MODIS-derived Snow Metrics



The National Park Service and Geographic Information Network of Alaska (GINA) developed an algorithm to derive snow cover climatology for Alaska using the MODIS snow cover daily product. 

The algorithm is two-fold and involves both data processing and the derivation of snow cover metrics. Terra MODIS snow cover daily 500m grid data (MOD10A1) are processed to reduce cloud obscuration through iterations of cloud reduction methods that include spatial, temporal, and snow cycle filtering. A total of 12 metrics (e.g. date of first snow, date of persistent snow cover) for each pixel are calculated.

Data Sources

The MODIS Terra Snow Cover Daily L3 Global 500m Grid data (MOD10A1) from the National Snow and Ice Data center (NSIDC) is used to calculate the snow metrics. Data description can be access at

The data files can be downloaded from

The MOD10A1 data contains snow cover, snow albedo, fractional snow cover, and Quality Assessment (QA) data along with corresponding metadata. It consists of 1200 km by 1200 km tiles of 500 m resolution data gridded in a sinusoidal map projection. For our purposes, we downloaded 26 tile files covering the Alaska region, created a mosaic, reprojected them into the Alaska Albers Projection (NAD83), and output the four scientific fields of snow cover, snow fraction, snow quality, and snow albedo into four single band GeoTIFF files, respectively. 

Product Details

The snow metrics will be updated yearly. Now 2001 to 2016 snow-year metrics are provided through the Web Coverage Service (WCS). 

URL for adding WCS in a GIS:

The snow metrics are also available through GINA's http server: Users should be aware of that files provided by the http server are in ENVI format. Each snow year metrics is composed of one image file and one header file.

Snow-metrics data file defines the following 12 snow metrics:

  1. first_snow_day, first day of the full snow season (FSS start day)
  2. last_snow_day, last day of the full snow season (FSS end day)
  3. fss_range, last_snow_day-first_snow_day +1
  4. longest_css_first_day, first day of the longest CSS segment (CSS start day)
  5. longest_css_last_day, last day of the longest CSS segment (CSS end day)
  6. longest_css_day_range, longest_css_last_day-longest_css_first_day +1
  7. snow_days, the number of snow days
  8. no_snow_days, the number of no snow days
  9. css_segment_num, the number of CSS segments
  10. mflag, pixel type (ocean, land, or lake/inland water) and type of snow (no snow, broken snow, or continuous snow)
  11. cloud_days, number of cloud days
  12. tot_css_days, total number of all days within CSS segments

Additional Notes:

  1. Days reported as metrics are counted as day of year beginning on Jan.1 of the year that precedes the name of the snow year. For example, the days reported in the snow metrics of the 2010 snow year are counted from Jan.1, 2009. The Snow_metrics_Calendar is available online.
  2. The values and their descriptions in mflag are defined in Table 1. Definition of mflag.
  3. The outputs are ENVI-format images. Each ENVI image actually includes a pair of files, a flat-binary data file and a ASCII head file. The Notes describes how to read ENVI image in ArcGIS, R, ans ENVI environments. 
Table 1. Definition of mflag
ocean (1) land (2) lake (3)
no-snow (10) 11 12 13
broken-snow (20) 21 22 23
css-snow (30) 31 32 33

Sample plots for snow metrics

Last Day Longest Css Modis Snow MetricsLast Day Longest Css Modis Snow MetricsRange Longest Css Modis Snow Metrics

Additional Information

Snow metrics algorithm documentation  is available here.

 MODIS Snow Cover Metrics for Alaska: Algorithm and Evaluation, was posted on the 47th Annual Alaska Survey and Mapping Conference, Feb,18-22, 2013 are available online.

Snow Cover Climatological analysis with MODIS Satellite Data for the National Park Service, Alaska - ArcGIS application, was posted on 49th Annual Alaska survey and Mapping Conference, Feb. 16-20, 2015, Anchorage.

Article: Deriving Snow Cover Metrics for Alaska from MODIS
by Chuck Lindsay, Jiang Zhu, Amy E. Miller, Peter Kirchner and Tammy L. Wilson
Remote Sens. 2015, 7(10), 12961-12985; doi:10.3390/rs71012961
Received: 27 May 2015 / Revised: 11 September 2015 / Accepted: 26 September 2015 / Published: 30 September 2015
Show/Hide Abstract | PDF Full-text (11511 KB)


Chuck Lindsay, Amy E. Miller, Parker Martyn from National Park Service Anchorage
for developing snow metrics algorithm

Michael E Budde for evaluation and feedback of snow metrics algorithm
Geographer - US Geological Survey
Earth Resources Observation and Science (EROS) Center

David K. Swanson for evaluation and feedback of snow metrics algorithm
Ecologist, Arctic Network
National Park Service

Tom Heinrichs at GINA for project management

Dayne Broderson at GINA for project management, helping develop the algorithm and documentation

Jay Cable at GINA for data management and presentation of metrics data in WCS

Will Fisher at GINA for editing the documentation