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Spatio‐temporal combination of MODIS images – potential for snow cover mapping
Author(s) -
Parajka J.,
Blöschl G.
Publication year - 2008
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2007wr006204
Subject(s) - snow , remote sensing , environmental science , cloud cover , filter (signal processing) , cloud computing , pixel , meteorology , computer science , geography , artificial intelligence , computer vision , operating system
MODIS snow cover products are appealing for hydrological applications because of their good accuracy and daily availability. Their main limitation, however, is cloud obscuration. In this study we evaluate simple mapping methods, termed temporal and spatial filters, that reduce cloud coverage by using information from neighboring non‐cloud covered pixels in time or space, and by combining MODIS data from the Terra and Aqua satellites. The accuracy of the filter methods is evaluated over Austria, using daily snow depth observations at 754 climate stations and daily MODIS images in the period 2003–2005. The results indicate that the filtering techniques are remarkably efficient in cloud reduction, and the resulting snow maps are still in good agreement with the ground snow observations. There exists a clear, seasonally dependent, trade off between accuracy and cloud coverage for the various filtering methods. An average of 63% cloud coverage of the Aqua images is reduced to 52% for combined Aqua‐Terra images, 46% for the spatial filter, 34% for the 1‐day temporal filter and 4% for the 7‐day temporal filter, and the corresponding overall accuracies are 95.5%, 94.9%, 94.2%, 94.4% and 92.1%, respectively.