## 4 Results and Discussion  
### LCZ Maps  
The LCZ maps obtained are shown in Figure 5, from which differences among different sensors as well as time periods can be outlined. Table 4 summarizes the OA for each classification output. OA is around 81% for the LCZ maps derived from Landsat 8, between 75% and 78% for the maps derived from Sentinel-2, and 71% for the maps derived from RapidEye. The slightly higher values registered for Landsat 8 can be mostly attributed to the pixel size and the choice of training datasets. In fact, in Landsat 8 imagery the pixel size allows smoothing reflectance information as well as noisy details such as buildings or tree shadows, which are instead more evident in Sentinel-2 and RapidEye imagery. Moreover, with the aim of comparing the classifications derived from the different satellite imagery, the same reference samples were used with a pixel size equal to the largest among the three, i.e., that of Landsat 8. A possible improvement of the OA values for the LCZ maps derived from Sentinel-2 and RapidEye might be achieved by selecting training samples more suited to the pixel size. In addition, the lower spectral resolution of RapidEye sensors negatively influences the classification results, as highlighted by the spectral signatures of the 8 LCZ classes reported in Figure 6. Except for the class water, which shows its peculiar spectral signature, the other classes have a similar spectral behavior. The main spectral differences are evident for infrared wavelengths, starting from 800 nm, represented by three bands in Landsat 8 imagery; four bands in Sentinel-2 dataset; and only one band in RapidEye collection. These considerably reduce OA values of the LCZ maps derived from RapidEye imagery. Comparing the non-vegetative and vegetative periods, OA values are almost the same for Landsat 8 and RapidEye, while for Sentinel-2 an improvement (more than 3%) is achieved for the map corresponding to the vegetative season. Major spectral differences among the classes are found for the summer period, with respect to the winter.  
In terms of PA and UA of each LCZ class, the LCZ maps derived from all the three satellites show very high values for some classes such as water, low plants or large low-rise (higher than 80% and up to 99% for the water class). Other classes, e.g., compact low-rise and open midrise, show instead very low values of UA and PA, e.g., 11% PA for compact low-rise in the RapidEye-derived classification. These low values point out the difficulty of the classification algorithm in correctly detecting some LCZ classes. Among built-up classes, compact midrise and large low-rise score high values of UA (more than 65% in all the cases), while the remaining classes cannot be distinguished. This aspect has to be considered for further analysis. Finally, as expected, a significant improvement is registered for the class scattered trees in the vegetative season, since during the non-vegetative period the absence of leaves causes confusion with the low plants class [56].  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Satellite**} & \multicolumn{2}{c}{**Period**} \\ \cline{2-3}  & **Non-Vegetative** & **Vegetative** \\ \hline Landsat 8 & 81.43 & 80.56 \\ Sentinel-2 & 75.57 & 78.70 \\ RapidEye & 71.79 & 71.61 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall Accuracy (OA) for the Random Forest (RF)-classification outputs [%].  
Figure 5: LCZ final maps: (**a**) Landsat 8, non-vegetative period; (**b**) Landsat 8, vegetative period; (**c**) Sentinel-2, non-vegetative period; (**d**) Sentinel-2, vegetative period; (**e**) RapidEye, non-vegetative period; (**f**) RapidEye, vegetative period. CRS: WGS84/UTM zone 32 N.  
Figure 6: Spectral signatures of the 8 different LCZ classes: (**a**) Landsat 8, non-vegetative period; (**b**) Landsat 8, vegetative period; (**c**) Sentinel-2, non-vegetative period; (**d**) Sentinel-2, vegetative period; (**e**) RapidEye, non-vegetative period, (**f**) RapidEye, vegetative period.