MODIS/Aqua Snow Cover Daily L3 Global 0.05Deg CMG, Version 4


MODIS/Aqua Snow Cover Daily L3 Global 0.05Deg CMG (MYD10C1) consists of 7200-column by 3600-row global arrays of snow cover in a 0.05 deg climate modeling grid (CMG). Data and quality assurance (QA) fields are in HDF-EOS format, along with corresponding metadata. Data extend from 04 July 2002 to 03 January 2007. MODIS snow cover data are based on a snow mapping algorithm that employs a Normalized Difference Snow Index (NDSI) and other criteria tests. 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

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.

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 (2000), data set title and version, date of the version you used, publishers (NSIDC), and digital media.

Hall, D.K., G.A. Riggs, and V.V. Salomonson. 2003, updated daily. MODIS/Aqua Snow Cover Daily L3 Global 0.05Deg CMG V004, January to March 2003. Boulder, CO, USA: National Snow and Ice Data Center. Digital media.

Overview Table

Category Description
Data format


Spatial coverage and resolution Coverage is global. Grid resolution is 0.05°.
Temporal coverage and resolution

Data extend from 04 July 2002 to 03 January 2007. Temporal resolution is daily.

Tools for accessing data


Data range

Pixel values are as follows:

0: Snow-free land
1-100: Percent snow in cell
111: Night
252: Antarctica
253: Data not mapped
254: Open water (ocean)
255: Fill

Grid type and size

The CMG products contain global snow cover arrays of 7200 columns by 3600 rows. Each cell is 0.05° resolution.

File naming convention

Example: "MYD10C1.A2001033.004.2001268165647.hdf"

File size

Each data granule is 101.36 MB.


The snow mapping algorithm for CMG products classifies pixels as snow, snow-free land, cloud, night, masked (Antarctica), or no data. Snow extent is the primary variable of interest in this data set.

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
University of Colorado
Boulder, CO 80309-0449  USA
phone: +1 303.492.6199
fax: +1 303.492.2468
form: Contact NSIDC User Services

2. Detailed Data Description

Algorithms that generate snow cover 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.

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


MYD10C1 consists of 7200-column by 3600-row global arrays of snow cover. MYD10A1 500 m cells that lie within a 0.05° resolution CMG cell are binned to determine surface features. Data are gridded in a geographic projection. Each data granule contains the following HDF-EOS fields:

Snow percentage in each cell of the Day CMG Snow Cover field is calculated using 500 m totals of the number of snow observations and count of land observations in that cell for the day. The percentage of snow-covered land is based on the clear-sky view of land in the CMG cell. So, the amount of snow observed in a CMG cell is based on the cloud-free observations mapped into the CMG grid cell for all land in that cell.

percent snow = 100 * count of snow observations/count of land

Cloud percentage in each cell of the Day CMG Cloud Obscured field is calculated in the same way as the percentage of snow, except that the count of cloud observations is used. Data from the Day CMG Snow Cover and Day CMG Cloud Obscured fields are used together to better understand the observed snow. For example, if MODIS views a snow-covered region and no clouds obstruct the view on that day, then the percentage of snow cover is 100 percent. If there is 30 percent cloud cover for that day, the percentage of snow cover is 70 percent.

percent cloud obscured = 100 * count of cloud observations/count of land

The Day CMG Confidence Index field represents an estimate of confidence of the data value in each cell. The index indicates how confident the algorithm is that snow percentage in a cell is correct based on which data (snow, snow-free land, cloud, and unknown) were binned into the grid cell.

The Snow Spatial QA field provides additional information on algorithm results for each pixel within a spatial context, and is used as a measure of usefulness for snow-cover data. QA data are stored as bit flags, and QA information is extracted by reading the bits within a byte. (See MODIS Snow Cover 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).


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:  "MYD10C1.A2003121.004.2003142152431.hdf"


2003 = Year of data acquisition
121 = Julian date of data acquisition (day 121)
004 = Version of data type (Version 4)
2003 = Year of production (2003)
142 = Julian date of production (day 142)
152431 = Hour/minute/second of production in GMT (15:24:31)

File Size

Each data granule is 101.36 MB.

Spatial Coverage

Coverage is global, but snow extent is calculated for land pixels only.

Spatial Resolution

Gridded resolution is 0.05°.


MYD10C1 is in a geographic projection.

Grid Description

The CMG products contain global snow cover arrays of 7200 columns by 3600 rows. Each cell is 0.05° resolution. The following is a sample image derived from MYD10C1 data.


See Geolocating MODIS Climate Modeling Grid (CMG) Data in ENVI.

Temporal Coverage

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

Temporal Resolution

Temporal resolution is daily for MYD10C1.

Parameter or Variable

Parameter Description

The snow mapping algorithm for CMG products classifies pixels as snow, snow-free land, cloud, night, masked (Antarctica), or no data. Snow extent is the primary variable of interest in this data set.

Parameter Range

Pixel values are as follows:

0: Snow-free land
1-100: Percent snow in cell
111: Night
252: Antarctica
253: Data not mapped
254: Open water (ocean)
255: Fill

Error Sources

Errors may exist in the reflectance calculations due to the anisotropy of snow and ice. Snow is not a Lambertian reflector and reflects more in a forward direction, particularly with aged snow. Thus, as snow ages, its anisotropy increases. The increase in forward scattering with snow age is greater in the near infrared wavelengths, relative to the visible wavelengths. Such errors will likely be greater at larger angles (30° or more) off nadir as the amount of reflected solar irradiance varies with view angle. Additionally, errors in precise reflectance value due to anisotropy related to topographic variability will be inherent in the data (Hall et al. 2001a).

Errors with the snow mapping algorithm are lowest in tundra and prairie regions. The maximum expected errors are 15 percent for forests, 10 percent for mixed agriculture and forest, and 5 percent for other land covers. Estimating snow cover is difficult in forests because trees partially or completely conceal underlying snow. These errors were used to estimate the expected maximum monthly and annual errors in Northern Hemisphere snow-mapping methods from the algorithm. The maximum monthly errors are expected to range from 5 percent to 9 percent for North America, and from 5 percent to 10 percent for Eurasia. The maximum aggregated Northern Hemisphere snow mapping error is estimated to be 7.5 percent. The error is highest, around 9 percent to 10 percent, when snow covers the Boreal Forest roughly between November and April (Hall et al. 2001b).

Quality Assessment

Quality indicators for MODIS snow data are represented by AutomaticQualityFlag and ScienceQualityFlag metadata objects and their corresponding explanations, AutomaticQualityFlagExplanation and ScienceQualityFlagExplanation, in the CoreMetadata.0 global attribute and also in the Snow Spatial QA data field. These are generated during production or in post-product scientific and quality checks of the data product.

The AutomaticQualityFlag is automatically set according to conditions for meeting data criteria in the snow mapping 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 are set after production, either after an automated QA program is run or after the data product is inspected by a qualified snow scientist. Content and explanation of this flag are dynamic so it should always be examined if present.

The Snow Cover PixelQA field provides additional information on algorithm results for each pixel within a spatial context, and is used as a measure of usefulness for snow-cover data. QA data are stored as bit flags, and QA information is extracted by reading the bits within a byte. (See MODIS Snow Cover 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). For example, intermediate checks for theoretical bounding of reflectance data and the NDSI ratio are made in the algorithm. In theory, reflectance values should lie within the 0-100 percent range, and the NDSI ratio should lie within the -1.0 to +1.0 range. Summary statistics are kept for pixels that exceed these theoretical limits; however, the test for snow is done regardless of violations of these limits. Violations are tracked and written as Auto_check_QA local attributes, as they suggest that error or other anomalies may have been introduced into the input data, indicating the need for further investigation.

The snow algorithm also identifies missing data and reports them in the output product. Certain expected anomalous conditions may exist with the input data, such as a few missing lines or unusable data from the MODIS sensor. In these cases, the snow algorithm makes no snow decision for an affected pixel. A snow spatial QA bit is set to indicate the cause, and the algorithm moves to the next pixel. Summary statistics are calculated for these conditions, and reported as Valid EV Obs Band x local attributes (Riggs, Hall, and Salomonson 2003).

The MODIS Land Quality Assurance Web site provides updated quality information for each product.

3. Data Access and Tools

Data Access

Contact NSIDC User Services to order data.

Software and Tools

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

Related Data Collections

See MODIS Data Summaries for other MODIS snow and sea ice products available from NSIDC.

4. Data Acquisition and Processing

Theory of Measurements

Satellites are well suited to the measurement of snow cover because the high albedo of snow presents a good contrast with most other natural surfaces except clouds. Spectral reflectivity of snow depends on grain size and shape, impurity content, liquid water content, depth, surface roughness, and solar elevation angle (Hall and Martinec 1985). Reflectance of fresh snow is very high in the visible wavelengths, but decreases in the near-infrared wavelengths especially as grain size increases. Because of natural aging and other factors such as soot or volcanic ash deposition, reflectance of snow decreases over time. Fresh snow can have a reflectance up to about 80 percent, but its reflectance may decrease to below 40 percent after snow crystals metamorphose (Hall et al. 1998).

Snow and Cloud Discrimination
Snow and cloud discrimination techniques are based on differences between cloud and snow/ice reflectance and emittance properties (Figure 1). Clouds typically have high reflectance in visible and near-infrared wavelengths while reflectance of snow decreases in shortwave infrared wavelengths (Hall et al. 1998).

Special Considerations for Dense Forests
The mapping of snow cover becomes limited in areas where snow cover is obscured by dense forest canopies. A forested landscape is never completely snow-covered because tree branches, trunks, and canopies may not be covered with snow. Often, in boreal forests, snow that falls on the coniferous tree canopy will not stay on the canopy for the entire winter because of sublimation. Thus, even in a continuously snow-covered area, much of the forested landscape will not be snow-covered. Furthermore, snow that falls onto the ground through the canopy may not be visible from above.

A canopy reflectance model (GeoSAIL) for discontinuous canopies is the basis for determining the fraction of sunlit crown, sunlit background, crown reflectance, canopy transmittance, shadowed crown, and shadowed background within a forest stand. Reflectances for the sunlit snow are calculated using the Wiscombe and Warren (1980) model, while reflectances from other components were measured from direct observations. A significant difference exists between the high reflectance of snow and the low reflectance of soil, leaves, and bark. Depending on the vegetation type, snow may also cause a decrease in the mid-infrared reflectance of the forest stand. In addition, reflectance in the visible spectrum often increases with respect to the near-infrared reflectance. This lowers the Normalized Difference Vegetation Index (NDVI), a complement to the NDSI (Figure 2).

The NDSI and NDVI are used together to discriminate between snow-free and snow- covered forests (Figure 2). Forested pixels have higher NDVI values compared with non-forested pixels. Thus, by using the NDSI and NDVI in combination, it is possible to lower the NDSI threshold in forested areas without compromising the algorithm performance in other land covers (Hall et al. 1998).

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°, driven by a motor encoder built to operate 100 percent of the time throughout the six year instrument design life. The optical system consists of a two-mirror off-axis afocal telescope which directs energy to four refractive objective assemblies: one each for the visible, near-infrared, shortwave-infrared, and longwave-infrared spectral regions (MODIS Web 2003).

Technical Specifications

Orbit 705 km, 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
1 620-670 nm 21.8
2 841-876 nm 24.7
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
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.


MODIS has a series of on-board calibrators that provide radiometric, spectral, and spatial calibration of the MODIS instrument. The blackbody calibrator is the primary calibration source for thermal bands between 3.5 µm and 14.4 µm, while the Solar Diffuser (SD) provides a diffuse, solar-illuminated calibration source for visible, near-infrared, and shortwave infrared bands. The Solar Diffuser Stability Monitor (SDSM) tracks changes in the reflectance of the SD with reference to the sun so that potential instrument changes are not incorrectly attributed to changes in this calibration source. The Spectroradiometric Calibration Assembly (SRCA) provides additional spectral, radiometric, and spatial calibration.

MODIS uses the moon as an additional calibration technique and for tracking degradation of the SD, by referencing the illumination of the moon since the moon's brightness is approximately the same as that of the Earth. Finally, MODIS deep space views provide a photon input signal of zero, which is used as a point of reference for calibration (MODIS Web 2003).

Data Acquisition Methods

Source or Platform Mission Objectives

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.

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 2003).

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 snow cover (MOD10_L2 and MYD10_L2), which is then used as input to create Level-3 gridded products for daily and 8-day snow cover (MOD10A1 and MOD10A2, respectively). The team bins the MOD10A1 and MOD10A2 data into corresponding cells of a 0.05° CMG to create daily and 8-day CMG products, respectively. These data are archived at the NSIDC DAAC and distributed to EOS investigators and other users via external networks and interfaces (MODIS Web 2003). Data are available to the public through the Warehouse Inventory Search Tool (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

The MODIS Science Investigator-led Processing System (SIPS) is responsible for algorithm development, product generation, and transfer of products to NSIDC.

A snow-mapping algorithm generates global daily and 8-day snow cover products from MODIS data. The algorithm identifies the presence of snow by reflectance or emittance properties in each 500 m pixel for each orbit. The snow mapping algorithm is based on the Normalized Difference Snow Index (NDSI). The NDSI is a measure of the difference between the infrared reflectance of snow in visible and shortwave wavelengths. The NDSI is adaptable for a number of illumination conditions, it does not depend on reflectance for a specific band, and it is partially normalized for atmospheric effects. The algorithm uses MODIS bands 4 (0.55 µm) and 6 (1.6 µm) from MOD02HKM to calculate the NDSI (Hall et al. 1998).

MODIS Level-1B Calibrated and Geolocated Radiances (MOD02HKM), available from the NASA Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC), are used as input to calculate reflectance, using the following equation (MCST 2000):

pcos(θ) = reflectance_scalesB(SIB - reflectance_offsetsB)

The reflectance_scales and reflectance_offsets are read from the metadata in each MOD02HKM granule, and they are computed from calibration parameters such that the reflectance product (p) is determined directly from the integer representation (SI) of the digital number. The subscript (B) refers to band. The result of this equation is then multiplied by 1/cos(θ) to give at-satellite reflectance (p):

p = pcos(θ) * 1/cos(θ)

Processing Steps

A binning algorithm maps MYD10A1 daily snow cover data at 500 m resolution into the corresponding cell of a 0.05° climate modeling grid (CMG) and computes snow and cloud percentages, QA, and a confidence index based on the mapping results. The algorithm generates these parameters based on the total number of observations of a class (snow, cloud, land, and others) and the total number of land observations mapped into a cell of the CMG. The objective of the algorithm and output product is to provide the user with an estimate of snow cover observed in a CMG cell, an estimate of how much of the land surface was obscured by clouds, and an index that determines the confidence of the estimates.

The binning algorithm places the different classes of observations into categories for each class. The MODIS snow algorithm analyzed all land pixels to determine if snow was present, during Level 2 processing. The snow algorithm processed only land pixels using the MOD03 land/water mask. A land bin sums all the observations made over land (snow, land, cloud, and others). This sum of land counts is the basis for expressing the percentage of snow, cloud, and the confidence index for each CMG cell.

A 0.05° land mask, derived from the University of Maryland 1 km global land cover data set, is used with the binning algorithm. If a CMG cell contains 12 percent or greater land, then it is classified as land and analyzed. If a cell contains less than 12 percent land, it is classified as ocean. This threshold was selected to minimize snow errors along coasts while being sensitive enough to map snow along coasts (Riggs, Hall, and Salomonson 2003).

Because Antarctica's surface is typically less than 1 percent snow-free (a value less than the global error rate for MODIS snow mapping), the algorithm classifies Antarctica as completely snow-covered. This also reduces confusion with cloud signatures (Riggs, Hall, and Salomonson 2003).

5. References and Related Publications

Diner, D.J., J.V. Martonchik, C. Borel, S.A.W. Gerstl, H.R. Gordon, Y. Knyazikhin, R. Myneni, B. Pinty, and M.M. Verstraete. 1999. MISR Level-2 surface retrieval Algorithm Theoretical Basis Document. Pasadena, CA: Jet Propulsion Laboratory.

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 2001a. Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow-, Lake Ice- and Sea Ice-Mapping Algorithms. Greenbelt, MD: Goddard Space Flight Center. <> .

Hall, D.K., J.L. Foster, V.V. Salomonson, A.G. Klein, and J.Y.L. Chien. 2001b. Development of a technique to assess snow-cover mapping accuracy from space. IEEE Transactions on Geoscience and Remote Sensing 39(2): 232-238.

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

Klein, A. MODIS Snow Albedo Prototype. 2003. <> Accessed July 2003.

Klein, A.G., and J. Stroeve. 2002. Development and validation of a snow albedo algorithm for the MODIS instrument. Annals of Glaciology 34: 45-52.

Klein, A.G., D.K. Hall, and G.A. Riggs. 1998. Improving snow-cover mapping in forests through the use of a canopy reflectance model. Hydrologic Processes 12(10-11): 1723-1744.

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. <> 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. January 2003. MODIS snow products user guide for collection 4 data products. <> .

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
CMG: Climate Modeling Grid
DAAC: Distributed Active Archive Center
EBNet: EOSDIS Backbone Network
ECS: EOSDIS Core System
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
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
ISIN: Integerized Sinusoidal
LP DAAC: Land Processes DAAC
MAS: MODIS Airborne Simulator
MCST: MODIS Characterization Support Team
MODIS: Moderate Resolution Imaging Spectroradiometer
MRT: MODIS Reprojection Tool
MSS: Multispectral Scanner
NASA: National Aeronautics and Space Administration
NCSA: National Center for Supercomputing Applications
NDSI: Normalized Difference Snow 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
SCA: Snow Covered Area
SCF: Science Computing Facility
SD: Solar Diffuser
SDP: Science Data Processing
SDSM: Solar Diffuser Stability Monitor
SIPS: Science Investigator-led Processing System
SRCA: Spectroradiometric Calibration Assembly
TM: Thematic Mapper
TOA: Top-of-atmosphere

Document Creation Date

February 2004

Document Revision Date

October 2009

Document URL