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Snow wetness estimation in Himalayan snow covered regions using ENVISAT-ASAR data

机译:使用Envisat-ASAR数据的喜马拉雅雪覆盖区域雪湿度估计

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The snow wetness in the Himalayan snow covered region is an important parameter, for the snow melt runoff modeling and forecasting. The main objective of the study is to estimate snow wetness in parts of Himalayan snow covered regions. Snow surface backscattering can expressed as function of permittivity of snow. Reflectivity at the air snow interface increases greatly with wetness and volume scattering decreases abruptly. ENIVISAT-ASAR dual polarization (HH&VV) data have been used to investigate permittivity and snow wetness in sub Himalayan region. Raw data have been processed for backscattering coefficient (BSC) image generation for HH and VV polarization. BSC image is geo-referenced and topographically corrected using high precision digital elevation model (DEM). The BSC images are despeckled using adaptive filter technique. For this study Physical optics Model (POM) for surface scattering based inversion model has been used. Physical Optics Model based inversion model gives the permittivity which can be further related for estimating snow wetness. A comparison was done between inversion model estimated snow wetness and field values of snow wetness in the study region. Comparison with field measurement showed that the correlation coefficient for snow wetness estimated from ASAR data was 0.8 at 95% confidence interval. The snow wetness ranges from 0-15% by volume.
机译:喜马拉雅雪覆盖地区的雪湿度是一个重要参数,用于雪熔径径流建模和预测。研究的主要目标是估算喜马拉雅雪覆盖地区的部分地区的雪湿度。雪表面反向散射可以表达为雪的介电常数。空气雪界面的反射率随着湿度和体积散射而突然下降大大增加。 Enivisat-ASAR双极化(HH&VV)数据已被用于研究喜马拉雅地区潜水率和雪湿度。已经处理了原始数据以用于HH和VV偏振的反向散射系数(BSC)图像生成。使用高精度数字高度模型(DEM),BSC图像是地理参考和地拓扑校正。使用自适应滤波技术,可以检测BSC图像。对于本研究的基于表面散射的反转模型的物理光学模型(POM)已经使用。基于物理光学模型的反转模型提供了对估计雪湿度的介电常数。研究区雪湿度的反转模型估计雪湿度与场值之间进行了比较。与场测量的比较表明,从ASAR数据估计的雪湿度的相关系数为95%置信区间为0.8。雪湿度范围为0-15%的体积。

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