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MODIS/Aqua Snow Cover Daily L3 Global 500m Grid (MYD10A1) contains snow cover and quality assurance (QA) data in HDF-EOS format, along with corresponding metadata. Data files after 14 September 2003 include a snow albedo array with a 'beta' status. MYD10A1 consists of 1200 km by 1200 km tiles of 500 m resolution data gridded in a sinusoidal map projection. MODIS/Aqua V004 data extend from 04 July 2002 to 03 January 2007. A sample of MODIS/Aqua V003 data from 29 August 2002 (day 241) through 07 October 2002 (day 280) allows users to correlate between V003 and later versions. This collection also includes several granules that were not reprocessed into V004. 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.
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 (2003), 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 500m Grid V004, January to March 2003. Boulder, CO, USA: National Snow and Ice Data Center. Digital media.
|Spatial coverage and resolution||
Coverage is global, but only land tiles are produced. Gridded resolution is 500 m.
|Temporal coverage and resolution||
Data extend from 04 July 2002 to present.
|Tools for accessing data||Data range||
|Grid type and size||
Data are gridded in equal-area tiles in a sinusoidal projection. Each tile consists of a 1200 km by 1200 km data array, which corresponds to 2400 pixels by 2400 pixels at 500 m resolution.
|File naming convention||
17 MB per data granule
The snow mapping algorithm classifies pixels as snow, snow-covered lake ice, cloud, water, land, or other. Snow extent is the primary variable of interest in this data set.
|Procedures for obtaining data||
Contact NSIDC User Services to order data.
Dorothy K. Hall
NASA Goddard Space Flight Center
Greenbelt, MD 20771
Vincent V. Salomonson
NASA Goddard Space Flight Center
Greenbelt, MD 20771
George A. Riggs
Science Systems and Applications, Inc. NASA Goddard Space Flight Center
Greenbelt, MD 20771
NSIDC User Services
National Snow and Ice Data Center
CIRES, 449 UCB
University of Colorado
Boulder, CO 80309-0449 USA
phone: +1 303.492.6199
fax: +1 303.492.2468
form: Contact NSIDC User Services
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 newest version available, which is the highest version number. MYD10A1 V004 granules after 14 September 2003 include a snow albedo data array with a beta status. This early-release data array allows users to gain familiarity with data formats and parameters. It is minimally validated and may contain significant errors. This product is not appropriate as the basis for quantitative scientific publications.
Please visit the following sites for more information about known data problems, production schedule, and future plans:
MODIS snow 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 software, support the HDF-EOS data format. See the Software section for more information.
MYD10A1 consists of 2400 x 2400 cells of tiled data in a sinusoidal projection. Each data granule contains the following HDF-EOS fields:
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 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 was investigated for quality assurance. The metadata file should be examined to determine if post-production QA was applied to the granule (Riggs, Hall, and Salomonson 2003).
MYD = Aqua
2003 = Year of data acquisition
138 = Julian date of data acquisition (day 138)
h03 = horizontal tile number (3)
v06 = vertical tile number (6)
004 = version number
2003 = Year of production (2003)
143 = Julian date of production (day 143)
062148 = Hour/minute/second of production in GMT (06:21:48)
Each data granule is 17 MB.
Coverage is global, but only land tiles are produced for MYD10A1. The following resources can help you select and work with MYD10A1 tiles:
Gridded resolution is 500 m.
MYD10A1 data are georeferenced to an equal-area sinusoidal projection. Many software packages support the sinusoidal projection, while few supported the integerized sinusoidal map projection. The MODIS Science Team implemented the change in response to user feedback and in an effort to make the data more accessible with existing software applications. The following software tools either read data in a sinusoidal projection or convert sinusoidal to other projections:
In the sinusoidal projection, areas on the data grids are proportional to same areas on the Earth, and distances are correct along all parallels and the central meridian(s). Shapes are increasingly distorted away from the central meridian(s) and near the poles. Finally, the data are not conformal, perspective, or equidistant (USGS 2003).
Meridians are represented by sinusoidal curves (except for the central meridian), and parallels are represented by straight lines. The central meridian and parallels are straight lines of true scale (Pearson 1990). Specific parameters follow:
Data are gridded in equal area tiles, each consisting of a 1200 km by 1200 km data array, which corresponds to 2400 x 2400 cells at 500 m resolution. The following image shows tile locations for MYD10A1 data in a sinusoidal projection. Click on the thumbnail to view a larger image.
The MODLAND Tile Calculator converts between MODIS tile numbers and latitude/longitude coordinates.
Data extend from 04 July 2002 to 03 January 2007.
Temporal resolution is daily for MYD10A1.
The snow mapping algorithm classifies pixels as snow, snow-covered lake ice, cloud, water, land, or other. Snow extent is the primary variable of interest in this data set.
1: No decision
3: Scan angle limit exceeded
4: Erroneous data
5: Non-production mask
7: Tile fill
8: No input tile expected
25: Snow-free land
37: Lake or inland water
39: Open water (ocean)
50: Cloud obscured
100: Snow-covered lake ice
254: Detector saturated
The snow albedo array has a different set of pixel values:
-6 or 250: Missing
0-100: Albedo (percentage)
101: No decision
125: Snow-free land
137: Lake or inland water
139: Open water (ocean)
252: Land mask mismatch
253: BRDF failure
254: Non-production mask
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).
Version 4 Level-2 snow products, which are input to creating MYD10A1, contain an additional cloud mask that is more liberal in mapping snow cover. The liberal cloud mask allows snow mapping to occur on more pixels, resulting in better accuracy of snow mapping in regions with mixed snow and clouds. The liberal cloud mask, however, has the potential to falsely label ice clouds as snow (Riggs, Hall, and Salomonson 2003).
Level-2 swaths contain a mix of visible and fill data wherever the MODIS instrument switches from day to night operation. This combination passes through to the CMG snow cover products which show a discontinuity of data through dark regions. Users will note a sharp change between daylight and polar darkness observations. Dark regions extend toward the poles but change irregularly to values of "no observation" (0), followed by another sharp change to fill the data. This happens because the top row of tiles in the integerized sinusoidal grid is not generated when the polar region is in complete darkness. Thus, MOD10C2 users should interpret all regions poleward of the first darkness data as being in darkness.
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 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 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). 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.
Contact NSIDC User Services to order data.
The following sites can help users select appropriate MODIS data for your study:
The Land Processes (LP) DAAC developed and distributes the MODIS Reprojection Tool (MRT), which reprojects MODIS data that are in the sinusoidal projection to other projections.
HDF-EOS to GeoTIFF converter (HEG): This free tool converts many HDF-EOS files to GeoTIFF, native binary, or HDF-EOS Grid. It also has reprojection, resampling, subsetting, stitching (mosaicing), and metadata creation capabilities.
Hierarchical Data Format - Earth Observing System (HDF-EOS): NSIDC provides more information about the HDF-EOS format, tools for extracting binary and ASCII objects from HDF, and a list of other HDF-EOS resources.
See MODIS Data Summaries for other MODIS snow and sea ice products available from NSIDC.
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).
Albedo refers to the percentage of incident solar radiation reflected back by an object. The albedo measured by MODIS is specifically termed a directional-hemispheric reflectance, as the incident irradiance is considered a directional collimated beam, and the reflected radiance is integrated over the upward hemisphere (Klein and Stroeve 2002).
There are several specific definitions of reflectance (see below), but in general, reflectance involves the diffuse scattering of light by a geometrically complex surface (Hapke 1993). Reflectance varies according to the degree of collimation, the incident irradiance, and the collimation of the detector. Collimation refers to the degree of angular diffusion of the the incident light or the size of the angular field of view of the detector. For instance, direct beam solar energy is considered highly collimated whereas the diffuse sky radiance is uncollimated; a narrow-angle field of view detector can be considered collimated while a hemispherical sensor such as a pyranometer is an uncollimated detector. The following table (modified from Diner et al. 1999) summarizes the most commonly-used terms for reflectance.
|Bidirectional Reflectance Distribution Function (BRDF)||Surface-leaving radiance divided by incident irradiance from a single direction|
|Bidirectional Reflectance Factor (BRF)||Surface-leaving radiance divided by radiance from a Lambertian reflector illuminated from a single direction|
|Hemispherical-Directional Reflectance Factor (HDRF)||Surface-leaving radiance divided by radiance from a Lamberian reflector illuminated under the same ambient conditions|
|Directional Hemispherical Reflectance (DHR)||Radiant exitance divided by irradiance under illumination from a single direction|
|Bihemispherical Reflectance (BHR)||Radiant exitance divided by irradiance under ambient illumination conditions|
The following figure (modified from Hapke 1993) summarizes how variations of reflectance depend upon the degree of collimation.
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).
|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|
|Power||162.5 W (single orbit average)|
|Data Rate||10.6 Mbps (peak daytime); 6.1 Mbps (orbital average)|
|Spatial Resolution||250 m (bands 1-2)
500 m (bands 3-7)
1000 m (bands 8-36)
|Design Life||Six years|
|Primary Use||Band||Bandwidth||Spectral Radiance|
|Atmospheric Water Vapor||17||890-920 nm||10.0|
|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)|
|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)|
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°.
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).
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.
A ±55° scanning pattern at 705 km achieves a 2330 km swath, with global coverage every one to two days.
The MODIS sensor contains a system whereby visible light from the earth passes through a scan aperture and into a scan cavity to a scan mirror. The double-sided scan mirror reflects incoming light onto an internal telescope, which in turn focuses the light onto four different detector assemblies. Before the light reaches the detector assemblies, it passes through beam splitters and spectral filters that divide the light into four broad wavelength ranges. Each time a photon strikes a detector assembly, an electron is created. Electrons are collected in a capacitor where they are eventually transferred into the preamplifier. Electrons are converted from an analog signal to digital data, and downlinked to ground receiving stations (MODIS Web 2003).
The EOS Ground System (EGS) consists of facilities, networks, and systems 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 eight-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 eight-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).
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.
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 eight-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)
See the MODIS/Terra Snow Cover 5-Min L2 Swath 500m documentation for processing steps used in MOD10_L2, the input to this data set.
In MYD10A1, a daily snow cover map is constructed by examining the multiple observations acquired for a day that are mapped to each grid cell. For Version 4 data, a scoring algorithm, based on pixel location and solar elevation, selects an observation for the day. Observations are scored based on distance from nadir, area of coverage in a grid cell, and solar elevation. The object of scoring is to select the observation nearest to nadir with greatest coverage at the highest solar elevation that was mapped into the grid cell. The scoring algorithm is represented by the following equation (Riggs, Hall, and Salomonson 2003):
score = 0.5 * (solar elevation) + 0.3 * (distance from nadir) + 0.2 * (observation coverage)
If the relative view azimuth of the selected observation is outside a set limit, a bit in the Snow Spatial QA field is set to indicate the condition.
Results of the snow cover algorithm are used to create a daily snow map, and are stored in the Day Tile Snow Cover field. The corresponding QA data are stored in the Snow Spatial QA field. Gridded snow cover data are stored as coded integer values, with values being the same as assigned for MOD10_L2.
MYD10A1 Snow Albedo
The prototype MODIS snow albedo algorithm for MYD10A1 is described in detail in Klein and Stroeve (2002) and MODIS Snow Albedo Prototype. Albedo is calculated only for areas identified as cloud-free by the MODIS cloud mask and as snow-covered by the MODIS snow algorithm. Once a pixel meets these criteria, atmospherically corrected surface reflectances are retrieved from the MODIS/Terra Surface Reflectance Daily L2G Global 500 m ISIN Grid product, available from The Land Processes (LP) DAAC (Klein and Stroeve 2002).
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.
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., 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. <http://modis-snow-ice.gsfc.nasa.gov/atbd.html> .
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.
Klein, A. MODIS Snow Albedo Prototype. 2003. <http://geog.tamu.edu/klein/modis_albedo/> Accessed July 2003.
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 Science and Instrument Team. MODIS Web. July 2003. <http://modis.gsfc.nasa.gov/> Accessed October 2000.
Riggs, G.A., D.K. Hall, and V.V. Salomonson. January 2003. MODIS snow products user guide for collection 4 data products. <http://modis-snow-ice.gsfc.nasa.gov/sug_main.html> .
United States Geological Survey. "Sinusoidal Equal Area." Map Projections. 2003. <http://erg.usgs.gov/isb/pubs/MapProjections/projections.html#sinusoidal> Accessed December 2000.
Please see the EOSDIS Glossary of Terms for a general list of terms.
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
MODLAND: MODIS Land
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