Effect of linear and non-linear mixing on hyper-spectral signatures of snow in the optical region (350-2500 nm)

Study of hyper-spectral behaviour of snow is important to interpret, analyse and validate optical remote sensing observations. To map and understand response of snow-mixed pixels in RS data, field experiments were conducted for linear mixing of external materials (i.e. Vegetation, Soil) with snow, using spectral-radiometer (350-2500 nm). Further, systematic non-linear mixing of snow contaminants (soil, coal, ash) in terms of size and concentration of contaminants is analysed to imitate and understand spectral response of actual field scenarios. Sensitivity of band indices along with absorption peak characteristics provide clues to discriminate the type of contaminants. SWIR region is found to be useful for discriminating size of external contaminants in snow e.g. Avalanche deposited snow from light contaminated forms. Present research provide inputs for mapping snow-mixed pixels in medium/coarse resolution remote sensing RS data (in terms of linear mixing) and suitable wavelength selections for identification and discriminating type/size of snow contaminants (in terms of non-linear mixing).