Development of a technique to assess snow-cover mapping errors fromspace
Hall, D.K.; Foster, J.L.; Salomonson, V.V.; Klein, A.G.; Chien, J.Y.L.
Geoscience and Remote Sensing, IEEE Transactions on
Volume 39, Issue 2, Feb 2001 Page(s):432 - 438
Digital Object Identifier 10.1109/36.905251
Summary:Following the December 18, 1999, launch of the Earth Observing
System (EOS) Terra satellite, daily snow-cover mapping is performed
automatically at a spatial resolution of 500 m, cloud-cover permitting,
using moderate resolution imaging spectroradiometer (MODIS) data. This
paper describes a technique for calculating global-scale snow mapping
errors and provides estimates of Northern Hemisphere snow mapping errors
based on prototype MODIS snow mapping algorithms. Field studies
demonstrate that under cloud-free conditions, when snow cover is
complete, snow mapping errors are small (<1%) in all land covers
studied except forests, where errors are often greater and more
variable. Thus, the accuracy of Northern Hemisphere snow-cover maps is
largely determined by percent of forest cover north of the snowline.
From the 17-class International Geosphere-Biosphere Program (IGBP)
land-cover maps of North America and Eurasia, the authors classify the
Northern Hemisphere into seven land-cover classes and water. Estimated
snow mapping errors in each of the land-cover classes are extrapolated
to the entire Northern Hemisphere for areas north of the average
continental snowline for each month. The resulting average monthly
errors are expected to vary, ranging from about 5-10%, with the larger
errors occurring during the months when snow covers the boreal forest in
the Northern Hemisphere. As determined using prototype MODIS data, the
annual average estimated error of the future Northern Hemisphere
snow-cover maps is approximately 8% in the absence of cloud cover,
assuming complete snow cover. Preliminary error estimates will be
refined after MODIS data have been available for about one year
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