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MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km, Version 4

Summary

MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km (MYD29) contains the following fields: sea ice by reflectance, sea ice by reflectance pixel quality assurance (QA), ice surface temperature (IST), IST pixel QA, sea ice by IST, combined sea ice, latitudes, and longitudes in 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. Version 4 (V004) MYD29 data uses Aqua/MODIS band seven instead of band six. 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 only data available for Version 4 (V004) is the Golden Month, which is a sample of V004 data covering the time period 29 August 2002 (day of year 241) through 7 October 2002 (day of year 280). The Golden Month is only available by special request by contacting NSIDC User Services.

Please note that NSIDC now has a complete series of Version 5 data, which is the highest version number now available and represents the best quality of data.

Citing These Data

We kindly request that you cite the use of this data set in a publication using the following citation example. For more information, see our Use and Copyright Web page.

The following example shows how to cite these data in a publication. List the principal investigators, year of data set release (2003), data set title and version, date of the version you used, publisher (NSIDC), and digital media.

Hall, D.K., G.A. Riggs, and V.V. Salomonson. 2003, updated daily. MODIS/Aqua Sea Ice Extent 5-Min L2 Swath 1km V004, December 2003 to January 2004. Boulder, CO, USA: National Snow and Ice Data Center. Digital media.

Overview Table

Category Description
Data format

HDF-EOS

Spatial coverage and resolution

Coverage is global, but the sea ice algorithm applies 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

Version 4 (V004) data extend from 04 July 2002 to 03 January 2007.

Temporal resolution is approximately five minutes.

Tools for accessing data

MODIS Swath-to-Grid Toolbox (MS2GT)
HDF-EOS to GeoTIFF converter (HEG)
Aqua orbital tracks
NSIDC's HDF-EOS site

Data range

Pixel values vary by field; see Parameter Range for details.

File naming convention

Example "MYD29.A2000055.0000.004.2002354200355.hdf"

File size

MYD29 with day input data: 19.73 MB
MYD29 with night input data: 11.67 MB
MYD29 with day and night input data: 19.73 MB

Parameter(s)

The sea ice algorithm classifies pixels as sea ice, cloud, open ocean, inland water, or land. The algorithm also calculates Ice Surface Temperature (IST) for each pixel.

Procedures for obtaining data

Contact NSIDC User Services to order data.

Table of Contents

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

1. Contacts and Acknowledgments

Investigator(s) Name and Title

Principal Investigators

Dorothy K. Hall
NASA Goddard Space Flight Center
Mailstop 974.0
Greenbelt, MD 20771

Vincent V. Salomonson
NASA Goddard Space Flight Center
Mailstop 974.0
Greenbelt, MD 20771

Support Investigator

George A. Riggs
Science Systems and Applications, Inc. NASA Goddard Space Flight Center
Mailstop 974.1
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. 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. MYD29 is available in Versions 3 and 4; however. See MODIS Product Versions for the reprocessing history and summary of changes in each version.

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

Format

MODIS products are archived in HDF-EOS format, which employs point, swath, and grid structures to geolocate parameters to geographic coordinates. Various software packages, including public domain, support the HDF-EOS data format. See the Software section for more information.

MYD29 is split into three different file types: (1) swaths acquired during daylight, (2) swaths acquired during night, and (3) swaths that were acquired in both day and night. The DayNightFlag object in the MYD29 CoreMetadata.0 global attribute specifies what input was used for a given MYD29 granule. 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 day or that observed 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. Each data file contains a mix of data fields, depending on whether the data were acquired at night or during the day:

Description of data fields

Sea Ice by Reflectance
The sea ice algorithm identifies pixels as being sea ice, ocean, cloud, land, inland water, or other condition. Sea ice is distinguished from open water based on reflective properties. Results are stored as integer values.


Ice Surface Temperature
IST data are expressed in Kelvins and are stored as scaled integer data in HDF-EOS calibrated form. You must convert data to Kelvins using the calibration data in the HDF predefined local attributes:

IST = 0.01 * (calibrated data - add_offset)

The valid range for IST is 243 to 271.5 K.


Sea Ice by Reflectance PixelQA and Ice Surface Temperature PixelQA
These fields store the quality of the algorithm on a pixel-by-pixel basis. QA information tells if the algorithm results were nominal, abnormal, cloud-obscured, invalid, 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 bit is set to nominal. If data showed abnormal values, for example out of range, the algorithm proceeds and outputs a value but flags it as abnormal. If the pixel is obscured by cloud, then the bit setting is cloud. If invalid data or calculations result in unacceptable values, the bit setting is invalid. See MODIS Sea Ice Quality Assurance Fields for more information about QA flags in sea ice products.


Sea Ice by IST
A pixel with an IST less than or equal to 271.5 K is classified as sea ice, and any pixel above that threshold is classified as open ocean.


Combined Sea Ice
This field represents the agreement or disagreement between sea ice identified by reflectance characteristics or by estimated IST. Data show pixels that were detected as sea ice in both the Sea Ice by Reflectance and Sea Ice by IST fields, and where the two techniques differed in detection of sea ice. Presence of other features, such as land, is consistent between these two fields.


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

geolocation mapping

The latitude and longitude data correspond to the center pixel of a 5 km by 5 km block of pixels in the sea ice field. Geolocation data are mapped to the sea ice data with an offset value of two and increment value of five. The offset indicates how far to move along a data dimension until reaching the first point with a corresponding entry along the geolocation dimension. The increment tells how many points to travel along the data dimension before the next point is found for which there is a corresponding entry along the geolocation dimension. In this case, the first element 0,0 in the latitude and longitude field corresponds to element 2,2 in the Sea Ice by Reflectance field. The algorithm then increments by five pixels in the cross track or along track direction to map geolocation data to the Sea Ice by Reflectance field elements.

Metadata

A separate ASCII text file containing metadata with a .met 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 assurance (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

Example: "MYD29.A2003141.0000.004.2003143091446.hdf"

Where:

MYD = MODIS/Aqua
2003 = Year of data acquisition (2003) 141 = Julian date of data acquisition (day 141)
0000 = Hour and minute of data acquisition in Greenwich Mean Time (GMT) (00:00)
004 = Version number
2003 = Year of production (2003)
143 = Julian date of production (day 143)
091446 = Hour/minute/second of production in GMT (09:14:46)

File Size

MYD29 with day input data: 19.73 MB
MYD29 with night input data: 11.67 MB
MYD29 with day and night input data: 19.73 MB

Spatial Coverage

Coverage is global; however, only ocean pixels are run through the sea ice algorithm.

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. See the Aqua orbital tracks to help select appropriate swath data for your study.

Temporal Coverage

Version 4 (V004) data extend from 04 July 2002 to 03 January 2007.

Temporal Resolution

Temporal resolution is approximately five minutes for MYD29.

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. Sea ice extent is determined by the number of pixels classified as sea ice. In the IST field, pixels classified as sea ice 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 areas over open ocean.

Parameter Range

Sea Ice by IST and Sea Ice by Reflectance:

0: Missing
1: No decision
11: Night
25: Land
37: Lake or inland water
39: Open water (ocean)
50: Cloud obscured
200: Sea ice
255: Fill

Combined Sea Ice:

0: Missing
1: No decision
11: Night
25: Land
37: Lake or inland water
39: Open water (ocean)
50: Cloud obscured
150: Sea ice by IST only
170: Sea ice by reflectance only
237: Sea ice by both reflectance and IST
255: Fill

Ice surface temperature:

You must convert data to Kelvins using the calibration data in the HDF predefined local attributes:
IST = 0.01 * (calibrated data - add_offset)

0.0: Missing
1.0: No decision
11.0: Night
25.0: Land
37.0: Lake or inland water
39.0: Open water (ocean)
50.0: Cloud obscured
243.0 to 271.5: IST (Kelvins)

Error Sources

Because sea ice varies in concentration from near zero to 100 percent, it can show different reflectances and temperatures within a pixel, due to sub-pixel effects. Sea ice can also have different reflectances depending on snow cover and presence of surface melt monds. Melt ponds and leads in the summer months affect the emissivity of the ice surface and, therefore, the calculation of ice surface temperature. Clouds may obscure sea ice observations, which is a problem when noting the movement of sea ice over an eight-day time series. Small ice floes, polynyas, and leads at subpixel resolution also contribute to errors in identification and mapping of sea ice (Hall et al. 1998).

Accuracy of IST is estimated to be 0.3 to 2.1 Kelvins (Key et al. 1997). MODIS Airborne Simulator (MAS) data and campaign field data are currently used to establish bounds for MODIS IST accuracy.

Quality Assessment

In MYD29 Version 4 (V004) data, the sea ice algorithm uses Aqua/MODIS band 7. Good quality has been observed in the sea ice maps; however, investigation of effects of the switch to band 7 is continuing. The cloud mask product, MYD35_L2, used as input to the MYD29 algorithm also changed to use of band 7. The effect of that change relative to sea ice/cloud discrimination is being investigated. The IST was not affected by the switch to band 7 except, possibly indirectly by the cloud mask switch to band 7. Validation status is set at provisional until further validation work specific to Aqua IST maps can be completed.

Provisional means that the products are partially validated; incremental improvements are still occurring. These are early science validated products and are useful for exploratory and process scientific studies. Quality may not be optimal since validation and quality assurance are ongoing. Users are urged to review product quality summaries before publication of results.

Analysis of the quality of the sea ice data products is an ongoing activity. 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 quality assessment 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 MOD29 and MOD29P1D data products there are two instances of the ScienceQualityFlagExplanation, one for sea ice determined by reflectance data and one for IST written in the metadata. Information on both is posted at that URL.

The Ice Surface Temperature PixelQA and the Sea Ice by Reflectance PixelQA data fields provide additional information on algorithm results for each pixel within a spatial context, and are used as a measure of usefulness for sea ice data. QA data are stored as bit flags. QA information is extracted by reading the bits within a byte (See MODIS Sea Ice Quality Assurance Fields). The QA information tells if algorithm results were nominal, abnormal, or if other defined conditions were encountered for a pixel (Riggs, Hall, and Salomonson 2003).

See MODIS Land Quality Assessment for further details.

3. Data Access and Tools

Data Access

Contact NSIDC User Services to order data.

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

Software and Tools

4. Data Acquisition and Processing

Theory of Measurements

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).

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° 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 2001).

Technical Specifications

Orbit 1:30 p.m. ascending node (Aqua), sun-synchronous, near-polar, circular
Scan Rate 20.3 rmp, 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

Primary Use Band Bandwidth Spectral Radiance
Land/Cloud/Aerosols
Boundaries
1 620-670 nm 21.8
2 841-876 nm 24.7
Land/Cloud/Aerosols
Properties
3 459-479 nm 35.3
4 545-565 nm 29.0
5 1230-1250 nm 5.4
6 1628-1652 nm 7.3
7 2105-2155 nm 1.0
Ocean Color
Phytoplankton
Biogeochemistry
8 405-420 nm 44.9
9 438-448 nm 41.9
10 483-493 nm 32.1
11 526-536 nm 27.9
12 546-556 nm 21.0
13 662-672 nm 9.5
14 673-683 nm 8.7
15 743-753 nm 10.2
16 862-877 nm 6.2
Atmospheric Water Vapor 17 890-920 nm 10.0
18 931-941 nm 3.6
19 915-965 nm 15.0
Surface/Cloud Temperature 20 3.660-3.840 µm 0.45 (300 K)
21 3.929-3.989 µm 2.38 (335 K)
22 3.929-3.989 µm 0.67 (300 K)
23 4.020-4.080 µm 0.79 (300 K)
Atmospheric Temperature 24 4.433-4.498 µm 0.17 (250 K)
25 4.482-4.549 µm 0.59 (275 K)
Cirrus Clouds
Water Vapor
26 1.360-1.390 µm 6.0
27 6.535-6.895 µm 1.16 (240 K)
28 7.175-7.475 µm 2.18 (250 K)
Cloud Properties 29 8.400-8.700 µm 9.58 (300 K)
Ozone 30 9.580-9.880 µm 3.69 (250 K)
Surface/Cloud Temperature 31 10.780-11.280 µm 9.55 (300 K)
32 11.770-12.270 µm 8.94 (300 K)
Cloud Top Attitude 33 13.185-13.485 µm 4.52 (260 K)
34 13.485-13.785 µm 3.76 (250 K)
35 13.785-14.085 µm 3.11 (240 K)
36 14.085-14.385 µm 2.08 (220 K)

Sensor or Instrument Measurement Geometry

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

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 2001).

Data Acquisition Methods

Source or Platform Mission Objectives

The objective of the mission is 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 subgrid-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).

Coverage Information

A ±55° scanning pattern at 705 km achieves a 2330 km swath, with global coverage every one to two days.

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 2001).

Data Acquisition and Processing

The EOS Ground System (EGS) consists of facilities, networks, and systems which archive, process, and distribute EOS and other NASA earth science data to the science and user community. The EOS Data and Operations System (EDOS) performs forward-link processing of data and return-link of science data from EOS spacecraft and instruments, processes telemetry to generate Level-0 products, and maintains a backup archive of Level-0 products.

EOSDIS ground stations are a component of EDOS, providing space to ground communication. EOSDIS ground stations comprise the Radio Frequency (RF) ground terminal, EDOS ground station interface, and the EOSDIS Backbone Network (EBnet) telecommunication system. The RF ground terminal provides space to ground link communication channels for receipt of science data, receipt of spacecraft telemetry data and transmission of spacecraft commands for two EOS spacecraft simultaneously, including X-band and S-band capabilities. The EDOS ground station interface monitors and captures the high-rate science data and transfers data to the EDOS Level-0 processing facility at the Goddard Space Flight Center (ESDIS 1996).

GSFC processes Level-1A data from Level-0 instrument packet data, then processes a Level-1B Calibrated Radiance product (MOD02) and Geolocation Fields (MOD03). The MODIS SIPS team creates a Level-2 product for sea ice (MOD29/MYD29), which is then used as input to create Level-3 gridded products for day and night sea ice data (MOD29P1D/MYD29P1D and MOD29P1N/MYD29P1N, respectively). These data are archived at the NSIDC DAAC and distributed to EOS investigators and other users via external networks and interfaces (MODIS Web 2000). Data are available to the public through the WIST.

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.

Derivation Techniques and Algorithms

Data products are generated by the MODIS Science Investigator-led Processing System (SIPS) and transferred to NSIDC. Figure 1 is a flowchart that summarizes the steps in the MODIS sea ice algorithm (Riggs, Hall, and Ackerman 1999), which identifies sea ice on the basis of reflectance characteristics in the visible and near infrared (IR) wavelengths, and also by IST. Algorithm criteria are based on the Normalized Difference Sea Ice Index (NDSI). 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 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 (MOD35_L2). Land and inland water bodies are masked with the MODIS 1 km mask contained within the MODIS geolocation product (MOD03).

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 ice surface temperature. This technique allows for correction of atmospheric effects (primarily water vapor). Relatively thin sea ice (less than 10 cm with no snow cover), which has a lower albedo and which may not be detected using the NDSI, is identified using the difference between ice surface and sea surface temperature. If the difference in surface temperature at 11.0 µm and 12.0 µm (MODIS channels 31 and 32, respectively) is small, then the algorithm assumes a clear atmosphere. Given this assumption, the surface temperature may then be estimated directly from the observed pixel brightness value at 11.0 µm, with an adjustment for the surface emissivity (0.985 for sea water). Sea ice is then identified as any pixel with a surface temperature less than or equal to the freezing point of sea water (271.5 K). Accuracy of the IST measurement may be increased by regression of the estimated IST with temperatures modeled from radiative transfer models or with observed surface temperatures (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))

where:

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 ice surface temperature (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)]

where:

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

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. Water vapor affects the accuracy of the IST calculation, so variable coefficients are used to correct for atmospheric water vapor (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.

5. 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, D.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, D.K. and J. Martinec. 1985. Remote sensing of ice and snow. London: Chapman and Hall.

Hall, D.K., G.A. Riggs, and V.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/atbd.html> .

Hall, D.K., G.A. Riggs, and V.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.

Hapke, B. 1993. Theory of reflectance and emittance spectroscopy. Cambridge: Cambridge University Press.

Key, J.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, J.R., J.A. Maslanik, T. Papakyriakou, M.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, G.A., D.K. Hall, and V.V. Salomonson. February 2003. MODIS sea ice products user guide. <http://modis-snow-ice.gsfc.nasa.gov/siugkc.html> .

Riggs, G.A., D.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.

6. Document Information

Glossary and Acronyms

Please see the EOSDIS Glossary of Terms for a general list of terms.

List of Acronyms

Please see the EOSDIS Acronyms list for a general list of Acronyms. The following acronyms are used in this document:

ATBD: Algorithm Theoretical Basis Document
AVHRR: Advanced Very High Resolution Radiometer
EASE-Grid: Equal Area Scalable Earth Grid
EBNet: EOSDIS Backbone Network
ECS: EOSDIS Core System
EDOS: EOSDIS Data and Operations System
EGS: EOSDIS Ground System
EOS: Earth Observing System
EOSDIS: Earth Observing System Data and Information System
ERS: European Remote Sensing
ESDIS: Earth Science Data and Information System
ESDT: Earth Science Data Type
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
MAS: MODIS Airborne Simulator
MCST: MODIS Characterization Support Team
MODIS: Moderate Resolution Imaging Spectroradiometer
MODLAND: MODIS Land
MSS: Multispectral Scanner
NASA: National Aeronautics and Space Administration
NCSA: National Center for Supercomputing Applications
NDSI: Normalized Difference Sea ice Index
NDVI: Normalized Difference Vegetation Index
NOAA: National Oceanic and Atmospheric Administration
NOHRSC: National Operational Hydrologic Remote Sensing Center
NSIDC: National Snow and Ice Data Center
PVL: Parameter Value Language
QA: Quality Assurance
RF: Radio Frequency
SAR: Synthetic Aperture Radar
SCF: Science Computing Facility
SD: Solar Diffuser
SDP: Science Data Processing
SDSM: Solar Diffuser Stability Monitor
SIPS: Science Investigator-led Processing System
SMMR: Scanning Multichannel Microwave Radiometer
SRCA: Spectroradiometric Calibration Assembly
SSM/I: Special Sensor Microwave/Imager
TM: Thematic Mapper

Document Creation Date

February 2004

Document Revision Date

October 2009

Document URL

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