MODIS/Aqua Sea Ice Extent and IST Daily L3 Global 4km EASE-Grid Day (MYD29E1D) data set contains fields for sea ice by reflectance and Ice Surface Temperature (IST). Each data granule covers the entire globe with two separate arrays of 4501 x 4501 pixels: one for the Arctic and one for the Antarctic. The MODIS sea ice algorithm uses a Normalized Difference Snow Index (NDSI) modified for sea ice to distinguish sea ice from open ocean based on reflective and thermal characteristics. Data are stored in HDF-EOS format, and are available from 4 July 2002 to present via FTP. Data can also be obtained in GeoTIFF format by ordering the data through the Data Pool.
All MODIS/Aqua sea ice products are considered validated at stage two, meaning that product accuracy has been assessed over a widely distributed set of locations and time periods via several ground truth and validation efforts. Quality indicators for MODIS sea ice data can be found in the following two places: ... * AutomaticQualityFlag and the ScienceQualityFlag metadata objects and their corresponding explanations: AutomaticQualityFlagExplanation and ScienceQualityFlagExplanation located in the CoreMetadata.0 global attributes * Custom local attributes associated with each SDS, for example Ice Surface Temperature. No automated quality assessment is done in this algorithm. All QA is inherited from the MOD29 product.
These quality indicators are generated during production or in post-production scientific and quality checks of the data product. For more information on local and global attributes, go to one of the following links: * MOD29E1D and MYD29E1D L ocal Sea Ice Attributes, Version 5 * MOD29E1D and MYD29E1D Global Sea Ice Attributes, Version 5
The AutomaticQualityFlag is automatically set according to conditions for meeting data criteria in the sea ice algorithm. In most cases, the flag is set to either Passed or Suspect, and in rare instances, it may be set to Failed. Suspect means that a significant percentage of the data were anomalous and that further analysis should be done to determine the source of anomalies. The AutomaticQualityFlagExplanation contains a brief message explaining the reason for the setting of the AutomaticQualityFlag. The ScienceQualityFlag and the ScienceQualityFlagExplanation maybe updated after production, either after an automated QA program is run or after the data product is inspected by a qualified scientist. Content and explanation of this flag are dynamic so it should always be examined if present in the external metadata file. A sampling of products will be inspected. Random sampling or support of specific events, such as field campaigns, may also be conducted. Specific information on the science quality of the sea ice data products is reported in the ScienceQualityFlagExplanation object in the CoreMetadata.0 global attribute. The URL for the QA site is given in the product metadata and is linked to from the EOS Data Gateway (EDG) when ordering data. The ScienceQualityFlagExplanation is changed in response to analysis and should be checked for updated information. In the MYD29E1D data product, there are four instances of the ScienceQualityFlagExplanation, one for each of the two parameters, sea ice determined by reflectance data and ice surface temperature, in each of the northern and southern grids written in the metadata. See the MODIS Land Quality Assessment Web site for further details.
LP DAAC User Services
U.S. Geological Survey (USGS)
Earth Resources Observation and Science (EROS) Center
47914 252nd Street
Province or State:
Earth Science Data and Information System (ESDIS). 1996. EOS Ground System (EGS) Systems and Operations Concept. Greenbelt, MD: Goddard Space Flight Center.
Hall, Dorothy K., J. L. Foster, D. L. Verbyla, A. G. Klein, and C. S. Benson. 1998. Assessment of Snow Cover Mapping Accuracy in a Variety of Vegetation Cover Densities in Central Alaska. Remote Sensing of the Environment 66:129-137.
... Hall, Dorothy K., Jeffrey R. Key, Kimberly A. Casey, George A. Riggs, and Donald Cavalieri. May 2004. Sea Ice Surface Temperature Product From MODIS. IEEE Transactions on Geoscience and Remote Sensing 42:5.
Hall, Dorothy K. and J. Martinec. 1985. Remote Sensing of Ice and Snow. London: Chapman and Hall.
Hall, Dorothy K., George A. Riggs, and Vincent V. Salomonson. 1995. Development of Methods for Mapping Global Snow Cover Using Moderate Resolution Imaging Spectroradiometer (MODIS). Remote Sensing of the Environment 54(2):127-140.
Hall, Dorothy K., George A. Riggs, and Vincent V. Salomonson. September 2001. Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow-, Lake Ice- and Sea Ice-Mapping Algorithms. Greenbelt, MD: Goddard Space Flight Center.
Hapke, B. 1993. Theory of Reflectance and Emittance Spectroscopy. Cambridge: Cambridge University Press.
Key, Jeffrey R., J. B. Collins, C. Fowler, and R. S. Stone. 1997. High Latitude Surface Temperature Estimates From Thermal Satellite Data. Remote Sensing of the Environment 61:302-309.
Key, Jeffrey R., J. A. Maslanik, T. Papakyriakou, Mark C. Serreze, and A. J. Schweiger. 1994. On the Validation of Satellite-Derived Sea Ice Surface Temperature. Arctic 47:280-287.
Markham, B. L. and J. L. Barker. 1986. Landsat MSS and TM Post-Calibration Dynamic Ranges, Exoatmospheric Reflectances and At-Satellite Temperatures. EOSAT Technical Notes 1:3-8.
MODIS Characterization and Support Team (MCST). 2000. MODIS Level-1B Product User's Guide for Level-1B Version 2.3.x Release 2. MCST Document #MCM-PUG-01-U-DNCN.
Pearson II, F. 1990. Map Projections: Theory and Applications. Boca Raton, FL: CRC Press, Inc.
Riggs, George A., Dorothy K. Hall, and Vincent V. Salomonson. February 2003. MODIS Sea Ice Products User Guide.
Riggs, George A., Dorothy K. Hall, and S. A. Ackerman. 1999. Sea Ice Extent and Classification Mapping With the Moderate Resolution Imaging Spectroradiometer Airborne Simulator. Remote Sensing of the Environment 68:152-163.
Scambos, Ted A., Terry M. Haran, and Robert Massom. In press. Validation of AVHRR and MODIS Ice Surface Temperature Products Using In Situ Radiometers. Annals of Glaciology 44.
Wiscombe, W. J. and S. G. Warren. 1980. A Model for the Spectral Albedo of Snow I: Pure Snow. Journal of the Atmospheric Sciences 37:2712-2733.