Abstract
Abstract. Remote sensing is the only feasible data source for distributed modelling of snow in mountain regions on medium to large scales, due to the limited access to these areas together with the lack of dense ground monitoring stations for snow variables. Observations worldwide identify snow cover persistence together with snowfall occurrence as the most affected variables by global warming. In Mediterranean regions, the spatiotemporal evolution of the snow cover can experiment quick changes that result in different accumulation-ablation cycles during the cold season. High frequency sensors are required to adequately monitor such shifts; however, for trend analyses, the Landsat time series constitute the only available source of data, being their frequency low for this regime, especially when cloudy conditions limit the available images. On the other hand, the MODIS daily series provide more than 15 years of continuous snow maps, despite the spatial resolution may pose a constraint in areas with abrupt topography; several approaches have been done to improve their spatial resolution from combining different information. This work presents a methodological approach to validate the improved MODIS daily snow cover maps from Notarnicola et al. (2013a, b), with 250 m spatial resolution, in Sierra Nevada (southern Spain), from a reference data set obtained by spectral mixture analyses of Landsat TM data by Pimentel et al. (2017b). This reference time series of fractional snow maps, with 30 m spatial resolution, were validated from high resolution local time series of snow maps obtained by terrestrial time-lapse cameras. The results show a significantly high correlation between the two snow map products both on a global and basin scales in the Sierra Nevada area. Selected areas and time periods are shown to address the convergence and divergence between both products and assess the development of a fusion algorithm to retrieve daily Landsat-resolution snow maps on a long term basis.
Highlights
Remote sensing techniques constitute the best source to provide distributed information about the snowpack evolution on medium to long time scales, complementing the traditional in situ field surveys and automatic ground measurements
Snow cover fraction (SCF) was calculated over the whole study area in Sierra Nevada and in each one of the five main headwaters regions: R1 – Adra, R2 – Andarax, R3 – Fardes, R4 – Genil and R5 – Guadalfeo, for both snow products, Landsat-mix and MODIS-EURAC
SCF follows the same trend for both products, with a clear overestimation of the SCF derived from MODISEURAC
Summary
Remote sensing techniques constitute the best source to provide distributed information about the snowpack evolution on medium to long time scales, complementing the traditional in situ field surveys and automatic ground measurements. The very strong spatiotemporal variability, which very often undergoes different accumulation-snowmelt cycles during the cold season in a given year, or the snow patched distribution around local singularities, such as rocks and vegetation, consequence of a very complex ablation process (Ménard et al, 2014; Pimentel et al, 2015, 2017b). Both high temporal and spatial resolutions are required to have a realistic representation of the snow cover
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