We present in this comment a Multi-Temporal SAR Interferometry (MT-InSAR) analysis showing that the results published by Milillo et al. (2019) in the Remote Sensing Journal, presenting the evidence of space geodetic observations relevant to displacements occurring before the collapse of the Morandi Bridge, happened in Genova (Italy) on the 14 August 2018, are questionable. In particular, we focus on the InSAR results obtained by Milillo et al. (2019) by processing the 3 m × 3 m resolution COSMO-SkyMed (CSK) data collected from ascending and descending orbits on the area of interest. These results, thanks to the high spatial resolution and the short revisit time characterizing this multi-orbit SAR dataset, represent the cornerstone of their analysis. The main findings of their study allow Milillo et al. to conclude that the InSAR processing of this COSMO-SkyMed dataset reveals the increased deformation magnitude over time of points located near the strands of the deck next to the collapsed pier, between 12 March 2017 and August 2018. In this comment, we show the results obtained by the IREA-CNR SAR team after processing the same ascending and descending CSK dataset, but by using two alternative and independent processing techniques: the Small BAseline Subset (SBAS) and the Advanced Tomographic SAR (TomoSAR) approaches, respectively. Our analysis shows that, although both the SBAS and the TomoSAR analyses allow achieving denser coherent pixel maps relevant to the Morandi bridge, nothing of the pre-collapse large displacements reported in Milillo et al. (2019) appears in our results, leading us to deeply disagree with the findings of their InSAR analysis.

This article presents an innovative, parallel implementation of the Small BAseline Subset (SBAS) approach to automatically and efficiently process large volumes of multitemporal differential synthetic aperture radar (SAR) interferometry (DInSAR) interferograms generated at the native full spatial resolution of the SAR images. The starting point of the developed full-resolution parallel-SBAS (FR P-SBAS) technique involves some algorithmic extensions for improving the quality of the DInSAR time series to effectively analyze extended deformations and localized displacement phenomena, such as those affecting single buildings and infrastructures. The main focus of the work is on the efficient and scalable FR P-SBAS processing chain implementation, extensively exploiting graphical processing unit (GPU) architectures. Moreover, the presented scalability analysis demonstrates the GPU capability of efficiently generating full-resolution displacement time series starting from large DInSAR datasets. Furthermore, it is also shown that the implemented processing solution easily allows us to deal with SAR data acquired through the Stripmap and TOPS modes. To assess the quality of the generated DInSAR products, an extensive experimental analysis is also shown, based on long sequences of X-Band COSMO-SkyMed Stripmap and C-Band Sentinel-1 TOPS acquisitions relevant to the Campi Flegrei Caldera (Southern Italy), which is monitored through a dense GNSS network. The presented results demonstrate the effectiveness of the FR P-SBAS processing chain in retrieving multifrequency and multiplatform displacement time series at the full spatial resolution with subcentimetric accuracy and in very short time frames, from a few hours for the COSMO-SkyMed datasets up to some tens of hours for the Sentinel-1 case.

We present in this work the new advances of the Differential SAR Interferometry (DInSAR) technique referred to as Parallel Small BAseline Subset (P-SBAS) approach, for the generation of displacement time series from the recently available L-band SAR image sequences acquired by the Argentinian SAOCOM-1 constellation. In particular, we first discuss the algorithmic extensions allowing us to retrieve SAOCOM-1 DInSAR products, with a main focus on the exploitation of Stripmap SAR images and multi-look interferograms. These algorithmic improvements are relevant to the adjacent Single Look Complex (SLC) image slices merging, to the multi-temporal residual orbital artifacts mitigation and to the phase unwrapping procedure enhancement. Subsequently, the effectiveness of the extended P-SBAS processing chain is shown through a comparative analysis between the achieved SAOCOM-1 DInSAR results and the corresponding ones relevant to Sentinel-1 and COSMO-SkyMed acquisitions. Moreover, a comparison between the SAOCOM-1 displacement time series and the GNSS measurements, relevant to the Campi Flegrei caldera (Italy), is presented. This shows that the mean standard deviation value of the differences between the DInSAR and the GNSS time series, the latter projected in the radar line of sight, is of about 1 cm, further confirming the effectiveness of the P-SBAS processing chain extension.