Articles | Volume 17, issue 1
https://doi.org/10.5194/tc-17-279-2023
© 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-17-279-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jan 2023
Research article |
| 20 Jan 2023
| 20 Jan 2023
Inter-comparison and evaluation of Arctic sea ice type products
Yufang Ye
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
Yanbing Luo
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
Mohammed Shokr
Meteorological Research Division, Environment and Climate Change
Canada, Toronto, Canada
Signe Aaboe
Division for Remote Sensing and Data Management, Norwegian
Meteorological Institute, Tromsø, Norway
Fanny Girard-Ardhuin
Laboratoire d'Océanographie Physique et Spatiale (LOPS),
Ifremer-Univ. Brest-CNRS-IRD, IUEM, Plouzané, France
Fengming Hui
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
Xiao Cheng
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
Zhuoqi Chen
CORRESPONDING AUTHOR
School of Geospatial Engineering and Science, Sun Yat-sen University
& Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),
Zhuhai, China
Related authors
Yan Sun, Shaoyin Wang, Xiao Cheng, Teng Li, Chong Liu, Yufang Ye, and Xi Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2760, https://doi.org/10.5194/egusphere-2024-2760, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
This manuscript proposes to combine semantic segmentation of ice region using a U-Net model and multi-stage detection of ice pixels using the Multi-textRG algorithm to achieve fine ice-water classification. Novel proccessings for the HV/HH polarization ratio and the GLCM textures, as well as the usage of regional growing, largely improve the method accuracy and robustness. The proposed algorithm framework achieved automated sea-ice labelling.
Yan Sun, Shaoyin Wang, Xiao Cheng, Teng Li, Chong Liu, Yufang Ye, and Xi Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-1177, https://doi.org/10.5194/egusphere-2024-1177, 2024
Preprint archived
Short summary
Short summary
Arctic sea ice has rapidly declined due to global warming, leading to extreme weather events. Accurate ice monitoring is vital for understanding and forecasting these impacts. Combining SAR and AMSR2 data with machine learning is efficient but requires sufficient labels. We propose a framework integrating the U-Net model with the Multi-textRG algorithm to achieve ice-water classification at SAR-level resolution and to generate accurate labels for improved U-Net model training.
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.
Yufang Ye, Mohammed Shokr, Signe Aaboe, Wiebke Aldenhoff, Leif E. B. Eriksson, Georg Heygster, Christian Melsheimer, and Fanny Girard-Ardhuin
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-200, https://doi.org/10.5194/tc-2019-200, 2019
Revised manuscript not accepted
Short summary
Short summary
Sea ice has been monitored with microwave satellite observations since the late 1970s. However, the question remains as to which sea ice type concentration (SITC) method is most appropriate for ice type distribution and hence climate monitoring. This paper presents key results of inter-comparison and evaluation for eight SITC methods. The SITC methods were inter-compared with sea ice age and sea ice type products. Their performances were evaluated quantitatively and qualitatively.
Anton Andreevich Korosov, Pierre Rampal, Leif Toudal Pedersen, Roberto Saldo, Yufang Ye, Georg Heygster, Thomas Lavergne, Signe Aaboe, and Fanny Girard-Ardhuin
The Cryosphere, 12, 2073–2085, https://doi.org/10.5194/tc-12-2073-2018, https://doi.org/10.5194/tc-12-2073-2018, 2018
Short summary
Short summary
A new algorithm for estimating sea ice age in the Arctic is presented. The algorithm accounts for motion, deformation, melting and freezing of sea ice and uses daily sea ice drift and sea ice concentration products. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel or, in other words, to provide a frequency distribution of the ice age. Multi-year ice concentration can be computed as a sum of all ice fractions older than 1 year.
Fukai Peng, Xiaoli Deng, Yunzhong Shen, and Xiao Cheng
Earth Syst. Sci. Data, 17, 1441–1460, https://doi.org/10.5194/essd-17-1441-2025, https://doi.org/10.5194/essd-17-1441-2025, 2025
Short summary
Short summary
A new reprocessed altimeter coastal sea level dataset, International Altimetry Service 2024 (IAS2024), for monitoring sea level changes along the world’s coastlines is presented. The evaluation and validation results confirm the reliability of this dataset. The altimeter-based virtual stations along the world’s coastlines can be built using this dataset to monitor the coastal sea level changes where tide gauges are unavailable. Therefore, it is beneficial for both oceanographic communities and policymakers.
Yan Sun, Shaoyin Wang, Xiao Cheng, Teng Li, Chong Liu, Yufang Ye, and Xi Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2760, https://doi.org/10.5194/egusphere-2024-2760, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
This manuscript proposes to combine semantic segmentation of ice region using a U-Net model and multi-stage detection of ice pixels using the Multi-textRG algorithm to achieve fine ice-water classification. Novel proccessings for the HV/HH polarization ratio and the GLCM textures, as well as the usage of regional growing, largely improve the method accuracy and robustness. The proposed algorithm framework achieved automated sea-ice labelling.
Yan Sun, Shaoyin Wang, Xiao Cheng, Teng Li, Chong Liu, Yufang Ye, and Xi Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-1177, https://doi.org/10.5194/egusphere-2024-1177, 2024
Preprint archived
Short summary
Short summary
Arctic sea ice has rapidly declined due to global warming, leading to extreme weather events. Accurate ice monitoring is vital for understanding and forecasting these impacts. Combining SAR and AMSR2 data with machine learning is efficient but requires sufficient labels. We propose a framework integrating the U-Net model with the Multi-textRG algorithm to achieve ice-water classification at SAR-level resolution and to generate accurate labels for improved U-Net model training.
Haihan Hu, Jiechen Zhao, Petra Heil, Zhiliang Qin, Jingkai Ma, Fengming Hui, and Xiao Cheng
The Cryosphere, 17, 2231–2244, https://doi.org/10.5194/tc-17-2231-2023, https://doi.org/10.5194/tc-17-2231-2023, 2023
Short summary
Short summary
The oceanic characteristics beneath sea ice significantly affect ice growth and melting. The high-frequency and long-term observations of oceanic variables allow us to deeply investigate their diurnal and seasonal variation and evaluate their influences on sea ice evolution. The large-scale sea ice distribution and ocean circulation contributed to the seasonal variation of ocean variables, revealing the important relationship between large-scale and local phenomena.
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.
Chong Liu, Xiaoqing Xu, Xuejie Feng, Xiao Cheng, Caixia Liu, and Huabing Huang
Earth Syst. Sci. Data, 15, 133–153, https://doi.org/10.5194/essd-15-133-2023, https://doi.org/10.5194/essd-15-133-2023, 2023
Short summary
Short summary
Rapid Arctic changes are increasingly influencing human society, both locally and globally. Land cover offers a basis for characterizing the terrestrial world, yet spatially detailed information on Arctic land cover is lacking. We employ multi-source data to develop a new land cover map for the circumpolar Arctic. Our product reveals regionally contrasting biome distributions not fully documented in existing studies and thus enhances our understanding of the Arctic’s terrestrial system.
Qi Liang, Wanxin Xiao, Ian Howat, Xiao Cheng, Fengming Hui, Zhuoqi Chen, Mi Jiang, and Lei Zheng
The Cryosphere, 16, 2671–2681, https://doi.org/10.5194/tc-16-2671-2022, https://doi.org/10.5194/tc-16-2671-2022, 2022
Short summary
Short summary
Using multi-temporal ArcticDEM and ICESat-2 altimetry data, we document changes in surface elevation of a subglacial lake basin from 2012 to 2021. The long-term measurements show that the subglacial lake was recharged by surface meltwater and that a rapid drainage event in late August 2019 induced an abrupt ice velocity change. Multiple factors regulate the episodic filling and drainage of the lake. Our study also reveals ~ 64 % of the surface meltwater successfully descended to the bed.
Yijing Lin, Yan Liu, Zhitong Yu, Xiao Cheng, Qiang Shen, and Liyun Zhao
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-325, https://doi.org/10.5194/tc-2021-325, 2021
Preprint withdrawn
Short summary
Short summary
We introduce an uncertainty analysis framework for comprehensively and systematically quantifying the uncertainties of the Antarctic mass balance using the Input and Output Method. It is difficult to use the previous strategies employed in various methods and the available data to achieve the goal of estimation accuracy. The dominant cause of the future uncertainty is the ice thickness data gap. The interannual variability of ice discharge caused by velocity and thickness is also nonnegligible.
Mengzhen Qi, Yan Liu, Jiping Liu, Xiao Cheng, Yijing Lin, Qiyang Feng, Qiang Shen, and Zhitong Yu
Earth Syst. Sci. Data, 13, 4583–4601, https://doi.org/10.5194/essd-13-4583-2021, https://doi.org/10.5194/essd-13-4583-2021, 2021
Short summary
Short summary
A total of 1975 annual calving events larger than 1 km2 were detected on the Antarctic ice shelves from August 2005 to August 2020. The average annual calved area was measured as 3549.1 km2, and the average calving rate was measured as 770.3 Gt yr-1. Iceberg calving is most prevalent in West Antarctica, followed by the Antarctic Peninsula and Wilkes Land in East Antarctica. This annual iceberg calving dataset provides consistent and precise calving observations with the longest time coverage.
Linlu Mei, Vladimir Rozanov, Evelyn Jäkel, Xiao Cheng, Marco Vountas, and John P. Burrows
The Cryosphere, 15, 2781–2802, https://doi.org/10.5194/tc-15-2781-2021, https://doi.org/10.5194/tc-15-2781-2021, 2021
Short summary
Short summary
This paper presents a new snow property retrieval algorithm from satellite observations. This is Part 2 of two companion papers and shows the results and validation. The paper performs the new retrieval algorithm on the Sea and Land
Surface Temperature Radiometer (SLSTR) instrument and compares the retrieved snow properties with ground-based measurements, aircraft measurements and other satellite products.
Yu Zhou, Jianlong Chen, and Xiao Cheng
Earth Surf. Dynam. Discuss., https://doi.org/10.5194/esurf-2021-21, https://doi.org/10.5194/esurf-2021-21, 2021
Preprint withdrawn
Mohammed E. Shokr, Zihan Wang, and Tingting Liu
The Cryosphere, 14, 3611–3627, https://doi.org/10.5194/tc-14-3611-2020, https://doi.org/10.5194/tc-14-3611-2020, 2020
Short summary
Short summary
This paper uses sequential daily SAR images covering the Robeson Channel to quantitatively study kinematics of individual ice floes with exploration of wind influence and the evolution of the ice arch at the entry of the channel. Results show that drift of ice floes within the Robeson Channel and the arch are both significantly influenced by wind. The study highlights the advantage of using the high-resolution daily SAR coverage in monitoring sea ice cover in narrow water passages.
Guillaume Boutin, Camille Lique, Fabrice Ardhuin, Clément Rousset, Claude Talandier, Mickael Accensi, and Fanny Girard-Ardhuin
The Cryosphere, 14, 709–735, https://doi.org/10.5194/tc-14-709-2020, https://doi.org/10.5194/tc-14-709-2020, 2020
Short summary
Short summary
We investigate the interactions of surface ocean waves with sea ice taking place at the interface between the compact sea ice cover and the open ocean. We use a newly developed coupling framework between a wave and an ocean–sea ice numerical model. Our results show how the push on sea ice exerted by waves changes the amount and the location of sea ice melting, with a strong impact on the ocean surface properties close to the ice edge.
Thomas Holding, Ian G. Ashton, Jamie D. Shutler, Peter E. Land, Philip D. Nightingale, Andrew P. Rees, Ian Brown, Jean-Francois Piolle, Annette Kock, Hermann W. Bange, David K. Woolf, Lonneke Goddijn-Murphy, Ryan Pereira, Frederic Paul, Fanny Girard-Ardhuin, Bertrand Chapron, Gregor Rehder, Fabrice Ardhuin, and Craig J. Donlon
Ocean Sci., 15, 1707–1728, https://doi.org/10.5194/os-15-1707-2019, https://doi.org/10.5194/os-15-1707-2019, 2019
Short summary
Short summary
FluxEngine is an open-source software toolbox designed to allow for the easy and accurate calculation of air–sea gas fluxes. This article describes new functionality and capabilities, which include the ability to calculate fluxes for nitrous oxide and methane, optimisation for running FluxEngine on a stand-alone desktop computer, and extensive new features to support the in situ measurement community. Four research case studies are used to demonstrate these new features.
Yufang Ye, Mohammed Shokr, Signe Aaboe, Wiebke Aldenhoff, Leif E. B. Eriksson, Georg Heygster, Christian Melsheimer, and Fanny Girard-Ardhuin
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-200, https://doi.org/10.5194/tc-2019-200, 2019
Revised manuscript not accepted
Short summary
Short summary
Sea ice has been monitored with microwave satellite observations since the late 1970s. However, the question remains as to which sea ice type concentration (SITC) method is most appropriate for ice type distribution and hence climate monitoring. This paper presents key results of inter-comparison and evaluation for eight SITC methods. The SITC methods were inter-compared with sea ice age and sea ice type products. Their performances were evaluated quantitatively and qualitatively.
Thomas Lavergne, Atle Macdonald Sørensen, Stefan Kern, Rasmus Tonboe, Dirk Notz, Signe Aaboe, Louisa Bell, Gorm Dybkjær, Steinar Eastwood, Carolina Gabarro, Georg Heygster, Mari Anne Killie, Matilde Brandt Kreiner, John Lavelle, Roberto Saldo, Stein Sandven, and Leif Toudal Pedersen
The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, https://doi.org/10.5194/tc-13-49-2019, 2019
Short summary
Short summary
The loss of polar sea ice is an iconic indicator of Earth’s climate change. Many satellite-based algorithms and resulting data exist but they differ widely in specific sea-ice conditions. This spread hinders a robust estimate of the future evolution of sea-ice cover.
In this study, we document three new climate data records of sea-ice concentration generated using satellite data available over the last 40 years. We introduce the novel algorithms, the data records, and their uncertainties.
Robert Ricker, Fanny Girard-Ardhuin, Thomas Krumpen, and Camille Lique
The Cryosphere, 12, 3017–3032, https://doi.org/10.5194/tc-12-3017-2018, https://doi.org/10.5194/tc-12-3017-2018, 2018
Short summary
Short summary
We present ice volume flux estimates through the Fram Strait using CryoSat-2 ice thickness data. This study presents a detailed analysis of temporal and spatial variability of ice volume export through the Fram Strait and shows the impact of ice volume export on Arctic ice mass balance.
Anton Andreevich Korosov, Pierre Rampal, Leif Toudal Pedersen, Roberto Saldo, Yufang Ye, Georg Heygster, Thomas Lavergne, Signe Aaboe, and Fanny Girard-Ardhuin
The Cryosphere, 12, 2073–2085, https://doi.org/10.5194/tc-12-2073-2018, https://doi.org/10.5194/tc-12-2073-2018, 2018
Short summary
Short summary
A new algorithm for estimating sea ice age in the Arctic is presented. The algorithm accounts for motion, deformation, melting and freezing of sea ice and uses daily sea ice drift and sea ice concentration products. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel or, in other words, to provide a frequency distribution of the ice age. Multi-year ice concentration can be computed as a sum of all ice fractions older than 1 year.
Justin E. Stopa, Fabrice Ardhuin, and Fanny Girard-Ardhuin
The Cryosphere, 10, 1605–1629, https://doi.org/10.5194/tc-10-1605-2016, https://doi.org/10.5194/tc-10-1605-2016, 2016
Short summary
Short summary
Satellite observations show the Arctic sea ice has decreased the last 30 years. From our wave model hindcast and satellite altimeter datasets we observe profound increasing wave heights, which are caused by the loss of sea ice and not the driving winds. If ice-free conditions persist later into fall, then regions like the Beaufort–Chukchi Sea will be prone to developing larger waves since the driving winds are strong this time of year.
T. Krumpen, M. Janout, K. I. Hodges, R. Gerdes, F. Girard-Ardhuin, J. A. Hölemann, and S. Willmes
The Cryosphere, 7, 349–363, https://doi.org/10.5194/tc-7-349-2013, https://doi.org/10.5194/tc-7-349-2013, 2013
Related subject area
Discipline: Sea ice | Subject: Remote Sensing
Novel methods to study sea ice deformation, linear kinematic features and coherent dynamic clusters from imaging remote sensing data
Snow depth estimation on leadless landfast ice using Cryo2Ice satellite observations
Updated Arctic melt pond fraction dataset and trends 2002–2023 using ENVISAT and Sentinel-3 remote sensing data
Impact assessment of snow thickness, sea ice density and water density in CryoSat-2-derived sea ice thickness
Pan-Arctic sea ice concentration from SAR and passive microwave
Assessing sea ice microwave emissivity up to submillimeter waves from airborne and satellite observations
The AutoICE Challenge
A study of sea ice topography in the Weddell and Ross seas using dual-polarimetric TanDEM-X imagery
Grounded Ridge Detection and Characterization along the Alaskan Arctic Coastline using ICESat-2 Surface Height Retrievals
Estimating differential penetration of green (532 nm) laser light over sea ice with NASA's Airborne Topographic Mapper: observations and models
Estimating the uncertainty of sea-ice area and sea-ice extent from satellite retrievals
Sea ice transport and replenishment across and within the Canadian Arctic Archipelago, 2016–2022
SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition
MMSeaIce: a collection of techniques for improving sea ice mapping with a multi-task model
Lead fractions from SAR-derived sea ice divergence during MOSAiC
Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach
New estimates of pan-Arctic sea ice–atmosphere neutral drag coefficients from ICESat-2 elevation data
Relevance of warm air intrusions for Arctic satellite sea ice concentration time series
Observing the evolution of summer melt on multiyear sea ice with ICESat-2 and Sentinel-2
Spaceborne thermal infrared observations of Arctic sea ice leads at 30 m resolution
Wind redistribution of snow impacts the Ka- and Ku-band radar signatures of Arctic sea ice
First observations of sea ice flexural–gravity waves with ground-based radar interferometry in Utqiaġvik, Alaska
Feasibility of retrieving Arctic sea ice thickness from the Chinese HY-2B Ku-band radar altimeter
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
A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval
Ice ridge density signatures in high-resolution SAR images
Rain on snow (ROS) understudied in sea ice remote sensing: a multi-sensor analysis of ROS during MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate)
Quantifying the effects of background concentrations of crude oil pollution on sea ice albedo
Characterizing the sea-ice floe size distribution in the Canada Basin from high-resolution optical satellite imagery
Generating large-scale sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission using the Environment and Climate Change Canada automated sea ice tracking system
Rotational drift in Antarctic sea ice: pronounced cyclonic features and differences between data products
Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data
Cross-platform classification of level and deformed sea ice considering per-class incident angle dependency of backscatter intensity
Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements
Antarctic snow-covered sea ice topography derivation from TanDEM-X using polarimetric SAR interferometry
Impacts of snow data and processing methods on the interpretation of long-term changes in Baffin Bay early spring sea ice thickness
A lead-width distribution for Antarctic sea ice: a case study for the Weddell Sea with high-resolution Sentinel-2 images
Satellite altimetry detection of ice-shelf-influenced fast ice
MOSAiC drift expedition from October 2019 to July 2020: sea ice conditions from space and comparison with previous years
Towards a swath-to-swath sea-ice drift product for the Copernicus Imaging Microwave Radiometer mission
Spaceborne infrared imagery for early detection of Weddell Polynya opening
Estimating instantaneous sea-ice dynamics from space using the bi-static radar measurements of Earth Explorer 10 candidate Harmony
Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning
An improved sea ice detection algorithm using MODIS: application as a new European sea ice extent indicator
Faster decline and higher variability in the sea ice thickness of the marginal Arctic seas when accounting for dynamic snow cover
Estimation of degree of sea ice ridging in the Bay of Bothnia based on geolocated photon heights from ICESat-2
Linking sea ice deformation to ice thickness redistribution using high-resolution satellite and airborne observations
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.
Monojit Saha, Julienne Stroeve, Dustin Isleifson, John Yackel, Vishnu Nandan, Jack Christopher Landy, and Hoi Ming Lam
The Cryosphere, 19, 325–346, https://doi.org/10.5194/tc-19-325-2025, https://doi.org/10.5194/tc-19-325-2025, 2025
Short summary
Short summary
Snow on sea ice is vital for near-shore sea ice geophysical and biological processes. Past studies have measured snow depths using the satellite altimeters Cryosat-2 and ICESat-2 (Cryo2Ice), but estimating sea surface height from leadless landfast sea ice remains challenging. Snow depths from Cryo2Ice are compared to in situ data after adjusting for tides. Realistic snow depths are retrieved, but differences in roughness, satellite footprints, and snow geophysical properties are identified.
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.
Imke Sievers, Henriette Skourup, and Till A. S. Rasmussen
The Cryosphere, 18, 5985–6004, https://doi.org/10.5194/tc-18-5985-2024, https://doi.org/10.5194/tc-18-5985-2024, 2024
Short summary
Short summary
To derive sea ice thickness (SIT) from satellite freeboard (FB) observations, assumptions about snow thickness, snow density, sea ice density and water density are needed. These parameters are impossible to observe alongside FB, so many existing products use empirical values. In this study, modeled values are used instead. The modeled values and otherwise commonly used empirical values are evaluated against in situ observations. In a further analysis, the influence on SIT is quantified.
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.
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.
Andreas Stokholm, Jørgen Buus-Hinkler, Tore Wulf, Anton Korosov, Roberto Saldo, Leif Toudal Pedersen, David Arthurs, Ionut Dragan, Iacopo Modica, Juan Pedro, Annekatrien Debien, Xinwei Chen, Muhammed Patel, Fernando Jose Pena Cantu, Javier Noa Turnes, Jinman Park, Linlin Xu, Katharine Andrea Scott, David Anthony Clausi, Yuan Fang, Mingzhe Jiang, Saeid Taleghanidoozdoozan, Neil Curtis Brubacher, Armina Soleymani, Zacharie Gousseau, Michał Smaczny, Patryk Kowalski, Jacek Komorowski, David Rijlaarsdam, Jan Nicolaas van Rijn, Jens Jakobsen, Martin Samuel James Rogers, Nick Hughes, Tom Zagon, Rune Solberg, Nicolas Longépé, and Matilde Brandt Kreiner
The Cryosphere, 18, 3471–3494, https://doi.org/10.5194/tc-18-3471-2024, https://doi.org/10.5194/tc-18-3471-2024, 2024
Short summary
Short summary
The AutoICE challenge encouraged the development of deep learning models to map multiple aspects of sea ice – the amount of sea ice in an area and the age and ice floe size – using multiple sources of satellite and weather data across the Canadian and Greenlandic Arctic. Professionally drawn operational sea ice charts were used as a reference. A total of 179 students and sea ice and AI specialists participated and produced maps in broad agreement with the sea ice charts.
Lanqing Huang and Irena Hajnsek
The Cryosphere, 18, 3117–3140, https://doi.org/10.5194/tc-18-3117-2024, https://doi.org/10.5194/tc-18-3117-2024, 2024
Short summary
Short summary
Interferometric synthetic aperture radar can measure the total freeboard of sea ice but can be biased when radar signals penetrate snow and ice. We develop a new method to retrieve the total freeboard and analyze the regional variation of total freeboard and roughness in the Weddell and Ross seas. We also investigate the statistical behavior of the total freeboard for diverse ice types. The findings enhance the understanding of Antarctic sea ice topography and its dynamics in a changing climate.
Kennedy A. Lange, Alice C. Bradley, Kyle Duncan, and Sinéad L. Farrell
EGUsphere, https://doi.org/10.5194/egusphere-2024-1885, https://doi.org/10.5194/egusphere-2024-1885, 2024
Short summary
Short summary
Grounded sea ice ridges stabilize nearshore sea ice by anchoring it in the seafloor. In this study, we develop a method to identify grounded ridges in satellite data, and measure the height, depth, distance from shore, and width of a thousand ridges across the Alaskan Arctic, finding regional differences in these metrics across the coastline. This method lays the groundwork for a better understanding of nearshore ice stability, holding importance for Arctic community food security and safety.
Michael Studinger, Benjamin E. Smith, Nathan Kurtz, Alek Petty, Tyler Sutterley, and Rachel Tilling
The Cryosphere, 18, 2625–2652, https://doi.org/10.5194/tc-18-2625-2024, https://doi.org/10.5194/tc-18-2625-2024, 2024
Short summary
Short summary
We use green lidar data and natural-color imagery over sea ice to quantify elevation biases potentially impacting estimates of change in ice thickness of the polar regions. We complement our analysis using a model of scattering of light in snow and ice that predicts the shape of lidar waveforms reflecting from snow and ice surfaces based on the shape of the transmitted pulse. We find that biased elevations exist in airborne and spaceborne data products from green lidars.
Andreas Wernecke, Dirk Notz, Stefan Kern, and Thomas Lavergne
The Cryosphere, 18, 2473–2486, https://doi.org/10.5194/tc-18-2473-2024, https://doi.org/10.5194/tc-18-2473-2024, 2024
Short summary
Short summary
The total Arctic sea-ice area (SIA), which is an important climate indicator, is routinely monitored with the help of satellite measurements. Uncertainties in observations of sea-ice concentration (SIC) partly cancel out when summed up to the total SIA, but the degree to which this is happening has been unclear. Here we find that the uncertainty daily SIA estimates, based on uncertainties in SIC, are about 300 000 km2. The 2002 to 2017 September decline in SIA is approx. 105 000 ± 9000 km2 a−1.
Stephen E. L. Howell, David G. Babb, Jack C. Landy, Isolde A. Glissenaar, Kaitlin McNeil, Benoit Montpetit, and Mike Brady
The Cryosphere, 18, 2321–2333, https://doi.org/10.5194/tc-18-2321-2024, https://doi.org/10.5194/tc-18-2321-2024, 2024
Short summary
Short summary
The CAA serves as both a source and a sink for sea ice from the Arctic Ocean, while also exporting sea ice into Baffin Bay. It is also an important region with respect to navigating the Northwest Passage. Here, we quantify sea ice transport and replenishment across and within the CAA from 2016 to 2022. We also provide the first estimates of the ice area and volume flux within the CAA from the Queen Elizabeth Islands to Parry Channel, which spans the central region of the Northwest Passage.
Karl Kortum, Suman Singha, Gunnar Spreen, Nils Hutter, Arttu Jutila, and Christian Haas
The Cryosphere, 18, 2207–2222, https://doi.org/10.5194/tc-18-2207-2024, https://doi.org/10.5194/tc-18-2207-2024, 2024
Short summary
Short summary
A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of the ice topography during the MOSAiC expedition is constructed and used to train a variety of deep learning algorithms. The results give realistic insights into the accuracy of retrieval of measured ice classes using modern deep learning models. The models able to learn from the spatial distribution of the measured sea ice classes are shown to have a clear advantage over those that cannot.
Xinwei Chen, Muhammed Patel, Fernando J. Pena Cantu, Jinman Park, Javier Noa Turnes, Linlin Xu, K. Andrea Scott, and David A. Clausi
The Cryosphere, 18, 1621–1632, https://doi.org/10.5194/tc-18-1621-2024, https://doi.org/10.5194/tc-18-1621-2024, 2024
Short summary
Short summary
This paper introduces an automated sea ice mapping pipeline utilizing a multi-task U-Net architecture. It attained the top score of 86.3 % in the AutoICE challenge. Ablation studies revealed that incorporating brightness temperature data and spatial–temporal information significantly enhanced model accuracy. Accurate sea ice mapping is vital for comprehending the Arctic environment and its global climate effects, underscoring the potential of deep learning.
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.
Qin Zhang and Nick Hughes
The Cryosphere, 17, 5519–5537, https://doi.org/10.5194/tc-17-5519-2023, https://doi.org/10.5194/tc-17-5519-2023, 2023
Short summary
Short summary
To alleviate tedious manual image annotations for training deep learning (DL) models in floe instance segmentation, we employ a classical image processing technique to automatically label floes in images. We then apply a DL semantic method for fast and adaptive floe instance segmentation from high-resolution airborne and satellite images. A post-processing algorithm is also proposed to refine the segmentation and further to derive acceptable floe size distributions at local and global scales.
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.
Philip Rostosky and Gunnar Spreen
The Cryosphere, 17, 3867–3881, https://doi.org/10.5194/tc-17-3867-2023, https://doi.org/10.5194/tc-17-3867-2023, 2023
Short summary
Short summary
During winter, storms entering the Arctic region can bring warm air into the cold environment. Strong increases in air temperature modify the characteristics of the Arctic snow and ice cover. The Arctic sea ice cover can be monitored by satellites observing the natural emission of the Earth's surface. In this study, we show that during warm air intrusions the change in the snow characteristics influences the satellite-derived sea ice cover, leading to a false reduction of the estimated ice area.
Ellen M. Buckley, Sinéad L. Farrell, Ute C. Herzfeld, Melinda A. Webster, Thomas Trantow, Oliwia N. Baney, Kyle A. Duncan, Huilin Han, and Matthew Lawson
The Cryosphere, 17, 3695–3719, https://doi.org/10.5194/tc-17-3695-2023, https://doi.org/10.5194/tc-17-3695-2023, 2023
Short summary
Short summary
In this study, we use satellite observations to investigate the evolution of melt ponds on the Arctic sea ice surface. We derive melt pond depth from ICESat-2 measurements of the pond surface and bathymetry and melt pond fraction (MPF) from the classification of Sentinel-2 imagery. MPF increases to a peak of 16 % in late June and then decreases, while depth increases steadily. This work demonstrates the ability to track evolving melt conditions in three dimensions throughout the summer.
Yujia Qiu, Xiao-Ming Li, and Huadong Guo
The Cryosphere, 17, 2829–2849, https://doi.org/10.5194/tc-17-2829-2023, https://doi.org/10.5194/tc-17-2829-2023, 2023
Short summary
Short summary
Spaceborne thermal infrared sensors with kilometer-scale resolution cannot support adequate parameterization of Arctic leads. For the first time, we applied the 30 m resolution data from the Thermal Infrared Spectrometer (TIS) on the emerging SDGSAT-1 to detect Arctic leads. Validation with Sentinel-2 data shows high accuracy for the three TIS bands. Compared to MODIS, the TIS presents more narrow leads, demonstrating its great potential for observing previously unresolvable Arctic leads.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
Short summary
Short summary
We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Dyre Oliver Dammann, Mark A. Johnson, Andrew R. Mahoney, and Emily R. Fedders
The Cryosphere, 17, 1609–1622, https://doi.org/10.5194/tc-17-1609-2023, https://doi.org/10.5194/tc-17-1609-2023, 2023
Short summary
Short summary
We investigate the GAMMA Portable Radar Interferometer (GPRI) as a tool for evaluating flexural–gravity waves in sea ice in near real time. With a GPRI mounted on grounded ice near Utqiaġvik, Alaska, we identify 20–50 s infragravity waves in landfast ice with ~1 mm amplitude during 23–24 April 2021. Observed wave speed and periods compare well with modeled wave propagation and on-ice accelerometers, confirming the ability to track propagation and properties of waves over hundreds of meters.
Zhaoqing Dong, Lijian Shi, Mingsen Lin, Yongjun Jia, Tao Zeng, and Suhui Wu
The Cryosphere, 17, 1389–1410, https://doi.org/10.5194/tc-17-1389-2023, https://doi.org/10.5194/tc-17-1389-2023, 2023
Short summary
Short summary
We try to explore the application of SGDR data in polar sea ice thickness. Through this study, we find that it seems difficult to obtain reasonable results by using conventional methods. So we use the 15 lowest points per 25 km to estimate SSHA to retrieve more reasonable Arctic radar freeboard and thickness. This study also provides reference for reprocessing L1 data. We will release products that are more reasonable and suitable for polar sea ice thickness retrieval to better evaluate HY-2B.
Wenkai Guo, Polona Itkin, Suman Singha, Anthony P. Doulgeris, Malin Johansson, and Gunnar Spreen
The Cryosphere, 17, 1279–1297, https://doi.org/10.5194/tc-17-1279-2023, https://doi.org/10.5194/tc-17-1279-2023, 2023
Short summary
Short summary
Sea ice maps are produced to cover the MOSAiC Arctic expedition (2019–2020) and divide sea ice into scientifically meaningful classes. We use a high-resolution X-band synthetic aperture radar dataset and show how image brightness and texture systematically vary across the images. We use an algorithm that reliably corrects this effect and achieve good results, as evaluated by comparisons to ground observations and other studies. The sea ice maps are useful as a basis for future MOSAiC studies.
Elie Dumas-Lefebvre and Dany Dumont
The Cryosphere, 17, 827–842, https://doi.org/10.5194/tc-17-827-2023, https://doi.org/10.5194/tc-17-827-2023, 2023
Short summary
Short summary
By changing the shape of ice floes, wave-induced sea ice breakup dramatically affects the large-scale dynamics of sea ice. As this process is also the trigger of multiple others, it was deemed relevant to study how breakup itself affects the ice floe size distribution. To do so, a ship sailed close to ice floes, and the breakup that it generated was recorded with a drone. The obtained data shed light on the underlying physics of wave-induced sea ice breakup.
Felix L. Müller, Stephan Paul, Stefan Hendricks, and Denise Dettmering
The Cryosphere, 17, 809–825, https://doi.org/10.5194/tc-17-809-2023, https://doi.org/10.5194/tc-17-809-2023, 2023
Short summary
Short summary
Thinning sea ice has significant impacts on the energy exchange between the atmosphere and the ocean. In this study we present visual and quantitative comparisons of thin-ice detections obtained from classified Cryosat-2 radar reflections and thin-ice-thickness estimates derived from MODIS thermal-infrared imagery. In addition to good comparability, the results of the study indicate the potential for a deeper understanding of sea ice in the polar seas and improved processing of altimeter data.
Maxim L. Lamare, John D. Hedley, and Martin D. King
The Cryosphere, 17, 737–751, https://doi.org/10.5194/tc-17-737-2023, https://doi.org/10.5194/tc-17-737-2023, 2023
Short summary
Short summary
The reflectivity of sea ice is crucial for modern climate change and for monitoring sea ice from satellites. The reflectivity depends on the angle at which the ice is viewed and the angle illuminated. The directional reflectivity is calculated as a function of viewing angle, illuminating angle, thickness, wavelength and surface roughness. Roughness cannot be considered independent of thickness, illumination angle and the wavelength. Remote sensors will use the data to image sea ice from space.
James Anheuser, Yinghui Liu, and Jeffrey R. Key
The Cryosphere, 16, 4403–4421, https://doi.org/10.5194/tc-16-4403-2022, https://doi.org/10.5194/tc-16-4403-2022, 2022
Short summary
Short summary
A prominent part of the polar climate system is sea ice, a better understanding of which would lead to better understanding Earth's climate. Newly published methods for observing the temperature of sea ice have made possible a new method for estimating daily sea ice thickness growth from space using an energy balance. The method compares well with existing sea ice thickness observations.
Mikko Lensu and Markku Similä
The Cryosphere, 16, 4363–4377, https://doi.org/10.5194/tc-16-4363-2022, https://doi.org/10.5194/tc-16-4363-2022, 2022
Short summary
Short summary
Ice ridges form a compressing ice cover. From above they appear as walls of up to few metres in height and extend even kilometres across the ice. Below they may reach tens of metres under the sea surface. Ridges need to be observed for the purposes of ice forecasting and ice information production. This relies mostly on ridging signatures discernible in radar satellite (SAR) images. New methods to quantify ridging from SAR have been developed and are shown to agree with field observations.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
Short summary
Short summary
Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
Benjamin Heikki Redmond Roche and Martin D. King
The Cryosphere, 16, 3949–3970, https://doi.org/10.5194/tc-16-3949-2022, https://doi.org/10.5194/tc-16-3949-2022, 2022
Short summary
Short summary
Sea ice is bright, playing an important role in reflecting incoming solar radiation. The reflectivity of sea ice is affected by the presence of pollutants, such as crude oil, even at low concentrations. Modelling how the brightness of three types of sea ice is affected by increasing concentrations of crude oils shows that the type of oil, the type of ice, the thickness of the ice, and the size of the oil droplets are important factors. This shows that sea ice is vulnerable to oil pollution.
Alexis Anne Denton and Mary-Louise Timmermans
The Cryosphere, 16, 1563–1578, https://doi.org/10.5194/tc-16-1563-2022, https://doi.org/10.5194/tc-16-1563-2022, 2022
Short summary
Short summary
Arctic sea ice has a distribution of ice sizes that provides insight into the physics of the ice. We examine this distribution from satellite imagery from 1999 to 2014 in the Canada Basin. We find that it appears as a power law whose power becomes less negative with increasing ice concentrations and has a seasonality tied to that of ice concentration. Results suggest ice concentration be considered in models of this distribution and are important for understanding sea ice in a warming Arctic.
Stephen E. L. Howell, Mike Brady, and Alexander S. Komarov
The Cryosphere, 16, 1125–1139, https://doi.org/10.5194/tc-16-1125-2022, https://doi.org/10.5194/tc-16-1125-2022, 2022
Short summary
Short summary
We describe, apply, and validate the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS) that routinely generates large-scale sea ice motion (SIM) over the pan-Arctic domain using synthetic aperture radar (SAR) images. The ECCC-ASITS was applied to the incoming image streams of Sentinel-1AB and the RADARSAT Constellation Mission from March 2020 to October 2021 using a total of 135 471 SAR images and generated new SIM datasets (i.e., 7 d 25 km and 3 d 6.25 km).
Wayne de Jager and Marcello Vichi
The Cryosphere, 16, 925–940, https://doi.org/10.5194/tc-16-925-2022, https://doi.org/10.5194/tc-16-925-2022, 2022
Short summary
Short summary
Ice motion can be used to better understand how weather and climate change affect the ice. Antarctic sea ice extent has shown large variability over the observed period, and dynamical features may also have changed. Our method allows for the quantification of rotational motion caused by wind and how this may have changed with time. Cyclonic motion dominates the Atlantic sector, particularly from 2015 onwards, while anticyclonic motion has remained comparatively small and unchanged.
Stefan Kern, Thomas Lavergne, Leif Toudal Pedersen, Rasmus Tage Tonboe, Louisa Bell, Maybritt Meyer, and Luise Zeigermann
The Cryosphere, 16, 349–378, https://doi.org/10.5194/tc-16-349-2022, https://doi.org/10.5194/tc-16-349-2022, 2022
Short summary
Short summary
High-resolution clear-sky optical satellite imagery has rarely been used to evaluate satellite passive microwave sea-ice concentration products beyond case-study level. By comparing 10 such products with sea-ice concentration estimated from > 350 such optical images in both hemispheres, we expand results of earlier evaluation studies for these products. Results stress the need to look beyond precision and accuracy and to discuss the evaluation data’s quality and filters applied in the products.
Wenkai Guo, Polona Itkin, Johannes Lohse, Malin Johansson, and Anthony Paul Doulgeris
The Cryosphere, 16, 237–257, https://doi.org/10.5194/tc-16-237-2022, https://doi.org/10.5194/tc-16-237-2022, 2022
Short summary
Short summary
This study uses radar satellite data categorized into different sea ice types to detect ice deformation, which is significant for climate science and ship navigation. For this, we examine radar signal differences of sea ice between two similar satellite sensors and show an optimal way to apply categorization methods across sensors, so more data can be used for this purpose. This study provides a basis for future reliable and constant detection of ice deformation remotely through satellite data.
Florent Garnier, Sara Fleury, Gilles Garric, Jérôme Bouffard, Michel Tsamados, Antoine Laforge, Marion Bocquet, Renée Mie Fredensborg Hansen, and Frédérique Remy
The Cryosphere, 15, 5483–5512, https://doi.org/10.5194/tc-15-5483-2021, https://doi.org/10.5194/tc-15-5483-2021, 2021
Short summary
Short summary
Snow depth data are essential to monitor the impacts of climate change on sea ice volume variations and their impacts on the climate system. For that purpose, we present and assess the altimetric snow depth product, computed in both hemispheres from CryoSat-2 and SARAL satellite data. The use of these data instead of the common climatology reduces the sea ice thickness by about 30 cm over the 2013–2019 period. These data are also crucial to argue for the launch of the CRISTAL satellite mission.
Lanqing Huang, Georg Fischer, and Irena Hajnsek
The Cryosphere, 15, 5323–5344, https://doi.org/10.5194/tc-15-5323-2021, https://doi.org/10.5194/tc-15-5323-2021, 2021
Short summary
Short summary
This study shows an elevation difference between the radar interferometric measurements and the optical measurements from a coordinated campaign over the snow-covered deformed sea ice in the western Weddell Sea, Antarctica. The objective is to correct the penetration bias of microwaves and to generate a precise sea ice topographic map, including the snow depth on top. Excellent performance for sea ice topographic retrieval is achieved with the proposed model and the developed retrieval scheme.
Isolde A. Glissenaar, Jack C. Landy, Alek A. Petty, Nathan T. Kurtz, and Julienne C. Stroeve
The Cryosphere, 15, 4909–4927, https://doi.org/10.5194/tc-15-4909-2021, https://doi.org/10.5194/tc-15-4909-2021, 2021
Short summary
Short summary
Scientists can estimate sea ice thickness using satellites that measure surface height. To determine the sea ice thickness, we also need to know the snow depth and density. This paper shows that the chosen snow depth product has a considerable impact on the findings of sea ice thickness state and trends in Baffin Bay, showing mean thinning with some snow depth products and mean thickening with others. This shows that it is important to better understand and monitor snow depth on sea ice.
Marek Muchow, Amelie U. Schmitt, and Lars Kaleschke
The Cryosphere, 15, 4527–4537, https://doi.org/10.5194/tc-15-4527-2021, https://doi.org/10.5194/tc-15-4527-2021, 2021
Short summary
Short summary
Linear-like openings in sea ice, also called leads, occur with widths from meters to kilometers. We use satellite images from Sentinel-2 with a resolution of 10 m to identify leads and measure their widths. With that we investigate the frequency of lead widths using two different statistical methods, since other studies have shown a dependency of heat exchange on the lead width. We are the first to address the sea-ice lead-width distribution in the Weddell Sea, Antarctica.
Gemma M. Brett, Daniel Price, Wolfgang Rack, and Patricia J. Langhorne
The Cryosphere, 15, 4099–4115, https://doi.org/10.5194/tc-15-4099-2021, https://doi.org/10.5194/tc-15-4099-2021, 2021
Short summary
Short summary
Ice shelf meltwater in the surface ocean affects sea ice formation, causing it to be thicker and, in particular conditions, to have a loose mass of platelet ice crystals called a sub‐ice platelet layer beneath. This causes the sea ice freeboard to stand higher above sea level. In this study, we demonstrate for the first time that the signature of ice shelf meltwater in the surface ocean manifesting as higher sea ice freeboard in McMurdo Sound is detectable from space using satellite technology.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
Short summary
Short summary
We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
Thomas Lavergne, Montserrat Piñol Solé, Emily Down, and Craig Donlon
The Cryosphere, 15, 3681–3698, https://doi.org/10.5194/tc-15-3681-2021, https://doi.org/10.5194/tc-15-3681-2021, 2021
Short summary
Short summary
Pushed by winds and ocean currents, polar sea ice is on the move. We use passive microwave satellites to observe this motion. The images from their orbits are often put together into daily images before motion is measured. In our study, we measure motion from the individual orbits directly and not from the daily images. We obtain many more motion vectors, and they are more accurate. This can be used for current and future satellites, e.g. the Copernicus Imaging Microwave Radiometer (CIMR).
Céline Heuzé, Lu Zhou, Martin Mohrmann, and Adriano Lemos
The Cryosphere, 15, 3401–3421, https://doi.org/10.5194/tc-15-3401-2021, https://doi.org/10.5194/tc-15-3401-2021, 2021
Short summary
Short summary
For navigation or science planning, knowing when sea ice will open in advance is a prerequisite. Yet, to date, routine spaceborne microwave observations of sea ice are unable to do so. We present the first method based on spaceborne infrared that can forecast an opening several days ahead. We develop it specifically for the Weddell Polynya, a large hole in the Antarctic winter ice cover that unexpectedly re-opened for the first time in 40 years in 2016, and determine why the polynya opened.
Marcel Kleinherenbrink, Anton Korosov, Thomas Newman, Andreas Theodosiou, Alexander S. Komarov, Yuanhao Li, Gert Mulder, Pierre Rampal, Julienne Stroeve, and Paco Lopez-Dekker
The Cryosphere, 15, 3101–3118, https://doi.org/10.5194/tc-15-3101-2021, https://doi.org/10.5194/tc-15-3101-2021, 2021
Short summary
Short summary
Harmony is one of the Earth Explorer 10 candidates that has the chance of being selected for launch in 2028. The mission consists of two satellites that fly in formation with Sentinel-1D, which carries a side-looking radar system. By receiving Sentinel-1's signals reflected from the surface, Harmony is able to observe instantaneous elevation and two-dimensional velocity at the surface. As such, Harmony's data allow the retrieval of sea-ice drift and wave spectra in sea-ice-covered regions.
Zhixiang Yin, Xiaodong Li, Yong Ge, Cheng Shang, Xinyan Li, Yun Du, and Feng Ling
The Cryosphere, 15, 2835–2856, https://doi.org/10.5194/tc-15-2835-2021, https://doi.org/10.5194/tc-15-2835-2021, 2021
Short summary
Short summary
MODIS thermal infrared (TIR) imagery provides promising data to study the rapid variations in the Arctic turbulent heat flux (THF). The accuracy of estimated THF, however, is low (especially for small leads) due to the coarse resolution of the MODIS TIR data. We train a deep neural network to enhance the spatial resolution of estimated THF over leads from MODIS TIR imagery. The method is found to be effective and can generate a result which is close to that derived from Landsat-8 TIR imagery.
Joan Antoni Parera-Portell, Raquel Ubach, and Charles Gignac
The Cryosphere, 15, 2803–2818, https://doi.org/10.5194/tc-15-2803-2021, https://doi.org/10.5194/tc-15-2803-2021, 2021
Short summary
Short summary
We describe a new method to map sea ice and water at 500 m resolution using data acquired by the MODIS sensors. The strength of this method is that it achieves high-accuracy results and is capable of attenuating unwanted resolution-breaking effects caused by cloud masking. Our resulting March and September monthly aggregates reflect the loss of sea ice in the European Arctic during the 2000–2019 period and show the algorithm's usefulness as a sea ice monitoring tool.
Robbie D. C. Mallett, Julienne C. Stroeve, Michel Tsamados, Jack C. Landy, Rosemary Willatt, Vishnu Nandan, and Glen E. Liston
The Cryosphere, 15, 2429–2450, https://doi.org/10.5194/tc-15-2429-2021, https://doi.org/10.5194/tc-15-2429-2021, 2021
Short summary
Short summary
We re-estimate pan-Arctic sea ice thickness (SIT) values by combining data from the Envisat and CryoSat-2 missions with data from a new, reanalysis-driven snow model. Because a decreasing amount of ice is being hidden below the waterline by the weight of overlying snow, we argue that SIT may be declining faster than previously calculated in some regions. Because the snow product varies from year to year, our new SIT calculations also display much more year-to-year variability.
Renée Mie Fredensborg Hansen, Eero Rinne, Sinéad Louise Farrell, and Henriette Skourup
The Cryosphere, 15, 2511–2529, https://doi.org/10.5194/tc-15-2511-2021, https://doi.org/10.5194/tc-15-2511-2021, 2021
Short summary
Short summary
Ice navigators rely on timely information about ice conditions to ensure safe passage through ice-covered waters, and one parameter, the degree of ice ridging (DIR), is particularly useful. We have investigated the possibility of estimating DIR from the geolocated photons of ICESat-2 (IS2) in the Bay of Bothnia, show that IS2 retrievals from different DIR areas differ significantly, and present some of the first steps in creating sea ice applications beyond e.g. thickness retrieval.
Luisa von Albedyll, Christian Haas, and Wolfgang Dierking
The Cryosphere, 15, 2167–2186, https://doi.org/10.5194/tc-15-2167-2021, https://doi.org/10.5194/tc-15-2167-2021, 2021
Short summary
Short summary
Convergent sea ice motion produces a thick ice cover through ridging. We studied sea ice deformation derived from high-resolution satellite imagery and related it to the corresponding thickness change. We found that deformation explains the observed dynamic thickness change. We show that deformation can be used to model realistic ice thickness distributions. Our results revealed new relationships between thickness redistribution and deformation that could improve sea ice models.
Cited articles
Aaboe, S., Sørensen, A., Eastwood, S., and Lavergne, T.: Sea ice edge and
type daily gridded data from 1978 to present derived from satellite
observations, Climate Data Store [data set], https://doi.org/10.24381/cds.29c46d83, 2020.
Aaboe, S., Down, E., and Eastwood, S.: Global Sea Ice Edge (OSI-402-d) and
Type (OSI-403-d) Validation Report, v3.1, in:
SAF/OSI/CDOP3/MET-Norway/SCI/RP/224, EUMETSAT OSISAF – Ocean and Sea Ice
Satellite Application Facility, 2021a.
Aaboe, S., Down, E., and Eastwood, S.: Algorithm Theoretical Basis Document
for the Global Sea-Ice Edge and Type, v3.4, in:
SAF/OSI/CDOP3/MET-Norway/TEC/MA/379, EUMETSAT OSISAF: Ocean and Sea Ice
Satellite Application Facility, 2021b.
Aaboe, S., Sørensen, A., Lavergne, T., and Eastwood, S.: Sea Ice Edge and
Sea Ice Type Climate Data Records Algorithm Theoretical Basis Document,
v3.1, EU C3S-Copernicus Climate Change Service, Copernicus Climate Change
Service, https://datastore.copernicus-climate.eu/documents/satellite-sea-ice-edge-type/v2.0/D1.SIETy.2-v2.0_ATBD-of-v2.0-SeaIceEdgeType-products_v3.1_APPROVED_Ver1.pdf (last access: 1 April 2022), 2021c.
Aldenhoff, W., Heuzé, C., and Eriksson, L. E. B.: Comparison of
ice/water classification in Fram Strait from C- and L-band SAR imagery,
Ann. Glaciol., 59, 112–123, https://doi.org/10.1017/aog.2018.7, 2018.
Alexandrov, V., Sandven, S., Wahlin, J., and Johannessen, O. M.: The relation between sea ice thickness and freeboard in the Arctic, The Cryosphere, 4, 373–380, https://doi.org/10.5194/tc-4-373-2010, 2010.
Andersen, S.: Evaluation of SSM/I Sea Ice Algorithms for use in the SAF on
Ocean and Sea Ice July 2000, DANISH METEOROLOGICAL INSTITUTE, https://www.dmi.dk/fileadmin/Rapporter/SR/sr00-10.pdf (last access: 1 April 2022), 2000.
Anderson, H. S. and Long, D. G.: Sea ice mapping method for SeaWinds, IEEE
T. Geosci. Remote, 43, 647–657,
https://doi.org/10.1109/TGRS.2004.842017, 2005.
Barber, D. G. and Thomas, A.: The influence of cloud cover on the radiation
budget, physical properties, and microwave scattering coefficient (/spl
sigma//spl deg/) of first-year and multiyear sea ice, IEEE T. Geosci. Remote, 36, 38–50, https://doi.org/10.1109/36.655316,
1998.
Belchansky, G. I. and Douglas, D. C.: Classification methods for monitoring
Arctic sea ice using OKEAN passive/active two-channel microwave data, Remote
Sens. Environ., 73, 307-322,
https://doi.org/10.1016/S0034-4257(00)00107-3, 2000.
Belmonte Rivas, M., Verspeek, J., Verhoef, A., and Stoffelen, A.: Bayesian
Sea Ice Detection With the Advanced Scatterometer ASCAT, IEEE T. Geosci. Remote, 50, 2649–2657,
https://doi.org/10.1109/tgrs.2011.2182356, 2012.
Belmonte Rivas, M., Otosaka, I., Stoffelen, A., and Verhoef, A.: A scatterometer record of sea ice extents and backscatter: 1992–2016, The Cryosphere, 12, 2941–2953, https://doi.org/10.5194/tc-12-2941-2018, 2018.
Belter, H. J., Krumpen, T., von Albedyll, L., Alekseeva, T. A., Birnbaum, G., Frolov, S. V., Hendricks, S., Herber, A., Polyakov, I., Raphael, I., Ricker, R., Serovetnikov, S. S., Webster, M., and Haas, C.: Interannual variability in Transpolar Drift summer sea ice thickness and potential impact of Atlantification, The Cryosphere, 15, 2575–2591, https://doi.org/10.5194/tc-15-2575-2021, 2021.
Berg, A. and Eriksson, L. E. B.: SAR Algorithm for Sea Ice
Concentration – Evaluation for the Baltic Sea, IEEE Geosci. Remote
Sens. Lett., 9, 938–942, https://doi.org/10.1109/LGRS.2012.2186280, 2012.
Bi, H. B., Liang, Y., Wang, Y. H., Liang, X., Zhang, Z. H., Du, T. Q., Yu,
Q. L., Huang, J., Kong, M., and Huang, H. J.: Arctic multiyear sea ice
variability observed from satellites: a review, J. Oceanol. Limnol., 38,
962–984, https://doi.org/10.1007/s00343-021-0382-9, 2020.
Boisvert, L. N., Wu, D. L., and Shie, C. L.: Increasing evaporation amounts
seen in the Arctic between 2003 and 2013 from AIRS data, J.
Geophys. Res.-Atmos., 120, 6865–6881,
https://doi.org/10.1002/2015JD023258, 2015.
Breivik, L.-A., Eastwood, S., and Lavergne, T.: Use of C-band scatterometer
for sea ice edge identification, IEEE T. Geosci. Remote, 50, 2669–2677, https://doi.org/10.1109/TGRS.2012.2188898, 2012.
Carsey, F.: Summer Arctic sea ice character from satellite microwave data,
J. Geophys. Res.-Oceans, 90, 5015–5034,
https://doi.org/10.1029/JC090iC03p05015, 1985.
Cavalieri, D. J., Gloersen, P., and Campbell, W. J.: Determination of sea
ice parameters with the NIMBUS 7 SMMR, J. Geophys. Res.-Atmos., 89, 5355–5369, https://doi.org/10.1029/JD089iD04p05355, 1984.
Comiso, J. C.: Sea ice effective microwave emissivities from satellite
passive microwave and infrared observations, J. Geophys.
Res.-Oceans, 88, 7686–7704, https://doi.org/10.1029/JC088iC12p07686,
1983.
Comiso, J. C., Parkinson, C. L., Gersten, R., and Stock, L.: Accelerated
decline in the Arctic sea ice cover, Geophys. Res. Lett., 35, L01703,
https://doi.org/10.1029/2007GL031972, 2008.
Dabboor, M. and Geldsetzer, T.: Towards sea ice classification using
simulated RADARSAT Constellation Mission compact polarimetric SAR imagery,
Remote Sens. Environ., 140, 189–195,
https://doi.org/10.1016/j.rse.2013.08.035, 2014.
Early, D. S. and Long, D. G.: Image reconstruction and enhanced resolution
imaging from irregular samples, IEEE T. Geosci. Remote, 39, 291–302, https://doi.org/10.1109/36.905237, 2001.
Emmerson, C. and Lahn, G.: Arctic opening: Opportunity and risk in the high north, 55, Lloyd's, http://library.arcticportal.org/1671/1/Arctic_O (last access: 1 April 2022), 2012.
Eppler, D. T., Farmer, L. D., Lohanick, A. W., Anderson, M. R., Cavalieri,
D. J., Comiso, J., Gloersen, P., Garrity, C., Grenfell, T. C., Hallikainen,
M., Maslanik, J. A., MäTzler, C., Melloh, R. A., Rubinstein, I., and
Swift, C. T.: Passive Microwave Signatures of Sea Ice, in: Microwave Remote
Sensing of Sea Ice, Geophys. Monogr. Ser., 47–71,
https://doi.org/10.1029/GM068p0047, 1992.
Ezraty, R. and Cavanie, A.: Construction and evaluation of 12.5-km grid
NSCAT backscatter maps over Arctic sea ice, IEEE T. Geosci. Remote, 37, 1685–1697, https://doi.org/10.1109/36.763289, 1999.
Ezraty, R. and Cavanié, A.: Intercomparison of backscatter maps over
Arctic sea ice from NSCAT and the ERS scatterometer, J. Geophys.
Res.-Oceans, 104, 11471–11483, https://doi.org/10.1029/1998JC900086,
1999.
Fowler, C., Emery, W. J., and Maslanik, J.: Satellite-derived evolution of
Arctic sea ice age: October 1978 to March 2003, IEEE Geosci. Remote
Sens. Lett., 1, 71–74, https://doi.org/10.1109/LGRS.2004.824741, 2004.
Girard-Ardhuin, F.: Multi-year Arctic sea ice extent estimate from
scatterometers onboard satellite since 2000, AGU Fall meeting, San
Francisco, CA, USA, 12–16 December, C42B-07, 2016.
Gloersen, P. and Cavalieri, D. J.: Reduction of weather effects in the
calculation of sea ice concentration from microwave radiances, J.
Geophys. Res.-Oceans, 91, 3913–3919,
https://doi.org/10.1029/JC091iC03p03913, 1986.
Gray, A., Hawkins, R., Livingstone, C., Arsenault, L., and Johnstone, W.:
Simultaneous scatterometer and radiometer measurements of sea-ice microwave
signatures, IEEE J. Ocean. Eng., 7, 20–32,
https://doi.org/10.1109/JOE.1982.1145506, 1982.
Hakkinen, S., Proshutinsky, A., and Ashik, I.: Sea ice drift in the Arctic
since the 1950s, Geophys. Res. Lett., 35, L19704,
https://doi.org/10.1029/2008GL034791, 2008.
Hallikainen, M. and Winebrenner, D. P.: The physical basis for sea ice
remote sensing, in: Microwave remote sensing of sea ice, edited by: Carsey,
F., American Geophysical Union, 29–46, https://doi.org/10.1029/GM068p0029, 1992.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A.,
Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I.,
Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5
hourly data on single levels from 1979 to present, Climate Data Store [data set],
https://doi.org/10.24381/cds.adbb2d47, 2018.
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.
IMarEST: Safety & Sustainability of Shipping and Offshore Activities in
the Arctic, Institute of Marine Engineering, Science & Technology,
IMarEST Report, London International Shipping Week, 2015.
Jung, T., Kasper, M. A., Semmler, T., and Serrar, S.: Arctic influence on
subseasonal midlatitude prediction, Geophys. Res. Lett., 41,
3676–3680, https://doi.org/10.1002/2014GL059961, 2014.
Kern, S., Rösel, A., Pedersen, L. T., Ivanova, N., Saldo, R., and Tonboe, R. T.: The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations, The Cryosphere, 10, 2217–2239, https://doi.org/10.5194/tc-10-2217-2016, 2016.
Kilic, L., Prigent, C., Aires, F., Boutin, J., Heygster, G., Tonboe, R. T.,
Roquet, H., Jimenez, C., and Donlon, C.: Expected Performances of the
Copernicus Imaging Microwave Radiometer (CIMR) for an All-Weather and High
Spatial Resolution Estimation of Ocean and Sea Ice Parameters, J.
Geophys. Res.-Oceans, 123, 7564–7580,
https://doi.org/10.1029/2018JC014408, 2018.
Kim, Y. S., Moore, R. K., Onstott, R. G., and Gogineni, S.: Towards
Identification of Optimum Radar Parameters for Sea-Ice Monitoring, J. Glaciol., 31, 214–219, https://doi.org/10.3189/S0022143000006523, 1985.
Korosov, A. A., Rampal, P., Pedersen, L. T., Saldo, R., Ye, Y., Heygster, G., Lavergne, T., Aaboe, S., and Girard-Ardhuin, F.: A new tracking algorithm for sea ice age distribution estimation, The Cryosphere, 12, 2073–2085, https://doi.org/10.5194/tc-12-2073-2018, 2018.
Kuang, H., Luo, Y., Ye, Y., Shokr, M., Chen, Z., Wang, S., Hui, F., Bi, H.,
and Cheng, X.: Arctic Multiyear Ice Areal Flux and Its Connection with
Large-Scale Atmospheric Circulations in the Winters of 2002–2021, Remote
Sens., 14, 3742, https://doi.org/10.3390/rs14153742, 2022.
Kwok, R.: Annual cycles of multiyear sea ice coverage of the Arctic Ocean:
1999–2003, J. Geophys. Res., 109, C11004,
https://doi.org/10.1029/2003JC002238, 2004.
Kwok, R.: Arctic sea ice thickness, volume, and multiyear ice coverage:
losses and coupled variability (1958–2018), Environ. Res. Lett.,
13, 105005, https://doi.org/10.1088/1748-9326/aae3ec, 2018.
Kwok, R. and Cunningham, G. F.: Variability of Arctic sea ice thickness and
volume from CryoSat-2, Philosophical Transactions of the Royal Society A:
Mathematical, Phys. Eng. Sci., 373, 20140157,
https://doi.org/10.1098/rsta.2014.0157, 2015.
Kwok, R., Cunningham, G. F., and Yueh, S.: Area balance of the Arctic Ocean
perennial ice zone: October 1996 to April 1997, J. Geophys.
Res.-Oceans, 104, 25747–25759, https://doi.org/10.1029/1999JC900234,
1999.
Kwok, R., Spreen, G., and Pang, S.: Arctic sea ice circulation and drift
speed: Decadal trends and ocean currents, J. Geophys. Res.-Oceans, 118, 2408–2425, https://doi.org/10.1002/jgrc.20191, 2013.
Lavergne, T., Sørensen, A., Tonboe, R. T., Saldo, R., Pedersen, L. T.,
Strong, C., Cherkaev, E., Golden, K. M., and Eastwood, S.: Algorithm
Theoretical Basis Document for the Global Sea-Ice Concentration Climate Data
Records, v3.0, In SAF/OSI/CDOP3/DMI_Met/SCI/MA/270, EUMETSAT
OSISAF – Ocean and Sea Ice Satellite Application Facility, https://osisaf-hl.met.no/sites/osisaf-hl/files/baseline_document/osisaf_cdop3_ss2_atbd_sea-ice-conc-climate-data-record_v3p0.pdf (last access: 1 April 2022), 2022.
Lee, S.-M., Sohn, B.-J., and Kim, S.-J.: Differentiating between first-year
and multiyear sea ice in the Arctic using microwave-retrieved ice
emissivities, J. Geophys. Res.-Atmos., 122, 5097–5112,
https://doi.org/10.1002/2016JD026275, 2017.
Lindell, D. B. and Long, D.: Multiyear Arctic Ice Classification Using ASCAT
and SSMIS, Remote Sens., 8, 294, https://doi.org/10.3390/rs8040294, 2016a.
Lindell, D. B. and Long, D. G.: Multiyear Arctic Sea Ice Classification
Using OSCAT and QuikSCAT, IEEE T. Geosci. Remote, 54, 167–175, https://doi.org/10.1109/tgrs.2015.2452215, 2016b.
Liu, J., Curry, J. A., Wang, H., Song, M., and Horton, R. M.: Impact of
declining Arctic sea ice on winter snowfall, P. Natl.
Acad. Sci. USA, 109, 4074–4079,
https://doi.org/10.1073/pnas.1114910109, 2012.
Liu, Q., Babanin, A. V., Zieger, S., Young, I. R., and Guan, C.: Wind and
Wave Climate in the Arctic Ocean as Observed by Altimeters, J.
Climate, 29, 7957–7975, https://doi.org/10.1175/JCLI-D-16-0219.1, 2016.
Lomax, A. S., Lubin, D., and Whritner, R. H.: The potential for interpreting
total and multiyear ice concentrations in SSM/I 85.5 GHz imagery, Remote
Sens. Environ., 54, 13–26,
https://doi.org/10.1016/0034-4257(95)00082-C, 1995.
Long, D. G., Hardin, P. J., and Whiting, P. T.: Resolution enhancement of
spaceborne scatterometer data, IEEE T. Geosci. Remote, 31, 700–715, https://doi.org/10.1109/36.225536, 1993.
Maaß, N. and Kaleschke, L.: Improving passive microwave sea ice
concentration algorithms for coastal areas: applications to the Baltic Sea,
Tellus A, 62, 393–410, https://doi.org/10.1111/j.1600-0870.2010.00452.x,
2010.
Maslanik, J., Stroeve, J., Fowler, C., and Emery, W.: Distribution and
trends in Arctic sea ice age through spring 2011, Geophys. Res. Lett., 38, L13502, https://doi.org/10.1029/2011GL047735, 2011.
Maslanik, J. A., Fowler, C., Stroeve, J., Drobot, S., Zwally, J., Yi, D.,
and Emery, W.: A younger, thinner Arctic ice cover: Increased potential for
rapid, extensive sea-ice loss, Geophys. Res. Lett., 34, L24501,
https://doi.org/10.1029/2007GL032043, 2007.
Meier, W. N., Hovelsrud, G. K., Van Oort, B. E. H., Key, J. R., Kovacs, K.
M., Michel, C., Haas, C., Granskog, M. A., Gerland, S., Perovich, D. K.,
Makshtas, A., and Reist, J. D.: Arctic sea ice in transformation: A review
of recent observed changes and impacts on biology and human activity,
Rev. Geophys., 52, 185–217, https://doi.org/10.1002/2013rg000431,
2014.
Onarheim, I. H., Eldevik, T., Smedsrud, L. H., and Stroeve, J. C.: Seasonal
and Regional Manifestation of Arctic Sea Ice Loss, J. Climate, 31,
4917–4932, https://doi.org/10.1175/jcli-d-17-0427.1, 2018.
Onstott, R. G.: SAR and Scatterometer Signatures of Sea Ice, in: Microwave
Remote Sensing of Sea Ice, Geophys. Monogr. Ser., 73–104,
https://doi.org/10.1029/GM068p0073, 1992.
Onstott, R. G., Moore, R. K., and Weeks, W. F.: Surface-Based Scatterometer
Results of Arctic Sea Ice, IEEE T. Geosci. Electron., 17,
78–85, https://doi.org/10.1109/TGE.1979.294616, 1979.
Perovich, D., Meier, W., Tschudi, M., Hendricks, S., Petty, A. A., Divine,
D., Farrell, S., Gerland, S., Haas, C., Kaleschke, L., Pavlova, O., Ricker,
R., Tian-Kunze, X., Webster, M., and Wood, K.: Sea ice, in: Arctic report card 2020, NOAA, https://doi.org/10.25923/n170-9h57, 2020.
Perovich, D., Meier, W., Tschudi, M., Farrell, S., Hendricks, S., Gerland,
S., Kaleschke, L., Ricker, R., Tian-Kunze, X., Webster, M., and Wood, K.:
Sea ice, in: Arctic Report Card 2021, NOAA,
https://doi.org/10.25923/y2wd-fn85, 2021.
Petty, A. A., Kurtz, N. T., Kwok, R., Markus, T., and Neumann, T. A.: Winter
Arctic sea ice thickness from ICESat-2 freeboards, J. Geophys.
Res.-Oceans, 125, e2019JC015764, https://doi.org/10.1029/2019JC015764,
2020.
Raphael, M. N. and Handcock, M. S.: A new record minimum for Antarctic sea
ice, Nat. Rev. Earth Environ., 3, 215–216,
https://doi.org/10.1038/s43017-022-00281-0, 2022.
Rostosky, P., Spreen, G., Farrell, S. L., Frost, T., Heygster, G., and
Melsheimer, C.: Snow Depth Retrieval on Arctic Sea Ice From Passive
Microwave RadiometersImprovements and Extensions to Multiyear Ice Using
Lower Frequencies, J. Geophys. Res.-Oceans, 123, 7120–7138,
https://doi.org/10.1029/2018jc014028, 2018.
Rothrock, D. A., Percival, D. B., and Wensnahan, M.: The decline in arctic
sea-ice thickness: Separating the spatial, annual, and interannual
variability in a quarter century of submarine data, J. Geophys.
Res.-Oceans, 113, C05003, https://doi.org/10.1029/2007JC004252, 2008.
Screen, J. A., Simmonds, I., Deser, C., and Tomas, R.: The atmospheric
response to three decades of observed Arctic sea ice loss, J.
Climate, 26, 1230–1248, https://doi.org/10.1175/JCLI-D-12-00063.1, 2013.
Shokr, M. E.: Field observations and model calculations of dielectric
properties of Arctic sea ice in the microwave C-band, IEEE T. Geosci. Remote, 36, 463–478,
https://doi.org/10.1109/36.662730, 1998.
Shokr, M. and Agnew, T. A.: Validation and potential applications of
Environment Canada Ice Concentration Extractor (ECICE) algorithm to Arctic
ice by combining AMSR-E and QuikSCAT observations, Remote Sens.
Environ., 128, 315–332, https://doi.org/10.1016/j.rse.2012.10.016, 2013.
Shokr, M. and Sinha, N. K.: Sea ice: physics and remote sensing, John Wiley
& Sons, American Geophysical Union, ISBN 978-1-119-02789-8, 2015.
Shokr, M., Lambe, A., and Agnew, T.: A New Algorithm (ECICE) to Estimate Ice
Concentration From Remote Sensing Observations: An Application to 85-GHz
Passive Microwave Data, IEEE T. Geosci. Remote,
46, 4104–4121, https://doi.org/10.1109/TGRS.2008.2000624, 2008.
Song, W., Li, M., Gao, W., Huang, D., Ma, Z., Liotta, A., and Perra, C.:
Automatic Sea-Ice Classification of SAR Images Based on Spatial and Temporal
Features Learning, IEEE T. Geosci. Remote, 59,
9887–9901, https://doi.org/10.1109/TGRS.2020.3049031, 2021.
Sumata, H., Lavergne, T., Girard-Ardhuin, F., Kimura, N., Tschudi, M. A.,
Kauker, F., Karcher, M., and Gerdes, R.: An intercomparison of Arctic ice
drift products to deduce uncertainty estimates, J. Geophys.
Res.-Oceans, 119, 4887–4921, https://doi.org/10.1002/2013JC009724,
2014.
Sun, Y. and Li, X.-M.: Denoising Sentinel-1 Extra-Wide Mode
Cross-Polarization Images Over Sea Ice, IEEE T. Geosci. Remote,
59, 2116–2131, https://doi.org/10.1109/TGRS.2020.3005831, 2021.
Swan, A. M. and Long, D. G.: Multiyear Arctic Sea Ice Classification Using
QuikSCAT, IEEE T. Geosci. Remote, 50, 3317–3326,
https://doi.org/10.1109/tgrs.2012.2184123, 2012.
Szanyi, S., Lukovich, J. V., Barber, D. G., and Haller, G.: Persistent
artifacts in the NSIDC ice motion data set and their implications for
analysis, Geophys. Res. Lett., 43, 10800–10807,
https://doi.org/10.1002/2016GL069799, 2016.
Tschudi, M. A., Stroeve, J. C., and Stewart, J. S.: Relating the Age of
Arctic Sea Ice to its Thickness, as Measured during NASA's ICESat and
IceBridge Campaigns, Remote Sens., 8, 457, https://doi.org/10.3390/rs8060457,
2016.
Tschudi, M. A., Meier, W. N., and Stewart, J. S.: An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC), The Cryosphere, 14, 1519–1536, https://doi.org/10.5194/tc-14-1519-2020, 2020.
Tschudi, M., Meier, W. N., Stewart, J. S., Fowler, C., and Maslanik, J.: EASE-Grid Sea Ice Age, Version 4, Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/UTAV7490FEPB, 2019.
Vant, M. R., Ramseier, R. O., and Makios, V.: The complex-dielectric
constant of sea ice at frequencies in the range 0.1–40 GHz, J.
Appl. Phys., 49, 1264–1280, https://doi.org/10.1063/1.325018, 1978.
Voss, S., Heygster, G., and Ezraty, R.: Improving sea ice type
discrimination by the simultaneous use of SSM/I and scatterometer data,
Polar Res., 22, 35–42,
https://doi.org/10.1111/j.1751-8369.2003.tb00093.x, 2003.
Walker, N. P., Partington, K. C., Woert, M. L. V., and Street, T. L. T.:
Arctic Sea Ice Type and Concentration Mapping Using Passive and Active
Microwave Sensors, IEEE T. Geosci. Remote, 44,
3574–3584, https://doi.org/10.1109/TGRS.2006.881116, 2006.
Weeks, W. F. and Ackley, S. F.: The Growth, Structure, and Properties of Sea
Ice, in: The Geophysics of Sea Ice, edited by: Untersteiner, N., Springer
US, Boston, MA, 9–164,
https://doi.org/10.1007/978-1-4899-5352-0_2, 1986.
Wentz, F. J.: A well-calibrated ocean algorithm for special sensor microwave/imager, J. Geophys. Res.-Oceans, 102, 8703–8718,
https://doi.org/10.1029/96JC01751, 1997.
Xu, R., Zhao, C., Zhai, X., and Chen, G.: Arctic Sea Ice Type Classification
by Combining CFOSCAT and AMSR-2 Data, Earth Space Sci., 9,
e2021EA002052, https://doi.org/10.1029/2021EA002052, 2022.
Ye, Y., Heygster, G., and Shokr, M.: Improving Multiyear Ice Concentration
Estimates With Reanalysis Air Temperatures, IEEE T. Geosci. Remote, 54, 2602–2614,
https://doi.org/10.1109/TGRS.2015.2503884, 2016a.
Ye, Y., Shokr, M., Heygster, G., and Spreen, G.: Improving multiyear sea ice
concentration estimates with sea ice drift, Remote Sens., 8, 397,
https://doi.org/10.3390/rs8050397, 2016b.
Ye, Y., Shokr, M., Aaboe, S., Aldenhoff, W., Eriksson, L. E. B., Heygster, G., Melsheimer, C., and Girard-Ardhuin, F.: Inter-comparison and evaluation of sea ice type concentration algorithms, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-200, 2019.
Yu, P., Clausi, D. A., and Howell, S.: Fusing AMSR-E and QuikSCAT Imagery
for Improved Sea Ice Recognition, IEEE T. Geosci. Remote, 47, 1980–1989, https://doi.org/10.1109/tgrs.2009.2013632, 2009.
Zhang, Z., Yu, Y., Li, X., Hui, F., Cheng, X., and Chen, Z.: Arctic Sea Ice
Classification Using Microwave Scatterometer and Radiometer Data During
2002–2017, IEEE T. Geosci. Remote, 57,
5319-5328, https://doi.org/10.1109/TGRS.2019.2898872, 2019.
Zhang, Z., Yu, Y., Shokr, M., Li, X., Ye, Y., Cheng, X., Chen, Z., and Hui,
F.: Intercomparison of Arctic Sea Ice Backscatter and Ice Type
Classification Using Ku-Band and C-Band Scatterometers, IEEE T. Geosci. Remote, 1–18,
https://doi.org/10.1109/TGRS.2021.3099835, 2021.
Short summary
Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study gives a systematic inter-comparison and evaluation of eight SITY products. Main results include differences in SITY products being significant, with average Arctic multiyear ice extent up to 1.8×106 km2; Ku-band scatterometer SITY products generally performing better; and factors such as satellite inputs, classification methods, training datasets and post-processing highly impacting their performance.
Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. This study...
