## 4 Results and Discussion
### Correlation Analysis  
Results from the the correlation analysis between LCZ classes and temperature are included in Figures 8-10, and Table 5. For the sake of clarity, some LCZ class compositions for buffers were collapsed into a single class. These are classes 2, 3, 4, and 5, which represent slightly different land cover features of medium compact built-up areas. As a matter of fact, it was observed that the contribution of these classes to the correlation tests was comparable, and generally not interesting in terms of influence on air temperature. On the other hand, this might be also due to an intrinsically lower interclass accuracy of the LCZ classification procedure, that might be biased by the similar surface characteristics of these classes compared to more extreme classes such as 1, 6 and 7. Class 8, i.e., water, was not considered in the correlation analysis because no significant fractions (i.e., always \(<\) 0.04% ) were detected in any of the station buffers. By observing the scatter-plots in Figures 8-10, it can be argued that the contribution of highly urbanized areas, such as the one expressed by the Class 1 fractions, is always positive on the air temperature. This is true for any satellite imagery and time period considered. The contribution of the collapsed Classes 2, 3, 4, and 5 shows almost no correlation evidence with air temperature. Regarding classes 6 and 7, a sensible negative correlation is detected in almost all the cases. Besides a general low linear correlation between the two variables that is expressed by the coefficient of determination (\(R^{2}\)) computed for each graph, regression lines show comparable trends by considering the same LCZ class and time period for different satellites. The different spatial and spectral resolutions of the different satellite imagery seems not to have a significant influence on this exploratory experiment. The results show how temperature patterns are connected with the land cover type that is a well known effect, also reported in literature [59]. Nevertheless, the data used in this example, as well as the analysis  
Figure 7: Temperature maps from the ordinary Kriging interpolation of the weekly average air temperature observations: Example of non-vegetative period (**a**) and vegetative period (**b**). Weekly averages were computed using the whole hourly observations for the week 11–17 January 2016 (**a**), and the week 20–26 June 2016 (**b**). CRS: WGS84/UTM zone 32 N.  
strategy adopted, would benefit from a further improvement in order to conceptualize robust models that can better explain this correlation. Figure 8: LCZ class fractions [%] computed into the 500 m buffer around the stations versus the measured air temperature at the stations. Air temperature measurements correspond to the hour of the Landsat 8 imagery acquisition. Acquisition times are 18 March 2016 (non-vegetative period) and 22 June 2016 (vegetative period) both at 10:10 am GMT. Red Lines and text boxes show the results of the linear regression for each plot. Figure 9: LCZ class fractions [%] computed into the 500 m buffer around the stations versus the measured air temperature at the stations. Air temperature measurements correspond to the hour of the Sentinel-2 imagery acquisition. Acquisition times are 13 January 2016 (non-vegetative period) and 22 May 2016 (vegetative period) both at 10:20 am GMT. Red Lines and text boxes show the results of the linear regression for each plot. The most important outcome is, however, the demonstration of how heterogeneous geo-data can be combined and processed to explore urban climate patterns and get insight into these [9]. With the second experiment, we aim at providing some quantitative evidence of land cover influence on air temperature instead of qualitative results as we did in the previous experiment. Table 5 reports the median and standard deviation of weekly temperatures by LCZ majority buffers, for each time period and satellite considered.