The MODIS/Terra Snow Cover Daily L3 Global 0.05Deg CMG (MOD10C1) data set contains snow cover and Quality Assessment (QA) data, latitudes and longitudes in compressed Hierarchical Data Format-Earth Observing System (HDF-EOS) format, and corresponding metadata. This data set consists of 7200 columns by 3600 rows of global arrays of snow cover in a 0.05 degree Climate Modeling Grid (CMG). MODIS snow cover data are based on a snow mapping algorithm that employs a Normalized Difference Snow Index (NDSI) and other criteria tests. Data are stored in HDF-EOS format, and are available from 24 February 2000 to present via FTP. Data can also be obtained in GeoTIFF format by ordering the data through the Data Pool.
Access the most current Nothern Hemisphere data as map images using the Open Geospatial Consortium (OGC) Web Map Service (WMS). WMS for Southern Hemisphere and for global coverages will be added soon.
Access the most current data in GeoTIFF format using the Open Geospatial Consortium (OGC) Web Coverage Service (WCS). WCS for Southern Hemisphere and for global coverages will be added soon.
Quality indicators for MODIS snow data can be found in the following 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, snow cover * Snow Spatial QA field. ... 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: * MOD10C1 and MYD10C1 Local Snow Cover Attributes, Version 5 * MOD10C1 and MYD10C1 Global Snow Cover Attributes, Version 5 The AutomaticQualityFlag is automatically set according to conditions for meeting data criteria in the snow mapping 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 snow scientist. Content and explanation of this flag are dynamic so it should always be examined if present in the external metadata file. The algorithm tests for a variety of anomalous conditions and sets the pixel value accordingly if such conditions are detected. Summary statistics about missing data, the percent cloud cover, the percent of good or other quality data, and snow cover percent are calculated and placed in the metadata for each product. The Snow Spatial QA data field provides additional information on algorithm results for each pixel within a spatial context, and is used as a measure of usefulness for snow-cover data. The QA information tells if algorithm results were nominal, abnormal, or if other defined conditions were encountered for a pixel (Riggs, Hall, and Salomonson 2006). The NASA Goddard Space Flight Center: MODIS Land Quality Assessment Web site provides updated quality information for each product.
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Province or State:
Diner, D. J., J. V. Martonchik, C. Borel, S. A. W. Gerstl, H. R. Gordon, Y. Knyazikhin, R. Myneni, B. Pinty, and M. M. Verstraete. 1999. MISR Level-2 Surface Retrieval Algorithm Theoretical Basis Document. Pasadena, CA: Jet Propulsion Laboratory.
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., George A. Riggs, and Vincent V. Salomonson. September 2001a. Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow-, Lake Ice- and Sea Ice-Mapping Algorithms. Greenbelt, MD: Goddard Space Flight Center.
Hall, Dorothy K. and J. Martinec. 1985. Remote Sensing of Ice and Snow. London: Chapman and Hall.
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., J. L. Foster, Vincent V. Salomonson, A. G. Klein, and J. Y. L. Chien. 2001b. Development of a Technique to Assess Snow-Cover Mapping Accuracy From Space. IEEE Transactions on Geoscience and Remote Sensing 39(2):232-238.
Hall, Dorothy K. and George A. Riggs. 2006. Assessment of Errors in the MODIS Suite of Snow-Cover Products. Hydrological Processes.
Hapke, B. 1993. Theory of Reflectance and Emittance Spectroscopy. Cambridge: Cambridge University Press. Klein, A. MODIS Snow Albedo Prototype. 2003.
Klein, A. G. and Julienne Stroeve. 2002. Development and Validation of a Snow Albedo Algorithm for the MODIS Instrument. Annals of Glaciology 34:45-52.
Klein, A. G., Dorothy K. Hall, and George A. Riggs. 1998. Improving Snow-Cover Mapping in Forests Through the Use of a Canopy Reflectance Model. Hydrologic Processes 12(10-11):1723-1744.
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. January 2006. MODIS Snow Products User Guide for Collection 4 Data Products.
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.