Abstract:
MODIS/Aqua Snow Cover Daily L3 Global 0.05Deg CMG (MYD10C1) 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. Data extend from 04 July 2002 to present. MODIS snow cover data are based on a snow mapping algorithm that employs a Normalized ... Difference Snow Index (NDSI) and other criteria tests. The only data available for Version 4 (V004) is the Golden Month, which is a sample of V004 data covering the time period 29 August 2002 (day of year 241) through 7 October 2002 (day of year 280). The Golden Month is only available by special request by contacting NSIDC User Services. Please note that NSIDC now has a complete series of Version 5 data, which is the highest version number now available and represents the best quality of data.
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 the 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 Cover PixelQA 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.
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
City:
Boulder
Province or State:
CO
Postal Code:
80309-0449
Country:
USA
Publications/References
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