Modeling of air temperatures by multiple linear regressions in the Rhône-Alpes region (France): enhancement of topographic and meteorological variables by remote sensing data

Lucille Alonso; Florent Renard

doi:10.1117/12.2532330

27 June 2019 Modeling of air temperatures by multiple linear regressions in the Rhône-Alpes region (France): enhancement of topographic and meteorological variables by remote sensing data

Lucille Alonso, Florent Renard

Author Affiliations +

Proceedings Volume 11174, Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019); 111740O (2019) https://doi.org/10.1117/12.2532330
Event: Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019), 2019, Paphos, Cyprus

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.

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Lucille Alonso and Florent Renard "Modeling of air temperatures by multiple linear regressions in the Rhône-Alpes region (France): enhancement of topographic and meteorological variables by remote sensing data", Proc. SPIE 11174, Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019), 111740O (27 June 2019); https://doi.org/10.1117/12.2532330

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KEYWORDS

Temperature metrology

Environmental sensing

Remote sensing

Earth observing sensors

Landsat

Data modeling

Atmospheric modeling

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