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. The Himalaya mountain range is characterized by highly glacierized, complex,dynamic topography. The ablation area of Himalayan glaciers often features a highlyheterogeneous debris mantle comprising ponds, steep and shallow slopes ofvarious aspects, variable debris thickness, and exposed ice cliffsassociated with differing ice ablation rates. Understanding the compositionof the supraglacial debris cover is essential for a proper understanding of glacierhydrology and glacier-related hazards. Until recently, efforts to mapdebris-covered glaciers from remote sensing focused primarily on glacierextent rather than surface characteristics and relied on traditionalwhole-pixel image classification techniques. Spectral unmixing routines,rarely used for debris-covered glaciers, allow decomposition of a pixel intoconstituting materials, providing a more realistic representation of glaciersurfaces. Here we use linear spectral unmixing of Landsat 8 Operational Land Imager (OLI) images (30 m) to obtain fractional abundance maps of the various supraglacial surfaces(debris material, clean ice, supraglacial ponds and vegetation) across theHimalaya around the year 2015. We focus on the debris-covered glacierextents as defined in the database of global distribution of supraglacial debris cover. The spectrallyunmixed surfaces are subsequently classified to obtain maps of compositionof debris-covered glaciers across sample regions. We test the unmixing approach in the Khumbu region of the central Himalaya,and we evaluate its performance for supraglacial ponds by comparison withindependently mapped ponds from high-resolution Pléiades (2 m) andPlanetScope imagery (3 m) for sample glaciers in two other regions withdiffering topo-climatic conditions. Spectral unmixing applied over theentire Himalaya mountain range (a supraglacial debris cover area of 2254 km2) indicates that at the end of the ablation season, debris-coveredglacier zones comprised 60.9 % light debris, 23.8 % dark debris, 5.6 % clean ice, 4.5 % supraglacial vegetation, 2.1 % supraglacialponds, and small amounts of cloud cover (2 %), with 1.2 % unclassifiedareas. The spectral unmixing performedsatisfactorily for the supraglacial pond and vegetation classes (an F scoreof ∼0.9 for both classes) and reasonably for the debrisclasses (F score of 0.7). Supraglacial ponds were more prevalent in the monsoon-influencedcentral-eastern Himalaya (up to 4 % of the debris-covered area) comparedto the monsoon-dry transition zone (only 0.3 %) and in regions with lowerglacier elevations. Climatic controls (higher average temperatures and moreabundant precipitation), coupled with higher glacier thinning rates andlower average glacier velocities, further favour pond incidence and thedevelopment of supraglacial vegetation. With continued advances in satellite data andfurther method refinements, the approach presented here provides avenuestowards achieving large-scale, repeated mapping of supraglacial features.

Surface composition of debris-covered glaciers across the Himalaya using linear spectral unmixing of Landsat 8 OLI imagery