doi.org/10.1029/1999JD900496, 1999. a, b, c
Grenfell, T. C., Neshyba, S. P., and Warren, S. G.: Representation of a nonspherical ice particle by a collection of independent spheres for scattering and absorption of radiation: 3. Hollow columns and plates, J. Geophys. Res.-Atmos., 110, 31697–31709, https://doi.org/10.1029/2005JD005811, 2005. a
Guanter, L., Richter, R., and Kaufmann, H.: On the application of the MODTRAN4 atmospheric radiative transfer code to optical remote sensing, Int. J. Remote Sens., 30, 1407–1424, https://doi.org/10.1080/01431160802438555, 2009. a
Hadley, O. L., Corrigan, C. E., Kirchstetter, T. W., Cliff, S. S., and Ramanathan, V.: Measured black carbon deposition on the Sierra Nevada snow pack and implication for snow pack retreat, Atmos. Chem. Phys., 10, 7505–7513, https://doi.org/10.5194/acp-10-7505-2010, 2010. a
Hao, D., Bisht, G., Rittger, K., Bair, E., He, C., Huang, H., Dang, C., Stillinger, T., Gu, Y., Wang, H., Qian, Y., and Leung, L. R.: Improving snow albedo modeling in the E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau, Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, 2023. a
He, C., Takano, Y., Liou, K.-N., Yang, P., Li, Q., and Chen, F.: Impact of snow grain shape and black carbon–snow internal mixing on snow optical properties: parameterizations for climate models, J. Climate, 30, 10019–10036, https://doi.org/10.1175/JCLI-D-17-0300.1, 2017. a
Hobbs, J., Braverman, A., Cressie, N., Granat, R., and Gunson, M.: Simulation-based uncertainty quantification for estimating atmospheric CO2 from satellite data, Int. J. Uncertain. Quan., 5, 956–985, https://doi.org/10.1137/16M1060765, 2017. a
Kaspari, S., Painter, T. H., Gysel, M., Skiles, S. M., and Schwikowski, M.: Seasonal and elevational variations of black carbon and dust in snow and ice in the Solu-Khumbu, Nepal and estimated radiative forcings, Atmos. Chem. Phys., 14, 8089–8103, https://doi.org/10.5194/acp-14-8089-2014, 2014. a
Kaspari, S., Skiles, S. M., Delaney, I., Dixon, D., and Painter, T. H.: Accelerated glacier melt on Snow Dome, Mt. Olympus, Washington, USA due to deposition of black carbon and mineral dust from wildfire, J. Geophys. Res.-Atmos., 120, 2793–2807, https://doi.org/10.1002/2014JD022676, 2015. a
Kohshima, S., Takeuchi, N., Uetake, J., Shiraiwa, T., Uemura, R., Yoshida, N., Matoba, S., and Godoi, M. A.: Estimation of net accumulation rate at a Patagonian glacier by ice core analyses using snow algae, Global Planet. Change, 59, 236–244, https://doi.org/10.1016/j.gloplacha.2006.11.014, 2007. a
Kokhanovsky, A. A. and Zege, E. P.: Scattering optics of snow, Appl. Optics, 43, 1589–1602, https://doi.org/10.1364/AO.43.001589, 2004. a
Kokhanovsky, A., Mauro, B. D., and Colombo, R.: Snow surface properties derived from PRISMA satellite data over the Nansen Ice Shelf (East Antarctica), Frontiers in Environmental Science, 10, 904585, https://doi.org/10.3389/fenvs.2022.904585, 2022. a, b
Lawrence, C. R., Painter, T. H., Landry, C. C., and Neff, J. C.: Contemporary geochemical composition and flux of aeolian dust to the San Juan Mountains, Colorado, US, J. Geophys. Res.-Biogeo., 115, G03007, https://doi.org/10.1029/2009JG001077, 2010. a, b
Lemke, P., Ren, J., Alley, R. B., Allison, I., Carrasco, J., Flato, G., Fujii, Y., Kaser, G., Mote, P., Thomas, R. H., and Zhang, T.: Observations: changes in snow, ice and frozen ground, in: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change edited by: Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller, Cambridge University Press, Cambridge, UK and New York, NY, USA, ISBN 978 0521 88009-1, 2007. a
Li, J., Okin, G. S., Skiles, S. M., and Painter, T. H.: Relating variation of dust on snow to bare soil dynamics in the western United States, Environ. Res. Lett., 8, 44–55, https://doi.org/10.1088/1748-9326/8/4/044054, 2013. a, b
Louis, J., Devignot, O., and Pessiot, L.: S2 MPC – Level-2A Algorithm Theoretical Basis Document, Tech. Rep. S2-PDGS-MPC-ATBD-L2A – 2.10, European Space Agency (ESA), Noordwijk, the Netherlands, S2-PDGS-MPC-L2A-SUM-V2.8, 2021. a
Mahowald, N., Albani, S., Kok, J. F., Engelstaeder, S., Scanza, R., Ward, D. S., and Flanner, M. G.: The size distribution of desert dust aerosols and its impact on the Earth system, Aeolian Res., 15, 53–71, https://doi.org/10.1016/j.aeolia.2013.09.002, 2014. a
Malinka, A. V.: Light scattering in porous materials: geometrical optics and stereological approach, J. Quant. Spectrosc. Ra., 141, 14–23, https://doi.org/10.1016/j.jqsrt.2014.02.022, 2014. a
Malinka, A. V.: Stereological approach to radiative transfer in porous materials. Application to the optics of snow, J. Quant. Spectrosc. Ra., 295, 108410, https://doi.org/10.1016/j.jqsrt.2022.108410, 2023. a
Martonchik, J. V., Bruegge, C. J., and Strahler, A.: A review of reflectance nomenclature used in remote sensing, Remote Sensing Reviews, 19, 9–20, https://doi.org/10.1080/02757250009532407, 2000. a
McEwan, C., Borrero, L. A., and Prieto, A.: Patagonia: Natural History, Prehistory, and Ethnography at the Uttermost End of the Earth, Princeton University Press, Princeton, NJ, USA, ISBN-13 978-0691601625, 1997. a
Miller, R. L., Tegen, I., and Perlwitz, J.: Surface radiative forcing by soil dust aerosols and the hydrologic cycle, J. Geophys. Res., 109, D04203, https://doi.org/10.1029/2003JD004085, 2004. a
Naegeli, K., Damm, A., Huss, M., Schaepman, M., and Hoelzle, M.: Imaging spectroscopy to assess the composition of ice surface materials and their impact on glacier mass balance, Remote Sens. Environ., 168, 388–402, https://doi.org/10.1016/j.rse.2015.07.006, 2015. a
National Academies of Sciences, Engineering, and Medicine: Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space, National Academies Press, Washington, DC, https://doi.org/10.17226/24938, 2018. a
Neshyba, S. P., Grenfell, T. C., and Warren, S. G.: Representation of a nonspherical ice particle by a collection of independent spheres for scattering and absorption of radiation: 2. Hexagonal columns and plates, J. Geophys. Res.-Atmos., 108, 4448, https://doi.org/10.1029/2002JD003302, 2003. a
Nicodemus, F. E., Richmond, J. C., Hsia, J. J., Ginsberg, I. W., and Limperis, T.: Geometrical Considerations and Nomenclature for Reflectance, National Bureau of Standards, US Department of Commerce, Washington, DC, USA, https://doi.org/10.6028/NBS.MONO.160, 1977. a
Oerlemans, J., Giesen, R. H., and Broeke, M. R. V. D.: Retreating alpine glaciers: increased melt rates due to accumulation of dust (Vadret da Morteratsch, Switzerland), J. Glaciol., 55, 729–736, https://doi.org/10.3189/002214309789470969, 2009. a
Painter, T. H. and Dozier, J.: The effect of anisotropic reflectance on imaging spectroscopy of snow properties, Remote Sens. Environ., 89, 409–422, https://doi.org/10.1016/j.rse.2003.09.007, 2004a. a, b, c, d
Painter, T. H. and Dozier, J.: Measurements of the hemispherical-directional reflectance of snow at fine spectral and angular resolution, J. Geophys. Res., 109, D18115, https://doi.org/10.1029/2003JD004458, 2004b. a, b, c
Painter, T. H., Duval, B., Thomas, W. H., Mendez, M., Heintzelman, S., and Dozier, J.: Detection and quantification of snow algae with an airborne imaging spectrometer, Appl. Environ. Microb., 67, 5267–5272, https://doi.org/10.1128/AEM.67.11.5267-5272.2001, 2001. a, b
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. a
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, 2012a. a, b
Painter, T. H., Skiles, S. M., Deems, J. S., Bryant, A. C., and Landry, C. C.: Dust radiative forcing in snow of the Upper Colorado River Basin: 1. A 6 year record of energy balance, radiation, and dust concentrations, Water Resour. Res., 48, W07522, https://doi.org/10.1029/2012WR011985, 2012b.. a, b, c
Painter, T. H., Flanner, M. G., Kaser, G., Marzeion, B., VanCuren, R. A., and Abdalati, W.: End of the Little Ice Age in the Alps forced by industrial black carbon, P. Natl. Acad. Sci. USA, 110, 15216–15221, https://doi.org/10.1073/pnas.1302570110, 2013a. a, b
Picard, G., Dumont, M., Lamare, M., Tuzet, F., Larue, F., Pirazzini, R., and Arnaud, L.: Spectral albedo measurements over snow-covered slopes: theory and slope effect corrections, The Cryosphere, 14, 1497–1517, https://doi.org/10.5194/tc-14-1497-2020, 2020. a, b, c, d
Polashenski, C. M., Dibb, J. E., Flanner, M. G., Chen, J. Y., Courville, Z. R., Lai, A. M., Schauer, J. J., Shafer, M. M., and Bergin, M.: Neither dust nor black carbon causing apparent albedo decline in Greenland's dry snow zone: implications for MODIS C5 surface reflectance, Geophys. Res. Lett., 42, 9319–9327, https://doi.org/10.1002/2015GL065912, 2015. a
Prospero, J. M.: Environmental characterization of global sources of atmospheric soil dust identified with the NIMBUS 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product, Rev. Geophys., 40, 9319–9327, https://doi.org/10.1029/2000RG000095, 2002. a
Rast, M., Ananasso, C., Bach, H., Dor, E. B., Chabrillat, S., Colombo, R., Bello, U. D., Feret, J.-B., Giardino, C., Green, R. O., Guanter, L., Marsh, S., Nieke, J., Ong, C., Rum, G., Schaepman, M., Schlerf, M., Skidmore, A. K., and Strobl, P.: Copernicus Hyperspectral Imaging Mission for the Environment – Mission Requirements Document, Tech. Rep. ESA-EOPSM-CHIM-MRD-3216, European Space Agency (ESA), Noordwijk, the Netherlands, ESA-EOPSM-CHIM-MRD-3216, 2019. a
Remias, D., Holzinger, A., Aigner, S., and Lütz, C.: Ecophysiology and ultrastructure of Ancylonema nordenskiöldii (Zygnematales, Streptophyta), causing brown ice on glaciers in Svalbard (high arctic), Polar Biol., 35, 899–908, https://doi.org/10.1007/s00300-011-1135-6, 2012. a
Reynolds, R. L., Goldstein, H. L., Bryant, B. M. M. A. C., Skiles, S. M., Kokaly, R. F., Flagg, C. B., Yauk, K., Berquó, T., Breit, G., Ketterer, M., Fernandez, D., Miller, M. E., and Painter, T. H.: Composition of dust deposited to snow cover in the Wasatch Range (Utah, USA): controls on radiative properties of snow cover and comparison to some dust-source sediments, Aeolian Res., 15, 73–90, https://doi.org/10.1016/j.aeolia.2013.08.001, 2014. a
Rhoades, C., Elder, K., and Greene, E.: The influence of an extensive dust event on snow chemistry in the Southern Rocky Mountains, Arct. Antarct. Alp. Res., 42, 98–105, https://doi.org/10.1657/1938-4246-42.1.98, 2010. a
Richter, R. and Schläpfer, D.: Atmospheric/Topographic Correction for Satellite Imagery (ATCOR-2/3, Version 9.1.1, February 2017) Theoretical Background Document, DLR-IB 565-01/2017, 2017. a
Rowe, P. M., Cordero, R. R., Warren, S. G., Stewart, E., Doherty, S. J., Pankow, A., Schrempf, M., Casassa, G., Carrasco, J., Pizarro, J., MacDonell, S., Damiani, A., Lambert, F., Rondanelli, R., Huneeus, N., Fernandoy, F., and Neshyba, S.: Black carbon and other light-absorbing impurities in snow in the Chilean Andes, Sci. Rep.-UK, 9, 4008, https://doi.org/10.1038/s41598-019-39312-0, 2019. a
Santini, F. and Palombo, A.: Impact of topographic correction on PRISMA, Sentinel 2, and Landsat 8 images, Remote Sens.-Basel, 14, 3903, https://doi.org/10.3390/rs14163903, 2022. a
Schepanski, K.: Transport of mineral dust and its impact on climate, Geosciences, 8, 151, https://doi.org/10.3390/geosciences8050151, 2018. a
Seidel, F. C., Rittger, K., Skiles, S. M., Molotch, N. P., and Painter, T. H.: Case study of spatial and temporal variability of snow cover, grain size, albedo and radiative forcing in the Sierra Nevada and Rocky Mountain snowpack derived from imaging spectroscopy, The Cryosphere, 10, 1229–1244, https://doi.org/10.5194/tc-10-1229-2016, 2016. a, b, c
Singh, D. and Flanner, M. G.: An improved carbon dioxide snow spectral albedo model: application to Martian conditions, J. Geophys. Res.-Planet., 121, 2037–2054, https://doi.org/10.1002/2016JE005040, 2016. a
Skiles, S. M. and Painter, T. H.: Daily evolution in dust and black carbon content, snow grain size, and snow albedo during snowmelt, Rocky Mountains, Colorado, J. Glaciol., 63, 118–132, https://doi.org/10.1017/jog.2016.125, 2017. a, b
Skiles, S. M., Flanner, M., Cook, J. M., Dumont, M., and Painter, T. H.: Radiative forcing by light-absorbing particles in snow, Nat. Clim. Change, 8, 964–971, https://doi.org/10.1038/s41558-018-0296-5, 2018. a
Stamnes, K., Tsay, S.-C., Wiscombe, W., and Jayaweera, K.: Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media, Appl. Optics, 27, 2502–2509, https://doi.org/10.1364/AO.27.002502, 1988. a
Sterle, K. M., McConnell, J. R., Dozier, J., Edwards, R., and Flanner, M. G.: Retention and radiative forcing of black carbon in eastern Sierra Nevada snow, The Cryosphere, 7, 365–374, https://doi.org/10.5194/tc-7-365-2013, 2013. a
Strub, G., Schaepman, M. E., Knyazikhin, Y., and Itten, K. I.: Evaluation of spectrodirectional Alfalfa Canopy Data acquired during DAISEX'99, IEEE T. Geosci. Remote, 41, 1034–1042, https://doi.org/10.1109/TGRS.2003.811555, 2003. a
Takeuchi, N. and Kohshima, S.: A snow algal community on Tyndall Glacier in the Southern Patagonia Icefield, Chile, Arct. Antarct. Alp. Res., 36, 92–99, https://doi.org/10.1657/1523-0430(2004)036[0092:ASACOT]2.0.CO;2, 2004. a
Thompson, D. R., Green, R. O., Bradley, C., Brodrick, P. G., Mahowald, N., Dor, E. B., Bennett, M., Bernas, M., Carmon, N., Chadwick, K. D., Clark, R. N., Coleman, R. W., Cox, E., Diaz, E., Eastwood, M. L., Eckert, R., Ehlmann, B. L., Ginoux, P., Ageitos, M. G., Grant, K., Guanter, L., Pearlshtien, D. H., Helmlinger, M., Herzog, H., Hoefen, T., Huang, Y., Keebler, A., Kalashnikova, O., Keymeulen, D., Kokaly, R., Klose, M., Li, L., Lundeen, S. R., Meyer, J., Middleton, E., Miller, R. L., Mouroulis, P., Oaida, B., Obiso, V., Ochoa, F., Olson-Duvall, W., Okin, G. S., Painter, T. H., García-Pando, C. P., Pollock, R., Realmuto, V., Shaw, L., Sullivan, P., Swayze, G., Thingvold, E., Thorpe, A. K., Vannan, S., Villarreal, C., Ung, C., Wilson, D. W., and Zandbergen, S.: On-orbit calibration and performance of the EMIT imaging spectrometer, Remote Sens. Environ., 303, 113986, https://doi.org/10.1016/j.rse.2023.113986, 2024. a, b
Verhoef, W. and Bach, H.: Coupled soil-leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data, Remote Sens. Environ., 109, 166–182, https://doi.org/10.1016/j.rse.2006.12.013, 2007. a
Vermote, E. F., Tanré, D., Deuzé, J. L., Herman, M., and Morcrette, J. J.: Second simulation of the satellite signal in the solar spectrum, 6S: an overview, IEEE T. Geosci. Remote, 35, 675–686, https://doi.org/10.1109/36.581987, 1997. a
Warren, S. G.: Optical properties of snow, Rev. Geophys., 20, 67–89, https://doi.org/10.1029/RG020i001p00067, 1982. a, b
Warren, S. G.: Can black carbon in snow be detected by remote sensing?, J. Geophys. Res.-Atmos., 118, 779–786, https://doi.org/10.1029/2012JD018476, 2013. a
Warren, S. G.: Optical properties of snow and ice, Philos. T. Roy. Soc. A, 337, 161, https://doi.org/10.1098/rsta.2018.0161, 2019. a, b
Whicker, C. A., Flanner, M. G., Dang, C., Zender, C. S., Cook, J. M., and Gardner, A. S.: SNICAR-ADv4: a physically based radiative transfer model to represent the spectral albedo of glacier ice, The Cryosphere, 16, 1197–1220, https://doi.org/10.5194/tc-16-1197-2022, 2022. a
Wientjes, I. G. M., Van de Wal, R. S. W., Reichart, G. J., Sluijs, A., and Oerlemans, J.: Dust from the dark region in the western ablation zone of the Greenland ice sheet, The Cryosphere, 5, 589–601, https://doi.org/10.5194/tc-5-589-2011, 2011. a
Wilder, B. A., Meyer, J., Enterkine, J., and Glenn, N. F.: Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements, The Cryosphere, 18, 5015–5029, https://doi.org/10.5194/tc-18-5015-2024, 2024. a, b, c, d
Williamson, C. J., Anesio, A. M., Cook, J., Tedstone, A., Poniecka, E., Holland, A., Fagan, D., Tranter, M., and Yallop, M. L.: Ice algal bloom development on the surface of the Greenland Ice Sheet, FEMS Microbiol. Ecol., 94, fiy025, https://doi.org/10.1093/femsec/fiy025, 2018. a
Yallop, M. L., Anesio, A. M., Perkins, R. G., Cook, J. M., Telling, J., Fagan, D., MacFarlane, J., Stibal, M., Barker, G., Bellas, C., Hodson, A., Tranter, M., Wadham, J., and Roberts, N. W.: Photophysiology and albedo-changing potential of the ice algal community on the surface of the Greenland ice sheet, ISME J., 6, 2302–2313, https://doi.org/10.1038/ismej.2012.107, 2012. a
Yin, H., Tan, B., Frantz, D., and Radeloff, V. C.: Integrated topographic corrections improve forest mapping using Landsat imagery, Int. J. Appl. Earth Obs., 108, 102716, https://doi.org/10.1016/j.jag.2022.102716, 2022. a
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.
A new type of Earth-observing satellite is measuring reflected sunlight in all its colors. These...
