Aims

The main aim of this monitoring is to characterize the snow cover in space and time, with basic information on parameters such as duration, extension, beginning and ending dates of the snow season, melt cycles, etc. All this is gained by downloading, processing, and analysing the images of the MODIS sensor of NASA, lodged in the satellite Terra.

Method and effort

Among the images taken by the MODIS sensor, two of its products were used: MOD10A1 and MOD10A2. The product MOD10A1 has a daily periodicity and provides values for the snowcover extent (% of surface area occupied by snow per pixel), the albedo, and the quality of observation. The product MOD10A2 has a periodicity of 8 days, and it is an aggregated product that indicates the maximum of snow cover in these 8 days, improving the sensitivity of the one-day model with respect to the presence of clouds. It provides values for: maximum cover and cover over 8 days.

We have created an automatic workflow that takes care of processing the satellite images and incorporating the raw data into a relational database. Afterwards, with the raw data, indicators are generated for the snowing profile, duration, and commencement dates and ending dates of the snow, the melting cycles for the different spatial entities existing in the Information System.

Periodicity

Taking into account the periodicity of the products of the MODIS sensor, 1 day for the MOD10A1 product, and 8 days for the MOD10A2 product.

Results

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References

Pérez-Pérez, R., Benito, B.M. y Bonet, F.J. 2012. ModeleR: an environmental model repository as knowledge base for experts. Expert Syst. Appl. http://dx.doi.org/10.1016/j.eswa.2012.01.180
Bonet-García, F.J. 2010. Coupling of snow cover and vegetation structure in Sierra Nevada (Spain). En: 2ª International GMBA-DIVERSITAS Conference “Functional significance of mountain biodiversity”. Chandolin, Switzerland. 27-30 Julio.
Conference abstracts, pp. 39–40
Hall, D.K., Riggs, G.A., Salomonson, V.V., DiGirolamo, N.E. y Bayr, K.J. 2002. MODIS snow-cover products. Remote Sens. Environ., 83 (1-2): 181–194.

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