Snow permittivity retrieval inversion algorithm for estimating snow wetness

Abstract

The environmental satellite (ENVISAT) advanced synthetic aperture radar (ASAR) offers the opportunity for monitoring snow parameters with dual polarization and multi-incidence angle. Snow wetness is an important index for indicating snow avalanche, snowmelt runoff modelling, water supply for irrigation and hydropower stations, weather forecasts and understanding climate change. We used a first-order scattering model that includes both volume and air/snow surface scattering based on a developed inversion model to estimate snow dielectric constant, which can be further related for estimating snow wetness. Comparison with field measurement showed that the correlation coefficient for snow permittivity estimated from ASAR data was observed to be 0.8 at 95% confidence interval and model bias was observed as 2.42% by volume at 95% confidence interval. The comparison of ASAR-derived snow permittivity with ground measurements shows the average absolute error 2.5%. The snow wetness range varies from 0 to 15% by volume.

Keywords:

• SAR
• remote sensing
• backscattering coefficient (BSC)
• snow wetness
• DEM

Acknowledgement

The authors would like to thank the European Space Agency for providing ENVISAT ASAR data through project No. 2524 category 1. Authors have greatly benefited from our technical interactions with Prof. Jiancheng Shi, University of California, USA regarding the model development.