MODIS/Aqua Sea Ice Extent and IST Daily L3 Global 4km EASE-Grid Day (MYD29E1D) data set contains fields for sea ice by reflectance and Ice Surface Temperature (IST). Each data granule covers the entire globe with two separate arrays of 4501 x 4501 pixels: one for the Arctic and one for the Antarctic. The MODIS 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/Aqua Sea Ice Extent and IST Daily L3 Global 4km EASE-Grid Day 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||Each file contains two data arrays: one for the Arctic and one for the Antarctic at 4 km gridded resolution. Both arrays combined provide global coverage.|
|Temporal coverage and resolution||MODIS data extends from 04 July 2002 to present.|
|Tools for accessing and analyzing data||Space Science and Engineering Center (SSEC): Aqua Orbit Tracks GLOBAL
NSIDC's Hierarchical Data Format - Earth Observing System (HDF-EOS) Web site
MODIS Rapid Response System
NASA Goddard Space Flight Center: MODIS Land Global Browse Images
National Center for Supercomputing Applications (NCSA) HDFView
The MODIS Conversion Toolkit (MCTK)
For more information regarding the scaled value and the coded integer value descriptions, please see the MOD29E1D and MYD29E1D Local Sea Ice Attributes, Version 5 document.
|Grid type and size||Arctic and Antarctic data arrays are 4501 x 4501 pixels.|
|File naming convention||Example: MYD29E1D.A2000070.005.2006256010347.hdf|
|File size||0.5 - 6.0 MB using HDF compression|
|Parameter(s)||Sea Ice by Reflectance NP
Ice Surface Temperature NP
Sea Ice by Reflectance SP
Ice Surface Temperature SP
|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:
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 most current version of MODIS data available, which is the highest version number. Version 5 (V005), also known as Collection 5, is the most current version of data available from NSIDC.
Please visit the following sites for more information about the V005 data, known data problems, 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 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.
Each data granule 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 = 0.0
The valid range for IST is 223.20 to 313.20 K
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 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 has been investigated for QA. 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 used for the MYD29E1D product is MYD29E1D.A2000070.005.2006256010347.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 70)|
|Year of production (2006)|
|Day of year of production (day 256)|
|Hour/minute/second of production in GMT (01:03:47)|
|HDF-EOS data format|
Data files are typically between 0.5 - 6.0 MB using HDF compression.
Note: New in V005, MYD29E1D 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.
Each file contains two data grids: one for the Arctic and one for the Antarctic at 4 km gridded resolution. Both grids combined provide global coverage; however, the sea ice algorithm is applied only to ocean pixels.
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.
Gridded resolution is 4 km.
MYD29E1D data are gridded to the NSIDC EASE-Grid, a Lambert Azimuthal Equal Area Projection. Please review All About EASE-Grid 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:
|North (90° latitude, 0° longitude)
South (-90° latitude, 0° longitude)
|North (0° longitude, oriented vertically at bottom)
South (0° longitude, oriented vertically at top)
Upper left corner point (m)
Lower right corner point (m)
True scale (m)
MYD29E1D data are gridded to North and South grids, each 4501 x 4501 pixels in size, which corresponds to approximately 18053 km by 18053 km at a resolution of 4010.8040 m per pixel.
The following maps show the North and South grids for MYD29E1D. Click on the thumbnail images to see the full-resolution images.
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.
Each granule contains a composite created from all of the data acquired over a 24 hour period.
IST values are given in kelvins and the values for all classes are scaled by 100.
Refer to the MOD29E1D and MYD29E1D Local Sea Ice Attributes, Version 5 document for a key to the meaning of the scaled values in the Sea Ice by Reflectance NP, the Sea Ice by Reflectance SP, the Ice Surface Temperature NP, and the Ice Surface Temperature SP.
Sea ice is a highly dynamic feature that requires satellite-based remote sensing to better understand its behavior. 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, 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 are 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.
See the MODIS/Aqua Sea Ice Extent Daily L3 Global 1km EASE-Grid Day documentation for processing steps used in MYD29P1D, which is the input to this data set.
Input data at approximately 1 km resolution are intermediately mapped to a polar grid at 1 km resolution; this is in turn mapped to the EASE-Grid at approximately 4 km. The gridded input observation nearest the center of an output grid cell is assigned as the output value for that grid cell. Approximately every fourth input grid cell is mapped into a sequential output grid cell.
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 MYD29E1D:
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).
All MODIS/Aqua sea ice products are considered validated at stage two, meaning that product accuracy has been assessed over a widely distributed set of locations and time periods via several ground truth and validation efforts.
Quality indicators for MODIS sea ice data can be found in the following two places:
No automated quality assessment is done in this algorithm. All QA is inherited from the MOD29 product. 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.
Specific information on the science quality of the sea ice data products is reported in the ScienceQualityFlagExplanation object in the CoreMetadata.0 global attribute. The URL for the QA site is given in the product metadata and is linked to from the Warehouse Inventory Search Tool (WIST) when ordering data. The ScienceQualityFlagExplanation is changed in response to analysis and should be checked for updated information. In the MYD29E1D data product, there are four instances of the ScienceQualityFlagExplanation, one for each of the two parameters, sea ice determined by reflectance data and ice surface temperature, in each of the northern and southern grids written in the metadata.
See the MODIS Land Quality Assessment Web site for further details.
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 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, 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|
|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 2001).
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)
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.
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.
The following acronyms and abbreviations are used in this document:
|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|
|NASA||National Aeronautics and Space Administration|
|NCSA||National Center for Supercomputing Applications|
|NSIDC||National Snow and Ice Data Center|
|SDP||Science Data Processing|
|SDS||Science Data Set|
|SDSM||Solar Diffuser Stability Monitor|
|SRCA||Spectroradiometric Calibration Assembly|