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Northern Hemisphere Snow Variations with Season and Elevation Using GIS and AMSR-E Data | OMICS International | Abstract
ISSN: 2157-7617

Journal of Earth Science & Climatic Change
Open Access

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Special Issue Article

Northern Hemisphere Snow Variations with Season and Elevation Using GIS and AMSR-E Data

Mukesh Singh Boori1,2* and Ralph R. Ferraro2
1National Research Council (NRC), USA
2NOAA/NESDIS/STAR/ Satellite Climate Studies Branch and Cooperative Institute for Climate and Satellites (CICS), ESSIC, University of Maryland, College Park, Maryland, USA
Corresponding Author : Mukesh SinghBoori
NOAA/NESDIS/STAR/ Satellite Climate Studies Branch
and Cooperative Institute for Climate and Satellites (CICS)
ESSIC, University of Maryland, College Park, Maryland, USA
E-mail: [email protected]
Received September 10, 2012; Accepted October 01, 2012; Published October 03, 2012
Citation: Boori MS, Ferraro RR (2012) Northern Hemisphere Snow Variations with Season and Elevation Using GIS and AMSR-E Data J Earth Sci Climat Change S12:001. doi: 10.4172/2157-7617.S12-001
Copyright: © 2012 Boori MS, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Seasonal snow cover and properties in the Northern Hemisphere(NH) was examined in this study using AMSR-E multispectral passive microwave satellite derived Snow Water Equivalent (SWE) and the Digital Elevation Model (DEM) for the different seasons (January, April, July and October months) from 2007 to 2011. The methodology involves conversion of satellite SWE data into 6 snow classes, computation of NDSI, determination of the boundary between snow classes from spectral response data and threshold slicing of the image data. Accuracy assessment of AMSR-E snow products was accomplished using Geographic Information System (GIS) techniques. The coldest month has all six snow type classes due to snow pack growth whereas the summer months only contain residual snow at the highest elevations. Sharp season-to-season differences were noted. The final results show the greatest snow cover extent in January whereas total snow in April is 60%, July 3% and in October near to 25%. In terms of inter-seasonal variations during the study period, the minimum (1.53 million km2) snow cover extent was observed in July 2008 and the maximum (60.0 km2) in January 2010. In terms of elevation, the maximum snow extent exists between 0 to 2000 m in winter and near to 5000 m in summer. Finally, this study shows how satellite remote sensing can be useful for the long-term observation of the intra and inter-annual variability of snowpacks in rather inaccessible regions and providing useful information on a critical component of the hydrological cycle, where the network of meteorological stations is deficient.

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