## 3 Methods
### LCZ and Air Temperature Correlation Analysis  
To investigate the correlation between the land cover type, expressed by the LCZ, and the air temperature, a 500 m buffer around each temperature station was considered. This relatively small buffer radius was introduced to account for the large variability of land cover features that characterize the urban environment. Moreover, temperature measurements are representative only in the spatial proximity of the sensors, where temperature changes can be assumed as negligible. By selecting a small buffer radius, also the point-wise nature of the temperature observations was taken into account for the correlation tests. Nevertheless, thanks to the abundance of weather stations across the Milan area, a satisfactory data coverage for the study area was achieved, especially within the city centre and the suburbs (Figure 2).  
The fraction of each LCZ class coverage within each buffer was then calculated as the ratio between the number of pixels of a class and the total number of pixels within a buffer. A second measure of the LCZ class composition inside buffers was computed. This was achieved by assigning to each buffer its corresponding majority LCZ class id, computed as the class having the highest coverage inside the buffer out of the total number of pixels. Both measures were computed for each satellite at the two considered time periods, i.e., the non-vegetative and the vegetative (see Table 1).  
These measures were used to run two separate correlation tests. The first one consisted of computing scatter-plots and connected regression lines by comparing the LCZ fractions at each station buffer with the temperature registered by the station at the exact acquisition time of satellites. This was accomplished using a custom Python script that combines _GeoPandas_ and _Rasterstat_ functionalities to process the station dataset and the LCZ raster maps. Scatter-plots were generated by using the _Matplotlib_ library, while the linear regression was computed by the _SciPy_ library. A positive slope of the regression line is interpreted as a positive contribution of the LCZ class to the temperature, while a negative slope as a mitigative effect. A scatter-plot was computed for each satellite at each time period and for each LCZ class. Results are reported and discussed in Section 4.  
In the second experiment, the buffers were grouped according to their majority LCZ class id. Descriptive statistics such as the weekly temperature median and standard deviation were computed  
Figure 4: Example of temperature time series for the Year 2016 from Weather Underground stations in Milan before (**left**) and after (**right**) the cleaning and smoothing procedures. Coarse outliers (**left**) are clearly removed from the data set (**right**).  
for each group of buffers from the temperature series of the stations included in each group (see Table 5). The reference week is the one in which the satellite image used for LCZ classification was acquired. The majority LCZ class and the descriptive statistics were both computed using the _QGIS_ Zonal Statistics Plugin as well as some Python scripts based on the _GeoPandas_ library. This second experiment was designed with the goal of finding evidence of the persistence of higher temperatures within buffers characterized by a heavy urbanization and lower temperatures in buffers characterized by a more natural land cover composition. Results are reported and discussed in Section 4.