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MODIS/Terra 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 Terra satellite.
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- Fractional Snow Cover has been replaced by Normalized Difference Snow Index (NDSI) snow cover.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;
- 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.
|Temporal Resolution:||5 minute|
|Data Contributor(s):||Miguel Román, Dorothy Hall, George Riggs|
|Metadata XML:||View Metadata Record|
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/Terra 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: http://dx.doi.org/10.5067/MODIS/MOD10_L2.006. [Date Accessed].
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 Terra 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.
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.
Example File Name:
Refer to Table 1 for descriptions of the file name variables listed above.
|A||Acquisition date follows|
|DDD||Acquisition day of year|
|HHMM||Acquisition hour and minute in Greenwich Mean Time (GMT)|
|VVV||Version (Collection) number|
|ddd||Production day of year|
|hhmmss||Production hour/minute/second in GMT|
|.hdf||HDF-EOS formatted data file|
Data files are approximately 6.5 MB.
Coverage is global. Terra's sun-synchronous, near-polar circular orbit is timed to cross the equator from north to south (descending node) at approximately 10:30 A.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 Terra's orbital path:
- Daily Terra Orbit Tracks, Space Science and Engineering Center, University of Wisconsin-Madison
- NASA LaRC Satellite Overpass Predictor (includes viewing zenith, solar zenith, and ground track distance to specified lat/lon)
500 m (at nadir) for data fields
5 km for geolocation fields
Projection and Grid Description
None (latitude, longitude referenced)
MODIS Terra data are available from 24 February 2000 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 Terra mission have resulted in minor data outages. If you cannot locate data for a particular date or time, check the MODIS/Terra Data Outages Web page.
Each data file contains five minutes of swath data (a scene). Complete global coverage occurs every one to two days.
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:
|Scientific Data Set||Description|
NDSI snow cover plus other results. Possible values are:
A basic estimate of the quality of the algorithm result. Possible values are:
|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:
|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
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—
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:
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.
Software and Tools
The following sites can help you identify the right MODIS data for your study:
- NASA's Earth Observing System Data and Information System | Near Real-Time Data
- NASA Goddard Space Flight Center | MODIS Land Global Browse Images
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.
Data Acquisition and Processing
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:
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.
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:
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).
Table 3 lists the MODIS products that are used as inputs to the snow detection algorithm:
MODIS/Terra 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/Terra Calibrated Radiances 5-Min L1B Swath 1km
Bands: 31 (11.03 μm )
MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km
Land/Water Mask (see note); Solar Zenith Angle; Latitude; Longitude; Geoid Height
MODIS/Terra Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km
Unobstructed Field of View Flag; Day/Night Flag
The new land/water mask greatly improves the accuracy of lake and river locations compared with Version 5. Users will likely notice that many larger rivers are more continuous and that the number of mapped lakes has increased, especially in regions with small lakes such as northern Minnesota to the Northwest Territories.
The algorithm reads radiance data from MOD02HKM, geolocation data and the land/water mask from MOD03, and the cloud mask and day/night flag from MOD35_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
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
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
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.
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.
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."
See the MODIS | Data Versions page for the history of MODIS snow and sea ice product versions.
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.
Quality Assessment (QA) in Version 6 consists of:
- Basic QA values stored in
- Bit flags stored in
NDSI_Snow_Cover_Algorithm_Flags_QAthat 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.
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:
|Orbit||705 km altitude, 10:30 A.M. descending node (Terra), sun-synchronous, near-polar, circular|
|Scan Rate||20.3 rpm, cross track|
|Swath Dimensions||2330 km (cross track) by 10 km (along track at nadir)|
|Telescope||17.78 cm diameter off-axis, afocal (collimated) with intermediate field stop|
|Size||1.0 m x 1.6 m 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||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.
References and Related Publications
Contacts and Acknowledgments
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
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