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.
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture
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
Siqi Liu, Shiming Xu, Wenkai Guo, Yanfei Fan, Lu Zhou, Jack Landy, Malin Johansson, Weixin Zhu, and Alek Petty
EGUsphere, https://doi.org/10.5194/egusphere-2025-1069, https://doi.org/10.5194/egusphere-2025-1069, 2025
Short summary
Short summary
In this study, we explore the potential of using synthetic aperture radars (SAR) to predict the sea ice height measurements by the airborne campaign of Operation IceBridge. In particular, we predict the meter-scale sea ice height with the statistical relationship between the two, overcoming the resolution limitation of SAR images from Sentinel-1 satellites. The prediction and ice drift correction algorithms can be applied to the extrapolation of ICESat-2 measurements in the Arctic region.
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.
Yi-Jie Yang, Suman Singha, Ron Goldman, and Florian Schütte
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-208, https://doi.org/10.5194/essd-2025-208, 2025
Preprint under review for ESSD
Short summary
Short summary
This data descriptor presents a dataset containing oil slicks, look-alikes, and other remarkable ocean phenomena in synthetic aperture radar (SAR) data, which can be used for training oil spill detection methods. It explains the formation of various oceanic phenomena, supported by examples and supporting materials. These insights can help researchers from diverse backgrounds, such as remote sensing, oceanography, and machine learning, to better understand the sources of the signatures.
Siqi Liu, Shiming Xu, Wenkai Guo, Yanfei Fan, Lu Zhou, Jack Landy, Malin Johansson, Weixin Zhu, and Alek Petty
EGUsphere, https://doi.org/10.5194/egusphere-2025-1069, https://doi.org/10.5194/egusphere-2025-1069, 2025
Short summary
Short summary
In this study, we explore the potential of using synthetic aperture radars (SAR) to predict the sea ice height measurements by the airborne campaign of Operation IceBridge. In particular, we predict the meter-scale sea ice height with the statistical relationship between the two, overcoming the resolution limitation of SAR images from Sentinel-1 satellites. The prediction and ice drift correction algorithms can be applied to the extrapolation of ICESat-2 measurements in the Arctic region.
Polona Itkin
The Cryosphere, 19, 1135–1151, https://doi.org/10.5194/tc-19-1135-2025, https://doi.org/10.5194/tc-19-1135-2025, 2025
Short summary
Short summary
Radar satellite images of sea ice were analyzed to understand how sea ice moves and deforms. These data are noisy, especially when looking at small details. A method was developed to filter out the noise. The filtered data were used to monitor how ice plates stretch and compress over time, revealing slow healing of ice fractures. Cohesive clusters of ice plates that move together were studied too. These methods provide climate-relevant insights into the dynamic nature of winter sea ice cover.
Larysa Istomina, Hannah Niehaus, and Gunnar Spreen
The Cryosphere, 19, 83–105, https://doi.org/10.5194/tc-19-83-2025, https://doi.org/10.5194/tc-19-83-2025, 2025
Short summary
Short summary
Melt water puddles, or melt ponds on top of the Arctic sea ice, are a good measure of the Arctic climate state. In the context of recent climate warming, the Arctic has warmed about 4 times faster than the rest of the world, and a long-term dataset of the melt pond fraction is needed to be able to model the future development of the Arctic climate. We present such a dataset, produce 2002–2023 trends and highlight a potential melt regime shift with drastic regional trends of + 20 % per decade.
Karl Kortum, Suman Singha, and Gunnar Spreen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3351, https://doi.org/10.5194/egusphere-2024-3351, 2024
Short summary
Short summary
Improved sea ice observations are essential to understanding the processes that lead to the strong warming effect currently being observed in the Arctic. In this work, we combine complementary satellite measurement techniques and find remarkable correlations between the two observations. This allows us to expand the coverage of ice topography measurements to a scope and resolution that could not previously be observed.
Polona Itkin and Glen E. Liston
EGUsphere, https://doi.org/10.5194/egusphere-2024-3402, https://doi.org/10.5194/egusphere-2024-3402, 2024
Short summary
Short summary
The MOSAiC project provided a year of observations of Arctic snow and sea ice, though some data were interrupted, especially during summer melt onset. We developed a data-model fusion system to produce continuous, high-resolution time series of snow and sea ice parameters. On all three analyzed three ice types snow redistribution correlated with sea ice deformation and level ice thickness was governed by the thinnest fraction of snow cover.
Tore Wulf, Jørgen Buus-Hinkler, Suman Singha, Hoyeon Shi, and Matilde Brandt Kreiner
The Cryosphere, 18, 5277–5300, https://doi.org/10.5194/tc-18-5277-2024, https://doi.org/10.5194/tc-18-5277-2024, 2024
Short summary
Short summary
Here, we present ASIP: a new and comprehensive deep-learning-based methodology to retrieve high-resolution sea ice concentration with accompanying well-calibrated uncertainties from satellite-based active and passive microwave observations at a pan-Arctic scale for all seasons. In a comparative study against pan-Arctic ice charts and well-established passive-microwave-based sea ice products, we show that ASIP generalizes well to the pan-Arctic region.
Rémy Lapere, Louis Marelle, Pierre Rampal, Laurent Brodeau, Christian Melsheimer, Gunnar Spreen, and Jennie L. Thomas
Atmos. Chem. Phys., 24, 12107–12132, https://doi.org/10.5194/acp-24-12107-2024, https://doi.org/10.5194/acp-24-12107-2024, 2024
Short summary
Short summary
Elongated open-water areas in sea ice, called leads, can release marine aerosols into the atmosphere. In the Arctic, this source of atmospheric particles could play an important role for climate. However, the amount, seasonality and spatial distribution of such emissions are all mostly unknown. Here, we propose a first parameterization for sea spray aerosols emitted through leads in sea ice and quantify their impact on aerosol populations in the high Arctic.
Hannah Niehaus, Gunnar Spreen, Larysa Istomina, and Marcel Nicolaus
EGUsphere, https://doi.org/10.5194/egusphere-2024-3127, https://doi.org/10.5194/egusphere-2024-3127, 2024
Short summary
Short summary
Melt ponds on Arctic sea ice affect how much solar energy is absorbed, influencing ice melt and climate change. This study used satellite data from 2017–2023 to examine how these ponds vary across regions and seasons. The results show that the surface fraction of melt ponds is more stable in the Central Arctic, with air temperature and ice surface roughness playing key roles in their formation. Understanding these patterns can help to improve climate models and predictions for Arctic warming.
Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell
The Cryosphere, 18, 4137–4163, https://doi.org/10.5194/tc-18-4137-2024, https://doi.org/10.5194/tc-18-4137-2024, 2024
Short summary
Short summary
Passive microwave observations from satellites are crucial for monitoring Arctic sea ice and atmosphere. To do this effectively, it is important to understand how sea ice emits microwaves. Through unique Arctic sea ice observations, we improved our understanding, identified four distinct emission types, and expanded current knowledge to include higher frequencies. These findings will enhance our ability to monitor the Arctic climate and provide valuable information for new satellite missions.
Manfred Wendisch, Susanne Crewell, André Ehrlich, Andreas Herber, Benjamin Kirbus, Christof Lüpkes, Mario Mech, Steven J. Abel, Elisa F. Akansu, Felix Ament, Clémantyne Aubry, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Marlen Brückner, Hans-Christian Clemen, Sandro Dahlke, Georgios Dekoutsidis, Julien Delanoë, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Irina V. Gorodetskaya, Sarah Grawe, Silke Groß, Jörg Hartmann, Silvia Henning, Lutz Hirsch, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsofia Jurányi, Michail Karalis, Mona Kellermann, Marcus Klingebiel, Michael Lonardi, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Marion Maturilli, Bernhard Mayer, Johanna Mayer, Stephan Mertes, Janosch Michaelis, Michel Michalkov, Guillaume Mioche, Manuel Moser, Hanno Müller, Roel Neggers, Davide Ori, Daria Paul, Fiona M. Paulus, Christian Pilz, Felix Pithan, Mira Pöhlker, Veronika Pörtge, Maximilian Ringel, Nils Risse, Gregory C. Roberts, Sophie Rosenburg, Johannes Röttenbacher, Janna Rückert, Michael Schäfer, Jonas Schaefer, Vera Schemann, Imke Schirmacher, Jörg Schmidt, Sebastian Schmidt, Johannes Schneider, Sabrina Schnitt, Anja Schwarz, Holger Siebert, Harald Sodemann, Tim Sperzel, Gunnar Spreen, Bjorn Stevens, Frank Stratmann, Gunilla Svensson, Christian Tatzelt, Thomas Tuch, Timo Vihma, Christiane Voigt, Lea Volkmer, Andreas Walbröl, Anna Weber, Birgit Wehner, Bruno Wetzel, Martin Wirth, and Tobias Zinner
Atmos. Chem. Phys., 24, 8865–8892, https://doi.org/10.5194/acp-24-8865-2024, https://doi.org/10.5194/acp-24-8865-2024, 2024
Short summary
Short summary
The Arctic is warming faster than the rest of the globe. Warm-air intrusions (WAIs) into the Arctic may play an important role in explaining this phenomenon. Cold-air outbreaks (CAOs) out of the Arctic may link the Arctic climate changes to mid-latitude weather. In our article, we describe how to observe air mass transformations during CAOs and WAIs using three research aircraft instrumented with state-of-the-art remote-sensing and in situ measurement devices.
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.
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.
Evelyn Jäkel, Sebastian Becker, Tim R. Sperzel, Hannah Niehaus, Gunnar Spreen, Ran Tao, Marcel Nicolaus, Wolfgang Dorn, Annette Rinke, Jörg Brauchle, and Manfred Wendisch
The Cryosphere, 18, 1185–1205, https://doi.org/10.5194/tc-18-1185-2024, https://doi.org/10.5194/tc-18-1185-2024, 2024
Short summary
Short summary
The results of the surface albedo scheme of a coupled regional climate model were evaluated against airborne and ground-based measurements conducted in the European Arctic in different seasons between 2017 and 2022. We found a seasonally dependent bias between measured and modeled surface albedo for cloudless and cloudy situations. The strongest effects of the albedo model bias on the net irradiance were most apparent in the presence of optically thin clouds.
Hannah Niehaus, Larysa Istomina, Marcel Nicolaus, Ran Tao, Aleksey Malinka, Eleonora Zege, and Gunnar Spreen
The Cryosphere, 18, 933–956, https://doi.org/10.5194/tc-18-933-2024, https://doi.org/10.5194/tc-18-933-2024, 2024
Short summary
Short summary
Melt ponds are puddles of meltwater which form on Arctic sea ice in the summer period. They are darker than the ice cover and lead to increased absorption of solar energy. Global climate models need information about the Earth's energy budget. Thus satellite observations are used to monitor the surface fractions of melt ponds, ocean, and sea ice in the entire Arctic. We present a new physically based algorithm that can separate these three surface types with uncertainty below 10 %.
Zuzanna M. Swirad, A. Malin Johansson, and Eirik Malnes
The Cryosphere, 18, 895–910, https://doi.org/10.5194/tc-18-895-2024, https://doi.org/10.5194/tc-18-895-2024, 2024
Short summary
Short summary
We used satellite images to create sea ice maps of Hornsund fjord, Svalbard, for nine seasons and calculated the percentage of the fjord that was covered by ice. On average, sea ice was present in Hornsund for 158 d per year, but it varied from year to year. April was the "iciest'" month and 2019/2020, 2021/22 and 2014/15 were the "iciest'" seasons. Our data can be used to understand sea ice conditions compared with other fjords of Svalbard and in studies of wave modelling and coastal erosion.
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.
Pablo Saavedra Garfias, Heike Kalesse-Los, Luisa von Albedyll, Hannes Griesche, and Gunnar Spreen
Atmos. Chem. Phys., 23, 14521–14546, https://doi.org/10.5194/acp-23-14521-2023, https://doi.org/10.5194/acp-23-14521-2023, 2023
Short summary
Short summary
An important Arctic climate process is the release of heat fluxes from sea ice openings to the atmosphere that influence the clouds. The characterization of this process is the objective of this study. Using synergistic observations from the MOSAiC expedition, we found that single-layer cloud properties show significant differences when clouds are coupled or decoupled to the water vapour transport which is used as physical link between the upwind sea ice openings and the cloud under observation.
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.
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., 23, 9963–9992, https://doi.org/10.5194/acp-23-9963-2023, https://doi.org/10.5194/acp-23-9963-2023, 2023
Short summary
Short summary
Lapse rate feedback (LRF) is a major driver of the Arctic amplification (AA) of climate change. It arises because the warming is stronger at the surface than aloft. Several processes can affect the LRF in the Arctic, such as the omnipresent temperature inversion. Here, we compare multimodel 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 and AA.
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.
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.
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.
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
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...