Improving Air Temperature Modelization by Means of Remote Sensing Variables

In this article we present a hybrid methodology between Remote Sensing and Geographical Information Systems to retrieve instantaneous, mean, maximum and minimum air temperatures for daily, monthly and annual periods between 2000 and 2005 on a regional scale (Catalonia, North-West Spain) by means of multiple regression analysis and spatial interpolation techniques. Best air temperature models are obtained when remote sensing variables are combined with geographical variables: averaged test R2=0.67 and averaged RMS error=1.22degC for daily temperatures and averaged test R2=0.90 and averaged RMS error =0.84degC for monthly and annual temperatures.

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