Articles | Volume 6, issue 1
https://doi.org/10.5194/tc-6-51-2012
© Author(s) 2012. 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-6-51-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
13 Jan 2012
Research article |
| 13 Jan 2012
Comparison of MODIS-derived land surface temperatures with ground surface and air temperature measurements in continuous permafrost terrain
S. Hachem
Interdisciplinary Centre on Climate Change and Department of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
C. R. Duguay
Interdisciplinary Centre on Climate Change and Department of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada
M. Allard
Centre d'études nordiques and Département de Géographie, Université Laval, Québec, Québec G1K 7P4, Canada
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
Short summary
Short summary
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
Short summary
Short summary
Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
Randall Bonnell, Daniel McGrath, Jack Tarricone, Hans-Peter Marshall, Ella Bump, Caroline Duncan, Stephanie Kampf, Yunling Lou, Alex Olsen-Mikitowicz, Megan Sears, Keith Williams, Lucas Zeller, and Yang Zheng
The Cryosphere, 18, 3765–3785, https://doi.org/10.5194/tc-18-3765-2024, https://doi.org/10.5194/tc-18-3765-2024, 2024
Short summary
Short summary
Snow provides water for billions of people, but the amount of snow is difficult to detect remotely. During the 2020 and 2021 winters, a radar was flown over mountains in Colorado, USA, to measure the amount of snow on the ground, while our team collected ground observations to test the radar technique’s capabilities. The technique yielded accurate measurements of the snowpack that had good correlation with ground measurements, making it a promising application for the upcoming NISAR satellite.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Automated stations measure snow properties at a single point but are frequently used to validate data that represent much larger areas. We use lidar snow depth data to see how often the mean snow depth surrounding a snow station is within 10 cm of the snow station depth at different scales. We found snow stations overrepresent the area-mean snow depth in ~ 50 % of cases, but the direction of bias at a site is temporally consistent, suggesting a site could be calibrated to the surrounding area.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains. The reflections were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The results demonstrate that C-band radar is sensitive to seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy and snow wetness may complicate satellite-based snow depth retrievals.
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
Short summary
Short summary
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
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.
James Dillon, Christopher Donahue, Evan Schehrer, Karl Birkeland, and Kevin Hammonds
The Cryosphere, 18, 2557–2582, https://doi.org/10.5194/tc-18-2557-2024, https://doi.org/10.5194/tc-18-2557-2024, 2024
Short summary
Short summary
Surface hoar crystals are snow grains that form when vapor deposits on a snow surface. They create a weak layer in the snowpack that can cause large avalanches to occur. Thus, determining when and where surface hoar forms is a lifesaving matter. Here, we developed a means of mapping surface hoar using remote-sensing technologies. We found that surface hoar displayed heightened texture, hence the variability of brightness. Using this, we created surface hoar maps with an accuracy upwards of 95 %.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
We compared infrared images taken by GOES-R satellites of an area with snow and forests against surface temperature measurements taken on the ground, from an aircraft, and by another satellite. We found that GOES-R measured warmer temperatures than the other measurements, especially in areas with more forest and when the Sun was behind the satellite. From this work, we learned that the position of the Sun and surface features such as trees that can cast shadows impact GOES-R infrared images.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
This study uses radar imagery from the Sentinel-1 satellite to derive snow depth from increases in the returning energy. These retrieved depths are then compared to nine lidar derived snow depths across the western United State to assess the ability of this technique to be used to monitor global snow distributions. We also qualitatively compare the changes in underlying Sentinel-1 amplitudes against both the total lidar snow depths and 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
Short summary
Short summary
Understanding snow phenology (SP) and its possible feedback are important. We reveal spatiotemporal heterogeneous SP on the Tibetan Plateau (TP) and the mediating effects from meteorological, topographic, and environmental factors on it. The direct effects of meteorology on SP are much greater than the indirect effects. Topography indirectly effects SP, while vegetation directly effects SP. This study contributes to understanding past global warming and predicting future trends on the TP.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
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
Short summary
Short summary
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
Short summary
Short summary
Seasonal snowfall accumulation plays a critical role in climate. The water stored in it is measured by the snow water equivalent (SWE), the amount of water released after completely melting. We demonstrate a Bayesian physical–statistical framework to estimate SWE from airborne X- and Ku-band synthetic aperture radar backscatter measurements constrained by physical snow hydrology and radar models. We explored spatial resolutions and vertical structures that agree well with ground observations.
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
Short summary
Short summary
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
Short summary
Short summary
The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
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
Short summary
Short summary
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
Short summary
Short summary
In situ techniques for snow depth (SD) measurement are not adequate to represent the spatiotemporal variability in SD in the Western Himalayan region. Therefore, this study focuses on the high-resolution mapping of daily snow depth in the Indian Western Himalayan region using passive microwave remote-sensing-based algorithms. Overall, the proposed multifactor SD models demonstrated substantial improvement compared to the operational products. However, there is a scope for further improvement.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Seasonal snow accumulates each winter, storing water to release later in the year and modulating both water and energy cycles, but the amount of seasonal snow is one of the most poorly measured components of the global water cycle. Satellite concepts to monitor snow accumulation have been proposed but not selected. This paper shows that snow accumulation can be measured using radar, and that (contrary to previous studies) does not require highly accurate information about snow microstructure.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Roads on permafrost have significant environmental effects. This study assessed the Inuvik to Tuktoyaktuk Highway (ITH) in Canada and its impact on snow accumulation, albedo and snowmelt timing. Our findings revealed that snow accumulation increased by up to 36 m from the road, 12-day earlier snowmelt within 100 m due to reduced albedo, and altered snowmelt patterns in seemingly undisturbed areas. Remote sensing aids in understanding road impacts on permafrost.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Snow conditions in the Northern Hemisphere are rapidly changing, and information on snow depth is important for decision-making. We present snow depth measurements using different drones throughout the winter at a subarctic site. Generally, all drones produced good estimates of snow depth in open areas. However, differences were observed in the accuracies produced by the different drones, and a reduction in accuracy was observed when moving from an open mire area to forest-covered areas.
Leo-Juhani Meriö, Anssi Rauhala, Pertti Ala-aho, Anton Kuzmin, Pasi Korpelainen, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4363–4380, https://doi.org/10.5194/tc-17-4363-2023, https://doi.org/10.5194/tc-17-4363-2023, 2023
Short summary
Short summary
Information on seasonal snow cover is essential in understanding snow processes and operational forecasting. We study the spatiotemporal variability in snow depth and snow processes in a subarctic, boreal landscape using drones. We identified multiple theoretically known snow processes and interactions between snow and vegetation. The results highlight the applicability of the drones to be used for a detailed study of snow depth in multiple land cover types and snow–vegetation interactions.
Kirsty Wivell, Stuart Fox, Melody Sandells, Chawn Harlow, Richard Essery, and Nick Rutter
The Cryosphere, 17, 4325–4341, https://doi.org/10.5194/tc-17-4325-2023, https://doi.org/10.5194/tc-17-4325-2023, 2023
Short summary
Short summary
Satellite microwave observations improve weather forecasts, but to use these observations in the Arctic, snow emission must be known. This study uses airborne and in situ snow observations to validate emissivity simulations for two- and three-layer snowpacks at key frequencies for weather prediction. We assess the impact of thickness, grain size and density in key snow layers, which will help inform development of physical snow models that provide snow profile input to emissivity simulations.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Ackerman, S. A., Strabala K. I., Menzel, P. W. P., Frey, R. A., Moeller, C. C., and Gumley, L. E: Discriminating clear sky from clouds with MODIS, J. Geophys. Res., 103, 32141–32157, 1998.
Bosilovich, M. G.: A comparison of MODIS land surface temperature with in situ observations, Geophys. Res. Lett., 33, L20112, https://doi.org/10.1029/2006GL027519, 2006.
Bouchard, C.: Simulation du régime thermique des sols pergélisolés: essai du modèle "Tone", Master thesis, Université Laval, 137 pp., 1990.
Coll, C., Caselles, V., Galve, J. M., Valor, E., Niclos, R., Sanchez, J. M., and Rivas, R.:Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data, Remote Sens. Environ., 97, 288–300, https://doi.org/10.1016/j.rse.2005.05.007, 2005.
Coll, C., Wan, Z., and Galvem, J. M.: Temperature-based and radiance- based validations of the V5 MODIS land surface temperature product, J. Geophys. Res., 114, D20102, https://doi.org/10.1029/2009JD012038, 2009.
Christensen, T. R., Johansson, T., Akerman, J. H., Mastepanov, M., Malmer, N., Friborg, T., Crill, P., and Svensson, B. H.: Thawing sub-arctic permafrost: effects on vegetation and methane emissions, Geophys. Res. Lett., 31, L04501, https://doi.org/10.1029/2003GL018680, 2004.
Comiso, J. C.: Warming trends in the Arctic from clear sky satellite observations, J. Climate, 16, 3498–3510, 2003.
Coops, N. C., Wulder, M. A., and Iwanicka, D.: Large area monitoring with a MODIS-based Disturbance Index (DI) sensitive to annual and seasonal variations, Remote Sens. Environ., 113, 1250–1261, https://doi.org/10.1016/j.rse.2010.01.005, 2009.
Crosman, E. T. and Horel, J. D.: MODIS-derived surface temperature of the Great Salt Lake, Remote Sens. Environ., 113, 73–81, https://doi.org/10.1016/j.rse.2008.08.013, 2009.
Eastman, R. and Warren, S. G.: Interannual Variations of Arctic Cloud Types in Relation to Sea Ice, J. Climate, 23, 4216–4232, https://doi.org/10.1175/2010JCLI3492.1, 2010.
Fernandes, R. A., Pavlic, G., Chen, W., and Fraser, R.: Canada-wide 1 km water fraction derived from National Topographic Data Base maps, Natural Resources Canada, Earth Science Sector, Ottawa, Ontario, Canada, availeble at: http://globalchange.nasa.gov/KeywordSearch/Metadata.do?Portal=GCMD&KeywordPath=Locations$\vert $VERTICAL+LOCATION$\vert $SEA+SURFACE&OrigMetadataNode=CANADA-CGDI&EntryId=Canada_GeoGratis_1kmWatFracNTDB&MetadataView=Full&MetadataType=0&lbnode=mdlb3, last access: 03 January 2012, 2002.
Gahé, E.: Géomorphologie cryogène et géophysique dans la région de Kangiqsualujjuaq, Ungava, Ph.D. thesis, Université Laval, 210 pp., 1987.
Galve, J. M., Coll, C., Caselles, V., Valor, E., Niclos, R., Sanchez, J. M., and Mira, M.: Simulation and validation of land surface temperature algorithms for MODIS and AATSR data, Tethys, 4, 27–32, https://doi.org/10.3369/tethys.2007.4.04, 2007.
Gleason, A. C. R., Prince, S. D., Goetz, S. J., and Small, J.: Effects of orbital drift on land surface temperature measured by AVHRR thermal sensors, Remote Sens. Environ., 79, 147–165, https://doi.org/S0034-4257(01)00269-3, 2002.
Goïta, K., Royer, A. B., and Bussières, N.: Characterization of land surface thermal structure from NOAA-AVHRR data over a Northern Ecosystem, Remote Sens. Environ., 60, 282–298, https://doi.org/S0034-4257(96)00211-8, 1997.
Goodrich, L.: The influence of snow cover on the ground thermal regime, Can. Geotech. J., 19, 421–432, 1982.
Hachem, S., Allard, M., and Duguay, C. R.: A new permafrost map of Québec-Labrador derived from near surface temperature data of the Moderate Resolution Imaging Spectroradiometer (MODIS), in: Proceedings of the 9th International Conference on Permafrost, 1, 591–596, 2008.
Hachem, S., Allard, M., and Duguay, C. R.: Using the MODIS land surface temperature product for mapping permafrost: an application to Northern Québec and Labrador, Canada, Permafrost Periglac., 20, 407–416, https://doi.org/10.1002/ppp.672, 2009.
Hall, D., Key, J., Casey, K., Riggs, G., and Cavalieri, D.: Sea ice surface temperature product from MODIS, IEEE Geosci. Remote S., 42, 1076–1087, https://doi.org/10.1109/TGRS.2004.825587, 2004.
Han, K.-S., Viau, A. A., and Anctil, F.: An Analysis of GOES and NOAA derived land surface temperatures estimated over a boreal forest, Int. J. Remote Sens., 25, 4761–4780, https://doi.org/10.1080/01431160410001680446, 2004.
Heim, B., Bartsch, A., Elger, K., Lantuit, H., Boike, J., Muster, S., Langer, M., Duguay, C., Hachem, S., Soliman, A., Paulik, C., Strozzi, T., and Seifert, F. M.: ESA DUE Permafrost: An earth observation (EO) permafrost monitoring system, EARSeL eProceedings, 10, 73–82, 2011.
Hinzman, L. D., Goering, D. J., and Kane, D. L.: A distributed thermal model for calculating soil temperature profiles and depth of thaw in permafrost regions, J. Geophys. Res., 103, 28975–28991, 1998.
Hinzman, L. D., Bettez, N. D., Bolton, W. R., Chapin III, F. S., Dyurgerov, M. B., Fastie, C. L., Griffith, B., Hollister, R. D., Hope, A., Huntington, H. P., Jensen, A. M., Jia, G. J., Jorgenson, T., Kane, D. L., Klein, D. R., Kofinas, G., Lynch, A. H., Lloyd, A. H., McGuire, A. D., Nelson, F. E., Oechel, W. C., Osterkamp, T. E., Racine, C. H., Romanowsky, V. E., Stone, R. S., Stow, D. A., Sturm, M., Tweedie, C. E., Vourlitis, G. L., Walker, M. D., Walker, D. A., Webber, P. J., Welker, J. M., Winker, K., and Yoshikawa, K.: Evidence and implications of recent climate change in Northern Alaska and other arctic regions, Climatic Change, 72, 251–298, https://doi.org/10.1007/s10584-005-5352-2, 2005.
Hook, S. J., Vaughan, R. G., Tonooka, H., and Schladow, S. G.: Absolute radiometric inflight validation of mid infrared and thermal infrared data from ASTER and MODIS on the Terra Spacecraft using the Lake Tahoe, CA/NV, USA, automated validation site, IEEE Geosci. Remote S., 45, 1798–1807, https://doi.org/10.1109/TGRS.2007.894564, 2007.
IPCC Report 2007: Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Core Writing Team, edited by: Pachauri, R. K. and Reisinger, A., IPCC, Geneva, Switzerland, pp.104, 2007.
Jia, G. J. and Epstein, H. E.: Controls over intra-seasonal dynamics of AVHRR NDVI for the Arctic tundra in Northern Alaska, Int. J. Remote Sens., 25, 1547–1564, https://doi.org/10.1080/0143116021000023925, 2004.
Jin, M. and Treadon, R. E.: Correcting the orbit drift effect on AVHRR land surface skin temperature measurements, Int. J. Remote Sens., 24, 4543–4558, https://doi.org/10.1080/0143116031000095943, 2003.
Kane, D. L.: High-latitude hydrology, what do we know?, Hydrol. Process., 19, 2453–2454, https://doi.org/10.1002/hyp.5929, 2005
Kane, D. L. and Hinzman, L. D.: Climate data from the North Slope Hydrology Research project, University of Alaska Fairbanks, Water and Environmental Research Center, available at: http://ine.uaf.edu/werc/projects/NorthSlope/ Fairbanks, Alaska, variously paged, Retrieval August 2009.
Klene, A. E., Nelson, F. E., and Shiklomanov, N. I.: The N-factor in Natural Landscapes: Variability of Air and Soil-Surface Temperatures, Kuparuk River Basin, Alaska,USA, Arctic and Alpine Research, 33, 140–148, 2001.
Kohn, J. and Royer, A.: AMSR-E data inversion for soil temperature estimation under snow cover, Remote Sens. Environ., 114, 2951–2961, https://doi.org/10.1016/j.rse.2010.08.002, 2010.
Langer, M., Westermann, S., and Boike, J.: Spatial and temporal variations of summer surface temperatures of wet polygonal tundra in Siberia – implications for MODIS LST based permafrost monitoring, Remote Sens. Environ., 114, 2059–2069, https://doi.org/10.1016/j.rse.2010.04.012, 2010.
Liu, Y., Ackerman, S., Maddux, B., Key, J., Frey, R.: Errors in cloud detection over the Arctic using a satellite imager and implications for observing feedback mechanisms, J. Climate, 23, 1894–1907, https://doi.org/10.1175/2009JCLI3386.1, 2010.
Mialon, A., Royer, A., Fily, M., and Picard, G.: Daily microwave-derived surface temperature over Canada/Alaska, J. Appl. Meteorol. Clim., 46, 591–604, https://doi.org/10.1175/JAM2485.1, 2007.
Marchenko, S., Hachem, S., Romanovsky, V., and Duguay, C.: Permafrost and active layer modeling in the Northern Eurasia using MODIS Land Surface Temperature as an input data, Geophys. Res. Abstracts, 11, 2009–11077, 2009.
Romanovsky, V. E., Burgess, M., Smith, S., Yoshikawa, K., and Brown, J.: Permafrost temperature records: indicators of climate change, EOS Transactions, 83, 593–594, 2002.
Royer, A. and Poirier, S.: Surface temperature spatial and temporal variations in North America from homogenized satellite SMMR – SSM/I microwave measurements and reanalysis for 1979–2008, J. Geophys. Res.-Atmos., 115, D08110, https://doi.org/10.1029/2009JD012760, 2010.
Sazonova, T. and Romanovsky, V. E.: A model for regional scale estimation of temporal and spatial variability of active layer thickness and mean annual ground temperatures, Permafrost Periglac., 14, 125–139, https://doi.org/10.1002/ppp.449, 2003.
Schubert, P., Eklundh, L., Lund, M., and Nilsson, M.: Estimations northern peatland CO2 exchange from MODIS time series data, Remote Sens. Environ., 114, 1178–1189, https://doi.org/10.1175/JAM2485.1, 2010.
Serreze, M. C., Walsh, J. E., Chapin III, F. S., Osterkamp, T. E., Dyurgerov, M. B., Romanovsky, V. E., Oechel, W. C., Morison, J., Zhang, T., and Barry, R. G.: The observational evidence of recent change in the northern high latitude environment, Climatic Change, 46, 159–207, https://doi.org/10.1023/A:1005504031923, 2000.
Shiklomanov, N. I. and Nelson, F. E.: Active-layer mapping at regional scales: a 13 yr spatial time series for the Kuparuk Region, North-Central Alaska, Permafrost Periglac., 13, 219–230, https://doi.org/10.1002/ppp.425, 2002.
Shiklomanov, N. I., Anisimov, O. A., Zhang, T., Marchenko, S., Nelson, F. E., and Oelke, C.: Comparison of model-produced active layer fields: results for northern Alaska, J. Geophys. Res., 112, F02S10, https://doi.org/10.1029/2006JF000571, 2007.
Snyder, W. C., Wan, Z., Zhang, Y., and Feng, Y.-Z.: Requirements for satellite land surface temperature validation using a Silt Playa, Remote Sens. Environ., 6, 279–289, 1997.
Traoré, P. C. S., Royer, A. B., and Goïta, K.: Land surface temperature time series derived from weekly AVHRR GVI composite datasets: Potential and constraints for Northern Latitudes, Can. J. Remote Sens., 4, 390–400, 1997.
Wan, Z.: MODIS Land Surface Temperature products user's guide, Institute for Computational Earth System Science, University of California, Santa Barbara, CA, availeble at: http://www.icess.ucsb.edu/modis/LstUsrGuide/MODIS_LST_products_Users_guide_C5.pdf, last access: 27 April 2011, 2007.
Wan, Z.: New refinements and validation of the MODIS land-surface temperature/emissivity products, Remote Sens. Environ., 112, 59–74, https://doi.org/10.1016/j.rse.2006.06.026, 2008.
Wan, Z. and Dozier, J.: A generalized split-window algorithm for retrieving land-surface temperature from space, IEEE Geosci. Remote S., 34, 892–905, https://doi.org/10.1109/36.508406, 1996.
Wan, Z. and Li, Z.-L.: A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data, IEEE Geosci. Remote S., 35, 980–996, https://doi.org/10.1109/36602541, 1997.
Wan, Z. and Li, Z-L.: Radiance-based validation of the V5 MODIS land-surface temperature product, Int. J. Remote Sens., 29, 5373–5395, https://doi.org/10.1080/01431160802036565, 2008.
Wan, Z., Zhang, Y., Li, Z.-L., Wang, R., Salomonson, V.V., Yves, A., Bosseno, R., and Hanocq, J. F.: Preliminary estimate of calibration of the Moderate Resolution Imaging Spectroradiometer thermal infrared data using Lake Titicaca, Remote Sens. Environ., 80, 497–515, https://doi.org/10.1016/S0034-4257(01)00327-3, 2002a.
Wan, Z., Zhang, Y., Zhang, Q., and Li Z.-L.: Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data, Remote Sens. Environ., 83, 163–180, https://doi.org/10.1016/S0034-4257(02)00093-7, 2002b.
Wan, Z., Zhang, Y., Zhang, Q., and Li Z.-L.: Quality assessment and validation of the MODIS global land surface temperature, Int. J. Remote Sens., 25, 261–274, https://doi.org/10.1080/0143116031000116417, 2004.
Westermann, S., Langer, M., and Boike, J.: Spatial and temporal variations of summer surface temperatures of high-arctic tundra on Svalbard – Implications for MODIS LST based permafrost monitoring, Remote Sens. Environ., 115, 908–922, https://doi.org/10.1016/j.rse.2010.11.018, 2011.
Wolfe, R. E., Nishihama, M., Fleig, A. J., Kuyper, J. A., Roy, D. P., Storey, J. C., Patt, F. S.: Achieving sub-pixel geolocation accuracy in support of MODIS land science, Remote Sens. Environ., 83, 31–49, https://doi.org/10.1016/S0034-4257(02)00085-8, 2002.
Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D. A., Shabanov, N. V., and Myneni, R. B.: Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999, J. Geophys. Res.-Atmos., 106, 20069–20083, 2001.
