Articles | Volume 5, issue 1
https://doi.org/10.5194/tc-5-271-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-5-271-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
29 Mar 2011
Research article |
| 29 Mar 2011
Co-registration and bias corrections of satellite elevation data sets for quantifying glacier thickness change
C. Nuth
Department of Geosciences, University of Oslo, 0316 Oslo, Norway
A. Kääb
Department of Geosciences, University of Oslo, 0316 Oslo, Norway
Related subject area
Remote Sensing
Assessing sea ice microwave emissivity up to submillimeter waves from airborne and satellite observations
Simulation of Arctic snow microwave emission in surface-sensitive atmosphere channels
AWI-ICENet1: a convolutional neural network retracker for ice altimetry
Monthly velocity and seasonal variations of the Mont Blanc glaciers derived from Sentinel-2 between 2016 and 2024
Retrieval of snow and soil properties for forward radiative transfer modeling of airborne Ku-band SAR to estimate snow water equivalent: the Trail Valley Creek 2018/19 snow experiment
Evaluating L-band InSAR snow water equivalent retrievals with repeat ground-penetrating radar and terrestrial lidar surveys in northern Colorado
Toward long-term monitoring of regional permafrost thaw with satellite interferometric synthetic aperture radar
Improved records of glacier flow instabilities using customized NASA autoRIFT (CautoRIFT) applied to PlanetScope imagery
Reanalyzing the spatial representativeness of snow depth at automated monitoring stations using airborne lidar data
The AutoICE Challenge
Observing glacier elevation changes from spaceborne optical and radar sensors – an inter-comparison experiment using ASTER and TanDEM-X data
Tower-based C-band radar measurements of an alpine snowpack
A study of sea ice topography in the Weddell and Ross seas using dual-polarimetric TanDEM-X imagery
Estimating differential penetration of green (532 nm) laser light over sea ice with NASA's Airborne Topographic Mapper: observations and models
Mapping surface hoar from near-infrared texture in a laboratory
Sentinel-1 detection of ice slabs on the Greenland Ice Sheet
Estimating the uncertainty of sea-ice area and sea-ice extent from satellite retrievals
A Framework for Automated Supraglacial Lake Detection and Depth Retrieval in ICESat-2 Photon Data Across the Greenland and Antarctic Ice Sheets
Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign
Sea ice transport and replenishment across and within the Canadian Arctic Archipelago, 2016–2022
SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition
Lake ice break-up in Greenland: timing and spatiotemporal variability
Evaluating Snow Depth Retrievals from Sentinel-1 Volume Scattering over NASA SnowEx Sites
Temperature-dominated spatiotemporal variability in snow phenology on the Tibetan Plateau from 2002 to 2022
Landcover succession for recently drained lakes in permafrost on the Yamal peninsula, Western Siberia
MMSeaIce: a collection of techniques for improving sea ice mapping with a multi-task model
Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method
Lead fractions from SAR-derived sea ice divergence during MOSAiC
Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17
A low-cost and open-source approach for supraglacial debris thickness mapping using UAV-based infrared thermography
Snow water equivalent retrieval over Idaho – Part 1: Using Sentinel-1 repeat-pass interferometry
Pan-Arctic Sea Ice Concentration from SAR and Passive Microwave
Passive microwave remote-sensing-based high-resolution snow depth mapping for Western Himalayan zones using multifactor modeling approach
Change in grounding line location on the Antarctic Peninsula measured using a tidal motion offset correlation method
Refined glacial lake extraction in a high-Asia region by deep neural network and superpixel-based conditional random field methods
Retrieval of snow water equivalent from dual-frequency radar measurements: using time series to overcome the need for accurate a priori information
Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach
Annual to seasonal glacier mass balance in High Mountain Asia derived from Pléiades stereo images: examples from the Pamir and the Tibetan Plateau
Snow accumulation, albedo and melt patterns following road construction on permafrost, Inuvik–Tuktoyaktuk Highway, Canada
Co-registration and residual correction of digital elevation models: a comparative study
Out-of-the-box calving-front detection method using deep learning
Mapping the extent of giant Antarctic icebergs with deep learning
Allometric scaling of retrogressive thaw slumps
Mapping Antarctic crevasses and their evolution with deep learning applied to satellite radar imagery
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 1: Measurements, processing, and accuracy assessment
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions
Evaluating Snow Microwave Radiative Transfer (SMRT) model emissivities with 89 to 243 GHz observations of Arctic tundra snow
Brief communication: Identification of tundra topsoil frozen/thawed state from SMAP and GCOM-W1 radiometer measurements using the spectral gradient method
Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data
New estimates of pan-Arctic sea ice–atmosphere neutral drag coefficients from ICESat-2 elevation data
Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell
The Cryosphere, 18, 4137–4163, https://doi.org/10.5194/tc-18-4137-2024, https://doi.org/10.5194/tc-18-4137-2024, 2024
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Passive microwave observations from satellites are crucial for monitoring Arctic sea ice and atmosphere. To do this effectively, it is important to understand how sea ice emits microwaves. Through unique Arctic sea ice observations, we improved our understanding, identified four distinct emission types, and expanded current knowledge to include higher frequencies. These findings will enhance our ability to monitor the Arctic climate and provide valuable information for new satellite missions.
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.
Veit Helm, Alireza Dehghanpour, Ronny Hänsch, Erik Loebel, Martin Horwath, and Angelika Humbert
The Cryosphere, 18, 3933–3970, https://doi.org/10.5194/tc-18-3933-2024, https://doi.org/10.5194/tc-18-3933-2024, 2024
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We present a new approach (AWI-ICENet1), based on a deep convolutional neural network, for analysing satellite radar altimeter measurements to accurately determine the surface height of ice sheets. Surface height estimates obtained with AWI-ICENet1 (along with related products, such as ice sheet height change and volume change) show improved and unbiased results compared to other products. This is important for the long-term monitoring of ice sheet mass loss and its impact on sea level rise.
Fabrizio Troilo, Niccolò Dematteis, Francesco Zucca, Martin Funk, and Daniele Giordan
The Cryosphere, 18, 3891–3909, https://doi.org/10.5194/tc-18-3891-2024, https://doi.org/10.5194/tc-18-3891-2024, 2024
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The study of glacier sliding along slopes is relevant in many aspects of glaciology. We processed Sentinel-2 satellite optical images of Mont Blanc, obtaining surface velocities of 30 glaciers between 2016 and 2024. The study revealed different behaviours and velocity variations that have relationships with glacier morphology. A velocity anomaly was observed in some glaciers of the southern side in 2020–2022, but its origin needs to be investigated further.
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.
Taha Sadeghi Chorsi, Franz J. Meyer, and Timothy H. Dixon
The Cryosphere, 18, 3723–3740, https://doi.org/10.5194/tc-18-3723-2024, https://doi.org/10.5194/tc-18-3723-2024, 2024
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The active layer thaws and freezes seasonally. The annual freeze–thaw cycle of the active layer causes significant surface height changes due to the volume difference between ice and liquid water. We estimate the subsidence rate and active-layer thickness (ALT) for part of northern Alaska for summer 2017 to 2022 using interferometric synthetic aperture radar and lidar. ALT estimates range from ~20 cm to larger than 150 cm in area. Subsidence rate varies between close points (2–18 mm per month).
Jukes Liu, Madeline Gendreau, Ellyn Mary Enderlin, and Rainey Aberle
The Cryosphere, 18, 3571–3590, https://doi.org/10.5194/tc-18-3571-2024, https://doi.org/10.5194/tc-18-3571-2024, 2024
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There are sometimes gaps in global glacier velocity records produced using satellite image feature-tracking algorithms during times of rapid glacier acceleration, which hinders the study of glacier flow processes. We present an open-source pipeline for customizing the feature-tracking parameters and for including images from an additional source. We applied it to five glaciers and found that it produced accurate velocity data that supplemented their velocity records during rapid acceleration.
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.
Andreas Stokholm, Jørgen Buus-Hinkler, Tore Wulf, Anton Korosov, Roberto Saldo, Leif Toudal Pedersen, David Arthurs, Ionut Dragan, Iacopo Modica, Juan Pedro, Annekatrien Debien, Xinwei Chen, Muhammed Patel, Fernando Jose Pena Cantu, Javier Noa Turnes, Jinman Park, Linlin Xu, Katharine Andrea Scott, David Anthony Clausi, Yuan Fang, Mingzhe Jiang, Saeid Taleghanidoozdoozan, Neil Curtis Brubacher, Armina Soleymani, Zacharie Gousseau, Michał Smaczny, Patryk Kowalski, Jacek Komorowski, David Rijlaarsdam, Jan Nicolaas van Rijn, Jens Jakobsen, Martin Samuel James Rogers, Nick Hughes, Tom Zagon, Rune Solberg, Nicolas Longépé, and Matilde Brandt Kreiner
The Cryosphere, 18, 3471–3494, https://doi.org/10.5194/tc-18-3471-2024, https://doi.org/10.5194/tc-18-3471-2024, 2024
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The AutoICE challenge encouraged the development of deep learning models to map multiple aspects of sea ice – the amount of sea ice in an area and the age and ice floe size – using multiple sources of satellite and weather data across the Canadian and Greenlandic Arctic. Professionally drawn operational sea ice charts were used as a reference. A total of 179 students and sea ice and AI specialists participated and produced maps in broad agreement with the sea ice charts.
Livia Piermattei, Michael Zemp, Christian Sommer, Fanny Brun, Matthias H. Braun, Liss M. Andreassen, Joaquín M. C. Belart, Etienne Berthier, Atanu Bhattacharya, Laura Boehm Vock, Tobias Bolch, Amaury Dehecq, Inés Dussaillant, Daniel Falaschi, Caitlyn Florentine, Dana Floricioiu, Christian Ginzler, Gregoire Guillet, Romain Hugonnet, Matthias Huss, Andreas Kääb, Owen King, Christoph Klug, Friedrich Knuth, Lukas Krieger, Jeff La Frenierre, Robert McNabb, Christopher McNeil, Rainer Prinz, Louis Sass, Thorsten Seehaus, David Shean, Désirée Treichler, Anja Wendt, and Ruitang Yang
The Cryosphere, 18, 3195–3230, https://doi.org/10.5194/tc-18-3195-2024, https://doi.org/10.5194/tc-18-3195-2024, 2024
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Satellites have made it possible to observe glacier elevation changes from all around the world. In the present study, we compared the results produced from two different types of satellite data between different research groups and against validation measurements from aeroplanes. We found a large spread between individual results but showed that the group ensemble can be used to reliably estimate glacier elevation changes and related errors from satellite data.
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.
Lanqing Huang and Irena Hajnsek
The Cryosphere, 18, 3117–3140, https://doi.org/10.5194/tc-18-3117-2024, https://doi.org/10.5194/tc-18-3117-2024, 2024
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Interferometric synthetic aperture radar can measure the total freeboard of sea ice but can be biased when radar signals penetrate snow and ice. We develop a new method to retrieve the total freeboard and analyze the regional variation of total freeboard and roughness in the Weddell and Ross seas. We also investigate the statistical behavior of the total freeboard for diverse ice types. The findings enhance the understanding of Antarctic sea ice topography and its dynamics in a changing climate.
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
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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.
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 %.
Riley Culberg, Roger J. Michaelides, and Julie Z. Miller
The Cryosphere, 18, 2531–2555, https://doi.org/10.5194/tc-18-2531-2024, https://doi.org/10.5194/tc-18-2531-2024, 2024
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Ice slabs enhance meltwater runoff from the Greenland Ice Sheet. Therefore, it is important to understand their extent and change in extent over time. We present a new method for detecting ice slabs in satellite radar data, which we use to map ice slabs at 500 m resolution across the entire ice sheet in winter 2016–2017. Our results provide better spatial coverage and resolution than previous maps from airborne radar and lay the groundwork for long-term monitoring of ice slabs from space.
Andreas Wernecke, Dirk Notz, Stefan Kern, and Thomas Lavergne
The Cryosphere, 18, 2473–2486, https://doi.org/10.5194/tc-18-2473-2024, https://doi.org/10.5194/tc-18-2473-2024, 2024
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The total Arctic sea-ice area (SIA), which is an important climate indicator, is routinely monitored with the help of satellite measurements. Uncertainties in observations of sea-ice concentration (SIC) partly cancel out when summed up to the total SIA, but the degree to which this is happening has been unclear. Here we find that the uncertainty daily SIA estimates, based on uncertainties in SIC, are about 300 000 km2. The 2002 to 2017 September decline in SIA is approx. 105 000 ± 9000 km2 a−1.
Philipp Sebastian Arndt and Helen Amanda Fricker
EGUsphere, https://doi.org/10.5194/egusphere-2024-1156, https://doi.org/10.5194/egusphere-2024-1156, 2024
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We develop a method for ice-sheet-scale retrieval of supraglacial meltwater depths using ICESat-2 photon data. We report results for two drainage basins in Greenland and Antarctica during two contrasting melt seasons, where our method reveals a total of 1249 lakes up to 25 m deep. The large volume and wide variety of accurate depth data that our method provides enables the development of data-driven models of meltwater volumes in satellite imagery.
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.
Stephen E. L. Howell, David G. Babb, Jack C. Landy, Isolde A. Glissenaar, Kaitlin McNeil, Benoit Montpetit, and Mike Brady
The Cryosphere, 18, 2321–2333, https://doi.org/10.5194/tc-18-2321-2024, https://doi.org/10.5194/tc-18-2321-2024, 2024
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The CAA serves as both a source and a sink for sea ice from the Arctic Ocean, while also exporting sea ice into Baffin Bay. It is also an important region with respect to navigating the Northwest Passage. Here, we quantify sea ice transport and replenishment across and within the CAA from 2016 to 2022. We also provide the first estimates of the ice area and volume flux within the CAA from the Queen Elizabeth Islands to Parry Channel, which spans the central region of the Northwest Passage.
Karl Kortum, Suman Singha, Gunnar Spreen, Nils Hutter, Arttu Jutila, and Christian Haas
The Cryosphere, 18, 2207–2222, https://doi.org/10.5194/tc-18-2207-2024, https://doi.org/10.5194/tc-18-2207-2024, 2024
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A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of the ice topography during the MOSAiC expedition is constructed and used to train a variety of deep learning algorithms. The results give realistic insights into the accuracy of retrieval of measured ice classes using modern deep learning models. The models able to learn from the spatial distribution of the measured sea ice classes are shown to have a clear advantage over those that cannot.
Christoph Posch, Jakob Abermann, and Tiago Silva
The Cryosphere, 18, 2035–2059, https://doi.org/10.5194/tc-18-2035-2024, https://doi.org/10.5194/tc-18-2035-2024, 2024
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Radar beams from satellites exhibit reflection differences between water and ice. This condition, as well as the comprehensive coverage and high temporal resolution of the Sentinel-1 satellites, allows automatically detecting the timing of when ice cover of lakes in Greenland disappear. We found that lake ice breaks up 3 d later per 100 m elevation gain and that the average break-up timing varies by ±8 d in 2017–2021, which has major implications for the energy budget of the lakes.
Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dunmire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall
EGUsphere, https://doi.org/10.5194/egusphere-2024-1018, https://doi.org/10.5194/egusphere-2024-1018, 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 9 automated snow monitoring stations.
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.
Clemens von Baeckmann, Annett Bartsch, Helena Bergstedt, Aleksandra Efimova, Barbara Widhalm, Dorothee Ehrich, Timo Kumpula, Alexander Sokolov, and Svetlana Abdulmanova
EGUsphere, https://doi.org/10.5194/egusphere-2024-699, https://doi.org/10.5194/egusphere-2024-699, 2024
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Lakes are common features in Arctic permafrost areas. Landcover change following their drainage needs to be monitored since it has implications for ecology and the carbon cycle. Satellite data are key in this context. We compared a common vegetation index approach with a novel landcover monitoring scheme. Landcover information provides specifically information on wetland features. We also showed that the bioclimatic gradients play a significant role after drainage within the first 10 years.
Xinwei Chen, Muhammed Patel, Fernando J. Pena Cantu, Jinman Park, Javier Noa Turnes, Linlin Xu, K. Andrea Scott, and David A. Clausi
The Cryosphere, 18, 1621–1632, https://doi.org/10.5194/tc-18-1621-2024, https://doi.org/10.5194/tc-18-1621-2024, 2024
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This paper introduces an automated sea ice mapping pipeline utilizing a multi-task U-Net architecture. It attained the top score of 86.3 % in the AutoICE challenge. Ablation studies revealed that incorporating brightness temperature data and spatial–temporal information significantly enhanced model accuracy. Accurate sea ice mapping is vital for comprehending the Arctic environment and its global climate effects, underscoring the potential of deep learning.
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.
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, https://doi.org/10.5194/tc-18-1259-2024, 2024
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Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
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.
Jérôme Messmer and Alexander Raphael Groos
The Cryosphere, 18, 719–746, https://doi.org/10.5194/tc-18-719-2024, https://doi.org/10.5194/tc-18-719-2024, 2024
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The lower part of mountain glaciers is often covered with debris. Knowing the thickness of the debris is important as it influences the melting and future evolution of the affected glaciers. We have developed an open-source approach to map variations in debris thickness on glaciers using a low-cost drone equipped with a thermal infrared camera. The resulting high-resolution maps of debris surface temperature and thickness enable more accurate monitoring and modelling of debris-covered glaciers.
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.
Tore Wulf, Jørgen Buus-Hinkler, Suman Singha, Hoyeon Shi, and Matilde Brandt Kreiner
EGUsphere, https://doi.org/10.5194/egusphere-2024-178, https://doi.org/10.5194/egusphere-2024-178, 2024
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Here, we present ASIP (Automated Sea Ice Products): a new and comprehensive deep learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from Sentinel-1 SAR and AMSR2 passive microwave observations at a pan-Arctic scale for all seasons. In a comparative study against pan-Arctic ice charts and passive microwave-based sea ice products, we show that ASIP generalizes well to the pan-Arctic region.
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.
Benjamin J. Wallis, Anna E. Hogg, Yikai Zhu, and Andrew Hooper
EGUsphere, https://doi.org/10.5194/egusphere-2023-2874, https://doi.org/10.5194/egusphere-2023-2874, 2024
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The grounding line, where ice begins to float, is an essential variable to understand ice dynamics, but in some locations it can be difficult to measure. Using satellite data and a new method, Wallis et al. measure the grounding line position of glaciers and ice shelves in the Antarctic Peninsula and find retreats of up to 16.3 km have occurred since the last time measurements were made in 1990s.
Yungang Cao, Rumeng Pan, Meng Pan, Ruodan Lei, Puying Du, and Xueqin Bai
The Cryosphere, 18, 153–168, https://doi.org/10.5194/tc-18-153-2024, https://doi.org/10.5194/tc-18-153-2024, 2024
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This study built a glacial lake dataset with 15376 samples in seven types and proposed an automatic method by two-stage (the semantic segmentation network and post-processing) optimizations to detect glacial lakes. The proposed method for glacial lake extraction has achieved the best results so far, in which the F1 score and IoU reached 0.945 and 0.907, respectively. The area of the minimum glacial lake that can be entirely and correctly extracted has been raised to the 100 m2 level.
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.
Qin Zhang and Nick Hughes
The Cryosphere, 17, 5519–5537, https://doi.org/10.5194/tc-17-5519-2023, https://doi.org/10.5194/tc-17-5519-2023, 2023
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To alleviate tedious manual image annotations for training deep learning (DL) models in floe instance segmentation, we employ a classical image processing technique to automatically label floes in images. We then apply a DL semantic method for fast and adaptive floe instance segmentation from high-resolution airborne and satellite images. A post-processing algorithm is also proposed to refine the segmentation and further to derive acceptable floe size distributions at local and global scales.
Daniel Falaschi, Atanu Bhattacharya, Gregoire Guillet, Lei Huang, Owen King, Kriti Mukherjee, Philipp Rastner, Tandong Yao, and Tobias Bolch
The Cryosphere, 17, 5435–5458, https://doi.org/10.5194/tc-17-5435-2023, https://doi.org/10.5194/tc-17-5435-2023, 2023
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Because glaciers are crucial freshwater sources in the lowlands surrounding High Mountain Asia, constraining short-term glacier mass changes is essential. We investigate the potential of state-of-the-art satellite elevation data to measure glacier mass changes in two selected regions. The results demonstrate the ability of our dataset to characterize glacier changes of different magnitudes, allowing for an increase in the number of inaccessible glaciers that can be readily monitored.
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.
Tao Li, Yuanlin Hu, Bin Liu, Liming Jiang, Hansheng Wang, and Xiang Shen
The Cryosphere, 17, 5299–5316, https://doi.org/10.5194/tc-17-5299-2023, https://doi.org/10.5194/tc-17-5299-2023, 2023
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Raw DEMs are often misaligned with each other due to georeferencing errors, and a co-registration process is required before DEM differencing. We present a comparative analysis of the two classical DEM co-registration and three residual correction algorithms. The experimental results show that rotation and scale biases should be considered in DEM co-registration. The new non-parametric regression technique can eliminate the complex systematic errors, which existed in the co-registration results.
Oskar Herrmann, Nora Gourmelon, Thorsten Seehaus, Andreas Maier, Johannes J. Fürst, Matthias H. Braun, and Vincent Christlein
The Cryosphere, 17, 4957–4977, https://doi.org/10.5194/tc-17-4957-2023, https://doi.org/10.5194/tc-17-4957-2023, 2023
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Delineating calving fronts of marine-terminating glaciers in satellite images is a labour-intensive task. We propose a method based on deep learning that automates this task. We choose a deep learning framework that adapts to any given dataset without needing deep learning expertise. The method is evaluated on a benchmark dataset for calving-front detection and glacier zone segmentation. The framework can beat the benchmark baseline without major modifications.
Anne Braakmann-Folgmann, Andrew Shepherd, David Hogg, and Ella Redmond
The Cryosphere, 17, 4675–4690, https://doi.org/10.5194/tc-17-4675-2023, https://doi.org/10.5194/tc-17-4675-2023, 2023
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In this study, we propose a deep neural network to map the extent of giant Antarctic icebergs in Sentinel-1 images automatically. While each manual delineation requires several minutes, our U-net takes less than 0.01 s. In terms of accuracy, we find that U-net outperforms two standard segmentation techniques (Otsu, k-means) in most metrics and is more robust to challenging scenes with sea ice, coast and other icebergs. The absolute median deviation in iceberg area across 191 images is 4.1 %.
Jurjen van der Sluijs, Steven V. Kokelj, and Jon F. Tunnicliffe
The Cryosphere, 17, 4511–4533, https://doi.org/10.5194/tc-17-4511-2023, https://doi.org/10.5194/tc-17-4511-2023, 2023
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There is an urgent need to obtain size and erosion estimates of climate-driven landslides, such as retrogressive thaw slumps. We evaluated surface interpolation techniques to estimate slump erosional volumes and developed a new inventory method by which the size and activity of these landslides are tracked through time. Models between slump area and volume reveal non-linear intensification, whereby model coefficients improve our understanding of how permafrost landscapes may evolve over time.
Trystan Surawy-Stepney, Anna E. Hogg, Stephen L. Cornford, and David C. Hogg
The Cryosphere, 17, 4421–4445, https://doi.org/10.5194/tc-17-4421-2023, https://doi.org/10.5194/tc-17-4421-2023, 2023
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The presence of crevasses in Antarctica influences how the ice sheet behaves. It is important, therefore, to collect data on the spatial distribution of crevasses and how they are changing. We present a method of mapping crevasses from satellite radar imagery and apply it to 7.5 years of images, covering Antarctica's floating and grounded ice. We develop a method of measuring change in the density of crevasses and quantify increased fracturing in important parts of the West Antarctic Ice Sheet.
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.
Konstantin Muzalevskiy, Zdenek Ruzicka, Alexandre Roy, Michael Loranty, and Alexander Vasiliev
The Cryosphere, 17, 4155–4164, https://doi.org/10.5194/tc-17-4155-2023, https://doi.org/10.5194/tc-17-4155-2023, 2023
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A new all-weather method for determining the frozen/thawed (FT) state of soils in the Arctic region based on satellite data was proposed. The method is based on multifrequency measurement of brightness temperatures by the SMAP and GCOM-W1/AMSR2 satellites. The created method was tested at sites in Canada, Finland, Russia, and the USA, based on climatic weather station data. The proposed method identifies the FT state of Arctic soils with better accuracy than existing methods.
Larysa Istomina, Hannah Niehaus, and Gunnar Spreen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-142, https://doi.org/10.5194/tc-2023-142, 2023
Revised manuscript accepted for TC
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Melt water puddles, or melt ponds on top of the Arctic sea ice are a good measure of the Arctic climate state. In the context of the recent climate warming, the Arctic has warmed about 4 times faster than the rest of the world, and a long-term dataset of the melt pond fraction is needed to be able to model the future development of the Arctic climate. We present such a dataset, produce 2002–2023 trends and highlight a potential melt regime shift with drastic regional trends of +20 % per decade.
Alexander Mchedlishvili, Christof Lüpkes, Alek Petty, Michel Tsamados, and Gunnar Spreen
The Cryosphere, 17, 4103–4131, https://doi.org/10.5194/tc-17-4103-2023, https://doi.org/10.5194/tc-17-4103-2023, 2023
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In this study we looked at sea ice–atmosphere drag coefficients, quantities that help with characterizing the friction between the atmosphere and sea ice, and vice versa. Using ICESat-2, a laser altimeter that measures elevation differences by timing how long it takes for photons it sends out to return to itself, we could map the roughness, i.e., how uneven the surface is. From roughness we then estimate drag force, the frictional force between sea ice and the atmosphere, across the Arctic.
Cited articles
Anderson, B., Lawson, W., Owens, I., and Goodsell, B.: Past and future mass balance of "Ka Roimata o Hine Hukatere" Franz Josef Glacier, New Zealand, J. Glaciol., 52, 597–607, https://doi.org/10.3189/172756506781828449, 2006.
Berthier, E.: Volume loss from Bering Glacier, Alaska, 1972–2003: comment on Muskett and others (2009), J. Glaciol., 56, 558–559, https://doi.org/10.3189/002214310792447716, 2010.
Berthier, E. and Toutin, T.: SPOT5-HRS digital elevation models and the monitoring of glacier elevation changes in North-West Canada and South-East Alaska, Remote Sens. Environ., 112, 2443–2454, https://doi.org/10.1016/j.rse.2007.11.004, 2008.
Berthier, E., Arnaud, Y., Baratoux, D., Vincent, C., and R{é}my, F.: Recent rapid thinning of the "Mer de Glace" glacier derived from satellite optical images, Geophys. Res. Lett., 31, L17401, https://doi.org/10.1029/2004GL020706, 2004.
Berthier, E., Arnaud, Y., Vincent, C., and R{é}my, F.: Biases of SRTM in high-mountain areas: Implications for the monitoring of glacier volume changes, Geophys. Res. Lett., 33, L08502, https://doi.org/10.1029/2006GL025862, 2006.
Berthier, E., Arnaud, Y., Kumar, R., Ahmad, S., Wagnon, P., and Chevallier, P.: Remote sensing estimates of glacier mass balances in the Himachal Pradesh (Western Himalaya, India), Remote Sens. Environ., 108, 327–338, https://doi.org/10.1016/j.rse.2006.11.017, 2007.
Berthier, E., Schiefer, E., Clarke, G. K. C., Menounos, B., and Remy, F.: Contribution of Alaskan glaciers to sea-level rise derived from satellite imagery, Nat. Geosci., 3, 92–95, https://doi.org/10.1038/ngeo737, 2010.
Bolch, T., Buchroithner, M., Pieczonka, T., and Kunert, A.: Planimetric and volumetric glacier changes in the Khumbu Himal, Nepal, since 1962 using Corona, Landsat TM and ASTER data, J. Glaciol., 54, 592–600, https://doi.org/10.3189/002214308786570782, 2008.
Bouillon, A., Bernard, M., Gigord, P., Orsoni, A., Rudowski, V., and Baudoin, A.: SPOT 5 HRS geometric performances: Using block adjustment as a key issue to improve quality of DEM generation, ISPRS J. Photogramm. Remote S., 60, 134–146, https://doi.org/10.1016/j.isprsjprs.2006.03.002, 2006.
Brenner, A. C., DiMarzio, J. R., and Zwally, H. J.: Precision and accuracy of satellite radar and laser altimeter data over the continental ice sheets, IEEE T. Geosci. Remote, 45, 321–331, https://doi.org/10.1109/TGRS.2006.887172, 2007.
Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M., and Bladé, I.: The Effective Number of Spatial Degrees of Freedom of a Time-Varying Field, J. Climate, 12, 1990–2009, 1999.
Burrough, P., McDonnell, R., and Burrough, P.: Principles of geographical information systems, Oxford University Press, Oxford, 1998.
Davis, J. C.: Statistics and data analysis in geology, J. Wiley, New York, 2002.
Dowdeswell, J. A. and Benham, T. J.: A surge of Perseibreen, Svalbard, examined using aerial photography and ASTER high resolution satellite imagery, Polar Res., 22, 373–383, https://doi.org/10.1111/j.1751-8369.2003.tb00118.x, 2003.
ERSDAC: ASTER User's Guide Part III, DEM Product (L4A01), Ver.1.1, Tech. rep., Earth Remote Sensing Data Analysis Center (ERSDAC), http://www.science.aster.ersdac.or.jp/en/documnts/users_guide/index.html (last access: 1 October 2010), 2005.
ERSDAC: ASTER User's Guide Part II Level 1 Data Products, Ver.5.1, Tech. rep., Earth Remote Sensing Data Analysis Center (ERSDAC), http://www.science.aster.ersdac.or.jp/en/documnts/users_guide/index.html (last access: 1 October 2010), 2007.
Etzelm{ü}ller, B.: On the Quantification of Surface Changes using Grid-based Digital Elevation Models (DEMs), Transact. GIS, 4, 129–143, https://doi.org/10.1111/1467-9671.00043, 2000.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007.
Fisher, P.: Improved Modeling of Elevation Error with Geostatistics, GeoInformatica, 2, 215–233, https://doi.org/10.1023/A:1009717704255, 1998.
Fitzharris, B., Lawson, W., and Owens, I.: Research on glaciers and snow in New Zealand, Prog. Phys. Geogr., 23, 469–500, https://doi.org/10.1177/030913339902300402, 1999.
Fricker, H. A., Borsa, A., Minster, B., Carabajal, C., Quinn, K., and Bills, B.: Assessment of ICESat performance at the Salar de Uyuni, Bolivia, Geophys. Res. Lett., 32(5), L21S06, https://doi.org/10.1029/2005GL023423, 2005.
Fujisada, H., Bailey, G., Kelly, G., Hara, S., and Abrams, M.: ASTER DEM performance, Geoscience and Remote Sensing, IEEE Transact., 43, 2707–2714, https://doi.org/10.1109/TGRS.2005.847924, 2005.
GADM: GADM database of Global Administrative Areas, http://www.gadm.org (last access: 21 September 2010), 2010.
Gjermundsen, E. F.: Recent changes in glacier area in the Central Southern Alps of New Zealand, Master's thesis, University of Oslo, 2007.
Gorokhovich, Y. and Voustianiouk, A.: Accuracy assessment of the processed SRTM-based elevation data by CGIAR using field data from USA and Thailand and its relation to the terrain characteristics, Remote Sens. Environ., 104, 409–415, https://doi.org/10.1016/j.rse.2006.05.012, 2006.
Gruen, A. and Akca, D.: Least squares 3D surface and curve matching, ISPRS J. Photogramm. Remote S., 59, 151–174, https://doi.org/10.1016/j.isprsjprs.2005.02.006, 2005.
Herman, F., Anderson, B., and Leprince, S.: Mountain glacier velocity variation during a retreat/advance cycle quantified using sub-pixel analysis of ASTER images, J. Glaciol., 57, 197–207, 2011.
Hirano, A., Welch, R., and Lang, H.: Mapping from ASTER stereo image data: DEM validation and accuracy assessment, ISPRS J. Photogramm. Remote S., 57, 356–370, https://doi.org/10.1016/S0924-2716(02)00164-8, 2003.
Hisdal, V.: Geography of Svalbard, vol. 2, Norwegian Polar Institue, Oslo, 1985.
Hochstein, M. P., Claridge, D., Henrys, S. A., Pyne, A., Nobes, D. C., and Leary, S. F.: Downwasting of the Tasman Glacier, South Island, New-zealand – Changes In the Terminus Region Between 1971 and 1993, New Zeal. J. Geol. Geop., 38, 1–16, https://doi.org/10.1080/00288306.1995.9514635, 1995.
Howat, I. M., Smith, B. E., Joughin, I., and Scambos, T. A.: Rates of southeast Greenland ice volume loss from combined ICESat and ASTER observations, Geophys. Res. Lett., 35, L17505, https://doi.org/10.1029/2008GL034496, 2008.
Iwasaki, A. and Fujisada, H.: ASTER geometric performance, Geoscience and Remote Sensing, IEEE Transact., 43, 2700–2706, https://doi.org/10.1109/TGRS.2005.849055, 2005.
K{ä}{ä}b, A.: Remote Sensing of Mountain Glaciers and Permafrost Creep, Geographisches Institut der Universit{ü}rich, Z{ü}rich, 2005.
K{ä}{ä}b, A.: Glacier Volume Changes Using ASTER Satellite Stereo and ICESat GLAS Laser Altimetry, A Test Study on Edgeøya, Eastern Svalbard, IEEE Int. Geosci. Remote, 46, 2823–2830, https://doi.org/10.1109/TGRS.2008.2000627, 2008.
K{ä}{ä}b, A., Huggel, C., Paul, F., Wessels, R., Raup, B., Kieffer, H., and Kargel, J.: Glacier monitoring from ASTER imagery: Accuracy and Applications, in: Proceedings of EARSeL-LISSIG-Workshop Observing our Cryosphere from Space, 2002.
Kirkbride, M. P.: Relationships Between Temperature and Ablation On the Tasman Glacier, Mount Cook National-park, New-zealand, New Zeal. J. Geol. Geop., 38, 17–27, https://doi.org/10.1080/00288306.1995.9514636, 1995.
Kn{ö}pfle, W., Strunz, G., and Roth, A.: Mosaicking of Digital Elevation Models derived by SAR interferometry, ISPRS Commission IV Symposium on GIS – Between Visions and Applications, 32, 1998.
Koblet, T., Gärtner-Roer, I., Zemp, M., Jansson, P., Thee, P., Haeberli, W., and Holmlund, P.: Reanalysis of multi-temporal aerial images of Storglaciären, Sweden (1959-99) - Part 1: Determination of length, area, and volume changes, The Cryosphere, 4, 333–343, https://doi.org/10.5194/tc-4-333-2010, 2010.
Korona, J., Berthier, E., Bernard, M., R{é}my, F., and Thouvenot, E.: SPIRIT. SPOT 5 stereoscopic survey of Polar Ice: Reference Images and Topographies during the fourth International Polar Year (2007–2009), ISPRS J. Photogramm. Remote S., 64, 204–212, https://doi.org/10.1016/j.isprsjprs.2008.10.005, 2009.
Larsen, C. F., Motyka, R. J., Arendt, A. A., Echelmeyer, K. A., and Geissler, P. E.: Glacier changes in southeast Alaska and northwest British Columbia and contribution to sea level rise, J. Geophys. Res.-Earth, 112, F01007, https://doi.org/10.1029/2006JF000586, 2007.
Leprince, S., Ayoub, F., Klingert, Y., and Avouac, J.-P.: Co-Registration of Optically Sensed Images and Correlation (COSI-Corr): an operational methodology for ground deformation measurements, in: Geoscience and Remote Sensing Symposium, 2007, IGARSS 2007, IEEE International, 1943–1946, https://doi.org/10.1109/IGARSS.2007.4423207, 2007.
Li, Z. L.: On the Measure of Digital Terrain Model Accuracy, Photogramm. Rec. 12, 873–877, https://doi.org/10.1111/j.1477-9730.1988.tb00636.x, 1988.
Lillesand, T., Kiefer, R., and Chipman, J.: Remote Sensing and Image Interpretation, 5 editon, John Wiley & Sons, Inc., Hoboken, NJ, 2004.
Luthcke, S. B., Rowlands, D. D., Williams, T. A., and Sirota, M.: Reduction of ICESat systematic geolocation errors and the impact on ice sheet elevation change detection, Geophys. Res. Lett., 32(4), L21S05, https://doi.org/10.1029/2005GL023689, 2005.
Magruder, L., Silverberg, E., Webb, C., and Schutz, B.: In situ timing and pointing verification of the ICESat altimeter using a ground-based system, Geophys. Res. Lett., 32(5), L21S04, https://doi.org/10.1029/2005GL023504, 2005.
METI/NASA/USGS: ASTER Global DEM Validation Summary Report, Tech. rep., METI/ERSDAC, NASA/LPDAAC, USGS/EROS, 2009.
Miller, P. E., Kunz, M., Mills, J. P., King, M. A., Murray, T., James, T. D., and Marsh, S. H.: Assessment of Glacier Volume Change Using ASTER-Based Surface Matching of Historical Photography, IEEE T. Geosci. Remote, 47, 1971–1979, https://doi.org/10.1109/TGRS.2009.2012702, 2009.
Moholdt, G., Hagen, J. O., Eiken, T., and Schuler, T. V.: Geometric changes and mass balance of the Austfonna ice cap, Svalbard, The Cryosphere, 4, 21–34, https://doi.org/10.5194/tc-4-21-2010, 2010a.
Moholdt, G., Nuth, C., Hagen, J., and Kohler, J.: Recent elevation changes of Svalbard glaciers derived from ICESat laser altimetry, Remote Sens. Environ., 114, 2756–2767, https://doi.org/10.1016/j.rse.2010.06.008, 2010b.
M{ü}ller, K.: Microwave penetration in polar snow and ice: Implications for GPR and SAR, Department of Geosciences, University of Oslo, 2011.
Muskett, R. R., Lingle, C. S., Sauber, J. M., Post, A. S., Tangborn, W. V., Rabus, B. T., and Echelmeyer, K. A.: Airborne and spaceborne DEM- and laser altimetry-derived surface elevation and volume changes of the Bering Glacier system, Alaska, USA, and Yukon, Canada, 1972–2006, J. Glaciol., 55, 316–326, https://doi.org/10.3189/002214309788608750, 2009.
NASA, NGA, DLR, and ASI: Shuttle Radar Topography Mission (SRTM) Elevation Data Set, http://seamless.usgs.gov (last access: 12 August 2010), 2002.
Nuth, C.: Quantification and interpretation of glacier elevation changes, Ph.D. thesis, Dept. of Geosciences, University of Oslo, 2011.
Nuth, C., Kohler, J., Aas, H. F., Brandt, O., and Hagen, J. O.: Glacier geometry and elevation changes on Svalbard (1936–90): a baseline dataset, Ann. Glaciol., 46, 106–116, https://doi.org/10.3189/172756407782871440, 2007.
Nuth, C., Moholdt, G., Kohler, J., Hagen, J. O., and K{ä}{ä}b, A.: Svalbard glacier elevation changes and contribution to sea level rise, J. Geophys. Res.-Earth, 115, F01008, https://doi.org/10.1029/2008JF001223, 2010.
Oerlemans, J., Bassford, R. P., Chapman, W., Dowdeswell, J. A., Glazovsky, A. F., Hagen, J. O., Melvold, K., de Wildt, M. D., and van de Wal, R. S. W.: Estimating the contribution of Arctic glaciers to sea-level change in the next 100 years, Ann. Glaciol., 42, 230–236, https://doi.org/10.3189/172756405781812745, 2005.
Paul, F.: Calculation of glacier elevation changes with SRTM: is there an elevation-dependent bias?, J. Glaciol., 54, 945–946, https://doi.org/10.3189/002214308787779960, 2008.
Paul, F. and Haeberli, W.: Spatial variability of glacier elevation changes in the Swiss Alps obtained from two digital elevation models, Geophys. Res. Lett., 35, L21502, https://doi.org/10.1029/2008GL034718, 2008.
Peduzzi, P., Herold, C., and Silverio, W.: Assessing high altitude glacier thickness, volume and area changes using field, GIS and remote sensing techniques: the case of Nevado Coropuna (Peru), The Cryosphere, 4, 313–323, https://doi.org/10.5194/tc-4-313-2010, 2010.
Pritchard, H. D., Arthern, R. J., Vaughan, D. G., and Edwards, L. A.: Extensive dynamic thinning on the margins of the Greenland and Antarctic ice sheets, Nature, 461, 971–975, https://doi.org/10.1038/nature08471, 2009.
Quincey, D. J. and Glasser, N. F.: Morphological and ice-dynamical changes on the Tasman Glacier, New Zealand, 1990–2007, Global Planet. Change, 68, 185–197, https://doi.org/10.1016/j.gloplacha.2009.05.003, 2009.
Rabus, B., Eineder, M., Roth, A., and Bamler, R.: The shuttle radar topography mission – a new class of digital elevation models acquired by spaceborne radar, ISPRS J. Photogramm. Remote S., 57, 241–262, https://doi.org/10.1016/S0924-2716(02)00124-7, 2003.
Racoviteanu, A. E., Manley, W. F., Arnaud, Y., and Williams, M. W.: Evaluating digital elevation models for glaciologic applications: An example from Nevado Coropuna, Peruvian Andes, Global Planet. Change, 59, 110–125, https://doi.org/10.1016/j.gloplacha.2006.11.036, 2007.
Rignot, E., Echelmeyer, K., and Krabill, W.: Penetration depth of interferometric synthetic-aperture radar signals in snow and ice, Geophys. Res. Lett., 28, 3501–3504, https://doi.org/10.1029/2000GL012484, 2001.
Rignot, E., Rivera, A., and Casassa, G.: Contribution of the Patagonia Icefields of South America to sea level rise, Science, 302, 434–437, https://doi.org/10.1126/science.1087393, 2003.
Rodriguez, E., Morris, C. S., and Belz, J. E.: A global assessment of the SRTM performance, Photogramm. Eng. Rem. S., 72, 249–260, 2006.
Rolstad, C., Haug, T., and Denby, B.: Spatially integrated geodetic glacier mass balance and its uncertainty based on geostatistical analysis: application to the western Svartisen ice cap, Norway, J. Glaciol., 55, 666–680, https://doi.org/10.3189/002214309789470950, 2009.
Rosen, P. A., Hensley, S., Joughin, I. R., Li, F. K., Madsen, S. N., Rodriguez, E., and Goldstein, R. M.: Synthetic aperture radar interferometry – Invited paper, Proceedings IEEE, 88, 333–382, https://doi.org/10.1109/5.838084, 2000.
Schenk, T.: Digital Photogrammetry, vol. 1, TerraScience, Laurelville, Ohio, 1999.
Schenk, T., Csatho, B., van der Veen, C. J., Brecher, H., Ahn, Y., and Yoon, T.: Registering imagery to ICESat data for measuring elevation changes on Byrd Glacier, Antarctica, Geophys. Res. Lett., 32(4), L23S05, https://doi.org/10.1029/2005GL024328, 2005.
Schiefer, E., Menounos, B., and Wheate, R.: Recent volume loss of British Columbian glaciers, Canada, Geophys. Res. Lett., 34, L16503, https://doi.org/10.1029/2007GL030780, 2007.
Shuman, C. A., Zwally, H. J., Schutz, B. E., Brenner, A. C., DiMarzio, J. P., Suchdeo, V. P., and Fricker, H. A.: ICESat Antarctic elevation data: Preliminary precision and accuracy assessment, Geophys. Res. Lett., 33(4), L07501, https://doi.org/10.1029/2005GL025227, 2006.
Skinner, B.: Measurement of twentieth century ice loss on the Tasman Glacier, New Zealand, New Zealand, J. Geol. Geophys., 7, 796–803, 1964.
Sund, M., Eiken, T., Hagen, J. O., and K{ä}{ä}b, A.: Svalbard surge dynamics derived from geometric changes, Ann. Glaciol., 50, 50-60, https://doi.org/10.3189/172756409789624265, 2009.
Surazakov, A. B. and Aizen, V. B.: Estimating volume change of mountain glaciers using SRTM and map-based topographic data, IEEE T. Geosci. Remote, 44, 2991–2995, https://doi.org/10.1109/TGRS.2006.875357, 2006.
Toutin, T.: Three-dimensional topographic mapping with ASTER stereo data in rugged topography, IEEE T. Geosci. Remote, 40, 2241–2247, https://doi.org/10.1109/TGRS.2002.80287, 2002.
Toutin, T.: Review article: Geometric processing of remote sensing images: models, algorithms and methods, Int. J. Remote Sens., 25, 1893–1924, https://doi.org/10.1080/0143116031000101611, 2004.
Toutin, T.: ASTER DEMs for geomatic and geoscientific applications: a review, Int. J. Remote Sens., 29, 1855–1875, https://doi.org/10.1080/01431160701408477, 2008.
Van Niel, T. G., McVicar, T. R., Li, L. T., Gallant, J. C., and Yang, Q. K.: The impact of misregistration on SRTM and DEM image differences, Remote Sens. Environ., 112, 2430–2442, https://doi.org/10.1016/j.rse.2007.11.003, 2008.
Wilson, J. and Gallant, J.: Terrain Analysis, Principles and Applications, John Wiley & Sons, Inc., New York, 2000.
Zwally, H. J., Schutz, R., Bentley, C., Bufton, J., Herring, T., Minster, J., Spinhirne, J., and Thomas, R.: GLAS/ICESat L2 Global ElevationData V031, Febuary 2003 to November 2009, http://nsidc.org/data/icesat/index.html, last access: 1 September 2010, Digital Media, National Snow and Ice Data Center, 2010a.
Zwally, H. J., Schutz, R., Bentley, C., Bufton, J., Herring, T., Minster, J., Spinhirne, J., and Thomas., R.: GLAS/ICESat L2 Global Land Surface Altimetry Data V531, Febuary 2003 to November 2009, http://nsidc.org/data/icesat/index.html, last access: 1 September 2010, Digital Media, National Snow and Ice Data Center, 2010b.
Zwally, H. J., Schutz, B., Abdalati, W., Abshire, J., Bentley, C., Brenner, A., Bufton, J., Dezio, J., Hancock, D., Harding, D., Herring, T., Minster, B., Quinn, K., Palm, S., Spinhirne, J., and Thomas, R.: ICESat's laser measurements of polar ice, atmosphere, ocean, and land, J. Geodynam., 34, 405–445, https://doi.org/10.1016/S0264-3707(02)00042-X, 2002.
