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Snow and ice cover

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Selkowitz, D. 2011. Exploring the potential for a fused Landsat-MODIS daily 30 meter snow covered area product. Proceedings of the 79th Annual Meeting of the Western Snow Conference, Stateline, NV, April 2011.

Selkowitz D. and Rittger, K. 2012. Developing a Landsat-MODIS Daily 30 m Snow Covered Area (SCA) Time Series Product. Abstract presented at the American Geophysical Union Fall Meeting 2012, San Francisco, CA, December 2012.

Multi-Sensor Snow Covered Area Monitoring

Landsat data indicating snow-covered (turquoise) and snow-free (all other colors) areas for the same location in the Sierra Nevada at approximately the same point during the snowmelt season for three different years. The similarity in spatial pattern of snow covered area despite differences in timing is the basis for combining daily 500 m fractional snow covered area from MODIS with sporadically acquired 30 m snow covered area from Landsat to produce a daily 30 m snow covered area time series.
Landsat data indicating snow-covered (turquoise) and snow-free (all other
colors) areas for the same location in the Sierra Nevada at approximately
the same point during the snowmelt season for three different years.
The similarity in spatial pattern of snow covered area despite differences
in timing is the basis for combining daily 500 m fractional snow covered
area from MODIS with sporadically acquired 30 m snow covered area from
Landsat to produce a daily 30 m snow covered area time series.
The focus of this project is the development and validation of methodology for the production of daily high spatial resolution snow covered area (SCA) time series datasets. Snow cover exhibits tremendous spatial and temporal variability and is often concentrated in remote or inaccessible regions, making space-borne remote sensing the most feasible approach for comprehensive SCA monitoring in most areas. The timing of the appearance and disappearance of snow cover at a specific location varies substantially between years in most regions. Despite substantial differences in the timing of snowmelt, however, the spatial patterns of snow cover at scales less than 1 km typically remain remarkably similar across most or all years. This is particularly true in areas where the interaction of wind, solar radiation, topography, and vegetation produce distinct patterns of snow cover distribution, as is the case in nearly all arctic and alpine environments as well as many montane and boreal environments.

Presently, no single existing or planned instrument provides daily high spatial resolution imagery suitable for SCA mapping. Combining daily imagery from a moderate resolution sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) with historical data from a higher spatial resolution sensor such as Landsat TM or Landsat ETM+, however, allows for the possibility of constructing a daily high spatial resolution SCA time series dataset.

The spatial and temporal distribution of SCA represents an important climate record useful for hydrologists, climatologists, ecologists, and other scientists and resource managers. A 30 m spatial resolution daily SCA dataset would allow for more accurate, higher spatial resolution reconstruction of maximum snow water equivalent (often considered the holy grail for hydrologists working in regions where snow cover is the primary source of runoff). Additionally, such a dataset would serve as a useful source of validation data for spatially distributed snow cover models. Finally, high spatial resolution maps of snow cover duration would provide a crucial input for ecological models mapping the present and future distribution of arctic and alpine plant communities.

Principal Investigator: David Selkowitz, dselkowitz@usgs.gov, Alaska Science Center, Anchorage, AK

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