## 3 Methods  
By taking into account the challenges outlined above, here we present a methodology to exploit the capabilities and potentialities supplied by the GPOD-SBAS service to retrieve the surface deformation information over high mountains. The VDA region is a representative area of investigation where we apply GPOD-SBAS processing. We consider SAR images collected by ERS-1/2, in ascending orbit, and ASAR Envisat missions from ascending and descending orbits (see Table 1). The tracks cover most of the VDA regional territory, excluding only about 10% of the entire region localized in the eastern portion (see Figure 4). The reference point is selected corresponding to an area assumed stable, on the basis of geological and geomorphological background analysis, during the investigation period, and where DInSAR coherence values are expected to be high (i.e., within the Aosta municipality, see Figure 4). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Satellite** & **Observation Period** & **Track** & **Frame** & **Orbit Acquisition** & **Number of Images** \\ \hline ERS-1/2 & April 1995–May 2000 & 301 & 909 & Ascending & 32 \\ ENVISAT & June 2004–October 2010 & 301 & 909 & Ascending & 50 \\ ENVISAT & September 2004–October 2010 & 294 & 2673 & Descending & 46 \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the ERS-1/2 and ASAR Envisat images considered for the VDA study area. Figure 3: Land cover map of the VDA region (redraw from CORINE 2006). Though the SBAS approach can be jointly applied to ERS-1/2 and ASAR Envisat datasets to retrieve a continuous time series covering almost 20 years [83], at the moment of the processing, the implementation available in GPDD allows us to process the datasets only separately. To retrieve reliable results and to maximize the spatial coverage of the achieved coherent points, we perform an iterative procedure in order to choose the most suitable SAR dataset, to minimize the temporal decorrelation effects mainly related to the presence of snow cover in the areas of interest, and to preserve the processing of the largest number of SAR scenes. We perform four tasks (named from T1 to T4) for each orbit, summarized in Table 2. It is worth noting that such an analysis phase, addressed to select the most suitable dataset, plays a key role when analyzing mountain areas. Indeed, the presence of several decorrelation sources, i.e., vegetation and snow cover, requires proper identification of the SAR acquisitions less affected by noise through an iterative mechanism. This analysis can be easily and efficiently carried out within the GPDD environment thanks to its capability to perform SBAS analyses in a reasonably short time (about 24 h). As a final remark, the following analysis is based on the two available Envisat datasets because they have a good number of SAR acquisitions (more than 50 acquisitions) uniformly distributed during the observed time period, and permit a better investigation of the impact of winter acquisition on the final results. As regards ERS-1/2 dataset, due to the limited number of available SAR acquisitions, it is directly processed by selecting the images and the processing parameters on the basis of the Envisat analyses. Finally, we advise that GPDD-SBAS operates with the three-arcsec DEM free available from the SRTM archive. Figure 4: Map of the VDA region study area (in **light grey**). The **red** box represents the extent of ERS-1/2 and ASAR Envisat track in ascending orbit, while the **blue** box represents the track in descending orbit. The reference point (REF) is located in the Aosta municipality. In T1, we processed all the available images for each Envisat dataset by keeping the default parameters of the GPDD-SBAS unmodified; this task is very important because it allows us to produce a large number of interferograms involving all the available SAR scenes and to retrieve a complete overview of the whole SAR dataset.