P. A. Bharathi

Indian Institute of Technology Bombay

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Avik Bhattacharya

Indian Institute of Technology Bombay

Gulab Singh

Indian Institute of Technology Bombay

G. Venkataraman

Indian Institute of Technology Bombay

M. Surendar

National Institute of Technology, Puducherry

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Indian Institute of Technology Bombay

Indian Space Research Organisation

Universidade Estadual Paulista (Unesp)

University of Pavia

Victoria University of Wellington

University of Massachusetts Amherst

Booz Allen Hamilton (United States)

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

Central Institute For Subtropical Horticulture

Weatherford College

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Improved snow wetness estimation from fully polarimetric SAR image

IEEE Asia-Pacific Conference on Synthetic Aperture Radar (2013)

M. Surendar Gulab Singh Avik Bhattacharya G. Venkataraman P. A. Bharathi

Snow wetness is an important parameter for forecasting of snow avalanche and snow melt run off modeling in cragged areas specifically Himalayan region of India. In this paper a new snow wetness estimation approach is used for fully polarimetric Synthetic Aperture Radar (SAR) data. In this new methodology Freeman surface scattering and Cloude volume scattering components are introduced, which account for all independent relative polarimetric phase parameters of the coherency matrix. These parameters have been inverted into surface and volume dielectric constant of the snow respectively. A generalized spheroidal shape is considered as a snow particle structure for volume scattering model. The estimated snow wetness is validated using the field data, which was collected, synchronized with the satellite pass. The results were also compared with the Shi and Dozier [1] inversion model based snow wetness.

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Snow wetness estimation based on Pol-SAR decomposition technique

M. Surendar Gulab Singh Avik Bhattacharya G. Venkataraman P. A. Bharathi

Snow wetness is a very important parameter for forecasting snow avalanche and for snow melt run off modeling in cragged areas specifically for Himalayan regions of India. In this paper, a new snow wetness estimation approach is used for fully polarimetric Synthetic Aperture Radar (SAR) data. In this new methodology, Freeman surface scattering and Cloude volume scattering components are introduced which account for all independent relative polarimetric phase parameters of the coherency matrix. Snow particle has been considered to be of spheroidal shape in volume scattering model. The estimated snow wetness is validated using the field data, which was collected, synchronized with the satellite pass. The results were also compared with the Shi and Dozier [1] inversion model based snow wetness estimation.

10.1109/igarss.2013.6723507

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Snow wetness estimation from polarimetric SAR image

Progress in Electromagnetics Research Symposium (2013)

M. Surendar Avik Bhattacharya G. Venkataraman P. A. Bharathi Gulab Singh

Snow pack parameters are very important for forecasting of snow avalanche and snow melt run ofi modeling in mountainous areas like Himalayan region of India. In this paper, a new algorithm for estimation of snow wetness based on SAR polarimetric decomposition has been described. This algorithm has been compared with the existing Shi and Dozzier (1) inversion snow wetness method and validated with the fleld measurements. Avalanche modeling, snow melt runofi and hydrological investigation required timely information about spatial variability of snow properties. Among these properties, the snow wetness of snowpack is very important. Estimation of snow pack parameters is very complicated due to their continuous spatial and temporal variation. Various active microwave remote sensing systems have been used for estimating snow pack parameters. Shi and Dozier (1) used polarimetric Shuttle Imaging Radar- C (SIR-C) mission data to derive snow wetness by considering the surface and volume scattering powers. Shi and Dozier algorithm has been modifled by Singh etal. (2) for dual polarization SAR data. But now the availability of fully polarimetric SAR data along with limited near-real time fleld observations insists and excites us to utilize full polarimetric SAR data for snow parameters estimation. Several assumptions have been made in the Shi and Dozier model to solve the regression equations. The second order statistics from the quad-pol SAR data can be used with an expectation to achieve a better accuracy. The scope of this study is to develop a methodology to retrieves now wetness using decomposition scheme from full polarimetric SAR data. In this study the Radarsat- 2 flne quad polarization data acquired on 7th February 2012 over Manali and Dhundi (center Lat/Long: N32 - 22 0 05 00 =77 - 15 0 00 00 , Himachal Pradesh, India) has been used.

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