## 3 Methods
Moreover, the results achieved represent a backbone analysis carried out with the maximum number of available acquisitions; thus, they provide a deformation time series with the highest temporal sampling. However, the spatial distribution of resulting coherent targets is limited. In order to increase our capacity for image surface deformation phenomena, in T2 we reprocessed only the images acquired from April to November without changing any other input or parameter, assuming that the snow season in VDA corresponds to the December-March period [84]. The approach followed in T2 can be particularly critical because it introduces very long temporal gaps in the SAR dataset during the winter periods and can strongly reduce the number of available acquisitions. Moreover, it does not guarantee the removal of all acquisitions affected by snow cover because the climatic conditions in mountain areas can significantly change year-by-year and zone-by-zone. However, the second analysis significantly improved the number of targets with respect to the first one: if we consider, for example, the Envisat ascending data, we registered an increase in the number of targets of 193% (Table 2). As stated above, snow seasons in VDA mainly range from December to March; however, there are exceptions. For example, the 2008-2009 winter registered extraordinary snow season from November to May [84]. According to this experience, in T3 we improved the SAR image selection method throughout the analysis of meteorological records coeval to SAR observation. Considering several weather stations of the regional meteorological network, a day-by-day analysis in correspondence to the satellite acquisition epochs is carried out. All the considered weather stations [85] are located above 2000 m a.s.l. to guarantee the significance of the meteo-climatic measurements for high mountain environments. This analysis is performed in two phases: (i) snow coverage identification and (ii) interferogram evaluation. In particular, we examined the daily rainfall (in mm) and the snow depth (in cm) parameters. The images characterized by abundant rain or snow precipitation were discarded from further analyses. Successively, an interferogram analysis was performed to identify SAR acquisition strongly affected by snow cover. During this analysis, arbitrary thresholds were considered. We observed that over 50 cm in snow depth could generate noisy interferograms. Regarding rainfall, only a single image was discarded from the analysis (28 May 2007), associated with a rainfall event of more than 30 mm/day, which has also generated, in this case, noisy interferograms. According to the analysis of the meteo-climatic parameters, we discarded only the images associated with the most critical values of daily rainfall and snow depth to ensure the SBAS processed the largest number of acquisitions. Using this fine-tuning, in T3 we still improved the number of targets: considering the Envisat ascending orbit again as a reference, the increment of coherent points with respect to the first analysis is of 313% (Table 2); with respect to T2, an increase in the number of targets to 5668 was revealed. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
It is worth noting that, for the reliability of the SBAS-DInSAR analysis, it is very important to keep in the processed dataset a number of SAR acquisitions as large as possible to preserve a good temporal sampling and to limit as much as possible the presence of temporal gaps [86]. Accordingly, we only removed the SAR acquisitions affected by a widespread snow cover that clear impacts on the spatial coherence of the interferograms. To this aim, it was crucial to crosscheck the meteo-climatic conditions that provide local information and with the quality of the interferograms that supply large-scale information.