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Transactions: WSEAS TRANSACTIONS ON SYSTEMS
Transactions ID Number: 32-783
Full Name: Tarendra Lakhankar
Position: Researcher
Age: ON
Sex: Male
Address: NOAA-Cooperative Remote Sensing Science & Technology Center (NOAA-CREST), City University of New York, NY 10031
Country: UNITED STATES
Tel: 2126505815
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E-mail address: tlakhankar@ccny.cuny.edu
Other E-mails: lakhankar@gmail.com
Title of the Paper: Neural Networks Application in Snow Cover Mapping using SSM/I Data
Authors as they appear in the Paper: Hosni Ghedira, Tarendra Lakhankar, Juan-Carlos Arevalo, Amir E. Azar, Reza Khanbilvardi and Reginald Blake
Email addresses of all the authors: hghedira@aud.edu, jcarevalol@hotmail.com, tlakhankar@ccny.cuny.edu, amir.eazar@gmail.com, khanbilvardi@ccny.cuny.edu; rblake@citytech.cuny.edu
Number of paper pages: 10
Abstract: In this paper neural network based approach is discussed to generate the spatial distribution of snow accumulation using multi-channel Special Sensor Microwave/Imager (SSM/I) data. Five SSM/I channels (19H, 19V, 22V, 37V, and 85V) were used to remotely sense snow accumulation during 2001/2002 winter season. Snow depth measurements were obtained from the National Climatic Data Center (NCDC) through the Cooperative Observer Network for snow monitoring in the United States. The snow depths were compiled and gridded into 25 km x 25 km grid to match the final SSM/I spatial resolution. The neural network based approach was tested and compared with the filtering algorithm developed by Grody and Basist [1] in the Northern Midwest region of the United States. The results indicate that the neural-network-based approach has a great potential in identifying snow pixels from SSM/I data by providing a significant improvement in snow mapping accuracy over the filtering algorithm.
Keywords: Snow mapping, SSM/I, Passive Microwave, Neural Network
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