Quantifying the role of mixed pixels in snow cover distribution in semiarid regions: A study case in Sierra Mountain (Spain)

Observations worldwide identify snow cover persistence together with snowfall occurrence as the most affected variables by global warming. In particular, Mediterranean mountain areas are pointed as climate warming hotspots. The characteristic snow-patched distribution shown over these areas, which result in different accumulation-ablation cycles during the cold season, usually makes spatial resolution the limiting factor for its correct representation. Remote sensing is the only feasible data source for distributed quantification of snow in mountain regions on medium to large scales, due among other to the limited access to these areas together with the lack of dense ground monitoring stations for snow variables. Among the numerous remote sensing sources, the Landsat constellation is those that better fit both basic requirements for studying snow over these areas, to cover a long period with observation and to have an high spatial resolution. However, the traditional classification algorithms for snow detection are usually based on normalized indexes that  provide a binary classification as snow and no-snow pixels throughout the study area; this simple classification may result in large error in heterogeneous and transitional areas within the snow-dominated domain. Alternatively, the spectral mixture analysis (SMA) approach provides a fraction of snow cover within each pixel and thus, constitutes a step forward to characterize heterogeneous and patchy snow areas in semiarid regions. 

This work analysed the role of mixed pixels, defined as pixels made up of different types of surfaces, in snow cover distribution over Mediterranean mountains. Sierra Nevada Mountain Range in southern Spain has been chosen as representative of a Mediterranean mountain area, which is characterized by strong altitudinal gradients with marked differences between the south (directly affected to the sea) and the north faces are found in the area. The fractional snow cover maps, at 30×30 m and 16 days spatial and temporal resolution respectively, derived from SMA of Landsat TM and ETM+ validated using as high resolution terrestrial photography (Pimentel et al., 2017) has been used for mixed pixel analysis. On the one hand, the results show the importance of mixed pixels, which can constitute more than 50% of the total pixels in some areas of the mountainous range and season of the year. On the other hand, the analysis carried out has allowed the identification of areas more prone to allocate this type of pixels, linking that fact to climatic drivers. 

This work has been funded by project MONADA - "Hydrometeorological trends in mountainous protected areas in Andalusia: examples of co-development of climatic services for strategies of adaptation to climatic change", with the economic collaboration of the European Funding for Rural Development (FEDER) and the Andalusian Ministry of Economic Transformation, Industry, Knowledge and Universities. R+D+i project 2020.