## 4 Discussion
In addition, they are characterized by different geometries of view (ENVISAT incidence angle from 15\({}^{\circ}\) to 45\({}^{\circ}\); CSK incidence angle from 20\({}^{\circ}\) to 59\({}^{\circ}\)), area coverage (ENVISAT \(\sim\)100 \(\times\) 100 km; CSK \(\sim\)40 \(\times\) 40 km), and spatial resolution (ENVISAT \(\sim\)20 \(\times\) 20 m; CSK \(\sim\)3 \(\times\) 3 m in StripMap mode), which are indeed elements to account for. In theory, such differences can be exploited as a sort of data cross-validation and improve the knowledge of a phenomenon according to its scale and orientation. However, they can create ambiguity and mislead non-expert users such as employees within the Civil Protection Department, Ministry of Environment, and so on. Therefore, in view of performing a near real-time coastline surveillance service, such differences in data and results have to be avoided, adopting the same protocol in all cases (including results updating, processing algorithms, and representation results). Moreover, other issues should be taken into account when considering SAR data: (a) the one-dimensionality of InSAR-based deformation measurements, i.e., along with the LOS direction. It does not enable providing a three-dimensional displacement field, especially when SAR acquisitions are available along one single orbit; (b) the limited spatial coverage (CSK \(\sim\)40 km; ENVISAT \(\sim\)100 km); (c) the processing resources, both in terms of hardware and software facilities, which require a high-performing workstation for the fast processing of long SAR image stacks, as well as resources for the storage of a great amount of SAR products (data and intermediate results). Some of these constraints can be easily overcome through the joint use of S1 acquisitions and the cloud-computing resources. The schedule of S1 acquisitions goes in the direction of reducing the number of variables, thus ensuring the comparability and homogeneity of the results. Also, the S1 mission provides acquisitions with a spatial coverage of about 250 km in the default interferometric wide (IW) Swath TOPSAR acquisition mode (along both the range and azimuth directions) and a revisit time of six days, thanks to its two satellite constellations. This means ensuring the coverage of the whole Italian territory with only four S1 images, with less than one-week temporal sampling (updating) of SAR deformation maps that are able to follow a high rate of displacement phenomena. Also, they are free and can be easily accessed by anyone. The main critical issue concerns the limited simultaneous download from the ESA data hub (maximum two images at a time) and the storage of these data (each S1 image has a size of \(\sim\)7 Gb each) and related derived products, which is becoming a predominant issue when dealing with the new ESA S1 missions. Then, it is strictly needed to have a high-power computing (HPC) service to automatically download the S1 data and upload semi-automatic processing tools to be run weekly or monthly according to the users' requirements. Once the infrastructure has been defined, the theoretical processing chain retrieving InSAR-based products, i.e., deformation rate maps and time series, could be as follows: (1) automatic S1 data download, every six days; (2) setting the maximum temporal baseline to be used for estimating the interferometric pairs and updating the results. For example, every new S1 SAR acquisition could be coupled with the six previous ones (i.e., maximum temporal baseline equal to 36 days) for the results updating. Restriction on the maximum perpendicular baseline should not be needed, since the S1 orbital tube is very stable; (3) choosing how often to update and provide the results (weekly, monthly, annually, etc.) and set the processing tools accordingly; (4) providing open access results through web-based geographical information system (GIS) infrastructure for monitoring and scientific purposes.