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MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid, Version 5
The MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid (MOD10A2) data set contains data fields for maximum snow cover extent over an eight-day compositing period and a chronology of snow occurrence observations in compressed Hierarchical Data Format-Earth Observing System (HDF-EOS) format, along with corresponding metadata. MOD10A2 consists of 1200 km by 1200 km tiles of 500 m resolution data gridded in a sinusoidal map projection. The Moderate Resolution Imaging Spectroradiometer (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.
As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our
The following example shows how to cite the use of this data set in a publication. List the principal investigators, year of data set release, data set title and version number, dates of the data you used (for example, December 2003 to March 2004), publisher: NSIDC, and digital media.
Hall, Dorothy K., George A. Riggs, and Vincent V. Salomonson. 2006, updated weekly. MODIS/Terra Snow Cover 8-day L3 Global 500m Grid V005, [list the dates of the data used]. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media.
Overview Table
Description
HDF-EOS version 2.9. Data can also be obtained in GeoTIFF format by ordering the data through the Data Pool.
Coverage is global, but only land tiles are produced. Gridded resolution is 500 m.
MODIS data extends from 24 February 2000 to present.
Products are produced every eight-days.
Maximum Snow Extent Coded Integer Values
Sample Value
Explanation
data missing
no decision
detector saturated
For more information regarding the coded integer value descriptions, please see the
for an interpretation of bit values and resulting integer values for the Eight Day Snow Cover field.
Data are gridded in equal-area tiles in a sinusoidal projection.
Each tile consists of a 1200 km by 1200 km data array which corresponds to 2400 pixels by 2400 pixels at 500 m resolution.
Example: MOD10A2.Av06.005.8.hdf
0.05 - 3 MB using HDF compression
Maximum Snow Extent
Eight Day Snow Cover
Please see
for a list of order options.
Table of Contents
Contacts and Acknowledgments
Data Access and Tools
Detailed Data Description
Data Processing
Data Acquisition
References and Related Publications
Document Information
Principal Investigators
Dorothy K. Hall
National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC)
Mailstop 614.1
Greenbelt, MD
Vincent V. Salomonson
Room 809 WBB
Department of Meteorology
University of Utah
Salt Lake City, UT 84112
Support Investigator
George A. Riggs
Science Systems and Applications, Inc.
Mailstop 614.1
Greenbelt, MD
NSIDC User Services
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO &&USA
phone: +1 303.492.6199
fax: +1 303.492.2468
The following sites can help you select appropriate MODIS data for your study:
Please see the
Web site for a list of order options.
: Software tools that reproject MODIS data
that are in the sinusoidal EASE-Grid or Climate Modeller's Grid projections to other projections.
: This free tool converts many types of HDF-EOS data to GeoTIFF, native binary, or HDF-EOS grid format. It also has reprojection, resampling, subsetting, stitching (mosaicing), and metadata creation capabilities.
: The HDFView is a visual tool for browsing and editing the National Center for Supercomputing Applications (NCSA) HDF4 and HDF5 files. Using HDFView, you can view a file hierarchy in a tree structure, create a new file, add or delete groups and datasets, view and modify the content of a dataset, add, delete, and modify attributes, and replace I/O and GUI components such as table view, image view, and metadata view.
: NSIDC provides more information about the HDF-EOS format, tools for extracting binary and ASCII objects from HDF, information about the hrepack tool for uncompressing HDF-EOS data files, and a list of other HDF-EOS resources.
: A free plugin for ENVI that can ingest, process, and georeference every known MODIS data product using either a graphical widget interface or a batch programmatic interface. This includes MODIS products distributed with EASE-Grid projections.
Web page for other MODIS snow and sea ice products available from NSIDC.
Algorithms that generate snow cover products are continually being improved as limitations become apparent in early versions of data. As a new algorithm becomes available, a new version of data is released. Users are encouraged to work with the most current version of data available, which is the highest version number. Version 5 (V005), also known as Collection 5, is the most current version of MODIS data available from NSIDC.
Please visit the following sites for more information about the V005 data, known data problems, the production schedules, and future plans:
MODIS snow products are archived in compressed HDF-EOS format, which employs point, swath, and grid structures to geolocate the data fields to geographic coordinates. This data compression should be transparent to most users since HDF capable software tools automatically uncompress the data. Various software packages, including several in the public domain, support the HDF-EOS data format. See the
section for details. Also, see the
Web site for more information about the HDF-EOS data format, as well as tutorials in uncompressing the data and converting data to binary format.
Data can also be obtained in GeoTIFF format by ordering the data through the Data Pool.
MOD10A2 consists of 2400 x 2400 cells of tiled data in a sinusoidal projection. Each data granule contains the following HDF-EOS local attribute fields, which are stored with their associated Scientific Data Set (SDS):
Each data granule also contains metadata either stored as
or as , which are stored with each SDS.
provides an interpretation of bit values and resulting integer values for the Eight Day Snow Cover Field.
External Metadata File
A separate ASCII text file containing metadata with
.xml file extension accompanies the HDF-EOS file. The metadata file contains some of the same metadata as in the product file, but also includes other information regarding archiving, user support, and post-production Quality Assessment (QA) relative to the granule ordered. The post-production QA metadata may or may not be present depending on whether or not the data granule was investigated for quality assessment. The metadata file should be examined to determine if post-production QA was applied to the granule ().
The following file naming convention is common to all Level 3 MODIS Land products: MOD10A2.Av06.005.48.hdf
Refer to Table 1 for an explanation of the variables used in the MODIS file naming convention.
Table 1. Variable Explanation for MODIS File Naming Convention
Explanation
MODIS/Terra
Type of product
Acquisition date
Year of data acquisition
Day of year of data acquisition (In this case, day 138. The date in the granule is the first day of data in the eight-day file.)
Horizontal tile number and vertical tile number. See the
as a reference.
Version number
Year of production (2006)
Day of year of production (Day 143)
Hour/minute/second of production in Greenwich Mean Time (GMT) (06:21:48)
HDF-EOS data forma
Data files are typically between 0.5 - 3 MB using HDF compression.
Note: New in V005, MOD10A2 data files now use HDF data compression. The extent to which compression reduces the file size varies from image to image, but generally it is a factor of 7 or more.
Coverage is global, but only land tiles are produced for MOD10A2.
The following resources can help you select and work with MOD10A2 tiles:
The local equatorial crossing time of the Terra satellite is approximately 10:30 A.M. in a descending node with a sun-synchronous, near-polar, circular orbit.
Grid resolution is 500 m.
MOD10A1 V005 data are georeferenced to an equal-area sinusoidal projection. The following Web sites provide links to the software tools that either read data in a sinusoidal projection or convert sinusoidal to other projections:
In the sinusoidal projections, areas on the data grids are proportional to the same areas on the Earth, and distances are correct along all parallels and the central meridian. Shapes are increasingly distorted away from the central meridian and near the poles. Finally, the data are neither conformal, perspective, nor equidistant ().
Meridians are represented by sinusoidal curves (except for the central meridian), and parallels are represented by straight lines. The central meridian and parallels are straight lines of true scale ().
Specific parameters are listed in Table 2.
Table 2. Sinusoidal Projection Parameters
Earth radius
000 meters
Projection origin
0& latitude, 0& longitude
Orientation
0& longitude, oriented vertically at top
Upper left corner point (m)
Lower right corner point (m)
True scale (m)
463.(x), 463.(y)
Level-3 eight-day data are gridded in equal-area tiles.
Each tile consists of a 1200 km by 1200 km data array, which corresponds to 2400 x 2400 pixels at 500 m resolution. The following image shows tile locations for MOD10A2 V005 data in a sinusoidal projection. Click on the thumbnail to view a larger image.
Although this product is referred to as having a 500 m grid, the true pixel resolution is 463. m in
both X and Y directions. Refer to . This allows for 2400 pixel by
2400 pixel tiles, each tile covering exactly 10 degrees of latitude vertically.
converts between MODIS tile/image cooordinates or map coordinates in meters and latitude/longitude coordinates.
For MOD10A2, eight-day periods begin on the first day of the year and extend into the next year.
In some cases, there may not be eight days of input. You should check the Number of Input Days, Days Input, and Eight Day Period global attributes to find out what days are covered. See the
section of the MOD10A2 and MYD10A2 Global Attributes document for these global attributes. The product is only produced if at least two days of input are available for the eight-day period. The data
indicates the first day in the eight-day period.
Table 3. Eight Day Input Periods
Over the course of the Terra mission, there have been a number of anomalies that have resulted in dropouts in the data. If you are looking for data for a particular date or time and can not find it, please visit the
Temporal resolution is eight days for MOD10A2, one half of the exact ground track repeat period of 16 days for the Terra satellite.
The snow mapping algorithm classifies pixels as snow, snow-covered lake ice, cloud, water, land, or other.
Maximum Snow Extent
and Eight Day Snow Cover are the primary variables of interest in this data set.
Refer to the MOD10A2 and MYD10A2 Local Snow Cover Attributes, Version 5 document for a key to the meaning of the coded integer values in the .
for an interpretation of bit values and resulting integer values for the Eight Day Snow Cover field.
For more information regarding this topic, please see the
section in the MODIS/Terra Snow Cover 5-Min L2 Swath 500m, Version 5 guide document (MOD10_L2).
The MODIS science team is responsible for algorithm development. MODAPS is responsible for product generation and transfer of products to NSIDC.
The algorithm first checks that the dates from MOD10A1 input data match those from the intended MOD10A2 time range, and then orders the data chronologically. Multiple days of observations for a cell are examined. If snow cover is found for any day, then the cell in the Maximum Snow Extent field is labeled as snow. If no snow is found, but there is one value that occurs more than once, that value is placed in the cell. For example, if a pixel is classified as water for five days, cloud for one day, land for one day, and night for one day, it would be ultimately labeled as water. If mixed observations occur, for example, land and cloud for more than one day in a given pixel, the algorithm assumes a cloud-free period and labels a pixel with the observed value. This logic minimizes cloud-cover extent, such that a cell needs to be cloud-obscured for all days in order to be labeled cloud. If all observations for a cell are analyzed but a classification cannot be determined, then that cell is labeled as no decision. A chronology of snow occurrence is recorded in the Eight Day Snow Cover field. The eight bits within a byte correspond to eight days of data. If snow is found in a pixel for a given day,
the corresponding bit in the Eight Day Snow Cover field is set to a value of one ().
As with any upper level product, the characteristics of and/or anomalies in input data may carry through to the output data product. The following product is input to the algorithms used to create the MOD10A2 product:
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 Scientific Data Set, for example, Maximum Snow Extent.
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 documents:
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 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.
Web site provides updated quality information for each product.
The MODIS instrument provides 12-bit radiometric sensitivity in 36 spectral bands, ranging in wavelength from 0.4 &m to 14.4 &m.
Two bands are imaged at a nominal resolution of 250 m at nadir, five bands at 500 m, and the remaining bands at 1000 m.
A &55 degree scanning pattern at 705 km altitude achieves a 2330 km swath with global coverage every one to two days.
The scan mirror assembly uses a continuously rotating double-sided scan mirror to scan &55 degrees, driven by a motor encoder built to operate 100 percent of the time throughout the six year instrument design life.
The optical system consists of a two-mirror off-axis afocal telescope which directs energy to four refractive objective assemblies: one each for the visible, near-infrared, and longwave-infrared spectral regions ().
Table 4. Technical Specifications
705 km, 10:30 A.M. descending node (Terra)
Swath Dimensions
2330 km (cross track) by 10 km (along track at nadir)
17.78 cm diameter off-axis, afocal (collimated) with intermediate field stop
1.0 x 1.6 x 1.0 m
162.5 W (single orbit average)
10.6 Mbps (peak daytime); 6.1 Mbps (orbital average)
Quantization
Spatial Resolution
250 m (bands 1-2)500 m (bands 3-7)1000 m (bands 8-36)
Design Life
Spectral Bands
For information on the 36 spectral bands provided by the MODIS instrument, see the .
The MODIS scan mirror assembly uses a continuously rotating double-sided scan mirror to scan &55 degrees with a 20.3 rpm.
The viewing swath is 10 km along track at nadir and 2330 km cross track at &55 degrees.
MODIS instruments were built to NASA specifications by Santa Barbara Remote Sensing, a division of Raytheon Electronics Systems.
MODIS has a series of on-board calibrators that provide radiometric, spectral, and spatial calibration of the MODIS instrument.
The blackbody calibrator is the primary calibration source for thermal bands between 3.5 &m and 14.4 &m, while the Solar Diffuser (SD) provides a diffuse, solar-illuminated calibration source for visible, near-infrared, and longwave infrared bands.
The Solar Diffuser Stability Monitor (SDSM) tracks changes in the reflectance of the SD with reference to the sun so that potential instrument changes are not incorrectly attributed to changes in this calibration source.
The Spectroradiometric Calibration Assembly (SRCA) provides additional spectral, radiometric, and spatial calibration.
MODIS uses the moon as an additional calibration technique and for tracking degradation of the SD by referencing the illumination of the moon since the moon's brightness is approximately the same as that of the Earth.
Finally, MODIS deep space views provide a photon input signal of zero, which is used as a point of reference for calibration ().
MODIS is a key instrument aboard the Terra satellite, the flagship of NASA?s Earth Observing System (EOS).
The EOS includes a series of satellites, a data system, and the world-wide community of scientists supporting a coordinated series of polar-orbiting and low inclination satellites for long-term global observations of the land surface, biosphere, solid Earth, atmosphere, and oceans that together enable an improved understanding of the Earth as an integrated system.
MODIS plays a vital role in the development of validated, global, and interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment , , and .
this overall context, the objectives of the MODIS snow and ice team
are to develop and implement algorithms that map snow and ice on a daily basis, and provide statistics of the extent and persistence of snow and ice over eight-day periods.
Data at 500 m resolution enables sub-pixel snow mapping for use in regional and global climate models.
A study of sub grid-scale snow-cover variability is expected to improve features of a model that simulates Earth radiation balance and land-surface hydrology.
The MODIS sensor contains a system whereby visible light from the earth passes through a scan aperture and into a scan cavity to a scan mirror. The double-sided scan mirror reflects incoming light onto an internal telescope, which in turn focuses the light onto four different detector assemblies.
Before the light reaches the detector assemblies, it passes through beam splitters and spectral filters that divide the light into four broad wavelength ranges.
Each time a photon strikes a detector assembly, an electron is created.
Electrons are collected in a capacitor where they are eventually transferred into the preamplifier.
Electrons are converted from an analog signal to digital data, and downlinked to ground receiving stations ().
The EOS Ground System (EGS) consists of facilities, networks, and systems that archive, process, and distribute EOS and other NASA earth science data to the science and user community. For example, ground stations provide space to ground communication. The EOS Data and Operations System (EDOS) processes telemetry from EOS spacecraft and instruments to generate Level-0 products, and maintains a backup archive of Level-0 products (). The
is currently responsible for generation of Level-1A data from Level-0 instrument packet data. These data are then used to generate higher level MODIS data products, including MOD10A2. MODIS snow and sea ice products are archived at the NSIDC Distributed Active Archive Center (DAAC) and distributed to EOS investigators and other users via external networks and interfaces (). Data are available to the public through a variety of interfaces.
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, in press.
Hapke, B. 1993. Theory of Reflectance and Emittance Spectroscopy. Cambridge: Cambridge University Press.
Klein, A. MODIS Snow Albedo Prototype. 2003. && Accessed July 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):.
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 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. && Accessed October 2000.
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.
United States Geological Survey. "Sinusoidal Equal Area." Map Projections. 2003. && 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:.
Acronyms and Abbreviations
The following acronyms and abbreviations are used in this document:
Table 5. Acronyms and Abbreviations
Algorithm Theoretical Basis Document
Distributed Active Archive Center
EOS Data and Operations System
EOS Ground System
Earth Observing System
Earth Observing System Data and Information System
Earth Science Data and Information System
File Transfer Protocol
Greenwich Mean Time
Goddard Space Flight Center
Hierarchical Data Format - Earth Observing System
MODIS Characterization Support Team
Moderate Resolution Imaging Spectroradiometer
MODIS Land Discipline Group
Multispectral Scanner
National Aeronautics and Space Administration
National Center for Supercomputing Applications
Normalized Difference Snow Index
National Snow and Ice Data Center
Quality Assessment
Solar Diffuser
Science Data Processing
Scientific Data Set
Solar Diffuser Stability Monitor
Sinusoidal Grid
Spectroradiometric Calibration Assembly
Thematic Mapper
February 2004
December 2006
http://nsidc.org/data/docs/daac/modis_v5/mod10a2_modis_terra_snow_8-day_global_500m_grid.gd.html