Sea ice in the Arctic and Antarctic can be classified into several types, the main ones being
The physical properties of sea ice differ significantly for the different ice types. Therefore. knowledge of the sea ice type is important for a number of activities including marine navigation and modelling of the ice-ocean-atmosphere system, and also for remote sensing of other quantities related to sea ice such as the depth of the snow layer on top of it. With the recent accelerated decrease of MYI in the Arctic, mapping this ice type on a daily basis has become important for many applications
The MYI data on this site are from a new satellite-based retrieval of sea ice type which can in principle distinguish YI, FYI and MYI. This retrieval was originally developed for the Arctic and has recently been adapted for Antarctic conditions as well, in the project "SITAnt" (Sea ice type distribution
in the Antarctic from microwave satellite observations), funded by Deutsche Forschungsgemeinschaft (DFG), grant SP1128/2-1, in the framework of the Antarctic priority programme SPP~1158 "Antarctic Research with comparative investigations in Arctic ice areas''.
The retrieval method uses data from active and passive microwave instruments (radar scatterometer and radiometer, respectively), but then in addition applies several correction schemes to account for the effect of melt-refreeze processes, snow metamorphosis and sea ice drift on the sea ice type retrieval. Both air temperature and sea ice drift data are used in the corrections.
As the retrieval does not work during the melt season, the data record usually spans October to May. Even during the winter season, the retrieval can be problematic - see details here.
For more information see the MYI User Guide
The retrieval algorithm has two main steps:
The passive microwave (i.e., radiometer) data are from either AMSR-E on the Aqua satellite by NASA (until 2011) or from AMSR2 on the GCOM-W1 satellite by JAXA (since 2012). The AMSR-E data of 2002-2009 are from the Microwave Earth Remote Sensing (MERS) laboratory of Brigham Young University (BYU), resampled to grids of 4.45 km using the Scatterometer Imager Reconstruction (SIR) resolution enhancement algorithm (Long and Daum, 1998).
The active microwave (i.e., scatterometer) data are either from the SeaWinds scatterometer onboard the satellite QuikSCAT (QSCAT) or from ASCAT on the Metop-A satellite. The QSCAT data (2002-2009) are from the MERS laboratory of BYU, resampled to grids of 4.45 km using the SIR algorithm with a technique by Early and Long, (2001). From 2009 to 2015, the ASCAT data regridded to the common 12.5 km North polar stereographic grid ("NSIDC grid") by Ifremer/CERSAT were taken (ASCAT-A 12.5km Arctic Sea-Ice Backscatter Maps) after that , near-real time ASCAT data have been regridded by ourselves.
For the correction scheme, surface temperature data and sea ice drift data are needed.
The surface temperature data are meteorological reanalysis data from the ECMWF (ECMWF), namely ERA-Interim data - for each day, the daily maximum of the 2m air temperature is taken.
The sea ice drift data are either to the to following sources
Because of different availability of the various input data sets, the following table is a detailed overview for which season which sources were taken, along with references to specific parameter settings for the correction schemes.
Season | Radiometer | Scatterometer | Ice Drift | Correction Parameter Set | Remarks |
---|---|---|---|---|---|
2002/2003 | AMSR-E | QSCAT (BYU) | NSIDC | A | 4.45 km |
2003/2004 | AMSR-E | QSCAT (BYU) | NSIDC | A | 4.45 km |
2004/2005 | AMSR-E | QSCAT (BYU) | NSIDC | A | 4.45 km |
2005/2006 | AMSR-E | QSCAT (BYU) | NSIDC | A | 4.45 km |
2006/2007 | AMSR-E | QSCAT (BYU) | NSIDC | A | 4.45 km |
2007/2008 | AMSR-E | QSCAT (BYU) | NSIDC | A | 4.45 km |
2008/2009 | AMSR-E | QSCAT (BYU) | NSIDC | A | 4.45 km |
2009/2010 | AMSR-E | ASCAT (Ifremer) | NSIDC | A | |
2010/2011 | AMSR-E | ASCAT (Ifremer) | OSISAF | A | |
2012/2013 | AMSR2 | ASCAT (Ifremer) | OSISAF | A | |
2013/2014 | AMSR2 | ASCAT (Ifremer) | OSISAF | A | |
2014/2015 | AMSR2 | ASCAT (Ifremer) | OSISAF | A | |
2015/2016 | AMSR2 | ASCAT (Ifremer) | OSISAF | A | |
2016/2017 | AMSR2 | ASCAT | OSISAF | A, coast correction | |
2017/2018 | AMSR2 | ASCAT | OSISAF | A, coast correction | |
2019/2019 | AMSR2 | ASCAT | OSISAF | A, coast correction | |
2019/2020 | AMSR2 | ASCAT | OSISAF | A, coast correction | ongoing |
Season | Radiometer | Scatterometer | Ice Drift | Correction Parameter Set | Remarks |
---|---|---|---|---|---|
2013 | AMSR2 | ASCAT (Ifremer) | OSISAF | A | |
2014 | AMSR2 | ASCAT (Ifremer) | OSISAF | A | |
2015 | AMSR2 | ASCAT (Ifremer) | OSISAF | A | |
2016 | AMSR2 | ASCAT (Ifremer) | OSISAF | A | |
2017 | AMSR2 | ASCAT | OSISAF | A | |
2018 | AMSR2 | ASCAT | OSISAF | A | |
2019 | AMSR2 | ASCAT | OSISAF | A | |
2020 | AMSR2 | ASCAT | OSISAF | A | ongoing |
For the current sesons (Arctic: 2019/2020, Antarctic: 2020), data processing is ongoing, this means:
Directly at a coast, the signal of the scatterometer or radiometer can contain contribution from the land ("land contamination"), resulting in erroneous high MYI concentration in pixels directly at the coast. This can lead to large errors later in the season. Therefore, during the first 10 days of the season retrieved (end of September), a zone that is one pixel wide, along all coast is cleared of MYI unless connected to MYI offshore. This is done before applying the correction schemes.
The correction schemes use a number of empiric parameters as documented in Ye et al. The specific parameters used for the retrieval of the listed data (Arctic and Antarctic) are
Temperature correction:
Drift correction:
Details ca be found in the MYI User Guide as well
While the MYI data retrieved from active and passive microwave data and corrected using temperature and ice drift data are considerably more realistic than data retrieved simply from radiometer data, some problems still remain:
Please help maintaining this service by properly citing and acknowledging if you use the data for publications:
Ye, Y., M. Shokr, G. Heygster, and G. Spreen (2016) Improving multiyear ice concentration estimates with ice drift. Remote Sens., 8(5), 397, doi:10.3390/rs8050397.
Ye, Y., G. Heygster, and M. Shokr (2016) Improving multiyear ice concentration estimates with air temperatures. IEEE Trans. Geosci. Remote Sens., 54(5), 2602–2614, doi:10.1109/TGRS.2015.2503884.
The data set of corrected multiyear ice is also availble from the data publisher PANGAEA:
Melsheimer, Christian; Spreen, Gunnar; Ye, Yufang; Shokr, Mohammed (2019): Multiyear Ice Concentration, Antarctic, 12.5 km grid, cold seasons 2013-2018 (from satellite). PANGAEA, https://doi.org/10.1594/PANGAEA.909054
Early, D. S., and Long, D. G. (2001). Image reconstruction and enhanced resolution imaging from irregular samples. IEEE Transactions on Geoscience and Remote Sensing, 39(2), 291-302.
Lavergne, T., Eastwood, S., Teffah, Z., Schyberg, H. and L.-A. Breivik (2010), Sea ice motion from low resolution satellite sensors: an alternative method and its validation in the Arctic. J. Geophys. Res., 115, C10032, doi:10.1029/2009JC005958
Long, D. G., and Daum, D. L. (1998). Spatial resolution enhancement of SSM/I data. IEEE Transactions on Geoscience and Remote Sensing, 36(2), 407-417.
Shokr, M., A. Lambe and T. Agnew (2008), A new algorithm (ECICE) to estimate ice concentration from remote sensing observations: An application to 85-GHz passive microwave data. IEEE Trans. Geosci. Remote Sens., 46(12), 4104-4121.
Shokr, M., T. Agnew (2013), Validation and potential applications of Environment Canada Ice Concentration Extractor (ECICE) algorithm to Arctic ice by combining AMSR-E and QuikSCAT observations, Remote Sensing of Environment 128, 315-332, doi:10.1016/j.rse.2012.10.016.
Tschudi, M., C. Fowler, J. Maslanik, J. S. Stewart, and W. Meier (2016), Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 3. (Arctic). Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi:10.5067/O57VAIT2AYYY.
Ye, Y., M. Shokr, G. Heygster, and G. Spreen (2016a), Improving multiyear ice concentration estimates with ice drift. Remote Sens., 8(5), 397, doi:10.3390/rs8050397.
Ye, Y., G. Heygster, and M. Shokr (2016b,) Improving multiyear ice concentration estimates with air temperatures. IEEE Trans. Geosci. Remote Sens., 54(5), 2602–2614, doi:10.1109/TGRS.2015.2503884.
For questions regarding the data please contact Gunnar Spreen, Christian Melsheimer, or Georg Heygster.
For questions related to the website and data-browser please contact Malte Gerken.
Institute of Environmental Physics, University of Bremen, Germany.