Hyper-spectral data based investigations for snow wetness mapping

Abstract

Spatial information on snow wetness content (SWC) is important for hydrology, climatology applications. Limited work is available on estimation of SWC using optical sensors. In present work, spectral signature characteristics of snow (~145 samples) acquired in winters of three years, using field spectral-radiometer (350–2500 nm) were correlated with synchronized SWC measurements. Correlation is found stronger in Near-Infra-Red (NIR) and Short-Wave-Infrared (SWIR) regions than Visible (VIS). Spectral peak width at 905 and 1240 nm is found negatively correlated with SWC, while positively correlated at 1025 nm. Asymmetry tends towards right as SWC increases and has stable positive correlations as compared to other characteristics. Sensitivity of widely used snow-related indices to SWC is also analyzed. Based on analysis, new ratio method at selected wavelengths is proposed to discriminate dry and wet snow zones using air/ground borne sensors. Proposed methodology is evaluated on air-borne hyper-spectral (AVIRIS-NG) data and 88% overall accuracy with kappa coefficient 77.6 observed after validation with reference observations.

Keywords:

• Snow wetness
• spectral peak characteristics
• correlation
• hyper-spectral

Acknowledgement

The authors would like to thank Sh. Ashwagosha Ganju and Sh. Snehmani from Snow and Avalanche Study Establishment (SASE), Chandigarh for the kind support to carry out investigations in snow bound remote field location. The author also expresses sincere thanks to Mr Shering and field facilitators for their help in conduct of experiments in harsh cold weather conditions of Himalaya. Author would like to thank Sh. S K Dewali, Sh. Diwakar Sharma and S. Paramveer Singh for the discussions and support.