Quantifying Snow Cover Distribution in Semiarid Regions Combining Satellite and Terrestrial Imagery
AbstractMediterranean mountainous regions constitute a climate change hotspot where snow plays a crucial role in water resources. The characteristic snow-patched distribution over these areas makes spatial resolution the limiting factor for its correct representation. This work assesses the estimation of snow cover area and the contribution of the patchy areas to the seasonal and annual regime of the snow in a semiarid mountainous range, the Sierra Nevada Mountains in southern Spain, by means of Landsat imagery combined with terrestrial photography (TP). Two methodologies were tested: (1) difference indexes to produce binary maps; and (2) spectral mixture analysis (SMA) to obtain fractional maps; their results were validated from “ground-truth” data by means of TP in a small monitored control area. Both methods provided satisfactory results when the snow cover was above 85% of the study area; below this threshold, the use of spectral mixture analysis is clearly recommended. Mixed pixels can reach up to 40% of the area during wet and cold years, their importance being larger as altitude increases, proving the usefulness of TP for assessing the accuracy of remote data sources. Mixed pixels identification allows for determining the more vulnerable areas facing potential changes of the snow regime due to global warming and climate variability. View Full-Text
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Pimentel, R.; Herrero, J.; Polo, M.J. Quantifying Snow Cover Distribution in Semiarid Regions Combining Satellite and Terrestrial Imagery. Remote Sens. 2017, 9, 995.
Pimentel R, Herrero J, Polo MJ. Quantifying Snow Cover Distribution in Semiarid Regions Combining Satellite and Terrestrial Imagery. Remote Sensing. 2017; 9(10):995.Chicago/Turabian Style
Pimentel, Rafael; Herrero, Javier; Polo, María J. 2017. "Quantifying Snow Cover Distribution in Semiarid Regions Combining Satellite and Terrestrial Imagery." Remote Sens. 9, no. 10: 995.
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