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Data Set ID:

MODIS/Aqua Snow Cover 5-Min L2 Swath 500m, Version 6

This data set reports the location of snow cover based on the Normalized Difference Snow Index (NDSI) and a series of screens designed to alleviate errors and flag uncertain snow cover detections. The NDSI is derived from radiance data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite.

Version Summary:

Changes for Version 6 include:

  • Fractional Snow Cover has been replaced by Normalized Difference Snow Index (NDSI) snow cover. Fractional Snow Cover is no longer calculated;
  • The binary Snow-Covered Area (SCA) map has been discontinued;
  • Aqua MODIS band 6 data have been restored to scientific quality using a Quantitative Image Restoration (QIR) technique. The snow detection algorithms are now the same for Aqua and Terra;
  • Data screens designed to reduce snow detection errors have been revised and several new screens have been added;
  • Data screen results, including snow detection reversals and detections with increased uncertainty, are provided in a new QA bit flag;
  • Basic pixel-level QA uses new criteria to indicate the overall quality of algorithm result.

Geographic Coverage

Spatial Coverage:
  • N: 90, S: -90, E: 180, W: -180

Spatial Resolution:
  • 500 m x 500 m
Temporal Coverage:
  • 4 July 2002
Temporal Resolution: 5 minute
  • Snow/Ice > Snow Cover
Platform(s) AQUA
Sensor(s): MODIS
Data Format(s):
Version: V6
Data Contributor(s): Miguel Román, Dorothy Hall, George Riggs

Data Citation

As a condition of using these data, you must cite the use of this data set using the following citation. For more information, see our Use and Copyright Web page.

Hall, D. K. and G. A. Riggs. 2016. MODIS/Aqua Snow Cover 5-Min L2 Swath 500m, Version 6. [Indicate subset used]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: [Date Accessed].

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Detailed Data Description

Snow covered land typically has a very high reflectance in visible bands and very low reflectance in the shortwave infrared. The Normalized Difference Snow Index (NDSI) reveals the magnitude of this difference. The snow cover algorithm in this data set calculates the NDSI for all land and inland water pixels in daylight using Aqua MODIS band 4 (visible green) and band 6 (shortwave near-infrared) and then applies a series of data screens to alleviate errors and flag uncertain snow detections.

Note: Version 6 incorporates a recently developed Quantitative Image Restoration (QIR) technique that restores Aqua MODIS band 6 data to scientific quality. Thus the snow detection algorithms are now the same for Aqua and Terra. See Derivation Techniques and Algorithms for additional details.


Data files are provided in HDF-EOS2 (V2.17). JPEG browse images are also available.

HDF-EOS (Hierarchical Data Format - Earth Observing System) is a self-describing file format based on HDF that was developed specifically for distributing and archiving data collected by NASA EOS satellites. For more information, visit the HDF-EOS Tools and Information Center.

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File Naming Convention

Example File Name:

  • MYD10_L2.A2003001.0045.006.2016058093750.hdf
  • MYD[PID].A[YYYY][DDD].[HHMM].[VVV].[yyyy][ddd][hhmmss].hdf

Refer to Table 1 for descriptions of the file name variables listed above.

Table 1. Variables in the MODIS File Naming Convention
Variable Description
PID Product ID
A Acquisition date follows
YYYY Acquisition year
DDD Acquisition day of year
HHMM Acquisition hour and minute in Greenwich Mean Time (GMT)
VVV Version (Collection) number
yyyy Production year
ddd Production day of year
hhmmss Production hour/minute/second in GMT
.hdf HDF-EOS formatted data file

Note: Data files contain important metadata including global attributes that are assigned to the file and local attributes like coded integer keys that provide details about the data fields. In addition, each HDF-EOS data file has a corresponding XML metadata file (.xml) which contains some of the same internal metadata as the HDF-EOS file plus additional information regarding user support, archiving, and granule-specific post-production. For detailed information about MODIS metadata fields and values, consult the MODIS Snow Products Collection 6 User Guide

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File Size

Data files are approximately 6.5 MB.

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Spatial Coverage

Note: MYD10_L2 data files contain five minutes of swath data (a scene). Five minutes of MODIS swath data typically comprises 203 full scans of the MODIS instrument and occasionally 204. With an along-track viewing path of 10 km, each scan acquires 20 pixels in the 500 m bands, and thus a scene typically contains 4060 pixels in the along-track direction and occasionally 4080. The instrument's ±55 degree scanning pattern yields 2708 pixels per scene in the cross-track direction. In general, 144 5-minute scenes are acquired during daylight.

Coverage is global. Aqua's sun-synchronous, near-polar circular orbit is timed to cross the equator from south to north (ascending node) at approximately 1:30 P.M. local time. Complete global coverage occurs every one to two days (more frequently near the poles). The following sites offer tools that track and predict Aqua's orbital path:

Spatial Resolution

500 m (at nadir) for data fields
5 km for geolocation fields

Projection and Grid Description

None (latitude, longitude referenced)

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Temporal Coverage

MODIS Aqua data are available from 04 July 2002 to present. However, because the NDSI depends on visible light, data are not produced for the night phase of each orbital period or for those portions of fall and winter in polar regions when viewing conditions are too dark. In addition, anomalies over the course of the Aqua mission have resulted in minor data outages. If you cannot locate data for a particular date or time, check the MODIS/Aqua Data Outages Web page.

Temporal Resolution

Each data file contains five minutes of swath data (a scene). Complete global coverage occurs every one to two days.

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Note: Starting with Version 6, MODIS snow cover data sets no longer report Fractional Snow Cover (FSC) and binary snow-covered area (SCA). See Processing Steps for details.

NDSI snow cover, raw NDSI, screen results, and basic QA for each pixel are written to HDF-EOS formatted files as Scientific Data Sets (SDSs) according to the HDF Scientific Data Set Data Model. The SDSs for this data set are listed in Table 2:

Table 2. Scientific Data Sets and Descriptions
Scientific Data Set Description

NDSI snow cover plus other results. Possible values are:

  • 0–100: NDSI snow cover
  • 200: missing data
  • 201: no decision
  • 211: night
  • 237: inland water
  • 239: ocean
  • 250: cloud
  • 254: detector saturated
  • 255: fill

A basic estimate of the quality of the algorithm result. Possible values are:

  • 0: best
  • 1: good
  • 2: OK
  • 3: poor (not currently in use)
  • 211: night
  • 239: ocean
  • 255: unusable input or no data 
NDSI_Snow_Cover_Algorithm_Flags_QA Bit flags indicating screen results and the presence of inland water. Bits are set to on (1) as follows:
  • Bit 0: Inland water
  • Bit 1: Low visible screen failed. Snow detection reversed.
  • Bit 2: Low NDSI screen failed. Snow detection reversed.
  • Bit 3: Combined temperature/height screen failed. On means either:
    • brightness temperature ≥ 281 K, pixel height < 1300 m, flag set, snow detection reversed to not snow, OR;
    • brightness temperature ≥ 281 K, pixel height ≥ 1300 m, flag set, snow detection NOT reversed.
  • Bit 4: Shortwave IR (SWIR) reflectance anomalously high. On means either:
    • Snow pixel with SWIR > 0.45, flag set, snow detection reversed to not snow, OR;
    • Snow pixel with 25% < SWIR <= 45%, flag set to indicate unusual snow conditon, snow detection NOT reversed.
  • Bit 5: spare
  • Bit 6: spare
  • Bit 7: solar zenith screen failed, uncertainty increased.
NDSI Raw NDSI (i.e. prior to screening) reported in the range 0–10,000. Values are scaled by 1 x 104.
Latitude Coarse resolution (5 km) latitudes for geolocating the SDSs. Values correspond to the center pixel of 5 km x 5 km blocks in the data arrays.
Longitude Coarse resolution (5 km) longitudes for geolocating the SDSs. Values correspond to the center pixel of 5 km x 5 km blocks in data arrays.

Geolocating MODIS 500 m Swath Data

The StructMetadata.0 metadata object contains a dimension map that specifies how each dimension of each geolocation field relates to the corresponding dimension in each data field. When a data field and a geolocation field share a named dimension, no explicit map is needed. However, for MODIS data sets in which the resolution of the geolocation dimension (5 km) differs from the resolution of the data dimension (500 m), two additional metadata objects—Offset and Increment—are needed to fully define the mapping.

Offset specifies the location along the data dimension of the first data point with a corresponding entry along the geolocation dimension. Increment then specifies the number of steps between subsequent points with corresponding entries along the geolocation dimension. For MODIS 500 m data sets, Offset = 5 and Increment = 10.

Unfortunately, HDF-EOS specifications only allow integer offsets in dimension maps, and MODIS 500 m data sets require fractional offsets to be correctly geolocated. Two product-specific metadata attributes were created to accomodate this additional mapping requirement: HDFEOS_FractionalOffset_Along_swath_lines_500m_MOD_Swath_Snow and HDFEOS_FractionalOffset_Cross_swath_pixels_500m_MOD_Swath_Snow.

These elements contain fractional offsets of 0.5 in the along-track direction and 0.0 in the cross-track direction that must be added to the integer offset stored with the dimension map. Thus the combined along-track offset of 5.5 indicates that the first element (0,0) in the latitude and longitude fields maps to (5, 5.5) in any of the data fields. Subsequent elements in the geolocation arrays then map to locations in the data fields at 10-pixel increments in the both the along-track and cross-track directions.

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Software and Tools

Get Data

Data are available via FTP and HTTPS.

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    Software and Tools

    The following sites can help you identify the right MODIS data for your study:

    The following resources are available to help users work with MODIS data:

    • The MODIS Reprojection Tool allows users to read data files in HDF-EOS format, specify geographic subsets or science data sets as input to processing, perform geographic transformations to different coordinate systems and cartographic projections, and write output files to formats other than HDF-EOS.
    • The HDF-EOS to GeoTIFF Conversion Tool (HEG) can reformat, re-project, and perform stitching/mosaicing and subsetting operations on HDF-EOS objects.
    • HDFView is a simple, visual interface for opening, inspecting, and editing HDF files. Users can view file hierarchy in a tree structure, modify the contents of a data set, add, delete and modify attributes, and create new files.
    • The MODIS Conversion Toolkit (MCTK) plug-in for ENVI can ingest, process, and georeference every known MODIS data set, including products distributed with EASE-Grid projections. The toolkit includes support for swath projection and grid reprojection and comes with an API for large batch processing jobs.
    • NSIDC's Hierarchical Data Format | Earth Observing System (HDF-EOS) Web page contains information about HDF-EOS, plus tools to extract binary and ASCII objects, instructions to uncompress and geolocate HDF-EOS data files, and links to obtain additional HDF-EOS resources.
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    Data Acquisition and Processing

    Mission Objectives

    MODIS is a key instrument onboard NASA's Earth Observing System (EOS) Aqua and Terra satellites. The EOS includes satellites, a data collection system, and the world-wide community of scientists supporting a coordinated series of polar-orbiting and low inclination satellites that provide long-term, global observations of the land surface, biosphere, solid Earth, atmosphere, and oceans. As a whole, EOS is improving our understanding of the Earth as an integrated system. MODIS plays a vital role in developing validated, global, and interactive Earth system models that can predict global change accurately enough to assist policy makers in making sound decisions about how best to protect our environment. For more information, see:

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    Data Acquisition

    The MODIS sensor contains a system whereby visible light from 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 generated. 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. 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.

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    Data Processing

    The MODIS science team continually seeks to improve the algorithms used to generate MODIS data sets. Whenever new algorithms become available, the MODIS Adaptive Processing System (MODAPS) reprocesses the entire MODIS collection—atmosphere, land, cryosphere, and ocean data sets—and a new version is released. Version 6 (also known as Collection 6) is the most recent version of MODIS snow cover data available from NSIDC. NSIDC strongly encourages users to work with the most recent version.

    Consult the following resources for more information about MODIS Version 6 data, including known problems, production schedules, and future plans:

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    Derivation Techniques and Algorithms

    Processing Steps

    Snow Cover

    The MODIS snow cover algorithm detects snow by computing the Normalized Difference Snow Index (NDSI) (Hall and Riggs, 2011) from MODIS Level 1B calibrated radiances. Data screens are then applied to alleviate errors of commission and to flag uncertain snow detections. The final output consists of NDSI snow cover plus the location of clouds, water bodies, and other algorithm results of interest to data users. The following sections briefly describe the approach used to detect snow. For a detailed description, see the Algorithm Theoretical Basis Document (ATBD).

    Input Products

    Table 3 lists the MODIS products that are used as inputs to the snow detection algorithm:

    Product ID

    Long Name

    Data Used

    Table 3. Inputs to the MODIS snow algorithm


    MODIS/Aqua Calibrated Radiances 5-Min L1B Swath 500m

    Band 1 (0.645 μm); Band 2 (0.865 μm); Band 4 (0.555 μm); Band 6 (1.640 μm)


    MODIS/Aqua Calibrated Radiances 5-Min L1B Swath 1km

    Bands: 31 (11.03 μm )


    MODIS/Aqua Geolocation Fields 5-Min L1A Swath 1km

    Land/Water Mask (see note); Solar Zenith Angle; Latitude; Longitude; Geoid Height


    MODIS/Aqua Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km

    Unobstructed Field of View Flag; Day/Night Flag

    Note: Version 6 utilizes a new land/water mask derived from the University of Maryland Global Land Cover Facility's UMD 250m MODIS Water Mask. To maintain continuity between Version 5 and Version 6, the UMD 250m MODIS Water Mask was converted from a 250 m, two-class map to 500 m and seven classes for use in all MODIS products. The conversion is detailed in Development of an Operational Land Water Mask for MODIS Collection 6.

    Fifteen of the 20 band 6 detectors on the Aqua MODIS failed shortly after launch, a 75 percent signal loss that has precluded using this band for snow detection. However, a Quantitative Image Restoration (QIR) technique was recently developed (Gladkova et al., 2012) that restores Aqua MODIS band 6 data to scientific quality. Version 6 incorporates this technique to produce an intermediate, calibrated radiances input product with band 6 restored: MYD02HKM_QIR (this product is not retained). Aside from this step, the snow detection algorithm is the same for Aqua and Terra.

    The algorithm reads the restored radiance data from MYD02HKM_QIR, geolocation data and the land/water mask from MYD03, and the cloud mask and day/night flag from MYD35_L2. The radiance data is checked for quality and converted to top of the atmosphere (TOA) reflectance. The NDSI is then computed for all land and inland water pixels in daylight using Band 4 (0.55 µm) and Band 6 (1.6 µm) reflectances as follows:

    NDSI = (Band 4 - Band 6) / (Band 4 + Band 6)

    Snow typically has a very high reflectance in visible bands and very low reflectance in the shortwave infrared, a characteristic which distinguishes snow cover from non snow-covered land and most cloud types. As such, pixels with NDSI > 0.0 are deemed to have some snow present. Pixels with NDSI ≤ 0.0 are classified as snow free land.

    In the previous version of this data set (Version 5), fractional snow cover was computed from the NDSI using a regression technique. This approach has been abandoned for Version 6 because the NDSI is directly related to the presence of snow in a pixel and thus more accurately describes snow detection compared with FSC. The MODIS Science Team believes this change will offer users more flexibility to apply MODIS snow cover data sets to their research. Importantly, the change does not disrupt data continuity because the snow detection algorithm in Version 6 is essentially the same as Version 5 without the FSC calculation. Users who wish to estimate FSC can apply the FSC regression equation from Version 5 to Version 6 NDSI snow cover data.

    In addition, the binary (snow/no snow) snow-covered area (SCA) map in Version 5 has been abandoned for Version 6. This SDS was computed by: a) setting a snow threshold of 0.4 ≤ NDSI ≤ 1; and b) applying an additional test to pixels with 0.1 ≤ NDSI ≤ 0.4 which used the Normalized Difference Vegetation Index (NDVI) to increase snow detection sensitivity in forested landscapes. However, this algorithm effectively prevented snow detections for NDSI < 0.4 on any landscape. Again, the MODIS Science Team believes this change offers the research community more flexibility. Users who wish to construct a binary SCA map can choose their own threshold for snow using the Version 6 NDSI Snow Cover, the raw NDSI data, or a combination of both.

    The NDSI has proven effective at detecting snow cover on the landscape given clear skies and good viewing geometry and solar illumination. However, other illumination conditions can diminish the technique's effectiveness and induce errors of commission or omission. During the course of the MODIS mission, the Science Team and user community have identified several frequently occuring sources or error, for example, confusion between snow-covered land and certain cloud types or surface features with snow-like reflectances.

    Examining the NDSI relationship more closely provides a means to circumvent many of these potential errors. For example, some bright surface features with snow-like NDSIs have MODIS Band 6 reflectances that exceed expected values for snow, while others have visible/near-infrared reflectance differences that are too low. As such, pixels determined to have some snow present are subjected to a series of screens that have been specifically developed to alleviate snow commission and omission associated with the most common error sources. In addition, snow-free pixels are screened for very low illumination conditions to prevent possible snow omission errors. The following sections describe these data screens.

    Low Visible Reflectance Screen

    This screen is applied to prevent errors from occuring when the reflectance is too low for the algorithm to perform well, such as in very low illumination or on surface features with very low reflectance. This screen is also applied to pixels that have no snow cover present (snow-free pixels) to prevent possible snow omission. If the MODIS Band 2 reflectance is ≤ 0.10 or the Band 4 reflectance is ≤ 0.11, the pixel fails the screen and is set to no decision in the NDSI snow cover SDS. The results of this screen are tracked in bit 1 of the NDSI_Snow_Cover_Algorithm_Flags_QA SDS.

    Low NDSI screen

    Pixels detected as having snow cover with 0.0 <  NDSI < 0.10 are reversed to no snow and flagged by setting bit 2 in the NDSI_Snow_Cover_Algorithm_Flags_QA SDS. This flag can be used to find pixels where snow cover detections were reversed to not snow.

    Estimated surface temperature and surface height screen

    This screen serves a dual purpose by linking estimated surface temperature with surface height.  It is used to alleviate errors of commission at low elevations that appear spectrally similar to snow but are too warm. It is also used to flag snow detections at high elevations that are warmer than expected. Using the estimated MODIS Band 31 brightness temperature (Tb), if snow is detected in a pixel with height < 1300 m and Tb ≥ 281 K, the pixel is reversed to not snow and bit 3 is set in the NDSI_Snow_Cover_Algorithm_Flags_QA SDS. If snow is detected in a pixel with height  ≥ 1300 m and Tb ≥ 281 K, the pixel is flagged as unusually warm by setting bit 3 in the NDSI_Snow_Cover_Algorithm_Flags_QA SDS.

    High SWIR reflectance screen

    This screen also serves a dual purpose by: a) preventing non-snow features that appear similar to snow from being detected as snow; b) allowing snow to be detected where snow-cover short-wave infrared reflectance (SWIR) is anomalously high. Snow typically has a SWIR reflectance of less than about 0.20; however, this value can be higher under certain conditions like a low sun angle. The SWIR reflectance screen thus utilizes two thresholds. Snow pixels with SWIR reflectance > 0.45 are reversed to not snow and bit 4 of NDSI_Snow_Cover_Algorithm_Flags_QA SDS is set. Snow pixels with 0.25 < SWIR reflectance ≤ 0.45 are flagged as having an unusually high SWIR for snow by setting bit 4 in the NDSI_Snow_Cover_Algorithm_Flags_QA SDS.

    Solar zenith screen

    When solar zenith angles exceed 70°, the low illumination challenges snow cover detection. As such, pixels with solar zenith angles > 70° are flagged by setting bit 7 in the NDSI_Snow_Cover_Algorithm_Flags_QA SDS. This solar zenith mask is set across the entire swath. Note: night is defined as a solar zenith angle ≥ 85°. Night pixels are assigned a value 211.

    Lake Ice

    Ice/snow covered lake ice are detected by applying the snow algorithm specifically to inland water bodies. These data are provided so that the MODIS user community can evaluate the efficacy of this technique. Inland water bodies are flagged by setting bit 0 in the NDSI_Snow_Cover_Algorithm_Flags_QA SDS. Users can extract or mask inland water in the NDSI snow cover SDS using this flag. The algorithm relies on the basic assumption that a water body is deep and clear and therefore absorbs all of the solar radiation incident upon it. Water bodies with algal blooms, high turbidity, or other relatively high reflectance conditions may be erroneously detected as snow/ice covered.

    Cloud Masking

    Clouds are masked using the Unobstructed Field of View (UFOV) cloud mask flag from MOD35_L2. Values in the 1 km mask value are applied to the four corresponding 500 m pixels.  If the cloud mask flags “certain cloud,” the pixels are masked as cloud.  Values of “confident clear," “probably clear,” or “uncertain clear” are interpreted as clear in the snow cover algorithm.

    Abnormal Condition Rules

    If radiance data are missing in any of the MODIS bands used by the algorithm, the pixel is set to "missing data" and is not processed for snow cover. Unusable radiance data are set to "no decision."

    Version History

    See the MODIS | Data Versions page for the history of MODIS snow and sea ice product versions.

    Error Sources

    Anomalies in the input data can propagate to the output. Table 3 lists the MODIS products that are used as input to the snow cover algorithm. Although developing a global snow cover detection algorithm presents a variety of challenges, the NDSI technique has proven to be a robust indicator. Numerous investigators have utilized MODIS snow cover data sets and reported accuracy in the range of 88% to 93%. Consult the MODIS Snow Products Collection 6 User Guide for more details about potential sources of error in the MODIS snow cover data sets.

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    Quality Assessment

    Quality Assessment (QA) in Version 6 consists of:

    • Basic QA values stored in NDSI_Snow_Cover_Basic_QA
    • Bit flags stored in NDSI_Snow_Cover_Algorithm_Flags_QA that report data screen results

    Basic QA values provide a qualitative estimate of the algorithm result for a pixel based on the input data and solar zenith data. The basic QA value is initialized to "best" and then adjusted as needed based on the quality of the MOD02HKM input radiance data and the solar zenith angle screen.  If the MOD02HKM data (TOA reflectance) lie outside the range of 5% to 100% but are still usable, the QA value is set to good.  If the solar zenith angle is in range of 70° ≤ solar zenith angle < 85°, the QA is set to okay to indicate the increased uncertainty stemming from low illumination. If the input data are unusable, the QA value is set to "other." The conditions for a poor result are not defined (i.e. this value is not currently used). Features that are masked, like night and ocean, use the same values as the snow cover SDS.

    Bit flags can be used to investigate results for all pixels which have been processed for snow. By examining the bit flags, users can determine if any of the data screens: a) changed a pixel's initial result from "snow" to "not snow"; or b) flagged snow cover in a pixel as uncertain. The Processing Steps section above describes each data screen and the location of its bit flag. Consult the Interpretation of Snow Cover Detection Accuracy, Uncertainty, and Errors section of the MODIS Snow Products Collection 6 User Guide to see how each screen should be interpreted.

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    Instrument Description

    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 an altitude of 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, and is 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, short- and mid-wavelength infrared, and long wavelength infrared spectral regions.

    The MODIS instruments on the Terra and Aqua space vehicles were built to NASA specifications by Santa Barbara Remote Sensing, a division of Raytheon Electronics Systems. Table 4 contains the instruments' technical specifications:

    Table 4. MODIS Technical Specifications
    Variable Description
    Orbit 705 km altitude, 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 m x 1.6 m 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 6 years


    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 short wave infrared bands. The Solar Diffuser Stability Monitor 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 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.

    For additional details about the MODIS instruments, see NASA's MODIS | About Web page.

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    References and Related Publications

    Contacts and Acknowledgments

    Principal Investigators

    Miguel O. Román
    NASA Goddard Space Flight Center
    Mail Code: 619
    Greenbelt , MD 20771

    Dorothy K. Hall
    NASA Goddard Space Flight Center
    Mail Code 615
    Greenbelt, MD 20771

    George A. Riggs
    NASA Goddard Space Flight Center
    Science Systems and Applications, Inc.
    Mail stop 615
    Greenbelt, MD 20771

    Document Information


    December 2006


    August 2007
    March 2016

    Questions? Please contact:

    NSIDC User Services
    Phone: 1 303 492-6199
    Fax: 1 303 492-2468
    Contact Address:
    National Snow and Ice Data Center
    CIRES, 449 UCB
    University of Colorado
    City: Boulder
    Province or State: CO
    Postal Code: 80309-0449
    Country: USA