Modeling air temperature through a combination of remote sensing and GIS data

• Published in: Journal of Geophysical Research: Atmospheres 2008-07, Vol.113 (D13), p.n/a
• Main Authors: Cristóbal, J., Ninyerola, M., Pons, X.
• Format: Article
• Language: English
• Subjects: Air temperature modeling; Albedo; Earth sciences; Earth, ocean, space; Exact sciences and technology; Geographic information systems; Land surface temperature; Mathematical models; Multiple regression analysis; NOAA; Remote sensing; Satellite navigation systems
• ISSN: 0148-0227; 2169-897X; 2156-2202; 2169-8996
• DOI: 10.1029/2007JD009318

Air temperature is involved in many environmental processes such as actual and potential evapotranspiration, net radiation and species distribution. Ground meteorological stations provide important local data of air temperature, but a continuous surface for large and heterogeneous areas is also needed. In this paper we present a hybrid methodology between Remote Sensing and Geographical Information Systems to retrieve daily instantaneous, mean, maximum and minimum air temperatures (2002–2004) as well as monthly and annual mean, maximum and minimum air temperatures (2000–2005) on a regional scale (Catalonia, northeast of the Iberian Peninsula) by means of multiple regression analysis and spatial interpolation techniques. To perform multiple regression analysis we have used geographical and multiresolution remotely sensed variables as predictors. The geographical variables we have included are altitude, latitude, continentality and solar radiation. As remote sensing predictors, we have selected those variables that are most closely related with air temperature such as albedo, land surface temperature (LST) and NDVI obtained from Landsat‐5 (TM), Landsat‐7 (ETM+), NOAA (AVHRR) and TERRA (MODIS) satellites. The best air temperature models are obtained when remote sensing variables are combined with geographical variables: averaged R2 = 0.60 and averaged root mean square error (RMSE) = 1.75°C for daily temperatures, and averaged R2 = 0.86 and averaged RMSE = 1.00°C for monthly and annual temperatures. The results also show that combined models appear in a higher frequency than only geographical or only remote sensing models (87%, 11% and 2% respectively) and that LST and NDVI are the most powerful remote sensing predictors in air temperature modeling.