NASA Team sea ice refers to a well-known algorithm used to estimate sea ice concentration from passive microwave brightness temperatures. This page discusses the algorithm itself and the long-term data set ("NSIDC 0051") produced by scientists at NASA Goddard and distributed by NSIDC. The NASA Team data are very widely used and are a key input into other data sets including NSIDC's Sea Ice Index, NSIDC's Near-Real-Time ice concentrations (NISE1 & NSIDC 0081), HadiSST, and NOAA OI. Therefore, users should be aware of the strengths and limitations of the algorithm and the data sets derived from it. Low-biased sea ice concentrations are especially apparent in the Antarctic winter, due in part to the scattering and reflectivity of layers of snow on top of sea ice. Relatively low concentrations are also often apparent in the Arctic, affecting calculations of ice area. A "NASA Team 2" (NT2) algorithm has been developed to address some of the weaknesses of the NASA Team algorithm. However, NASA Team is used for the longest data sets as NT2 depends on the 85GHz channel that was not available on the SMMR (1978-1987).
Key Strengths:
- Long-term record (1979- almost present)
- Consistent algorithm through time
- Effects of surface temperature variations tend to cancel out due to use of brightness temperature ratios
Key Limitations:
- Tends to under-estimate sea ice concentration compared to ship- and ground- based observations and other algorithms, especially in the Antarctic
- Weather, snow and ice layering effects may not be fully removed (see Anderson et al., 2007)
- Underestimates thin ice and ice with melting or refreezing. Can interpret melt ponds as open water.
Years of Record
Timestep
Domain
Spatial Resolution
Ocean or Land
Missing Data Flag
Vertical Levels
Input Data
Suggested Data Citation
From NSIDC: Cavalieri, D., C. Parkinson, P. Gloersen, and H. J. Zwally. 1996, updated yearly. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, [list dates of temporal coverage used]. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media.
Data Access: Please Cite data sources, following the data providers' instructions.
Usage Restrictions
- Cavalieri, D. J., P. Gloersen, and W. J. Campbell (1984), Determination of Sea Ice Parameters With the NIMBUS 7 SMMR, J. Geophys. Res., 89(D4), 5355–5369, doi:10.1029/JD089iD04p05355.
- Gloersen, P., and D. J. Cavalieri (1986), Reduction of Weather Effects in the Calculation of Sea Ice Concentration From Microwave Radiances, J. Geophys. Res., 91(C3), 3913–3919, doi:10.1029/JC091iC03p03913.
- Cavalieri, D. J., C. L. Parkinson, P. Gloersen, J. C. Comiso, and H. J. Zwally (1999), Deriving long-term time series of sea ice cover from satellite passive-microwave multisensor data sets, J. Geophys. Res., 104(C7), 15,803–15,814, doi:10.1029/1999JC9000
- Cavalieri, D. J., P. Gloersen, C. l. Parkinson, J. C. Comiso, and H. J. Zwally. 1997. Observed Hemispheric Asymmetry in Global Sea Ice Changes. Science 278(5340): 1104-1106.
- Cavalieri, D. J., and C. L. Parkinson (2008), Antarctic sea ice variability and trends, 1979–2006, J. Geophys. Res., 113, C07004, doi:10.1029/2007JC004564.
- Parkinson, C. L., and D. J. Cavalieri (2008), Arctic sea ice variability and trends, 1979–2006, J. Geophys. Res., 113, C07003, doi:10.1029/2007JC004558.
- Comiso, J. C., D. J. Cavalieri, C. L. Parkinson, and P. Gloersen (1997), Passive microwave algorithms for sea ice concentration: A comparison of two techniques, Remote Sens. Environ., 60, 357–384.
- Markus, T. and D.J. Cavalieri, An enhancement of the NASA Team sea ice algorithm, IEEE Trans. Geoscience Remote Sensing, 38, 1387-1398, 2000.
- Andersen, S., R. Tonboe, L. Kaleschke, G. Heygster, and L. T. Pedersen (2007), Intercomparison of passive microwave sea ice concentration retrievals over the high-concentration Arctic sea ice, J. Geophys. Res., 112, C08004, doi:10.1029/2006JC003543.
- Meier, W. (2005), Comparison of passive microwave ice concentration algorithm retrievals with AVHRR imagery in arctic peripheral seas, IEE Transactions on Geosciences and Remote Sensing, 43(6), 1324-1337.