## 4 Experimental Results
### SBAS-DInSAR Analysis  
We applied the SBAS-DInSAR technique, discussed in Section 3.2, to the available C- and X-band SAR datasets. In particular, we selected SAR data pairs with a perpendicular baseline <800 m and a temporal baseline <400 days; accordingly, we generated \(M_{1}\) = 91 and \(M_{2}\) = 155 interferograms from the ASAR/ENVISAT and the CSK datasets, respectively. For the sake of convenience, the selected SAR data pairs are also represented in the temporal/perpendicular baseline plane of Figure 2a,b, through connecting segments between the SAR data acquisition points. The interferograms were generated by computing the phase difference between the co-registered SAR images pairs [58], and by subtracting the relevant topographic phase contributions, as synthesized using the Shuttle Radar Topography Mission (SRTM) DEM (with a spatial sampling of 90 m \(\times\) 90 m) of the area [59]. To mitigate the effects of the decorrelation noise, we also independently carried out (on each single interferogram) a complex multilook operation [60] (with 30 looks in the azimuth and range directions for the CSK case, and with 20 looks in the azimuth, and 4 looks in the range direction for the ASAR/ENVISAT case, thus resulting, for both SAR datasets, in a radar pixel dimension of about 90 \(\times\) 90 m). To each interferogram, a noise-filtering operation [61; 62] was also applied. A selection of four interferograms, characterized by different values of the spatial and temporal baselines, is shown in Figure 3. It is worth noting that the temporal decorrelation noise artifacts corrupt the interferograms as the temporal baseline increases, and this effect is more evident in coastal areas. The generated interferograms were unwrapped using the Extended Minimum Cost Flow (EMCF) algorithm [52]. It is worth noting that phase unwrapping (phU) operations were only involved in the sparse group of coherent pixels common to all the generated interferograms; accordingly, the incoherent areas and the sea have automatically been masked out. Moreover, unwrapped phase signals were calibrated to one selected ground reference point (typically a high coherent pixel), which has been assumed to be motionless [63; 64; 28]. As is evident, the southeastern sector of the Shanghai megacity looks to be affected more by temporal decorrelation noise artifacts than the highly urbanized city center. This finding is, however, somewhat expected, as coastal areas have changed significantly over time, due to active reclamation processes; in addition, temporal decorrelation is more severe in the X-band than in the C-band [35]. Nevertheless, this does not represent a serious drawback, since DInSAR allows the identification of reliable groups of targets that preserve their coherence [28]. SBAS-DInSAR processing has been exclusively performed on such sparse grids of coherent pixels. Figure 3: Four selected DInSAR interferograms relevant to the Shanghai area, achieved by exploiting ENVISAT/ASAR acquisitions over ascending orbits (**a**,**b**); and CSK SAR data over descending orbits (**c**,**d**): (**a**) 29 October 2007–3 December 2007, ENVISAT interferogram, with a perpendicular baseline = 64 m, and temporal baseline = 35 days; (**b**) 2 March 2009–20 July 2009, ENVISAT interferogram, with a perpendicular baseline = 5 m, and a temporal baseline = 140 days; (**c**) 8 January 2014–9 February 2014, CSK interferogram, with a perpendicular baseline = 70 m, and a temporal baseline = 32 days; and (**d**) 27 January 2015–2 October 2015, CSK interferogram, with a perpendicular baseline = 45 m, and a temporal baseline = 248 days. SBAS-DInSAR processing allowed the generation, for both of the two SAR datasets, of a map of the average LOS deformation velocity of the Shanghai area district and, for each coherent pixel, of the corresponding LOS displacement time-series. Maps showing the rates and spatial patterns of surface deformation, observed by both the ASAR/ENVISAT and CSK satellite orbits, are shown in Figure 4a,b, respectively.