# A GeoNode-Based Platform for an Effective Exploitation of Advanced DInSAR Measurements
## 1 Introduction  
Earth Observation (EO), defined as the process of acquiring observations of the Earth's surface and atmosphere via remote sensing instruments [1], allow unique investigation opportunities of our planet [2; 3]. In particular, the EO techniques have proven to be very powerful for the analysis of surface deformation phenomena, providing critical insights into several processes of great interest for science and society, especially from the perspective of further understanding the Earth System [4; 5; 6; 7; 8; 9; 10; 11] and the impact of human activities [12; 13; 14; 15; 16; 17; 18; 19]. In this scenario, differential interferometric synthetic aperture radar (DInSAR) is regarded as one of the key EO methods for its ability to investigate surface displacements affecting large areas of the Earth with centimeter- to millimeter level accuracy and a remarkable cost effectiveness [20; 21; 22]. Basically, the DInSAR technique allows generation of spatially dense deformation maps by computing the phase difference (interferogram) between pairs of complex SAR images, relevant to acquisitions carried out at different times but with nearly the same illumination geometry and from sufficiently close flight tracks, whose separation is typically referred to as the baseline [23; 24; 25]. The DInSAR methodology has been originally applied to analyze single deformation episodes such as earthquakes and volcanic unrests [6; 7; 8; 9]. However, thanks to the availability of long SAR data time-series, it is also possible to study the temporal evolution of the detected surface deformations. This is carried out through the exploitation of the so-called advanced DInSAR techniques,which properly combine the information available from a set of multi-temporal SAR acquisitions relevant to an area of interest, in order to compute the deformation time-series [26; 27; 28; 29; 30; 31; 32; 33; 34]. Currently, the DInSAR scenario is characterized by a huge availability of SAR data acquired during the last 25 years, comprising the long-term C-band European Space Agency (ESA) archives (e.g., ERS-1, ERS-2, and ENVISAT), the RADARSAT-1 and RADARSAT-2 C-band data sequences, those provided by the L-band ALOS and ALOS-2 sensors and by the X-band generation of SAR sensors, such as the COSMO-SkyMed (CSK) and TerraSAR-X constellations. Moreover, a massive and ever increasing data flow is currently supplied by the C-band Sentinel-1 (S1) constellation of the European Copernicus program [35; 36] that is composed of twin SAR satellites, Sentinel-1A and Sentinel-1B, which were launched on April 2014 and April 2016, respectively, and are specifically oriented to DInSAR applications, for the imaging of land surfaces [36]. This Big Data DInSAR scenario needs the development of advanced methodologies and techniques to manage, visualize and analyze these data, and to integrate them with other sources. In this context the Spatial Data Infrastructures (SDI) may play a key role because they implement a framework of geographic data, metadata, users and tools that are interactively connected [37]. Moreover, a SDI is relevant because it represents a collection of technologies, policies, standards, human resources, and related activities permitting the acquisition, processing, distribution, use, maintenance, and preservation of spatial data [38]. We remark that in the SDI framework the technologies used have significantly changed over time and will certainly continue to evolve, thus implying a significant effort to integrate distributed data among the geospatial data producers, to fully benefit from new technologies. The Open Geospatial Consortium standards (OGC) [39] are an important component in this context, because the SDIs are data and network services dependent by open standards. Making policies that maximize the use of geospatial products, solutions and services compliant with the OGC standards, represents the best strategy to capitalize the results obtained from the implemented data services, supporting the interoperability functionalities.