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Modeling of air temperatures by multiple linear regressions in the Rhône-Alpes region (France): enhancement of topographic and meteorological variables by remote sensing data
Author(s): Lucille Alonso; Florent Renard
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Paper Abstract

With the phenomenon of urban heat island and thermal discomfort felt in urban areas, exacerbated by climate change, it is necessary to best estimate the air temperature in every part of a territory, especially in the context of the on-going rationalization of the Météo-France network. This study proposes to estimate the temperature of the air from 35 explanatory variables, notably from remote sensing using multiple linear regressions. The collinearity of the explanatory variables is analyzed by the Pearson correlation matrix and the Variance Inflation Factor. In fine, for each day of study in each study area, the part of the variance explained is very high (greater than 73%). On the other hand, this estimate of the air temperature can never be a substitute for measurements on the ground.

Paper Details

Date Published: 27 June 2019
PDF: 8 pages
Proc. SPIE 11174, Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019), 111740O (27 June 2019); doi: 10.1117/12.2532330
Show Author Affiliations
Lucille Alonso, Environment Ville Société, CNRS, Univ. de Lyon (France)
Florent Renard, Environment Ville Société, CNRS, Univ. de Lyon (France)


Published in SPIE Proceedings Vol. 11174:
Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019)
Kyriacos Themistocleous; Giorgos Papadavid; Silas Michaelides; Vincent Ambrosia; Diofantos G. Hadjimitsis, Editor(s)

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