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Abstract
The present study demonstrates the potential of hyperspectral imaging Hyperion data for the mapping of snow grain size and snow mixed objects in the Himalayan region. The spectral signatures collected using a field spectroradiometer for different types of snow grain size and vegetation-/soil-mixed snow were used for the identification/comparison of collected image spectra. Snow grain size was estimated using the spectral angle mapper (SAM) method and compared/validated with the grain sizes obtained from grain index and asymptotic radiative transfer (ART) theory methods. The overall matching area was 81% among different snow grain size classes using SAM and grain index methods. A good match was observed between the class-wise (i.e. fine, medium, and coarse) grain sizes and quantitatively obtained grain sizes using the ART theory; however, the grain diameters obtained from the ART method were small, which may be due to the difference between equivalent grain and effective optical grain size. The obtained grain size was also supported by the field snow conditions of the region. The spectra of mixed snow cover were collected from Hyperion images and compared with the spectra collected from snow mixed objects during field experiments. The vegetation-mixed and contamination/patchy (soil-mixed) snow-cover areas were identified in Hyperion scenes and the results supported using high-resolution images and snow conditions of the region. This study is of importance in the mapping of snow-cover characteristics, which can provide valuable input for climatology, hydrology, and mountain hazard applications.
Acknowledgements
The authors thank the USGS EROS Data Center, USA, for acquiring and providing Hyperion images. The authors are grateful to D. Ramakrishnan, IIT Bombay, India, and A. Kokhanovsky, IUP Bremen, Germany, for technical support and discussion. The authors also thank all the SASE persons who have collected the snow meteorological data at field locations.