Abstract:
The MODIS/Aqua Coarse Snow Cover 5-Min L2 Swath 5km (MOD10L2C) 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. Latitude and longitude geolocation fields are at 5 km resolution while all other fields are at 500 m resolution. Version 5 (V005), the latest ... version of the Moderate Resolution Imaging Spectroradiometer (MODIS) data available has two separate snow fields. The first field, snow cover, classifies each cloud-free land or inland water body pixel as snow-covered or snow-free, the second field, fractional snow cover, provides the percent of snow cover within each pixel for land and inland water bodies. 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.
Latitude Resolution:
5 km
Longitude Resolution:
5 km
Horizontal Resolution Range:
1 km - < 10 km or approximately .01 degree - < .09 degree
Temporal Resolution:
5 min
Temporal Resolution Range:
1 minute - < 1 hour
Quality
Quality indicators for MODIS snow data can be found in three 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 Scientific Data Set (SDS), for example snow cover * Snow Cover ... Pixel QA data field. These quality indicators are generated during production or in post-production 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 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 snow algorithm 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. Summary statistics are calculated for these conditions and reported as Valid EV Obs Band x percent and Saturated EV Obs Band 1 percent local attributes Riggs, Hall, and Salomonson 2006). In addition to these data values, the product contains quality information at the pixel level. The Snow Cover Pixel QA data field provides additional information on algorithm results for each pixel within a MODIS scene and is used as a measure of usefulness for snow-cover data. The QA data are stored as coded integer values and tell if algorithm results were nominal, abnormal, or if other defined conditions were encountered for a pixel (Riggs, Hall, and Salomonson 2006). 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. The NASA Goddard Space Flight Center: MODIS Land Quality Assessment Web site provides updated quality information for each product.
NASA Goddard Space Flight Center (GSFC)
Science Systems and Applications, Inc.
Code 614.1
City:
Greenbelt
Province or State:
MD
Postal Code:
20771
Country:
USA
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 2001a. 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., G.A. Riggs, V.V. Salomonson, N.E. DiGirolamo, and K.J. Bayr. 2002. MODIS snow-cover products. Remote Sensing of the Environment 83: 181-194. 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. 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, D.K., G.A. Riggs. 2006. Assessment of errors in the MODIS suite of snow-cover products. Hydrological Processes, in press. 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 2006. MODIS snow products user guide for collection 4 data products. <http://modis-snow-ice.gsfc.nasa.gov/sug_main.html>. Salomonson, V. and I. Appel. 2006. Development of the Aqua MODIS NDSI Fractional Snow Cover Algorithm and Validation Results. Transactions on Geoscience and Remote Sensing 44(7):1747-1756. Salomonson, V. and I. Appel. 2004. Estimating fractional snow cover from MODIS using the normalized difference snow index (NDSI). Remote Sensing of the Environment 89:351-360. 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.
