Articles | Volume 11, issue 2
https://doi.org/10.5194/tc-11-755-2017
© Author(s) 2017. 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-11-755-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 Mar 2017
Research article |
| 23 Mar 2017
Signature of Arctic first-year ice melt pond fraction in X-band SAR imagery
Ane S. Fors
CORRESPONDING AUTHOR
Department of Physics and Technology, University of Tromsø – The Arctic University of Norway, 9037 Tromsø, Norway
Dmitry V. Divine
Norwegian Polar Institute, FRAM Centre, 9296 Tromsø, Norway
Department of Mathematics and Statistics, University of Tromsø – The Arctic University of Norway, 9037 Tromsø, Norway
Anthony P. Doulgeris
Department of Physics and Technology, University of Tromsø – The Arctic University of Norway, 9037 Tromsø, Norway
Angelika H. H. Renner
Norwegian Polar Institute, FRAM Centre, 9296 Tromsø, Norway
Institute of Marine Research, 9294 Tromsø, Norway
Sebastian Gerland
Norwegian Polar Institute, FRAM Centre, 9296 Tromsø, Norway
Related authors
Ane S. Fors, Camilla Brekke, Anthony P. Doulgeris, Torbjørn Eltoft, Angelika H. H. Renner, and Sebastian Gerland
The Cryosphere, 10, 401–415, https://doi.org/10.5194/tc-10-401-2016, https://doi.org/10.5194/tc-10-401-2016, 2016
Short summary
Short summary
This paper demonstrates how sea ice segmentation using high-resolution multi-polarisation synthetic aperture radar (SAR) can be used to retrieve valuable information about sea ice type during late summer. It adds knowledge to how choice of SAR features influence the information gain and highlights the sea ice segmentation capability of both the C and X band in late summer. The study contributes to an increased understanding of sea ice mapping and monitoring with SAR in the melt season.
Andrea Spolaor, Federico Scoto, Catherine Larose, Elena Barbaro, Francois Burgay, Mats P. Bjorkman, David Cappelletti, Federico Dallo, Fabrizio de Blasi, Dmitry Divine, Giuliano Dreossi, Jacopo Gabrieli, Elisabeth Isaksson, Jack Kohler, Tonu Martma, Louise S. Schmidt, Thomas V. Schuler, Barbara Stenni, Clara Turetta, Bartłomiej Luks, Mathieu Casado, and Jean-Charles Gallet
The Cryosphere, 18, 307–320, https://doi.org/10.5194/tc-18-307-2024, https://doi.org/10.5194/tc-18-307-2024, 2024
Short summary
Short summary
We evaluate the impact of the increased snowmelt on the preservation of the oxygen isotope (δ18O) signal in firn records recovered from the top of the Holtedahlfonna ice field located in the Svalbard archipelago. Thanks to a multidisciplinary approach we demonstrate a progressive deterioration of the isotope signal in the firn core. We link the degradation of the δ18O signal to the increased occurrence and intensity of melt events associated with the rapid warming occurring in the archipelago.
Emma Nilsson, Carmen Paulina Vega, Dmitry Divine, Anja Eichler, Tonu Martma, Robert Mulvaney, Elisabeth Schlosser, Margit Schwikowski, and Elisabeth Isaksson
EGUsphere, https://doi.org/10.5194/egusphere-2023-3156, https://doi.org/10.5194/egusphere-2023-3156, 2024
Short summary
Short summary
To project future climate change it is necessary to understand paleoclimate including past sea ice conditions. We have investigated methane sulphonic acid (MSA) in Antarctic firn and ice cores to reconstruct sea ice extent (SIE) and found that the MSA – SIE as well as the MSA – phytoplankton biomass relationship varies across the different firn and ice cores. These inconsistencies in correlations across records suggest that MSA in Fimbul Ice Shelf cores does not reliably indicate regional SIE.
Laust Færch, Wolfgang Dierking, Nick Hughes, and Anthony P. Doulgeris
The Cryosphere, 17, 5335–5355, https://doi.org/10.5194/tc-17-5335-2023, https://doi.org/10.5194/tc-17-5335-2023, 2023
Short summary
Short summary
Icebergs in open water are a risk to maritime traffic. We have compared six different constant false alarm rate (CFAR) detectors on overlapping C- and L-band synthetic aperture radar (SAR) images for the detection of icebergs in open water, with a Sentinel-2 image used for validation. The results revealed that L-band gives a slight advantage over C-band, depending on which detector is used. Additionally, the accuracy of all detectors decreased rapidly as the iceberg size decreased.
Wenkai Guo, Polona Itkin, Suman Singha, Anthony P. Doulgeris, Malin Johansson, and Gunnar Spreen
The Cryosphere, 17, 1279–1297, https://doi.org/10.5194/tc-17-1279-2023, https://doi.org/10.5194/tc-17-1279-2023, 2023
Short summary
Short summary
Sea ice maps are produced to cover the MOSAiC Arctic expedition (2019–2020) and divide sea ice into scientifically meaningful classes. We use a high-resolution X-band synthetic aperture radar dataset and show how image brightness and texture systematically vary across the images. We use an algorithm that reliably corrects this effect and achieve good results, as evaluated by comparisons to ground observations and other studies. The sea ice maps are useful as a basis for future MOSAiC studies.
Vidar S. Lien, Angelika H. H. Renner, Mari S. Myksvoll, Johnny A. Johannessen, Jeremy Cook, Helene Spurkeland, and Ronald Toppe
State Planet Discuss., https://doi.org/10.5194/sp-2022-12, https://doi.org/10.5194/sp-2022-12, 2022
Preprint withdrawn
Short summary
Short summary
The One Ocean Expedition, a part of the United Nations Ocean Decade, is a sailing voyage where a traditional tall ship equipped with state-of-the-art ocean observation technology is circumnavigating the globe with a crew consisting of students, scientists, trainees and professionals. The focus for the expedition is awareness raising and education through showcasing ocean science using a traditional tall ship as a platform.
Øyvind Lundesgaard, Arild Sundfjord, Sigrid Lind, Frank Nilsen, and Angelika H. H. Renner
Ocean Sci., 18, 1389–1418, https://doi.org/10.5194/os-18-1389-2022, https://doi.org/10.5194/os-18-1389-2022, 2022
Short summary
Short summary
In this study, 2-year mooring observations show the evolution of temperature, salinity, and currents in the northern Barents Sea. Inflow of Atlantic Water from the north in autumn and winter was the main driver of the seasonal cycle in the ocean. Winds modulated the inflow on shorter timescales. The upper-ocean state reflected how much sea ice had previously melted in the area. The import of ocean water and sea ice from adjacent regions plays a key role in the complex air–ice–ocean interplay.
Wenkai Guo, Polona Itkin, Johannes Lohse, Malin Johansson, and Anthony Paul Doulgeris
The Cryosphere, 16, 237–257, https://doi.org/10.5194/tc-16-237-2022, https://doi.org/10.5194/tc-16-237-2022, 2022
Short summary
Short summary
This study uses radar satellite data categorized into different sea ice types to detect ice deformation, which is significant for climate science and ship navigation. For this, we examine radar signal differences of sea ice between two similar satellite sensors and show an optimal way to apply categorization methods across sensors, so more data can be used for this purpose. This study provides a basis for future reliable and constant detection of ice deformation remotely through satellite data.
Anja Rösel, Sinead Louise Farrell, Vishnu Nandan, Jaqueline Richter-Menge, Gunnar Spreen, Dmitry V. Divine, Adam Steer, Jean-Charles Gallet, and Sebastian Gerland
The Cryosphere, 15, 2819–2833, https://doi.org/10.5194/tc-15-2819-2021, https://doi.org/10.5194/tc-15-2819-2021, 2021
Short summary
Short summary
Recent observations in the Arctic suggest a significant shift towards a snow–ice regime caused by deep snow on thin sea ice which may result in a flooding of the snowpack. These conditions cause the brine wicking and saturation of the basal snow layers which lead to a subsequent underestimation of snow depth from snow radar mesurements. As a consequence the calculated sea ice thickness will be biased towards higher values.
Bronwen L. Konecky, Nicholas P. McKay, Olga V. Churakova (Sidorova), Laia Comas-Bru, Emilie P. Dassié, Kristine L. DeLong, Georgina M. Falster, Matt J. Fischer, Matthew D. Jones, Lukas Jonkers, Darrell S. Kaufman, Guillaume Leduc, Shreyas R. Managave, Belen Martrat, Thomas Opel, Anais J. Orsi, Judson W. Partin, Hussein R. Sayani, Elizabeth K. Thomas, Diane M. Thompson, Jonathan J. Tyler, Nerilie J. Abram, Alyssa R. Atwood, Olivier Cartapanis, Jessica L. Conroy, Mark A. Curran, Sylvia G. Dee, Michael Deininger, Dmitry V. Divine, Zoltán Kern, Trevor J. Porter, Samantha L. Stevenson, Lucien von Gunten, and Iso2k Project Members
Earth Syst. Sci. Data, 12, 2261–2288, https://doi.org/10.5194/essd-12-2261-2020, https://doi.org/10.5194/essd-12-2261-2020, 2020
Lisa Claire Orme, Xavier Crosta, Arto Miettinen, Dmitry V. Divine, Katrine Husum, Elisabeth Isaksson, Lukas Wacker, Rahul Mohan, Olivier Ther, and Minoru Ikehara
Clim. Past, 16, 1451–1467, https://doi.org/10.5194/cp-16-1451-2020, https://doi.org/10.5194/cp-16-1451-2020, 2020
Short summary
Short summary
A record of past sea temperature in the Indian sector of the Southern Ocean, spanning the last 14 200 years, has been developed by analysis of fossil diatoms in marine sediment. During the late deglaciation the reconstructed temperature changes were highly similar to those over Antarctica, most likely due to a reorganisation of global ocean and atmospheric circulation. During the last 11 600 years temperatures gradually cooled and became increasingly variable.
Tine Nilsen, Dmitry V. Divine, Annika Hofgaard, Andreas Born, Johann Jungclaus, and Igor Drobyshev
Clim. Past Discuss., https://doi.org/10.5194/cp-2019-123, https://doi.org/10.5194/cp-2019-123, 2019
Revised manuscript not accepted
Short summary
Short summary
Using a set of three climate model simulations we cannot find a consistent relationship between atmospheric conditions favorable for forest fire activity in northern Scandinavia and weaker ocean circulation in the North Atlantic subpolar gyre on seasonal timescales. In the literature there is support of such a relationship for longer timescales. With the motivation to improve seasonal prediction systems, we conclude that the gyre circulation alone does not indicate forthcoming model drought.
Caixin Wang, Robert M. Graham, Keguang Wang, Sebastian Gerland, and Mats A. Granskog
The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, https://doi.org/10.5194/tc-13-1661-2019, 2019
Short summary
Short summary
A warm bias and higher total precipitation and snowfall were found in ERA5 compared with ERA-Interim (ERA-I) over Arctic sea ice. The warm bias in ERA5 was larger in the cold season when 2 m air temperature was < −25 °C and smaller in the warm season than in ERA-I. Substantial anomalous Arctic rainfall in ERA-I was reduced in ERA5, particularly in summer and autumn. When using ERA5 and ERA-I to force a 1-D sea ice model, the effects on ice growth are very small (cm) during the freezing period.
Tine Nilsen, Johannes P. Werner, Dmitry V. Divine, and Martin Rypdal
Clim. Past, 14, 947–967, https://doi.org/10.5194/cp-14-947-2018, https://doi.org/10.5194/cp-14-947-2018, 2018
Short summary
Short summary
The BARCAST climate field reconstruction method is tested using synthetic data experiments. It is demonstrated that the output reconstructions have altered statistical properties compared with the input data, but they are also not necessarily consistent with the model assumption of the reconstruction method. The conclusion is that the statistical properties of a reconstruction not only reflect the statistics of the real climate, but they may very well be affected by the manipulation of the data.
John Faulkner Burkhart, Arve Kylling, Crystal B. Schaaf, Zhuosen Wang, Wiley Bogren, Rune Storvold, Stian Solbø, Christina A. Pedersen, and Sebastian Gerland
The Cryosphere, 11, 1575–1589, https://doi.org/10.5194/tc-11-1575-2017, https://doi.org/10.5194/tc-11-1575-2017, 2017
Short summary
Short summary
We present the first use of spectrometer measurements from a drone to assess reflectance and albedo over the Greenland Ice Sheet. In order to measure albedo – a critical parameter in the earth's energy balance – a drone was flown along 200 km transects coincident with Terra and Aqua satellites flying MODIS. We present a direct comparison of UAV-measured reflectance with satellite data over Greenland and provide a new method to study cryospheric surfaces using UAV with spectral instruments.
Ane S. Fors, Camilla Brekke, Anthony P. Doulgeris, Torbjørn Eltoft, Angelika H. H. Renner, and Sebastian Gerland
The Cryosphere, 10, 401–415, https://doi.org/10.5194/tc-10-401-2016, https://doi.org/10.5194/tc-10-401-2016, 2016
Short summary
Short summary
This paper demonstrates how sea ice segmentation using high-resolution multi-polarisation synthetic aperture radar (SAR) can be used to retrieve valuable information about sea ice type during late summer. It adds knowledge to how choice of SAR features influence the information gain and highlights the sea ice segmentation capability of both the C and X band in late summer. The study contributes to an increased understanding of sea ice mapping and monitoring with SAR in the melt season.
M. -A. N. Moen, A. P. Doulgeris, S. N. Anfinsen, A. H. H. Renner, N. Hughes, S. Gerland, and T. Eltoft
The Cryosphere, 7, 1693–1705, https://doi.org/10.5194/tc-7-1693-2013, https://doi.org/10.5194/tc-7-1693-2013, 2013
Related subject area
Remote Sensing
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
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
Temperature-dominated spatiotemporal variability in snow phenology on the Tibetan Plateau from 2002 to 2022
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
Retrieval of airborne Ku-Band SAR Using Forward Radiative Transfer Modeling to Estimate Snow Water Equivalent: The Trail Valley Creek 2018/19 Snow Experiment
Assessing the sea ice microwave emissivity up to submillimeter waves from airborne and satellite observations
Improved records of glacier flow instabilities using customized NASA autoRIFT applied to PlanetScope imagery
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
Evaluating L-band InSAR Snow Water Equivalent Retrievals with Repeat Ground-Penetrating Radar and Terrestrial Lidar Surveys in Northern Colorado
Passive microwave remote-sensing-based high-resolution snow depth mapping for Western Himalayan zones using multifactor modeling approach
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
Toward Long-Term Monitoring of Regional Permafrost Thaw with Satellite InSAR
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
The AutoICE Challenge
Morphodynamics of the Mont Blanc glaciers and their recent evolution
Out-of-the-box calving-front detection method using deep learning
Reanalyzing the spatial representativeness of snow depth at automated monitoring stations using airborne Lidar data
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
New estimates of pan-Arctic sea ice–atmosphere neutral drag coefficients from ICESat-2 elevation data
GLAcier Feature Tracking testkit (GLAFT): a statistically and physically based framework for evaluating glacier velocity products derived from optical satellite image feature tracking
Evaluating the utility of active microwave observations as a snow mission concept using observing system simulation experiments
Relevance of warm air intrusions for Arctic satellite sea ice concentration time series
Observing the evolution of summer melt on multiyear sea ice with ICESat-2 and Sentinel-2
Cast shadows reveal changes in glacier surface elevation
AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini
Characterizing the surge behaviour and associated ice-dammed lake evolution of the Kyagar Glacier in the Karakoram
Spaceborne thermal infrared observations of Arctic sea ice leads at 30 m resolution
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.
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.
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.
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-651, https://doi.org/10.5194/egusphere-2024-651, 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.
Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell
EGUsphere, https://doi.org/10.5194/egusphere-2024-179, https://doi.org/10.5194/egusphere-2024-179, 2024
Short summary
Short summary
Satellite passive microwave observations are crucial for monitoring the Arctic's sea ice and atmosphere. To do this effectively, it's important to understand how sea ice emits microwaves. Through unique Arctic sea ice observations, we've improved our understanding, identifying four distinct emission types and expanding current knowledge towards higher frequencies. These findings will enhance our ability to monitor the Arctic climate and provide valuable information for new satellite missions.
Jukes Liu, Madeline Gendreau, Ellyn Mary Enderlin, and Rainey Aberle
EGUsphere, https://doi.org/10.5194/egusphere-2024-374, https://doi.org/10.5194/egusphere-2024-374, 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 5 glaciers and found that it produced accurate velocity data that supplemented their velocity records during rapid acceleration.
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-236, https://doi.org/10.5194/egusphere-2024-236, 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.
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.
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.
Taha Sadeghi Chorsi, Franz J. Meyer, and Timothy H. Dixon
EGUsphere, https://doi.org/10.5194/egusphere-2023-2605, https://doi.org/10.5194/egusphere-2023-2605, 2023
Short summary
Short summary
Active layer, the soil or sediment layer above permafrost, thaws and freezes seasonally. The annual freeze-thaw cycle of the active layer causes significant surface height change. We estimate subsidence rate and active layer thickness (ALT) for part of northern Alaska’s permafrost zone for summer 2017 to 2022 using satellite radar interferometry and LiDAR. ALT estimates range from ~20 cm to larger than 150 cm. Subsidence rate varies ranging from ~3–20 cm/year during the thaw season.
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.
Andreas R. Stokholm, Jørgen Buus-Hinkler, Tore Wulf, Anton Korosov, Roberto Saldo, Leif T. Pedersen, David Arthurs, Ionut Dragan, Iacopo Modica, Juan Pedro, Annekatrien Debien, Xinwei Chen, Muhammed Patel, Fernando J. P. Cantu, Javier N. Turnes, Jinman Park, Linlin Xu, Andrea K. Scott, David A. Clausi, Yuan Fang, Mingzhe Jiang, Saeid Taleghanidoozdoozan, Neil C. Brubacher, Armina Soleymani, Zacharie Gousseau, Michał Smaczny, Patryk Kowalski, Jacek Komorowski, David Rijlaarsdam, Jan N. van Rijn, Jens Jakobsen, Martin S. J. Rogers, Nick Hughes, Tom Zagon, Rune Solberg, Nicolas Longépé, and Matilde B. Kreiner
EGUsphere, https://doi.org/10.5194/egusphere-2023-2648, https://doi.org/10.5194/egusphere-2023-2648, 2023
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, the age and ice floe size using multiple sources of satellite and weather data across the Canadian and Greenlandic Arctic. To judge the solution, professionally drawn operational sea ice charts were used as reference. 179 students and sea ice and AI specialists participated and produced maps in broad agreement with the sea ice charts.
Fabrizio Troilo, Niccolò Dematteis, Francesco Zucca, Martin Funk, and Daniele Giordan
EGUsphere, https://doi.org/10.5194/egusphere-2023-2771, https://doi.org/10.5194/egusphere-2023-2771, 2023
Short summary
Short summary
One of the main features of glaciers is their movement along slopes. The study of this process is relevant in many aspects of glaciology. In the present study, optical satellite images of Mont Blanc were processed, obtaining surface velocities of 30 glaciers between 2016–2022. The study highlighted different behaviours and velocity variations that have relationships with the glacier morphology. An unexpected accelerating trend was observed, but its origin needs to be further investigated.
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.
Jordan N. Herbert, Mark S. Raleigh, and Eric E. Small
EGUsphere, https://doi.org/10.5194/egusphere-2023-2543, https://doi.org/10.5194/egusphere-2023-2543, 2023
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.
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.
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.
Whyjay Zheng, Shashank Bhushan, Maximillian Van Wyk De Vries, William Kochtitzky, David Shean, Luke Copland, Christine Dow, Renette Jones-Ivey, and Fernando Pérez
The Cryosphere, 17, 4063–4078, https://doi.org/10.5194/tc-17-4063-2023, https://doi.org/10.5194/tc-17-4063-2023, 2023
Short summary
Short summary
We design and propose a method that can evaluate the quality of glacier velocity maps. The method includes two numbers that we can calculate for each velocity map. Based on statistics and ice flow physics, velocity maps with numbers close to the recommended values are considered to have good quality. We test the method using the data from Kaskawulsh Glacier, Canada, and release an open-sourced software tool called GLAcier Feature Tracking testkit (GLAFT) to help users assess their velocity maps.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
Short summary
Short summary
As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
Philip Rostosky and Gunnar Spreen
The Cryosphere, 17, 3867–3881, https://doi.org/10.5194/tc-17-3867-2023, https://doi.org/10.5194/tc-17-3867-2023, 2023
Short summary
Short summary
During winter, storms entering the Arctic region can bring warm air into the cold environment. Strong increases in air temperature modify the characteristics of the Arctic snow and ice cover. The Arctic sea ice cover can be monitored by satellites observing the natural emission of the Earth's surface. In this study, we show that during warm air intrusions the change in the snow characteristics influences the satellite-derived sea ice cover, leading to a false reduction of the estimated ice area.
Ellen M. Buckley, Sinéad L. Farrell, Ute C. Herzfeld, Melinda A. Webster, Thomas Trantow, Oliwia N. Baney, Kyle A. Duncan, Huilin Han, and Matthew Lawson
The Cryosphere, 17, 3695–3719, https://doi.org/10.5194/tc-17-3695-2023, https://doi.org/10.5194/tc-17-3695-2023, 2023
Short summary
Short summary
In this study, we use satellite observations to investigate the evolution of melt ponds on the Arctic sea ice surface. We derive melt pond depth from ICESat-2 measurements of the pond surface and bathymetry and melt pond fraction (MPF) from the classification of Sentinel-2 imagery. MPF increases to a peak of 16 % in late June and then decreases, while depth increases steadily. This work demonstrates the ability to track evolving melt conditions in three dimensions throughout the summer.
Monika Pfau, Georg Veh, and Wolfgang Schwanghart
The Cryosphere, 17, 3535–3551, https://doi.org/10.5194/tc-17-3535-2023, https://doi.org/10.5194/tc-17-3535-2023, 2023
Short summary
Short summary
Cast shadows have been a recurring problem in remote sensing of glaciers. We show that the length of shadows from surrounding mountains can be used to detect gains or losses in glacier elevation.
Enze Zhang, Ginny Catania, and Daniel T. Trugman
The Cryosphere, 17, 3485–3503, https://doi.org/10.5194/tc-17-3485-2023, https://doi.org/10.5194/tc-17-3485-2023, 2023
Short summary
Short summary
Glacier termini are essential for studying why glaciers retreat, but they need to be mapped automatically due to the volume of satellite images. Existing automated mapping methods have been limited due to limited automation, lack of quality control, and inadequacy in highly diverse terminus environments. We design a fully automated, deep-learning-based method to produce termini with quality control. We produced 278 239 termini in Greenland and provided a way to deliver new termini regularly.
Guanyu Li, Mingyang Lv, Duncan J. Quincey, Liam S. Taylor, Xinwu Li, Shiyong Yan, Yidan Sun, and Huadong Guo
The Cryosphere, 17, 2891–2907, https://doi.org/10.5194/tc-17-2891-2023, https://doi.org/10.5194/tc-17-2891-2023, 2023
Short summary
Short summary
Kyagar Glacier in the Karakoram is well known for its surge history and its frequent blocking of the downstream valley, leading to a series of high-magnitude glacial lake outburst floods. Using it as a test bed, we develop a new approach for quantifying surge behaviour using successive digital elevation models. This method could be applied to other surge studies. Combined with the results from optical satellite images, we also reconstruct the surge process in unprecedented detail.
Yujia Qiu, Xiao-Ming Li, and Huadong Guo
The Cryosphere, 17, 2829–2849, https://doi.org/10.5194/tc-17-2829-2023, https://doi.org/10.5194/tc-17-2829-2023, 2023
Short summary
Short summary
Spaceborne thermal infrared sensors with kilometer-scale resolution cannot support adequate parameterization of Arctic leads. For the first time, we applied the 30 m resolution data from the Thermal Infrared Spectrometer (TIS) on the emerging SDGSAT-1 to detect Arctic leads. Validation with Sentinel-2 data shows high accuracy for the three TIS bands. Compared to MODIS, the TIS presents more narrow leads, demonstrating its great potential for observing previously unresolvable Arctic leads.
Cited articles
Airbus Defence and Space: Basic product specifications, 2013.
Barber, D. G., Yackel, J. J., and Hanesiak, J.: Sea Ice, RADARSAT-1 and Arctic Climate Processes: A Review and Update, Can. J. Remote Sens., 27, 51–61, 2001.
Beckers, J. F., Renner, A. H. H., Spreen, G., Gerland, S., and Haas, C.: Sea-ice surface roughness estimates from airborne laser scanner and laser altimeter observations in Fram Strait and north of Svalbard, Ann. Glaciol., 56, 235–244, https://doi.org/10.3189/2015AoG69A717, 2015.
Cloude, S. R.: The dual polarisation entropy/alpha decomposition: A PALSAR case study, in: Proc. POLinSAR 2007, 22–26 Januar 2007, European Space Agency (ESA SP-644), Frascati, Italy, 2007.
Cloude, S. R. and Pottier, E.: An entropy based classification scheme for land applications of polarimetric SAR, IEEE T. Geosci. Remote, 35, 68–78, https://doi.org/10.1109/36.551935, 1997.
Comiso, J. C. and Kwok, R.: Surface and radiative characteristics of the summer Arctic sea ice cover from multisensor satellite observations, J. Geophys. Res., 101, 28397–28416, https://doi.org/10.1029/96JC02816, 1996.
Corder, G. W. and Foreman, D. I.: Nonparametric Statistics for Non-Statisticians: A Step-by-step approach, John Wiley & Sons, Inc., Hoboken, USA, https://doi.org/10.1002/9781118165881, 2009.
Divine, D. V., Granskog, M. A., Hudson, S. R., Pedersen, C. A., Karlsen, T. I., Divina, S. A., Renner, A. H. H., and Gerland, S.: Regional melt-pond fraction and albedo of thin Arctic first-year drift ice in late summer, The Cryosphere, 9, 255–268, https://doi.org/10.5194/tc-9-255-2015, 2015.
Divine, D. V., Pedersen, C. A., Karlsen, T. I., Granskog, M. A., Aas, H. F., Hudson, S. R., and Gerland, S.: Photogrammetric retreval and analysis of small scale sea ice topography during summer melt, Cold Reg. Sci. Technol., 129, 77–84, https://doi.org/10.1016/j.coldregions.2016.06.006, 2016.
Divine, D., Pedersen, C. A., Karlsen, T. I., Aas, H. F., Granskog, M. A., Hudson, S. R., and Gerland, S.: Regional melt-pond and open water fractions of thin Arctic first-year drift ice during ICE12 drift experiment 26.07–03.08.2012 north of Svalbard [Data set], Norwegian Polar Institute, https://doi.org/10.21334/npolar.2017.5de6b1e4, 2017a.
Divine, D., Aas, H. F., Karlsen, T. I., Pedersen, C. A., and Gerland, S.: Sea ice surface topography during ICE12 drift from photogrammetric measurements [Data set], Norwegian Polar Institute, https://doi.org/10.21334/npolar.2017.aef50040, 2017b.
Donelan, M. A. and Pierson, W. J.: Radar scattering and equilibrium ranges in wind-generated waves with application to scatterometry, J. Geophys. Res., 92, 4971–5029, https://doi.org/10.1029/JC092iC05p04971, 1987.
Doulgeris, A. P. and Eltoft, T.: Scale Mixture of Gaussian Modelling of Polarimetric SAR Data, EURASIP J. Adv. Sig. Pr., 2010, 1–13, https://doi.org/10.1155/2010/874592, 2010.
Drinkwater, M., Kwok, R., Rignot, E., Israelsson, H., Onstott, R. G., and Winebrenner, D. P.: Potential Applications of Polarimetry to the Classification of Sea Ice, in: Microwave Remote Sensing of Sea Ice, edited by: Carsey, F. D., vol. 68 of Geophysical Monograph Series, 419–430, American Geophysical Union, Washington, DC, USA, https://doi.org/10.1029/GM068, 1992.
Ehn, J. K., Mundy, C. J., Barber, D. G., Hop, H., Rossnagel, A., and Stewart, J.: Impact of horizontal spreading on light propagation in melt pond covered seasonal sea ice in the Canadian Arctic, J. Geophys. Res., 116, C00G02, https://doi.org/10.1029/2010JC006908, 2011.
Eicken, H., Grenfell, T. C., Perovich, D. K., Richter-Menge, J. A., and Frey, K.: Hydraulic controls of summer Arctic pack ice albedo, J. Geophys. Res., 109, C08007, https://doi.org/10.1029/2003JC001989, 2004.
Eom, H. J. and Boerner, W.-M.: Statistical properties of the phase difference between two orthogonally polarized SAR signals, Geosci. Remote Sens., 29, 182–184, 1991.
European Space Agency: Sentinel-1 toolbox – SNAP, available at: http://step.esa.int/main/toolboxes/snap/ (last access: 13 May 2016), 2016.
Fetterer, F. and Untersteiner, N.: Observations of melt ponds on Arctic sea ice, J. Geophys. Res.-Oceans, 103, 24821–24835, https://doi.org/10.1029/98JC02034, 1998.
Flocco, D., Schroeder, D., Feltham, D. L., and Hunke, E. C.: Impact of melt ponds on Arctic sea ice simulations from 1990 to 2007, J. Geophys. Res.-Oceans, 117, C09032, https://doi.org/10.1029/2012JC008195, 2012.
Flocco, D., Feltham, D. L., Bailey, E., and Schroeder, D.: The refreezing of melt ponds on Arctic sea ice, J. Geophys. Res.-Oceans, 120, 647–659, https://doi.org/10.1002/2014JC010140, 2015.
Fors, A. S., Doulgeris, A. P., Renner, A. H. H., Brekke, C., and Gerland, S.: On the releation between polarimetric synthetic aperture radar (SAR) features and sea ice melt pond fraction, in: Proc. IGARSS 2015, 3441–3445, Milano, Italy, 2015.
Fors, A. S., Brekke, C., Doulgeris, A. P., Eltoft, T., Renner, A. H. H., and Gerland, S.: Late-summer sea ice segmentation with multi-polarisation SAR features in C and X band, The Cryosphere, 10, 401–415, https://doi.org/10.5194/tc-10-401-2016, 2016a.
Fors, A. S., Brekke, C., Gerland, S., Doulgeris, A. P., and Beckers, J. F.: Late Summer Arctic Sea Ice Surface Roughness Signatures in C-Band SAR Data, IEEE J. Sel. Top. Appl., 9, 1199–1215, https://doi.org/10.1109/JSTARS.2015.2504384, 2016b.
Geldsetzer, T. and Yackel, J. J.: Sea ice type and open water discrimination using dual co-polarized C-band SAR, Can. J. Remote Sens., 35, 73–84, https://doi.org/10.5589/m08-075, 2009.
Hajnsek, I., Pottier, E., and Cloude, S. R.: Inversion of surface parameters from polarimetric SAR, IEEE T. Geosci. Remote, 41, 727–744, https://doi.org/10.1109/TGRS.2003.810702, 2003.
Han, H., Im, J., Kim, M., Sim, S., Kim, J., Kim, D.-J., and Kang, S.-H.: Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data, Remote Sensing, 8, 57 pp., https://doi.org/10.3390/rs8010057, 2016.
Hoaglin, D. C., Mosteller, F., and Tukey, J. W.: Understanding robust and exploratory data analysis, John Wiley & Sons, Inc, New York, 1983.
Holland, M. M., Bailey, D. A., Briegleb, B. P., Light, B., and Hunke, E.: Improved sea ice shortwave radiation physics in CCSM4: The impact of melt ponds and aerosols on Arctic sea ice, J. Climate, 25, 1413–1430, https://doi.org/10.1175/JCLI-D-11-00078.1, 2012.
Howell, S. E. L., Tivy, A., Yackel, J. J., and Scharien, R. K.: Application of a SeaWinds/QuikSCAT sea ice melt algorithm for assessing melt dynamics in the Canadian Arctic Archipelago, J. Geophys. Res.-Oceans, 111, C07025, https://doi.org/10.1029/2005JC003193, 2006.
Hudson, S. R., Granskog, M. A., Sundfjord, A., Randelhoff, A., Renner, A. H. H., and Divine, D. V.: Energy budget of first-year Arctic sea ice in advanced stages of melt, Geophys. Res. Lett., 40, 2679–2683, https://doi.org/10.1002/grl.50517, 2013.
Inoue, J., Curry, J. A., and Maslanik, J. A.: Application of Aerosondes to Melt-Pond Observations over Arctic Sea Ice, J. Atmos. Ocean. Techn., 25, 327–334, https://doi.org/10.1175/2007JTECHA955.1, 2008.
Istomina, L., Heygster, G., Huntemann, M., Schwarz, P., Birnbaum, G., Scharien, R., Polashenski, C., Perovich, D., Zege, E., Malinka, A., Prikhach, A., and Katsev, I.: Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 1: Validation against in situ, aerial, and ship cruise data, The Cryosphere, 9, 1551–1566, https://doi.org/10.5194/tc-9-1551-2015, 2015.
Jeffries, M. O., Schwartz, K., and Li, S.: Arctic summer sea-ice SAR signatures, melt-season characteristics, and melt-pond fractions, Polar Rec., 33, 101–112, https://doi.org/10.1017/S003224740001442X, 1997.
Kellndorfer, J. M., Pierce, L. E., Dobson, M. C., and Ulaby, F. T.: Toward consistent regional-to-global-scale vegetation characterization using orbital SAR systems, IEEE T. Geosci. Remote, 36, 1396–1411, https://doi.org/10.1109/36.718844, 1998.
Kern, S., Brath, M., and Stammer, D.: Melt Ponds as Observed with a Helicopter-Borne, Multi-Frequency Scatterometer in the Arctic Ocean in 2007, in: Proc. of ESA Living Planet Symp., European Space Agency (ESA SP-686), Bergen, Norway, 2010.
Kim, D. J., Hwang, B., Chung, K. H., Lee, S. H., Jung, H. S., and Moon, W. M.: Melt pond mapping with high-resolution SAR: The first view, Proceedings of the IEEE, 101, 748–758, 2013.
Lee, J.-S. and Pottier, E.: Polarimetric radar imaging: From basics to applications, CRC Press, Taylor and Francis Group, Boca Raton, FL, USA, 2009.
Light, B., Grenfell, T. C., and Perovich, D. K.: Transmission and absorption of solar radiation by Arctic sea ice during the melt season, J. Geophys. Res.-Oceans, 113, C03023, https://doi.org/10.1029/2006JC003977, 2008.
Mäkynen, M., Kern, S., Rösel, A., and Pedersen, L. T.: On the Estimation of Melt Pond Fraction on the Arctic Sea Ice With ENVISAT WSM Images, IEEE T. Geosci. Remote, 52, 7366–7379, https://doi.org/10.1109/TGRS.2014.2311476, 2014.
Mardia, K. V.: Measure of multivariate skewness and kurtosis with applications, Biometrica, 57, 519—-530, 1970.
Markus, T., Cavalieri, D. J., Tschudi, M. A., and Ivanoff, A.: Comparison of aerial video and Landsat 7 data over ponded sea ice, Remote Sens. Environ., 86, 458–469, https://doi.org/10.1016/S0034-4257(03)00124-X, 2003.
Minchew, B., Jones, C. E., and Holt, B.: Polarimetric Analysis of Backscatter From the Deepwater Horizon Oil Spill Using L-Band Synthetic Aperture Radar, IEEE T. Geosci. Remote, 50, 3812–3830, https://doi.org/10.1109/TGRS.2012.2185804, 2012.
Moen, M.-A. N., Doulgeris, A. P., Anfinsen, S. N., Renner, A. H. H., Hughes, N., Gerland, S., and Eltoft, T.: Comparison of feature based segmentation of full polarimetric SAR satellite sea ice images with manually drawn ice charts, The Cryosphere, 7, 1693–1705, https://doi.org/10.5194/tc-7-1693-2013, 2013.
Nicolaus, M., Katlein, C., Maslanik, J., and Hendricks, S.: Changes in Arctic sea ice result in increasing light transmittance and absorption, Geophys. Res. Lett., 39, L24501, https://doi.org/10.1029/2012GL053738, 2012.
Perovich, D. K.: Aerial observations of the evolution of ice surface conditions during summer, J. Geophys. Res., 107, 8048, https://doi.org/10.1029/2000JC000449, 2002.
Perovich, D. K. and Polashenski, C.: Albedo evolution of seasonal Arctic sea ice, Geophys. Res. Lett., 39, L08501, https://doi.org/10.1029/2012GL051432, 2012.
Perovich, D. K., Grenfell, T. C., Light, B., Elder, B. C., Harbeck, J., Polashenski, C., Tucker, W. B., and Stelmach, C.: Transpolar observations of the morphological properties of Arctic sea ice, J. Geophys. Res., 114, C00A04, https://doi.org/10.1029/2008JC004892, 2009.
Perovich, D. K., Meier, W. N., Tschudi, M. A., Farrell, S., Gerland, S., and Hendricks, S.: Sea ice, in: Arctic Report Card 2015, available at: http://www.arctic.noaa.gov/reportcard (last access: 13 May 2016), 2015.
Polashenski, C., Perovich, D., and Courville, Z.: The mechanisms of sea ice melt pond formation and evolution, J. Geophys. Res., 117, C01001, https://doi.org/10.1029/2011JC007231, 2012.
Renner, A. H., Hendricks, S., Gerland, S., Beckers, J., Haas, C., and Krumpen, T.: Large-scale ice thickness distribution of first-year sea ice in spring and summer north of Svalbard, Ann. Glaciol., 54, 13–18, https://doi.org/10.3189/2013AoG62A146, 2013.
Rösel, A. and Kaleschke, L.: Comparison of different retrieval techniques for melt ponds on Arctic sea ice from Landsat and MODIS satellite data, Ann. Glaciol., 52, 185–191, 2011.
Rösel, A. and Kaleschke, L.: Exceptional melt pond occurrence in the years 2007 and 2011 on the Arctic sea ice revealed from MODIS satellite data, J. Geophys. Res.-Oceans, 117, C05018, https://doi.org/10.1029/2011JC007869, 2012.
Rösel, A., Kaleschke, L., and Birnbaum, G.: Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network, The Cryosphere, 6, 431–446, https://doi.org/10.5194/tc-6-431-2012, 2012.
Scharien, R. K., Yackel, J. J., Barber, D. G., Asplin, M., Gupta, M., and Isleifson, D.: Geophysical controls on C band polarimetric backscatter from melt pond covered Arctic first-year sea ice: Assessment using high-resolution scatterometry, J. Geophys. Res.-Oceans, 117, C00G18, https://doi.org/10.1029/2011JC007353, 2012.
Scharien, R. K., Hochheim, K., Landy, J., and Barber, D. G.: First-year sea ice melt pond fraction estimation from dual-polarisation C-band SAR - Part 2: Scaling in situ to Radarsat-2, The Cryosphere, 8, 2163—2176, https://doi.org/10.5194/tc-8-2163-2014, 2014a.
Scharien, R. K., Landy, J., and Barber, D. G.: First-year sea ice melt pond fraction estimation from dual-polarisation C-band SAR – Part 1: In situ observations, The Cryosphere, 8, 2147–2162, https://doi.org/10.5194/tc-8-2147-2014, 2014b.
Schröder, D., Feltham, D. L., Flocco, D., and Tsamados, M.: September Arctic sea-ice minimum predicted by spring melt-pond fraction, Nature Climate Change, 4, 353–357, https://doi.org/10.1038/nclimate2203, 2014.
Skrunes, S., Brekke, C., and Eltoft, T.: Characterization of Marine Surface Slicks by Radarsat-2 Multipolarization Features, IEEE T. Geosci. Remote, 52, 5302–5319, https://doi.org/10.1109/TGRS.2013.2287916, 2014.
Tschudi, M. A., Maslanik, J. A., and Perovich, D. K.: Derivation of melt pond coverage on Arctic sea ice using MODIS observations, Remote Sens. Environ., 112, 2605–2614, https://doi.org/10.1016/j.rse.2007.12.009, 2008.
van Zyl, J. J. and Kim, Y.: Synthetic Aperture Radar Polarimetry, John Wiley & Sons, Inc, Hoboken, USA, 2011.
Yackel, J. J. and Barber, D. G.: Melt ponds on sea ice in the Canadian Archipelago: 2. On the use of RADARSAT-1 synthetic aperture radar for geophysical inversion, J. Geophys. Res., 105, 22061, https://doi.org/10.1029/2000JC900076, 2000.
Yackel, J. J., Barber, D. G., Papakyriakou, T. N., and Breneman, C.: First-year sea ice spring melt transitions in the Canadian Arctic Archipelago from time-series synthetic aperture radar data, 1992–2002, Hydrol. Process., 21, 253–265, https://doi.org/10.1002/hyp.6240, 2007.
Zege, E., Malinka, A., Katsev, I., Prikhach, A., Heygster, G., Istomina, L., Birnbaum, G., and Schwarz, P.: Algorithm to retrieve the melt pond fraction and the spectral albedo of Arctic summer ice from satellite optical data, Remote Sens. Environ., 163, 153–164, https://doi.org/10.1016/j.rse.2015.03.012, 2015.
Short summary
This paper investigates the signature of melt ponds in satellite-borne synthetic aperture radar (SAR) imagery. A comparison between helicopter-borne images of drifting first-year ice and polarimetric X-band SAR images shows relations between observed melt pond fraction and several polarimetric SAR features. Melt ponds strongly influence the Arctic sea ice energy budget, and the results imply prospective opportunities for expanded monitoring of melt ponds from space.
This paper investigates the signature of melt ponds in satellite-borne synthetic aperture radar...
