Articles | Volume 17, issue 1
https://doi.org/10.5194/essd-17-233-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-233-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An Arctic sea ice concentration data record on a 6.25 km polar stereographic grid from 3 years of Landsat-8 imagery
Hee-Sung Jung
Department of Earth and Environmental Sciences, Seoul National University, Seoul, 08826, Republic of Korea
Sang-Moo Lee
CORRESPONDING AUTHOR
Department of Earth and Environmental Sciences, Seoul National University, Seoul, 08826, Republic of Korea
Institute for Data Innovation in Science, Seoul National University, Seoul, 08826, Republic of Korea
Joo-Hong Kim
Division of Ocean and Atmosphere Sciences, Korea Polar Research Institute, Incheon, 21990, Republic of Korea
Kyungsoo Lee
Department of Earth and Environmental Sciences, Seoul National University, Seoul, 08826, Republic of Korea
Related authors
No articles found.
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, and Youngchae Kwon
Geosci. Model Dev., 16, 5365–5382, https://doi.org/10.5194/gmd-16-5365-2023, https://doi.org/10.5194/gmd-16-5365-2023, 2023
Short summary
Short summary
This is the first attempt to assimilate the observations of microwave temperature sounders into the global climate forecast model in which the satellite observations have not been assimilated in the past. To do this, preprocessing schemes are developed to make the satellite observations suitable to be assimilated. In the assimilation experiments, the model analysis is significantly improved by assimilating the observations of microwave temperature sounders.
Jinyoung Jung, Yuzo Miyazaki, Jin Hur, Yun Kyung Lee, Mi Hae Jeon, Youngju Lee, Kyoung-Ho Cho, Hyun Young Chung, Kitae Kim, Jung-Ok Choi, Catherine Lalande, Joo-Hong Kim, Taejin Choi, Young Jun Yoon, Eun Jin Yang, and Sung-Ho Kang
Atmos. Chem. Phys., 23, 4663–4684, https://doi.org/10.5194/acp-23-4663-2023, https://doi.org/10.5194/acp-23-4663-2023, 2023
Short summary
Short summary
This study examined the summertime fluorescence properties of water-soluble organic carbon (WSOC) in aerosols over the western Arctic Ocean. We found that the WSOC in fine-mode aerosols in coastal areas showed a higher polycondensation degree and aromaticity than in sea-ice-covered areas. The fluorescence properties of atmospheric WSOC in the summertime marine Arctic boundary can improve our understanding of the WSOC chemical and biological linkages at the ocean–sea-ice–atmosphere interface.
Cited articles
Agnew, T. and Howell, S.: The use of operational ice charts for evaluating passive microwave ice concentration data, Atmos. Ocean, 41, 317–331, https://doi.org/10.3137/ao.410405, 2010.
Andersen, S., Tonboe, R., Kaleschke, L., Heygster, G., and Pedersen, L. T.: Intercomparison of passive microwave sea ice concentration retrievals over the high-concentration Arctic sea ice, J. Geophys. Res., 112, C08004, https://doi.org/10.1029/2006JC003543, 2007.
Cavalieri, D. J., Gloersen, P., and Campbell, W. J.: Determination of sea ice parameters with the Nimbus 7 SMMR, J. Geophys. Res., 89, 5355–5369, https://doi.org/10.1029/JD089iD04p05355, 1984.
Cavalieri, D. J., Germain, K. M., and Swift, C. T.: Reduction of weather effects in the calculation of sea-ice concentration with the DMSP SSM/I, J. Glaciol., 41, 455–464, https://doi.org/10.3189/S0022143000034791, 1995.
Cavalieri, D. J., Markus, T., Hall, D. K., Gasiewski, A. J., Klein, M., and Ivanoff, A.: Assessment of EOS Aqua AMSR-E Arctic Sea Ice Concentration Using Landsat-7 and Airborne Microwave Imagery, IEEE T. Geosci. Remote, 44, 3057–3069, https://doi.org/10.1109/TGRS.2006.878445, 2006.
Cavalieri, D. J., Markus, T., Hall, D. K., Ivanoff A., and Glick E.: Assessment of AMSR-E Antarctic Winter Sea-Ice Concentrations Using Aqua MODIS, IEEE T. Geosci. Remote, 48, 3331–3339, https://doi.org/10.1109/TGRS.2010.2046495, 2010.
Cavalieri, D. J. and Parkinson, C. L.: Arctic sea ice variability and trends, 1979–2010, The Cryosphere, 6, 881–889, https://doi.org/10.5194/tc-6-881-2012, 2012.
Cheng, A., Casati, B., Tivy, A., Zagon, T., Lemieux, J.-F., and Tremblay, L. B.: Accuracy and inter-analyst agreement of visually estimated sea ice concentrations in Canadian Ice Service ice charts using single-polarization RADARSAT-2, The Cryosphere, 14, 1289–1310, https://doi.org/10.5194/tc-14-1289-2020, 2020.
Chi, J., Kim, H. C., Lee, S., and Crawford, M. M.: Deep learning based retrieval algorithm for Arctic sea ice concentration from AMSR2 passive microwave and MODIS optical data, Remote Sens. Environ., 231, 111204, https://doi.org/10.1016/j.rse.2019.05.023, 2019.
Comiso, J. C.: SSM/I Sea Ice Concentrations Using the Bootstrap Algorithm, NASA Reference Publication 1380, NASA Center for Aerospace Information, 800 Elkridge Landing Road, Linthicum Heights, MD, 49 pp., https://www.geobotany.uaf.edu/library/pubs/ComisoJC1995_nasa_1380_53.pdf (last access: 18 January 2025), 1995.
Comiso, J. C., Ackley, S. F., and Gordon, A. L.: Antarctic sea ice microwave signatures and their correlation with in situ ice observations, J. Geophys. Res.-Oceans, 89, 662–672, https://doi.org/10.1029/JC089iC01p00662, 1984.
Comiso, J. C., Cavalieri, D. J., Parkinson, C. L., and Gloersen, P.: Passive Microwave Algorithm for Sea Ice Concentration: A Comparison of Two Techniques, Remote Sens. Environ., 60, 357–384, https://doi.org/10.1016/S0034-4257(96)00220-9, 1997.
Comiso, J. C., Meier, W. N., and Gersten, R.: Variability and trends in the Arctic Sea ice cover: Results from different techniques, J. Geophys. Res.-Oceans, 122, 6883–6900, https://doi.org/10.1002/2017JC012768, 2017.
Earth Resources Observation and Science (EROS) Center: Landsat 8–9 Operational Land Imager/Thermal Infrared Sensor Level-1, Collection 2, U.S. Geological Survey [data set], https://doi.org/10.5066/P975CC9B, 2020.
E.U. Copernicus Marine Service Information (CMEMS): Arctic Ocean-Sea Ice Concentration Charts – Svalbard and Greenland, Marine Data Store (MDS) [data set], https://doi.org/10.48670/moi-00128, 2024.
Fetterer, F. and Untersteiner, N.: Observations of melt ponds on Arctic sea ice, J. Geophys. Res., 103, 24821–24825, https://doi.org/10.1029/98JC02034, 1998.
Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley, R. D., Beckmann, T., Schmidt, G. L., Dwyer, J. L., Hughes, M. J., and Laue, B.: Cloud detection algorithm comparison and validation for operational Landsat data products, Remote Sens. Environ., 194, 379–390, https://doi.org/10.1016/j.rse.2017.03.026, 2017.
Gloersen, P. and Cavalieri, D. J.: Reduction of weather effects in the calculation of sea ice concentration from microwave radiances, J. Geophys. Res., 91, 3913–3919, https://doi.org/10.1029/JC091iC03p03913, 1986.
Hall, D. K., Riggs, G. A., and Salomonson, V. V.: Development of Methods for Mapping Global Snow Cover Using Moderate Resolution Imaging Spectroradiometer Data, Remote Sens. Environ., 54, 127–140, https://doi.org/10.1016/0034-4257(95)00137-P, 1995.
Han, H. and Kim, H. C.: Evaluation of summer passive microwave sea ice concentrations in the Chukchi Sea based on KOMPSAT-5 SAR and numerical weather prediction data, Remote Sens. Environ., 209, 343–362, https://doi.org/10.1016/j.rse.2018.02.058, 2018.
Honda, M., Inoue, J., and Yamane, S.: Influence of low Arctic sea-ice minima on anomalously cold Eurasian winters, Geophys. Res. Lett., 36, L08707, https://doi.org/10.1029/2008GL037079, 2009.
Horvat, C., Buckley, E., Stewart, M., Yoosiri, P., and Wilhelmus, M. M.: Linear Ice Fraction: Sea Ice Concentration Estimates from the ICESat-2 Laser Altimeter, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2312, 2023.
Ivanova, N., Johannessen, O. M., Pedersen, L. T., and Tonboe, R. T.: Retrieval of Arctic Sea Ice Parameters by Satellite Passive Microwave Sensors: A Comparison of Eleven Sea Ice Concentration Algorithms, IEEE T. Geosci. Remote, 52, 7233–7246, https://doi.org/10.1109/TGRS.2014.2310136, 2014.
Ivanova, N., Pedersen, L. T., Tonboe, R. T., Kern, S., Heygster, G., Lavergne, T., Sørensen, A., Saldo, R., Dybkjær, G., Brucker, L., and Shokr, M.: Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations, The Cryosphere, 9, 1797–1817, https://doi.org/10.5194/tc-9-1797-2015, 2015.
Jaiser, R., Dethloff, K., Handorf, D., Rinke, A., and Cohen, J.: Impact of sea ice cover changes on the Northern Hemisphere atmospheric winter circulation, Tellus A, 64, 11595, https://doi.org/10.3402/tellusa.v64i0.11595, 2012.
Jung, H. S.: Software of Arctic sea ice concentration data record in 6.25 km polar stereographic grid from three-year Landsat-8 imagery (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.12754602, 2024.
Jung, H. S., Lee, S. M., Kim, J. H., and Lee, K.: Arctic sea ice concentration data record in 6.25 km polar stereographic grid from three-year Landsat-8 imagery (Version v5), Zenodo [data set], https://doi.org/10.5281/zenodo.10973297, 2024.
Kaleschke, L., Lüpkes, C., Vihma, T., Harrpaintner, J., Bochert, A., Hartmann, J., and Heygster, G.: SSM/I sea ice remote sensing for mesoscale ocean-atmosphere interaction studies, Can. J. Remote Sens., 27, 526–537, https://doi.org/10.1080/07038992.2001.10854892, 2001.
Karvonen, J.: Baltic Sea Ice Concentration Estimation Using SENTINEL-1 SAR and AMSR2 Microwave Radiometer Data, IEEE. T. Geosci. Remote., 55, 2871–2883, https://doi.org/10.1109/TGRS.2017.2655567, 2017.
Kern, S.: Landsat surface type over water from supervised classification of surface broadband albedo estimates (Version_2021_fv0.01), Universität Hamburg [data set], https://doi.org/10.25592/uhhfdm.9181, 2021.
Kern, S., Lavergne, T., Pedersen, L. T., Tonboe, R. T., Bell, L., Meyer, M., and Zeigermann, L.: Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data, The Cryosphere, 16, 349–378, https://doi.org/10.5194/tc-16-349-2022, 2022.
Kim, B. M., Son, S. W., Min, S. K., Jeong, J. H., Kim, S. J., Zhang, X., Shim, T., and Yoon, J. H.: Weakening of the stratospheric polar vortex by Arctic sea-ice loss, Nat. Commun., 5, 4646, https://doi.org/10.1038/ncomms5646, 2014.
Liu, Y., Key, J., and Mahoney, R.: Sea and Freshwater Ice Concentration from VIIRS on Suomi NPP and the Future JPSS Satellites, Remote Sens., 8, 523, https://doi.org/10.3390/rs8060523, 2016.
Ludwig, V., Spreen, G., and Pedersen, L. T.: Evaluation of a New Merged Sea-Ice Concentration Dataset at 1km Resolution from Thermal Infrared and Passive Microwave Satellite Data in the Arctic, Remote Sens., 12, 3183, https://doi.org/10.3390/rs12193183, 2020.
Malinka, A., Zege, E., Istomina, L., Heygster, G., Spreen, G., Perovich, D., and Polashenski, C.: Reflective properties of melt ponds on sea ice, The Cryosphere, 12, 1921–1937, https://doi.org/10.5194/tc-12-1921-2018, 2018.
Markus, T., Cavalieri, D. J., and Ivanoff, A.: The potential of using Landsat 7 ETM+ for the classification of sea-ice surface conditions during summer, Ann. Glaciol, 34, 415–419, https://doi.org/10.3189/172756402781817536, 2002.
Meier, W. N. and Notz, D.: A note on the accuracy and reliability of satellite-derived passive microwave estimates of sea-ice extent, Clic Arctic sea ice working group, Consensus document, CLIC International Project Office, Tromsø, Norway, https://www.arcus.org/files/sio/936/clicseaicereliabilityreport.pdf (last access: 18 January 2025), 2010.
Meier, W. N. and Stewart, J. S.: Arctic and Antarctic Regional Masks for Sea Ice and Related Data Products, Version 1, National Snow and Ice Data Center [data set], https://doi.org/10.5067/CYW3O8ZUNIWC, 2023a.
Meier, W. N. and Stewart, J. S.: NSIDC Land, Ocean, Coast, Ice, and Sea Ice Region Masks, NSIDC Special Report 25, National Snow and Ice Data Center, https://nsidc.org/sites/default/files/documents/technical-reference/nsidc-special-report-25.pdf (last access: 18 January 2025), 2023b.
Meier, W. N. and Stroeve, J.: An updated assessment of the changing Arctic sea ice cover, Oceanography, 35, 10–19, https://doi.org/10.5670/oceanog.2022.114, 2022.
Meier, W. N., Hovelsrud, G. K., van Oort, B. E.H., Key, J. R., Kovacs, K. M., Michel, C., Hass, 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., 51, 185–217, https://doi.org/10.1002/2013RG000431, 2014.
Meier, W. N., Fetterer, F., Windnagel, A. K., and Stewart, J. S.: NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4, National Snow and Ice Data Center [data set], https://doi.org/10.7265/efmz-2t65, 2021.
Niehaus, H. and Spreen, G.: Melt pond fraction on Arctic sea-ice from Sentinel-2 satellite optical imagery (2017–2021), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.950885, 2022.
Niehaus, H., Spreen, G., Birnbaum, G., Istomina, L., Jäkel, E., and Linhardt, F.: Sea Ice Melt Pond Fraction Derived From Sentinel-2 Data: Along the MOSAiC Drift and Arctic-Wide, Geophys. Res. Lett., 50, e2022GL102102, https://doi.org/10.1029/2022GL102102, 2023.
Park, J.-W., Korosov, A. A., Babiker, M., Won, J.-S., Hansen, M. W., and Kim, H.-C.: Classification of sea ice types in Sentinel-1 synthetic aperture radar images, The Cryosphere, 14, 2629–2645, https://doi.org/10.5194/tc-14-2629-2020, 2020.
Parkinson, C. L., Comiso, J. C., Zwally, H. J., Cavalieri, D. J., Gloersen, P., and Campbell, W. J.: Arctic Sea Ice, 1973–1976: Satellite Passive-Microwave Observations, NASA SP-489, National Aeronautics and Space Administration, 296 pp., https://ntrs.nasa.gov/citations/19870015437 (last access: 18 January 2025), 1987.
Perovich, D. K.: The optical properties of sea ice, CRREL Monograph 9-61, US Army Corps of Engineers, https://apps.dtic.mil/sti/tr/pdf/ADA310586.pdf (last access: 18 January 2025), 1996.
Pinto, C. T., Jing, X., and Leigh, L.: Evaluation Analysis of Landsat Level-1 and Level-2 Data Products Using In Situ Measurements, Remote Sens., 12, 2597, https://doi.org/10.3390/rs12162597, 2020.
Qiu, S., Zhu, Z., and He, B.: Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 imagery, Remote Sens. Environ., 231, 111205, https://doi.org/10.1016/j.rse.2019.05.024, 2019.
Riggs, G. A., Hall, D. K., and Salomonson, V. V.: Recent Progress in Development of the Moderate Resolution Imaging Spectroradiometer Snow Cover Algorithm and Product, 1996 International Geoscience and Remote Sensing Symposium, Lincoln, NE, USA, 1, 139–141, https://doi.org/10.1109/IGARSS.1996.516270, 1996.
Riggs, G. A., Hall, D. K, and Ackerman, S. A.: Sea Ice Extent and Classification Mapping with the Moderate Resolution Imaging Spectroradiometer Airborne Simulator, Remote Sens. Environ., 68, 152–163, https://doi.org/10.1016/S0034-4257(98)00107-2, 1999.
Rösel, A. and Kaleschke, L.: Comparison of different retrieval techniques for melt ponds on Arctic sea ice from Landsat and MODIS satellite data, Ann. Glaciol., 52, 185–191, https://doi.org/10.3189/172756411795931606, 2011.
Rösel, A., Kaleschke, L., and Birnbaum, G.: Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network, The Cryosphere, 6, 431–446, https://doi.org/10.5194/tc-6-431-2012, 2012.
Shi, H., Lee, S. M., Sohn, B. J., Gasiewski, A. J., Meier, W. N., Dybkjær, G., and Kim, S. W.: Estimation of Arctic Winter Snow Depth, Sea Ice Thickness and Bulk Density, and Ice Freeboard by Combining CryoSat-2, AVHRR, and AMSR Measurements, IEEE T. Geosci. Remote., 61, 1–18, https://doi.org/10.1109/tgrs.2023.3265274, 2023.
Song, K. and Minnett, P. J.: Evaluation of Summertime Passive Microwave and Reanalysis Sea-Ice Concentration in the Central Arctic, Earth and Space Science, 11, e2023EA003214, https://doi.org/10.1029/2023EA003214, 2024.
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using AMSR-E 89-GHz channels, J. Geophys. Res., 113, C02S03, https://doi.org/10.1029/2005JC003384, 2008.
Tanaka, Y. and Lu, J.: A New Sea Ice Concentration Retrieval Algorithm Based on Relationship Between AMSR2 89-GHz Polarization and Landsat 8 Observations, IEEE. T. Geosci. Remote, 61, 1–15, https://doi.org/10.1109/TGRS.2023.3257401, 2023.
Tarrio, K., Tang, X., Masek, J. G., Claverie, M., Ju, J., Qiu, S., Zhu, Z., and Woodcock, C. E.: Comparison of cloud detection algorithms for Sentinel-2 imagery, Sci. Remote Sens. 2, 100010, https://doi.org/10.1016/j.srs.2020.100010, 2020.
Tonboe, R. T., Eastwood, S., Lavergne, T., Sørensen, A. M., Rathmann, N., Dybkjær, G., Pedersen, L. T., Høyer, J. L., and Kern, S.: The EUMETSAT sea ice concentration climate data record, The Cryosphere, 10, 2275–2290, https://doi.org/10.5194/tc-10-2275-2016, 2016.
Trewin, B., Cazenave, A., Howell, S., Huss, M., Isensee, K., Palmer, M. D., Tarasova, O., and Vermeulen, A.: Headline Indicators for Global Climate Monitoring, B. Am. Meteorol. Soc., 102, E20–E37, https://doi.org/10.1175/BAMS-D-19-0196.1, 2021.
Untersteiner, N.: On the mass and heat budget of arctic sea ice, Arch. Meteor., Geophy. A, 12, 151–182, https://doi.org/10.1007/bf02247491, 1961.
Zanter, K.: Landsat 8 (L8) Data Users Handbook, LDSD-1574, Version 5, https://www.usgs.gov/landsat-missions/landsat-8-data-users-handbook (last access: 16 March 2024), 2019.
Zhu, Z. and Woodcock, C. E.: Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sens. Environ., 118, 83–94, https://doi.org/10.1016/j.rse.2011.10.028, 2012.
Zhu, Z., Wang, S., and Woodcock, C. E.: Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images, Remote Sens. Environ., 159, 269–277, https://doi.org/10.1016/j.rse.2014.12.014, 2015.
Short summary
This dataset consists of reference sea ice concentration (SIC) data records over the Arctic Ocean, which were derived from the 30 m resolution imagery from the Operational Land Imager (OLI) on board Landsat-8. Each SIC map is given in a 6.25 km polar stereographic grid and is catalogued into one of the 12 regions of the Arctic Ocean. This dataset was produced to be used as a reference in the validation of various SIC products.
This dataset consists of reference sea ice concentration (SIC) data records over the Arctic...
Altmetrics
Final-revised paper
Preprint