## 2 Materials and Methods
### The Ground Segment
#### 2.2.4 SAR Data Processing Chain
To overcome this drawback, in recent years, many efforts have been devoted to develop algorithms aimed at reducing the computational effort required by time-domain approaches [54; 55; 56; 57]. Moreover, the recent widespread proliferation of multi-core hardware architectures, coupled with the development of parallel programming solutions, has helped to reduce even more the processing time required by time-domain algorithms, which, as observed above, are based on a pixel-by-pixel approach, resulting intrinsically tailored to parallel processing strategies. In the processing chain of the IREA-CNR infrastructure, we implement a time-domain algorithm that was recently developed and presented in [42]. The algorithm accounts not only for flight track deviations but also for the space variant nature (in both range and azimuth direction) of the squint angle induced by variations in the pointing direction of the radar antenna during acquisition. More specifically, to focus each target, we first calculate the antenna-to-target distance by using an available external DEM of the observed area and the flight data recorded by the navigation system. Then, we evaluate the number of samples of the synthetic aperture length according to the desired azimuth resolution. Finally, we use the navigation data to select the track portion from which the radar antenna illuminates the considered target within its main beam [42]. Finally, it is remarked that the AC step may be integrated with data processing procedures aimed at estimating some electronic parameters necessary for the accurate implementation of the AC step itself [40; 41]. The implementation of this time-domain AC procedure on the IREA-CNR IT platform takes full advantage of the multi-core capability of the platform. Indeed, a parallel programming approach is implemented for each image to be focused by splitting the corresponding output grid into a number of portions equal to the number of WNs and cores available on the platform. A set of independent jobs (one for each portion of data to be focused) is then executed on the selected WNs and cores. The procedure is managed by Linux Bash scripts concurrently running on the different WNs and cores. Following this parallel computation, a straightforward mosaicking of the different portions of the focused data is carried out to achieve the final SLC. The same rationale is also applied to achieve the parallel generation of different SLCs; in this case, each job is responsible for the processing of one portion of one image of the selected dataset. Following the SAR focusing step, the overall data processing chain of the IREA-CNR infrastructure, as mentioned above, also includes some processing steps leading from SAR SLCs to higher-level products, such as radiometrically calibrated images, DEMs and polarimetric maps. More details on the generation of these higher-level products are provided in paragraphs (a)-(c). (a) External Radiometric Calibration  
External radiometric calibration is a procedural step used to convert the Digital Numbers (DNs) that compose SLC images into meaningful physical descriptors for the targets in the observed scene, such as the Normalized Radar Cross Section (NRCS). To this end, based on the radar equation, the procedure aims at compensating for constant and variable factors introduced into the recorded signals by the radar electronics, the SAR acquisition geometry, the surface topography and the focusing procedure [58]. In particular, radiometric calibration is typically split into the following two steps: relative and absolute calibration. Relative calibration compensates for the space variant terms that account for the range spreading loss, the used Tx/Rx radar antenna patterns, the azimuth compression gain and topography dependent radiometric distortions [59; 60]. Depending on the particular processing needs, this step can be performed during the SAR focusing operation or using post-processing approaches applied to uncalibrated SLC images.