Articles | Volume 17, issue 3
https://doi.org/10.5194/tc-17-1279-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-1279-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Mar 2023
Research article |
| 16 Mar 2023
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture
Wenkai Guo
CORRESPONDING AUTHOR
Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
Polona Itkin
Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
Suman Singha
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Bremen, Germany
currently at: National Center for Climate Research (NCKF), Danish Meteorological Institute (DMI), Copenhagen, Denmark
Anthony P. Doulgeris
Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
Malin Johansson
Department of Physics and Technology, UiT The Arctic University of Norway, Tromsø, Norway
Gunnar Spreen
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Related authors
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.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
Short summary
Short summary
We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Philip Rostosky and Gunnar Spreen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-69, https://doi.org/10.5194/tc-2023-69, 2023
Preprint under review for TC
Short summary
Short summary
During winter, storms entering the Arctic ocean can bring warm air into the cold environment. Strong increases in air temperature modify the characteristic 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 warming, the change in the snow characteristic influences the satellite derived sea ice cover leading to a false reduction of the estimated ice area.
Pablo Saavedra Garfias, Heike Kalesse-Los, Luisa von Albedyll, Hannes Griesche, and Gunnar Spreen
EGUsphere, https://doi.org/10.5194/egusphere-2023-623, https://doi.org/10.5194/egusphere-2023-623, 2023
Short summary
Short summary
Wintertime Arctic clouds act as warming mechanism since they trap heat to the lower atmosphere. This surface surplus of heat influences the thermal interaction between the lower atmosphere and the sea ice. Sea ice openings impact the atmosphere since the exposed ocean sources heat and moisture. This intricate feedback is studied by classifying the degree of interaction of sea ice with clouds, uncovering asymmetries in properties like cloud depth, height, liquid water content, ice water fraction.
Laust Færch, Wolfgang Dierking, Nick Hughes, and Anthony P. Doulgeris
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-17, https://doi.org/10.5194/tc-2023-17, 2023
Preprint under review for TC
Short summary
Short summary
Icebergs in open water are a risk to maritime traffic. In this study, we have compared 6 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.
Alexander Mchedlishvili, Christof Lüpkes, Alek Petty, Michel Tsamados, and Gunnar Spreen
EGUsphere, https://doi.org/10.5194/egusphere-2023-187, https://doi.org/10.5194/egusphere-2023-187, 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 which 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 atmosphere, across the Arctic.
Christian Melsheimer, Gunnar Spreen, Yufang Ye, and Mohammed Shokr
The Cryosphere, 17, 105–126, https://doi.org/10.5194/tc-17-105-2023, https://doi.org/10.5194/tc-17-105-2023, 2023
Short summary
Short summary
It is necessary to know the type of Antarctic sea ice present – first-year ice (grown in one season) or multiyear ice (survived one summer melt) – to understand and model its evolution, as the ice types behave and react differently. We have adapted and extended an existing method (originally for the Arctic), and now, for the first time, daily maps of Antarctic sea ice types can be derived from microwave satellite data. This will allow a new data set from 2002 well into the future to be built.
Olivia Linke, Johannes Quaas, Finja Baumer, Sebastian Becker, Jan Chylik, Sandro Dahlke, André Ehrlich, Dörthe Handorf, Christoph Jacobi, Heike Kalesse-Los, Luca Lelli, Sina Mehrdad, Roel A. J. Neggers, Johannes Riebold, Pablo Saavedra Garfias, Niklas Schnierstein, Matthew D. Shupe, Chris Smith, Gunnar Spreen, Baptiste Verneuil, Kameswara S. Vinjamuri, Marco Vountas, and Manfred Wendisch
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-836, https://doi.org/10.5194/acp-2022-836, 2023
Preprint under review for ACP
Short summary
Short summary
The lapse-rate feedback (LRF) is a major driver of the "Arctic amplification" of climate change. It arises since the warming is more pronounced at the surface than aloft. There are several processes mediating the LRF in the Arctic, for instance the omnipresent temperature inversion. Here, we compare multi-model climate simulations to Arctic-based observations from a large research consortium to broaden our understanding of these processes, find synergy among them, and constrain the Arctic LRF.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
Short summary
Short summary
Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
Short summary
Short summary
Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
Alexander Mchedlishvili, Gunnar Spreen, Christian Melsheimer, and Marcus Huntemann
The Cryosphere, 16, 471–487, https://doi.org/10.5194/tc-16-471-2022, https://doi.org/10.5194/tc-16-471-2022, 2022
Short summary
Short summary
In this paper we show that the activity leading to the open-ocean polynyas near the Maud Rise seamount that have occurred repeatedly from 1974–1976 as well as 2016–2017 does not simply stop for polynya-free years. Using apparent sea ice thickness retrieval, we have identified anomalies where there is thinning of sea ice on a scale that is comparable to that of the polynya events of 2016–2017. These anomalies took place in 2010, 2013, 2014 and 2018.
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.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
Short summary
Short summary
We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
Susanne Crewell, Kerstin Ebell, Patrick Konjari, Mario Mech, Tatiana Nomokonova, Ana Radovan, David Strack, Arantxa M. Triana-Gómez, Stefan Noël, Raul Scarlat, Gunnar Spreen, Marion Maturilli, Annette Rinke, Irina Gorodetskaya, Carolina Viceto, Thomas August, and Marc Schröder
Atmos. Meas. Tech., 14, 4829–4856, https://doi.org/10.5194/amt-14-4829-2021, https://doi.org/10.5194/amt-14-4829-2021, 2021
Short summary
Short summary
Water vapor (WV) is an important variable in the climate system. Satellite measurements are thus crucial to characterize the spatial and temporal variability in WV and how it changed over time. In particular with respect to the observed strong Arctic warming, the role of WV still needs to be better understood. However, as shown in this paper, a detailed understanding is still hampered by large uncertainties in the various satellite WV products, showing the need for improved methods to derive WV.
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.
Yu Zhang, Tingting Zhu, Gunnar Spreen, Christian Melsheimer, Marcus Huntemann, Nick Hughes, Shengkai Zhang, and Fei Li
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-85, https://doi.org/10.5194/tc-2021-85, 2021
Revised manuscript not accepted
Short summary
Short summary
We developed an algorithm for ice-water classification using Sentinel-1 data during melting seasons in the Fram Strait. The proposed algorithm has the OA of nearly 90 % with STD less than 10 %. The comparison of sea ice concentration demonstrate that it can provide detailed information of sea ice with the spatial resolution of 1km. The time series shows the average June to September sea ice area does not change so much in 2015–2017 and 2019–2020, but it has a significant decrease in 2018.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Rasmus Tonboe, Stefan Hendricks, Robert Ricker, James Mead, Robbie Mallett, Marcus Huntemann, Polona Itkin, Martin Schneebeli, Daniela Krampe, Gunnar Spreen, Jeremy Wilkinson, Ilkka Matero, Mario Hoppmann, and Michel Tsamados
The Cryosphere, 14, 4405–4426, https://doi.org/10.5194/tc-14-4405-2020, https://doi.org/10.5194/tc-14-4405-2020, 2020
Short summary
Short summary
This study provides a first look at the data collected by a new dual-frequency Ka- and Ku-band in situ radar over winter sea ice in the Arctic Ocean. The instrument shows potential for using both bands to retrieve snow depth over sea ice, as well as sensitivity of the measurements to changing snow and atmospheric conditions.
Larysa Istomina, Henrik Marks, Marcus Huntemann, Georg Heygster, and Gunnar Spreen
Atmos. Meas. Tech., 13, 6459–6472, https://doi.org/10.5194/amt-13-6459-2020, https://doi.org/10.5194/amt-13-6459-2020, 2020
Arantxa M. Triana-Gómez, Georg Heygster, Christian Melsheimer, Gunnar Spreen, Monia Negusini, and Boyan H. Petkov
Atmos. Meas. Tech., 13, 3697–3715, https://doi.org/10.5194/amt-13-3697-2020, https://doi.org/10.5194/amt-13-3697-2020, 2020
Short summary
Short summary
In the Arctic, in situ measurements are sparse and standard remote sensing retrieval methods have problems. We present advances in a retrieval algorithm for vertically integrated water vapour tuned for polar regions. In addition to the initial sensor used (AMSU-B), we can now also use data from the successor instrument (MHS). Additionally, certain artefacts are now filtered out. Comparison with radiosondes shows the overall good performance of the updated algorithm.
Thomas Krumpen, Florent Birrien, Frank Kauker, Thomas Rackow, Luisa von Albedyll, Michael Angelopoulos, H. Jakob Belter, Vladimir Bessonov, Ellen Damm, Klaus Dethloff, Jari Haapala, Christian Haas, Carolynn Harris, Stefan Hendricks, Jens Hoelemann, Mario Hoppmann, Lars Kaleschke, Michael Karcher, Nikolai Kolabutin, Ruibo Lei, Josefine Lenz, Anne Morgenstern, Marcel Nicolaus, Uwe Nixdorf, Tomash Petrovsky, Benjamin Rabe, Lasse Rabenstein, Markus Rex, Robert Ricker, Jan Rohde, Egor Shimanchuk, Suman Singha, Vasily Smolyanitsky, Vladimir Sokolov, Tim Stanton, Anna Timofeeva, Michel Tsamados, and Daniel Watkins
The Cryosphere, 14, 2173–2187, https://doi.org/10.5194/tc-14-2173-2020, https://doi.org/10.5194/tc-14-2173-2020, 2020
Short summary
Short summary
In October 2019 the research vessel Polarstern was moored to an ice floe in order to travel with it on the 1-year-long MOSAiC journey through the Arctic. Here we provide historical context of the floe's evolution and initial state for upcoming studies. We show that the ice encountered on site was exceptionally thin and was formed on the shallow Siberian shelf. The analyses presented provide the initial state for the analysis and interpretation of upcoming biogeochemical and ecological studies.
Xiaoyong Yu, Annette Rinke, Wolfgang Dorn, Gunnar Spreen, Christof Lüpkes, Hiroshi Sumata, and Vladimir M. Gryanik
The Cryosphere, 14, 1727–1746, https://doi.org/10.5194/tc-14-1727-2020, https://doi.org/10.5194/tc-14-1727-2020, 2020
Short summary
Short summary
This study presents an evaluation of Arctic sea ice drift speed for the period 2003–2014 in a state-of-the-art coupled regional model for the Arctic, called HIRHAM–NAOSIM. In particular, the dependency of the drift speed on the near-surface wind speed and sea ice conditions is presented. Effects of sea ice form drag included by an improved parameterization of the transfer coefficients for momentum and heat over sea ice are discussed.
Christine Pohl, Larysa Istomina, Steffen Tietsche, Evelyn Jäkel, Johannes Stapf, Gunnar Spreen, and Georg Heygster
The Cryosphere, 14, 165–182, https://doi.org/10.5194/tc-14-165-2020, https://doi.org/10.5194/tc-14-165-2020, 2020
Short summary
Short summary
A spectral to broadband conversion is developed empirically that can be used in combination with the Melt Pond Detector algorithm to derive broadband albedo (300–3000 nm) of Arctic sea ice from MERIS data. It is validated and shows better performance compared to existing conversion methods. A comparison of MERIS broadband albedo with respective values from ERA5 reanalysis suggests a revision of the albedo values used in ERA5. MERIS albedo might be useful for improving albedo representation.
Valentin Ludwig, Gunnar Spreen, Christian Haas, Larysa Istomina, Frank Kauker, and Dmitrii Murashkin
The Cryosphere, 13, 2051–2073, https://doi.org/10.5194/tc-13-2051-2019, https://doi.org/10.5194/tc-13-2051-2019, 2019
Short summary
Short summary
Sea-ice concentration, the fraction of an area covered by sea ice, can be observed from satellites with different methods. We combine two methods to obtain a product which is better than either of the input measurements alone. The benefit of our product is demonstrated by observing the formation of an open water area which can now be observed with more detail. Additionally, we find that the open water area formed because the sea ice drifted in the opposite direction and faster than usual.
Cătălin Paţilea, Georg Heygster, Marcus Huntemann, and Gunnar Spreen
The Cryosphere, 13, 675–691, https://doi.org/10.5194/tc-13-675-2019, https://doi.org/10.5194/tc-13-675-2019, 2019
Short summary
Short summary
Sea ice thickness is important for representing atmosphere–ocean interactions in climate models. A validated satellite sea ice thickness measurement algorithm is transferred to a new sensor. The results offer a better temporal and spatial coverage of satellite measurements in the polar regions. Here we describe the calibration procedure between the two sensors, taking into account their technical differences. In addition a new filter for interference from artificial radio sources is implemented.
Aleksey Malinka, Eleonora Zege, Larysa Istomina, Georg Heygster, Gunnar Spreen, Donald Perovich, and Chris Polashenski
The Cryosphere, 12, 1921–1937, https://doi.org/10.5194/tc-12-1921-2018, https://doi.org/10.5194/tc-12-1921-2018, 2018
Short summary
Short summary
Melt ponds occupy a large part of the Arctic sea ice in summer and strongly affect the radiative budget of the atmosphere–ice–ocean system. The melt pond reflectance is modeled in the framework of the radiative transfer theory and validated with field observations. It improves understanding of melting sea ice and enables better parameterization of the surface in Arctic atmospheric remote sensing (clouds, aerosols, trace gases) and re-evaluating Arctic climatic feedbacks at a new accuracy level.
Gunnar Spreen, Ron Kwok, Dimitris Menemenlis, and An T. Nguyen
The Cryosphere, 11, 1553–1573, https://doi.org/10.5194/tc-11-1553-2017, https://doi.org/10.5194/tc-11-1553-2017, 2017
Ane S. Fors, Dmitry V. Divine, Anthony P. Doulgeris, Angelika H. H. Renner, and Sebastian Gerland
The Cryosphere, 11, 755–771, https://doi.org/10.5194/tc-11-755-2017, https://doi.org/10.5194/tc-11-755-2017, 2017
Short summary
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.
Paul Vallelonga, Niccolo Maffezzoli, Andrew D. Moy, Mark A. J. Curran, Tessa R. Vance, Ross Edwards, Gwyn Hughes, Emily Barker, Gunnar Spreen, Alfonso Saiz-Lopez, J. Pablo Corella, Carlos A. Cuevas, and Andrea Spolaor
Clim. Past, 13, 171–184, https://doi.org/10.5194/cp-13-171-2017, https://doi.org/10.5194/cp-13-171-2017, 2017
Short summary
Short summary
We present a study of bromine, iodine and sodium in an ice core from Law Dome, in coastal East Antarctica. We find that bromine and iodine variability at Law Dome is correlated to changes in the area of sea ice along the Law Dome coast as observed by satellite since the early 1970s. These findings are in agreement with a previous study based on MSA and confirm a long-term trend of sea ice decrease for this sector of Antarctica over the 20th century.
T. Krumpen, R. Gerdes, C. Haas, S. Hendricks, A. Herber, V. Selyuzhenok, L. Smedsrud, and G. Spreen
The Cryosphere, 10, 523–534, https://doi.org/10.5194/tc-10-523-2016, https://doi.org/10.5194/tc-10-523-2016, 2016
Short summary
Short summary
We present an extensive data set of ground-based and airborne electromagnetic ice thickness measurements covering Fram Strait in summer between 2001 and 2012. An investigation of back trajectories of surveyed sea ice using satellite-based sea ice motion data allows us to examine the connection between thickness variability, ice age and source area. In addition, we determine across and along strait gradients in ice thickness and associated volume fluxes.
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.
A. Spolaor, T. Opel, J. R. McConnell, O. J. Maselli, G. Spreen, C. Varin, T. Kirchgeorg, D. Fritzsche, A. Saiz-Lopez, and P. Vallelonga
The Cryosphere, 10, 245–256, https://doi.org/10.5194/tc-10-245-2016, https://doi.org/10.5194/tc-10-245-2016, 2016
Short summary
Short summary
The role of sea ice in the Earth climate system is still under debate, although it is known to influence albedo, ocean circulation, and atmosphere-ocean heat and gas exchange. Here we present a reconstruction of 1950 to 1998 AD sea ice in the Laptev Sea based on the Akademii Nauk ice core (Severnaya Zemlya, Russian Arctic) and halogen measurements. The results suggest a connection between bromine and sea ice, as well as a connection between iodine concentration in snow and summer sea ice.
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
Discipline: Sea ice | Subject: Remote Sensing
Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice
First observations of sea ice flexural–gravity waves with ground-based radar interferometry in Utqiaġvik, Alaska
Feasibility of retrieving Arctic sea ice thickness from the Chinese HY-2B Ku-band radar altimeter
Aerial observations of sea ice breakup by ship waves
Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery
The effects of surface roughness on the calculated, spectral, conical–conical reflectance factor as an alternative to the bidirectional reflectance distribution function of bare sea ice
Inter-comparison and evaluation of Arctic sea ice type products
A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval
Ice ridge density signatures in high-resolution SAR images
Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate)
Quantifying the effects of background concentrations of crude oil pollution on sea ice albedo
Characterizing the sea-ice floe size distribution in the Canada Basin from high-resolution optical satellite imagery
Generating large-scale sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission using the Environment and Climate Change Canada automated sea ice tracking system
Rotational drift in Antarctic sea ice: pronounced cyclonic features and differences between data products
Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data
Cross-platform classification of level and deformed sea ice considering per-class incident angle dependency of backscatter intensity
Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements
Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry
Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay early spring sea ice thickness
A lead-width distribution for Antarctic sea ice: a case study for the Weddell Sea with high-resolution Sentinel-2 images
Satellite altimetry detection of ice-shelf-influenced fast ice
MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years
Towards a swath-to-swath sea-ice drift product for the Copernicus Imaging Microwave Radiometer mission
Spaceborne infrared imagery for early detection of Weddell Polynya opening
Estimating instantaneous sea-ice dynamics from space using the bi-static radar measurements of Earth Explorer 10 candidate Harmony
Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning
An improved sea ice detection algorithm using MODIS: application as a new European sea ice extent indicator
Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover
Estimation of degree of sea ice ridging in the Bay of Bothnia based on geolocated photon heights from ICESat-2
Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations
Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice
Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery
Spring melt pond fraction in the Canadian Arctic Archipelago predicted from RADARSAT-2
Simultaneous estimation of wintertime sea ice thickness and snow depth from space-borne freeboard measurements
Observations of sea ice melt from Operation IceBridge imagery
Estimating statistical errors in retrievals of ice velocity and deformation parameters from satellite images and buoy arrays
Brief Communication: Mesoscale and submesoscale dynamics in the marginal ice zone from sequential synthetic aperture radar observations
Classification of sea ice types in Sentinel-1 synthetic aperture radar images
A linear model to derive melt pond depth on Arctic sea ice from hyperspectral data
Satellite passive microwave sea-ice concentration data set inter-comparison for Arctic summer conditions
Opportunistic evaluation of modelled sea ice drift using passively drifting telemetry collars in Hudson Bay, Canada
Combining TerraSAR-X and time-lapse photography for seasonal sea ice monitoring: the case of Deception Bay, Nunavik
Satellite observations of unprecedented phytoplankton blooms in the Maud Rise polynya, Southern Ocean
Effects of decimetre-scale surface roughness on L-band brightness temperature of sea ice
Brief communication: Conventional assumptions involving the speed of radar waves in snow introduce systematic underestimates to sea ice thickness and seasonal growth rate estimates
Broadband albedo of Arctic sea ice from MERIS optical data
Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations
Estimating the sea ice floe size distribution using satellite altimetry: theory, climatology, and model comparison
The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset
Estimation of turbulent heat flux over leads using satellite thermal images
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
Short summary
Short summary
We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Dyre Oliver Dammann, Mark A. Johnson, Andrew R. Mahoney, and Emily R. Fedders
The Cryosphere, 17, 1609–1622, https://doi.org/10.5194/tc-17-1609-2023, https://doi.org/10.5194/tc-17-1609-2023, 2023
Short summary
Short summary
We investigate the GAMMA Portable Radar Interferometer (GPRI) as a tool for evaluating flexural–gravity waves in sea ice in near real time. With a GPRI mounted on grounded ice near Utqiaġvik, Alaska, we identify 20–50 s infragravity waves in landfast ice with ~1 mm amplitude during 23–24 April 2021. Observed wave speed and periods compare well with modeled wave propagation and on-ice accelerometers, confirming the ability to track propagation and properties of waves over hundreds of meters.
Zhaoqing Dong, Lijian Shi, Mingsen Lin, Yongjun Jia, Tao Zeng, and Suhui Wu
The Cryosphere, 17, 1389–1410, https://doi.org/10.5194/tc-17-1389-2023, https://doi.org/10.5194/tc-17-1389-2023, 2023
Short summary
Short summary
We try to explore the application of SGDR data in polar sea ice thickness. Through this study, we find that it seems difficult to obtain reasonable results by using conventional methods. So we use the 15 lowest points per 25 km to estimate SSHA to retrieve more reasonable Arctic radar freeboard and thickness. This study also provides reference for reprocessing L1 data. We will release products that are more reasonable and suitable for polar sea ice thickness retrieval to better evaluate HY-2B.
Elie Dumas-Lefebvre and Dany Dumont
The Cryosphere, 17, 827–842, https://doi.org/10.5194/tc-17-827-2023, https://doi.org/10.5194/tc-17-827-2023, 2023
Short summary
Short summary
By changing the shape of ice floes, wave-induced sea ice breakup dramatically affects the large-scale dynamics of sea ice. As this process is also the trigger of multiple others, it was deemed relevant to study how breakup itself affects the ice floe size distribution. To do so, a ship sailed close to ice floes, and the breakup that it generated was recorded with a drone. The obtained data shed light on the underlying physics of wave-induced sea ice breakup.
Felix L. Müller, Stephan Paul, Stefan Hendricks, and Denise Dettmering
The Cryosphere, 17, 809–825, https://doi.org/10.5194/tc-17-809-2023, https://doi.org/10.5194/tc-17-809-2023, 2023
Short summary
Short summary
Thinning sea ice has significant impacts on the energy exchange between the atmosphere and the ocean. In this study we present visual and quantitative comparisons of thin-ice detections obtained from classified Cryosat-2 radar reflections and thin-ice-thickness estimates derived from MODIS thermal-infrared imagery. In addition to good comparability, the results of the study indicate the potential for a deeper understanding of sea ice in the polar seas and improved processing of altimeter data.
Maxim L. Lamare, John D. Hedley, and Martin D. King
The Cryosphere, 17, 737–751, https://doi.org/10.5194/tc-17-737-2023, https://doi.org/10.5194/tc-17-737-2023, 2023
Short summary
Short summary
The reflectivity of sea ice is crucial for modern climate change and for monitoring sea ice from satellites. The reflectivity depends on the angle at which the ice is viewed and the angle illuminated. The directional reflectivity is calculated as a function of viewing angle, illuminating angle, thickness, wavelength and surface roughness. Roughness cannot be considered independent of thickness, illumination angle and the wavelength. Remote sensors will use the data to image sea ice from space.
Yufang Ye, Yanbing Luo, Yan Sun, Mohammed Shokr, Signe Aaboe, Fanny Girard-Ardhuin, Fengming Hui, Xiao Cheng, and Zhuoqi Chen
The Cryosphere, 17, 279–308, https://doi.org/10.5194/tc-17-279-2023, https://doi.org/10.5194/tc-17-279-2023, 2023
Short summary
Short summary
Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study gives a systematic inter-comparison and evaluation of eight SITY products. Main results include differences in SITY products being significant, with average Arctic multiyear ice extent up to 1.8×106 km2; Ku-band scatterometer SITY products generally performing better; and factors such as satellite inputs, classification methods, training datasets and post-processing highly impacting their performance.
James Anheuser, Yinghui Liu, and Jeffrey R. Key
The Cryosphere, 16, 4403–4421, https://doi.org/10.5194/tc-16-4403-2022, https://doi.org/10.5194/tc-16-4403-2022, 2022
Short summary
Short summary
A prominent part of the polar climate system is sea ice, a better understanding of which would lead to better understanding Earth's climate. Newly published methods for observing the temperature of sea ice have made possible a new method for estimating daily sea ice thickness growth from space using an energy balance. The method compares well with existing sea ice thickness observations.
Mikko Lensu and Markku Similä
The Cryosphere, 16, 4363–4377, https://doi.org/10.5194/tc-16-4363-2022, https://doi.org/10.5194/tc-16-4363-2022, 2022
Short summary
Short summary
Ice ridges form a compressing ice cover. From above they appear as walls of up to few metres in height and extend even kilometres across the ice. Below they may reach tens of metres under the sea surface. Ridges need to be observed for the purposes of ice forecasting and ice information production. This relies mostly on ridging signatures discernible in radar satellite (SAR) images. New methods to quantify ridging from SAR have been developed and are shown to agree with field observations.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
Short summary
Short summary
Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
Benjamin Heikki Redmond Roche and Martin D. King
The Cryosphere, 16, 3949–3970, https://doi.org/10.5194/tc-16-3949-2022, https://doi.org/10.5194/tc-16-3949-2022, 2022
Short summary
Short summary
Sea ice is bright, playing an important role in reflecting incoming solar radiation. The reflectivity of sea ice is affected by the presence of pollutants, such as crude oil, even at low concentrations. Modelling how the brightness of three types of sea ice is affected by increasing concentrations of crude oils shows that the type of oil, the type of ice, the thickness of the ice, and the size of the oil droplets are important factors. This shows that sea ice is vulnerable to oil pollution.
Alexis Anne Denton and Mary-Louise Timmermans
The Cryosphere, 16, 1563–1578, https://doi.org/10.5194/tc-16-1563-2022, https://doi.org/10.5194/tc-16-1563-2022, 2022
Short summary
Short summary
Arctic sea ice has a distribution of ice sizes that provides insight into the physics of the ice. We examine this distribution from satellite imagery from 1999 to 2014 in the Canada Basin. We find that it appears as a power law whose power becomes less negative with increasing ice concentrations and has a seasonality tied to that of ice concentration. Results suggest ice concentration be considered in models of this distribution and are important for understanding sea ice in a warming Arctic.
Stephen E. L. Howell, Mike Brady, and Alexander S. Komarov
The Cryosphere, 16, 1125–1139, https://doi.org/10.5194/tc-16-1125-2022, https://doi.org/10.5194/tc-16-1125-2022, 2022
Short summary
Short summary
We describe, apply, and validate the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS) that routinely generates large-scale sea ice motion (SIM) over the pan-Arctic domain using synthetic aperture radar (SAR) images. The ECCC-ASITS was applied to the incoming image streams of Sentinel-1AB and the RADARSAT Constellation Mission from March 2020 to October 2021 using a total of 135 471 SAR images and generated new SIM datasets (i.e., 7 d 25 km and 3 d 6.25 km).
Wayne de Jager and Marcello Vichi
The Cryosphere, 16, 925–940, https://doi.org/10.5194/tc-16-925-2022, https://doi.org/10.5194/tc-16-925-2022, 2022
Short summary
Short summary
Ice motion can be used to better understand how weather and climate change affect the ice. Antarctic sea ice extent has shown large variability over the observed period, and dynamical features may also have changed. Our method allows for the quantification of rotational motion caused by wind and how this may have changed with time. Cyclonic motion dominates the Atlantic sector, particularly from 2015 onwards, while anticyclonic motion has remained comparatively small and unchanged.
Stefan Kern, Thomas Lavergne, Leif Toudal Pedersen, Rasmus Tage Tonboe, Louisa Bell, Maybritt Meyer, and Luise Zeigermann
The Cryosphere, 16, 349–378, https://doi.org/10.5194/tc-16-349-2022, https://doi.org/10.5194/tc-16-349-2022, 2022
Short summary
Short summary
High-resolution clear-sky optical satellite imagery has rarely been used to evaluate satellite passive microwave sea-ice concentration products beyond case-study level. By comparing 10 such products with sea-ice concentration estimated from > 350 such optical images in both hemispheres, we expand results of earlier evaluation studies for these products. Results stress the need to look beyond precision and accuracy and to discuss the evaluation data’s quality and filters applied in the products.
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.
Florent Garnier, Sara Fleury, Gilles Garric, Jérôme Bouffard, Michel Tsamados, Antoine Laforge, Marion Bocquet, Renée Mie Fredensborg Hansen, and Frédérique Remy
The Cryosphere, 15, 5483–5512, https://doi.org/10.5194/tc-15-5483-2021, https://doi.org/10.5194/tc-15-5483-2021, 2021
Short summary
Short summary
Snow depth data are essential to monitor the impacts of climate change on sea ice volume variations and their impacts on the climate system. For that purpose, we present and assess the altimetric snow depth product, computed in both hemispheres from CryoSat-2 and SARAL satellite data. The use of these data instead of the common climatology reduces the sea ice thickness by about 30 cm over the 2013–2019 period. These data are also crucial to argue for the launch of the CRISTAL satellite mission.
Lanqing Huang, Georg Fischer, and Irena Hajnsek
The Cryosphere, 15, 5323–5344, https://doi.org/10.5194/tc-15-5323-2021, https://doi.org/10.5194/tc-15-5323-2021, 2021
Short summary
Short summary
This study shows an elevation difference between the radar interferometric measurements and the optical measurements from a coordinated campaign over the snow-covered deformed sea ice in the western Weddell Sea, Antarctica. The objective is to correct the penetration bias of microwaves and to generate a precise sea ice topographic map, including the snow depth on top. Excellent performance for sea ice topographic retrieval is achieved with the proposed model and the developed retrieval scheme.
Isolde A. Glissenaar, Jack C. Landy, Alek A. Petty, Nathan T. Kurtz, and Julienne C. Stroeve
The Cryosphere, 15, 4909–4927, https://doi.org/10.5194/tc-15-4909-2021, https://doi.org/10.5194/tc-15-4909-2021, 2021
Short summary
Short summary
Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.
Marek Muchow, Amelie U. Schmitt, and Lars Kaleschke
The Cryosphere, 15, 4527–4537, https://doi.org/10.5194/tc-15-4527-2021, https://doi.org/10.5194/tc-15-4527-2021, 2021
Short summary
Short summary
Linear-like openings in sea ice, also called leads, occur with widths from meters to kilometers. We use satellite images from Sentinel-2 with a resolution of 10 m to identify leads and measure their widths. With that we investigate the frequency of lead widths using two different statistical methods, since other studies have shown a dependency of heat exchange on the lead width. We are the first to address the sea-ice lead-width distribution in the Weddell Sea, Antarctica.
Gemma M. Brett, Daniel Price, Wolfgang Rack, and Patricia J. Langhorne
The Cryosphere, 15, 4099–4115, https://doi.org/10.5194/tc-15-4099-2021, https://doi.org/10.5194/tc-15-4099-2021, 2021
Short summary
Short summary
Ice shelf meltwater in the surface ocean affects sea ice formation, causing it to be thicker and, in particular conditions, to have a loose mass of platelet ice crystals called a sub‐ice platelet layer beneath. This causes the sea ice freeboard to stand higher above sea level. In this study, we demonstrate for the first time that the signature of ice shelf meltwater in the surface ocean manifesting as higher sea ice freeboard in McMurdo Sound is detectable from space using satellite technology.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
Short summary
Short summary
We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
Thomas Lavergne, Montserrat Piñol Solé, Emily Down, and Craig Donlon
The Cryosphere, 15, 3681–3698, https://doi.org/10.5194/tc-15-3681-2021, https://doi.org/10.5194/tc-15-3681-2021, 2021
Short summary
Short summary
Pushed by winds and ocean currents, polar sea ice is on the move. We use passive microwave satellites to observe this motion. The images from their orbits are often put together into daily images before motion is measured. In our study, we measure motion from the individual orbits directly and not from the daily images. We obtain many more motion vectors, and they are more accurate. This can be used for current and future satellites, e.g. the Copernicus Imaging Microwave Radiometer (CIMR).
Céline Heuzé, Lu Zhou, Martin Mohrmann, and Adriano Lemos
The Cryosphere, 15, 3401–3421, https://doi.org/10.5194/tc-15-3401-2021, https://doi.org/10.5194/tc-15-3401-2021, 2021
Short summary
Short summary
For navigation or science planning, knowing when sea ice will open in advance is a prerequisite. Yet, to date, routine spaceborne microwave observations of sea ice are unable to do so. We present the first method based on spaceborne infrared that can forecast an opening several days ahead. We develop it specifically for the Weddell Polynya, a large hole in the Antarctic winter ice cover that unexpectedly re-opened for the first time in 40 years in 2016, and determine why the polynya opened.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
Short summary
Short summary
Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
Zhixiang Yin, Xiaodong Li, Yong Ge, Cheng Shang, Xinyan Li, Yun Du, and Feng Ling
The Cryosphere, 15, 2835–2856, https://doi.org/10.5194/tc-15-2835-2021, https://doi.org/10.5194/tc-15-2835-2021, 2021
Short summary
Short summary
MODIS thermal infrared (TIR) imagery provides promising data to study the rapid variations in the Arctic turbulent heat flux (THF). The accuracy of estimated THF, however, is low (especially for small leads) due to the coarse resolution of the MODIS TIR data. We train a deep neural network to enhance the spatial resolution of estimated THF over leads from MODIS TIR imagery. The method is found to be effective and can generate a result which is close to that derived from Landsat-8 TIR imagery.
Joan Antoni Parera-Portell, Raquel Ubach, and Charles Gignac
The Cryosphere, 15, 2803–2818, https://doi.org/10.5194/tc-15-2803-2021, https://doi.org/10.5194/tc-15-2803-2021, 2021
Short summary
Short summary
We describe a new method to map sea ice and water at 500 m resolution using data acquired by the MODIS sensors. The strength of this method is that it achieves high-accuracy results and is capable of attenuating unwanted resolution-breaking effects caused by cloud masking. Our resulting March and September monthly aggregates reflect the loss of sea ice in the European Arctic during the 2000–2019 period and show the algorithm's usefulness as a sea ice monitoring tool.
Robbie D. C. Mallett, Julienne C. Stroeve, Michel Tsamados, Jack C. Landy, Rosemary Willatt, Vishnu Nandan, and Glen E. Liston
The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, https://doi.org/10.5194/tc-15-2429-2021, 2021
Short summary
Short summary
We re-estimate pan-Arctic sea ice thickness (SIT) values by combining data from the Envisat and CryoSat-2 missions with data from a new, reanalysis-driven snow model. Because a decreasing amount of ice is being hidden below the waterline by the weight of overlying snow, we argue that SIT may be declining faster than previously calculated in some regions. Because the snow product varies from year to year, our new SIT calculations also display much more year-to-year variability.
Renée Mie Fredensborg Hansen, Eero Rinne, Sinéad Louise Farrell, and Henriette Skourup
The Cryosphere, 15, 2511–2529, https://doi.org/10.5194/tc-15-2511-2021, https://doi.org/10.5194/tc-15-2511-2021, 2021
Short summary
Short summary
Ice navigators rely on timely information about ice conditions to ensure safe passage through ice-covered waters, and one parameter, the degree of ice ridging (DIR), is particularly useful. We have investigated the possibility of estimating DIR from the geolocated photons of ICESat-2 (IS2) in the Bay of Bothnia, show that IS2 retrievals from different DIR areas differ significantly, and present some of the first steps in creating sea ice applications beyond e.g. thickness retrieval.
Luisa von Albedyll, Christian Haas, and Wolfgang Dierking
The Cryosphere, 15, 2167–2186, https://doi.org/10.5194/tc-15-2167-2021, https://doi.org/10.5194/tc-15-2167-2021, 2021
Short summary
Short summary
Convergent sea ice motion produces a thick ice cover through ridging. We studied sea ice deformation derived from high-resolution satellite imagery and related it to the corresponding thickness change. We found that deformation explains the observed dynamic thickness change. We show that deformation can be used to model realistic ice thickness distributions. Our results revealed new relationships between thickness redistribution and deformation that could improve sea ice models.
Rasmus T. Tonboe, Vishnu Nandan, John Yackel, Stefan Kern, Leif Toudal Pedersen, and Julienne Stroeve
The Cryosphere, 15, 1811–1822, https://doi.org/10.5194/tc-15-1811-2021, https://doi.org/10.5194/tc-15-1811-2021, 2021
Short summary
Short summary
A relationship between the Ku-band radar scattering horizon and snow depth is found using a radar scattering model. This relationship has implications for (1) the use of snow climatology in the conversion of satellite radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both 1 and 2, the impact of using a snow climatology versus the actual snow depth is relatively small.
Stephan Paul and Marcus Huntemann
The Cryosphere, 15, 1551–1565, https://doi.org/10.5194/tc-15-1551-2021, https://doi.org/10.5194/tc-15-1551-2021, 2021
Short summary
Short summary
Cloud cover in the polar regions is difficult to identify at night when using only thermal-infrared data. This is due to occurrences of warm clouds over cold sea ice and cold clouds over warm sea ice. Especially the standard MODIS cloud mask frequently tends towards classifying open water and/or thin ice as cloud cover. Using a neural network, we present an improved discrimination between sea-ice, open-water and/or thin-ice, and cloud pixels in nighttime MODIS thermal-infrared satellite data.
Stephen E. L. Howell, Randall K. Scharien, Jack Landy, and Mike Brady
The Cryosphere, 14, 4675–4686, https://doi.org/10.5194/tc-14-4675-2020, https://doi.org/10.5194/tc-14-4675-2020, 2020
Short summary
Short summary
Melt ponds form on the surface of Arctic sea ice during spring and have been shown to exert a strong influence on summer sea ice area. Here, we use RADARSAT-2 satellite imagery to estimate the predicted peak spring melt pond fraction in the Canadian Arctic Archipelago from 2009–2018. Our results show that RADARSAT-2 estimates of peak melt pond fraction can be used to provide predictive information about summer sea ice area within certain regions of the Canadian Arctic Archipelago.
Hoyeon Shi, Byung-Ju Sohn, Gorm Dybkjær, Rasmus Tage Tonboe, and Sang-Moo Lee
The Cryosphere, 14, 3761–3783, https://doi.org/10.5194/tc-14-3761-2020, https://doi.org/10.5194/tc-14-3761-2020, 2020
Short summary
Short summary
To estimate sea ice thickness from satellite freeboard measurements, snow depth information has been required; however, the snow depth estimate has been considered largely uncertain. We propose a new method to estimate sea ice thickness and snow depth simultaneously from freeboards by imposing a thermodynamic constraint. Obtained ice thicknesses and snow depths were consistent with airborne measurements, suggesting that uncertainty of ice thickness caused by uncertain snow depth can be reduced.
Nicholas C. Wright, Chris M. Polashenski, Scott T. McMichael, and Ross A. Beyer
The Cryosphere, 14, 3523–3536, https://doi.org/10.5194/tc-14-3523-2020, https://doi.org/10.5194/tc-14-3523-2020, 2020
Short summary
Short summary
This work presents a new dataset of sea ice surface fractions along NASA Operation IceBridge flight tracks created by processing hundreds of thousands of aerial images. These results are then analyzed to investigate the behavior of meltwater on first-year ice in comparison to multiyear ice. We find preliminary evidence that first-year ice frequently has a lower melt pond fraction than adjacent multiyear ice, contrary to established knowledge in the sea ice community.
Wolfgang Dierking, Harry L. Stern, and Jennifer K. Hutchings
The Cryosphere, 14, 2999–3016, https://doi.org/10.5194/tc-14-2999-2020, https://doi.org/10.5194/tc-14-2999-2020, 2020
Short summary
Short summary
Monitoring deformation of sea ice is useful for studying effects of ice compression and divergent motion on the ice mass balance and ocean–ice–atmosphere interactions. In calculations of deformation parameters not only the measurement uncertainty of drift vectors has to be considered. The size of the area and the time interval used in the calculations have to be chosen within certain limits to make sure that the uncertainties of deformation parameters are smaller than their real magnitudes.
Igor E. Kozlov, Evgeny V. Plotnikov, and Georgy E. Manucharyan
The Cryosphere, 14, 2941–2947, https://doi.org/10.5194/tc-14-2941-2020, https://doi.org/10.5194/tc-14-2941-2020, 2020
Short summary
Short summary
Here we demonstrate a recently emerged opportunity to retrieve high-resolution surface current velocities from sequential spaceborne radar images taken over low-concentration ice regions of polar oceans. Such regularly available data uniquely resolve complex surface ocean dynamics even at small scales and can be used in operational applications to assess and predict transport and distribution of biogeochemical substances and pollutants in ice-covered waters.
Jeong-Won Park, Anton Andreevich Korosov, Mohamed Babiker, Joong-Sun Won, Morten Wergeland Hansen, and Hyun-Cheol Kim
The Cryosphere, 14, 2629–2645, https://doi.org/10.5194/tc-14-2629-2020, https://doi.org/10.5194/tc-14-2629-2020, 2020
Short summary
Short summary
A new Sentinel-1 radar-based sea ice classification algorithm is proposed. We show that the readily available ice charts from operational ice services can reduce the amount of manual work in preparation of large amounts of training/testing data and feed highly reliable data to the trainer in an efficient way. Test results showed that the classifier is capable of retrieving three generalized cover types with overall accuracy of 87 % and 67 % in the winter and summer seasons, respectively.
Marcel König and Natascha Oppelt
The Cryosphere, 14, 2567–2579, https://doi.org/10.5194/tc-14-2567-2020, https://doi.org/10.5194/tc-14-2567-2020, 2020
Short summary
Short summary
We used data that we collected on RV Polarstern cruise PS106 in summer 2017 to develop a model for the derivation of melt pond depth on Arctic sea ice from reflectance measurements. We simulated reflectances of melt ponds of varying color and water depth and used the sun zenith angle and the slope of the log-scaled reflectance at 710 nm to derive pond depth. We validated the model on the in situ melt pond data and found it to derive pond depth very accurately.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, and Rasmus Tonboe
The Cryosphere, 14, 2469–2493, https://doi.org/10.5194/tc-14-2469-2020, https://doi.org/10.5194/tc-14-2469-2020, 2020
Short summary
Short summary
Arctic sea-ice concentration (SIC) estimates based on satellite passive microwave observations are highly inaccurate during summer melt. We compare 10 different SIC products with independent satellite data of true SIC and melt pond fraction (MPF). All products disagree with the true SIC. Regional and inter-product differences can be large and depend on the MPF. An inadequate treatment of melting snow and melt ponds in the products’ algorithms appears to be the main explanation for our findings.
Ron R. Togunov, Natasha J. Klappstein, Nicholas J. Lunn, Andrew E. Derocher, and Marie Auger-Méthé
The Cryosphere, 14, 1937–1950, https://doi.org/10.5194/tc-14-1937-2020, https://doi.org/10.5194/tc-14-1937-2020, 2020
Short summary
Short summary
Sea ice drift affects important geophysical and biological processes in the Arctic. Using the motion of dropped polar bear GPS collars, our study evaluated the accuracy of a popular satellite-based ice drift model in Hudson Bay. We observed that velocity was underestimated, particularly at higher speeds. Direction was unbiased, but it was less precise at lower speeds. These biases should be accounted for in climate and ecological research relying on accurate/absolute drift velocities.
Sophie Dufour-Beauséjour, Anna Wendleder, Yves Gauthier, Monique Bernier, Jimmy Poulin, Véronique Gilbert, Juupi Tuniq, Amélie Rouleau, and Achim Roth
The Cryosphere, 14, 1595–1609, https://doi.org/10.5194/tc-14-1595-2020, https://doi.org/10.5194/tc-14-1595-2020, 2020
Short summary
Short summary
Inuit have reported greater variability in seasonal sea ice conditions. For Deception Bay (Nunavik), an area prized for seal and caribou hunting, an increase in snow precipitation and a shorter snow cover period is expected in the near future. In this context, and considering ice-breaking transport in the fjord by mining companies, we combined satellite images and time-lapse photography to monitor sea ice in the area between 2015 and 2018.
Babula Jena and Anilkumar N. Pillai
The Cryosphere, 14, 1385–1398, https://doi.org/10.5194/tc-14-1385-2020, https://doi.org/10.5194/tc-14-1385-2020, 2020
Short summary
Short summary
Records of multiple ocean color satellite data indicated unprecedented phytoplankton blooms on the Maud Rise with a backdrop of anomalous upper ocean warming and sea ice loss in 2017. The bloom appearance may indicate it as a potential sink of atmospheric CO2 through biological pumping, and it can be a major source of carbon and energy for the regional food web. The reoccurrence of the bloom is important considering the high-nutrient low-chlorophyll conditions of the Southern Ocean.
Maciej Miernecki, Lars Kaleschke, Nina Maaß, Stefan Hendricks, and Sten Schmidl Søbjærg
The Cryosphere, 14, 461–476, https://doi.org/10.5194/tc-14-461-2020, https://doi.org/10.5194/tc-14-461-2020, 2020
Robbie D. C. Mallett, Isobel R. Lawrence, Julienne C. Stroeve, Jack C. Landy, and Michel Tsamados
The Cryosphere, 14, 251–260, https://doi.org/10.5194/tc-14-251-2020, https://doi.org/10.5194/tc-14-251-2020, 2020
Short summary
Short summary
Soils store large carbon and are important for global warming. We do not know what factors are important for soil carbon storage in the alpine Andes and how they work. We studied how rainfall affects soil carbon storage related to soil structure. We found soil structure is not important, but soil carbon storage and stability controlled by rainfall are dependent on rocks under the soils. The results indicate that we should pay attention to the rocks when studying soil carbon storage in the Andes.
Christine Pohl, Larysa Istomina, Steffen Tietsche, Evelyn Jäkel, Johannes Stapf, Gunnar Spreen, and Georg Heygster
The Cryosphere, 14, 165–182, https://doi.org/10.5194/tc-14-165-2020, https://doi.org/10.5194/tc-14-165-2020, 2020
Short summary
Short summary
A spectral to broadband conversion is developed empirically that can be used in combination with the Melt Pond Detector algorithm to derive broadband albedo (300–3000 nm) of Arctic sea ice from MERIS data. It is validated and shows better performance compared to existing conversion methods. A comparison of MERIS broadband albedo with respective values from ERA5 reanalysis suggests a revision of the albedo values used in ERA5. MERIS albedo might be useful for improving albedo representation.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, Rasmus Tage Tonboe, Roberto Saldo, and Atle MacDonald Sørensen
The Cryosphere, 13, 3261–3307, https://doi.org/10.5194/tc-13-3261-2019, https://doi.org/10.5194/tc-13-3261-2019, 2019
Short summary
Short summary
A systematic evaluation of 10 global satellite data products of the polar sea-ice area is performed. Inter-product differences in evaluation results call for careful consideration of data product limitations when performing sea-ice area trend analyses and for further mitigation of the effects of sensor changes. We open a discussion about evaluation strategies for such data products near-0 % and near-100 % sea-ice concentration, e.g. with the aim to improve high-concentration evaluation accuracy.
Christopher Horvat, Lettie A. Roach, Rachel Tilling, Cecilia M. Bitz, Baylor Fox-Kemper, Colin Guider, Kaitlin Hill, Andy Ridout, and Andrew Shepherd
The Cryosphere, 13, 2869–2885, https://doi.org/10.5194/tc-13-2869-2019, https://doi.org/10.5194/tc-13-2869-2019, 2019
Short summary
Short summary
Changes in the floe size distribution (FSD) are important for sea ice evolution but to date largely unobserved and unknown. Climate models, forecast centres, ship captains, and logistic specialists cannot currently obtain statistical information about sea ice floe size on demand. We develop a new method to observe the FSD at global scales and high temporal and spatial resolution. With refinement, this method can provide crucial information for polar ship routing and real-time forecasting.
Valentin Ludwig, Gunnar Spreen, Christian Haas, Larysa Istomina, Frank Kauker, and Dmitrii Murashkin
The Cryosphere, 13, 2051–2073, https://doi.org/10.5194/tc-13-2051-2019, https://doi.org/10.5194/tc-13-2051-2019, 2019
Short summary
Short summary
Sea-ice concentration, the fraction of an area covered by sea ice, can be observed from satellites with different methods. We combine two methods to obtain a product which is better than either of the input measurements alone. The benefit of our product is demonstrated by observing the formation of an open water area which can now be observed with more detail. Additionally, we find that the open water area formed because the sea ice drifted in the opposite direction and faster than usual.
Meng Qu, Xiaoping Pang, Xi Zhao, Jinlun Zhang, Qing Ji, and Pei Fan
The Cryosphere, 13, 1565–1582, https://doi.org/10.5194/tc-13-1565-2019, https://doi.org/10.5194/tc-13-1565-2019, 2019
Short summary
Short summary
Can we ignore the contribution of small ice leads when estimating turbulent heat flux? Combining bulk formulae and a fetch-limited model with surface temperature from MODIS and Landsat-8 Thermal Infrared Sensor (TIRS) images, we found small leads account for 25 % of the turbulent heat flux, due to its large total area. Estimated turbulent heat flux is larger from TIRS than that from MODIS with a coarser resolution and larger using a fetch-limited model than that using bulk formulae.
Cited articles
Baraldi, A. and Parmiggiani, F.: An investigation of the textural
characteristics associated with gray level cooccurrence matrix statistical
parameters, IEEE T. Geosci. Remote, 33,
293–304, https://doi.org/10.1109/TGRS.1995.8746010, 1995. a
Barber, D. G., Ehn, J. K., Pućko, M., Rysgaard, S., Deming, J. W.,
Bowman, J. S., Papakyriakou, T., Galley, R. J., and Søgaard, D. H.: Frost
flowers on young Arctic sea ice: The climatic, chemical, and microbial
significance of an emerging ice type, J. Geophys. Res.-Atmos., 119, 11593–11612, https://doi.org/10.1002/2014JD021736, 2014. a, b
Bliss, A., Hutchings, J., Anderson, P., Anhaus, P., and Jakob Belter, H.:
Sea ice drift tracks from the Distributed Network of autonomous buoys
deployed during the Multidisciplinary drifting Observatory for the Study of
Arctic Climate (MOSAiC) expedition 2019–2021, Arctic Data Center [data set], https://doi.org/10.18739/A2Q52FD8S,
2021. a
Bogdanov, A. V., Sandven, S., Johannessen, O. M., Alexandrov, V. Y., and
Bobylev, L. P.: Multi-sensor approach to automated classification of sea ice
image data, Image Processing for Remote Sensing, 43, 293–324,
https://doi.org/10.1201/9781420066654, 2007. a
Boulze, H., Korosov, A., and Brajard, J.: Classification of sea ice types in
sentinel-1 SAR data using convolutional neural networks, Remote Sens., 12,
1–20, https://doi.org/10.3390/rs12132165, 2020. a
Cafarella, S. M., Scharien, R., Geldsetzer, T., Howell, S., Haas, C., Segal,
R., and Nasonova, S.: Estimation of Level and Deformed First-Year Sea Ice
Surface Roughness in the Canadian Arctic Archipelago from C- and L-Band
Synthetic Aperture Radar, Can. J. Remote Sens., 45, 457–475,
https://doi.org/10.1080/07038992.2019.1647102, 2019. a
Clausi, D. A.: Comparison and fusion of co-occurrence, Gabor and MRF texture
features for classification of SAR sea-ice imagery, Atmosphere-Ocean, 39,
183–194, https://doi.org/10.1080/07055900.2001.9649675, 2001. a
Clausi, D. A. and Yu, B.: Comparing cooccurrence probabilities and Markov
random fields for texture analysis of SAR sea ice imagery, IEEE T. Geosci. Remote, 42, 215–228,
https://doi.org/10.1109/TGRS.2003.817218, 2004. a, b
Cox, C., Gallagher, M., Shupe, M., Persson, O., Solomon, A., Blomquist, B.,
Brooks, I., Costa, D., Gottas, D., and Hutchings, J.: 10-meter (m)
meteorological flux tower measurements (Level 1 Raw), Multidisciplinary
drifting observatory for the study of arctic climate (MOSAiC), central
Arctic, October 2019–September 2020, https://doi.org/10.18739/A2VM42Z5F, 2021. a
Daniel, W. W.: Applied nonparametric statistics, Boston (Mass.): PWS-KENT,
2nd edn., http://lib.ugent.be/catalog/rug01:000283035 (last access: 1 October 2022),
1990. a
Dierking, W.: Mapping of Different Sea Ice Regimes Using Images From
Sentinel-1 and ALOS Synthetic Aperture Radar, IEEE T. Geosci. Remote, 48, 1045–1058,
https://doi.org/10.1109/TGRS.2009.2031806, 2010. a
Dierking, W. and Dall, J.: Sea-ice deformation state from synthetic aperture
radar imagery – Part I: Comparison of C- and L-Band and different
polarization, IEEE T. Geosci. Remote, 45,
3610–3621, https://doi.org/10.1109/TGRS.2007.903711, 2007. a, b
Doulgeris, A. P.: An automatic U-distribution and markov random field
segmentation algorithm for PolSAR images, IEEE T. Geosci. Remote, 53, 1819–1827, https://doi.org/10.1109/TGRS.2014.2349575, 2015. a, b
European Space Agency: SNAP – ESA Sentinel Application Platform v7.0.4,
http://step.esa.int (last access: 28 March 2022), 2020. a
European Space Agency: Copernicus Sentinel data,
https://scihub.copernicus.eu (last access: 28 March 2022), 2021. a
Gegiuc, A., Similä, M., Karvonen, J., Lensu, M., Mäkynen, M., and Vainio, J.: Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data, The Cryosphere, 12, 343–364, https://doi.org/10.5194/tc-12-343-2018, 2018. a, b
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore,
R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone,
Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031,
2017. a
Guo, W., Itkin, P., Lohse, J., Johansson, M., and Doulgeris, A. P.: Cross-platform classification of level and deformed sea ice considering per-class incident angle dependency of backscatter intensity, The Cryosphere, 16, 237–257, https://doi.org/10.5194/tc-16-237-2022, 2022. a, b
Guo, W., Itkin, P., Singha, S., Doulgeris, A. P., Johansson, M., and Spreen, G.: TSX_SC_MOSAiC, https://www.dropbox.com/sh/edx4eq2oux0fqdg/AAB5CXZ8ReTwZNpXe48mpoZYa?dl=0, Dropbox [data set] (last access: 15 November 2022), 2023.
Hall, D. K. and Riggs, G. A.: MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km,
Version 61, National Snow and Ice Data Center [data set], https://doi.org/10.5067/MODIS/MOD29.061, 2021. a, b
Haralick, R., Shanmugan, K., and Dinstein, I.: Textural features f
or image classification, IEEE T. Syst. Man Cyb., 3, 610–621, https://doi.org/10.1109/TSMC.1973.4309314, 1973. a, b, c, d
Hendricks, S., Itkin, P., Ricker, R., Webster, M., von Albedyll, L., Rohde, J.,
Raphael, I., Jaggi, M., and Arndt, S.: GEM-2 quicklook total thickness
measurements from the 2019–2020 MOSAiC expedition, PANGAEA [data set],
https://doi.org/10.1594/PANGAEA.943666, 2022. a
Holmes, Q. A., Nuesch, D. R., and Shuchman, R. A.: Textural Analysis and
Real-Time Classification of Sea-Ice Types Using Digital SAR Data, IEEE T. Geosci. Remote, GE-22, 113–120,
https://doi.org/10.1109/TGRS.1984.350602, 1984. a, b
Iqbal, M., Chen, J., Yang, W., Wang, P., and Sun, B.: Kalman filter for
removal of scalloping and inter-scan banding in scansar images, Prog.
Electromagn. Res., 132, 443–461, https://doi.org/10.2528/PIER12082107, 2012. a
Isleifson, D., Hwang, B., Barber, D. G., Scharien, R. K., and Shafai, L.:
C-band polarimetric backscattering signatures of newly formed sea ice during
fall freeze-up, IEEE T. Geosci. Remote, 48,
3256–3267, https://doi.org/10.1109/TGRS.2010.2043954, 2010. a
Isleifson, D., Galley, R. J., Firoozy, N., Landy, J. C., and Barber, D. G.:
Investigations into frost flower physical characteristics and the C-band
scattering response, Remote Sens., 10, 1–16, https://doi.org/10.3390/rs10070991,
2018. a
Itkin, P., Hendricks, S., Webster, M., Albedyll, L. V., Arndt, S., Divine, D.,
Jaggi, M., Oggier, M., Raphael, I., Ricker, R., Rohde, J., Schneebeli, M.,
and Liston, G.: Sea ice and snow mass balance from transects in the MOSAiC
Central Observatory, Elementa: Science of the Anthropocene, in review, 2023. a, b, c, d, e
Johansson, A. M., Brekke, C., Spreen, G., and King, J. A.: X-, C-, and L-band
SAR signatures of newly formed sea ice in Arctic leads during winter and
spring, Remote Sens. Environ., 204, 162–180,
https://doi.org/10.1016/j.rse.2017.10.032, 2018. a
Komarov, A. S. and Buehner, M.: Automated Detection of Ice and Open Water from
Dual-Polarization RADARSAT-2 Images for Data Assimilation, IEEE T. Geosci. Remote, 55, 5755–5769,
https://doi.org/10.1109/TGRS.2017.2713987, 2017. a
Kortum, K., Singha, S., and Spreen, G.: Robust Multiseasonal Ice
Classification From High-Resolution X-Band SAR, IEEE T. Geosci. Remote, 60, 1–12, https://doi.org/10.1109/TGRS.2022.3144731,
2022. a, b
Krumpen, T. and Sokolov, V.: The Expedition AF122/1: Setting up the MOSAiC
Distributed Network in October 2019 with Research Vessel AKADEMIK FEDOROV,
Berichte zur Polar-und Meeresforschung,
744, 2020. a
Krumpen, T., Birrien, F., Kauker, F., Rackow, T., von Albedyll, L., Angelopoulos, M., Belter, H. J., Bessonov, V., Damm, E., Dethloff, K., Haapala, J., Haas, C., Harris, C., Hendricks, S., Hoelemann, J., Hoppmann, M., Kaleschke, L., Karcher, M., Kolabutin, N., Lei, R., Lenz, J., Morgenstern, A., Nicolaus, M., Nixdorf, U., Petrovsky, T., Rabe, B., Rabenstein, L., Rex, M., Ricker, R., Rohde, J., Shimanchuk, E., Singha, S., Smolyanitsky, V., Sokolov, V., Stanton, T., Timofeeva, A., Tsamados, M., and Watkins, D.: The MOSAiC ice floe: sediment-laden survivor from the Siberian shelf, The Cryosphere, 14, 2173–2187, https://doi.org/10.5194/tc-14-2173-2020, 2020. a
Krumpen, T., von Albedyll, L., Goessling, H. F., Hendricks, S., Juhls, B., Spreen, G., Willmes, S., Belter, H. J., Dethloff, K., Haas, C., Kaleschke, L., Katlein, C., Tian-Kunze, X., Ricker, R., Rostosky, P., Rückert, J., Singha, S., and Sokolova, J.: MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years, The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, 2021. a, b, c, d
Leigh, S., Wang, Z., and Clausi, D. A.: Automated ice-water classification
using dual polarization SAR satellite imagery, IEEE T. Geosci. Remote, 52, 5529–5539,
https://doi.org/10.1109/TGRS.2013.2290231, 2014. a, b, c
Liu, H., Guo, H., and Liu, G.: A Two-Scale Method of Sea Ice Classification
Using TerraSAR-X ScanSAR Data During Early Freeze-Up, IEEE J.
Sel. Top. Appl. Earth Obs., 14,
10919–10928, https://doi.org/10.1109/JSTARS.2021.3122546, 2021. a, b
Lohse, J., Doulgeris, A. P., and Dierking, W.: Mapping sea-ice types from S
entinel-1 considering the surface-type dependent effect of incidence angle, Ann. Glaciol., 61, 260–270, https://doi.org/10.1017/aog.2020.45, 2020. a, b
Mahmud, M. S., Geldsetzer, T., Howell, S. E., Yackel, J. J., Nandan, V., and
Scharien, R. K.: Incidence angle dependence of HH-polarized C- A nd L-band
wintertime backscatter over arctic sea ice, IEEE T. Geosci.
Remote, 56, 6686–6698, https://doi.org/10.1109/TGRS.2018.2841343, 2018. a
Mäkynen, M. and Hallikainen, M.: Investigation of C- and X-band
backscattering signatures of Baltic Sea ice, Int. J. Remote
Sens., 25, 2061–2086, https://doi.org/10.1080/01431160310001647697, 2004. a
Mäkynen, M. and Karvonen, J.: Incidence Angle Dependence of First-Year
Sea Ice Backscattering Coefficient in Sentinel-1 SAR Imagery over the Kara
Sea, IEEE T. Geosci. Remote, 55, 6170–6181,
https://doi.org/10.1109/TGRS.2017.2721981, 2017. a
Mäkynen, M. P., Manninen, A. T., Similä, M. H., Karvonen, J. A.,
and Hallikainen, M. T.: Incidence angle dependence of the statistical
properties of C-band HH-polarization backscattering signatures of the Baltic
Sea ice, IEEE T. Geosci. Remote, 40, 2593–2605,
https://doi.org/10.1109/TGRS.2002.806991, 2002. a, b
Marcel, W., Clauss, K., Valgur, M., and Sølvsteen, J.: Sentinelsat Python
API, GNU General Public License v3.0+,
https://github.com/sentinelsat/sentinelsat/tree/a551d071f9c5faae09603ec4a3ef9dc3dd3ef833 (last access: 28 March 2022),
2021. a
Martin, S., Drucker, R. M., and Fort, M.: A laboratory study of frost flower
growth on the surface of young sea ice, J. Geophys. Res.,
100, 7027–7036, 1995. a
Murashkin, D., Spreen, G., Huntemann, M., and Dierking, W.: Method for
detection of leads from Sentinel-1 SAR images, Ann. Glaciol., 59,
124–136, https://doi.org/10.1017/aog.2018.6, 2018. a
Nicolaus, M., Arndt, S., Birnbaum, G., and Katlein, C.: Visual panoramic
photographs of the surface conditions during the MOSAiC campaign 2019/20, PANGAEA [data set],
https://doi.org/10.1594/PANGAEA.938534, 2021. a, b
Nicolaus, M., Perovich, D. K., Spreen, G., Granskog, M. A., von Albedyll, L.,
Angelopoulos, M., Anhaus, P., Arndt, S., Belter, H. J., Bessonov, V.,
Birnbaum, G., Brauchle, J., Calmer, R., Cardellach, E., Cheng, B.,
Clemens-Sewall, D., Dadic, R., Damm, E., de Boer, G., Demir, O., Dethloff,
K., Divine, D. V., Fong, A. A., Fons, S., Frey, M. M., Fuchs, N.,
Gabarró, C., Gerland, S., Goessling, H. F., Gradinger, R., Haapala, J.,
Haas, C., Hamilton, J., Hannula, H.-R., Hendricks, S., Herber, A.,
Heuzé, C., Hoppmann, M., Høyland, K. V., Huntemann, M., Hutchings,
J. K., Hwang, B., Itkin, P., Jacobi, H.-W., Jaggi, M., Jutila, A., Kaleschke,
L., Katlein, C., Kolabutin, N., Krampe, D., Kristensen, S. S., Krumpen, T.,
Kurtz, N., Lampert, A., Lange, B. A., Lei, R., Light, B., Linhardt, F.,
Liston, G. E., Loose, B., Macfarlane, A. R., Mahmud, M., Matero, I. O., Maus,
S., Morgenstern, A., Naderpour, R., Nandan, V., Niubom, A., Oggier, M.,
Oppelt, N., Pätzold, F., Perron, C., Petrovsky, T., Pirazzini, R.,
Polashenski, C., Rabe, B., Raphael, I. A., Regnery, J., Rex, M., Ricker, R.,
Riemann-Campe, K., Rinke, A., Rohde, J., Salganik, E., Scharien, R. K.,
Schiller, M., Schneebeli, M., Semmling, M., Shimanchuk, E., Shupe, M. D.,
Smith, M. M., Smolyanitsky, V., Sokolov, V., Stanton, T., Stroeve, J.,
Thielke, L., Timofeeva, A., Tonboe, R. T., Tavri, A., Tsamados, M., Wagner,
D. N., Watkins, D., Webster, M., and Wendisch, M.: Overview of the MOSAiC
expedition: Snow and sea ice, Elementa: Science of the Anthropocene, 10, 46,
https://doi.org/10.1525/elementa.2021.000046, 2022. a
Nixdorf, U., Dethloff, K., Rex, M., Shupe, M., Sommerfeld, A., Perovich, D.,
Nicolaus, M., Heuzé, C., Rabe, B., Loose, B., Damm, E., Gradinger, R.,
Fong, A., Maslowski, W., Rinke, A., Kwok, R., Hirsekorn, M., Spreen, G.,
Mohaupt, V., Wendisch, M., Frickenhaus, S., Mengedoht, D., Herber, A.,
Immerz, A., Regnery, J., Weiss-tuider, K., Gerchow, P., Haas, C.,
König, B., Ransby, D., Kanzow, T., Krumpen, T., Rack, F. R.,
Morgenstern, A., Saitzev, V., Sokolov, V., Makarov, A., Schwarze, S., and
Wunderlich, T.: MOSAiC Extended Acknowledgement, Zenodo [data set],
https://doi.org/10.5281/ZENODO.5541624, 2021. a
Park, J.-W., Korosov, A. A., Babiker, M., Won, J.-S., Hansen, M. W., and Kim, H.-C.: Classification of sea ice types in Sentinel-1 synthetic aperture radar images, The Cryosphere, 14, 2629–2645, https://doi.org/10.5194/tc-14-2629-2020, 2020. a, b
Ressel, R., Frost, A., and Lehner, S.: A Neural Network-Based Classification
for Sea Ice Types on X-Band SAR Images, IEEE J. Sel. Top.
Appl. Earth Obs., 8, 3672–3680,
https://doi.org/10.1109/JSTARS.2015.2436993, 2015. a, b
Sanden, J. J. D. and Hoekman, D. H.: Review of relationships between grey-tone
co-occurrence, semivariance, and autocorrelation based image texture analysis
approaches, Can. J. Remote Sens., 31, 207–213,
https://doi.org/10.5589/m05-008, 2005. a
Scharien, R. K. and Nasonova, S.: Incidence Angle Dependence of Texture
Statistics From Sentinel-1 HH-Polarization Images of Winter Arctic Sea Ice,
IEEE Geosci. Remote Sens. Lett., 19, 1–5,
https://doi.org/10.1109/LGRS.2020.3039739, 2020. a, b
Segal, R. A., Scharien, R. K., Cafarella, S., and Tedstone, A.: Characterizing
winter landfast sea-ice surface roughness in the Canadian Arctic Archipelago
using Sentinel-1 synthetic aperture radar and the Multi-angle Imaging
SpectroRadiometer, Ann. Glaciol., 61, 284–298,
https://doi.org/10.1017/aog.2020.48, 2020. a
Shokr, M. E.: Evaluation of second-order texture parameters for sea ice
classification from radar images, J. Geophys. Res.-Oceans,
96, 10625–10640, https://doi.org/10.1029/91JC00693, 1991. a, b
Shupe, M. D., Rex, M., Blomquist, B., Persson, P. O. G., Schmale, J., Uttal,
T., Althausen, D., Angot, H., Archer, S., Bariteau, L., Beck, I., Bilberry,
J., Bucci, S., Buck, C., Boyer, M., Brasseur, Z., Brooks, I. M., Calmer, R.,
Cassano, J., Castro, V., Chu, D., Costa, D., Cox, C. J., Creamean, J.,
Crewell, S., Dahlke, S., Damm, E., de Boer, G., Deckelmann, H., Dethloff, K.,
Dütsch, M., Ebell, K., Ehrlich, A., Ellis, J., Engelmann, R., Fong,
A. A., Frey, M. M., Gallagher, M. R., Ganzeveld, L., Gradinger, R., Graeser,
J., Greenamyer, V., Griesche, H., Griffiths, S., Hamilton, J., Heinemann, G.,
Helmig, D., Herber, A., Heuzé, C., Hofer, J., Houchens, T., Howard, D.,
Inoue, J., Jacobi, H.-W., Jaiser, R., Jokinen, T., Jourdan, O., Jozef, G.,
King, W., Kirchgaessner, A., Klingebiel, M., Krassovski, M., Krumpen, T.,
Lampert, A., Landing, W., Laurila, T., Lawrence, D., Lonardi, M., Loose, B.,
Lüpkes, C., Maahn, M., Macke, A., Maslowski, W., Marsay, C., Maturilli,
M., Mech, M., Morris, S., Moser, M., Nicolaus, M., Ortega, P., Osborn, J.,
Pätzold, F., Perovich, D. K., Petäjä, T., Pilz, C.,
Pirazzini, R., Posman, K., Powers, H., Pratt, K. A., Preußer, A.,
Quéléver, L., Radenz, M., Rabe, B., Rinke, A., Sachs, T., Schulz,
A., Siebert, H., Silva, T., Solomon, A., Sommerfeld, A., Spreen, G.,
Stephens, M., Stohl, A., Svensson, G., Uin, J., Viegas, J., Voigt, C.,
von der Gathen, P., Wehner, B., Welker, J. M., Wendisch, M., Werner, M., Xie,
Z., and Yue, F.: Overview of the MOSAiC expedition: Atmosphere, Elementa:
Science of the Anthropocene, 10, 60, https://doi.org/10.1525/elementa.2021.00060, 2022. a, b
Soh, L. K. and Tsatsoulis, C.: Texture analysis of sar sea ice imagery using
gray level co-occurrence matrices, IEEE T. Geosci. Remote, 37, 780–795, https://doi.org/10.1109/36.752194, 1999. a, b
The Mathworks Inc.: MATLAB R2021b,
http://www.mathworks.com/ (last access: 15 October 2022), 2021. a
Unser, M.: Texture classification and segmentation using wavelet frames, IEEE
T. Image Process., 4, 1549–1560, 1995. a
WMO: Sea Ice Nomenclature, WMO/OMM/BMO – No. 259, Terminology, Volume I,
1970–2017 edn., 2018. a
Yang, W., Li, Y., Liu, W., Chen, J., Li, C., and Men, Z.: Scalloping
Suppression for ScanSAR Images Based on Modified Kalman Filter With
Preprocessing, IEEE T. Geosci. Remote, 59,
7535–7546, https://doi.org/10.1109/tgrs.2020.3034098, 2020. a
Zakhvatkina, N., Korosov, A., Muckenhuber, S., Sandven, S., and Babiker, M.: Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images, The Cryosphere, 11, 33–46, https://doi.org/10.5194/tc-11-33-2017, 2017. a, b
Zakhvatkina, N., Smirnov, V., and Bychkova, I.: Satellite SAR Data-based Sea
Ice Classification: An Overview, Geosciences, 9, 152, https://doi.org/10.3390/geosciences9040152, 2019. a, b, c
Zakhvatkina, N. Y., Alexandrov, V. Y., Johannessen, O. M., Sandven, S., and
Frolov, I. Y.: Classification of sea ice types in ENVISAT synthetic aperture
radar images, IEEE T. Geosci. Remote, 51,
2587–2600, https://doi.org/10.1109/TGRS.2012.2212445, 2013. a
Zhang, L., Liu, H., Gu, X., Guo, H., Chen, J., and Liu, G.: Sea Ice
Classification Using TerraSAR-X ScanSAR Data With Removal of Scalloping and
Interscan Banding, IEEE J. Sel. Top. Appl. Earth
Obs., 12, 589–598,
https://doi.org/10.1109/JSTARS.2018.2889798, 2019. a, b
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.
Sea ice maps are produced to cover the MOSAiC Arctic expedition (2019–2020) and divide sea ice...
