## 5 Conclusions  
This work was devoted to the exploration of long MTInSAR displacement time series derived from both CSK and S1 for investigating ground instabilities related, in particular, to landslide events. In fact, in order to fully exploit the information content of MTInSAR products covering long time periods, it is crucial to analyze not only the mean rate of displacements, but also their full time series, looking for signals related to the ground phenomena of interest that can be also highly non-linear. Examples are periodic signals related to structural thermal oscillations or seasonal ground subsidence, in addition to high-rate and non-linear trends that characterize the warning signals related to landslides or the pre-failure of artificial infrastructures. However, this detailed analysis is often hindered by the large number of coherent targets (up to millions) required to be inspected by expert users to recognize different signal components and also possible artifacts affecting the MTInSAR products, such as, for instance, those related to phase unwrapping errors. In the present work the displacement time series analysis was supported by automated procedures recently developed for identifying targets with peculiar, nonlinear signals, one based on fuzzy entropy, and the other performing nonlinear trend analysis based on the Fisher statistics. This allowed the focus on a smaller set of coherent targets showing nonlinear displacements and the identification of trends affected by residual unwrapping errors. We investigated the ground stability of two hilly villages located in the Southern Italian Apennines, namely the towns of Montescaligioso and Pomario, where landslides occurred in the recent past, causing damage to houses, commercial buildings, and infrastructures. Specifically, we presented an example of slope pre-failure monitoring on the Pomario landslide, an example of slope post-failure monitoring on the Montescaligioso landslide, and a few examples of buildings and roads affected by instability related to different causes (soil swelling, for example). The analysis of MTInSAR displacement time series showed that the joint exploitation of different MTInSAR datasets provides valuable information about the ground instabilities, with CSK more effective over man-made structures, and S1 over outcrops. These results once more confirm the potential role of MTInSAR technology in supporting both regional and local civil protection organizations in all phases of risk management: identification, analysis and monitoring, assessment, and mitigation. To achieve this aim, it is very important that MTInSAR datasets are regularly acquired over long time periods with different wavelengths and acquisition modes, and to perform detailed analysis of displacement time series through reliable and automated procedures. Finally, despite the potential of both FE and NLTA in selecting reliable targets and characterizing their temporal behavior, an operational service devoted to slope instability early warning still needs more development. In particular, concerning the time series analysis, the NLTA should be followed by specific processing steps aimed, for instance, at deriving the time corresponding to a breakpoint [33], at forecasting future displacement behavior [45], and at predicting failure time for landslides [46]. The development of these processing steps is also the subject of ongoing research by exploring machine learning techniques. Conceptualization, F.B. and I.A. ; methodology, F.B., A.R. and G.P. ; investigation, I.A. and G.S. ; formal analysis, F.B. ; resources and software, R.N. and D.O.N. ; validation, F.B., I.A. and G.S. ; writing--original draft preparation, F.B. and I.A. ; writing--review and editing, A.R., R.N., D.O.N. and G.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the Italian Ministry of Education, University and Research, D.D.