Retrieval algorithm

The retrieval algorithm has two main steps:

1. A constrained optimisation technique that uses different sets of microwave satellite data  and the probability distributions of radiometric signatures of different ice types whose concentration is to be retrieved. This is the Environment Canada Ice Concentration Extractor (ECICE) [Shokr et al. 2008, Shokr and Agnew 2013]. The input data used here are microwave radiometer data at 18 and 37 GHz (horizontal and vertical polarisation), and microwave scatterometer data at 5.3 GHz
2. Two correction schemes for the output from ECICE  in order to  correct for anomalous radiometric backscatter observations. Such anomalies are caused by snow wetness and metamorphism. This happens when air temperature rises to approach the melting point. Air temperature is used in one correction scheme and ice drift is used in the second [Ye et al. 2016a, 2016b].

 Main input

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.

Data for the correction schemes

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

• National Snow and Ice Data Center, NSIDC: Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 3 [Tschudi et al. 2016, doi:10.5067/O57VAIT2AYYY], based on  active and passive microave and visible/infrared data as well as buoy data.
• Ocean and Sea Ice Satellite Application Facility, OSISAF: Low Resolution Sea Ice Drift product (OSI-405), based on active and passive microwave data

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.

Remarks

For the current sesons (Arctic: 2019/2020, Antarctic: 2020), data processing is ongoing, this means:

• Uncorrected ice type data ("raw" data: YI, FYI, MYI) are updated daily with a time lag of 1 day
• Corrected MYI data are updated daily with a time lag of 17 days

Coast correction

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.

Correction Parameter Set

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

Set A:

Temperature correction:

• T₁ (threshold for beginning of warm episode): -2°C
• T₂ (threshold for end of warm episode): +1°C
• DeltaC_M (min change of MYI concentration to be considered): 10%
• maximum duration of warm episode: 10 days

  Drift correction:

• minimum rise of MYI concentration: 20%
• minimum drop of T_B,37H: 20 K
  
• minimum drop of  T_B,19H - T_B,37H: 10 K
  

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:

• Weather influence on the surface of the MYI (including the snow cover) can cause considerable day-to-day fluctuation of MYI concentration - a moving average or several might reduce them, but is currently not applied.
• In the Eastern Arctic (Kara and Laptev Sea), unrealistically high MYI concentrations occur in March and April. They are probably caused by small areas of spurious MYI caused by rough young ice (possible with wet snow) in the previous autumn.
• A similar effect occurs in some years in the Northern Ross Sea in August and September.
• In the Antarctic, large grounded icebergs can be misclassified as sea ice and might even give rise to growing areas of erroneously high MYI concentration.
• We are grateful of any feedback on unexpectued features and possible problems of the data, see email links below.

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.