## 4 Methods
### Sentinel-1
Finally, we employed the Range-Doppler terrain correction method, which used the 10 m Aosta Valley DTM implemented in the SNAP repository, selecting ED50-UTM 32 N (EPSG: 23032) as the projected reference system, and selecting an average output resolution of 10 m. The output coherence image consisted of two different bands, reporting interferometric coherence values (from 0 to 1) for the two polarizations (VH and VV). It is worth noting that the coherence between the two SAR images expressed the similarity of the radar reflection between them. Any changes in the complex reflectivity function of the scene were manifested as a decorrelation in the phase of the appropriate pixels between the two images. Within this type of raster, it was possible to extract the urban footprint by applying supervised (and unsupervised) classification algorithms. In this case, a supervised classification was performed starting with the training set. Since we were interested in distinguishing only two different classes, i.e., urban and non-urban areas, we aggregated all non-urban land cover types into the same class (such as glaciers, lawn pastures, needle \begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{4}{c}{**S1 Pairs Ascending Orbit**} \\ \multicolumn{4}{c}{**(Product n\({}^{\circ}\), Baseline, Temporal Distance in Days between the Two Acquisitions)**} \\ \hline S1B\_IW\_SLC\_1 SDV\_20200903T172 & S1A\_IW\_SLC\_1 SDV\_2020 & \\
253\_20200903T172320\_023214\_02C15 & 0909T172335\_2020090T172 & 159 m & 6 \\ A\_3R08 & 402\_034285\_03 FC20\_A288 & \\ \hline S1B\_IW\_SLC\_1 SDV\_20201009T1725 & S1A\_IW\_SLC\_1 SDV\_2020 & \\
254\_20201009T172321\_023739\_02D1 & 10151T2336\_202010151T172 & 134 m & 6 \\ C8\_57D8 & 402\_0348100409B\_A403 & \\ \hline S1B\_IW\_SLC\_1 SDV\_20201114T172 & S1A\_IW\_SLC\_1 SDV\_2020 & \\
240\_20201114T172307\_024264\_02E2 & 1120T12335\_20201120T172 & 144 m & 7 \\ F\_4D7 & 402\_035335\_0420C3\_E828 & \\ \hline \multicolumn{4}{c}{**S1 Pairs Ascending orbit**} \\ \hline S1A\_IW\_SLC\_1 SDV\_2020012T053 & S1B\_IW\_SLC\_1 SDV\_20200 & \\
523\_20200112T053550\_030763\_03871 & 1187053455\_20200118T0535 & 165 m & 5 \\ C\_D73E & 22\_019867\_02592E\_ADCO & \\ \hline S1B\_IW\_SLC\_1 SDV\_20200211T053 & S1A\_IW\_SLC\_1 SDV\_2020 & \\
455\_20200211T05352\_020217102647 & 217150352\_20200217T0535 & 155 m & 7 \\
9\_49 FE & 48\_031288\_03996E\_2722 & \\ \hline S1A\_IW\_SLC\_1 SDV\_20200324T053 & S1B\_IW\_SLC\_1 SDV\_2020 & \\
522\_20200324T05349\_031813\_03 AB & 330710535\_200303T0535 & 129 m & 5 \\ B5\_4955 & 22\_020917\_027 ABA\_DC4C & \\ \hline S1B\_IW\_SLC\_1 SDV\_2020050T053 & S1A\_IW\_SLC\_1 SDV\_2020 & \\
456\_2020050T053523\_021442\_028B5 & 511705352\_20200511T0535 & 138 m & 7 \\ C\_A52F & 50\_032513\_03C3F4\_2251 & \\ \hline S1B\_IW\_SLC\_1 SDV\_20200118T053 & S1A\_IW\_SLC\_1 SDV\_2020 & \\
455\_20200118T053522\_019867\_02592 & 0124T053522\_20200124T053 & 147 m & 7 \\ E\_ADCO & 549\_030938\_038D40\_8123 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._forests. etc.). A random forest classifier was performed in the SNAP. We specified the maximum number of decision trees in the RF classifier at 500 as the optimal value to achieve good noise removal and a homogeneous response [63; 64; 65]. Following the instructions in the ESA online material [66], we applied the interferometric coherence processing methodology outlined in the previous section to a set of S1 data obtained from January to December 2020. The classification images produced from S1 imagery consisted of a discrete raster, with all the pixels classified into either \"urban\" or \"non-urban\" values (with values of 1 and 0, respectively) and water or \"non-water\".