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MODIS/Aqua Snow Cover 8-Day L3 Global 0.05Deg CMG, Version 4

Entry ID: MYD10C2

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Summary

Abstract: MODIS/Aqua Snow Cover 8-Day L3 Global 0.05Deg CMG (MYD10C2) consists of 7200-column by 3600-row global arrays of snow cover in a 0.05 deg climate modeling grid (CMG). Data and quality assurance (QA) fields are in HDF-EOS format, along with corresponding metadata. MODIS snow cover data are based on a snow mapping algorithm that employs a Normalized Difference Snow Index (NDSI) and other criteria ...

Related URL

Link: VIEW PROJECT HOME PAGE

Multimedia Sample

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Caption: MYD10C2 Version 4 data, 06-13 September 2002

Geographic Coverage

(Click for Interactive Map)

Spatial coordinates

N: 90.0

S: -90.0

E: 180.0

W: -180.0

Data Set Citation

Dataset Originator/Creator: D. Hall, V. Salomonson

Dataset Title: MODIS/Aqua Snow Cover 8-Day L3 Global 0.05Deg CMG, Version 4

Dataset Release Date: 2000-09-01

Dataset Release Place: Boulder, CO U.S.A.

Dataset Publisher: National Snow and Ice Data Center (NSIDC)

Version: 4

Online Resource: http://nsidc.org/data/myd10c2.html

Temporal Coverage

Start Date: 2002-07-04

Stop Date: 2006-12-31

Data Resolution

Latitude Resolution: 0.05 deg
Longitude Resolution: 0.05 deg
Horizontal Resolution Range: 1 km - < 10 km or approximately .01 degree - < .09 degree
Temporal Resolution: 8 days
Temporal Resolution Range: Weekly - < Monthly

Science Keywords

CRYOSPHERE > SNOW/ICE > SNOW COVER

TERRESTRIAL HYDROSPHERE > SNOW/ICE > SNOW COVER

ISO Topic Category

CLIMATOLOGY/METEOROLOGY/ATMOSPHERE

Platform

AQUA > Earth Observing System, AQUA

Instrument

MODIS > Moderate-Resolution Imaging Spectroradiometer

SPECTRORADIOMETERS

Project

EOSDIS > Earth Observing System Data Information System

ESIP > Earth Science Information Partners Program

Quality Quality indicators for MODIS snow data are represented by AutomaticQualityFlag and ScienceQualityFlag metadata objects and their corresponding explanations, AutomaticQualityFlagExplanation and ScienceQualityFlagExplanation, in the CoreMetadata.0 global attribute and also in the Snow Spatial QA data field. These are generated during production or in post-product scientific and quality checks of the ... data product. 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 ScienceQualityFlagExplanation are set 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. The Snow Spatial QA 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. QA data are stored as bit flags, and QA information is extracted by reading the bits within a byte. (See MODIS Snow Cover Quality Assurance Fields.) The QA information tells if algorithm results were nominal, abnormal, or if other defined conditions were encountered for a pixel (Riggs, Hall, and Salomonson 2003). For example, intermediate checks for theoretical bounding of reflectance data and the NDSI ratio are made in the algorithm. In theory, reflectance values should lie within the 0-100 percent range, and the NDSI ratio should lie within the -1.0 to +1.0 range. Summary statistics are kept for pixels that exceed these theoretical limits; however, the test for snow is done regardless of violations of these limits. Violations are tracked and written as Auto_check_QA local attributes as they suggest that error or other anomalies may have been introduced into the input data, indicating the need for further investigation. The snow algorithm also identifies missing data and reports them in the output product. Certain expected anomalous conditions may exist with the input data, such as a few missing lines or unusable data from the MODIS sensor. In these cases, the snow algorithm makes no snow decision for an affected pixel. A snow spatial QA bit is set to indicate the cause, and the algorithm moves to the next pixel. Summary statistics are calculated for these conditions and reported as Valid EV Obs Band x local attributes (Riggs, Hall, and Salomonson 2003). The MODIS Land Quality Assurance Web site provides updated quality information for each product.

Ancillary Keywords

Antarctic

Arctic

Data Set Progress

COMPLETE

Data Center

NSIDC Distributed Active Archive Center

Data Center URL: http://nsidc.org/daac/index.html
Dataset ID: MYD10C24

Data Center Personnel
Name: NSIDC USER SERVICES Phone: +1 (303) 492-6199 Fax: +1 (303) 492-2468 Email: nsidc at nsidc.org Contact Address: National Snow and Ice Data Center CIRES, 449 UCB University of Colorado City: Boulder Province or State: CO Postal Code: 80309-0449 Country: USA

National Snow and Ice Data Center

Data Center URL: http://nsidc.org
Dataset ID: MYD10C24

Data Center Personnel
Name: NSIDC USER SERVICES Phone: +1 (303) 492-6199 Fax: +1 (303) 492-2468 Email: nsidc at nsidc.org Contact Address: National Snow and Ice Data Center CIRES, 449 UCB University of Colorado City: Boulder Province or State: CO Postal Code: 80309-0449 Country: USA

Distribution

Distribution Media: upon request

Distribution Size: 101.36 MB

Distribution Format: HDF-EOS

Personnel

DOROTHY K. HALL

Role: INVESTIGATOR

Contact Address:

National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC)

Mail stop 614.1

City: Greenbelt

Province or State: MD

Postal Code: 20771

Publications/References

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, D. K., G. A. Riggs, and V. 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. http://modis-snow-ice.gsfc.nasa.gov/atbd.html Hall, D. K. and J. Martinec. 1985. Remote sensing of ice and snow. London: Chapman and Hall. Hall, D. 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, D. K., J. L. Foster, V. V. Salomonson, A. G. Klein, and J. Y. L. Chien. 2001. Development of a technique to assess snow-cover mapping accuracy from space. IEEE Transactions on Geoscience and Remote Sensing 39(2):232-238. Hapke, B. 1993. Theory of reflectance and emittance spectroscopy. Cambridge: Cambridge University Press. Klein, A. MODIS Snow Albedo Prototype. 2003. http://geog.tamu.edu/klein/modis_albedo/ Accessed July 2003. Klein, A. G. and J. Stroeve. 2002. Development and validation of a snow albedo algorithm for the MODIS instrument. Annals of Glaciology 34:45-52. Klein, A. G., D. K. Hall, and G. 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. MODIS Science and Instrument Team. MODIS Web. July 2003. http://modis.gsfc.nasa.gov/ Accessed October 2000. Pearson II, F. 1990. Map projections: theory and applications. Boca Raton, FL:CRC Press, Inc. Riggs, G. A., D. K. Hall, and V. V. Salomonson. January 2003. MODIS snow products user guide for collection 4 data products. http://modis-snow-ice.gsfc.nasa.gov/sug_main.html. United States Geological Survey. 'Sinusoidal Equal Area.' Map Projections. 2003. http://mac.usgs.gov/mac/isb/pubs/MapProjections/projections.html#si... Accessed December 2000. 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.

Creation and Review Dates

DIF Creation Date: 2004-02-13

Last DIF Revision Date: 2011-02-11

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