MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Night, Version 5

Summary

MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Night (MYD29P1N) data set, new for Version 5 (V005), contains fields for Ice Surface Temperature (IST) and IST Spatial Quality Assessment (QA) in Hierarchical Data Format-Earth Observing System (HDF-EOS) format along with corresponding metadata. MYD29P1N V005, the latest version of the Moderate Resolution Imaging Spectroradiometer (MODIS) data, consists of 951 x 951 km files of 1 km resolution data gridded in the Lambert Azimuth Equal Area map projection.

Data are stored in HDF-EOS format, and are available from 04 July 2002 to present via FTP. Data can also be obtained in GeoTIFF format by ordering the data through the Data Pool.

Citing These Data

The following example shows how to cite the use of this data set in a publication. For more information, see our Use and Copyright Web page.

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. 2007, updated daily. MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Night V005, [list the dates of the data used]. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media.

Overview Table

1. Contacts and Acknowledgments

Investigator(s) Name and Title

Principal Investigators

Dorothy K. Hall
National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC)
Mail stop 614.1
Greenbelt, MD 20771

Vincent V. Salomonson
Room 809 WBB
Department of Meteorology
University of Utah
Salt Lake City, UT 84112

Support Investigator

George A. Riggs
NASA GSFC
Science Systems and Applications, Inc.
Mail stop 614.1
Greenbelt, MD 20771

Technical Contact

NSIDC User Services
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO 80309-0449 USA
phone: +1 303.492.6199
fax: +1 303.492.2468
form: Contact NSIDC User Services
e-mail: nsidc@nsidc.org

2. Data Access and Analysis Tools

Data Access Aids

The following site can help you select appropriate MODIS data for your study:

Data Access Tools

Please see the Ordering MODIS Products from NSIDC Web site for a list of order options.

Data Analysis Tools

The following software tools can help you analyze the data:

HDF-EOS to GeoTIFF Conversion Tool (HEG): This free tool converts many kinds of HDF-EOS swath and grid data such as MODIS, ASTER, MISR, AIRS, and AMSR-E, into flat binary, HDF-EOS, or GeoTIFF gridded files. It also has reprojection, resampling, subsetting, stitching (mosaicing), and metadata creation capabilities.

HDF Java Products: NCSA HDFView: 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 data sets, view and modify the content of a data set, add, delete, and modify attributes, and replace I/O and GUI components such as table view, image view, and metadata view.

NSIDC's Hierarchical Data Format - Earth Observing System (HDF-EOS) site: NSIDC provides more information about the HDF-EOS format, tools for extracting binary and ASCII objects from HDF, and a list of other HDF-EOS resources.

MODIS: MODIS Land Discipline Group (MODLAND) Tile Calculator

The MODIS Conversion Toolkit (MCTK): 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.

3. Detailed Data Description

Algorithms that generate sea ice 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 latest version available, which is the highest version number. Version 5 (V005) is the most current version of MODIS data available from NSIDC. For V005, the Science Data Sets (SDS) Sea Ice by Ice Surface Temperature and Combined Sea Ice present in Version 4 (V004) were deleted from the product.

Please visit the following sites for more information about the V005 data, known data problems, the production schedule, and future plans:

Format

MODIS sea ice 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 Software section for details. Also, see the Hierarchical Data Format - Earth Observing System (HDF-EOS) 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.

Visible data are not acquired when the sensor is observing the surface in darkness; therefore, this night product only contains IST data fields, which are based on thermal data. MYD29P1D consists of 951 x 951 cells of tiled data in the Lambert Azimuth Equal Area Projection.Each data granule contains the following HDF-EOS local attribute fields, which are stored with each SDS:

Each data granule also contains global attributes and HDF-predefined fields, which are stored with their associated SDS.

Description of Data Fields

Ice Surface Temperature (IST) - IST data are expressed in kelvins and are stored as scaled integer data in HDF-EOS calibrated form. The data must be converted to kelvins using the calibration data in the HDF predefined local attributes:

Where:

Ice Surface Temperature Spatial Quality Assessment - this field stores the quality of the algorithm on a pixel-by-pixel basis. QA information tells if the algorithm results were good quality or not, or if other defined conditions were encountered for a pixel. If all the input data and calculations in the algorithm were nominal for a pixel, the QA field is set to good. See MOD29P1N and MYD29P1N Local Sea Ice Attributes, Version 5 document for more information about QA flags in sea ice products.

External Metadata File

A separate ASCII text file containing metadata with a .xml file extension accompanies the HDF-EOS file. This ASCII text file contains some of the same metadata as in the HDF-EOS file, but also includes other information regarding archiving, user support, and post production QA relative to the granule ordered. The post-production QA metadata may or may not be present in the .xml file depending on whether or not the data granule was investigated for Quality Assessment. The ASCII text file should be examined to determine if post-production QA was applied to the granule (Riggs, Hall, and Salomonson 2006).

File Naming Convention

The file naming convention used for the MYD29P1N product is MYD29P1N.A2000057.h12v07.005.2006252061042.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
Variable	Explanation
MYD	MODIS/Aqua
29P1N	Type of product
A	Acquisition date
2000	Year of data acquisition
057	Day of year of data acquisition (day 57)
h12v07	Horizontal tile number and vertical tile number. For further information, see the Grid Description section in this document.
005	Version number
2006	Year of production (2006)
252	Day of year of production (day 252)
061042	Hour/minute/second of production in GMT (06:10:42)
hdf	HDF-EOS data format

File Size

Data files are typically between 0.05 -1.5 MB using HDF compression.

Note: New in V005, MYD29P1N 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 5 or more.

Spatial Coverage

Coverage is global; however, only ocean granules are produced. A �55 degree scanning pattern at 705 km altitude achieves a 2330 km swath with global coverage every one to two days. The following resources can help you select and work with MYD29P1N tiles:

Latitude Crossing Times

The local equatorial crossing time of the Aqua satellite is approximately 1:30 p.m. in an ascending node with a sun-synchronous, near-polar, circular orbit.

Spatial Resolution

Gridded resolution is 1 km.

Projection

MYD29P1N data are gridded to the NSIDC EASE-Grid in a Lambert Azimuthal Equal Area projection. Please review the All About EASE-Grid document for general information on the EASE-Grid.

The following Web sites provide links to the software tools that reformat, re-project, and perform stitching/mosaicing, and subsetting operations on HDF-EOS objects:

In the polar aspect of the Lambert Azimuthal Equal Area Projection, all meridians are straight lines and all parallels are circles with either the north or south pole as the center of the projection (Snyder 1987). Areas on the map are shown in true proportion to the same areas on the Earth. Directions are true only from the pole. Scale decreases gradually away from the pole. Distortion of shapes increases away from the pole. Any straight line drawn through the pole is on a great circle. Finally, the map is equal area but neither conformal, perspective, nor equidistant (USGS 2003). Specific parameters are listed in Table 2.

**Table 2.** Lambert Azimuthal Equal Area Map Projection Parameters
Earth radius	6371228.0 meters
Projection origin	North (90° latitude, 0° longitude) South (-90° latitude, 0° longitude)
Orientation	North (0° longitude, oriented vertically at bottom) South (0° longitude, oriented vertically at top)
Upper left corner point (m)	-9058902.1845(x), 9058902.1845(y)
Lower right corner point (m)	9058902.1845(x), -9058902.1845(y)
True scale (m)	1002.7010(x), 1002.7010(y)

Grid Description

MYD29P1N data are gridded to Polar EASE-Grid tiles, each 951 x 951 pixels in size, which corresponds to approximately 954 km by 954 km at a resolution of 1002.7010 m per pixel.

The following maps show tile locations for MYD29P1N. Click on the thumbnail images to see the full-resolution images.

North

South

Tiles are 951 pixels by 951 pixels and 10 degrees by 10 degrees. Half of the tiles (313) are in the north polar grid; the other half are in the south polar grid.
The north polar grid tile coordinate system starts at (0, 0) (horizontal tile number, vertical tile number) in the upper left corner and proceeds right (horizontal) and downward (vertical). The tile in the bottom right corner is (18, 18).
The south polar grid tile coordinate system starts at (0, 20) and the tile in the bottom right corner is (18, 38).
The polar grids are based on the Lambert Azimuthal Equal Area map projection centered on each pole. The grids are compatible with the NSIDC EASE-Grid.

See the following documents for bounding coordinate information for each tile:

MODIS Sea Ice Tile Bounding Coordinates, Northern Hemisphere
MODIS Sea Ice Tile Bounding Coordinates, Southern Hemisphere
MODIS MODLAND Tile Calculator: converts between MODIS tile numbers and latitude/longitude coordinates.

Temporal Coverage

MODIS data extends from 04 July 2002 to present.

Over the course of the Aqua 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 MODIS/Aqua Data Outages Web page.

Temporal Resolution

Temporal resolution is daily. The time between repeat coverage of a given point on the earth depends on latitude with the most frequent coverage occurring near the poles. Areas pole ward of ±30 degrees latitude are observed at least daily.

Parameter or Variable

Parameter Description

In the IST field, pixels classified as cloud free ocean contain an IST value in kelvins, and pixel values are scaled by 100 for all classes. The IST algorithm was designed for sea ice; however, IST values are provided for all ocean areas that are not classified as cloud.

Parameter Range

Refer to the MOD29P1N and MYD29P1N Local Sea Ice Attributes, Version 5 document for a key to the meaning of the scaled values in the Ice Surface Temperature Field, and the Ice Surface Temperature Pixel QA Field.

4. Data Processing

Theory of Measurements

For information regarding the theory for sea ice mapping and ice suface temperature retrieval, please see the Theory of Measurements section in the MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km, Version 5 guide document (MOD29).

Processing Steps

For more information regarding the processing steps used as the input to this data set, please see the Processing Steps section in the MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km, Version 5 guide document (MOD29).

MYD29P1N is generated from a temporary product (MYD29PGN) which was created by mapping each night pixel in each of the MYD29 products acquired over a 24 hour period to their earth locations on the Lambert Azimuthal Equal Area projection. As a result, multiple observations are associated with each cell in the grid. For each grid cell, the algorithm that creates the MYD29P1N product, then selects the observation that is nearest nadir and has the greatest coverage of the grid cell using a weighted scoring function. The observation with the highest score is selected as the observation for that grid cell (Riggs, Hall, and Salomonson, 2006).

Calculated Variables

IST is the primary variable of interest in this data set.

Error Sources

As with any upper level product, the characteristics of or anomalies in input data may carry through to the output data product. The following products are input to MYD29P1N:

MYD29PGN- MODIS/Aqua Sea Ice Extent Daily L2G Global 1km EASE-Grid Night , which is further decsribed in the MODIS Sea Ice Products User Guide to Collection 5.

MYDPTPGN - MODIS/Aqua Observation Pointers Daily L2G Global 1km Polar Grid Night - This product is not available; however, the product (MYDPT1KN) Observation Pointers Daily L2G Global 1 km Night is input to this product and is available from the LP DAAC Web site.

Because sea ice varies in concentration from near zero to 100 percent, it can show temperatures within a pixel due to sub-pixel effects. Melt ponds and leads in the summer months affect the emissivity of the ice surface; therefore, affecting the calculation of ice surface temperature (Hall et al. 1998). The presence of even very thin clouds or fog within the field of view prevent obtaining an accurate IST (Hall et al. 2004). Recent studies in the arctic and antarctic have shown that under clear sky conditions the IST are accurate to better than � 1.5 over the 245-270 K range for all ice types (Hall et al. 2004) (Scambos, Haran, and Massom).

Quality Assessment

All MODIS/Aqua sea ice products are considered validated or at stage 2 meaning that accuracy has been assessed over a widely distributed set of locations and time periods using several ground-truth and validation campaigns.

Quality indicators for MODIS sea ice data can be found in the following 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 SDS, for example Ice Surface Temperature
The Pixel QA SDS that accompanies each data field, for example, Ice Surface Temperature Spatial QA.

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:

The AutomaticQualityFlag is automatically set according to conditions for meeting data criteria in the sea ice 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 scientist. Content and explanation of this flag are dynamic so it should always be examined if present in the external metadata file. A sampling of products will be inspected. Random sampling or support of specific events, such as field campaigns, may also be conducted.

The IST 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 sea ice data. QA data are stored as coded integer values and tells if algorithm results were good or not, or if other defined conditions were encountered (Riggs, Hall, and Salomonson 2003).

See the MODIS Land Quality Assessment Web page for further details.

5. Data Acquisition

Sensor or Instrument Description

Principles of Operation

The MODIS instrument provides 12-bit radiometric sensitivity in 36 spectral bands ranging in wavelength from 0.4 µm to 14.4 µm. 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 a 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, shortwave-infrared, and longwave-infrared spectral regions (MODIS Web 2003).

Technical Specifications

**Table 3.** Technical Specifications
Orbit	705 km, 1:30 p.m. ascending node (Aqua), sun-synchronous, near-polar, circular
Scan Rate	20.3 rpm, cross track
Swath Dimensions	2330 km (cross track) by 10 km (along track at nadir)
Telescope	17.78 cm diameter off-axis, afocal (collimated) with intermediate field stop
Size	1.0 x 1.6 x 1.0 m
Weight	228.7 kg
Power	162.5 W (single orbit average)
Data Rate	10.6 Mbps (peak daytime); 6.1 Mbps (orbital average)
Quantization	12 bits
Spatial Resolution	250 m (bands 1-2) 500 m (bands 3-7) 1000 m (bands 8-36)
Design Life	Six years

Spectral Bands

For information on the 36 spectral bands provided by the MODIS instrument, see the Spectral Bands Table.

Sensor or Instrument Measurement Geometry

The MODIS scan mirror assembly uses a continuously rotating double-sided scan mirror to scan ±55 degrees with a 20.3 rpm cross track. The viewing swath is 10 km along track at nadir, and 2330 km cross track at ±55 degrees.

Manufacturer of Sensor or Instrument

MODIS instruments were built to NASA specifications by Santa Barbara Remote Sensing, a division of Raytheon Electronics Systems.

Calibration

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 µm and 14.4 µm, while the Solar Diffuser (SD) provides a diffuse, solar-illuminated calibration source for visible, near-infrared, and shortwave-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 Web 2003).

Data Acquisition Methods

Source or Platform Mission Objectives

MODIS is a key instrument aboard the Aqua satellite, one component 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 is playing 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. (NASA's MODIS Web Site 2006), (NASA's Aqua Web Site 2006), and (NASA's EOS Web Site 2006)

MODIS Snow and Sea Ice Global Mapping Project Objectives

Within 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 (Hall et al. 1998).

Data Collection System

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 down linked to ground receiving stations (MODIS Web 2003).

Data Acquisition and Processing

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 (ESDIS 1996). The NASA Goddard Space Flight Center: MODIS Adaptive Processing System (MODAPS) Services 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 MYD29. MODIS snow and 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 (MODIS Web 2003). Data are available to the public through a variety of interfaces.

6. References and Related Publications

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., 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., Jeffrey R. Key, Kimberly A. Casey, George A. Riggs, and Donald Cavalieri. May 2004. Sea Ice Surface Temperature Product From MODIS. IEEE Transactions on Geoscience and Remote Sensing 42:5.

Hall, Dorothy K. and J. Martinec. 1985. Remote Sensing of Ice and Snow. London: Chapman and Hall.

Hall, Dorothy K., George A. Riggs, and Vincent V. Salomonson. 1995. Development of Methods for Mapping Global Snow Cover Using Moderate Resolution Imaging Spectroradiometer (MODIS). Remote Sensing of the Environment 54(2):127-140.

Hall, Dorothy K., George A. Riggs, and Vincent 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/?c=atbd&t=atbd> .

Hapke, B. 1993. Theory of Reflectance and Emittance Spectroscopy. Cambridge: Cambridge University Press.

Key, Jeffrey R., J. B. Collins, C. Fowler, and R. S. Stone. 1997. High Latitude Surface Temperature Estimates From Thermal Satellite Data. Remote Sensing of the Environment 61:302-309.

Key, Jeffrey R., J. A. Maslanik, T. Papakyriakou, Mark C. Serreze, and A. J. Schweiger. 1994. On the validation of satellite-derived sea ice surface temperature. Arctic 47:280-287.

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, George A., Dorothy K. Hall, and Vincent V. Salomonson. December 2006. MODIS Sea Ice Products User Guide to Collection 5. http://nsidc.org/data/docs/daac/modis_v5/dorothy_ice_doc.pdf

Riggs, George A., Dorothy K. Hall, and S. A. Ackerman. 1999. Sea Ice Extent and Classification Mapping With the Moderate Resolution Imaging Spectroradiometer Airborne Simulator. Remote Sensing of the Environment 68:152-163.

Scambos, Ted A., Terry M. Haran, and Robert Massom. In press. Validation of AVHRR and MODIS Ice Surface Temperature Products Using In Situ Radiometers. Annals of Glaciology 44.

Snyder, John P. 1987. Map Projections: a Working Manual. U. S. Geological Survey Professional Paper 1395. Department of the Interior. Washington, D. C.

United States Geological Survey. 2003. Lambert Azimuthal Equal Area Map Projections. http://erg.usgs.gov/isb/pubs/MapProjections/projections.html#lambert. Accessed April 2007.

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.

7. Document Information

Acronyms and Abbreviations

The following acronyms and abbreviations are used in this document:

**Table 4.** Acronyms and Abbreviations
ATBD	Algorithm Theoretical Basis Document
AVHRR	Advanced Very High Resolution Radiometer
DAAC	Distributed Active Archive Center
EASE-Grid	Equal Area Scalable Earth Grid
EDOS	EOS Data and Operations System
EGS	EOS Ground System
EOS	Earth Observing System
EOSDIS	Earth Observing System Data and Information System
ESDIS	Earth Science Data and Information System
FTP	File Transfer Protocol
GMT	Greenwich Mean Time
GSFC	Goddard Space Flight Center
HDF-EOS	Hierarchical Data Format - Earth Observing System
IST	Ice Surface Temperature
LP DAAC	Land Processes Distributive Active Archive Center
MCST	MODIS Characterization Support Team
MODIS	Moderate Resolution Imaging Spectroradiometer
MODLAND	MODIS Land Discipline Group
MSS	Multispectral Scanner
NASA	National Aeronautics and Space Administration
NCSA	National Center for Supercomputing Applications
NSIDC	National Snow and Ice Data Center
QA	Quality Assessment
SD	Solar Diffuser
SDP	Science Data Processing
SDS	Science Data Set
SDSM	Solar Diffuser Stability Monitor
SRCA	Spectroradiometric Calibration Assembly
TM	Thematic Mapper

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