हमारा समूह 1000 से अधिक वैज्ञानिक सोसायटी के सहयोग से हर साल संयुक्त राज्य अमेरिका, यूरोप और एशिया में 3000+ वैश्विक सम्मेलन श्रृंखला कार्यक्रम आयोजित करता है और 700+ ओपन एक्सेस जर्नल प्रकाशित करता है जिसमें 50000 से अधिक प्रतिष्ठित व्यक्तित्व, प्रतिष्ठित वैज्ञानिक संपादकीय बोर्ड के सदस्यों के रूप में शामिल होते हैं।
ओपन एक्सेस जर्नल्स को अधिक पाठक और उद्धरण मिल रहे हैं
700 जर्नल और 15,000,000 पाठक प्रत्येक जर्नल को 25,000+ पाठक मिल रहे हैं
Mukesh Singh Boori* and Ralph R. Ferraro
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.