Articles | Volume 19, issue 3
https://doi.org/10.5194/tc-19-975-2025
© 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-19-975-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Understanding biases in ICESat-2 data due to subsurface scattering using Airborne Topographic Mapper waveform data
University of Washington Applied Physics Laboratory Polar Science Center, Seattle, WA 98122, USA
Michael Studinger
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Tyler Sutterley
University of Washington Applied Physics Laboratory Polar Science Center, Seattle, WA 98122, USA
Zachary Fair
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Thomas Neumann
Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Related authors
Allison M. Chartrand, Ian M. Howat, Ian R. Joughin, and Benjamin E. Smith
The Cryosphere, 18, 4971–4992, https://doi.org/10.5194/tc-18-4971-2024, https://doi.org/10.5194/tc-18-4971-2024, 2024
Short summary
Short summary
This study uses high-resolution remote-sensing data to show that shrinking of the West Antarctic Thwaites Glacier’s ice shelf (floating extension) is exacerbated by several sub-ice-shelf meltwater channels that form as the glacier transitions from full contact with the seafloor to fully floating. In mapping these channels, the position of the transition zone, and thinning rates of the Thwaites Glacier, this work elucidates important processes driving its rapid contribution to sea level rise.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
Short summary
Short summary
We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
Inès N. Otosaka, Andrew Shepherd, Erik R. Ivins, Nicole-Jeanne Schlegel, Charles Amory, Michiel R. van den Broeke, Martin Horwath, Ian Joughin, Michalea D. King, Gerhard Krinner, Sophie Nowicki, Anthony J. Payne, Eric Rignot, Ted Scambos, Karen M. Simon, Benjamin E. Smith, Louise S. Sørensen, Isabella Velicogna, Pippa L. Whitehouse, Geruo A, Cécile Agosta, Andreas P. Ahlstrøm, Alejandro Blazquez, William Colgan, Marcus E. Engdahl, Xavier Fettweis, Rene Forsberg, Hubert Gallée, Alex Gardner, Lin Gilbert, Noel Gourmelen, Andreas Groh, Brian C. Gunter, Christopher Harig, Veit Helm, Shfaqat Abbas Khan, Christoph Kittel, Hannes Konrad, Peter L. Langen, Benoit S. Lecavalier, Chia-Chun Liang, Bryant D. Loomis, Malcolm McMillan, Daniele Melini, Sebastian H. Mernild, Ruth Mottram, Jeremie Mouginot, Johan Nilsson, Brice Noël, Mark E. Pattle, William R. Peltier, Nadege Pie, Mònica Roca, Ingo Sasgen, Himanshu V. Save, Ki-Weon Seo, Bernd Scheuchl, Ernst J. O. Schrama, Ludwig Schröder, Sebastian B. Simonsen, Thomas Slater, Giorgio Spada, Tyler C. Sutterley, Bramha Dutt Vishwakarma, Jan Melchior van Wessem, David Wiese, Wouter van der Wal, and Bert Wouters
Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, https://doi.org/10.5194/essd-15-1597-2023, 2023
Short summary
Short summary
By measuring changes in the volume, gravitational attraction, and ice flow of Greenland and Antarctica from space, we can monitor their mass gain and loss over time. Here, we present a new record of the Earth’s polar ice sheet mass balance produced by aggregating 50 satellite-based estimates of ice sheet mass change. This new assessment shows that the ice sheets have lost (7.5 x 1012) t of ice between 1992 and 2020, contributing 21 mm to sea level rise.
Benjamin E. Smith, Brooke Medley, Xavier Fettweis, Tyler Sutterley, Patrick Alexander, David Porter, and Marco Tedesco
The Cryosphere, 17, 789–808, https://doi.org/10.5194/tc-17-789-2023, https://doi.org/10.5194/tc-17-789-2023, 2023
Short summary
Short summary
We use repeated satellite measurements of the height of the Greenland ice sheet to learn about how three computational models of snowfall, melt, and snow compaction represent actual changes in the ice sheet. We find that the models do a good job of estimating how the parts of the ice sheet near the coast have changed but that two of the models have trouble representing surface melt for the highest part of the ice sheet. This work provides suggestions for how to better model snowmelt.
Brooke Medley, Thomas A. Neumann, H. Jay Zwally, Benjamin E. Smith, and C. Max Stevens
The Cryosphere, 16, 3971–4011, https://doi.org/10.5194/tc-16-3971-2022, https://doi.org/10.5194/tc-16-3971-2022, 2022
Short summary
Short summary
Satellite altimeters measure the height or volume change over Earth's ice sheets, but in order to understand how that change translates into ice mass, we must account for various processes at the surface. Specifically, snowfall events generate large, transient increases in surface height, yet snow fall has a relatively low density, which means much of that height change is composed of air. This air signal must be removed from the observed height changes before we can assess ice mass change.
Andrew O. Hoffman, Knut Christianson, Daniel Shapero, Benjamin E. Smith, and Ian Joughin
The Cryosphere, 14, 4603–4609, https://doi.org/10.5194/tc-14-4603-2020, https://doi.org/10.5194/tc-14-4603-2020, 2020
Short summary
Short summary
The West Antarctic Ice Sheet has long been considered geometrically prone to collapse, and Thwaites Glacier, the largest glacier in the Amundsen Sea, is likely in the early stages of disintegration. Using observations of Thwaites Glacier velocity and elevation change, we show that the transport of ~2 km3 of water beneath Thwaites Glacier has only a small and transient effect on glacier speed relative to ongoing thinning driven by ocean melt.
Zachary Fair, Carrie Vuyovich, Thomas Neumann, Justin Pflug, David Shean, Ellyn M. Enderlin, Karina Zikan, Hannah Besso, Jessica Lundquist, Cesar Deschamps-Berger, and Désirée Treichler
EGUsphere, https://doi.org/10.5194/egusphere-2024-3992, https://doi.org/10.5194/egusphere-2024-3992, 2025
Short summary
Short summary
Lidar is commonly used to measure snow over global water reservoirs. However, ground-based and airborne lidar surveys are expensive, so satellite-based methods are needed. In this review, we outline the latest research using satellite-based lidar to monitor snow. Best practices for lidar-based snow monitoring are given, as is a discussion on challenges in this field of research.
Beata Csatho, Tony Schenk, and Tom Neumann
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 83–88, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-83-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-83-2024, 2024
Allison M. Chartrand, Ian M. Howat, Ian R. Joughin, and Benjamin E. Smith
The Cryosphere, 18, 4971–4992, https://doi.org/10.5194/tc-18-4971-2024, https://doi.org/10.5194/tc-18-4971-2024, 2024
Short summary
Short summary
This study uses high-resolution remote-sensing data to show that shrinking of the West Antarctic Thwaites Glacier’s ice shelf (floating extension) is exacerbated by several sub-ice-shelf meltwater channels that form as the glacier transitions from full contact with the seafloor to fully floating. In mapping these channels, the position of the transition zone, and thinning rates of the Thwaites Glacier, this work elucidates important processes driving its rapid contribution to sea level rise.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
Short summary
Short summary
We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
Alice C. Frémand, Peter Fretwell, Julien A. Bodart, Hamish D. Pritchard, Alan Aitken, Jonathan L. Bamber, Robin Bell, Cesidio Bianchi, Robert G. Bingham, Donald D. Blankenship, Gino Casassa, Ginny Catania, Knut Christianson, Howard Conway, Hugh F. J. Corr, Xiangbin Cui, Detlef Damaske, Volkmar Damm, Reinhard Drews, Graeme Eagles, Olaf Eisen, Hannes Eisermann, Fausto Ferraccioli, Elena Field, René Forsberg, Steven Franke, Shuji Fujita, Yonggyu Gim, Vikram Goel, Siva Prasad Gogineni, Jamin Greenbaum, Benjamin Hills, Richard C. A. Hindmarsh, Andrew O. Hoffman, Per Holmlund, Nicholas Holschuh, John W. Holt, Annika N. Horlings, Angelika Humbert, Robert W. Jacobel, Daniela Jansen, Adrian Jenkins, Wilfried Jokat, Tom Jordan, Edward King, Jack Kohler, William Krabill, Mette Kusk Gillespie, Kirsty Langley, Joohan Lee, German Leitchenkov, Carlton Leuschen, Bruce Luyendyk, Joseph MacGregor, Emma MacKie, Kenichi Matsuoka, Mathieu Morlighem, Jérémie Mouginot, Frank O. Nitsche, Yoshifumi Nogi, Ole A. Nost, John Paden, Frank Pattyn, Sergey V. Popov, Eric Rignot, David M. Rippin, Andrés Rivera, Jason Roberts, Neil Ross, Anotonia Ruppel, Dustin M. Schroeder, Martin J. Siegert, Andrew M. Smith, Daniel Steinhage, Michael Studinger, Bo Sun, Ignazio Tabacco, Kirsty Tinto, Stefano Urbini, David Vaughan, Brian C. Welch, Douglas S. Wilson, Duncan A. Young, and Achille Zirizzotti
Earth Syst. Sci. Data, 15, 2695–2710, https://doi.org/10.5194/essd-15-2695-2023, https://doi.org/10.5194/essd-15-2695-2023, 2023
Short summary
Short summary
This paper presents the release of over 60 years of ice thickness, bed elevation, and surface elevation data acquired over Antarctica by the international community. These data are a crucial component of the Antarctic Bedmap initiative which aims to produce a new map and datasets of Antarctic ice thickness and bed topography for the international glaciology and geophysical community.
Inès N. Otosaka, Andrew Shepherd, Erik R. Ivins, Nicole-Jeanne Schlegel, Charles Amory, Michiel R. van den Broeke, Martin Horwath, Ian Joughin, Michalea D. King, Gerhard Krinner, Sophie Nowicki, Anthony J. Payne, Eric Rignot, Ted Scambos, Karen M. Simon, Benjamin E. Smith, Louise S. Sørensen, Isabella Velicogna, Pippa L. Whitehouse, Geruo A, Cécile Agosta, Andreas P. Ahlstrøm, Alejandro Blazquez, William Colgan, Marcus E. Engdahl, Xavier Fettweis, Rene Forsberg, Hubert Gallée, Alex Gardner, Lin Gilbert, Noel Gourmelen, Andreas Groh, Brian C. Gunter, Christopher Harig, Veit Helm, Shfaqat Abbas Khan, Christoph Kittel, Hannes Konrad, Peter L. Langen, Benoit S. Lecavalier, Chia-Chun Liang, Bryant D. Loomis, Malcolm McMillan, Daniele Melini, Sebastian H. Mernild, Ruth Mottram, Jeremie Mouginot, Johan Nilsson, Brice Noël, Mark E. Pattle, William R. Peltier, Nadege Pie, Mònica Roca, Ingo Sasgen, Himanshu V. Save, Ki-Weon Seo, Bernd Scheuchl, Ernst J. O. Schrama, Ludwig Schröder, Sebastian B. Simonsen, Thomas Slater, Giorgio Spada, Tyler C. Sutterley, Bramha Dutt Vishwakarma, Jan Melchior van Wessem, David Wiese, Wouter van der Wal, and Bert Wouters
Earth Syst. Sci. Data, 15, 1597–1616, https://doi.org/10.5194/essd-15-1597-2023, https://doi.org/10.5194/essd-15-1597-2023, 2023
Short summary
Short summary
By measuring changes in the volume, gravitational attraction, and ice flow of Greenland and Antarctica from space, we can monitor their mass gain and loss over time. Here, we present a new record of the Earth’s polar ice sheet mass balance produced by aggregating 50 satellite-based estimates of ice sheet mass change. This new assessment shows that the ice sheets have lost (7.5 x 1012) t of ice between 1992 and 2020, contributing 21 mm to sea level rise.
Benjamin E. Smith, Brooke Medley, Xavier Fettweis, Tyler Sutterley, Patrick Alexander, David Porter, and Marco Tedesco
The Cryosphere, 17, 789–808, https://doi.org/10.5194/tc-17-789-2023, https://doi.org/10.5194/tc-17-789-2023, 2023
Short summary
Short summary
We use repeated satellite measurements of the height of the Greenland ice sheet to learn about how three computational models of snowfall, melt, and snow compaction represent actual changes in the ice sheet. We find that the models do a good job of estimating how the parts of the ice sheet near the coast have changed but that two of the models have trouble representing surface melt for the highest part of the ice sheet. This work provides suggestions for how to better model snowmelt.
Brooke Medley, Thomas A. Neumann, H. Jay Zwally, Benjamin E. Smith, and C. Max Stevens
The Cryosphere, 16, 3971–4011, https://doi.org/10.5194/tc-16-3971-2022, https://doi.org/10.5194/tc-16-3971-2022, 2022
Short summary
Short summary
Satellite altimeters measure the height or volume change over Earth's ice sheets, but in order to understand how that change translates into ice mass, we must account for various processes at the surface. Specifically, snowfall events generate large, transient increases in surface height, yet snow fall has a relatively low density, which means much of that height change is composed of air. This air signal must be removed from the observed height changes before we can assess ice mass change.
Zachary Fair, Mark Flanner, Adam Schneider, and S. McKenzie Skiles
The Cryosphere, 16, 3801–3814, https://doi.org/10.5194/tc-16-3801-2022, https://doi.org/10.5194/tc-16-3801-2022, 2022
Short summary
Short summary
Snow grain size is important to determine the age and structure of snow, but it is difficult to measure. Snow grain size can be found from airborne and spaceborne observations by measuring near-infrared energy reflected from snow. In this study, we use the SNICAR radiative transfer model and a Monte Carlo model to examine how snow grain size measurements change with snow structure and solar zenith angle. We show that improved understanding of these variables improves snow grain size precision.
Michael Studinger, Serdar S. Manizade, Matthew A. Linkswiler, and James K. Yungel
The Cryosphere, 16, 3649–3668, https://doi.org/10.5194/tc-16-3649-2022, https://doi.org/10.5194/tc-16-3649-2022, 2022
Short summary
Short summary
The footprint density and high-resolution imagery of airborne surveys reveal details in supraglacial hydrological features that are currently not obtainable from spaceborne data. The accuracy and resolution of airborne measurements complement spaceborne measurements, can support calibration and validation of spaceborne methods, and provide information necessary for process studies of the hydrological system on ice sheets that currently cannot be achieved from spaceborne observations alone.
Joseph A. MacGregor, Winnie Chu, William T. Colgan, Mark A. Fahnestock, Denis Felikson, Nanna B. Karlsson, Sophie M. J. Nowicki, and Michael Studinger
The Cryosphere, 16, 3033–3049, https://doi.org/10.5194/tc-16-3033-2022, https://doi.org/10.5194/tc-16-3033-2022, 2022
Short summary
Short summary
Where the bottom of the Greenland Ice Sheet is frozen and where it is thawed is not well known, yet knowing this state is increasingly important to interpret modern changes in ice flow there. We produced a second synthesis of knowledge of the basal thermal state of the ice sheet using airborne and satellite observations and numerical models. About one-third of the ice sheet’s bed is likely thawed; two-fifths is likely frozen; and the remainder is too uncertain to specify.
Christian J. Taubenberger, Denis Felikson, and Thomas Neumann
The Cryosphere, 16, 1341–1348, https://doi.org/10.5194/tc-16-1341-2022, https://doi.org/10.5194/tc-16-1341-2022, 2022
Short summary
Short summary
Outlet glaciers are projected to account for half of the total ice loss from the Greenland Ice Sheet over the 21st century. We classify patterns of seasonal dynamic thickness changes of outlet glaciers using new observations from the Ice, Cloud and land Elevation Satellite-2 (ICESat-2). Our results reveal seven distinct patterns that differ across glaciers even within the same region. Future work can use our results to improve our understanding of processes that drive seasonal ice sheet changes.
Karen E. Alley, Christian T. Wild, Adrian Luckman, Ted A. Scambos, Martin Truffer, Erin C. Pettit, Atsuhiro Muto, Bruce Wallin, Marin Klinger, Tyler Sutterley, Sarah F. Child, Cyrus Hulen, Jan T. M. Lenaerts, Michelle Maclennan, Eric Keenan, and Devon Dunmire
The Cryosphere, 15, 5187–5203, https://doi.org/10.5194/tc-15-5187-2021, https://doi.org/10.5194/tc-15-5187-2021, 2021
Short summary
Short summary
We present a 20-year, satellite-based record of velocity and thickness change on the Thwaites Eastern Ice Shelf (TEIS), the largest remaining floating extension of Thwaites Glacier (TG). TG holds the single greatest control on sea-level rise over the next few centuries, so it is important to understand changes on the TEIS, which controls much of TG's flow into the ocean. Our results suggest that the TEIS is progressively destabilizing and is likely to disintegrate over the next few decades.
Joseph A. MacGregor, Michael Studinger, Emily Arnold, Carlton J. Leuschen, Fernando Rodríguez-Morales, and John D. Paden
The Cryosphere, 15, 2569–2574, https://doi.org/10.5194/tc-15-2569-2021, https://doi.org/10.5194/tc-15-2569-2021, 2021
Short summary
Short summary
We combine multiple recent global glacier datasets and extend one of them (GlaThiDa) to evaluate past performance of radar-sounding surveys of the thickness of Earth's temperate glaciers. An empirical envelope for radar performance as a function of center frequency is determined, its limitations are discussed and its relevance to future radar-sounder survey and system designs is considered.
Andrew O. Hoffman, Knut Christianson, Daniel Shapero, Benjamin E. Smith, and Ian Joughin
The Cryosphere, 14, 4603–4609, https://doi.org/10.5194/tc-14-4603-2020, https://doi.org/10.5194/tc-14-4603-2020, 2020
Short summary
Short summary
The West Antarctic Ice Sheet has long been considered geometrically prone to collapse, and Thwaites Glacier, the largest glacier in the Amundsen Sea, is likely in the early stages of disintegration. Using observations of Thwaites Glacier velocity and elevation change, we show that the transport of ~2 km3 of water beneath Thwaites Glacier has only a small and transient effect on glacier speed relative to ongoing thinning driven by ocean melt.
Zachary Fair, Mark Flanner, Kelly M. Brunt, Helen Amanda Fricker, and Alex Gardner
The Cryosphere, 14, 4253–4263, https://doi.org/10.5194/tc-14-4253-2020, https://doi.org/10.5194/tc-14-4253-2020, 2020
Short summary
Short summary
Ice on glaciers and ice sheets may melt and pond on ice surfaces in summer months. Detection and observation of these meltwater ponds is important for understanding glaciers and ice sheets, and satellite imagery has been used in previous work. However, image-based methods struggle with deep water, so we used data from the Ice, Clouds, and land Elevation Satellite-2 (ICESat-2) and the Airborne Topographic Mapper (ATM) to demonstrate the potential for lidar depth monitoring.
Michael Studinger, Brooke C. Medley, Kelly M. Brunt, Kimberly A. Casey, Nathan T. Kurtz, Serdar S. Manizade, Thomas A. Neumann, and Thomas B. Overly
The Cryosphere, 14, 3287–3308, https://doi.org/10.5194/tc-14-3287-2020, https://doi.org/10.5194/tc-14-3287-2020, 2020
Short summary
Short summary
We use repeat airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88° S. We find small–scale variability in snow accumulation based on the snow radar subsurface layering, indicating areas of strong wind redistribution are prevalent at 88° S. There is no slope–independent relationship between surface roughness and accumulation.
Cited articles
Abdalati, W., Zwally, H. J., Bindschadler, R., Csatho, B., Farrell, S. L., Fricker, H. A., Harding, D., Kwok, R., Lefsky, M., Markus, T., Marshak, A., Neumann, T., Palm, S., Schutz, B., Smith, B., Spinhirne, J., and Webb, C.: The ICESat-2 Laser Altimetry Mission, Proc. IEEE, 98, 735–751, https://doi.org/10.1109/jproc.2009.2034765, 2010.
Allgaier, M. and Smith, B. J.: Diffuse optics for glaciology, Opt. Express, 29, 18845, https://doi.org/10.1364/oe.425630, 2021.
Allgaier, M., Cooper, M. G., Carlson, A. E., Cooley, S. W., Ryan, J. C., and Smith, B. J.: Direct measurement of optical properties of glacier ice using a photon-counting diffuse LiDAR, J. Glaciol., 68, 1210–1220, https://doi.org/10.1017/jog.2022.34, 2022.
Brunt, K. M., Smith, B. E., Sutterley, T. C., Kurtz, N. T., and Neumann, T. A.: Comparisons of Satellite and Airborne Altimetry With Ground-Based Data From the Interior of the Antarctic Ice Sheet, Geophys. Res. Lett., 48, e2020GL090572, https://doi.org/10.1029/2020gl090572, 2021.
Chapman, J.: SIMPL grain-size data, https://popo.jpl.nasa.gov/avng/y19/2019_greenland_grainsize_data.zip, last access: 23 June 2023.
Cooper, M. G., Smith, L. C., Rennermalm, A. K., Miège, C., Pitcher, L. H., Ryan, J. C., Yang, K., and Cooley, S. W.: Meltwater storage in low-density near-surface bare ice in the Greenland ice sheet ablation zone, The Cryosphere, 12, 955–970, https://doi.org/10.5194/tc-12-955-2018, 2018.
Fair, Z., Flanner, M., Neumann, T., Vuyovich, C., Smith, B., and Schneider, A.: Quantifying Volumetric Scattering Bias in ICESat-2 and Operation IceBridge Altimetry Over Greenland Firn and Aged Snow, Earth Space Sci., 11, e2022EA002479, https://doi.org/10.1029/2022ea002479, 2024.
Fausto, R. S., Box, J. E., Vandecrux, B., As, D. van, Steffen, K., MacFerrin, M. J., Machguth, H., Colgan, W., Koenig, L. S., McGrath, D., Charalampidis, C., and Braithwaite, R. J.: A Snow Density Dataset for Improving Surface Boundary Conditions in Greenland Ice Sheet Firn Modeling, Front. Earth Sci., 6, 51, https://doi.org/10.3389/feart.2018.00051, 2018.
Flock, S. T., Patterson, M. S., Wilson, B. C., and Wyman, D. R.: Monte Carlo modeling of light propagation in highly scattering tissues. I. Model predictions and comparison with diffusion theory, IEEE T. Bio-Med. Eng., 36, 1162–1168, https://doi.org/10.1109/tbme.1989.1173624, 1989.
Gardner, A. and Sharp, M.: A review of snow and ice albedo and the development of a new physically based broadband albedo parameterization, J. Geophys. Res.-Earth, 115, F01009, https://doi.org/10.1029/2009jf001444, 2010.
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, 31697–31709, https://doi.org/10.1029/1999jd900496, 1999.
Haran, T., Bohlander, J., Scambs, T., Painter, T., and Fahnestock, M.: MEaSUREs MODIS Mosaic of Greenland (MOG) 2005, 2010, and 2015 Image Maps, (NSIDC-0547, Version 2), NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/9zo79photye5, 2018.
Harding, D., Dabney, P., Valett, S., Yu, A., Vasilyev, A., and Kelly, A.: AIRBORNE POLARIMETRIC, TWO-COLOR LASER ALTIMETER MEASUREMENTS OF LAKE ICE COVER: A PATHFINDER FOR NASA'S ICESAT-2 SPACEFLIGHT MISSION, 2011 IEEE Int. Geosci. Remote Sens. Symp., Vancouver, British Columbia, Canada, 24–29 July 2011, 1, 3598–3601, https://doi.org/10.1109/igarss.2011.6050002, 2011.
Hofton, M. A., Blair, J. B., Luthcke, S. B., and Rabine, D. L.: Assessing the performance of 20–25 m footprint waveform lidar data collected in ICESat data corridors in Greenland, Geophys. Res. Lett., 35, L24501, https://doi.org/10.1029/2008gl035774, 2008.
Hu, Y., Lu, X., Zeng, X., Stamnes, S. A., Neuman, T. A., Kurtz, N. T., Zhai, P., Gao, M., Sun, W., Xu, K., Liu, Z., Omar, A. H., Baize, R. R., Rogers, L. J., Mitchell, B. O., Stamnes, K., Huang, Y., Chen, N., Weimer, C., Lee, J., and Fair, Z.: Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements, Front. Remote Sens., 3, 855159, https://doi.org/10.3389/frsen.2022.855159, 2022.
Kokhanovsky, A., Lamare, M., Danne, O., Brockmann, C., Dumont, M., Picard, G., Arnaud, L., Favier, V., Jourdain, B., Meur, E. L., Mauro, B. D., Aoki, T., Niwano, M., Rozanov, V., Korkin, S., Kipfstuhl, S., Freitag, J., Hoerhold, M., Zuhr, A., Vladimirova, D., Faber, A. K., Steen-Larsen, H. C., Wahl, S., Andersen, J. K., Vandecrux, B., As, D. van, Mankoff, K. D., Kern, M., Zege, E., and Box, J. E.: Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument, Remote Sens.-Basel, 11, 2280, https://doi.org/10.3390/rs11192280, 2019.
Krabill, W. B., Abdalati, W., Frederick, E. B., Manizade, S. S., Martin, C. F., Sonntag, J. G., Swift, R. N., Thomas, R. H., and Yungel, J. G.: Aircraft laser altimetry measurement of elevation changes of the greenland ice sheet: technique and accuracy assessment, J. Geodyn., 34, 357–376, https://doi.org/10.1016/s0264-3707(02)00040-6, 2002.
Lu, X., Hu, Y., Zeng, X., Stamnes, S. A., Neuman, T. A., Kurtz, N. T., Yang, Y., Zhai, P.-W., Gao, M., Sun, W., Xu, K., Liu, Z., Omar, A. H., Baize, R. R., Rogers, L. J., Mitchell, B. O., Stamnes, K., Huang, Y., Chen, N., Weimer, C., Lee, J., and Fair, Z.: Deriving Snow Depth From ICESat-2 Lidar Multiple Scattering Measurements: Uncertainty Analyses, Front. Remote Sens., 3, 891481, https://doi.org/10.3389/frsen.2022.891481, 2022.
MacGregor, J. A., Boisvert, L. N., Medley, B., Petty, A. A., Harbeck, J. P., Bell, R. E., Blair, J. B., Blanchard-Wrigglesworth, E., Buckley, E. M., Christoffersen, M. S., Cochran, J. R., Csathó, B. M., Marco, E. L., Dominguez, R. T., Fahnestock, M. A., Farrell, S. L., Gogineni, S. P., Greenbaum, J. S., Hansen, C. M., Hofton, M. A., Holt, J. W., Jezek, K. C., Koenig, L. S., Kurtz, N. T., Kwok, R., Larsen, C. F., Leuschen, C. J., Locke, C. D., Manizade, S. S., Martin, S., Neumann, T. A., Nowicki, S. M. J., Paden, J. D., Richter-Menge, J. A., Rignot, E. J., Rodríguez-Morales, F., Siegfried, M. R., Smith, B. E., Sonntag, J. G., Studinger, M., Tinto, K. J., Truffer, M., Wagner, T. P., Woods, J. E., Young, D. A., and Yungel, J. K.: The Scientific Legacy of NASA's Operation IceBridge, Rev. Geophys., 59, e2020RG000712, https://doi.org/10.1029/2020rg000712, 2021.
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B., Farrell, S., Fricker, H., Gardner, A., Harding, D., Jasinski, M., Kwok, R., Magruder, L., Lubin, D., Luthcke, S., Morison, J., Nelson, R., Neuenschwander, A., Palm, S., Popescu, S., Shum, C. K., Schutz, B. E., Smith, B., Yang, Y. K., and Zwally, J.: The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): Science requirements, concept, and implementation, Remote Sens. Environ., 190, 260–273, https://doi.org/10.1016/j.rse.2016.12.029, 2017.
Medley, B., Neumann, T. A., Zwally, H. J., Smith, B. E., and Stevens, C. M.: Simulations of firn processes over the Greenland and Antarctic ice sheets: 1980–2021, The Cryosphere, 16, 3971–4011, https://doi.org/10.5194/tc-16-3971-2022, 2022.
Mei, L., Rozanov, V., Pohl, C., Vountas, M., and Burrows, J. P.: The retrieval of snow properties from SLSTR Sentinel-3 – Part 1: Method description and sensitivity study, The Cryosphere, 15, 2757–2780, https://doi.org/10.5194/tc-15-2757-2021, 2021.
Nolin, A. W. and Dozier, J.: A hyperspectral method for remotely sensing the grain size of snow, Remote Sens. Environ., 74, 207–216, https://doi.org/10.1016/s0034-4257(00)00111-5, 2000.
Nolin, A. W. and Payne, M. C.: Classification of glacier zones in western Greenland using albedo and surface roughness from the Multi-angle Imaging SpectroRadiometer (MISR), Remote Sens. Environ., 107, 264–275, https://doi.org/10.1016/j.rse.2006.11.004, 2007.
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.
Petty, A. A., Keeney, N., Cabaj, A., Kushner, P., and Bagnardi, M.: Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection, The Cryosphere, 17, 127–156, https://doi.org/10.5194/tc-17-127-2023, 2023.
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P.: Numerical recipes : the art of scientific computing, Cambridge University Press, New York, 492–496, ISBN 978-0-511-33555-6, 2007.
Smith, B., Fricker, H. A., Holschuh, N., Gardner, A. S., Adusumilli, S., Brunt, K. M., Csatho, B., Harbeck, K., Huth, A., Neumann, T., Nilsson, J., and Siegfried, M. R.: Land ice height-retrieval algorithm for NASA's ICESat-2 photon-counting laser altimeter, Remote Sens. Environ., 233, 111352, https://doi.org/10.1016/j.rse.2019.111352, 2019a.
Smith, B., Fricker, H. A., Holschuh, N., Gardner, A. S., Adusumilli, S., Brunt, K. M., Csatho, B., Harbeck, K., Huth, A., Neumann, T., Nilsson, J., and Siegfried, M. R.: Land ice height-retrieval algorithm for NASA's ICESat-2 photon-counting laser altimeter, Remote Sens. Environ., 233, 111352, https://doi.org/10.1016/j.rse.2019.111352, 2019b.
Smith, B. E., Gardner, A., Schneider, A., and Flanner, M.: Modeling biases in laser-altimetry measurements caused by scattering of green light in snow, Remote Sens. Environ., 215, 398–410, https://doi.org/10.1016/j.rse.2018.06.012, 2018.
Smith, B. E., Fricker, H. A., Gardner, A. S., Medley, B., Nilsson, J., Paolo, F. S., Holschuh, N., Adusumilli, S., Brunt, K., Csatho, B., Harbeck, K., Markus, T., Neumann, T., Siegdried, M. R., and Zwally, H. J.: Pervasive ice sheet mass loss driven by competing, ocean, and atmosphere processes, Science, 368, 1239–1242, https://doi.org/10.1126/science.aaz5845, 2020.
Smith, B., Sutterley, T., and Studinger, M.: IceBridge ATM Waveform Derived Snow Effective Grain Radius. (ILATMGR, Version 1), Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/1207YUVC7KOO, 2024.
Studinger, M.: IceBridge ATM L1B Elevation and Return Strength with Waveforms, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/ezq5u3r3xwbs, 2018a.
Studinger, M.: IceBridge Narrow Swath ATM L1B Elevation and Return Strength with Waveforms, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], Boulder, Colorado, USA, https://doi.org/10.5067/v25x7lhdpmzy, 2018b.
Studinger, M., Manizade, S. S., Linkswiler, M. A., and Yungel, J. K.: High-resolution imaging of supraglacial hydrological features on the Greenland Ice Sheet with NASA's Airborne Topographic Mapper (ATM) instrument suite, The Cryosphere, 16, 3649–3668, https://doi.org/10.5194/tc-16-3649-2022, 2022a.
Studinger, M., Linkswiler, M. A., Manizade, S. S., and Yungel, J. K.: NASA's Airborne Topographic Mapper (ATM) ground calibration data for waveform data products (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7225937, 2022b.
Studinger, M., Smith, B. E., Kurtz, N., Petty, A., Sutterley, T., and Tilling, R.: Estimating differential penetration of green (532 nm) laser light over sea ice with NASA's Airborne Topographic Mapper: observations and models, The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, 2024.
Sutterley, T. C., Markus, T., Neumann, T. A., van den Broeke, M., van Wessem, J. M., and Ligtenberg, S. R. M.: Antarctic ice shelf thickness change from multimission lidar mapping, The Cryosphere, 13, 1801–1817, https://doi.org/10.5194/tc-13-1801-2019, 2019.
Thompson, D. R., Boardman, J. W., Eastwood, M. L., Green, R. O., Haag, J. M., Mouroulis, P., and Gorp, B. V.: Imaging spectrometer stray spectral response: In-flight characterization, correction, and validation, Remote Sens. Environ., 204, 850–860, https://doi.org/10.1016/j.rse.2017.09.015, 2018.
Vandecrux, B., Box, J., Mankoff, K., and Wehrlé, A.: Snow broadband albedo, specific surface area and optical grain diameter from Sentinel-3's OLCI, daily 1 km mosaics, Greenland, GEUS dataverse [data set], https://doi.org/10.22008/fk2/oiajvo, 2022a.
Vandecrux, B., Box, J. E., Wehrle, A., Kokhanovsky, A. A., Picard, G., Niwano, M., Hoerhold, M., Faber, A. K., and Steen-Larsen, H. C.: The Determination of the Snow Optical Grain Diameter and Snowmelt Area on the Greenland Ice Sheet Using Spaceborne Optical Observations, Remote Sens.-Basel, 14, 932, https://doi.org/10.3390/rs14040932, 2022b.
Warren, S. G.: Optical properties of snow, Rev. Geophys., 20, 67–89, https://doi.org/10.1029/rg020i001p00067, 1982.
Warren, S. G. and Brandt, R. E.: Optical constants of ice from the ultraviolet to the microwave: A revised compilation, J. Geophys. Res.-Atmos., 113, D14220, https://doi.org/10.1029/2007jd009744, 2008.
Wiscombe, W. J. and Warren, S. G.: A Model for the Spectral Albedo of Snow. I: Pure Snow, J. Atmos. Sci., 37, 2712–2733, https://doi.org/10.1175/1520-0469(1980)037<2712:amftsa>2.0.co;2, 1980.
Yi, D., Zwally, H. J., and Sun, X.: ICESat measurement of Greenland ice sheet surface slope and roughness, Ann. Glaciol., 42, 83–89, https://doi.org/10.3189/172756405781812691, 2005.
Yu, A. W., Harding, D. J., and Dabney, P. W.: Laser transmitter design and performance for the slope imaging multi-polarization photon-counting lidar (SIMPL) instrument, in: Solid State Lasers XXV: Technol. Devices, San Francisco, California, USA, 13–14 February 2016, 97260J-97260J–8, https://doi.org/10.1117/12.2213005, 2016.
Short summary
This study investigates errors (biases) that may result when green lasers are used to measure the elevation of glaciers and ice sheets. These biases are important because if the snow or ice on top of the ice sheet changes, it can make the elevation of the ice appear to change by the wrong amount. We measure these biases over the Greenland Ice Sheet with a laser system on an airplane and explore how the use of satellite data can let us correct for the biases.
This study investigates errors (biases) that may result when green lasers are used to measure...