Ground motion, such as land subsidence, can be due to human (groundwater extraction, artificial loading) and natural causes. The latter are related to the geological setting, properties of soil and also to climatic stress (drought periods) and they can affect human-induced subsidence. It is possible that more than one cause contributes to the ground deformation, and it can be difficult to determine and quantify the contribution of each cause. In addition, also the socio-economic development factors, such as the increase in water demand and urbanization and population growth, can contribute to worsening subsidence, especially regarding subsidence due to groundwater extraction which is often overexploited. InSAR data can be a valid support in the study of the ground movements, providing valid products (starting from time series up to displacement maps) that can cover wide areas even where in situ monitoring instruments may be missing. This work will focus on the analysis of A-DInSAR time series by applying several methodologies and additional factors, such as analysis of topography, lithology, land use and geological setting, will be taken into account. In particular, ONtheMOVE methodology (InterpolatiON of InSAR Time series for the dEtection of ground deforMatiOn eVEnts) will allow to classify the A-DInSAR TS trend (uncorrelated, linear, non-linear) and to identify areas with non-linear target clusters. Wavelet analysis and Independent Component Analysis will be performed both on A-DInSAR data and piezometric data in order to unravel and correlate the main components of both TS. Satellite data that will be used cover a period from 2015 to 2021 in a test site in Brescia province (Lombardia region, Italy), in which subsidence is related not only to groundwater extraction but also to compaction of clay, peat oxidation and compaction due to artificial loading. The results of this study will contribute to improve the knowledge of ground deformations in the test site, and they will be helpful in the characterization of aquifer parameters to fill gaps in data especially when in situ monitoring systems are scarce.