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Measuring terrain deformation over several spatial and temporal scales is relevant for many applications in Earth Sciences (i.e. active faults, volcanoes, landslides or glaciers understanding). The growing volume of freely available data represents nowadays a challenge in terms of storage capacity and computing resources which, together with the complexity of the processing (code parameterization, combination of the image sequences, co-registration of the images) may prevent the exploitation of long time series. We propose here a new version of the Multiple-Pairwise Image Correlation toolbox for processing OPTical images (MPIC-OPT). The toolbox proposes an end-to-end solution to compute the horizontal sub-pixel ground deformation time series from large Sentinel-2 datasets. In addition to time series inversion, several corrections and filtering options are integrated to reduce the noise and improve the accuracy and precision of the measurements. In particular, an automatic jitter correction based on wavelet filtering is proposed. Moreover, the MPIC-OPT service is deployed on the Tier 1.5 High-Performance Computing cluster (e.g. Datacentre/EOST-A2S) of the University of Strasbourg and is accessible on-line through the ESA Geohazards Exploitation Platform (GEP) and the ForM@Ter Solid Earth computing infrastructure with a user-friendly environment to query the satellite data catalogues, parameterize the processing and visualize the outputs. We test the performances of MPIC-OPT on several use cases: the measurement of the co-seismic ground deformation of the 2019 Ridgecrest earthquake sequence (USA), of the rapid motion of the Slumgullion landslide (USA) and of the glaciers of the Mont-Blanc massif (France/Italy). We show that the results of MPIC-OPT are in agreement with in-situ data. The jitter correction significantly improves the precision (RMSE jitter =0.3 m vs. RMSE nojitter =0.5 m) and the accuracy (RMSE jitter =0.3 m vs. RMSE nojitter =1.3 m) of the measurement of the co-seismic displacement of the Rigdecrest seismic deformation. We show that the precision and accuracy of the terrain deformation estimation depend mainly on the correlation threshold and the temporal matching range parameters and we quantify and discuss their impacts. This work opens new perspectives to monitor automatically surface displacements/velocities of natural hazards over large scales and large periods of time. [Display omitted] [ABSTRACT FROM AUTHOR]