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MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km, Version 5

Summary

MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km (MYD29) 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 compressed 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 characteristics.

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 5-Min L2 Swath 1km V005, [list the dates of the data used]. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media.

Overview Table

Category Description
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 04 July 2002 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): Aqua 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
NCSA HDFView

Data range
Sea Ice by Reflectance Field Coded Integer Values
Value Description
0
missing data
1
no decision
11
night
25
land
37
inland water
39
ocean
50
cloud
100
lake ice
200
sea ice
254
detector saturated
255
fill
Ice Surface Temperature (IST) Field Scaled Values1
Value Description
0.0
missing data
1.0
no decision
11.0
night
25.0
land
37.0
inland water
39.0
open ocean
50.0
cloud
243.0 - 273.0
expected IST range
655.35
fill
1 A scaled integer is created after multiplying the 16-bit integer values by the scaling factor of 0.01.

For more information regarding the scaled value and the coded integer value descriptions, please see the MOD29 and MYD29 Local Sea Ice Attributes, Version 5 document.
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
IST
Latitude
Longitude
Procedures for obtaining data Please see the Ordering MODIS Products from NSIDC Web site for a list of order options.

Table of Contents

1. Contacts and Acknowledgments
2. Data Access and Tools
3. Detailed Data Description
4. Data Processing
5. Data Acquisition
6. References and Related Publications
7. Document Information

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 sites 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:

Related Data Collections

See the MODIS Data at NSIDC: Data Summaries Web page for other MODIS snow and sea ice products available from NSIDC.

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 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, 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 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.

MYD29 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):

Each data granule also contains metadata either stored as global attributes or as HDF-predefined fields, which are stored with each SDS.

Description of Data Fields

Click the following thumbnail to see a larger diagram of how latitude and longitude fields are mapped to the sea ice fields.

geolocation mapping

External Metadata File

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).

File Naming Convention

The file naming convention common to all MODIS Level 2 data products is MYD29.A2000055.1630.005.2006251012020.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
29
Type of product
A
Acquisition date
2000
Year of data acquisition
055
Day of year of data acquisition (day 55)
1630
Hour and minute of data acquisition in Greenwich Mean Time (GMT) (16:30)
005
Version number
2006
Year of production (2006)
251
Day of year of production (day 251)
012020
Hour/minute/second of production in GMT (01:20:20)
hdf
HDF-EOS data format

File Size

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.

Spatial Coverage

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.

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

Resolution at nadir is 1 km for the sea ice fields and 5 km for the latitude and longitude geolocation fields.

Swath Description

MYD29 is produced in five-minute segments, which corresponds to approximately 203 scans. Visit the Space Science and Engineering Center (SSEC): Aqua Orbit Tracks GLOBAL Web site to help select appropriate swath data for your study.

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

Data are produced in five-minute segments. 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.

Parameter or Variable

Parameter Description

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.

Parameter Range

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.

4. Data Processing

Theory of Measurements

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.

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).

Derivation Techniques and Algorithms

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 MYD29 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. Due to failures with the majority of the detectors for Band 6, NDSI is calculated using MODIS bands 4 (0.55 µm) and 7 (2.1µm) radiances:

NDSI = (band 4 - band 7)/(band 4 + band 7)

Processing Steps

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 (MYD35_L2). Land and inland water bodies are masked with the MODIS 1 km mask contained within the MODIS geolocation product (MYD03).

Reflectance Criteria

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.

Ice Surface Temperature

A split-window technique is used to determine sea surface temperature and 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))

Table 2. Radiance Data Conversions
Variable
Description
T
brightness temperature in kelvins (K)
c1
1.1910659 * 10-5 mW m-2 sr cm-4
c2
1.438833 cm deg K
v
central wavelength in cm-1
E
radiance from sensor in mW m-2 sr cm-4
e
emissivity


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)]

Table 3. Key's Equation to Estimate IST
Variable
Description
a,b,c,d
coefficients determined from multilinear regression of brightness temperatures to estimated surface temperatures
T31
brightness temperature of MODIS channel 31 (11 µm)
T32
brightness temperature of MODIS channel 32 (12 µm)
θ
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.

Cloud masks

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).

Calculated Variables

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.

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 MYD29:

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 ).

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 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.

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 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).

Technical Specifications

Table 4. 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 of the flagships 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 downlinked 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> .

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.

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. 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.

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.

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 5. Acronyms and Abbreviations
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
IR Infrared
IST Ice Surface Temperature
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
QA Quality Assessment
SD Solar Diffuser
SDS Scientific Data Set
SDSM Solar Diffuser Stability Monitor
SRCA Spectroradiometric Calibration Assembly

Document Creation Date

February 2004

Document Revision Date

January 2007

Document URL

http://nsidc.org/data/docs/daac/modis_v5/myd29_modis_aqua_seaice_5min_swath_1km.gd.html