Articles | Volume 15, issue 12
https://doi.org/10.5194/tc-15-5323-2021
© Author(s) 2021. 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-15-5323-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Dec 2021
Research article |
| 02 Dec 2021
Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry
Institute of Environmental Engineering, Swiss Federal Institute of Technology in Zurich (ETH), 8093 Zurich, Switzerland
Georg Fischer
Microwaves and Radar Institute, German Aerospace Center (DLR), 82234 Wessling, Germany
Irena Hajnsek
Institute of Environmental Engineering, Swiss Federal Institute of Technology in Zurich (ETH), 8093 Zurich, Switzerland
Microwaves and Radar Institute, German Aerospace Center (DLR), 82234 Wessling, Germany
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Marcel Stefko, Silvan Leinss, Othmar Frey, and Irena Hajnsek
The Cryosphere, 16, 2859–2879, https://doi.org/10.5194/tc-16-2859-2022, https://doi.org/10.5194/tc-16-2859-2022, 2022
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The coherent backscatter opposition effect can enhance the intensity of radar backscatter from dry snow by up to a factor of 2. Despite widespread use of radar backscatter data by snow scientists, this effect has received notably little attention. For the first time, we characterize this effect for the Earth's snow cover with bistatic radar experiments from ground and from space. We are also able to retrieve scattering and absorbing lengths of snow at Ku- and X-band frequencies.
Philipp Bernhard, Simon Zwieback, and Irena Hajnsek
The Cryosphere, 16, 2819–2835, https://doi.org/10.5194/tc-16-2819-2022, https://doi.org/10.5194/tc-16-2819-2022, 2022
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With climate change, Arctic hillslopes above ice-rich permafrost are vulnerable to enhanced carbon mobilization. In this work elevation change estimates generated from satellite observations reveal a substantial acceleration of carbon mobilization on the Taymyr Peninsula in Siberia between 2010 and 2021. The strong increase occurring in 2020 coincided with a severe Siberian heatwave and highlights that carbon mobilization can respond sharply and non-linearly to increasing temperatures.
Philipp Bernhard, Simon Zwieback, Nora Bergner, and Irena Hajnsek
The Cryosphere, 16, 1–15, https://doi.org/10.5194/tc-16-1-2022, https://doi.org/10.5194/tc-16-1-2022, 2022
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We present an investigation of retrogressive thaw slumps in 10 study sites across the Arctic. These slumps have major impacts on hydrology and ecosystems and can also reinforce climate change by the mobilization of carbon. Using time series of digital elevation models, we found that thaw slump change rates follow a specific type of distribution that is known from landslides in more temperate landscapes and that the 2D area change is strongly related to the 3D volumetric change.
Simon Zwieback, Steven V. Kokelj, Frank Günther, Julia Boike, Guido Grosse, and Irena Hajnsek
The Cryosphere, 12, 549–564, https://doi.org/10.5194/tc-12-549-2018, https://doi.org/10.5194/tc-12-549-2018, 2018
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We analyse elevation losses at thaw slumps, at which icy sediments are exposed. As ice requires a large amount of energy to melt, one would expect that mass wasting is governed by the available energy. However, we observe very little mass wasting in June, despite the ample energy supply. Also, in summer, mass wasting is not always energy limited. This highlights the importance of other processes, such as the formation of a protective veneer, in shaping mass wasting at sub-seasonal scales.
Vanessa Round, Silvan Leinss, Matthias Huss, Christoph Haemmig, and Irena Hajnsek
The Cryosphere, 11, 723–739, https://doi.org/10.5194/tc-11-723-2017, https://doi.org/10.5194/tc-11-723-2017, 2017
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Recent surging of Kyagar Glacier (Karakoram) caused a hazardous ice-dammed lake to form and burst in 2015 and 2016. We use remotely sensed glacier surface velocities and surface elevation to observe dramatic changes in speed and mass distribution during the surge. The surge was hydrologically controlled with rapid summer onset and dramatic termination following lake outburst. Since the surge, the potential outburst hazard has remained high, and continued remote monitoring is crucial.
Silvan Leinss, Henning Löwe, Martin Proksch, Juha Lemmetyinen, Andreas Wiesmann, and Irena Hajnsek
The Cryosphere, 10, 1771–1797, https://doi.org/10.5194/tc-10-1771-2016, https://doi.org/10.5194/tc-10-1771-2016, 2016
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Four years of anisotropy measurements of seasonal snow are presented in the paper. The anisotropy was measured every 4 h with a ground-based polarimetric radar. An electromagnetic model has been developed to measured the anisotropy with radar instruments from ground and from space. The anisotropic permittivity was derived with Maxwell–Garnett-type mixing formulas which are shown to be equivalent to series expansions of the permittivity tensor based on spatial correlation function of snow.
Related subject area
Discipline: Sea ice | Subject: Remote Sensing
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Feasibility of retrieving Arctic sea ice thickness from the Chinese HY-2B Ku-band radar altimeter
Sea ice classification of TerraSAR-X ScanSAR images for the MOSAiC expedition incorporating per-class incidence angle dependency of image texture
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
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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
Wind Transport of Snow Impacts Ka- and Ku-band Radar Signatures on Arctic Sea Ice
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
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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
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Estimation of degree of sea ice ridging in the Bay of Bothnia based on geolocated photon heights from ICESat-2
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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
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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
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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
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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
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The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset
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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
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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
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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.
Wenkai Guo, Polona Itkin, Suman Singha, Anthony P. Doulgeris, Malin Johansson, and Gunnar Spreen
The Cryosphere, 17, 1279–1297, https://doi.org/10.5194/tc-17-1279-2023, https://doi.org/10.5194/tc-17-1279-2023, 2023
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, John Yackel, 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 Hoppman
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-116, https://doi.org/10.5194/tc-2022-116, 2022
Revised manuscript accepted for TC
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We show that, wind blows and redistributes snow on sea ice, and Ka- and Ku-band radar signatures detect both newly deposited and buried snow layers that can critically affect snow depth measurements on ice. Radar measurements, meteorological and snow physical data were collected during the MOSAiC Expedition. With frequent occurrence of storms in the Arctic, our results provide baseline information that are vitally important for accurately calculating snow depth on sea ice from satellite radars.
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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
Abdalati, W., Zwally, H. J., Bindschadler, R., Csatho, B., Farrell, S. L., Fricker, H. A., Harding, D., Kwok, R., Lefsky, M., Markus, T., and Marshak, A.: The ICESat-2 laser altimetry mission, Proc. IEEE, 98, 735–751, https://doi.org/10.1109/JPROC.2009.2034765, 2010. a
Albert, M. D., Lee, Y. J., Ewe, H.-T., and Chuah, H.-T.: Multilayer model
formulation and analysis of radar backscattering from sea ice, Prog. Electromagn. Res., 128, 267–290, https://doi.org/10.2528/PIER12020205, 2012. a, b, c, d
Castellani, G., Lüpkes, C., Hendricks, S., and Gerdes, R.: Variability of
Arctic sea-ice topography and its impact on the atmospheric surface drag, J. Geophys. Res.-Oceans, 119, 6743–6762, https://doi.org/10.1002/2013JC009712, 2014. a
Cox, G. F. and Weeks, W. F.: Salinity variations in sea ice, J. Glaciol., 13,
109–120, https://doi.org/10.1017/S0022143000023418, 1974. a, b
Dall, J.: InSAR elevation bias caused by penetration into uniform volumes, IEEE T. Geosci. Remote, 45, 2319–2324, https://doi.org/10.1109/TGRS.2007.896613, 2007. a, b
Dammann, D. O., Eriksson, L. E. B., Nghiem, S. V., Pettit, E. C., Kurtz, N. T., Sonntag, J. G., Busche, T. E., Meyer, F. J., and Mahoney, A. R.: Iceberg
topography and volume classification using TanDEM-X interferometry, The
Cryosphere, 13, 1861–1875, https://doi.org/10.5194/tc-13-1861-2019, 2019. a
Dierking, W.: Laser profiling of the ice surface topography during the Winter
Weddell Gyre Study 1992, J. Geophys. Res.-Oceans, 100, 4807–4820,
https://doi.org/10.1029/94JC01938, 1995. a, b
Dierking, W. and Davidson, M.: Use of L-and C-Band SAR Satellites for Sea Ice
and Iceberg Monitoring (LC-ICE), in: EGU General Assembly Conference
Abstracts, online, 19–30 April 2021, EGU21-3916, https://doi.org/10.5194/egusphere-egu21-3916, 2021. a
Divine, D. V., Pedersen, C. A., Karlsen, T. I., Aas, H. F., Granskog, M. A.,
Hudson, S. R., and Gerland, S.: Photogrammetric retrieval and analysis of
small scale sea ice topography during summer melt, Cold Reg. Sci. Technol.,
129, 77–84, https://doi.org/10.1016/j.coldregions.2016.06.006, 2016. a
DLR – German Aerospace Center: Earth Observation on the Web (EOWEB),
available at: https://eoweb.dlr.de, last access: 1 December 2021. a
Dominguez, R.: IceBridge DMS L1B Geolocated and Orthorectified Images,
Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA, https://doi.org/10.5067/OZ6VNOPMPRJ0, 2018. a, b
Drinkwater, M. R. and Crocker, G.: Modelling changes in scattering properties
of the dielectric and young snow-covered sea ice at GHz requencies, J. Glaciol., 34, 274–282, https://doi.org/10.3189/S0022143000007012, 1988. a
Dufour-Beauséjour, S., Wendleder, A., Gauthier, Y., Bernier, M., Poulin,
J., Gilbert, V., Tuniq, J., Rouleau, A., and Roth, A.: Combining TerraSAR-X
and time-lapse photography for seasonal sea ice monitoring: the case of
Deception Bay, Nunavik, The Cryosphere, 14, 1595–1609,
https://doi.org/10.5194/tc-14-1595-2020, 2020. a
Duque, S., Balss, U., Rossi, C., Fritz, T., and Balzer, W.: TanDEM-X payload
ground segment, CoSSC generation and interferometric considerations, German
Aerospace Center, Oberpfaffenhofen, Germany, available at:
https://tandemx-science.dlr.de/pdfs/TD-PGS-TN-3129_CoSSCGenerationInterferometricConsiderations_1.0.pdf (last access: 1 December 2021), 2012. a
Eineder, M., Fritz, T., Mittermayer, J., Roth, A., Boerner, E., and Breit, H.: TerraSAR-X ground segment, basic product specification document, Tech. rep., CAF – Cluster Applied Remote Sensing, Oberpfaffenhofen, Germany, 2008. a
Farrell, S., Duncan, K., Buckley, E., Richter-Menge, J., and Li, R.: Mapping
sea ice surface topography in high fidelity with ICESat-2, Geophys. Res.
Lett., 47, e2020GL090708, https://doi.org/10.1029/2020GL090708, 2020. a
Farrell, S. L., Markus, T., Kwok, R., and Connor, L.: Laser altimetry sampling strategies over sea ice, Ann. Glaciol., 52, 69–76,
https://doi.org/10.3189/172756411795931660, 2011. a
Fischer, G., Papathanassiou, K. P., and Hajnsek, I.: Modeling multifrequency
Pol-InSAR data from the percolation zone of the Greenland Ice Sheet, IEEE
T. Geosci. Remote, 57, 1963–1976, https://doi.org/10.1109/TGRS.2018.2870301, 2018. a, b, c
Garbrecht, T., Lüpkes, C., Hartmann, J., and Wolff, M.: Atmospheric drag
coefficients over sea ice–validation of a parameterisation concept, Tellus A, 54, 205–219, https://doi.org/10.3402/tellusa.v54i2.12129, 2002. a
Gloersen, P.: Arctic and Antarctic sea ice, 1978–1987: Satellite
passive-microwave observations and analysis, 511, Scientific and Technical Information Program, National Aeronautics and Space Administration, Michigan State University, Michigan, USA, 1992. a
Gow, A., Ackley, S., Weeks, W., and Govoni, J.: Physical and structural
characteristics of Antarctic sea ice, Ann. Glaciol., 3, 113–117,
https://doi.org/10.3189/S0260305500002627, 1982. a
Haas, C., Liu, Q., and Martin, T.: Retrieval of Antarctic sea-ice pressure
ridge frequencies from ERS SAR imagery by means of in situ laser profiling
and usage of a neural network, Int. J. Remote Sens., 20, 3111–3123,
https://doi.org/10.1080/014311699211642, 1999. a
Haykin, S., Lewis, E. O., Raney, R. K., and Rossiter, J. R.: Remote sensing of sea ice and icebergs, in: vol. 13, John Wiley & Sons, New York, 1994. a
Hibler, W., Mock, S. J., and Tucker, W.: Classification and variation of sea
ice ridging in the western Arctic Basin, J. Geophys. Res., 79, 2735–2743,
https://doi.org/10.1029/JC079i018p02735, 1974. a
Jeffries, M., Morris, K., Weeks, W., and Worby, A.: Seasonal variations in the properties and structural composition of sea ice and snow cover in the
Bellingshausen and Amundsen Seas, Antarctica, J. Glaciol., 43, 138–151,
https://doi.org/10.3189/S0022143000002902, 1997. a, b
Jeffries, M., Li, S., Jana, R., Krouse, H., and Hurst-Cushing, B.: Late winter first-year ice floe thickness variability, seawater flooding and snow ice formation in the Amundsen and Ross Seas, in: Antarctic Sea Ice: Physical
processes, interactions and variability, edited by: Jeffries, M.,
Wiley Online Library, 69–88, https://doi.org/10.1029/AR074p0069, 1998. a
Jeffries, M. O., Krouse, H. R., Hurst-Cushing, B., and Maksym, T.: Snow-ice
accretion and snow-cover depletion on Antarctic first-year sea-ice floes,
Ann. Glaciol., 33, 51–60, https://doi.org/10.3189/172756401781818266, 2001. a, b, c
Joerg, H., Pardini, M., Hajnsek, I., and Papathanassiou, K. P.: 3-D scattering characterization of agricultural crops at C-Band using SAR tomography, IEEE T. Geosci. Remote, 56, 3976–3989, https://doi.org/10.1109/TGRS.2018.2818440, 2018. a
Kasilingam, D., Schuler, D., and Lee, J.-S.: The depolarization of radar
backscatter from rough surfaces due to surface roughness and slopes, in:
Proc. IGARSS., vol. 2, IEEE, 9–13 July 2001, Sydney, NSW, Australia, 925–927, https://doi.org/10.1109/IGARSS.2001.976682 2001. a
Kim, J.-W., Kim, D.-J., and Hwang, B. J.: Characterization of Arctic sea ice
thickness using high-resolution spaceborne polarimetric SAR data, IEEE T. Geosci. Remote, 50, 13–22, https://doi.org/10.1109/TGRS.2011.2160070, 2011. a
Krieger, G., Moreira, A., Fiedler, H., Hajnsek, I., Werner, M., Younis, M., and Zink, M.: TanDEM-X: A satellite formation for high-resolution SAR
interferometry, IEEE T. Geosci. Remote, 45, 3317–3341, https://doi.org/10.1109/TGRS.2007.900693, 2007. a, b
Kugler, F., Lee, S.-K., Hajnsek, I., and Papathanassiou, K. P.: Forest height
estimation by means of Pol-InSAR data inversion: The role of the vertical
wavenumber, IEEE T. Geosci. Remote, 53, 5294–5311, https://doi.org/10.1109/TGRS.2015.2420996, 2015. a, b, c, d
Kurtz, N. T. and Markus, T.: Satellite observations of Antarctic sea ice
thickness and volume, J. Geophys. Res.-Oceans, 117, C08025, https://doi.org/10.1029/2012JC008141, 2012. a
Lee, J.-S. and Pottier, E.: Polarimetric radar imaging: from basics to
applications, CRC Press, Boca Raton, Florida, USA, https://doi.org/10.1080/01431161.2010.519925, 2009. a
Li, T., Zhang, B., Cheng, X., Westoby, M. J., Li, Z., Ma, C., Hui, F., Shokr,
M., Liu, Y., Chen, Z., Zhai, M., and Li, X.: Resolving fine-scale surface
features on polar sea ice: A first assessment of UAS photogrammetry
without ground control, Remote Sens., 11, 784, https://doi.org/10.3390/rs11070784, 2019. a
Lindsay, R. and Schweiger, A.: Arctic sea ice thickness loss determined using
subsurface, aircraft, and satellite observations, The Cryosphere, 9, 269–283, https://doi.org/10.5194/tc-9-269-2015, 2015. a
Lopes, A., Mougin, E., Beaudoin, A., Goze, S., Nezry, E., Touzi, R., Karam, M., and Fung, A.: Phase difference statistics related to sensor and forest parameters, in: Proc. IGARSS, 26–29 May 1992, Houston, Texas, 779–781, https://doi.org/10.1109/IGARSS.1992.576832, 1992. a, b
Lytle, V. and Ackley, S.: Sea ice ridging in the eastern Weddell Sea, J.
Geophys. Res.-Oceans, 96, 18411–18416, https://doi.org/10.1029/91JC01978, 1991. a, b
Lytle, V. I., Worby, A., and Massom, R.: Sea-ice pressure ridges in East
Antarctica, Ann. Glaciol., 27, 449–454, https://doi.org/10.3189/1998AoG27-1-449-454,
1998. a
Meier, W. N., Markus, T., and Comiso, J. C.: AMSR-E/AMSR2 Unified L3 Daily
12.5 km Brightness Temperatures, Sea Ice Concentration, Motion & Snow Depth
Polar Grids, Version 1, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado, USA, https://doi.org/10.5067/RA1MIJOYPK3P, 2018. a
Melling, H. and Riedel, D. A.: The underside topography of sea ice over the
continental shelf of the Beaufort Sea in the winter of 1990, J. Geophys.
Res.-Oceans, 100, 13641–13653, https://doi.org/10.1029/95JC00309, 1995. a
Nandan, V., Geldsetzer, T., Islam, T., Yackel, J. J., Gill, J. P., Fuller, M. C., Gunn, G., and Duguay, C.: Ku-, X-and C-band measured and modeled microwave backscatter from a highly saline snow cover on first-year sea ice,
Remote Sens. Environ., 187, 62–75, https://doi.org/10.1016/j.rse.2016.10.004, 2016. a
Nghiem, S., Borgeaud, M., Kong, J., and Shin, R.: Polarimetric remote sensing
of geophysical media with layer random medium model, Prog. Electromagn. Res.,
3, 1–73, https://doi.org/10.1007/978-1-4899-3677-6_50, 1990. a, b, c
Nghiem, S., Kwok, R., Yueh, S., and Drinkwater, M.: Polarimetric signatures of sea ice: 2. Experimental observations, J. Geophys. Res.-Oceans, 100,
13681–13698, https://doi.org/10.1029/95JC00938, 1995b. a, b, c
Nghiem, S., Busche, T., Kraus, T., Bachmann, M., Kurtz, N., Sonntag, J., Woods, J., Ackley, S., Xie, H., Maksym, T., and Tinto, K.: Remote sensing of Antarctic sea ice with coordinated aircraft and satellite data acquisitions, in: Proc. IGARSS, IEEE, 22–27 July 2018, Valencia, Spain, 8531–8534, https://doi.org/10.1109/IGARSS.2018.8518550, 2018. a, b, c
NSIDC – National Snow and Ice Data Center: Distributed Active Archive Center (DAAC) IceBridge Data, NSIDC [data set], https://nsidc.org/data/icebridge,
last access: 1 December 2021. a
Ozsoy-Cicek, B., Ackley, S., Xie, H., Yi, D., and Zwally, J.: Sea ice thickness retrieval algorithms based on in situ surface elevation and thickness values for application to altimetry, J. Geophys. Res.-Oceans, 118, 3807–3822, https://doi.org/10.1002/jgrc.20252, 2013. a, b
Papathanassiou, K. P. and Cloude, S. R.: Single-baseline polarimetric SAR
interferometry, IEEE T. Geosci. Remote, 39, 2352–2363, https://doi.org/10.1109/36.964971, 2001. a, b
Petty, A. A., Tsamados, M. C., Kurtz, N. T., Farrell, S. L., Newman, T.,
Harbeck, J. P., Feltham, D. L., and Richter-Menge, J. A.: Characterizing Arctic sea ice topography using high-resolution IceBridge data, The
Cryosphere, 10, 1161–1179, https://doi.org/10.5194/tc-10-1161-2016, 2016. a, b, c, d
Rampal, P., Weiss, J., and Marsan, D.: Positive trend in the mean speed and
deformation rate of Arctic sea ice, 1979–2007, J. Geophys. Res.-Oceans, 114, C05013, https://doi.org/10.1029/2008JC005066, 2009. a
Reimnitz, E. and Kempema, E.: Field observations of slush ice generated during freeze-up in Arctic coastal waters, Mar. Geol., 77, 219–231,
https://doi.org/10.1016/0025-3227(87)90113-7, 1987. a
Rodriguez, E. and Martin, J.: Theory and design of interferometric synthetic
aperture radars, Radar and Signal Processing, IEE Proc. F, 139, 147–159, https://doi.org/10.1049/ip-f-2.1992.0018, 1992. a
Schutz, B. E., Zwally, H. J., Shuman, C. A., Hancock, D., and DiMarzio, J. P.: Overview of the ICESat mission, Geophys. Res. Lett., 32, L21S01, https://doi.org/10.1029/2005GL024009, 2005. a
Sharma, J. J., Hajnsek, I., Papathanassiou, K. P., and Moreira, A.: Estimation of glacier ice extinction using long-wavelength airborne Pol-InSAR, IEEE T. Geosci. Remote, 51, 3715–3732, https://doi.org/10.1109/TGRS.2012.2220855, 2012. a, b
Sturm, M. and Massom, R. A.: Snow and sea ice, in: Sea Ice, chap 5, edited by: Thomas, D. N. and Dieckmann, G. S., Wiley Online Library, 153–204, https://doi.org/10.1002/9781444317145.ch5, 2009. a
Timco, G. and Burden, R.: An analysis of the shapes of sea ice ridges, Cold
Reg. Sci. Technol., 25, 65–77, https://doi.org/10.1016/S0165-232X(96)00017-1, 1997. a
Tin, T. and Jeffries, M. O.: Morphology of deformed first-year sea ice features in the Southern Ocean, Cold Reg. Sci. Technol., 36, 141–163,
https://doi.org/10.1016/S0165-232X(03)00008-9, 2003. a
Tin, T., Jeffries, M. O., Lensu, M., and Tuhkuri, J.: Estimating the thickness of ridged sea ice from ship observations in the Ross Sea, Antarct. Sci., 15, 47–54, https://doi.org/10.1017/S0954102003001056, 2003.
a
Touzi, R. and Lopes, A.: Statistics of the Stokes parameters and of the complex coherence parameters in one-look and multilook speckle fields, IEEE T. Geosci. Remote, 34, 519–531, https://doi.org/10.1109/36.485128, 1996. a, b
Toyota, T., Massom, R., Tateyama, K., Tamura, T., and Fraser, A.: Properties
of snow overlying the sea ice off East Antarctica in late winter, 2007, Deep-Sea Res. Pt. II, 58, 1137–1148, https://doi.org/10.1016/j.dsr2.2010.12.002, 2011. a, b, c
Tucker, W. and Govoni, J.: Morphological investigations of first-year sea ice
pressure ridge sails, Cold Reg. Sci. Technol., 5, 1–12,
https://doi.org/10.1016/0165-232X(81)90036-7, 1981. a
Tucker, W. B., Sodhi, D. S., and Govoni, J. W.: Structure of first-year
pressure ridge sails in the Prudhoe Bay region, in: The Alaskan Beaufort Sea, edited by: Barnes, P. W., Schell, D. M., and Reimnitz, E.,
Academic Press, New York, 115–135, https://doi.org/10.1016/B978-0-12-079030-2.50012-5, 1984. a
Wakabayashi, H., Matsuoka, T., Nakamura, K., and Nishio, F.: Polarimetric characteristics of sea ice in the sea of Okhotsk observed by airborne L-band SAR, IEEE T. Geosci. Remote, 42, 2412–2425, https://doi.org/10.1109/TGRS.2004.836259, 2004. a
Walsh, J. E.: A comparison of Arctic and Antarctic climate change, present and future, Antarct. Sci., 21, 179–188, https://doi.org/10.1017/S0954102009001874, 2009. a
Willatt, R. C., Giles, K. A., Laxon, S. W., Stone-Drake, L., and Worby, A. P.: Field investigations of Ku-band radar penetration into snow cover on
Antarctic sea ice, IEEE T. Geosci. Remote, 48, 365–372,
https://doi.org/10.1109/TGRS.2009.2028237, 2009. a
Worby, A. P., Geiger, C. A., Paget, M. J., Van Woert, M. L., Ackley, S. F., and DeLiberty, T. L.: Thickness distribution of Antarctic sea ice, J. Geophys. Res.-Oceans, 113, C05S92, https://doi.org/10.1029/2007JC004254, 2008. a
Yitayew, T. G., Dierking, W., Divine, D. V., Eltoft, T., Ferro-Famil, L.,
Rösel, A., and Negrel, J.: Validation of sea-ice topographic heights
derived from TanDEM-X interferometric SAR data with results from laser profiler and photogrammetry, IEEE T. Geosci. Remote, 56, 6504–6520, https://doi.org/10.1109/TGRS.2018.2839590, 2018. a
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.
This study shows an elevation difference between the radar interferometric measurements and the...
