## 3 Sublook Generation and Temporal Sublook Coherence Evaluation  
This Section presents a brief review of the TSC approach in order to ease the comprehension of the rest of the manuscript. The maps obtained by means of TSC are compared with the traditional phase quality estimators for the GB-SAR and TerraSAR-X datasets described in the previous section. Figure 4: Amplitude in dB covering the lower part of the landslide of El Forn de Canillo of a SAR image collected by the RiskSAR-X sensor in (**a**) cartesian and (**b**) polar coordinates. The blue arrows refer to man-made structures, while the red arrows refer to high-reflectivity areas related to forested areas, which decorrelate fast at the X-band. (**c**) Amplitude in dB of a SAR image collected by the TerraSAR-X sensor in sliding spotlight mode covering the whole sliding mass. The objective of the TSC approach is to find the so-called SCS, which refers to those targets that behave as point-like scatterers in terms of the spectrum properties throughout the whole multi-temporal set of SAR images available [25]. This means looking for those targets characterized by a high correlated spectrum in range, azimuth and height for all of the acquisitions. Since some scatterers usually present a non-uniform azimuth scattering pattern, the assumption of the correlated spectrum is typically applied only in the range direction [26]. Working with the spectral properties of point-like scatterers presents clear advantages [25]. In contrast to the PS approach, the TSC is not affected by the possible amplitude fluctuations of point-like scatterers along the temporal axis. The amplitude, thus, plays no role in the TSC estimation, and for this reason, a radiometric calibration of data is not required. Moreover, the resolution of the images is better preserved in comparison with the coherence stability pixel selection approach. Only a loss of a factor of two is produced in the range direction due to the sublook generation process [26]. This resolution loss may be partially overcome using the spatial variant apodization (SVA) filtering technique, as suggested in [25, 31]. Finally, due to the nature of the estimator, which is based on exploiting the coherence between different sublooks of the image spectrum, it allows reliable full-resolution detection capabilities, even when a few number of SAR images are at our disposal, as will be demonstrated throughout this manuscript. The TSC evaluation requires, beforehand, a sublook generation process. A sublook may be defined as a spectrum portion of the full available system bandwidth. This process can be summarized in the following points [26]:  
* First of all, the SLC spectrum must be unweighted. SAR images are typically filtered with linear windows in order to reduce the impact of side-lobes. Taking the sublooks directly from the tapered spectra may lead to unbalanced distributions of energy in the sublooks, which could have a negative impact during the later TSC evaluation. * Once the image spectrum is unweighted, it is divided into two non-overlapping sublooks, which, at the same time, are base-banded to the same center frequency. This step is carried out in order to avoid linear phase terms during the TSC evaluation. * Each sublook spectrum may be additionally weighted to reduce the side-lobes in the detection. * An inverse Fourier transform is finally applied to each sublook in order to obtain them in the spatial domain. Once the sublooks of each image are calculated, the TSC may be evaluated in order to detect SCS, which are directly related to the deterministic point-like scatterers within the area of interest.