EARSeL eProceedings x, issue/year 1 Mapping Daily Snow Cover Extent over Land Surfaces using NOAA AVHRR Imagery
The Global Climate Observing System (GCOS) has identified snow cover, mapped on a daily basis at 1km resolution or better, as an essential climate variable. GCOS specifically highlighted the need to produce historical snow cover maps from NOAA AVHRR sensors. We present an algo-rithm, SnowCover, for...
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| Format: | Text |
| Language: | English |
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| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.502.9193 http://www.earsel.org/workshops/LISSIG2008/Papers_and_Presentations/Final_Papers/Fernandes_Paper.pdf |
| Summary: | The Global Climate Observing System (GCOS) has identified snow cover, mapped on a daily basis at 1km resolution or better, as an essential climate variable. GCOS specifically highlighted the need to produce historical snow cover maps from NOAA AVHRR sensors. We present an algo-rithm, SnowCover, for mapping snow cover from polar orbiting optical sensors with frequent (~daily) repeat passes. The algorithm is based on a new time series filter, adaptive to local cloud conditions, and pixel wise calibration of snow and snow free end members. SnowCover was ap-plied to a 1km resolution climate data archive of NOAA AVHRR data over the Western Arctic to produce daily snow cover maps from 1982 to present with, on average, 90 % temporal coverage. Comparison of the maps with snow cover estimates derived from 83 in-situ long term snow depth sites in Canada indicate year round agreement rates above of 90 % at the 50%ile (85 % at the 95%ile). Agreement rates during the spring melt transition increases to 87 % at 50%ile indicating that the temporal filtering can preserve this phenomenon. |
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