Hyper-spectral data based investigations for snow wetness mapping. Issue 6 (12th May 2019) - Shekhar, Chander; Srivastava, Sunita; Negi, Harendra Singh; Dwivedi, Manish
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.