Deriving long term snow cover extent dataset from AVHRR and MODIS data: Central Asia case study

We computed the daily AVHRR snow cover from all available AVHRR level1b raw data over central Asia (CA) with an aggregated rating based snow identification scheme. The daily AVHRR snow cover was further processed into an 8-day maximum-snow-extent data, and then went through a set of spatial and temporal filters to fill cloud or gap pixels. A correction method based on known long term snow probability in small sub-regions has been developed for computing corrected 8-day AVHRR snow cover, which is comparable to 8-day MODIS snow data. Validation of the daily AVHRR snow product against ground snow survey suggested a high accuracy of the snow identification scheme. Comparison of the daily AVHRR snow cover with the daily MODIS snow cover in Amu Dar'ya River basin within CA showed high accuracy of daily AVHRR snow, with a general accuracy of 99.60% and Kappa Coefficient, of 0.92 in the basin. Comparison of the corrected 8-day AVHRR snow cover with 8-day MODIS cloud/gap free snow cover in the same basin also showed high comparability between both data, with a general accuracy of 95.61% and Kappa Coefficient of 0.84. Seasonal snow cover analysis in Amu Dar'ya River basin revealed the spatial and temporal patterns of snow distribution and negative trends in snow cover duration in 1986 to 2008 due to earlier snow melting dates. The newly developed long-term snow dataset from AVHRR and MODIS data over CA, with its high accuracy and internal comparability, is suitable for seasonal snow cover studies in mountainous regions over CA (C) 2013 Elsevier Inc. All rights reserved.