MODIS/Terra Sea Ice Extent 5-Min L2 Swath 1km (MOD29) data set contains fields for sea ice by reflectance, sea ice by reflectance pixel Quality Assessment (QA), Ice Surface Temperature (IST), IST pixel QA, latitudes, and longitudes in Hierarchical Data Format-Earth Observing System (HDF-EOS) format, along with corresponding metadata. Latitude and longitude geolocation fields are at 5 km resolution, while all other fields are at 1 km resolution. The sea ice algorithm uses a Normalized Difference Snow Index (NDSI) modified for sea ice to distinguish sea ice from open ocean based on reflective and thermal characteristics.
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/Terra Sea Ice Extent 5-min L2 Swath 1km V005, [list the dates of the data used]. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media.
|Data format||HDF-EOS version 2.9. GeoTIFF available through Reverb | ECHO, NASA's Next Generation Earth Science Discovery tool.|
|Spatial coverage and resolution||Coverage is global, but the sea ice algorithm is applied only to ocean pixels. Spatial resolution at nadir is approximately 1 km for the data fields and 5 km for the latitude and longitude geolocation fields.|
|Temporal coverage and resolution||MODIS data extends from 24 February 2000 to present.
The time between repeat coverage of a given point on the earth depends on latitude with the most frequent coverage occuring near the poles. Areas poleward of ±30 degrees latitude are observed at least daily.
|Tools for accessing and analyzing data||Land Processes Distributive Active Archive Center: MODIS Swath Reprojection Tool Distribution Page
HEG HDF-EOS to GeoTIFF Conversion Tool
Space Science and Engineering Center (SSEC): Terra Orbit Tracks GLOBAL
NSIDC's Hierarchical Data Format - Earth Observing System (HDF-EOS) Web site
The MODIS Conversion Toolkit (MCTK)
MODIS Rapid Response System
NASA Goddard Space Flight Center: MODIS Land Global Browse Images
|File naming convention||Example: MYD29.A2000055.1630.005.2006251012020.hdf|
|File size||0.5 - 6.0 MB using HDF compression|
|Parameter(s)||Sea Ice by Reflectance|
Ice Surface Temperature (IST)
|Procedures for obtaining data||Please see the Ordering MODIS Products from NSIDC Web site for a list of order options.|
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
George A. Riggs
Science Systems and Applications, Inc.
Mail stop 614.1
Greenbelt, MD 20771
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
The following sites can help you select appropriate MODIS data for your study:
Please see the Ordering MODIS Products from NSIDC Web site for a list of order options.
The following software tools can help you analyze the data:
See the MODIS Data at NSIDC: Data Summaries Web page for other MODIS snow and sea ice products available from NSIDC.
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 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:
MODIS sea ice products are archived in compressed HDF-EOS format, which employs point, swath, and grid structures to geolocate the data fill 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.
Data are produced in five minute segments of the orbital swath, which corresponds to approximately 203 scans. With 10 lines per scan, individual products have approximately 2030 pixels in the along track direction and 1354 pixels in the cross track direction. At the Earth's surface, the coverage of a single MYD29 data granule is approximately 2030 km along track by 2330 km cross track.
MOD29 is split into three different file types:
The DayNightFlag object, which is a CoreMetadata.0 Global Attribute, specifies what input was used for a given MYD29 granule. The content of sea ice data products is different between day and night because MODIS visible data are not acquired when the sensor is observing the surface in darkness. Thermal data are acquired day and night. Swaths acquired during the day, or those observed as a combination of day and night, contain fields based on reflective and thermal data. In swaths that were acquired in night mode, only data fields based on thermal data are included; Sea Ice by Relectance and Sea Ice by Relectance Pixel QA fields are not included. Each data file contains a mix of data fields depending on whether the data were acquired at night or during the day. And each data file contains the following HDF-EOS local attribute fields, which are stored with their associated Scientific Data Set (SDS):
IST = scale_factor * (data value - add_offset)
scale_factor = 0.01
data value = ice surface temperature
add_offset = 0
The valid range for IST is 243 to 271.5 K.
Click the following thumbnail to see a larger diagram of how latitude and longitude fields are mapped to the sea ice fields.
A separate ASCII text file containing metadata with a .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 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 has been investigated for quality assurance. The metadata file should be examined to determine if post-production QA has been applied to the granule (Riggs, Hall, and Salomonson 2003).
The file naming convention common to all MODIS Level 2 data products is MOD29.A2000055.1630.005.2006251012020.hdf
Refer to Table 1 for an explanation of the variables used in the MODIS file naming convention.
|Type of product|
|Year of data acquisition|
|Day of year of data acquisition (day 55)|
|Hour and minute of data acquisition in Greenwich Mean Time (GMT) (16:30)|
|Year of production (2006)|
|Day of year of production (day 251)|
|Hour/minute/second of production in GMT (01:20:20)|
|HDF-EOS data format|
Data files are typically between 0.9 - 6.0 MB using HDF compression.
Note: New in V005, MYD29 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 10 or more.
Coverage is global; however, only ocean pixels are run through the sea ice algorithm. A ±55 degree scanning pattern at 705 km altitude achieves a 2330 km swath with global coverage every one to two days.
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.
Resolution at nadir is 1 km for the sea ice fields and 5 km for the latitude and longitude geolocation fields.
MOD29 is produced in five-minute segments, which corresponds to approximately 203 scans. Visit the Space Science and Engineering Center (SSEC): Terra Orbit Tracks GLOBAL Web site to help select appropriate swath data for your study.
MODIS data extends from 24 February 2000 to present.
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 MODIS/Terra Data Outages Web page.
Data are produced in five-minute segments. The time between repeat coverage of a given point on the earth depends on latitude with multiple pass coverage near the poles, and at least daily coverage of locations poleward of ±30 degrees latitude. The nominal repeat period of the satellite is 16 days.
The sea ice algorithm classifies pixels as sea ice, cloud, open ocean, inland water, or land. In the Sea Ice by Reflectance field, sea ice is distinguished from open water based on reflective properties. In the IST field, pixels contain an IST value in kelvins, scaled by 100 for all classes. The IST algorithm was designed for sea ice; however, IST values are provided for areas over open ocean.
Refer to the MOD29 and MYD29 Local Sea Ice Attributes, Version 5 document for a key to the meaning of the coded integer values in the Sea Ice by Reflectance Field, the Sea Ice by Reflectance Pixel QA Field, the Ice Surface Temperature Field, the Ice Surface Temperature Pixel QA Field, the Latitude Field, and the Longitude Field.
Sea ice is a highly dynamic feature that requires satellite-based remote sensing to better understand its behavior. A strong insulator, sea ice restricts the exchange of heat, mass, and momentum between the ocean and atmosphere; influences circulation patterns; and reduces the amount of solar radiation absorbed by the ocean (Riggs, Hall, and Ackerman 1999). Newly formed, smooth, thin sea ice is changed by temperature fluctuations, compressive and shear forces, surface currents, and winds. Sea ice usually becomes snow-covered only a few days after formation. As snow melts on sea ice, albedo decreases across all wavelengths. Sea ice has a much higher albedo compared to open ocean. Specific reflective characteristics of sea ice depend on the age of the ice. Snow-covered, opaque, white sea ice, thick first-year ice, and multiyear ice typically show maximum reflectance between 0.4 µm and 0.8 µm, and again at 1.9 µm. Young sea ice has a lower spectral albedo, 10-40 percent, than older sea ice when measured in this spectral range. Sea ice in the process of ablation and formation of melt ponds shows a decrease in reflectance from 0.6 µm to 0.8 µm, followed by a consistent decrease to approximately 1.6 µm. Sea ice reflectance criteria are used to identify snow-covered sea ice and the age of the ice (Hall and Martinec 1985) and (Hall et al. 1998).
Measurement of IST is useful for determining ice type and estimating radiative and turbulent heat fluxes for large-scale climate studies. IST estimates can be used as an additional discriminatory variable for the identification of sea ice cover. Studies of MODIS Airborne Spectrometer (MAS) images in the Beaufort Sea, near St. Lawrence Island, Alaska, show that the surface temperature of water is typically greater than 271.4 kelvins, while the surface temperature of saline ice is less than 271.4 kelvins (Hall et al. 1998). These thresholds take into account the emissivity of sea ice. First-year ice has an emissivity of about 0.92, and multiyear ice has an emissivity of about 0.84. The difference in ice emissivities results in a difference in recorded surface temperatures, allowing a researcher to distinguish the relative age of ice and infer relative ice thickness (Hall and Martinec 1985).
The MODIS science team is responsible for algorithm development. The MODIS Data Processing System (MODAPS) is responsible for product generation and transfer of products to NSIDC.
Figure 1 is a flowchart that summarizes the steps in the MOD29 sea ice algorithm, which identifies sea ice on the basis of reflectance characteristics in the visible and Near Infrared (IR) wavelengths (Riggs, Hall, and Ackerman 1999). The algorithm assumes that the sea ice is snow covered and that the NDSI can be used to detect sea ice. The NDSI is used to detect the high reflectance of sea ice at visible wavelengths, and the low reflectance at approximately 1.6 µm. NDSI is calculated using MODIS bands 4 (0.55 µm) and 6 (1.6 µm) radiances:
NDSI = (band 4 - band 6)/(band 4 + band 6)
Analysis of sea ice in a MODIS swath is constrained to pixels that:
Constraints are applied in the order listed. After they are applied, only pixels having a 95 percent or greater probability of being unobstructed by cloud over an ocean surface are analyzed for sea ice. Clouds are masked with the MODIS Cloud Mask data product (MOD35_L2). Land and inland water bodies are masked with the MODIS 1 km mask contained within the MODIS geolocation product (MOD03).
Refer to Figure 2. Sea ice detection is achieved with a criteria test for sea ice reflectance characteristics in the visible and near-infrared regions. A pixel is identified as sea ice if all the following conditions are met: (Hall et al. 1998, Riggs, Hall, and Salomonson 2003)
Intermediate checks for theoretical bounding of reflectance data and the NDSI ratio are made in the algorithm. Reflectance values should be between 0 -100 percent, and the NDSI ratio should be within -1.0 to +1.0. Summary statistics are kept for pixels that exceed these theoretical limits; however, the test for sea ice is done regardless. A quality flag is set in the QA data array to indicate the occurrence of sea ice.
A split-window technique is used to determine sea surface temperature and Ice Surface Temperature (IST). This technique allows for correction of atmospheric effects, primarily water vapor (Hall et al. 1998, Riggs, Hall, and Salomonson 2003).
Radiance data from MODIS Channels 31 and 32 (11 µm and 12 µm, respectively) are first converted to brightness temperatures with an inversion of Planck's equation (Key et al. 1994):
T = c2v / ln(1 + ((ec1v3)/E))
|brightness temperature in kelvins (K)|
|1.1910659 * 10-5 mW m-2 sr cm-4|
|1.438833 cm deg K|
|central wavelength in cm-1|
|radiance from sensor in mW m-2 sr cm-4|
The following equation, based on the technique of Key et al. (1997), is then used to estimate IST. Key's equation originally developed for the Advanced Very High Resolution Radiometer (AVHRR) was adapted for use with MODIS Channels 31 and 32.
IST = a + bT31 + c(T31 - T32) + d[(T31 - T32)sec(θ - 1)]
|coefficients determined from multilinear regression of brightness temperatures to estimated surface temperatures|
|brightness temperature of MODIS channel 31 (11 µm)|
|brightness temperature of MODIS channel 32 (12 µm)|
|sensor scan angle from nadir|
Different coefficients are used for each of the three temperature ranges in the Northern and Southern hemispheres, a total of six coefficient sets.
Coefficients for the Northern Hemisphere
|IST coefficients, 240 = -1.5711228087,1.0054774067,1.8532794923,-0.7905176303|
|IST coefficients, 240-260 = -2.3726968515,1.0086040702,1.6948238801,-0.2052523236|
|IST coefficients, >260 = -4.2953046345,1.0150179031,1.9495254583,0.197132579|
Coefficients for the Southern Hemisphere
|IST coefficients, <240 = -0.1594802497,0.9999256454,1.3903881106,-0.4135749071|
|IST coefficients, 240-260 = -3.3294560023,1.0129459037,1.2145725772,0.1310171301|
|IST coefficients, >260 = -5.207360416,1.0194285947,1.5102495616,0.2603553496|
The major caveat with the IST algorithm is that it is only applicable to clear-sky conditions. Inadequate cloud masking may result in significant error in estimating the IST. The MODIS cloud mask is used to identify clear sky conditions since only pixels with a 95 percent or greater probability of being unobstructed by cloud cover will be considered (Hall et al. 1998, Riggs, Hall, and Salomonson 2003).
The sea ice algorithm classifies pixels as sea ice, cloud, open ocean, inland water, or land. Sea ice extent and IST are the primary variables of interest in this data set.
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 MOD29:
This product is not available; however, a document describing the product, [put name of document and link to it here], is available from the
For example, the sea ice detection algorithm is sensitive to the presense of clouds within the field of view, and it will map clouds as sea ice if for some reason the cloud mask product fails to mask a cloud (Hall et al. 2004). The algorithm assumes that sea ice is snow covered and that snow dominates the reflectance characteristics. As a consequence, the presence of surface melt ponds, small ice floes, polynyas, and leads at subpixel resolution will contribute to errors in identification and mapping of sea ice (Hall et al. 1998).
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 2006 ).
All MODIS/Terra 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 via several ground-truth and validation campaigns.
Quality indicators for MODIS sea ice data can be found in the following three places:
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:
An AutomaticQualityFlag for each SDS is automatically set according to conditions for meeting data criteria in the 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. In the MYD29 data product, there are two instances of the ScienceQualityFlagExplanation, one for sea ice determined by reflectance data and one for IST written in the metadata
The sea ice 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 algorithm makes no 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 X percent local attributes. Where X equals 2, 4, or 6 for Sea Ice by Reflectance and 31 or 32 for IST (Riggs, Hall, and Salomonson 2003).
The IST Pixel QA and the Sea Ice by Reflectance Pixel QA data fields provide additional information on algorithm results for each pixel within a MODIS scene, and are used as a measure of usefulness for sea ice data. The QA data are stored as coded integer values and tells if algorithm results were nominal, abnormal, or if other defined conditions were encountered for a pixel. For example, intermediate checks for theoretical bounding of reflectance data and the NDSI ratio are made in the algorithm. Reflectance values should lie within the 0-100 percent range, and the NDSI ratio should lie within the -1.0 to +1.0 range. If these limits are violated, the test for sea ice is still done, but the quality flag is set to Other quality in the Pixel QA field (Riggs, Hall, and Salomonson 2003).
The NASA Goddard Space Flight Center: MODIS Land Quality Assessment 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 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 705 km altitudes 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).
|Orbit||705 km, 10:30 a.m. descending node (Terra), 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|
|Power||162.5 W (single orbit average)|
|Data Rate||10.6 Mbps (peak daytime); 6.1 Mbps (orbital average)|
|Spatial Resolution||250 m (bands 1-2)
500 m (bands 3-7)
1000 m (bands 8-36)
|Design Life||Six years|
For information on the 36 spectral bands provided by the MODIS instrument, see the Spectral Bands Table.
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.
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 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).
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 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 Terra Web Site 2006), and (NASA's EOS Web Site 2006)
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).
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 (MODIS Web 2003).
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.
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., 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> .
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 Science and Instrument Team. MODIS Web. July 2003. <http://modis.gsfc.nasa.gov/> Accessed October 2000.
Riggs, George A., Dorothy K. Hall, and Vincent V. Salomonson. February 2003. MODIS Sea Ice Products User Guide. <http://modis-snow-ice.gsfc.nasa.gov/siugkc.html> .
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.
The following acronyms and abbreviations are used in this document:
|AVHRR||Advanced Very High Resolution Radiometer|
|DAAC||Distributed Active Archive Center|
|EDOS||EOS Data and Operations System|
|EGS||EOS Ground System|
|EOS||Earth Observing 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 DAAC|
|MODIS||Moderate Resolution Imaging Spectroradiometer|
|NASA||National Aeronautics and Space Administration|
|NCSA||National Center for Supercomputing Applications|
|NDSI||Normalized Difference Snow Index|
|NSIDC||National Snow and Ice Data Center|
|SDSM||Solar Diffuser Stability Monitor|
|SRCA||Spectroradiometric Calibration Assembly|