Integrating Satellite-Derived Data as Spatial Predictors in Multiple Regression Models to Enhance the Knowledge of Air Temperature Patterns
UMR CNRS Environment City Society, University Jean Moulin Lyon 3, 69007 Lyon, France
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Urban Sci. 2019, 3(4), 101; https://doi.org/10.3390/urbansci3040101
Received: 24 August 2019
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Revised: 10 September 2019
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Accepted: 17 September 2019
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Published: 21 September 2019
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 an area, especially in the context of the on-going rationalization weather stations network. In addition, the comprehension of air temperature patterns is essential for multiple applications in the fields of agriculture, hydrology, land development or public health. Thus, this study proposes to estimate the air temperature from 28 explanatory variables, using multiple linear regressions. The innovation of this study is to integrate variables from remote sensing into the model in addition to the variables traditionally used like the ones from the Land Use Land Cover. The contribution of spectral indices is significant and makes it possible to improve the quality of the prediction model. However, modeling errors are still present. Their locations and magnitudes are analyzed. However, although the results provided by modelling are of good quality in most cases, particularly thanks to the introduction of explanatory variables from remote sensing, this can never replace dense networks of ground-based measurements. Nevertheless, the methodology presented, applicable to any territory and not requiring specific computer resources, can be highly useful in many fields, particularly for urban planners.
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Keywords:
multiple linear regression; remote sensing; spectral indices; air temperature; urban heat island; land use land cover
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
MDPI and ACS Style
Alonso, L.; Renard, F. Integrating Satellite-Derived Data as Spatial Predictors in Multiple Regression Models to Enhance the Knowledge of Air Temperature Patterns. Urban Sci. 2019, 3, 101. https://doi.org/10.3390/urbansci3040101
AMA Style
Alonso L, Renard F. Integrating Satellite-Derived Data as Spatial Predictors in Multiple Regression Models to Enhance the Knowledge of Air Temperature Patterns. Urban Science. 2019; 3(4):101. https://doi.org/10.3390/urbansci3040101
Chicago/Turabian StyleAlonso, Lucille, and Florent Renard. 2019. "Integrating Satellite-Derived Data as Spatial Predictors in Multiple Regression Models to Enhance the Knowledge of Air Temperature Patterns" Urban Science 3, no. 4: 101. https://doi.org/10.3390/urbansci3040101
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