## 2 Materials, Methods, and Discussion
### Land Cover Constraints  
Land cover represents a critical factor reducing InSAR capabilities, in terms of accuracy of the results, due to temporal decorrelation effects. In order to remove or at least minimize such effects, two main families of methods were developed over the last decade [4]: the PSInSARTM [33] and SBAS [34]. With PS, temporal decorrelation effects are reduced by considering targets with low amplitude dispersion, while SBAS family relies on forming interferograms only between images  
Figure 5: Identification of image distortions to the Sentinel-1 IW ascending (**a**) and descending (**b**) modes over Great Britain. Figure 6: Total areas of simulated Sentinel-1 IW geometric distortions (shadow and layover include both active and passive distortions) across Great Britain, based on the 90 m resolution SRTM DSM. separated by a short time interval and with a small difference in look and squint angle. However, the predominance of non-irrigated arable land and pastures, covering \(\sim\)130,000 km\({}^{2}\) across the landmass, exerts significant control on the potential for InSAR methods to identify scatterers in Great Britain, with particularly critical effects on rural and grassland regions where only a few radar targets per square kilomet can be extracted and monitored via InSAR processing of C-band data [10]. Novel InSAR methods, such as SqueeSAR(tm) [35] and ISBAS [36;37], aim to increase the coverage of results in non-urban regions by considering, respectively, distributed scatterers (e.g., debris, non-cultivated land, or low vegetation cover), or intermittently coherent surfaces. We assess ISBAS density using the freely-available CORINE land cover data for the reference year 2012 (CLC2012) and at 100 m resolution, following the empirical approach illustrated in [38]. Therefore, for each single land cover class the number of ISBAS targets N falling inside the land cover class is calculated. This is implemented by counting the number of geocoded ISBAS Nk (of the reference SAR data) for each single subarea k of each land cover class (i) and then summing up the results (N = \(\Sigma\)Nk). Then, the entire area of the i-th class within each site A was calculated. Next, the ISBAS density p for each class i (ISBAS targets per km\({}^{2}\)) was derived:  
\[\mathrm{p_{i}}=\mathrm{N_{i}}/\mathrm{A_{i}} \tag{3}\]  
The procedure described above was implemented in a geographical information system (GIS) and executed for a number of calibration sites. In particular, the p\({}_{\mathrm{i}}\) was estimated from a total of 64,844 km\({}^{2}\) including six subareas (East Anglia, East Midlands, Hampshire, Lancashire, Yorkshire, and Southern Scotland) where 36 out of the 39 land cover types of Great Britain occur and 5,155,619 ISBAS pixels have been identified (Table 1). The exploitation of existing ISBAS datasets over different areas has been the input data for the quantitative calibration of countrywide land cover data towards extending the pre-survey feasibility assessment to the entire landmass and land cover types mapped across the country (Figure 7). By using the same sensor with the same wavelength over the two acquisition geometries, normalization of absolute ISBAS densities has not been required. Furthermore, climate conditions are similar in the seven calibration sites (Met Office, [22]), therefore, no initial climate classification and compensation were performed. Standing at the ISBAS average density, ISBAS standard deviation and extension of each land cover types (Figure 8), ranging from the 122 ISBAS/km\({}^{2}\) over continuous urban fabric to just 9 ISBAS/km\({}^{2}\) over estuaries, and accounting for the topographic constraints, \(\sim\)22,000,000 ISBAS points can be acquired, corresponding to an area of \(\sim\)170,000 km\({}^{2}\), namely 70% of the Great Britain landmass. No ISBAS points were detected over burnt areas, costal logoons, permanently irrigated land, sea and ocean, water bodies, and water courses.