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
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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...
