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Landsat Collection 2 Level-3 Fractional Snow Covered Area Science Product

Snow cover is spatially and temporally variable and is often concentrated in remote or inaccessible land regions making spaceborne remote sensing the most feasible approach to measure and monitor snow cover change. Landsat’s spatial resolution offers the capability to map snow cover patterns across topographically complex mountainous regions.

Return to Landsat Fractional Snow Covered Area Product Overview

Return to Landsat Science Products Overview

 

While Landsat’s acquisition frequency limits analysis of short-term snow cover variations, longer term changes in snow cover duration and persistence can be detected. The Landsat Collection 2 (C2) Fractional Snow Covered Area (fSCA) product provides per-pixel fractional snow cover maps that indicate the percentage of pixels covered by snow for Landsat 4-9 data.

Example of the Landsat Collection 2 Fractional Snow Covered Snow product
Example of the Landsat Collection 2 Fractional Snow Covered Area (fSCA) Science Product showing an area in the Dixie National Forest in Utah on February 28, 2021 for tile h007V010. Left: Landsat 8 Collection 2 U.S. Analysis Ready Data Surface Reflectance image (Bands 6,5,4), Middle: fSCA, and Right: Canopy Adjusted fSCA.

Available for Landsat 4-5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8-9 Operational Land Imager (OLI) data, C2 fSCA is generated from Landsat Collection 2 U.S. Analysis Ready Data (ARD) Surface Reflectance and Top of Atmosphere Reflectance data.   

All fSCA products are processed to 30-meter spatial resolution in Albers Equal Area (AEA) projection using the World Geodetic System 1984 (WGS84) datum and gridded to the ARD tiling scheme consisting of fixed 5000-meter squared non-overlapping tiles. Products are delivered in various formats, including Cloud Optimized GeoTIFF (COG) files for the fSCA raster files and Extensible Markup Language (XML) for metadata files. Spatial reference information is embedded within the COG files.

NOTE: this page provides basic information about the Landsat C2 fSCA product.  Please refer to the Landsat Collection 2 Fractional Snow Covered Area Data Format Control Book for more descriptive information. 

Product Availability

The Landsat C2 fSCA product is available for the western, northern, and northeastern CONUS, and Alaska for the following date ranges:

  • Landsat 9 OLI: October 2021 to present
  • Landsat 8 OLI: April 2013 to present
  • Landsat 7 ETM+: July 1999 to April 2022
  • Landsat 5 TM: March 1984 to May 2012
  • Landsat 4 TM: November 1982 to December 1993

Collection 2 fSCA Product Improvements

  • fSCA science product expanded to include northern areas of the conterminous U.S. (see CONUS map below)
  • fSCA science product expanded to include the Aleutian Islands (see Alaska map below). Note: The H/V tile grid numbers of the Alaska C2 ARD and ARD-derived products has changed from C1.  
  • The C1 SNOW band has been expanded in C2 to separate VIEWABLE and GROUND SNOW bands to show whether the SCAG results are adjusted for the canopy cover.
  • New Shade fractions (SCAG_SHADE) and SCAG Model Mask (SCAG_MASK) bands have been added. (See fSCA Band Files table below)
  • fSCA Statistics for  monthly and annual fractions and clear pixel counts for the following time spans:
    • 5-year (7): 1986-1990, 1991-1995, 1996-2000, 2001-2005, 2006-2010, 2011-2015, 2016-2020, etc.
    • Whole stack (1): starting year of the ARD stack to ending year of stack.

The maps below shows where Landsat C2 fSCA and Canopy Adjusted fSCA data are available for the CONUS and Alaska. (Click to view a larger image.)

Landsat Collection 2 fSCA Grid Map

Acquisition-based C2 fSCA Package Contents

The Landsat C2 fSCA product contains an acquisition-based per-pixel snow cover fraction, a canopy adjusted fSCA, a fractional shade map, a model mask, a quality assessment mask, and a product metadata file.  The bands included in the C2 fSCA acquisition-based product are listed below and displayed in the following table. 

  • Fractional Snow Covered Area (VIEWABLE_SNOW)Raster Layer: Indicates the percentage of the pixel covered by snow visible from the instrument (determined by Thematic Mapper- Based Fractional Snow Cover and Grain Size (TMSCAG).
    Delivered file name: tileID_VIEWABLE_SNOW.TIF
  • Canopy Adjusted Fractional Snow Covered Area (GROUND_SNOW) *Raster Layer: Indicates the canopy adjusted percentage of the pixel covered by snow.
    Delivered file name: tileID_GROUND_SNOW.TIF
  • SCAG Shade Endmember (SCAG_SHADE)* - Raster Layer: Indicates the percentage of the pixel covered by shade visible from the instrument (determined by TMSCAG).
    Delivered file name:  tileID_SCAG_SHADE.TIF
  • SCAG Model Mask (SCAG_MASK)* - Raster Layer: Identifies whether pixels were successfully modeled in TMSCAG, and for TM/ETM+ which saturation run was used to populate the pixel.
  • Delivered file name:  tileID_SCAG_MASK.TIF
  • fSCA QA (FSCA_QA)**Raster Layer: This mask (based on the input Level 1 pixel QA) flags pixels identified as fill, cloud, cirrus, revised cloud, water, terrain shadow, and NLCD fill.
    Delivered file name: tileID_FSCA_QA.TIF

*New Bands in the C2 fSCA product
**FSCA_QA was named Revised Cloud Mask (REVCM) in the Collection 1 fSCA product. 

 
File Specifications of Collection 2 Fractional Snow Covered Area Products
Band Name
Description 
Data
Type
Units Range Valid
Range
Fill
Value
Scale
Factor
VIEWABLE_SNOW
fSCA as identified by
TMSCAG algorithm
INT16 Fraction -32768
to 32767
0
to
1000
-9999 0.001
GROUND_SNOW
Canopy Adjusted fSCA
INT16 Fraction 0
to
1000
0 to 1000 -9999 0.001
SCAG_SNOW
Shade Fractions from
TMSCAG algorithm
 
INT16 Fraction -32768
to 32767
0 to 1000 -9999 0.001
SCAG_MASK
SCAG Model Mask from
TMSCAG algorithm
 
UINT8 Flag 0-255 1-3, 99 0 N/A
FSCA_QA
Level-3 fSCA QA Mask
UINT8 Flag 0-255 0-255 1 N/A

Caveats and Constraints

  • The C2 fSCA product is derived from available Landsat C2 U.S. ARD SR products. SR products are generated using two algorithms, the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) for Landsat 4-7 and the Land Surface Reflectance Code (LaSRC) for Landsat 8. Occasionally, Landsat data cannot be processed to SR due to missing auxiliary data. More information pertaining to the auxiliary data characteristics is described in the Landsat Atmospheric Auxiliary Data DFCB. Date ranges of missing auxiliary data are listed under “Caveats and Constraints” on the Landsat Collection 2 Surface Reflectance webpage.
  • The fSCA algorithm tends to flag water boundaries and dark water pixels as false positives. The fSCA QA is designed to use the Landsat C2 Level 3 DSWE science product to alleviate many of these false-positives.
  • The fSCA algorithm uses National Land Cover Database (NLCD) canopy cover and land cover products for canopy adjustment to address canopy covered areas of snow. If the NLCD canopy cover or land cover products are not accurate for a particular area due to fire, logging, urbanization, etc., the canopy-adjusted results may have issues with accuracy.
  • The fill value for the viewable snow (VIEWABLE_SNOW) and shade fractions (SCAG_SHADE) fall in the valid data range. Users are advised to use the fill bit in FSCA_QA band to mask NoData pixels.

Landsat Collection 2 fSCA Statistics Product

The fSCA mean snow cover fraction and clear pixel count statistics are available for unique time steps between 1984 and the current year. These time steps include 5-year date ranges and the entire U.S. ARD stack period. Each fSCA Statistic package contains monthly (entire U.S. ARD stack only), annual, and mean annual viewable or ground snow cover statistics files for each unique time step, over a single tile location (e.g., 2001-2005 statistics for tile h002v005). fSCA Statistics will be updated annually each February. 

C2 fSCA Statistics Package Contents

The entire stack time period will have 24 monthly, 2 annual, and 1 mean annual file, all described in a single XML metadata file for the that time period. Each unique 5-year time period will have two annual files, listed in a single XML metadata file for that time period. The statistics files are described below.

  • Monthly fractions – “MFRACTIONS” : The mean snow cover of all acquisitions within month “x” for each year in the n-year time step.
    Delivered file name: tileID_MFRACTIONS
  • Monthly cloud free count – “MCLDFREE”: The number of clear pixels used for the monthly snow cover fractions.
    Delivered file name: tileID_MCLDFREE
  • Annual fractions – “AFRACTIONS”: The mean snow cover fractions of all acquisitions in the n-year span.
    Delivered file name: tileID_AFRACTIONS
  • Annual cloud free count – “ACLDFREE”: The number of clear pixels used for the annual snow cover fractions.
    Delivered file name: tileID_ACLDFREE
  • Mean annual fractions – “MEAN_AFRACTIONS”: The mean of all monthly snow cover fractions over the n-year span.
    Delivered file name: tileID_MEAN_AFRACTIONS

Data Access

The Landsat Fractional Snow Covered Area product is available for download from EarthExplorer or through the commercial cloud

In EarthExplorer, the data are located under the Landsat category, Landsat Collection 2 Level-3 Science Products subcategory, and listed as Landsat 4-8 C2 Fractional Snow Covered Area.

The Fractional Snow Covered Area Statistics are also available in the same location on EarthExplorer – these are contained as a separate dataset and listed as Landsat 4-8 C2 fSCA Statistics.   

Visit the Landsat Data Access webpage for additional information about cloud access and bulk download option.    

Documentation

Landsat Collection 2 Level-3 Fractional Snow Covered Area Data Format Control Book 

Landsat Collection 2 Fractional Snow Covered Area Algorithm Description Document (ADD)

Landsat Collection 2 Fractional Snow Covered Area Digital Object Identifier (DOI): 10.5066/P97ALZ2X

Landsat Collection 2 Fractional Snow Covered Area Statistics Digital Object Identifier (DOI): 10.5066/F7VQ31ZQ

Citation Information

There are no restrictions on the use of Landsat Science Products. It is not a requirement of data use, but the following citation may be used in publication or presentation materials to acknowledge the USGS as a data source and to credit the original research.

Landsat Collection 2 Level-3 Fractional Snow Covered Area Science Product courtesy of the U.S. Geological Survey. Digital Object Identifier https://doi.org/10.5066/P97ALZ2X

Landsat Collection 2 Level-3 Fractional Snow Covered Area Statistics Product Digital Object Identifier https://doi.org/10.5066/F7VQ31ZQ

The Fractional Snow Covered Area product is based directly on work described in this publication

Rittger, K., Bormann, K.J., Blair, E.H., Dozier, J., Painter, T.H., 2021. Evaluation of VIIRS and MODIS Snow Cover Fraction in High-Mountain Asia Using Landsat 8 OLI. Frontiers in Remote Sensing, 2, 2673-6187 DOI: 10.3389/frsen.2021.647154

Painter, T. H., Rittger, K., McKenzie, C., Slaughter, P., Davis, R. E., & Dozier, J., 2009. Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sensing of Environment, 113(4), 868-879. DOI: 10.1016/S0034-4257(02)00187-6

 

References

Selkowitz, D.J., Painter, T.H., Rittger, K.E., Schmidt, G., & Forster, R., 2017. "The USGS Landsat Snow Covered Area Products: Methods and Preliminary Validation." Automated Approaches for Snow and Ice Cover Monitoring Using Optical Remote Sensing. D. Selkowitz. Salt Lake City, UT: The University of Utah. pp. 76-119. Available online.

Selkowitz, D. J., & Forster, R. R., 2016. Automated mapping of persistent ice and snow cover across the western U.S. with Landsat. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 126-140. DOI: 10.1016/j.isprsjprs.2016.04.001

Selkowitz, D.J, Painter, T., Schmidt, G., Rittger, K., and Forster, R., 2015, The USGS Landsat Snow Covered Area Science Data Product [poster], in Fall Meeting, San Francisco, Calif., 14-18 December 2015, Fall Meeting Abstracts: Washington, D.C., American Geophysical Union, abstract number C41D-0759.

Selkowitz, David. 2011. Landsat-derived Patterns of Snow Covered Area (SCA) and the Potential for Enhancing the Spatial Resolution of MODIS-derived SCA Estimates. AGU Fall Meeting Abstracts 05.