The effect of contaminated snow reflectance using hyperspectral remote sensing – a review. (3rd April 2019) - Saha, Arnab; Garg, Pradeep Kumar; Patil, Manti
ABSTRACT: Snow is composed of small crystalline ice particles consisting of multitude of snowflakes that fall from clouds. This review paper highlights the scope and nature of the research work done in North West Himalayan region. Spectral signatures were collected for varying snow grain size, contamination, adjacency factors and other ambient objects. The retrieval of snow parameters such as grain size, contamination, spectral albedo using high resolution imaging data at different wavelength are discussed in this paper. Wavelengths 550, 1240 and 1660 nm are found to be useful wavelength for discriminating different snow feature. Spectral unmixing (SU) or the disintegration of individual spectra into a mixture of a small number of end members represents the spectra of pure and contaminated components. Many linear SU techniques exploit this notion in a way or another. For instance, where the pixel purity index algorithm projects the spectra of every pixel onto random vectors in spectral space, and tags the extremities. The spectra that got tagged the most is considered as end members. The N-findR algorithm searches for the largest volume simplex via an iterative procedure, and assigns the vertices of this simplex as end members. These algorithms of end member extraction are based on the assumption that spectra of pure pixel exist in data and form the extremes of a simplex embedded in the data cloud. But, in reality this is often not the case as with multiple scattering in wet environments, secondary reflections through vegetation canopies or between fuzzy surface materials. Nonlinear algorithm is made upon an assumption that the pixel reflectance results from nonlinear function of the abundance vectors associated with the pure spectra of snow with ambiguity of unknown spectral signatures of the pure snow and nonlinear function.