Next Article in Journal
Aerosol Optical Properties and Direct Radiative Effects over Central China
Next Article in Special Issue
Spatiotemporal Variation of Snow Cover in Tianshan Mountains, Central Asia, Based on Cloud-Free MODIS Fractional Snow Cover Product, 2001–2015
Previous Article in Journal
Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops
Previous Article in Special Issue
Fractional Snow Cover Mapping from FY-2 VISSR Imagery of China
Open AccessArticle

Quantifying Snow Cover Distribution in Semiarid Regions Combining Satellite and Terrestrial Imagery

Fluvial Dynamics and Hydrology Research Group, Andalusian Institute for Earth System Research, University of Córdoba, Campus Rabanales, Edificio Leonardo da Vinci, Área de Ingeniería Hidráulica, 14017 Córdoba, Spain
Hydrology Research Unit, Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(10), 995;
Received: 29 August 2017 / Revised: 15 September 2017 / Accepted: 22 September 2017 / Published: 26 September 2017
(This article belongs to the Special Issue Snow Remote Sensing)
PDF [5699 KB, uploaded 26 September 2017]


Mediterranean 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
Keywords: snow; semiarid regions; terrestrial photography; Landsat TM and ETM+ snow; semiarid regions; terrestrial photography; Landsat TM and ETM+

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Pimentel, R.; Herrero, J.; Polo, M.J. Quantifying Snow Cover Distribution in Semiarid Regions Combining Satellite and Terrestrial Imagery. Remote Sens. 2017, 9, 995.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top