Sounding of the Deforming Solid Earth Surface: New Opportunities for Spaceborne Differential SAR Interferometry Techniques to Detect and Monitor Displacements Affecting the Ground and the Built-up Environment

Sounding of the Deforming Solid Earth Surface: New Opportunities for Spaceborne Differential SAR Interferometry Techniques to Detect and Monitor Displacements Affecting the Ground and the Built-up Environment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sounding of the Deforming Solid Earth Surface: New Opportunities for Spaceborne Differential SAR Interferometry Techniques to Detect and Monitor Displacements Affecting the Ground and the Built-up Environment Riccardo Lanari, Federica Casamento, Francesco Casu, Federica Cotugno, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8904298/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract We discuss in this work, through the analysis of four selected case studies, some new opportunities for the detection and monitoring of displacements affecting the ground surface and the built-up environment, through Differential SAR Interferometry (DInSAR) techniques. This is done by exploiting the data acquired by currently operating SAR constellations, such as SAOCOM-1 (providing L-band data), Sentinel-1 (providing C-band data), and COSMO-SkyMed (providing X-band data), and by also envisaging those that will be soon available through the forthcoming missions, as for the case of the NIMBUS SAR constellation (that will also provide X-band data). The first case study shows how the L-band SAR data, in particular those systematically acquired by the SAOCOM-1 twin systems, allow to retrieve surface displacements in areas characterized by vegetation, where shorter-wavelength sensors have typically limited performance. The second experiment is based on the Sentinel-1 C-band DInSAR measurements and permits us to envisage new scenarios for improving surface deformation analysis of very wide areas by exploiting external data, such as the ECMWF ERA5 and the ETAD ones, with the aim of mitigating possible atmospheric artifacts, thus enhancing the retrieved surface displacements accuracy. The third experiment highlights how the high resolution SAR data acquired by the X-band sensors of the COSMO-SkyMed constellation permit the investigation of small-scale displacements relevant to single buildings or infrastructures of the built-up environments, even when they are located in areas where extended deformation phenomena occur. The last case study explores the potentials of the forthcoming NIMBUS SAR X-band constellation in future DInSAR scenarios, assessing its prospective impact on the displacement monitoring capabilities for the entire Italian territory. The final discussion is devoted to summarizing the main findings learned through the presented case studies and to highlighting further possible impacts on the future developments of the DInSAR techniques. Synthetic Aperture Radar (SAR) DInSAR Ground displacements Built-up environment Deformation monitoring Sentinel-1 COSMO-SkyMed SAOCOM-1 ERA5 ETAD IRIDE NIMBUS SAR Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Imaging the Earth surface both day and night and (nearly) in any atmospheric condition is possible thanks to the Synthetic Aperture Radar (SAR), a sensor which, generally mounted on board satellites or aircrafts (and drones, more recently), operates in the microwave band of the electromagnetic spectrum. In particular, the SAR sensors are equipped with a transmitting and a receiving system through which, by sending appropriate signals, they "illuminate" the areas of interest and record the back-scattered electromagnetic radiations, whose appropriate digital processing allows us to generate high resolution microwave images of the observed zones (Curlander and McDonough, 1991; Franceschetti and Lanari, 1999 ) An important feature of these radar systems is the possibility of applying the Differential Interferometry SAR (DInSAR) techniques, which permit the measurement of surface deformations affecting large areas on Earth, with centimeter (sub-centimeter, in some cases) accuracy (Gabriel et al. ,1989; Massonnet and Feigl 1998 ; Burgmann et al. ,2000). More specifically, the DInSAR technique exploits the phase difference (often referred to as interferogram) between SAR image pairs acquired at different times (whose separation is referred to as temporal baseline) but with the same illumination geometry and from sufficiently close flight tracks (whose separation, in the direction perpendicular to the radar line of sight, hereinafter referred to as LOS, is known as spatial or perpendicular baseline) (Franceschetti and Lanari 1999 ). The topography-related phase component, which is present in the original interferograms, typically requires being properly compensated; this is done through the exploitation of an external Digital Elevation Model (DEM) and of the satellite orbital information, in order to retrieve the so-called differential interferograms (Massonnet and Feigl 1998 ; Burgmann et al., 2000). It is worth remarking that the DInSAR technique is able to estimate the projection of the surface displacement component along the sensor LOS, for the interferometrically coherent pixels, i.e., for the pixels that are not significantly affected by phase noise effects, which are referred to as decorrelation phenomena (Zebker and Villasenor 1992). The DInSAR techniques have undergone a continuous evolution over the last decades, becoming a very important geodetic tool for effectively sounding the deforming solid Earth surface behaviour. In fact, these methods are nowadays widely used for the study of deformations linked to both natural phenomena (seismic, volcanic, hydrogeological), and anthropic activities (subsoil exploitation, for instance), also allowing the investigation of possible displacements at the scale of single buildings and infrastructures (Massonnet et al., 1993; Peltzer and Rosen 1995; Rignot 1998 ; Manunta et al., 2008 ; Tizzani et al., 2013). Moreover, originally designed and applied to investigate single deformation episodes, such as an earthquake or a volcanic eruption, the “original” DInSAR methodology has evolved toward the study of the temporal evolution of the detected displacements, thanks to the development of multi-temporal (MT), also referred to as advanced, DInSAR techniques (Ferretti et al., 2000 ; Berardino et al., 2002 ; Mora et al., 2003 ; Werner et al., 2003 ; Lanari et al., 2004 ; Hooper 2008 ; Sansosti et al., 2010; Ferretti et al., 2011 ). Such multi-temporal approaches, which are based on the exploitation of SAR image sequences relevant to an area of interest, provide helpful information on the spatial and temporal characteristics of the detected deformations, through the generation of displacement time series. These algorithmic advancements have paved the way to the concurrent development of SAR sensors and satellite missions, including the long-term C-band (approximately 5.6 cm wavelength) ERS-1/2, ENVISAT and RADARSAT-1/2 systems, and the L-band (23–24 cm wavelength) JERS-1 and ALOS-1 sensors, characterized by different frequency bands, spatial resolutions, and ground coverage, whose exploitation in advanced DInSAR contexts allowed the achievement of a sub-centimetric accuracy for the retrieved ground displacement measurements (Casu et al., 2006 ; Bonano et al., 2013 ). However, due to their relatively long (typically monthly) revisit times, these “first-generation” SAR systems proved to be inadequate for deformation monitoring. This limitation led to the development of new space-borne missions, the so-called “second generation” SAR systems, characterized by shorter revisit times, improved orbital information, and advanced operational capabilities. Among these, very relevant are the COSMO-SkyMed (CSK) (Covello et al. 2010) and the TerraSAR-X (TSX) (Werninhaus and Buckreuss 2010) SAR systems, launched in 2006. These sensors operate in the X-band (approximately 3.1 cm wavelength), allowing for high resolution imaging and increased sensitivity to surface displacements. This positively impacts the investigation of the space–time characteristics of the revealed displacement phenomena, particularly those related to single buildings and infrastructures. Moreover, the combination of reduced, with respect to first-generation SAR satellite, revisit time (11–16 days for each single satellite) and higher sensitivity, due to the exploited X-band frequencies, has made these missions well-suited for detailed deformation analysis and monitoring in urban scenarios. Furthermore, a notable step forward was achieved some years later with the advent of the C-band Sentinel − 1 (S-1) mission of the European Copernicus program (Torres et al. 2012). Indeed, the launch of this constellation, with the first Sentinel-1 sensor (S-1A) placed in orbit in April 2014, changed significantly the DInSAR scenario of the following decade, thanks to the unprecedented characteristics of this system in terms of spatial coverage, acquisition frequency, and data access policy. In particular, the S-1 sensors are designed with a focus on interferometric applications and can operate in different imaging modes; specifically, the TOPS one, which is the default mode over land, permits achieving a wide-swath coverage of 250 km. Moreover, the single S-1 sensors are characterized by a revisit time of 12 days, which reduces to 6 days when two satellites of the S-1 constellation operate simultaneously. Furthermore, the perpendicular baselines of the S-1 data pairs are smaller than 300 m (except for some degradations that are affecting the S-1A acquisitions after 2024), and the overall acquired data are fully available with a free and open access policy. It is also worth noting that in April 2016, two years after the launch of the first S-1A sensor, the twin Sentinel − 1B (S-1B) satellite was put into orbit. It delivered data up to December 2021, when it experienced an anomaly related to the instrument electronics power supply that caused, in August 2022, the end of the S-1B mission. Moreover, in December 2024 Sentinel-1C (S-1C) was launched, restoring the capability of acquiring interferometrically compatible SAR images over the same area on the ground every 6 days and, finally, in November 2025, the last sensor of the constellation, Sentinel-1D (S-1D), which will replace the outdated S-1A, was placed in orbit. Thanks also to the characteristics of free and rapid product delivery, the S-1 constellation has definitely changed the DInSAR monitoring scenario, moving toward the operational capability to perform advanced DInSAR analyses at very large scales and in rather short times, through the development of techniques aimed to process huge SAR image sets in an automatic and efficient way [Zinno et al., 2015 ; De Luca et al., 2017 ; Zinno et al., 2017 ; Zinno et al., 2018 ; Lanari et al., 2020 ; Cigna et al., 2021 ; Li et al., 2022 ; Wu et al., 2024 ]. More recently, significant efforts have been devoted to the design, setup and development of new L-band SAR constellations, aimed at overcoming some of the intrinsic limitations of the X- and C-band SAR systems, particularly in decorrelation-prone environments. Some of these systems are already in orbit, including ALOS-2 [Shimada, 2013 ], SAOCOM-1 [Delgado et al., 2024 ], and the recently launched ALOS-4 [Motohka et al., 2019 ] and NISAR [Kellogg et al., 2020 ] satellites, while others that are currently in an advanced development phase, as for the new European ROSE-L mission [Rostan et al., 2024 ]. Operating at longer wavelengths, L-band SAR systems exhibit enhanced penetration capabilities through vegetation, resulting in improved temporal coherence over long observation periods. This characteristic makes these sensors particularly suitable for deformation monitoring in vegetated regions, agricultural areas, wetlands, glaciers and ice sheets, where shorter-wavelength systems often suffer from decorrelation. Furthermore, L-band sensors are less sensitive to phase unwrapping errors [Yu et al., 2019 ; Zhang et al., 2019 ; Onorato et el., 2025], enabling more reliable long-term displacement measurements and increasing the robustness of multi-temporal interferometric techniques. The advent of new L-band sensors is also accompanied by substantial improvements in system architecture, including high radiometric stability, wide swath coverage, and short revisit times. These capabilities significantly enhance the potential for large-scale, systematic deformation monitoring and allow for the integration of L-band data into operational services for geohazard assessment, land subsidence monitoring, and infrastructure surveillance [De Luca et al., 2025 ]. We remark that all the previously discussed SAR systems exploit a Sun-Synchronous Orbit (SSO) configuration, which ensures consistent illumination conditions and helps systematic, repeat-pass interferometric acquisitions. However, recent investments have been increasingly focused on alternative orbital concepts, aimed at further enhancing temporal resolutions and operational flexibility. In this framework, a growing attention has been devoted to inclined orbital configurations, such as the Mid-Inclination Orbit (MIO), designed to provide high revisit frequencies and spatial coverage over a mid-latitude area of interest. Moreover, MIO systems are also characterized by enhanced sensitivity to the North-South deformation components, with respect to the SSO-based DInSAR exploitation [Cotugno et al., 2025 a]. Within this context, it is worth citing the already operating Iceye [Ignatenko et al., 2020 ] and Capella Space [Castelletti et al., 2021 ; Yague-Martinez et al., 2025 ] constellations and the new-generation Earth Observation (EO) space program referred to as IRIDE, developed in Italy under the National Recovery and Resilience Plan ( PNRR) framework of the European Union, with the management of the European Space Agency (ESA) and the support of the Italian Space Agency (ASI) [webportal: https://iridespazio.it ]. In particular, IRIDE is designed as a collection of small satellites positioned in Low Earth Orbits (LEO), with the aim of providing dedicated operational applications and services to institutional entities, thus supporting, among various applications, the systematic monitoring of the Italian territory for risk prevention, emergency management, and post-event damage assessment. In this framework, the NIMBUS SAR X-band sub-constellation of the IRIDE program is expected to complement conventional SSO SAR systems, particularly the COSMO-SkyMed ones, thus contributing to a more resilient, responsive, and comprehensive EO framework for deformation monitoring and hazard assessment of the Italian territory [Cotugno et al., 2025 a]. In this work, we discuss, through the analysis of four selected case studies, some possible future development trends of the Differential SAR Interferometry (DInSAR) techniques for the detection and monitoring of displacements affecting the ground surface and the built-up environment. This is done by considering SAR constellations that are currently operating, such as SAOCOM-1 (providing L-band data), Sentinel-1 (providing C-band data) and COSMO-SkyMed (providing X-band data), or forthcoming missions, as for the case of the NIMBUS SAR one (that will also provide X-band data). Finally, we summarize the lessons learned through the presented case studies and identify further possible impacts on the future developments of the DInSAR techniques for surface deformation retrieval. 2. L-band SAR data exploitation for extended Multi-Temporal DInSAR analysis in vegetated areas: the SAOCOM-1 case study Over the last decade, an increasing effort has been devoted by several space agencies toward the development and deployment of spaceborne SAR systems operating at rather low frequencies of the microwave spectrum, particularly in L-band. This trend reflects the growing recognition of the intrinsic advantages of long-wavelength SAR observations in various frameworks and, for interferometric applications, especially in challenging scenarios represented by vegetated regions, agricultural areas, wetlands, glaciers and ice sheets, and long temporal baselines. Some L-band missions are already operational, including ALOS-2, SAOCOM-1 and ALOS-4, while large international investments are represented by the recently launched (July 30th 2025) NASA–ISRO (NISAR) mission and by the forthcoming European ROSE-L system. For what concerns the DInSAR scenario, as said, the L-band SAR signals ensure a significantly enhanced temporal coherence with respect to higher-frequency satellites, such as the C-band and the X-band systems, and a strong reduction of phase unwrapping errors, which are particularly relevant in multi-temporal interferometric analyses. In this work, following the lines of the study presented in [De Luca et al., 2025 ], the benefits of L-band observations are clearly demonstrated through a comparative analysis performed in a vegetated area, using Sentinel-1 (C-band) and SAOCOM-1 (L-band) data processed through the Parallel Small BAseline Subset (P-SBAS) approach [Casu et al., 2014 ; Manunta et al., 2019 ; De Luca et al., 2025 ]. In particular, the analyzed area is close to the Berceto municipality, within the Apennines of the Tuscany region (Northern Italy), which is historically affected by several landslide phenomena, also threatening urbanized zones. The exploited S-1 dataset is composed of 160 ascending orbits IWS SAR images, specifically collected from Track 15 through the TOPS acquisition mode, during the January 2021 – May 2025 time interval, covering the area over the Apennines of the Tuscany Region (Northern Italy). Moreover, to perform our comparative P-SBAS analysis, we also exploited the corresponding SAOCOM-1 acquisitions (64 Stripmap mode SAR images, ascending swath S3) collected over the area of interest, during the same time interval, to guarantee the consistency of the compared datasets. The generated DInSAR results show that, by considering the same observation period and spatial resolution interferometric products (30 m x 30 m), the SAOCOM-1 measurements preserve coherence in a wider area, with a marked density increase of the reliable pixels. In particular, in the investigated vegetated and mountainous zone, the L-band time series allow a more effective detection and spatial delineation of the deformation phenomena, such as slow-moving landslides, that are only partially captured by C-band data. In Fig. 1 -(a), we show the mean deformation velocity map retrieved through the SAOCOM-1 P-SBAS processing (see the red box in Fig. 1 -(a)), overlapped on the corresponding one relevant to the S-1 P-SBAS results. In particular, Fig. 1 -(b) shows an insight of Fig. 1 -(a), around the area imaged by the SAOCOM-1 footprint. The coverage extension achieved thanks to the L-band imaging is evident. To further emphasize the achieved coverage improvement, Fig. 2 shows two zoomed-in views of the area identified by the white rectangle in Fig. 1 -(b). In particular, Figs. 2 -(a) and 2-(b) report the mean deformation velocity maps of this zone, obtained by processing Sentinel-1 and SAOCOM-1 data, respectively. Note that the investigated area is affected by several slow-moving landslides, whose bodies are clearly and fully delineated by the L-band SAOCOM-1 DInSAR results (Fig. 2 -(b)). Furthermore, the displacement time series of three selected pixels, labelled as P1, P2 and P3 in Fig. 2 and common to both the C-band and L-band P-SBAS analyses, are also displayed, where the S-1 deformation time series are represented in green, while the SAOCOM-1 ones are in red. We remark that the Sentinel-1 and SAOCOM-1 displacement time series comparison shows a good agreement in terms of the retrieved deformation signals, whereas some slight differences can be attributed to the different impact of the atmospheric phase contributions and to the difference in the look angle values between the Sentinel-1 (about 37°) and the SAOCOM-1 (about 30°) acquisitions over the considered area. These experimental findings provide clear evidence of the positive impact of space-borne L-band SAR systems for operational ground deformation monitoring. In this perspective, the above-mentioned NISAR and the future ESA’s ROSE-L missions are expected to significantly enhance the capability of multi-temporal DInSAR analyses by providing systematic, long-term L-band acquisitions with wide spatial coverage. The availability of such datasets will enable more robust and spatially continuous deformation products, particularly in regions affected by dense vegetation. Moreover, the integration of the L-band results with existing C- and X-band missions will further foster multi-frequency approaches, opening new perspectives for the robust characterization of both natural and anthropogenic deformation processes. 3. External data exploitation for atmospheric noise filtering of DInSAR time series: the Sentinel-1 case study The operational wide area DInSAR deformation investigation and monitoring scenario has been characterized by an authentic revolution, thanks to the extended ground coverage (about 250 km in the range direction), frequent revisit time (6 days), short perpendicular baseline (less than 300 m), and free and open data policy of the Sentinel-1 constellation [Lanari et al., 2020 ; webportal: https://egms.land.copernicus.eu ]. In this framework, we are witnessing the development and the diffusion of auxiliary data aimed at improving the accuracy of the generated DInSAR results. In particular, we focus on the exploitation of external data to reduce the atmospheric noise affecting DInSAR measurements. Indeed, the proper filtering of the atmospheric disturbances in DInSAR products still remains one of the most challenging tasks, due to the difficulty of correctly discriminating interferometric phase delays, relevant to atmospheric variations, from deformation signals [Zebker et al.,1997; Hanssen 2001 ; Parizzi et al., 2021 ]. Among the several techniques adopted in literature for the atmospheric filtering, those exploiting external data coming from Numerical Weather Prediction (NWP) models present the advantages of being independent of DInSAR measurements, thus allowing us to estimate the atmospheric contributions without impacting on the investigated ground displacements. Nowadays, the ECMWF ERA5 reanalysis dataset [web portal: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview ], which provides measurements of atmospheric parameters, such as temperature, pressure and humidity, with hourly update, is one of the most exploited for estimating the atmospheric contribution, thanks to its characteristics of global coverage, easy download and rapid usability [Hersback et al., 2020; Hu and Mallorquí, 2019 ; Zinno et al., 2022 ]. On the contrary, the main drawback of the ERA5 data is the coarse spatial resolution of about 31 km in horizontal, significantly larger than the DInSAR products resolution. Moreover, recently, the European Space Agency (ESA) has started distributing a new auxiliary product named the Extended Timing Annotation Dataset (ETAD) [Fritz et al., 2021 ; Gisinger et al., 2022 ], specifically designed for the Sentinel-1 SAR data, which supplies users with a set of correction layers to improve the geometric accuracy of S-1 images to centimetric levels. These layers also include, among others, the corrections to be applied to take into account the tropospheric delay, the ionospheric delay, and the solid Earth tides effects. The ETAD corrections are provided with a predefined grid spacing of about 200 m ground sampling for both range and azimuth coordinates. In this Section, we present the results of an extensive performance analysis aimed at evaluating the effectiveness of the above mentioned ERA5- and ETAD-based APS correction data when applied to large Sentinel-1 datasets, following the lines of the studies presented in [Zinno et al., 2025 ; Casamento et al., 2025 ]. The calculation of the ERA5 atmospheric corrections is performed through the PyAPS software [webportal: https://github.com/insarlab/PyAPS ], properly integrated within the P-SBAS automatic processing chain for correcting the generated DInSAR products online [Casamento et al., 2025 ]. The application of the ETAD corrections is carried out through specifically developed algorithms, which also account for a native issue of the ETAD product that causes artifacts, within the APS corrected medium resolution interferograms, by properly removing them [Zinno et al., 2025 ]. The S-1 dataset exploited for the experimental analysis is composed of 104 images acquired from the ascending orbits (Track 44) between 8 January 2018 and 23 December 2020, covering a wide region of approximately 490 km × 270 km over southern Italy, including the Mt. Vesuvius and the Campi Flegrei caldera volcanic sites. A total of 278 interferograms were generated and subsequently exploited to retrieve, through the P-SBAS processing chain [Manunta et al., 2019 ], the displacement time series used for the analysis presented in the following. In particular, we computed and compared the standard deviation (SD) of the original P-SBAS non-filtered time series and that of the ERA5 and ETAD corrected ones. Note that all the above-mentioned statistical metrics were applied only to the coherent pixels of the study area, for which the obtained DInSAR measurements can be considered highly reliable. Moreover, to avoid the variance calculation being biased by possible deformation signals, such as in the case of the Campi Flegrei caldera (characterized by a significant uplifting behaviour), the low-pass signal component related to the displacement signals was first estimated and removed from the P-SBAS time series, and the variance was then computed. The performed statistical analysis highlights that both ERA5 and ETAD corrections generally improve the generated DInSAR results, though not removing the APS component completely. In particular, it came out that both the deformation time series corrected with ETAD and ERA5 data present reduced SD with respect to those of the P-SBAS non-filtered time series. More specifically, considering the overall number of coherent points, which is 2.6*10 6 , both the ETAD and the ERA5 corrected time series show a reduced SD in the 97.3% and in the 99.3% of the measurement points, respectively. Moreover, the comparison between the SDs of the ETAD and ERA5 corrected time series highlights that the former is smaller than the latter in 70.5% of the considered points. On the contrary, ERA5 corrected time series show a SD smaller than the ETAD one in the 29.5% of the considered points. According to these results, the ETAD corrections appear to perform better. To further investigate the APS correction performance obtained for the two generated datasets, we show in Fig. 3 the histograms of the SD of the displacement time series before the APS correction (black dashed line), and after the correction with ERA5 (red line) and ETAD (blue line) data, respectively. Such an analysis confirms that both corrections are effective in improving the distribution of the SD of the original time series, reducing its dispersion significantly. In addition, Fig. 3 clearly shows that the SD histogram of the ETAD-corrected deformation time series exhibits a narrower distribution (lower dispersion) compared to that of the ERA5-corrected series and is shifted leftward toward lower SD values. Moreover, Table 1 shows the mean, mode, and interquartile range (IQR) values for each histogram; also in this case, the ETAD corrections clearly appear to perform better. As a further remark, we underline that our experimental results show that both the ERA5 and ETAD data achieve the best APS correction performance over mountainous areas, since they are capable of retrieving well the atmospheric contribution correlated with the topography, i.e., the stratified atmospheric component. On the contrary, both the ERA5 and ETAD datasets cannot optimize the estimation of the small-scale atmospheric contributions, such as the turbulence, due to their coarse spatial resolution. Table 1 Mean, Mode and IQR values of the SD distributions depicted in Fig. 3 P-SBAS non-corrected deformation time series ETAD-corrected deformation time series ERA5-corrected deformation time series SD mean [mm] 24 16 17 SD mode [mm] 26 16 18 SD IQR [mm] 10 3 4 Figure 4 depicts the final mean deformation velocity map of the area under investigation. Moreover, Fig. 5 shows the deformation time series of some pixels of the analyzed area that are located both in deforming and non-deforming sites, before and after the application of the APS corrections. We depict the original P-SBAS time series (first row) and the corresponding ones after both the APS corrections (the ETAD-corrected and ERA5-corrected time series are shown in the second and third rows, respectively). By considering the ETAD and ERA5-corrected displacement time series shown in Fig. 5 , it is clear that the ETAD ones appear more smoothed and “clean”, thus confirming the achieved better atmospheric correction. It is also shown that both ETAD and ERA5 corrections perform better on mountainous areas compared to flat ones, where they less significantly impact the APS removal (see, for instance point C of Fig. 4 and the corresponding time series shown in Fig. 5 ). The presented experimental analysis shows that both the ERA5 and ETAD datasets allow us to improve the DInSAR time series accuracy, although their major drawback is due to the coarse spatial resolution. However, it is worth noting that the production of new data with enhanced characteristics is proceeding fast. Indeed, the Copernicus Climate Change Service has recently started delivering the Copernicus European Regional Reanalysis (CERRA) dataset [webportal: https://climate.copernicus.eu/copernicus-regional-reanalysis-europe-cerra ], available over Europe, which is produced with a horizontal resolution of 5.5 km, with the purpose of adding value to ERA5 data. Moreover, starting from 2027, the ERA6 global climate reanalysis is expected to be released, offering significant improvements over its predecessor, ERA5, primarily through a higher spatial resolution of about 18 km, more advanced Earth system modeling, and the assimilation of a wider range of reprocessed observations, thus enabling better representation of smaller spatial scale atmospheric phenomena. 4. High resolution SAR data exploitation for displacements investigation in urbanized areas: the COSMO-SkyMed case study The wide area imaging, medium spatial resolution SAR sensors, like those of the C-band Sentinel-1 constellation, have extensively demonstrated proven capabilities to detect and monitor large-scale deformation phenomena, such as regional subsidence, landslide dynamics, volcanic displacements, and other geologically driven ground motions over wide and very wide areas [Lanari et al., 2020 ; webportal: https://egms.land.copernicus.eu .; Crosetto et al., 2025 ]. However, when deformation phenomena become highly localized and spatially heterogeneous, as often occurs in densely urbanized areas, the spatial resolution limitation of these SAR systems may limit the capability to discriminate small-scale displacement patterns or to reliably associate observed ground motions with individual buildings, infrastructures or portions of them. Within this framework, the exploitation of high resolution SAR data, as for the X-band images provided by the Italian COSMO-SkyMed constellation, including both the First-Generation (CSK) and the Second-Generation (CSG) missions, allows performing detailed DInSAR investigations of the built-up environment. In particular, the fine spatial resolution (about 2.7 m in the Stripmap mode but down to less than 1 m in the Spotlight one) of the COSMO-SkyMed SAR acquisitions represents a key asset for the detection and monitoring of surface deformations at the scale of built-up structures, thus supporting infrastructure resilience, preventive maintenance, and risk mitigation strategies through satellite-based displacements. Indeed, since 2009, the use of the CSK data has proven its high effectiveness in capturing detailed deformation patterns related to complex anthropogenic hazard scenarios [Sansosti et al., 2014 ; Crosetto et al., 2015 ; Calamita et al., 2019; Tapete and Cigna, 2020 ; Talledo et al., 2022 ; Miano et al., 2022 ; Ho Tong Minh et al., 2025 ; Miano et al., 2025 ], thanks to a dense distribution of coherent radar targets and the possibility of generating long-term interferometric time series. The launch of the Second-Generation COSMO-SkyMed (CSG) mission, started in 2020, further enhanced these capabilities, guaranteeing temporal continuity and high spatial density of reliable DInSAR measurements. These characteristics are particularly advantageous for multi-temporal DInSAR applications in extended urban areas, where localized deformation signals may coexist with broader regional-scale patterns, and where the capability to isolate, track, and interpret small-scale displacements is essential for risk assessment and infrastructure monitoring. This Section is aimed to highlight how this kind of analysis can be carried out by using high resolution X-band CSK/CSG SAR acquisitions. In particular, we focus on the multi-temporal DInSAR technique referred to as Full Resolution Parallel-SBAS (FR P-SBAS) approach [Bonano et al., 2025 ]. This method is suitable for profitably detecting localized displacements affecting individual buildings or transport infrastructures, while preserving the capability to efficiently and automatically perform advanced DInSAR explorations, across multiple spatial resolution scales, for regional and local-scale deformation retrieval. In the following, we present the main outcomes achieved by processing, through the FR P-SBAS approach, large sequences of ascending and descending X-Band CSK/CSG, Stripmap mode data collected in the 2017–2025 time interval over the extended urban area of Bologna (northern Italy), located along the Po Plain, which is affected by a well-known regional-scale subsidence pattern, primarily induced by the natural compaction of the Quaternary deposits and by groundwater withdrawal [Stramondo et al., 2007 ; Antonelli et al., 2016; Navarro et al., 2025 ]. Specifically, the exploited CSK/CSG dataset is composed of 136 ascending and 131 descending SLC images, collected during the January 2017-April 2025 time interval, within the so-called ASI MapItaly programme [webportal: https://www.asi.it/en/2023/12/asi-italian-space-agency-upgrades-access-to-mapitaly-data ]. The FR P-SBAS processing of such large X-band data sequences allowed the generation of the full resolution (FR) DInSAR products, consisting of LOS FR displacement time series and of the corresponding mean deformation velocity maps for both ascending and descending orbits. In Fig. 6 , we present the FR LOS mean deformation velocity maps achieved by processing the ascending (A) and descending (B) CSK/CSG datasets relevant to the extended urban area of Bologna. The well-known subsidence pattern across the city and the surrounding areas is quite evident in the achieved velocity maps, where it is possible to measure a LOS deformation trend exceeding 1 cm/year; this is observed along the industrial belt and the peri-urban zones of the Bologna municipality. These results further confirm the reliability of the FR P-SBAS algorithm for long-term deformation monitoring and its suitability for supporting regional-scale land management and planning activities. However, when a prevailing regional-scale deformation pattern occurs in the investigated zone, as for the widespread subsidence phenomena characterizing the Bologna area, the identification of possible localized, differential displacements at the scale of single buildings/infrastructures, or specific structural elements becomes not trivial. In this scenario, the capability of the FR P-SBAS approach to perform deformation analyses at different spatial resolution scales can be effectively exploited to enhance the detection and analysis of possible localized signals with respect to the regional trend movements [Lanari et al., 2004 ; Manunta et al., 2008 ]. This is done through the decomposition of the FR displacement time series into the spatially low-pass and high-pass components. Indeed, the former primarily reflects deformation processes related to the soil and the regional-scale ground movements, whereas the high-pass component emphasizes localized displacements that may affect individual structures or portions of them. To further clarify this issue, in Fig. 7 we show the comparison between the FR LOS mean velocity maps achieved by processing (through the FR P-SBAS approach) the ascending CSK/CSG dataset, relevant to the overall FR analysis (A), and to the spatially high-pass displacement component, respectively. It is worth noting that the high-pass deformation velocity map displayed in Fig. 7 - (B), obtained after the removal of the spatially low-pass (or regional) displacement component, in a sort of “Differential of the Differential SAR Interferometry”, is characterized by a generally low displacement signal behaviour. Nevertheless, it allows us to zoom in and easily detect possible anomalies, with respect to the regional deformation trend, associated with infrastructures or single constructions, as, for instance, the one depicted in the inset of Fig. 7 -(B). This is related to a building located in the upper part of the city center that is affected by localized differential displacements. In this case, we also present one of the spatially high-pass displacement time series, shown in the plots of Fig. 7 , which further highlights how the removal of the regional component allows the retrieval of subtle, non-linear, structure-related displacement signals that are superimposed to the long-term, regional subsidence trend. We remark that the presented exploitation of such CSK/CSG observations enables both retrospective investigations and advanced monitoring strategies, thus supporting a comprehensive assessment of regional and localized displacement phenomena affecting the ground surface and the built-up environment, particularly in highly urbanized zones. This represents a crucial step toward more reliable and operational DInSAR-based services devoted to extended urban areas, complementing the information provided by wide area imaging SAR missions operating at medium spatial resolutions, as for the Sentinel-1 case, and paving the way for future high resolution SAR constellations. 5. Mid-Inclination Orbit constellations for advanced DInSAR monitoring: the NIMBUS SAR IRIDE case study In the last decades, Sun-Synchronous Orbits, often in a Dawn-Dusk configuration, have been the standard architecture for many EO missions because they guarantee global coverage, repeatable illumination geometry, and operational simplicity. This approach is consistent with the economic and technological rationale of the historically “big” satellites: complex, expensive platforms with few units available, for which optimization meant maximizing utility on a global scale and reducing management complexity [Yu et al., 2017; Ullo et al., 2019 ; NG et al., 2017 ]. However, the transition to constellations of small satellites, including both commercial and national SAR constellations, is changing this paradigm. Indeed, the availability of more units, faster production, and the possibility of deploying an entire constellation with a single launch require the exploitation of orbital configurations that can better meet specific mission objectives, such as the systematic coverage of specific latitudes of interest, shorter interferometric revisit times, diversified observation geometries, and on-demand services [Ignatenko et al., 2020 ; Castelletti et al., 2021 ; Orzel et al.,. 2023]. In this context, Mid-Inclination Orbits (MIO) become an interesting option for missions that do not need to cover the entire globe but aim to maximize performance over a specific area of interest located at mid-latitude. Indeed, the geometry of MIO ground tracks involves a progressive reduction of the inter-track distance at latitudes close to the orbital inclination, which is particularly valuable for small SAR satellites with limited swaths and typically high/very high spatial resolutions. In the case of DInSAR applications, the drivers are not only the optimization of the spatial coverage of a specific area and the short temporal revisit, but also some rather stringent geometric constraints, such as the use of Repeat Ground Track (RGT) orbits and manageable perpendicular baselines. Furthermore, DInSAR measurement is intrinsically a LOS measurement. Specifically, SSO (quasi-polar) missions are very robust for what concerns the Vertical and East-West displacement components retrieval, but they are not very sensitive to the North-South component [Guzzetti et al., 2009 ; Casu and Manconi, 2016 ]. On the other hand, MIOs would introduce a geometry that increases sensitivity to the North-South direction, thus opening more favourable prospects for retrieving the investigated three-dimensional (3D) deformation field [Cotugno et al., 2025 b]. In this context, IRIDE is one of the emerging new constellations of small satellites. This program, developed under the management of the European Space Agency (ESA) and with the support of the Italian Space Agency (ASI), is conceived as a “constellation of constellations” in Low Earth Orbit (LEO) equipped with different sensors (SAR and optical, at various spatial resolutions and bands), aimed of responding to specific institutional services and operational applications, primarily to support the monitoring of the Italian territory, in addition to on-demand capabilities [webportal: https://iridespazio.it ]. One of IRIDE's SAR sub-constellations is NIMBUS SAR, an X-band system that will consist of two different batches and will also have a high resolution (0.5–3 m) capability. The first batch, scheduled for launch between the end of 2026 and the beginning of 2027, will include 6 satellites that will acquire, on the Italian territory, VV-polarized SAR data in Stripmap mode with a swath width on the order of a few tens of kilometers (in our analysis: ~25–30 km, with an average value used of ~ 27.5 km) [webportal: https://iridespazio.it ; webportal: https://www.thalesaleniaspace.com/en/press-releases/thales-alenia-space-winscontracts-iride-radar-and-optical-satellites ]. The narrow swath width makes the choice of orbital configuration crucial. In fact, a conventional SSO risks not guaranteeing systematic coverage of Italy with short interferometric revisit times. Conversely, a MIO can maximize both spatial and temporal coverage capabilities in the latitudes of interest. Within this framework, this Section aims to show, following the lines of [Cotugno et al., 2025 a], an optimal orbital solution for NIMBUS SAR. We therefore design and compare several repeat ground track (RGT) configurations, evaluating the corresponding interferometric coverage over Italy and the resulting DInSAR performance. A key element of the proposed methodology is the beam assignment strategy: to quickly fill the gap between consecutive orbital ground tracks, each satellite in the constellation is stably associated with a “beam” during the entire N-day repetition cycle, so that after N nodal days, the same area is observed again by the satellite with the same angle of incidence (an essential condition for DInSAR) [Cotugno et al., 2025 a]. Applying this methodology to the NIMBUS SAR case and imposing an interferometric temporal revisit of 6 nodal days (consistent with the current interest to investigate rather fast-developing phenomena), we identify a particularly effective configuration: MIO with an orbital inclination of 49° and right-looking Stripmap mode (nominal altitude 548 km) [Cotugno et al., 2025 a]. The coverage of the Italian territory, relevant to this configuration, is illustrated in Fig. 8 . The comparison with a 6-nodal days RGT SSO shows a clear result, as depicted in Fig. 8 : with the same number of satellites and repeat cycle, the SSO cannot guarantee systematic coverage of Italy. The only way to improve coverage by exploiting an SSO is to increase the repeat cycle to N = 12 days or N = 18 days, which inevitably reduces the temporal imaging frequency. On the other hand, a 49° MIO achieves very satisfactory coverage, except for a few residual areas in southern Italy, which is consistent with the progressive increase in inter-track distance towards the equator. The analysis carried out in [Cotugno et al., 2025 b] also shows that, for the proposed MIO configuration, there are no substantial limitations related to critical perpendicular baseline or geometric distortions (foreshortening/layover/shadow) that could compromise DInSAR applications over the Italian territory [Cotugno et al., 2025 a]. Moreover, as anticipated, another distinctive advantage of exploiting this configuration is the retrieved sensitivity to the North-South deformation component. This enhanced capability is clearly illustrated in Fig. 9 , where the LOS projection of a 1-cm North-South displacement is mapped over Italy (assuming an off-nadir angle of 30°), showing non-negligible contributions and a marked increase as we move toward the northern latitudes. For the sake of completeness, we also report, in Fig. 9 , the LOS projection of a 1-cm deformation in the East-West direction, clearly showing that the improved North-South displacement retrieval capability is at some expense of the East-West one. Looking beyond the first NIMBUS SAR batch, these findings open up several promising perspectives. The first implication concerns the design of the second batch of NIMBUS SAR. If additional spacecraft are deployed in MIOs with different orbital inclinations (e.g., ~ 43°- 44°) and an alternative viewing geometry (e.g., left-looking), the resulting acquisition geometry would be both effective and complementary to that of the first batch. In this way, the constellation could more naturally fill residual coverage gaps (particularly in the southern areas of Italy), while also reducing local limitations such as persistent geometric distortions on specific mountain slopes [Cotugno et al., 2025 a]. At the same time, the availability of an independent viewing geometry would further strengthen the inversion of the system to retrieve the three deformation components, especially for what attains the North-South one [Cotugno et al., 2025 b]. Moreover, the envisaged exploitation of a HH polarization, for the NIMBUS SAR second batch, represents an additional source of information, for what concerns the investigation of the imaged backscattering surface characteristics, integrating the VV-based imaging capability of the first batch [Franceschetti and Lanari, 1999 ]. We further remark that a second, very relevant implication concerns the synergy between the two NIMBUS SAR satellite batches, disposed in MIO configurations, and other available X-band sensors operating at comparable spatial resolution but along SSOs, with a specific focus on the CSK and CSG sensors. Indeed, such a multi-orbit framework would enable a highly robust 3D deformation retrieval capability, paving the way for obtaining high resolution displacement information with unprecedented accuracy. In particular, because of the high coherence of multi-temporal DInSAR products relevant to the built-up environment, this may allow to develop an extremely effective monitoring scenario for the buildings and infrastructures of the overall Italian territory. 6. Conclusion We discussed, through the analysis of four selected case studies, some new development opportunities for the investigation of displacements affecting the ground surface and the built-up environment through advanced DInSAR techniques In this framework, the presented experimental results clearly show the relevance of the synergic exploitation of multi-frequency SAR constellations. In particular, the integration of L-band measurements with those available through the already existing, long-term C- and X-band missions, such as the Sentinel-1 and COSMO-SkyMed SAR constellation (whose continuity is already ensured by their new generations), will further foster multi-frequency DInSAR-based approaches for the robust characterization of both natural and anthropogenic deformation processes. Indeed, the rather long wavelength of the L-band SAR signals ensures a significantly enhanced temporal coherence and a remarkable mitigation of phase unwrapping errors, with respect to higher-frequency systems that, on the other hand, provide higher sensitivity in coherent areas. Accordingly, the envisaged multi-frequency DInSAR analysis will significantly extend the interferometric applications, especially in challenging scenarios represented by vegetated regions, agricultural areas, wetlands, glaciers and ice sheets, and long temporal baselines. In this perspective, the new L-band SAR missions such as ALOS-4 [Motohka et al., 2019 ], NISAR [Kellogg et al., 2020 ], and the future ESA’s ROSE-L [Rostan et al., 2024 ] are expected to represent a very relevant development boost. The presented results also show relevant aspects emerging from the exploitation of multi-resolution SAR data sources, particularly those provided by high resolution sensors. These systems, as for the COSMO-SkyMed constellation of first and second generation, may allow the detection and monitoring of surface displacements at the scale of single infrastructures, buildings or even portions of them, and/or to investigate areas where localized deformation signals coexist with broader regional-scale patterns, where the capability to isolate, track, and interpret small-scale displacements can be essential for risk assessment and monitoring. Indeed, thanks to their high resolution imaging capability, the possibility to “zoom in” areas of interest and to easily detect displacement anomalies of a single structure may represent a key element for supporting resilience increase, preventive maintenance, and risk mitigation strategies for the built-up environment. With this regard, the availability of high resolution L-band SAR systems may represent a very relevant topic for future developments of the SAR technology, as well as the possibility to explore multi-static, in particular bistatic, configurations. The latter will be the case of the forthcoming ESA Harmony bistatic radar mission [López-Dekker et al., 2019 ], composed of two passive sensors, which will run in tandem with the Sentinel-1 ones (very likely, the Sentinel-1D), starting from 2029. One of the main tasks of this system will consist of the possibility to retrieve, for the planned five years of operation, the North-South displacements component in addition to the vertical and East-West ones, which are routinely obtained thanks to the Sentinel-1 sensors. In parallel to the synergic use of multi-frequency/multi-resolution SAR constellations, another highlighted opportunity of development is provided by the availability of external data for improving the quality of the generated DInSAR products. In particular, it is worth noting that there are different kinds of external data, provided by Global Atmospheric Models (GAMs) and Numerical Weather Prediction (NWP) models, which can be used to reduce the atmospheric artifacts affecting the DInSAR measurements, thus improving their accuracy. In particular, we remark that a major influence is due to the troposphere, which highly impacts higher-frequency SAR systems, like those operating at C- and X-band. Among the most used external data, there are the investigated ECMWF ERA5 data [web portal: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview ] and the GACOS corrections [Yu et al., 2020 ]. Moreover, ESA has recently distributed the ETAD product [Gisinger et al., 2022 ], which is specifically designed for improving the accuracy of Sentinel-1 SAR products and also includes some layers for the APS corrections, as shown in the presented results. The main issue of the currently available atmospheric correction data is the spatial resolution, which is generally coarser with respect to that of the typical DInSAR products. Moreover, additional issues are represented by their availability in terms of spatial and temporal coverage. For what concerns the GAM derived data, such as the ERA5 ones, they are available at a global scale but are provided with a rough spatial resolution (~ 31 km), allowing us to estimate only the stratified APS component, but not the finer-scale APS component, such as the turbulent one [Zinno et al., 2025 ]. Differently, NWP-derived data have a finer spatial resolution, but they are available only over limited areas or in restricted time spans and, therefore, cannot be exploited from a perspective of large-scale, automatic, interferometric processing. One of the most recent datasets is the Copernicus European Regional Reanalysis (CERRA) one, which is delivered by the Copernicus Climate Change Service, with the purpose of adding value to ERA5 data; this is produced with a horizontal resolution of 5.5 km and is available over Europe. In this framework, the good news is represented by the fact that a new generation of these external data, with enhanced characteristics, is approaching fast. Above all, it is expected that the ERA6 global climate reanalysis will be released in 2027, offering significant improvements over its predecessor ERA5 through a higher spatial resolution of about 18 km, a more advanced Earth system modelling, and the assimilation of a wider range of reprocessed observations, thus enabling better representation of smaller spatial scale atmospheric phenomena. In this framework, the synergy with the measurements available through the GNSS network can be highly beneficial, thanks also to the growing expansion of these geodetic networks. Furthermore, the multi-frequency characteristics of the GNSS measurements can be also beneficial for the mitigation of phase artifacts induced by the ionosphere, which may have a very significant impact on the low frequency SAR sensors as for the case of the above mentioned L-band SAR systems, and even more on the P-band ones, as already clearly shown through the first results of the ESA BIOMASS mission launched on 2025 [Quegan et al., 2019 ]. Overall, it is highly expected that the large availability of these forthcoming external data will play a key role in improving the quality of the generated DInSAR products. As a final element of discussion, the presented analysis has been focused on the new opportunities offered by constellations of small satellites. With respect to the conventional space-borne SAR systems, i) characterized by large dimensions, ii) typically operating from Sun-Synchronous Orbits, and iii) guaranteeing global coverage, repeatable illumination geometry and operational simplicity, the small satellites constellations represent a change of paradigm. In particular, the availability of more units, faster production, and the possibility of deploying an entire constellation with a single launch allow the exploitation of orbital configurations that better meet specific mission objectives such as the systematic coverage of specific latitudes of interest, shorter interferometric revisit times, diversified observation geometries, and on-demand services [Castelletti et al., 2021 ; Orzel et al., 2023 ]. This has already been shown by the Iceye [Ignatenko et al., 2020 ] and Capella Space [Yague-Martinez et al., 2025 ] constellations. In this context, MIOs become an interesting option for missions that do not need to cover the entire globe but aim to maximize performance over a specific area of interest located at mid-latitude. Moreover, for what concerns DInSAR applications, MIOs would introduce a geometry that increases sensitivity to the North-South direction and, thus, in connection with SAR imaging from SSOs, opens very favourable prospects for retrieving the three-dimensional (3D) deformation field [Cotugno et al., 2025 b]. This is the scenario discussed in the fourth presented case study, which is focused on one of the SAR sub-constellations of the Italian IRIDE program, referred to as NIMBUS SAR, represented by an X-band SAR system allowing high spatial resolution (0.5-3 m) imaging. NIMBUS SAR will include two batches of 6 small (less than 300 kg) satellites each, operating along MIOs and at altitudes between 490–550 km. In particular, the first batch of the NIMBUS SAR constellation will be deployed in 49° right-looking (to be launched at the end of 2026), while it is expected for the second batch a 43° − 44° left-looking orbital configuration (to be launched at the end of 2027). This will allow us to “interferometrically image” the whole Italian territory in 6 days from each orbit and, through the DInSAR technique exploitation, to measure also the North-South deformation component, thus permitting us to investigate the three-dimensional behaviour of the detected displacements. As an additional remark, we highlight that the contribution of small satellites to high-end architectures does not reside, at least for now, in the intrinsic value of the single elements, but rather in the collective capacity of the constellation. Accordingly, it is also foreseen the future possibility to launch swarms of several microsatellites with the possibility to operate cooperatively and also to exploit Artificial-Intelligence (AI) capabilities. To our knowledge, none of these systems is currently operational, but it is already clear that the implementation of such a new concept of observational radar could really boost applications in several scenarios, particularly for emergency response and continuous monitoring. We finally underline that, although the presented case studies provide some good examples of development opportunities for further extending the investigation capability of spaceborne DInSAR techniques, our analysis cannot be fully exhaustive because other elements can play a relevant role. Among these, a key aspect we highlight is related to the capability of efficiently and effectively carrying out a full exploitation of the huge amount of SAR data we are already experiencing, which will exponentially increase in the near future thanks to the foreseen new SAR sensors. In this framework, a major drawback (affecting mainly time series analyses) is certainly represented by the required great increase of both the amount of data to be handled and stored, and the computing time needed to process them. Such characteristics necessarily force a performance optimization of the exploited Information and Communication Technologies (ICT) infrastructures. In this scenario, Cloud Computing Environments [Zinno et al., 2017 ; Wu et al., 2024 ] may represent an opportunity to achieve both large storage and high computing performance capabilities. Moreover, ad-hoc algorithms able to exploit these computing facilities must be developed to ensure algorithm efficiency and portability to such distributed hardware architectures (Lee et al. 2011 ; Sadashiv and Kumar 2011), allowing the effective processing of the available large SAR data volumes. In addition, another key element to be carefully accounted for consists of the capability to promptly and automatically analyze the huge amount of generated DInSAR products. In this direction, the application of AI techniques, to extract added-value information, is another extremely relevant topic for further developments [Anantrasirichai et al., 2018 ; Anantrasirichai et al., 2019 ; Brengman et al., 2021; Zhou et al., 2021 ; Festa et al., 2022 ; Fusco et al., 2023 ]. Declarations Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Funding This work was partly supported by the Italian Civil Protection Department (DPC) in the frame of the IREA-DPC (2022–2024) and (2025–2027) agreements; by the Directorate General for Energy Sources and Enabling Titles – Ministry of the Environment and Energy Security of Italy, through the IREA-MASE agreement; by the EPOS-RI, including the support obtained through the EPOS-Italia JRU and EPOS-ON (GA 101131592). Moreover, we also partially benefited from the European Union— Next Generaton EU (PNRR-M4C2) through the ICSC – CN-HPC (CN00000013) and its HaMMon (Hazard Mapping and vulnerability Monitoring) Innovation Grant Spoke 3 Project, CN-MOST (CN00000023), MEET IR (IR0000025), and GeoSciences IR (IR0000037) projects, and by the GRINT (PIR01_00013) and IbiSCo (PIR01_00011) projects, funded by the National Operational Programme Infrastructures and Networks 2014/202 of the Italian Ministry of Infrastructure and Transports. Author Contribution Conceptualization: R.L., C.d.L., M.M., I.Z., M.B. Data Curation: R.L., Fe.Ca., Fr.Ca., Fe.Co., C.d.L., M.M., Y.B.L.R., P.S., I.Z., M.B.Formal analysis: all Funding acquisition: R.L., Fr.Ca., C.d.L., M.M., I.Z., M.B.Investigation: allMethodology: R.L., Fr.Ca., C.d.L., M.M., G.O., I.Z., M.B.Project administration: R.L., Fr.Ca., C.d.L., M.M., I.Z., M.B.Resources: allSoftware: Fe.Ca., Fr.Ca., Fe.Co., C.d.L., M.M., G.O.,Y.B.L.R., P.S., I.Z., M.B. Supervision: R.L., C.d.L., M.B.Validation: allVisualization: Fe.Ca., Fe.Co., C.d.L., Y.B.L.R., P.S., I.Z., M.B.Writing – Original Draft: R.L., Fe.Co., C.d.L., I.Z., M.B.Writing – Review & Editing: all Acknowledgement The contents of this paper represent the authors’ ideas and do not necessarily correspond to the official opinion and policies of the Italian Civil Protection Department – Presidency of the Council of Ministers and of the Directorate General for Energy Sources and Enabling Titles – Ministry of the Environment and Energy Security of Italy. The Sentinel-1 data have been provided through the Copernicus Program of the European Union. The authors acknowledge the Italian Space Agency (ASI) for acquiring and providing the COSMO-SkyMed) data under the License to Use Agreement (ID 1056 ASI Project Card). The authors would like to thank ASI for providing also the SAOCOM-1 data, under the ASI-CONAE SAOCOM-1 License to Use Agreement. The DEM of the investigated zones was acquired through the SRTM archive. References Anantrasirichai N et al (2018) Application of machine learning to classification of volcanic deformation in routinely generated InSAR data. J Geophys Research: Solid Earth 123:6592–6606 Anantrasirichai N et al (2019) The application of convolutional neural networks to detect slow, sustained deformation in InSAR time series. Geophys Res Lett 46:11850–11858 Antonielli B, Monserrat O, Bonini M, Cenni N, Devanthéry N, Righini G, Sani F (2016) Persistent Scatterer Interferometry analysis of ground deformation in the Po Plain (Piacenza-Reggio Emilia sector, Northern Italy): seismo-tectonic implications, Geophysical Journal International , Volume 206, Issue 2, August Pages 1440–1455. https://doi.org/10.1093/gji/ggw227 ASI, Italian Space Agency Upgrades Access To MAPITALY Data. [Online]. Available: https://www.asi.it/en/2023/12/asi-italian-space-agency-upgrades-access-to-mapitaly-data (Accessed: February 2nd, (2026) Berardino P, Fornaro G, Lanari R, Sansosti E (2002) A new Algorithm for Surface Deformation Monitoring based on Small Baseline Differential SAR Interferograms. IEEE Trans Geosci Remote Sens 40:11, 2375–2383 Bonano M, Manunta M, Pepe A, Paglia L, Lanari R (2013) From Previous C-Band to New X-Band SAR Systems: Assessment of the DInSAR Mapping Improvement for Deformation Time-Series Retrieval in Urban Areas, IEEE Trans. Geosci. Remote Sens. , vol. 51, no. 4, pp. 1973–1984, Apr Bonano M et al (2025) New Advances of the P-SBAS Approach for an Efficient Parallel Processing of Large Volumes of Full-Resolution Multitemporal DInSAR Interferograms. IEEE J Sel Top Appl Earth Observations Remote Sens 18:2317–2341. 10.1109/JSTARS.2024.3507542 Brengman CMJ, Barnhart WD (2021) Identification of surface deformation in InSAR using machine learning. Geochem Geophys Geosyst 22.3 : e2020GC009204. Burgmann R, Rosen PA, Fielding EJ (May 2000) Synthetic aperture radar interferometry to measure Earth's surface topography and its deformation. Annu Rev Earth Planet Sci 28:169–209 Calamita G et al (2009) An integrated geophysical approach for urban underground characterization: The Avigliano town (southern Italy) case study. Geomatics Nat Hazards Risk 10(1):412–432 Casamento F, Zinno I, Bonano M, Casu F, De Luca C, Lanari R (2025) The APS filtering approach of the Parallel Small BAseline Subset DInSAR processing chain: methodology and performance analysis, IGARSS 2025–2025 IEEE International Geoscience and Remote Sensing Symposium , Brisbane, Australia, pp. 9158–9162. 10.1109/IGARSS55030.2025.11242517 Castelletti D, Farquharson G, Stringham C, Duersch M, Eddy D (2021) Capella space first operational SAR satellite, in Proc. IEEE Int. Geosci. Remote Sens. Symp. IGARSS, Jul. pp. 1483–1486 Casu F, Manzo M, Lanari R (2006) A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data, Remote Sens. Environ. , vol. 102, no. 3–4, pp. 195–210, Jun Casu F, Elefante S, Imperatore P, Zinno I, Manunta M, De Luca C, Lanari R (2014) SBAS-DInSAR Parallel Processing for Deformation Time-Series Computation, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. , vol. 7, no. 8, pp. 3285–3296, Aug Casu F, Manconi A (2016) Four-dimensional surface evolution of active rifting from spaceborne SAR data, Geosphere, vol. 12, no. 3, pp. 697–705, Jun Cerra Reanalysis data (2026) [Online]. Available: https://climate.copernicus.eu/copernicus-regional-reanalysis-europe-cerra (Accessed: February 2nd Cigna F, Ramírez RE, Tapete D (2021) Accuracy of sentinel-1 PSI and SBAS InSAR displacement velocities against GNSS and geodetic leveling monitoring data. Remote Sens 13(23). 10.3390/rs13234800 Covello F, Battazza F, Coletta A, Lopinto E, Fiorentino C, Pietranera L, Valentini G, Zoffoli S COSMO-SkyMed an existing opportunity for observing the Earth. J Geodyn, 49, 3/4, pp. 171– 180, Apr. 2010. Cotugno F et al (2025) a Constellation Design and Analysis for Spaceborne DInSAR Mapping in Mid-Inclination Orbits: The IRIDE NIMBUS Mission, in IEEE Transactions on Geoscience and Remote Sensing , vol. 63, pp. 1–20, Art no. 1000120. 10.1109/TGRS.2024.3519212 Cotugno F et al (2025) First Results of the 3D Ground Displacements Retrieval through Capella Space Mid-Inclination Orbit DInSAR Measurements: the Campi Flegrei Caldera Case Study, IGARSS 2025–2025 IEEE International Geoscience and Remote Sensing Symposium, Brisbane, Australia, pp. 9145–9149, 2025b Crosetto M, Monserrat O, Cuevas-González M, Devanthéry N, G.Luzi, and, Crippa B (2015) Measuring thermal expansion using X-band persistent scatterer interferometry. ISPRS J Photogrammetry Remote Sens 100:84–91. 10.1016/j.isprsjprs.2014.05.006 Crosetto M, Crippa B, Mróz M, Cuevas-González M, Shahbazi S (2025) Applications based on EGMS products: A review, Remote Sensing Applications: Society and Environment, Vol. 37. https://doi.org/10.1016/j.rsase.2025.101452 Curlander JC, Robert N, McDonough (1991) Synthetic aperture radar, vol 11. Wiley, New York Delgado F, Shreve T, Borgstrom S, León-Ibanez P, Castillo J, Poland M (2024) A global assessment of SAOCOM-1 L-band stripmap data for InSAR characterization of volcanic, tectonic, cryospheric, and anthropogenic deformation, IEEE Trans. Geosci. Remote Sens. , vol. 62, Art. no. 5216821. 10.1109/TGRS.2024.3423792 De Luca C, Zinno I, Manunta M, Lanari R, Casu F (2017) Large areas surface deformation analysis through a cloud computing P-SBAS approach for massive processing of DInSAR time series. Remote Sens Environ 202:3–17. 10.1016/j.rse.2017.05.022 De Luca C et al (2025) SAOCOM-1 L-band DInSAR Time Series generation through the P-SBAS approach: algorithm extension and products analysis. IEEE J Sel Top Appl Earth Observations Remote Sens 18:2680–2703 ECMWF ERA5 data (2026) [Online]. Available: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview (Accessed: February 2nd European Ground Motion Service, [Online] (2026) Available: https://egms.land.copernicus.eu . (Accessed: February 2nd Ferretti A, Prati C, Rocca F (2000) Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Trans Geosci Remote Sens 38(5). 10.1109/36.868878 Ferretti A, Fumagalli A, Novali F, Prati C, Rocca F (2011) Rucci A New Algorithm for Processing Interferometric Data- Stacks: SqueeSAR. IEEE Trans Geosci Remote Sens 49:3460–3470 Festa D, Bonano M, Casagli N, Confuorto P, De Luca C, Del Soldato M, Casu F (2022) Nation-wide mapping and classification of ground deformation phenomena through the spatial clustering of P-SBAS InSAR measurements: Italy case study. ISPRS J Photogrammetry Remote Sens 189:1–22 Franceschetti G, Lanari R (1999) Synthetic Aperture Radar Processing , Boca Raton, FL, CRC, Mar Fritz T, Krieger L, Gisinger C, Lachaise M (2021) S1-ETAD project product definition document, ESA Technical Document, Doc. ETAD-DLR-PS-0002, 2.1 Fusco A et al (2023) A CNN-Based Interferogram Filtering Approach to Enhance the Co-Seismic Surface Displacements Identification by Exploiting the EPOSAR DInSAR Maps Global Archive, IGARSS 2023–2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 5230–5233. 10.1109/IGARSS52108.2023.10282743 Gabriel AK, Goldstein RM, Zebker HA (1989) Mapping small elevation changes over large areas: Differential interferometry, J. Geophys. Res ., vol. 94, no. B7, pp. 9183–9191, Mar Gisinger C et al (2022) The extended timing annotation dataset for Sentinel-1—Product description and first evaluation results, IEEE Trans. Geosci. Remote Sens. , vol. 60, Art. no. 5232622. 10.1109/TGRS.2022.3194216 Guzzetti F et al (2009) Analysis of ground deformation detected using the SBAS-DInSAR technique in Umbria, central Italy, Pure Appl. Geophys., vol. 166, nos. 8–9, pp. 1425–1459, Jun Hanssen RF (2001) Radar Interferometry: Data Interpretation and Error Analysis. Springer-, Berlin, Germany Hersbach H et al (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146:1999–2049. https://doi.org/10.1002/qj.3803 Ho Tong Minh D, Ngo Y-N, Le TC, Nguyen QD (2025) The Sinking Ho Chi Minh Megacity by Radar Interferometry. IEEE J Sel Top Appl Earth Observations Remote Sens 18:23228–23241. 10.1109/JSTARS.2025.3607038 Hooper AJ (2008) A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches. Geophys Res Lett 35(16). 10.1029/2008GL034654 Hu Z, Mallorquí JJ (2019) An accurate method to correct atmospheric phase delay for InSAR with the ERA5 global atmospheric model. Remote Sens, 11, no. 17, Art. 1969 Ignatenko V, Laurila P, Radius A, Lamentowski L, Antropov O, Muff D (2020) ICEYE microsatellite SAR constellation status update: Evaluation of first commercial imaging modes, in Proc. IEEE Int. Geosci. Remote Sens. Symp., Sep. pp. 3581–3584 Iride official website (2025) iride official website. [online]. available at: https://iridespazio.it/ Kellogg K NASA-ISRO synthetic aperture radar (NISAR) Mission. in Proc. IEEE Aerosp Conf., Sky B et al (2020) MT, USA, 2020, pp. 1–21. 10.1109/AERO47225.2020.9172638 Lanari R, Mora O, Manunta M, Mallorquì JJ, Berardino P, Sansosti E (2004) A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms. IEEE Trans Geosci Remote Sens 42:7, 1377–1386 Lanari R et al (2020) Automatic generation of Sentinel-1 continental scale DInSAR deformation time series through an extended P-SBAS processing pipeline in a cloud computing environment. Remote Sens 12(18). 10.3390/RS12182961 Lee CA, Gasster SD, Plaza A, Chang C-I, Huang B (2011) Recent Developments in High Performance Computing for Remote Sensing: A Review. IEEE J Sel Top Appl Earth Observations Remote Sens 4(3):508–527. 10.1109/JSTARS.2011.2162643 Li Y, Jiang W, Zhang J (2022) A time series processing chain for geological disasters based on a GPU-assisted Sentinel-1 InSAR processor, Natural Hazards , vol. 111, pp. 803–815. 10.1007/s11069-021-05079-9 López-Dekker P, Rott H, Prats-Iraola P, Chapron B, Scipal K, Witte ED (2019) Harmony: an Earth Explorer 10 Mission Candidate to Observe Land, Ice, and Ocean Surface Dynamics, IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, pp. 8381–8384. 10.1109/IGARSS.2019.8897983 Manunta M, Marsella M, Zeni G, Sciotti M, Atzori S, Lanari R (2008) Two-scale surface deformation analysis using the SBAS‐DInSAR technique: a case study of the city of Rome, Italy, Int. J. Remote Sens. , vol. 29, no. 6, pp. 1665–1684, Mar Manunta M et al (2019) The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment, in IEEE Transactions on Geoscience and Remote Sensing , vol. 57, no. 9, pp. 6259–6281, Sept. 10.1109/TGRS.2019.2904912 Massonnet D, Rossi M, Carmona C, Adragna F, Peltzer G, Feigl K, Rabaute T (Jul. 1993) The displacement field of the Landers earthquake mapped by radar interferometry. Nature 364(6433):138–142 Massonnet D, Feigl KL (1998) Radar Interferometry and its application to changes in the Earth’s surface. Rev Geophys 36:441–500 Miano A, Di Carlo F, Mele A, Giannetti I, Nappo N, Rompato M, Striano P, Bonano M, Bozzano F, Lanari R et al (2022) GIS Integration of DInSAR Measurements, Geological Investigation and Historical Surveys for the Structural Monitoring of Buildings and Infrastructures: An Application to the Valco San Paolo Urban Area of Rome. Infrastructures 7:89. https://doi.org/10.3390/infrastructures7070089 Miano A, Mele A, Silla M et al (2025) Space-borne DInSAR measurements exploitation for risk classification of bridge networks. J Civil Struct Health Monit 15:731–744. https://doi.org/10.1007/s13349-024-00832-7 Mora O, Mallorquí JJ, Broquetas A (2003) Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images. IEEE Trans Geosci Remote Sens 41(10):2243–2253. 10.1109/TGRS.2003.814657 Motohka T, Kankaku Y, Miura S, Suzuki S (2019) Alos-4 L-Band SAR Mission and Observation, in Proc. IEEE Int. Geosci. Remote Sens. Symp. , Yokohama, Japan, pp. 5271–5273. 10.1109/IGARSS.2019.8898169 Navarro AMG, Eid C, Rocca V, Benetatos C, De Luca C, Onorato G, Lanari R (2025) Integrated Analysis of Satellite and Geological Data to Characterize Ground Deformation in the Area of Bologna (Northern Italy) Using a Cluster Analysis-Based Approach. Remote Sens 17(15):2645. https://doi.org/10.3390/rs17152645 Ng H-M, Ge L, Du Z, Wang S, Ma C (2017) Satellite radar interferometry for monitoring subsidence induced by longwall mining activity using Radarsat-2, Sentinel-1 and ALOS-2 data, Int. J. Appl. Earth Observ. Geoinf., vol. 61, pp. 92–103, Sep Onorato G et al (2025) Identification and Correction of Phase Unwrapping Errors in Multitemporal Small Baseline DInSAR Interferograms: A Three-Step Cascade Algorithm. IEEE J Sel Top Appl Earth Observations Remote Sens 18:8602–8616. 10.1109/JSTARS.2025.3546824 Orzel K et al (2023) Synspective SAR Constellation Status Update: Recent Calval Activities and the Automatic Data Quality Assessment, IGARSS 2023–2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, pp. 1517–1520. 10.1109/IGARSS52108.2023.10282327 Parizzi A, Brcic R, De Zan F (2021) InSAR performance for large-scale deformation measurement, IEEE Trans. Geosci. Remote Sens. , vol. 59, no. 10, pp. 8510–8520, Oct Peltzer G, Rosen PA (June 1995) Surface displacement of the 17 May 1993 Eureka Valley earthquake observed by SAR interferometry. Science 268:1333–1336 Quegan S, Le Toan T, Chave J, Dall J, Exbrayat JF, Minh DHT, Williams M (2019) The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space. Remote Sens Environ 227:44–60 Rignot E (1998) Fast recession of a west Antarctic glacier. Science 281:549–551 Rostan F, Mak K, von Alberti M, Bauleo A, Gebert N (2024) Rose-L SAR Instrument Development Status and Mission Performance Prediction. IGARSS 2024–2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 6618–6620. 10.1109/IGARSS53475.2024.10642943 Sadashiv N, Kumar SMD Cluster, grid and cloud computing: A detailed comparison, (2011) 6th International Conference on Computer Science & Education (ICCSE) , Singapore, 2011, pp. 477–482. 10.1109/ICCSE.2011.6028683 Sansosti E, Casu F, Manzo M, Lanari R (October 2010) Space-borne radar interferometry techniques for the generation of deformation time series: an advanced tool for Earth’s surface displacement analysis. Geophys Res Lett 37:L20305. 10.1029/2010GL044379 Sansosti E et al (2014) How second generation SAR systems are impacting the analysis of ground deformation. Int J Appl Earth Observ Geoinf 28(1):1–11. 10.1016/j.jag.2013.10.007 Stramondo S, Saroli M, Tolomei C, Moro M, Doumaz F, Pesci A, Boschi E (2007) Surface movements in Bologna (Po plain—Italy) detected by multitemporal DInSAR. Remote Sens Environ 110(3):304–316 Talledo DA et al (2022) Satellite radar interferometry: Potential and limitations for structural assessment and monitoring, J. Building Eng. , vol. 46, Art. no. 103756. 10.1016/j.jobe.2021.103756 Tapete D, Cigna F (2020) Poorly Known 2018 Floods in Bosra UNESCO Site and Sergiopolis in Syria Unveiled from Space Using Sentinel-1/2 and COSMO-Skymed. Sci Rep 10(1):1–16. https://doi.org/10.1038/s41598-020-69181-x Thales Alenia Space, Italy M (2023) Italy’s New Constellation of Innovative and Flexible Earth Observation Satellites to Monitor Italy and Europe. [Online]. Available: https://www.thalesaleniaspace.com/en/press-releases/thales-alenia-space-winscontracts-iride-radar-and-optical-satellites The PyAPS software (2026) [Online]. Available: https://github.com/insarlab/PyAPS (Accessed: February 2nd Shimada M (2013) ALOS-2 science program, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS , Melbourne, VIC, Australia, pp. 2400–2403. 10.1109/IGARSS.2013.6723303 Tizzani P et al (February 2013) New insights into the 2012 Emilia (Italy) seismic sequence through advanced numerical modeling of ground deformation InSAR measurements. Geophys Res Lett 40. https://doi.org/10.1002/grl.50290 Torres R, Snoeij P, Geudtner D, Bibby D, Davidson M, Attema E, Potin P, Rommen B, Floury N, Brown M, Traver IN, Deghaye P, Duesmann B, Rosich B, Miranda N, Bruno C, L’Abbate M, Croci R, Pietropaolo A, Huchler M, Rostan F (May 2012) GMES Sentinel-1 mission. Remote Sens Environ 120:9–24 Ullo SL et al (2019) Application of DInSAR technique to high coherence Sentinel-1 images for dam monitoring and result validation through in situ measurements, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 12, no. 3, pp. 875–890, Mar Werner C, Wegmüller U, Strozzi T, Wiesmann A (2003) Interferometric Point Target Analysis for Deformation Mapping, in International Geoscience and Remote Sensing Symposium (IGARSS). 10.1109/igarss.2003.1295516 Werninghaus R, Buckreuss S (2010) The TerraSAR-X mission and system design, IEEE Trans. Geosci. Remote Sens. , vol. 48, no. 2, pp. 606–614, Feb Wu Z, Ma P, Zhang X, Ye G (2024) Efficient management and processing of massive InSAR images using an HPC-based cloud platform. IEEE J Sel Top Appl Earth Observ Remote Sens 17:2866–2876. 10.1109/JSTARS.2023.3349214 Yague-Martinez N, Cazcarra-Bes V, Farquharson G, Stringham C, De S (2025) Capella Space SAR Interferometry Demonstration: Recent developments and use of Mid-Inclination Orbits, IGARSS 2025–2025 IEEE International Geoscience and Remote Sensing Symposium , Brisbane, Australia, pp. 5428–5432. 10.1109/IGARSS55030.2025.11243117 Yu L, Yang TL, Zhao Q, Liu M, Pepe A (Nov. 2017) The 2015–2016 ground displacements of the Shanghai coastal area inferred from a combined COSMO-SkyMed/Sentinel-l DInSAR analysis. Remote Sens 9(11):1–19 Yu H, Lan Y, Yuan Z, Xu J, Lee H (2019) Phase Unwrapping in InSAR: A review. IEEE Geosci Remote Sens Mag 7(1):40–58. 10.1109/MGRS.2018.2873644 Yu C, Li Z, Penna NT (2020) Triggered afterslip on the southern Hikurangi subduction interface following the 2016 Kaikōura earthquake from InSAR time series with atmospheric corrections. Remote Sens Environ 251:112097 Zebker HA, Rosen PA, Hensley S (1997) Atmospheric effects in interferometric synthetic aperture radar surface deformation and topographic maps. J Geophys Res Solid Earth 102(B4):7547–7563 Zhang Y, Heresh F, Falk A (2019) Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction, Comput. Geosci. , vol. 133, Art. no. 104331. 10.31223/osf.io/9sz6m Zhou L, Yu H, Lan Y, Xing M (2021) Artificial Intelligence in Interferometric Synthetic Aperture Radar Phase Unwrapping: A Review. IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 2, pp. 10–28, June 2021. 10.1109/MGRS.2021.3065811 Zinno I, Elefante S, Mossucca L, De Luca C, Manunta M, Terzo O, Lanari R, Casu F (2015) A First Assessment of the P-SBAS DInSAR Algorithm Performances Within a Cloud Computing Environment. IEEE J Sel Top Appl Earth Obs Remote Sens 8:4675–4686 Zinno I, Casu F, De Luca C, Elefante S, Lanari R, Manunta M (2017) A Cloud Computing Solution for the Efficient Implementation of the P-SBAS DInSAR Approach. IEEE J Sel Top Appl Earth Obs Remote Sens 10:802–817 Zinno I, Bonano M, Buonanno S, Casu F, De Luca C, Manunta M, Manzo M, Lanari R (2018) National scale surface deformation time series generation through advanced DInSAR processing of sentinel-1 data within a cloud computing environment. IEEE Trans Big Data 6(3):558–571. 10.1109/TBDATA.2018.2863558 Zinno I, Casamento F, Casu F, De Luca C, Lanari R (2022) A Comparative Analysis of Different DInsar Approaches to Filter Out Atmospheric Phase Artifacts in Active Volcano Scenarios Characterized by High Topography Surfaces, IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, pp. 1099–1102. 10.1109/IGARSS46834.2022.9884047 Zinno I, Casamento F, Lanari R (2025) On the Exploitation of the ETAD Product for Filtering Out the Atmospheric Phase Screen From Medium Resolution DInSAR Measurements: An Extensive Performance Analysis. IEEE J Sel Top Appl Earth Observations Remote Sens 18:712–727. 10.1109/JSTARS.2024.3488494 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviews received at journal 12 Mar, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 18 Feb, 2026 Submission checks completed at journal 17 Feb, 2026 First submitted to journal 17 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8904298","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596402628,"identity":"19961186-b523-49a1-a297-8a8eae079461","order_by":0,"name":"Riccardo Lanari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACPjBpwMDAzwwXY2zAq4UNpkWyGaGlEa8eNhjD4ABCEL81bOxnD36uKGDINz7O+/DBDwa7PPMG5vYHeLXw5CVLnjFgsNx2mN3YsIchuVjmAEGH5RhINhgwGJgdZmOT4GE4kDiDoF/43xj/BGkxbmZj//mHKC0SOWZgWwyY2diYibNF4o2ZZYOBhIHEYTZmaRmD5MQZzIyNM/Bp4efPMb7Z8MfGgL//GOPHNxV2iTPY2x98wKcFCiSgNDBOGZjxKRwFo2AUjIJRQBQAAN9VOfIoGO7dAAAAAElFTkSuQmCC","orcid":"","institution":"Istituto per il Rilevamento Elettromagnetico dell'Ambiente","correspondingAuthor":true,"prefix":"","firstName":"Riccardo","middleName":"","lastName":"Lanari","suffix":""},{"id":596402636,"identity":"9a0645c3-2e3e-48dc-ad49-5c2abf88da34","order_by":1,"name":"Federica Casamento","email":"","orcid":"","institution":"Istituto per il Rilevamento Elettromagnetico dell'Ambiente","correspondingAuthor":false,"prefix":"","firstName":"Federica","middleName":"","lastName":"Casamento","suffix":""},{"id":596402639,"identity":"004c6bc0-915d-418b-b589-c22457c1d938","order_by":2,"name":"Francesco Casu","email":"","orcid":"","institution":"Istituto per il Rilevamento Elettromagnetico dell'Ambiente","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Casu","suffix":""},{"id":596402640,"identity":"81979c55-dcdf-42e3-8854-06f2ba8c6ae6","order_by":3,"name":"Federica Cotugno","email":"","orcid":"","institution":"ATG Italy S.R.L c/o ESA/ESRIN","correspondingAuthor":false,"prefix":"","firstName":"Federica","middleName":"","lastName":"Cotugno","suffix":""},{"id":596402648,"identity":"6502854a-a79e-458e-8643-5137255c4c8f","order_by":4,"name":"Claudio De Luca","email":"","orcid":"","institution":"Istituto per il Rilevamento Elettromagnetico dell'Ambiente","correspondingAuthor":false,"prefix":"","firstName":"Claudio","middleName":"","lastName":"De Luca","suffix":""},{"id":596402652,"identity":"369bedfa-8c1f-4433-b9f6-39cdd13c3319","order_by":5,"name":"Michele Manunta","email":"","orcid":"","institution":"Istituto per il Rilevamento Elettromagnetico dell'Ambiente","correspondingAuthor":false,"prefix":"","firstName":"Michele","middleName":"","lastName":"Manunta","suffix":""},{"id":596402654,"identity":"1a698d6c-81f2-4cdb-9e60-e54e1c1d2c64","order_by":6,"name":"Giovanni Onorato","email":"","orcid":"","institution":"Istituto per il Rilevamento Elettromagnetico dell'Ambiente","correspondingAuthor":false,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Onorato","suffix":""},{"id":596402657,"identity":"3a32cea3-7a32-4517-b9c5-61c570d2967c","order_by":7,"name":"Yenni Lorena Belen Roa","email":"","orcid":"","institution":"Istituto per il Rilevamento Elettromagnetico dell'Ambiente","correspondingAuthor":false,"prefix":"","firstName":"Yenni","middleName":"Lorena Belen","lastName":"Roa","suffix":""},{"id":596402662,"identity":"98eaef1b-07e7-40dd-bf13-0305332893d7","order_by":8,"name":"Pasquale Striano","email":"","orcid":"","institution":"Istituto per il Rilevamento Elettromagnetico dell'Ambiente","correspondingAuthor":false,"prefix":"","firstName":"Pasquale","middleName":"","lastName":"Striano","suffix":""},{"id":596402665,"identity":"8e6b60fa-3f16-46fb-8591-7e31d6a68e97","order_by":9,"name":"Ivana Zinno","email":"","orcid":"","institution":"Istituto per il Rilevamento Elettromagnetico dell'Ambiente","correspondingAuthor":false,"prefix":"","firstName":"Ivana","middleName":"","lastName":"Zinno","suffix":""},{"id":596402676,"identity":"aeb48891-76f1-41ae-b1fc-d27d533e7c95","order_by":10,"name":"Manuela Bonano","email":"","orcid":"","institution":"Istituto per il Rilevamento Elettromagnetico dell'Ambiente","correspondingAuthor":false,"prefix":"","firstName":"Manuela","middleName":"","lastName":"Bonano","suffix":""}],"badges":[],"createdAt":"2026-02-17 21:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8904298/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8904298/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104399078,"identity":"be9714df-6316-4b1c-9224-38c7d3e242bb","added_by":"auto","created_at":"2026-03-11 12:04:41","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":598482,"visible":true,"origin":"","legend":"\u003cp\u003eMean LOS deformation velocity maps [cm/year], retrieved by processing the Sentinel-1 and SAOCOM-1 images through the P-SBAS processing chain, geocoded and superimposed on an optical image of the area. Note that the white and red rectangles in (a) show the Sentinel-1 and SAOCOM-1 ground coverage, respectively. (b) Zoomed-in view of panel (a), where the white box is relevant to a further zoomed-in view that has been selected for for a more detailed analysis of the coherent zones extension, which is presented in Figure 2.\u003c/p\u003e","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8904298/v1/04b360d6984d84d42cbc9a9c.jpg"},{"id":103596701,"identity":"3cea5f24-9fd0-498c-b4a1-e6ff25ed5c10","added_by":"auto","created_at":"2026-02-27 13:18:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":696808,"visible":true,"origin":"","legend":"\u003cp\u003eZoomed-in views of the area identified by the white box in Figure 1-(b). In particular, (a) shows the mean LOS velocity map retrieved by processing Sentinel-1 SAR data through the P-SBAS processing chain. (b) is relevant to the \u0026nbsp;mean LOS velocity map relevant to the SAOCOM-1 SAR dataset. The comparative plots of the corresponding P-SBAS displacement time series for three pixels labelled as P1, P2 and P3 are also presented, where the S-1 measurements are represented in green, while the SAOCOM-1 ones are in red.\u003c/p\u003e","description":"","filename":"image2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8904298/v1/de97ce0b3d8cee93711f945a.jpg"},{"id":103596702,"identity":"998464ef-9dac-42bb-b316-fca0a4eb598b","added_by":"auto","created_at":"2026-02-27 13:18:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":98388,"visible":true,"origin":"","legend":"\u003cp\u003eHistograms of the displacement time series standard deviations before the APS corrections (black-dashed line), and after the correction with ERA5 (red line) and ETAD (blue line) data, respectively.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8904298/v1/1a9d45aece0a4409c23cf128.png"},{"id":104398878,"identity":"bb1a8115-d0d7-4d90-948b-9296e30a26dc","added_by":"auto","created_at":"2026-03-11 12:04:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1265212,"visible":true,"origin":"","legend":"\u003cp\u003eMean deformation velocity map, expressed in [cm/year], of the analyzed southern Italy area. The labels A-C indicate the position of the pixels whose deformation time series are shown in Figure 5.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8904298/v1/d822ad7cf58704518ffd7632.png"},{"id":104399271,"identity":"228c6584-52f8-4872-94a7-8c7c5b430ad7","added_by":"auto","created_at":"2026-03-11 12:05:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":544731,"visible":true,"origin":"","legend":"\u003cp\u003eP-SBAS deformation time series relevant to the points labelled as A, B, and C in Figure 4. In particular, the first row represents the original non-filtered P-SBAS time series, whereas the second and third rows show the ETAD- and the ERA5-corrected time series, respectively.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8904298/v1/c8c0578d083c6aa875b991f2.png"},{"id":104399397,"identity":"f71094ad-656c-4aaf-9bad-f07f5abe16d8","added_by":"auto","created_at":"2026-03-11 12:05:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":947533,"visible":true,"origin":"","legend":"\u003cp\u003eLOS deformation velocity maps achieved by processing, through the FR P-SBAS approach, the ascending (A) and descending (B) CSK/CSG datasets relevant to the Bologna area.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8904298/v1/85a398eee038d0b9cb6905cc.png"},{"id":104399073,"identity":"a732daba-6431-4f93-86c8-cc726ccd56b2","added_by":"auto","created_at":"2026-03-11 12:04:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1133678,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between the FR and the spatially high-pass component of the P-SBAS LOS results relevant to the processed ascending CSK/CSG SAR dataset. A) Full resolution LOS displacement velocity map. B) LOS deformation velocity map relevant to the high-pass component, after removing the spatially low-pass displacements. The zoomed-in views, highlighted by the white box in the velocity maps, are relevant to a building in the upper part of the city center, which is affected by a differential displacement behavior with respect to the regional trend.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8904298/v1/8e8c69db9f4139217004dedb.png"},{"id":103596707,"identity":"f0438e92-fea8-44cf-a801-15542bbab1d7","added_by":"auto","created_at":"2026-02-27 13:18:45","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1013311,"visible":true,"origin":"","legend":"\u003cp\u003eCoverage map over Italy. (Top) 49° MIO, right-looking acquisition mode (a) ascending passes, (b) descending passes. (Bottom) SSO, right-looking acquisition mode, (c) ascending passes, (d) descending passes.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8904298/v1/1ee3a1846c52ea1aa7d8a5f9.png"},{"id":103596704,"identity":"1be0ecf0-619c-4d60-b467-c34e3b4f3d6d","added_by":"auto","created_at":"2026-02-27 13:18:45","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":349428,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated maps of the LOS-projection associated with a 1-cm deformation in the (a) North-South direction, (b) East-West direction, for the selected 49° MIO configuration, assuming an off-nadir angle of 30°.\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8904298/v1/13bd7a5f7db918d2d106b7d0.jpeg"},{"id":104407739,"identity":"6aaee392-16cd-4cd2-a942-2f3a6a416209","added_by":"auto","created_at":"2026-03-11 12:39:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7415419,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8904298/v1/c0889bbe-24b7-4f6d-8e31-d113091d72d9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sounding of the Deforming Solid Earth Surface: New Opportunities for Spaceborne Differential SAR Interferometry Techniques to Detect and Monitor Displacements Affecting the Ground and the Built-up Environment","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eImaging the Earth surface both day and night and (nearly) in any atmospheric condition is possible thanks to the Synthetic Aperture Radar (SAR), a sensor which, generally mounted on board satellites or aircrafts (and drones, more recently), operates in the microwave band of the electromagnetic spectrum. In particular, the SAR sensors are equipped with a transmitting and a receiving system through which, by sending appropriate signals, they \"illuminate\" the areas of interest and record the back-scattered electromagnetic radiations, whose appropriate digital processing allows us to generate high resolution microwave images of the observed zones (Curlander and McDonough, 1991; Franceschetti and Lanari, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAn important feature of these radar systems is the possibility of applying the Differential Interferometry SAR (DInSAR) techniques, which permit the measurement of surface deformations affecting large areas on Earth, with centimeter (sub-centimeter, in some cases) accuracy (Gabriel et al. ,1989; Massonnet and Feigl \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Burgmann et al. ,2000).\u003c/p\u003e \u003cp\u003eMore specifically, the DInSAR technique exploits the phase difference (often referred to as interferogram) between SAR image pairs acquired at different times (whose separation is referred to as temporal baseline) but with the same illumination geometry and from sufficiently close flight tracks (whose separation, in the direction perpendicular to the radar line of sight, hereinafter referred to as LOS, is known as spatial or perpendicular baseline) (Franceschetti and Lanari \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The topography-related phase component, which is present in the original interferograms, typically requires being properly compensated; this is done through the exploitation of an external Digital Elevation Model (DEM) and of the satellite orbital information, in order to retrieve the so-called differential interferograms (Massonnet and Feigl \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Burgmann et al., 2000). It is worth remarking that the DInSAR technique is able to estimate the projection of the surface displacement component along the sensor LOS, for the interferometrically coherent pixels, i.e., for the pixels that are not significantly affected by phase noise effects, which are referred to as decorrelation phenomena (Zebker and Villasenor 1992).\u003c/p\u003e \u003cp\u003eThe DInSAR techniques have undergone a continuous evolution over the last decades, becoming a very important geodetic tool for effectively sounding the deforming solid Earth surface behaviour. In fact, these methods are nowadays widely used for the study of deformations linked to both natural phenomena (seismic, volcanic, hydrogeological), and anthropic activities (subsoil exploitation, for instance), also allowing the investigation of possible displacements at the scale of single buildings and infrastructures (Massonnet et al., 1993; Peltzer and Rosen 1995; Rignot \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Manunta et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Tizzani et al., 2013). Moreover, originally designed and applied to investigate single deformation episodes, such as an earthquake or a volcanic eruption, the \u0026ldquo;original\u0026rdquo; DInSAR methodology has evolved toward the study of the temporal evolution of the detected displacements, thanks to the development of multi-temporal (MT), also referred to as advanced, DInSAR techniques (Ferretti et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Berardino et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Mora et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Werner et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Lanari et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Hooper \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sansosti et al., 2010; Ferretti et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Such multi-temporal approaches, which are based on the exploitation of SAR image sequences relevant to an area of interest, provide helpful information on the spatial and temporal characteristics of the detected deformations, through the generation of displacement time series.\u003c/p\u003e \u003cp\u003eThese algorithmic advancements have paved the way to the concurrent development of SAR sensors and satellite missions, including the long-term C-band (approximately 5.6 cm wavelength) ERS-1/2, ENVISAT and RADARSAT-1/2 systems, and the L-band (23\u0026ndash;24 cm wavelength) JERS-1 and ALOS-1 sensors, characterized by different frequency bands, spatial resolutions, and ground coverage, whose exploitation in advanced DInSAR contexts allowed the achievement of a sub-centimetric accuracy for the retrieved ground displacement measurements (Casu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Bonano et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, due to their relatively long (typically monthly) revisit times, these \u0026ldquo;first-generation\u0026rdquo; SAR systems proved to be inadequate for deformation monitoring. This limitation led to the development of new space-borne missions, the so-called \u0026ldquo;second generation\u0026rdquo; SAR systems, characterized by shorter revisit times, improved orbital information, and advanced operational capabilities. Among these, very relevant are the COSMO-SkyMed (CSK) (Covello et al. 2010) and the TerraSAR-X (TSX) (Werninhaus and Buckreuss 2010) SAR systems, launched in 2006. These sensors operate in the X-band (approximately 3.1 cm wavelength), allowing for high resolution imaging and increased sensitivity to surface displacements. This positively impacts the investigation of the space\u0026ndash;time characteristics of the revealed displacement phenomena, particularly those related to single buildings and infrastructures. Moreover, the combination of reduced, with respect to first-generation SAR satellite, revisit time (11\u0026ndash;16 days for each single satellite) and higher sensitivity, due to the exploited X-band frequencies, has made these missions well-suited for detailed deformation analysis and monitoring in urban scenarios.\u003c/p\u003e \u003cp\u003eFurthermore, a notable step forward was achieved some years later with the advent of the C-band Sentinel\u0026thinsp;\u0026minus;\u0026thinsp;1 (S-1) mission of the European Copernicus program (Torres et al. 2012). Indeed, the launch of this constellation, with the first Sentinel-1 sensor (S-1A) placed in orbit in April 2014, changed significantly the DInSAR scenario of the following decade, thanks to the unprecedented characteristics of this system in terms of spatial coverage, acquisition frequency, and data access policy. In particular, the S-1 sensors are designed with a focus on interferometric applications and can operate in different imaging modes; specifically, the TOPS one, which is the default mode over land, permits achieving a wide-swath coverage of 250 km. Moreover, the single S-1 sensors are characterized by a revisit time of 12 days, which reduces to 6 days when two satellites of the S-1 constellation operate simultaneously. Furthermore, the perpendicular baselines of the S-1 data pairs are smaller than 300 m (except for some degradations that are affecting the S-1A acquisitions after 2024), and the overall acquired data are fully available with a free and open access policy. It is also worth noting that in April 2016, two years after the launch of the first S-1A sensor, the twin Sentinel \u0026minus;\u0026thinsp;1B (S-1B) satellite was put into orbit. It delivered data up to December 2021, when it experienced an anomaly related to the instrument electronics power supply that caused, in August 2022, the end of the S-1B mission. Moreover, in December 2024 Sentinel-1C (S-1C) was launched, restoring the capability of acquiring interferometrically compatible SAR images over the same area on the ground every 6 days and, finally, in November 2025, the last sensor of the constellation, Sentinel-1D (S-1D), which will replace the outdated S-1A, was placed in orbit. Thanks also to the characteristics of free and rapid product delivery, the S-1 constellation has definitely changed the DInSAR monitoring scenario, moving toward the operational capability to perform advanced DInSAR analyses at very large scales and in rather short times, through the development of techniques aimed to process huge SAR image sets in an automatic and efficient way [Zinno et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; De Luca et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zinno et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zinno et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lanari et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cigna et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMore recently, significant efforts have been devoted to the design, setup and development of new L-band SAR constellations, aimed at overcoming some of the intrinsic limitations of the X- and C-band SAR systems, particularly in decorrelation-prone environments. Some of these systems are already in orbit, including ALOS-2 [Shimada, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2013\u003c/span\u003e], SAOCOM-1 [Delgado et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e], and the recently launched ALOS-4 [Motohka et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e] and NISAR [Kellogg et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] satellites, while others that are currently in an advanced development phase, as for the new European ROSE-L mission [Rostan et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e]. Operating at longer wavelengths, L-band SAR systems exhibit enhanced penetration capabilities through vegetation, resulting in improved temporal coherence over long observation periods. This characteristic makes these sensors particularly suitable for deformation monitoring in vegetated regions, agricultural areas, wetlands, glaciers and ice sheets, where shorter-wavelength systems often suffer from decorrelation. Furthermore, L-band sensors are less sensitive to phase unwrapping errors [Yu et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Onorato et el., 2025], enabling more reliable long-term displacement measurements and increasing the robustness of multi-temporal interferometric techniques.\u003c/p\u003e \u003cp\u003eThe advent of new L-band sensors is also accompanied by substantial improvements in system architecture, including high radiometric stability, wide swath coverage, and short revisit times. These capabilities significantly enhance the potential for large-scale, systematic deformation monitoring and allow for the integration of L-band data into operational services for geohazard assessment, land subsidence monitoring, and infrastructure surveillance [De Luca et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe remark that all the previously discussed SAR systems exploit a Sun-Synchronous Orbit (SSO) configuration, which ensures consistent illumination conditions and helps systematic, repeat-pass interferometric acquisitions. However, recent investments have been increasingly focused on alternative orbital concepts, aimed at further enhancing temporal resolutions and operational flexibility. In this framework, a growing attention has been devoted to inclined orbital configurations, such as the Mid-Inclination Orbit (MIO), designed to provide high revisit frequencies and spatial coverage over a mid-latitude area of interest. Moreover, MIO systems are also characterized by enhanced sensitivity to the North-South deformation components, with respect to the SSO-based DInSAR exploitation [Cotugno et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea]. Within this context, it is worth citing the already operating Iceye [Ignatenko et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] and Capella Space [Castelletti et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yague-Martinez et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] constellations and the new-generation Earth Observation (EO) space program referred to as IRIDE, developed in Italy under the National Recovery and Resilience Plan \u003cb\u003e(\u003c/b\u003ePNRR) framework of the European Union, with the management of the European Space Agency (ESA) and the support of the Italian Space Agency (ASI) [webportal: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iridespazio.it\u003c/span\u003e\u003cspan address=\"https://iridespazio.it\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. In particular, IRIDE is designed as a collection of small satellites positioned in Low Earth Orbits (LEO), with the aim of providing dedicated operational applications and services to institutional entities, thus supporting, among various applications, the systematic monitoring of the Italian territory for risk prevention, emergency management, and post-event damage assessment. In this framework, the NIMBUS SAR X-band sub-constellation of the IRIDE program is expected to complement conventional SSO SAR systems, particularly the COSMO-SkyMed ones, thus contributing to a more resilient, responsive, and comprehensive EO framework for deformation monitoring and hazard assessment of the Italian territory [Cotugno et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea].\u003c/p\u003e \u003cp\u003eIn this work, we discuss, through the analysis of four selected case studies, some possible future development trends of the Differential SAR Interferometry (DInSAR) techniques for the detection and monitoring of displacements affecting the ground surface and the built-up environment. This is done by considering SAR constellations that are currently operating, such as SAOCOM-1 (providing L-band data), Sentinel-1 (providing C-band data) and COSMO-SkyMed (providing X-band data), or forthcoming missions, as for the case of the NIMBUS SAR one (that will also provide X-band data). Finally, we summarize the lessons learned through the presented case studies and identify further possible impacts on the future developments of the DInSAR techniques for surface deformation retrieval.\u003c/p\u003e"},{"header":"2. L-band SAR data exploitation for extended Multi-Temporal DInSAR analysis in vegetated areas: the SAOCOM-1 case study","content":"\u003cp\u003eOver the last decade, an increasing effort has been devoted by several space agencies toward the development and deployment of spaceborne SAR systems operating at rather low frequencies of the microwave spectrum, particularly in L-band. This trend reflects the growing recognition of the intrinsic advantages of long-wavelength SAR observations in various frameworks and, for interferometric applications, especially in challenging scenarios represented by vegetated regions, agricultural areas, wetlands, glaciers and ice sheets, and long temporal baselines. Some L-band missions are already operational, including ALOS-2, SAOCOM-1 and ALOS-4, while large international investments are represented by the recently launched (July 30th 2025) NASA\u0026ndash;ISRO (NISAR) mission and by the forthcoming European ROSE-L system.\u003c/p\u003e \u003cp\u003eFor what concerns the DInSAR scenario, as said, the L-band SAR signals ensure a significantly enhanced temporal coherence with respect to higher-frequency satellites, such as the C-band and the X-band systems, and a strong reduction of phase unwrapping errors, which are particularly relevant in multi-temporal interferometric analyses. In this work, following the lines of the study presented in [De Luca et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], the benefits of L-band observations are clearly demonstrated through a comparative analysis performed in a vegetated area, using Sentinel-1 (C-band) and SAOCOM-1 (L-band) data processed through the Parallel Small BAseline Subset (P-SBAS) approach [Casu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Manunta et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; De Luca et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e]. In particular, the analyzed area is close to the Berceto municipality, within the Apennines of the Tuscany region (Northern Italy), which is historically affected by several landslide phenomena, also threatening urbanized zones.\u003c/p\u003e \u003cp\u003eThe exploited S-1 dataset is composed of 160 ascending orbits IWS SAR images, specifically collected from Track 15 through the TOPS acquisition mode, during the January 2021 \u0026ndash; May 2025 time interval, covering the area over the Apennines of the Tuscany Region (Northern Italy). Moreover, to perform our comparative P-SBAS analysis, we also exploited the corresponding SAOCOM-1 acquisitions (64 Stripmap mode SAR images, ascending swath S3) collected over the area of interest, during the same time interval, to guarantee the consistency of the compared datasets.\u003c/p\u003e \u003cp\u003eThe generated DInSAR results show that, by considering the same observation period and spatial resolution interferometric products (30 m x 30 m), the SAOCOM-1 measurements preserve coherence in a wider area, with a marked density increase of the reliable pixels. In particular, in the investigated vegetated and mountainous zone, the L-band time series allow a more effective detection and spatial delineation of the deformation phenomena, such as slow-moving landslides, that are only partially captured by C-band data.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-(a), we show the mean deformation velocity map retrieved through the SAOCOM-1 P-SBAS processing (see the red box in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-(a)), overlapped on the corresponding one relevant to the S-1 P-SBAS results. In particular, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-(b) shows an insight of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-(a), around the area imaged by the SAOCOM-1 footprint. The coverage extension achieved thanks to the L-band imaging is evident. To further emphasize the achieved coverage improvement, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows two zoomed-in views of the area identified by the white rectangle in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-(b). In particular, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-(a) and 2-(b) report the mean deformation velocity maps of this zone, obtained by processing Sentinel-1 and SAOCOM-1 data, respectively.\u003c/p\u003e \u003cp\u003eNote that the investigated area is affected by several slow-moving landslides, whose bodies are clearly and fully delineated by the L-band SAOCOM-1 DInSAR results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-(b)). Furthermore, the displacement time series of three selected pixels, labelled as P1, P2 and P3 in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and common to both the C-band and L-band P-SBAS analyses, are also displayed, where the S-1 deformation time series are represented in green, while the SAOCOM-1 ones are in red.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe remark that the Sentinel-1 and SAOCOM-1 displacement time series comparison shows a good agreement in terms of the retrieved deformation signals, whereas some slight differences can be attributed to the different impact of the atmospheric phase contributions and to the difference in the look angle values between the Sentinel-1 (about 37\u0026deg;) and the SAOCOM-1 (about 30\u0026deg;) acquisitions over the considered area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese experimental findings provide clear evidence of the positive impact of space-borne L-band SAR systems for operational ground deformation monitoring. In this perspective, the above-mentioned NISAR and the future ESA\u0026rsquo;s ROSE-L missions are expected to significantly enhance the capability of multi-temporal DInSAR analyses by providing systematic, long-term L-band acquisitions with wide spatial coverage. The availability of such datasets will enable more robust and spatially continuous deformation products, particularly in regions affected by dense vegetation. Moreover, the integration of the L-band results with existing C- and X-band missions will further foster multi-frequency approaches, opening new perspectives for the robust characterization of both natural and anthropogenic deformation processes.\u003c/p\u003e"},{"header":"3. External data exploitation for atmospheric noise filtering of DInSAR time series: the Sentinel-1 case study","content":"\u003cp\u003eThe operational wide area DInSAR deformation investigation and monitoring scenario has been characterized by an authentic revolution, thanks to the extended ground coverage (about 250 km in the range direction), frequent revisit time (6 days), short perpendicular baseline (less than 300 m), and free and open data policy of the Sentinel-1 constellation [Lanari et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; webportal: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://egms.land.copernicus.eu\u003c/span\u003e\u003cspan address=\"https://egms.land.copernicus.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. In this framework, we are witnessing the development and the diffusion of auxiliary data aimed at improving the accuracy of the generated DInSAR results. In particular, we focus on the exploitation of external data to reduce the atmospheric noise affecting DInSAR measurements. Indeed, the proper filtering of the atmospheric disturbances in DInSAR products still remains one of the most challenging tasks, due to the difficulty of correctly discriminating interferometric phase delays, relevant to atmospheric variations, from deformation signals [Zebker et al.,1997; Hanssen \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Parizzi et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e]. Among the several techniques adopted in literature for the atmospheric filtering, those exploiting external data coming from Numerical Weather Prediction (NWP) models present the advantages of being independent of DInSAR measurements, thus allowing us to estimate the atmospheric contributions without impacting on the investigated ground displacements. Nowadays, the ECMWF ERA5 reanalysis dataset [web portal: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e], which provides measurements of atmospheric parameters, such as temperature, pressure and humidity, with hourly update, is one of the most exploited for estimating the atmospheric contribution, thanks to its characteristics of global coverage, easy download and rapid usability [Hersback et al., 2020; Hu and Mallorqu\u0026iacute;, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zinno et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2022\u003c/span\u003e]. On the contrary, the main drawback of the ERA5 data is the coarse spatial resolution of about 31 km in horizontal, significantly larger than the DInSAR products resolution. Moreover, recently, the European Space Agency (ESA) has started distributing a new auxiliary product named the Extended Timing Annotation Dataset (ETAD) [Fritz et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gisinger et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], specifically designed for the Sentinel-1 SAR data, which supplies users with a set of correction layers to improve the geometric accuracy of S-1 images to centimetric levels. These layers also include, among others, the corrections to be applied to take into account the tropospheric delay, the ionospheric delay, and the solid Earth tides effects. The ETAD corrections are provided with a predefined grid spacing of about 200 m ground sampling for both range and azimuth coordinates.\u003c/p\u003e \u003cp\u003eIn this Section, we present the results of an extensive performance analysis aimed at evaluating the effectiveness of the above mentioned ERA5- and ETAD-based APS correction data when applied to large Sentinel-1 datasets, following the lines of the studies presented in [Zinno et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Casamento et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e]. The calculation of the ERA5 atmospheric corrections is performed through the PyAPS software [webportal: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/insarlab/PyAPS\u003c/span\u003e\u003cspan address=\"https://github.com/insarlab/PyAPS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e], properly integrated within the P-SBAS automatic processing chain for correcting the generated DInSAR products online [Casamento et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e]. The application of the ETAD corrections is carried out through specifically developed algorithms, which also account for a native issue of the ETAD product that causes artifacts, within the APS corrected medium resolution interferograms, by properly removing them [Zinno et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2025\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe S-1 dataset exploited for the experimental analysis is composed of 104 images acquired from the ascending orbits (Track 44) between 8 January 2018 and 23 December 2020, covering a wide region of approximately 490 km \u0026times; 270 km over southern Italy, including the Mt. Vesuvius and the Campi Flegrei caldera volcanic sites. A total of 278 interferograms were generated and subsequently exploited to retrieve, through the P-SBAS processing chain [Manunta et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], the displacement time series used for the analysis presented in the following.\u003c/p\u003e \u003cp\u003eIn particular, we computed and compared the standard deviation (SD) of the original P-SBAS non-filtered time series and that of the ERA5 and ETAD corrected ones. Note that all the above-mentioned statistical metrics were applied only to the coherent pixels of the study area, for which the obtained DInSAR measurements can be considered highly reliable. Moreover, to avoid the variance calculation being biased by possible deformation signals, such as in the case of the Campi Flegrei caldera (characterized by a significant uplifting behaviour), the low-pass signal component related to the displacement signals was first estimated and removed from the P-SBAS time series, and the variance was then computed. The performed statistical analysis highlights that both ERA5 and ETAD corrections generally improve the generated DInSAR results, though not removing the APS component completely. In particular, it came out that both the deformation time series corrected with ETAD and ERA5 data present reduced SD with respect to those of the P-SBAS non-filtered time series. More specifically, considering the overall number of coherent points, which is 2.6*10\u003csup\u003e6\u003c/sup\u003e, both the ETAD and the ERA5 corrected time series show a reduced SD in the 97.3% and in the 99.3% of the measurement points, respectively. Moreover, the comparison between the SDs of the ETAD and ERA5 corrected time series highlights that the former is smaller than the latter in 70.5% of the considered points. On the contrary, ERA5 corrected time series show a SD smaller than the ETAD one in the 29.5% of the considered points. According to these results, the ETAD corrections appear to perform better.\u003c/p\u003e \u003cp\u003eTo further investigate the APS correction performance obtained for the two generated datasets, we show in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e the histograms of the SD of the displacement time series before the APS correction (black dashed line), and after the correction with ERA5 (red line) and ETAD (blue line) data, respectively. Such an analysis confirms that both corrections are effective in improving the distribution of the SD of the original time series, reducing its dispersion significantly. In addition, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e clearly shows that the SD histogram of the ETAD-corrected deformation time series exhibits a narrower distribution (lower dispersion) compared to that of the ERA5-corrected series and is shifted leftward toward lower SD values. Moreover, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the mean, mode, and interquartile range (IQR) values for each histogram; also in this case, the ETAD corrections clearly appear to perform better.\u003c/p\u003e \u003cp\u003eAs a further remark, we underline that our experimental results show that both the ERA5 and ETAD data achieve the best APS correction performance over mountainous areas, since they are capable of retrieving well the atmospheric contribution correlated with the topography, i.e., the stratified atmospheric component. On the contrary, both the ERA5 and ETAD datasets cannot optimize the estimation of the small-scale atmospheric contributions, such as the turbulence, due to their coarse spatial resolution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean, Mode and IQR values of the SD distributions depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-SBAS non-corrected deformation time series\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eETAD-corrected deformation time series\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eERA5-corrected deformation time series\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD mean [mm]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD mode [mm]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD IQR [mm]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e depicts the final mean deformation velocity map of the area under investigation. Moreover, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the deformation time series of some pixels of the analyzed area that are located both in deforming and non-deforming sites, before and after the application of the APS corrections. We depict the original P-SBAS time series (first row) and the corresponding ones after both the APS corrections (the ETAD-corrected and ERA5-corrected time series are shown in the second and third rows, respectively).\u003c/p\u003e \u003cp\u003eBy considering the ETAD and ERA5-corrected displacement time series shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, it is clear that the ETAD ones appear more smoothed and \u0026ldquo;clean\u0026rdquo;, thus confirming the achieved better atmospheric correction. It is also shown that both ETAD and ERA5 corrections perform better on mountainous areas compared to flat ones, where they less significantly impact the APS removal (see, for instance point C of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and the corresponding time series shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe presented experimental analysis shows that both the ERA5 and ETAD datasets allow us to improve the DInSAR time series accuracy, although their major drawback is due to the coarse spatial resolution. However, it is worth noting that the production of new data with enhanced characteristics is proceeding fast. Indeed, the Copernicus Climate Change Service has recently started delivering the Copernicus European Regional Reanalysis (CERRA) dataset [webportal: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://climate.copernicus.eu/copernicus-regional-reanalysis-europe-cerra\u003c/span\u003e\u003cspan address=\"https://climate.copernicus.eu/copernicus-regional-reanalysis-europe-cerra\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e], available over Europe, which is produced with a horizontal resolution of 5.5 km, with the purpose of adding value to ERA5 data. Moreover, starting from 2027, the ERA6 global climate reanalysis is expected to be released, offering significant improvements over its predecessor, ERA5, primarily through a higher spatial resolution of about 18 km, more advanced Earth system modeling, and the assimilation of a wider range of reprocessed observations, thus enabling better representation of smaller spatial scale atmospheric phenomena.\u003c/p\u003e"},{"header":"4. High resolution SAR data exploitation for displacements investigation in urbanized areas: the COSMO-SkyMed case study","content":"\u003cp\u003eThe wide area imaging, medium spatial resolution SAR sensors, like those of the C-band Sentinel-1 constellation, have extensively demonstrated proven capabilities to detect and monitor large-scale deformation phenomena, such as regional subsidence, landslide dynamics, volcanic displacements, and other geologically driven ground motions over wide and very wide areas [Lanari et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; webportal: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://egms.land.copernicus.eu\u003c/span\u003e\u003cspan address=\"https://egms.land.copernicus.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.; Crosetto et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e]. However, when deformation phenomena become highly localized and spatially heterogeneous, as often occurs in densely urbanized areas, the spatial resolution limitation of these SAR systems may limit the capability to discriminate small-scale displacement patterns or to reliably associate observed ground motions with individual buildings, infrastructures or portions of them.\u003c/p\u003e \u003cp\u003eWithin this framework, the exploitation of high resolution SAR data, as for the X-band images provided by the Italian COSMO-SkyMed constellation, including both the First-Generation (CSK) and the Second-Generation (CSG) missions, allows performing detailed DInSAR investigations of the built-up environment. In particular, the fine spatial resolution (about 2.7 m in the Stripmap mode but down to less than 1 m in the Spotlight one) of the COSMO-SkyMed SAR acquisitions represents a key asset for the detection and monitoring of surface deformations at the scale of built-up structures, thus supporting infrastructure resilience, preventive maintenance, and risk mitigation strategies through satellite-based displacements.\u003c/p\u003e \u003cp\u003eIndeed, since 2009, the use of the CSK data has proven its high effectiveness in capturing detailed deformation patterns related to complex anthropogenic hazard scenarios [Sansosti et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Crosetto et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Calamita et al., 2019; Tapete and Cigna, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Talledo et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Miano et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ho Tong Minh et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Miano et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e], thanks to a dense distribution of coherent radar targets and the possibility of generating long-term interferometric time series. The launch of the Second-Generation COSMO-SkyMed (CSG) mission, started in 2020, further enhanced these capabilities, guaranteeing temporal continuity and high spatial density of reliable DInSAR measurements. These characteristics are particularly advantageous for multi-temporal DInSAR applications in extended urban areas, where localized deformation signals may coexist with broader regional-scale patterns, and where the capability to isolate, track, and interpret small-scale displacements is essential for risk assessment and infrastructure monitoring.\u003c/p\u003e \u003cp\u003eThis Section is aimed to highlight how this kind of analysis can be carried out by using high resolution X-band CSK/CSG SAR acquisitions. In particular, we focus on the multi-temporal DInSAR technique referred to as Full Resolution Parallel-SBAS (FR P-SBAS) approach [Bonano et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e]. This method is suitable for profitably detecting localized displacements affecting individual buildings or transport infrastructures, while preserving the capability to efficiently and automatically perform advanced DInSAR explorations, across multiple spatial resolution scales, for regional and local-scale deformation retrieval.\u003c/p\u003e \u003cp\u003eIn the following, we present the main outcomes achieved by processing, through the FR P-SBAS approach, large sequences of ascending and descending X-Band CSK/CSG, Stripmap mode data collected in the 2017\u0026ndash;2025 time interval over the extended urban area of Bologna (northern Italy), located along the Po Plain, which is affected by a well-known regional-scale subsidence pattern, primarily induced by the natural compaction of the Quaternary deposits and by groundwater withdrawal [Stramondo et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Antonelli et al., 2016; Navarro et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSpecifically, the exploited CSK/CSG dataset is composed of 136 ascending and 131 descending SLC images, collected during the January 2017-April 2025 time interval, within the so-called ASI MapItaly programme [webportal: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.asi.it/en/2023/12/asi-italian-space-agency-upgrades-access-to-mapitaly-data\u003c/span\u003e\u003cspan address=\"https://www.asi.it/en/2023/12/asi-italian-space-agency-upgrades-access-to-mapitaly-data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. The FR P-SBAS processing of such large X-band data sequences allowed the generation of the full resolution (FR) DInSAR products, consisting of LOS FR displacement time series and of the corresponding mean deformation velocity maps for both ascending and descending orbits.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e, we present the FR LOS mean deformation velocity maps achieved by processing the ascending (A) and descending (B) CSK/CSG datasets relevant to the extended urban area of Bologna. The well-known subsidence pattern across the city and the surrounding areas is quite evident in the achieved velocity maps, where it is possible to measure a LOS deformation trend exceeding 1 cm/year; this is observed along the industrial belt and the peri-urban zones of the Bologna municipality. These results further confirm the reliability of the FR P-SBAS algorithm for long-term deformation monitoring and its suitability for supporting regional-scale land management and planning activities. However, when a prevailing regional-scale deformation pattern occurs in the investigated zone, as for the widespread subsidence phenomena characterizing the Bologna area, the identification of possible localized, differential displacements at the scale of single buildings/infrastructures, or specific structural elements becomes not trivial. In this scenario, the capability of the FR P-SBAS approach to perform deformation analyses at different spatial resolution scales can be effectively exploited to enhance the detection and analysis of possible localized signals with respect to the regional trend movements [Lanari et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Manunta et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2008\u003c/span\u003e]. This is done through the decomposition of the FR displacement time series into the spatially low-pass and high-pass components. Indeed, the former primarily reflects deformation processes related to the soil and the regional-scale ground movements, whereas the high-pass component emphasizes localized displacements that may affect individual structures or portions of them.\u003c/p\u003e \u003cp\u003eTo further clarify this issue, in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e we show the comparison between the FR LOS mean velocity maps achieved by processing (through the FR P-SBAS approach) the ascending CSK/CSG dataset, relevant to the overall FR analysis (A), and to the spatially high-pass displacement component, respectively. It is worth noting that the high-pass deformation velocity map displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e- (B), obtained after the removal of the spatially low-pass (or regional) displacement component, in a sort of \u0026ldquo;Differential of the Differential SAR Interferometry\u0026rdquo;, is characterized by a generally low displacement signal behaviour. Nevertheless, it allows us to zoom in and easily detect possible anomalies, with respect to the regional deformation trend, associated with infrastructures or single constructions, as, for instance, the one depicted in the inset of Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e-(B). This is related to a building located in the upper part of the city center that is affected by localized differential displacements. In this case, we also present one of the spatially high-pass displacement time series, shown in the plots of Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e, which further highlights how the removal of the regional component allows the retrieval of subtle, non-linear, structure-related displacement signals that are superimposed to the long-term, regional subsidence trend.\u003c/p\u003e \u003cp\u003eWe remark that the presented exploitation of such CSK/CSG observations enables both retrospective investigations and advanced monitoring strategies, thus supporting a comprehensive assessment of regional and localized displacement phenomena affecting the ground surface and the built-up environment, particularly in highly urbanized zones. This represents a crucial step toward more reliable and operational DInSAR-based services devoted to extended urban areas, complementing the information provided by wide area imaging SAR missions operating at medium spatial resolutions, as for the Sentinel-1 case, and paving the way for future high resolution SAR constellations.\u003c/p\u003e"},{"header":"5. Mid-Inclination Orbit constellations for advanced DInSAR monitoring: the NIMBUS SAR IRIDE case study","content":"\u003cp\u003eIn the last decades, Sun-Synchronous Orbits, often in a Dawn-Dusk configuration, have been the standard architecture for many EO missions because they guarantee global coverage, repeatable illumination geometry, and operational simplicity. This approach is consistent with the economic and technological rationale of the historically \u0026ldquo;big\u0026rdquo; satellites: complex, expensive platforms with few units available, for which optimization meant maximizing utility on a global scale and reducing management complexity [Yu et al., 2017; Ullo et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; NG et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e]. However, the transition to constellations of small satellites, including both commercial and national SAR constellations, is changing this paradigm. Indeed, the availability of more units, faster production, and the possibility of deploying an entire constellation with a single launch require the exploitation of orbital configurations that can better meet specific mission objectives, such as the systematic coverage of specific latitudes of interest, shorter interferometric revisit times, diversified observation geometries, and on-demand services [Ignatenko et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Castelletti et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Orzel et al.,. 2023]. In this context, Mid-Inclination Orbits (MIO) become an interesting option for missions that do not need to cover the entire globe but aim to maximize performance over a specific area of interest located at mid-latitude. Indeed, the geometry of MIO ground tracks involves a progressive reduction of the inter-track distance at latitudes close to the orbital inclination, which is particularly valuable for small SAR satellites with limited swaths and typically high/very high spatial resolutions. In the case of DInSAR applications, the drivers are not only the optimization of the spatial coverage of a specific area and the short temporal revisit, but also some rather stringent geometric constraints, such as the use of Repeat Ground Track (RGT) orbits and manageable perpendicular baselines. Furthermore, DInSAR measurement is intrinsically a LOS measurement. Specifically, SSO (quasi-polar) missions are very robust for what concerns the Vertical and East-West displacement components retrieval, but they are not very sensitive to the North-South component [Guzzetti et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Casu and Manconi, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e]. On the other hand, MIOs would introduce a geometry that increases sensitivity to the North-South direction, thus opening more favourable prospects for retrieving the investigated three-dimensional (3D) deformation field [Cotugno et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003eb].\u003c/p\u003e \u003cp\u003eIn this context, IRIDE is one of the emerging new constellations of small satellites. This program, developed under the management of the European Space Agency (ESA) and with the support of the Italian Space Agency (ASI), is conceived as a \u0026ldquo;constellation of constellations\u0026rdquo; in Low Earth Orbit (LEO) equipped with different sensors (SAR and optical, at various spatial resolutions and bands), aimed of responding to specific institutional services and operational applications, primarily to support the monitoring of the Italian territory, in addition to on-demand capabilities [webportal: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iridespazio.it\u003c/span\u003e\u003cspan address=\"https://iridespazio.it\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. One of IRIDE's SAR sub-constellations is NIMBUS SAR, an X-band system that will consist of two different batches and will also have a high resolution (0.5\u0026ndash;3 m) capability. The first batch, scheduled for launch between the end of 2026 and the beginning of 2027, will include 6 satellites that will acquire, on the Italian territory, VV-polarized SAR data in Stripmap mode with a swath width on the order of a few tens of kilometers (in our analysis: ~25\u0026ndash;30 km, with an average value used of ~\u0026thinsp;27.5 km) [webportal: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iridespazio.it\u003c/span\u003e\u003cspan address=\"https://iridespazio.it\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; webportal: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.thalesaleniaspace.com/en/press-releases/thales-alenia-space-winscontracts-iride-radar-and-optical-satellites\u003c/span\u003e\u003cspan address=\"https://www.thalesaleniaspace.com/en/press-releases/thales-alenia-space-winscontracts-iride-radar-and-optical-satellites\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. The narrow swath width makes the choice of orbital configuration crucial. In fact, a conventional SSO risks not guaranteeing systematic coverage of Italy with short interferometric revisit times. Conversely, a MIO can maximize both spatial and temporal coverage capabilities in the latitudes of interest.\u003c/p\u003e \u003cp\u003eWithin this framework, this Section aims to show, following the lines of [Cotugno et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea], an optimal orbital solution for NIMBUS SAR. We therefore design and compare several repeat ground track (RGT) configurations, evaluating the corresponding interferometric coverage over Italy and the resulting DInSAR performance. A key element of the proposed methodology is the beam assignment strategy: to quickly fill the gap between consecutive orbital ground tracks, each satellite in the constellation is stably associated with a \u0026ldquo;beam\u0026rdquo; during the entire N-day repetition cycle, so that after N nodal days, the same area is observed again by the satellite with the same angle of incidence (an essential condition for DInSAR) [Cotugno et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea]. Applying this methodology to the NIMBUS SAR case and imposing an interferometric temporal revisit of 6 nodal days (consistent with the current interest to investigate rather fast-developing phenomena), we identify a particularly effective configuration: MIO with an orbital inclination of 49\u0026deg; and right-looking Stripmap mode (nominal altitude 548 km) [Cotugno et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea]. The coverage of the Italian territory, relevant to this configuration, is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The comparison with a 6-nodal days RGT SSO shows a clear result, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e: with the same number of satellites and repeat cycle, the SSO cannot guarantee systematic coverage of Italy. The only way to improve coverage by exploiting an SSO is to increase the repeat cycle to N\u0026thinsp;=\u0026thinsp;12 days or N\u0026thinsp;=\u0026thinsp;18 days, which inevitably reduces the temporal imaging frequency. On the other hand, a 49\u0026deg; MIO achieves very satisfactory coverage, except for a few residual areas in southern Italy, which is consistent with the progressive increase in inter-track distance towards the equator. The analysis carried out in [Cotugno et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003eb] also shows that, for the proposed MIO configuration, there are no substantial limitations related to critical perpendicular baseline or geometric distortions (foreshortening/layover/shadow) that could compromise DInSAR applications over the Italian territory [Cotugno et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea]. Moreover, as anticipated, another distinctive advantage of exploiting this configuration is the retrieved sensitivity to the North-South deformation component. This enhanced capability is clearly illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e, where the LOS projection of a 1-cm North-South displacement is mapped over Italy (assuming an off-nadir angle of 30\u0026deg;), showing non-negligible contributions and a marked increase as we move toward the northern latitudes. For the sake of completeness, we also report, in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e, the LOS projection of a 1-cm deformation in the East-West direction, clearly showing that the improved North-South displacement retrieval capability is at some expense of the East-West one.\u003c/p\u003e \u003cp\u003eLooking beyond the first NIMBUS SAR batch, these findings open up several promising perspectives. The first implication concerns the design of the second batch of NIMBUS SAR. If additional spacecraft are deployed in MIOs with different orbital inclinations (e.g., ~\u0026thinsp;43\u0026deg;- 44\u0026deg;) and an alternative viewing geometry (e.g., left-looking), the resulting acquisition geometry would be both effective and complementary to that of the first batch. In this way, the constellation could more naturally fill residual coverage gaps (particularly in the southern areas of Italy), while also reducing local limitations such as persistent geometric distortions on specific mountain slopes [Cotugno et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003ea]. At the same time, the availability of an independent viewing geometry would further strengthen the inversion of the system to retrieve the three deformation components, especially for what attains the North-South one [Cotugno et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003eb]. Moreover, the envisaged exploitation of a HH polarization, for the NIMBUS SAR second batch, represents an additional source of information, for what concerns the investigation of the imaged backscattering surface characteristics, integrating the VV-based imaging capability of the first batch [Franceschetti and Lanari, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1999\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe further remark that a second, very relevant implication concerns the synergy between the two NIMBUS SAR satellite batches, disposed in MIO configurations, and other available X-band sensors operating at comparable spatial resolution but along SSOs, with a specific focus on the CSK and CSG sensors. Indeed, such a multi-orbit framework would enable a highly robust 3D deformation retrieval capability, paving the way for obtaining high resolution displacement information with unprecedented accuracy. In particular, because of the high coherence of multi-temporal DInSAR products relevant to the built-up environment, this may allow to develop an extremely effective monitoring scenario for the buildings and infrastructures of the overall Italian territory.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eWe discussed, through the analysis of four selected case studies, some new development opportunities for the investigation of displacements affecting the ground surface and the built-up environment through advanced DInSAR techniques\u003c/p\u003e \u003cp\u003eIn this framework, the presented experimental results clearly show the relevance of the synergic exploitation of multi-frequency SAR constellations. In particular, the integration of L-band measurements with those available through the already existing, long-term C- and X-band missions, such as the Sentinel-1 and COSMO-SkyMed SAR constellation (whose continuity is already ensured by their new generations), will further foster multi-frequency DInSAR-based approaches for the robust characterization of both natural and anthropogenic deformation processes. Indeed, the rather long wavelength of the L-band SAR signals ensures a significantly enhanced temporal coherence and a remarkable mitigation of phase unwrapping errors, with respect to higher-frequency systems that, on the other hand, provide higher sensitivity in coherent areas. Accordingly, the envisaged multi-frequency DInSAR analysis will significantly extend the interferometric applications, especially in challenging scenarios represented by vegetated regions, agricultural areas, wetlands, glaciers and ice sheets, and long temporal baselines. In this perspective, the new L-band SAR missions such as ALOS-4 [Motohka et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], NISAR [Kellogg et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e], and the future ESA\u0026rsquo;s ROSE-L [Rostan et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] are expected to represent a very relevant development boost.\u003c/p\u003e \u003cp\u003eThe presented results also show relevant aspects emerging from the exploitation of multi-resolution SAR data sources, particularly those provided by high resolution sensors. These systems, as for the COSMO-SkyMed constellation of first and second generation, may allow the detection and monitoring of surface displacements at the scale of single infrastructures, buildings or even portions of them, and/or to investigate areas where localized deformation signals coexist with broader regional-scale patterns, where the capability to isolate, track, and interpret small-scale displacements can be essential for risk assessment and monitoring. Indeed, thanks to their high resolution imaging capability, the possibility to \u0026ldquo;zoom in\u0026rdquo; areas of interest and to easily detect displacement anomalies of a single structure may represent a key element for supporting resilience increase, preventive maintenance, and risk mitigation strategies for the built-up environment.\u003c/p\u003e \u003cp\u003eWith this regard, the availability of high resolution L-band SAR systems may represent a very relevant topic for future developments of the SAR technology, as well as the possibility to explore multi-static, in particular bistatic, configurations. The latter will be the case of the forthcoming ESA Harmony bistatic radar mission [L\u0026oacute;pez-Dekker et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e], composed of two passive sensors, which will run in tandem with the Sentinel-1 ones (very likely, the Sentinel-1D), starting from 2029. One of the main tasks of this system will consist of the possibility to retrieve, for the planned five years of operation, the North-South displacements component in addition to the vertical and East-West ones, which are routinely obtained thanks to the Sentinel-1 sensors.\u003c/p\u003e \u003cp\u003eIn parallel to the synergic use of multi-frequency/multi-resolution SAR constellations, another highlighted opportunity of development is provided by the availability of external data for improving the quality of the generated DInSAR products. In particular, it is worth noting that there are different kinds of external data, provided by Global Atmospheric Models (GAMs) and Numerical Weather Prediction (NWP) models, which can be used to reduce the atmospheric artifacts affecting the DInSAR measurements, thus improving their accuracy. In particular, we remark that a major influence is due to the troposphere, which highly impacts higher-frequency SAR systems, like those operating at C- and X-band. Among the most used external data, there are the investigated ECMWF ERA5 data [web portal: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e] and the GACOS corrections [Yu et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e]. Moreover, ESA has recently distributed the ETAD product [Gisinger et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e], which is specifically designed for improving the accuracy of Sentinel-1 SAR products and also includes some layers for the APS corrections, as shown in the presented results. The main issue of the currently available atmospheric correction data is the spatial resolution, which is generally coarser with respect to that of the typical DInSAR products. Moreover, additional issues are represented by their availability in terms of spatial and temporal coverage. For what concerns the GAM derived data, such as the ERA5 ones, they are available at a global scale but are provided with a rough spatial resolution (~\u0026thinsp;31 km), allowing us to estimate only the stratified APS component, but not the finer-scale APS component, such as the turbulent one [Zinno et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2025\u003c/span\u003e]. Differently, NWP-derived data have a finer spatial resolution, but they are available only over limited areas or in restricted time spans and, therefore, cannot be exploited from a perspective of large-scale, automatic, interferometric processing. One of the most recent datasets is the Copernicus European Regional Reanalysis (CERRA) one, which is delivered by the Copernicus Climate Change Service, with the purpose of adding value to ERA5 data; this is produced with a horizontal resolution of 5.5 km and is available over Europe. In this framework, the good news is represented by the fact that a new generation of these external data, with enhanced characteristics, is approaching fast. Above all, it is expected that the ERA6 global climate reanalysis will be released in 2027, offering significant improvements over its predecessor ERA5 through a higher spatial resolution of about 18 km, a more advanced Earth system modelling, and the assimilation of a wider range of reprocessed observations, thus enabling better representation of smaller spatial scale atmospheric phenomena. In this framework, the synergy with the measurements available through the GNSS network can be highly beneficial, thanks also to the growing expansion of these geodetic networks. Furthermore, the multi-frequency characteristics of the GNSS measurements can be also beneficial for the mitigation of phase artifacts induced by the ionosphere, which may have a very significant impact on the low frequency SAR sensors as for the case of the above mentioned L-band SAR systems, and even more on the P-band ones, as already clearly shown through the first results of the ESA BIOMASS mission launched on 2025 [Quegan et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e]. Overall, it is highly expected that the large availability of these forthcoming external data will play a key role in improving the quality of the generated DInSAR products.\u003c/p\u003e \u003cp\u003eAs a final element of discussion, the presented analysis has been focused on the new opportunities offered by constellations of small satellites. With respect to the conventional space-borne SAR systems, i) characterized by large dimensions, ii) typically operating from Sun-Synchronous Orbits, and iii) guaranteeing global coverage, repeatable illumination geometry and operational simplicity, the small satellites constellations represent a change of paradigm. In particular, the availability of more units, faster production, and the possibility of deploying an entire constellation with a single launch allow the exploitation of orbital configurations that better meet specific mission objectives such as the systematic coverage of specific latitudes of interest, shorter interferometric revisit times, diversified observation geometries, and on-demand services [Castelletti et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Orzel et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e]. This has already been shown by the Iceye [Ignatenko et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e] and Capella Space [Yague-Martinez et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2025\u003c/span\u003e] constellations. In this context, MIOs become an interesting option for missions that do not need to cover the entire globe but aim to maximize performance over a specific area of interest located at mid-latitude. Moreover, for what concerns DInSAR applications, MIOs would introduce a geometry that increases sensitivity to the North-South direction and, thus, in connection with SAR imaging from SSOs, opens very favourable prospects for retrieving the three-dimensional (3D) deformation field [Cotugno et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003eb]. This is the scenario discussed in the fourth presented case study, which is focused on one of the SAR sub-constellations of the Italian IRIDE program, referred to as NIMBUS SAR, represented by an X-band SAR system allowing high spatial resolution (0.5-3 m) imaging. NIMBUS SAR will include two batches of 6 small (less than 300 kg) satellites each, operating along MIOs and at altitudes between 490\u0026ndash;550 km. In particular, the first batch of the NIMBUS SAR constellation will be deployed in 49\u0026deg; right-looking (to be launched at the end of 2026), while it is expected for the second batch a 43\u0026deg; \u0026minus;\u0026thinsp;44\u0026deg; left-looking orbital configuration (to be launched at the end of 2027). This will allow us to \u0026ldquo;interferometrically image\u0026rdquo; the whole Italian territory in 6 days from each orbit and, through the DInSAR technique exploitation, to measure also the North-South deformation component, thus permitting us to investigate the three-dimensional behaviour of the detected displacements.\u003c/p\u003e \u003cp\u003eAs an additional remark, we highlight that the contribution of small satellites to high-end architectures does not reside, at least for now, in the intrinsic value of the single elements, but rather in the collective capacity of the constellation. Accordingly, it is also foreseen the future possibility to launch swarms of several microsatellites with the possibility to operate cooperatively and also to exploit Artificial-Intelligence (AI) capabilities. To our knowledge, none of these systems is currently operational, but it is already clear that the implementation of such a new concept of observational radar could really boost applications in several scenarios, particularly for emergency response and continuous monitoring.\u003c/p\u003e \u003cp\u003eWe finally underline that, although the presented case studies provide some good examples of development opportunities for further extending the investigation capability of spaceborne DInSAR techniques, our analysis cannot be fully exhaustive because other elements can play a relevant role. Among these, a key aspect we highlight is related to the capability of efficiently and effectively carrying out a full exploitation of the huge amount of SAR data we are already experiencing, which will exponentially increase in the near future thanks to the foreseen new SAR sensors. In this framework, a major drawback (affecting mainly time series analyses) is certainly represented by the required great increase of both the amount of data to be handled and stored, and the computing time needed to process them. Such characteristics necessarily force a performance optimization of the exploited Information and Communication Technologies (ICT) infrastructures. In this scenario, Cloud Computing Environments [Zinno et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e] may represent an opportunity to achieve both large storage and high computing performance capabilities. Moreover, \u003cem\u003ead-hoc\u003c/em\u003e algorithms able to exploit these computing facilities must be developed to ensure algorithm efficiency and portability to such distributed hardware architectures (Lee et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sadashiv and Kumar 2011), allowing the effective processing of the available large SAR data volumes. In addition, another key element to be carefully accounted for consists of the capability to promptly and automatically analyze the huge amount of generated DInSAR products. In this direction, the application of AI techniques, to extract added-value information, is another extremely relevant topic for further developments [Anantrasirichai et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Anantrasirichai et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Brengman et al., 2021; Zhou et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Festa et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fusco et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e].\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eOpen Access\u003c/h2\u003e \u003cp\u003eThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://creativecommons.org/licenses/by/4.0/\u003c/span\u003e\u003cspan address=\"http://creativecommons.org/licenses/by/4.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was partly supported by the Italian Civil Protection Department (DPC) in the frame of the IREA-DPC (2022\u0026ndash;2024) and (2025\u0026ndash;2027) agreements; by the Directorate General for Energy Sources and Enabling Titles \u0026ndash; Ministry of the Environment and Energy Security of Italy, through the IREA-MASE agreement; by the EPOS-RI, including the support obtained through the EPOS-Italia JRU and EPOS-ON (GA 101131592). Moreover, we also partially benefited from the European Union\u0026mdash; Next Generaton EU (PNRR-M4C2) through the ICSC \u0026ndash; CN-HPC (CN00000013) and its HaMMon (Hazard Mapping and vulnerability Monitoring) Innovation Grant Spoke 3 Project, CN-MOST (CN00000023), MEET IR (IR0000025), and GeoSciences IR (IR0000037) projects, and by the GRINT (PIR01_00013) and IbiSCo (PIR01_00011) projects, funded by the National Operational Programme Infrastructures and Networks 2014/202 of the Italian Ministry of Infrastructure and Transports.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: R.L., C.d.L., M.M., I.Z., M.B. Data Curation: R.L., Fe.Ca., Fr.Ca., Fe.Co., C.d.L., M.M., Y.B.L.R., P.S., I.Z., M.B.Formal analysis: all Funding acquisition: R.L., Fr.Ca., C.d.L., M.M., I.Z., M.B.Investigation: allMethodology: R.L., Fr.Ca., C.d.L., M.M., G.O., I.Z., M.B.Project administration: R.L., Fr.Ca., C.d.L., M.M., I.Z., M.B.Resources: allSoftware: Fe.Ca., Fr.Ca., Fe.Co., C.d.L., M.M., G.O.,Y.B.L.R., P.S., I.Z., M.B. Supervision: R.L., C.d.L., M.B.Validation: allVisualization: Fe.Ca., Fe.Co., C.d.L., Y.B.L.R., P.S., I.Z., M.B.Writing \u0026ndash; Original Draft: R.L., Fe.Co., C.d.L., I.Z., M.B.Writing \u0026ndash; Review \u0026amp; Editing: all\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe contents of this paper represent the authors\u0026rsquo; ideas and do not necessarily correspond to the official opinion and policies of the Italian Civil Protection Department \u0026ndash; Presidency of the Council of Ministers and of the Directorate General for Energy Sources and Enabling Titles \u0026ndash; Ministry of the Environment and Energy Security of Italy. The Sentinel-1 data have been provided through the Copernicus Program of the European Union. The authors acknowledge the Italian Space Agency (ASI) for acquiring and providing the COSMO-SkyMed) data under the License to Use Agreement (ID 1056 ASI Project Card). The authors would like to thank ASI for providing also the SAOCOM-1 data, under the ASI-CONAE SAOCOM-1 License to Use Agreement. The DEM of the investigated zones was acquired through the SRTM archive.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnantrasirichai N et al (2018) Application of machine learning to classification of volcanic deformation in routinely generated InSAR data. J Geophys Research: Solid Earth 123:6592\u0026ndash;6606\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnantrasirichai N et al (2019) The application of convolutional neural networks to detect slow, sustained deformation in InSAR time series. Geophys Res Lett 46:11850\u0026ndash;11858\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntonielli B, Monserrat O, Bonini M, Cenni N, Devanth\u0026eacute;ry N, Righini G, Sani F (2016) Persistent Scatterer Interferometry analysis of ground deformation in the Po Plain (Piacenza-Reggio Emilia sector, Northern Italy): seismo-tectonic implications, \u003cem\u003eGeophysical Journal International\u003c/em\u003e, Volume 206, Issue 2, August Pages 1440\u0026ndash;1455. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/gji/ggw227\u003c/span\u003e\u003cspan address=\"10.1093/gji/ggw227\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eASI, Italian Space Agency Upgrades Access To MAPITALY Data. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.asi.it/en/2023/12/asi-italian-space-agency-upgrades-access-to-mapitaly-data\u003c/span\u003e\u003cspan address=\"https://www.asi.it/en/2023/12/asi-italian-space-agency-upgrades-access-to-mapitaly-data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed: February 2nd, (2026)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerardino P, Fornaro G, Lanari R, Sansosti E (2002) A new Algorithm for Surface Deformation Monitoring based on Small Baseline Differential SAR Interferograms. IEEE Trans Geosci Remote Sens 40:11, 2375\u0026ndash;2383\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonano M, Manunta M, Pepe A, Paglia L, Lanari R (2013) From Previous C-Band to New X-Band SAR Systems: Assessment of the DInSAR Mapping Improvement for Deformation Time-Series Retrieval in Urban Areas, \u003cem\u003eIEEE Trans. Geosci. Remote Sens.\u003c/em\u003e, vol. 51, no. 4, pp. 1973\u0026ndash;1984, Apr\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonano M et al (2025) New Advances of the P-SBAS Approach for an Efficient Parallel Processing of Large Volumes of Full-Resolution Multitemporal DInSAR Interferograms. IEEE J Sel Top Appl Earth Observations Remote Sens 18:2317\u0026ndash;2341. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/JSTARS.2024.3507542\u003c/span\u003e\u003cspan address=\"10.1109/JSTARS.2024.3507542\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrengman CMJ, Barnhart WD (2021) Identification of surface deformation in InSAR using machine learning. Geochem Geophys Geosyst 22.3 : e2020GC009204.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgmann R, Rosen PA, Fielding EJ (May 2000) Synthetic aperture radar interferometry to measure Earth's surface topography and its deformation. Annu Rev Earth Planet Sci 28:169\u0026ndash;209\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalamita G et al (2009) An integrated geophysical approach for urban underground characterization: The Avigliano town (southern Italy) case study. Geomatics Nat Hazards Risk 10(1):412\u0026ndash;432\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasamento F, Zinno I, Bonano M, Casu F, De Luca C, Lanari R (2025) The APS filtering approach of the Parallel Small BAseline Subset DInSAR processing chain: methodology and performance analysis, \u003cem\u003eIGARSS 2025\u0026ndash;2025 IEEE International Geoscience and Remote Sensing Symposium\u003c/em\u003e, Brisbane, Australia, pp. 9158\u0026ndash;9162. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/IGARSS55030.2025.11242517\u003c/span\u003e\u003cspan address=\"10.1109/IGARSS55030.2025.11242517\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastelletti D, Farquharson G, Stringham C, Duersch M, Eddy D (2021) Capella space first operational SAR satellite, in Proc. IEEE Int. Geosci. Remote Sens. Symp. IGARSS, Jul. pp. 1483\u0026ndash;1486\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasu F, Manzo M, Lanari R (2006) A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data, \u003cem\u003eRemote Sens. Environ.\u003c/em\u003e, vol. 102, no. 3\u0026ndash;4, pp. 195\u0026ndash;210, Jun\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasu F, Elefante S, Imperatore P, Zinno I, Manunta M, De Luca C, Lanari R (2014) SBAS-DInSAR Parallel Processing for Deformation Time-Series Computation, \u003cem\u003eIEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.\u003c/em\u003e, vol. 7, no. 8, pp. 3285\u0026ndash;3296, Aug\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasu F, Manconi A (2016) Four-dimensional surface evolution of active rifting from spaceborne SAR data, Geosphere, vol. 12, no. 3, pp. 697\u0026ndash;705, Jun\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCerra Reanalysis data (2026) [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://climate.copernicus.eu/copernicus-regional-reanalysis-europe-cerra\u003c/span\u003e\u003cspan address=\"https://climate.copernicus.eu/copernicus-regional-reanalysis-europe-cerra\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed: February 2nd\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCigna F, Ram\u0026iacute;rez RE, Tapete D (2021) Accuracy of sentinel-1 PSI and SBAS InSAR displacement velocities against GNSS and geodetic leveling monitoring data. Remote Sens 13(23). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/rs13234800\u003c/span\u003e\u003cspan address=\"10.3390/rs13234800\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCovello F, Battazza F, Coletta A, Lopinto E, Fiorentino C, Pietranera L, Valentini G, Zoffoli S COSMO-SkyMed an existing opportunity for observing the Earth. J Geodyn, 49, 3/4, pp. 171\u0026ndash; 180, Apr. 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCotugno F et al (2025) a Constellation Design and Analysis for Spaceborne DInSAR Mapping in Mid-Inclination Orbits: The IRIDE NIMBUS Mission, in \u003cem\u003eIEEE Transactions on Geoscience and Remote Sensing\u003c/em\u003e, vol. 63, pp. 1\u0026ndash;20, Art no. 1000120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/TGRS.2024.3519212\u003c/span\u003e\u003cspan address=\"10.1109/TGRS.2024.3519212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCotugno F et al (2025) First Results of the 3D Ground Displacements Retrieval through Capella Space Mid-Inclination Orbit DInSAR Measurements: the Campi Flegrei Caldera Case Study, IGARSS 2025\u0026ndash;2025 IEEE International Geoscience and Remote Sensing Symposium, Brisbane, Australia, pp. 9145\u0026ndash;9149, 2025b\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrosetto M, Monserrat O, Cuevas-Gonz\u0026aacute;lez M, Devanth\u0026eacute;ry N, G.Luzi, and, Crippa B (2015) Measuring thermal expansion using X-band persistent scatterer interferometry. ISPRS J Photogrammetry Remote Sens 100:84\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.isprsjprs.2014.05.006\u003c/span\u003e\u003cspan address=\"10.1016/j.isprsjprs.2014.05.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrosetto M, Crippa B, Mr\u0026oacute;z M, Cuevas-Gonz\u0026aacute;lez M, Shahbazi S (2025) Applications based on EGMS products: A review, Remote Sensing Applications: Society and Environment, Vol. 37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rsase.2025.101452\u003c/span\u003e\u003cspan address=\"10.1016/j.rsase.2025.101452\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCurlander JC, Robert N, McDonough (1991) Synthetic aperture radar, vol 11. Wiley, New York\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelgado F, Shreve T, Borgstrom S, Le\u0026oacute;n-Ibanez P, Castillo J, Poland M (2024) A global assessment of SAOCOM-1 L-band stripmap data for InSAR characterization of volcanic, tectonic, cryospheric, and anthropogenic deformation, \u003cem\u003eIEEE Trans. Geosci. Remote Sens.\u003c/em\u003e, vol. 62, Art. no. 5216821. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/TGRS.2024.3423792\u003c/span\u003e\u003cspan address=\"10.1109/TGRS.2024.3423792\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Luca C, Zinno I, Manunta M, Lanari R, Casu F (2017) Large areas surface deformation analysis through a cloud computing P-SBAS approach for massive processing of DInSAR time series. Remote Sens Environ 202:3\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.rse.2017.05.022\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2017.05.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Luca C et al (2025) SAOCOM-1 L-band DInSAR Time Series generation through the P-SBAS approach: algorithm extension and products analysis. IEEE J Sel Top Appl Earth Observations Remote Sens 18:2680\u0026ndash;2703\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eECMWF ERA5 data (2026) [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed: February 2nd\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean Ground Motion Service, [Online] (2026) Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://egms.land.copernicus.eu\u003c/span\u003e\u003cspan address=\"https://egms.land.copernicus.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (Accessed: February 2nd\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerretti A, Prati C, Rocca F (2000) Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Trans Geosci Remote Sens 38(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/36.868878\u003c/span\u003e\u003cspan address=\"10.1109/36.868878\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerretti A, Fumagalli A, Novali F, Prati C, Rocca F (2011) Rucci A New Algorithm for Processing Interferometric Data- Stacks: SqueeSAR. IEEE Trans Geosci Remote Sens 49:3460\u0026ndash;3470\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFesta D, Bonano M, Casagli N, Confuorto P, De Luca C, Del Soldato M, Casu F (2022) Nation-wide mapping and classification of ground deformation phenomena through the spatial clustering of P-SBAS InSAR measurements: Italy case study. ISPRS J Photogrammetry Remote Sens 189:1\u0026ndash;22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranceschetti G, Lanari R (1999) \u003cem\u003eSynthetic Aperture Radar Processing\u003c/em\u003e, Boca Raton, FL, CRC, Mar\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFritz T, Krieger L, Gisinger C, Lachaise M (2021) S1-ETAD project product definition document, ESA Technical Document, Doc. ETAD-DLR-PS-0002, 2.1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFusco A et al (2023) A CNN-Based Interferogram Filtering Approach to Enhance the Co-Seismic Surface Displacements Identification by Exploiting the EPOSAR DInSAR Maps Global Archive, IGARSS 2023\u0026ndash;2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 5230\u0026ndash;5233. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/IGARSS52108.2023.10282743\u003c/span\u003e\u003cspan address=\"10.1109/IGARSS52108.2023.10282743\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGabriel AK, Goldstein RM, Zebker HA (1989) Mapping small elevation changes over large areas: Differential interferometry, \u003cem\u003eJ. Geophys. Res\u003c/em\u003e., vol. 94, no. B7, pp. 9183\u0026ndash;9191, Mar\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGisinger C et al (2022) The extended timing annotation dataset for Sentinel-1\u0026mdash;Product description and first evaluation results, \u003cem\u003eIEEE Trans. Geosci. Remote Sens.\u003c/em\u003e, vol. 60, Art. no. 5232622. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/TGRS.2022.3194216\u003c/span\u003e\u003cspan address=\"10.1109/TGRS.2022.3194216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuzzetti F et al (2009) Analysis of ground deformation detected using the SBAS-DInSAR technique in Umbria, central Italy, Pure Appl. Geophys., vol. 166, nos. 8\u0026ndash;9, pp. 1425\u0026ndash;1459, Jun\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanssen RF (2001) Radar Interferometry: Data Interpretation and Error Analysis. Springer-, Berlin, Germany\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHersbach H et al (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146:1999\u0026ndash;2049. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/qj.3803\u003c/span\u003e\u003cspan address=\"10.1002/qj.3803\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo Tong Minh D, Ngo Y-N, Le TC, Nguyen QD (2025) The Sinking Ho Chi Minh Megacity by Radar Interferometry. IEEE J Sel Top Appl Earth Observations Remote Sens 18:23228\u0026ndash;23241. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/JSTARS.2025.3607038\u003c/span\u003e\u003cspan address=\"10.1109/JSTARS.2025.3607038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHooper AJ (2008) A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches. Geophys Res Lett 35(16). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1029/2008GL034654\u003c/span\u003e\u003cspan address=\"10.1029/2008GL034654\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Z, Mallorqu\u0026iacute; JJ (2019) An accurate method to correct atmospheric phase delay for InSAR with the ERA5 global atmospheric model. Remote Sens, 11, no. 17, Art. 1969\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIgnatenko V, Laurila P, Radius A, Lamentowski L, Antropov O, Muff D (2020) ICEYE microsatellite SAR constellation status update: Evaluation of first commercial imaging modes, in Proc. IEEE Int. Geosci. Remote Sens. Symp., Sep. pp. 3581\u0026ndash;3584\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIride official website (2025) iride official website. [online]. available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iridespazio.it/\u003c/span\u003e\u003cspan address=\"https://iridespazio.it/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKellogg K NASA-ISRO synthetic aperture radar (NISAR) Mission. in Proc. IEEE Aerosp Conf., Sky B et al (2020) MT, USA, 2020, pp. 1\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/AERO47225.2020.9172638\u003c/span\u003e\u003cspan address=\"10.1109/AERO47225.2020.9172638\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLanari R, Mora O, Manunta M, Mallorqu\u0026igrave; JJ, Berardino P, Sansosti E (2004) A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms. IEEE Trans Geosci Remote Sens 42:7, 1377\u0026ndash;1386\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLanari R et al (2020) Automatic generation of Sentinel-1 continental scale DInSAR deformation time series through an extended P-SBAS processing pipeline in a cloud computing environment. Remote Sens 12(18). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/RS12182961\u003c/span\u003e\u003cspan address=\"10.3390/RS12182961\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee CA, Gasster SD, Plaza A, Chang C-I, Huang B (2011) Recent Developments in High Performance Computing for Remote Sensing: A Review. IEEE J Sel Top Appl Earth Observations Remote Sens 4(3):508\u0026ndash;527. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/JSTARS.2011.2162643\u003c/span\u003e\u003cspan address=\"10.1109/JSTARS.2011.2162643\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Jiang W, Zhang J (2022) A time series processing chain for geological disasters based on a GPU-assisted Sentinel-1 InSAR processor, \u003cem\u003eNatural Hazards\u003c/em\u003e, vol. 111, pp. 803\u0026ndash;815. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11069-021-05079-9\u003c/span\u003e\u003cspan address=\"10.1007/s11069-021-05079-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-Dekker P, Rott H, Prats-Iraola P, Chapron B, Scipal K, Witte ED (2019) Harmony: an Earth Explorer 10 Mission Candidate to Observe Land, Ice, and Ocean Surface Dynamics, IGARSS 2019\u0026ndash;2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, pp. 8381\u0026ndash;8384. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/IGARSS.2019.8897983\u003c/span\u003e\u003cspan address=\"10.1109/IGARSS.2019.8897983\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManunta M, Marsella M, Zeni G, Sciotti M, Atzori S, Lanari R (2008) Two-scale surface deformation analysis using the SBAS‐DInSAR technique: a case study of the city of Rome, Italy, \u003cem\u003eInt. J. Remote Sens.\u003c/em\u003e, vol. 29, no. 6, pp. 1665\u0026ndash;1684, Mar\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManunta M et al (2019) The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment, in \u003cem\u003eIEEE Transactions on Geoscience and Remote Sensing\u003c/em\u003e, vol. 57, no. 9, pp. 6259\u0026ndash;6281, Sept. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/TGRS.2019.2904912\u003c/span\u003e\u003cspan address=\"10.1109/TGRS.2019.2904912\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMassonnet D, Rossi M, Carmona C, Adragna F, Peltzer G, Feigl K, Rabaute T (Jul. 1993) The displacement field of the Landers earthquake mapped by radar interferometry. Nature 364(6433):138\u0026ndash;142\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMassonnet D, Feigl KL (1998) Radar Interferometry and its application to changes in the Earth\u0026rsquo;s surface. Rev Geophys 36:441\u0026ndash;500\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiano A, Di Carlo F, Mele A, Giannetti I, Nappo N, Rompato M, Striano P, Bonano M, Bozzano F, Lanari R et al (2022) GIS Integration of DInSAR Measurements, Geological Investigation and Historical Surveys for the Structural Monitoring of Buildings and Infrastructures: An Application to the \u003cem\u003eValco San Paolo\u003c/em\u003e Urban Area of Rome. Infrastructures 7:89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/infrastructures7070089\u003c/span\u003e\u003cspan address=\"10.3390/infrastructures7070089\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiano A, Mele A, Silla M et al (2025) Space-borne DInSAR measurements exploitation for risk classification of bridge networks. J Civil Struct Health Monit 15:731\u0026ndash;744. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13349-024-00832-7\u003c/span\u003e\u003cspan address=\"10.1007/s13349-024-00832-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMora O, Mallorqu\u0026iacute; JJ, Broquetas A (2003) Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images. IEEE Trans Geosci Remote Sens 41(10):2243\u0026ndash;2253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/TGRS.2003.814657\u003c/span\u003e\u003cspan address=\"10.1109/TGRS.2003.814657\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMotohka T, Kankaku Y, Miura S, Suzuki S (2019) Alos-4 L-Band SAR Mission and Observation, in \u003cem\u003eProc. IEEE Int. Geosci. Remote Sens. Symp.\u003c/em\u003e, Yokohama, Japan, pp. 5271\u0026ndash;5273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/IGARSS.2019.8898169\u003c/span\u003e\u003cspan address=\"10.1109/IGARSS.2019.8898169\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNavarro AMG, Eid C, Rocca V, Benetatos C, De Luca C, Onorato G, Lanari R (2025) Integrated Analysis of Satellite and Geological Data to Characterize Ground Deformation in the Area of Bologna (Northern Italy) Using a Cluster Analysis-Based Approach. Remote Sens 17(15):2645. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs17152645\u003c/span\u003e\u003cspan address=\"10.3390/rs17152645\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNg H-M, Ge L, Du Z, Wang S, Ma C (2017) Satellite radar interferometry for monitoring subsidence induced by longwall mining activity using Radarsat-2, Sentinel-1 and ALOS-2 data, Int. J. Appl. Earth Observ. Geoinf., vol. 61, pp. 92\u0026ndash;103, Sep\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnorato G et al (2025) Identification and Correction of Phase Unwrapping Errors in Multitemporal Small Baseline DInSAR Interferograms: A Three-Step Cascade Algorithm. IEEE J Sel Top Appl Earth Observations Remote Sens 18:8602\u0026ndash;8616. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/JSTARS.2025.3546824\u003c/span\u003e\u003cspan address=\"10.1109/JSTARS.2025.3546824\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrzel K et al (2023) Synspective SAR Constellation Status Update: Recent Calval Activities and the Automatic Data Quality Assessment, IGARSS 2023\u0026ndash;2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, pp. 1517\u0026ndash;1520. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/IGARSS52108.2023.10282327\u003c/span\u003e\u003cspan address=\"10.1109/IGARSS52108.2023.10282327\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParizzi A, Brcic R, De Zan F (2021) InSAR performance for large-scale deformation measurement, \u003cem\u003eIEEE Trans. Geosci. Remote Sens.\u003c/em\u003e, vol. 59, no. 10, pp. 8510\u0026ndash;8520, Oct\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeltzer G, Rosen PA (June 1995) Surface displacement of the 17 May 1993 Eureka Valley earthquake observed by SAR interferometry. Science 268:1333\u0026ndash;1336\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuegan S, Le Toan T, Chave J, Dall J, Exbrayat JF, Minh DHT, Williams M (2019) The European Space Agency BIOMASS mission: Measuring forest above-ground biomass from space. Remote Sens Environ 227:44\u0026ndash;60\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRignot E (1998) Fast recession of a west Antarctic glacier. Science 281:549\u0026ndash;551\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRostan F, Mak K, von Alberti M, Bauleo A, Gebert N (2024) Rose-L SAR Instrument Development Status and Mission Performance Prediction. IGARSS 2024\u0026ndash;2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 6618\u0026ndash;6620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/IGARSS53475.2024.10642943\u003c/span\u003e\u003cspan address=\"10.1109/IGARSS53475.2024.10642943\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadashiv N, Kumar SMD Cluster, grid and cloud computing: A detailed comparison, (2011) \u003cem\u003e6th International Conference on Computer Science \u0026amp;\u003c/em\u003e Education \u003cem\u003e(ICCSE)\u003c/em\u003e, Singapore, 2011, pp. 477\u0026ndash;482. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/ICCSE.2011.6028683\u003c/span\u003e\u003cspan address=\"10.1109/ICCSE.2011.6028683\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSansosti E, Casu F, Manzo M, Lanari R (October 2010) Space-borne radar interferometry techniques for the generation of deformation time series: an advanced tool for Earth\u0026rsquo;s surface displacement analysis. Geophys Res Lett 37:L20305. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1029/2010GL044379\u003c/span\u003e\u003cspan address=\"10.1029/2010GL044379\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSansosti E et al (2014) How second generation SAR systems are impacting the analysis of ground deformation. Int J Appl Earth Observ Geoinf 28(1):1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jag.2013.10.007\u003c/span\u003e\u003cspan address=\"10.1016/j.jag.2013.10.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStramondo S, Saroli M, Tolomei C, Moro M, Doumaz F, Pesci A, Boschi E (2007) Surface movements in Bologna (Po plain\u0026mdash;Italy) detected by multitemporal DInSAR. Remote Sens Environ 110(3):304\u0026ndash;316\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalledo DA et al (2022) Satellite radar interferometry: Potential and limitations for structural assessment and monitoring, \u003cem\u003eJ. Building Eng.\u003c/em\u003e, vol. 46, Art. no. 103756. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jobe.2021.103756\u003c/span\u003e\u003cspan address=\"10.1016/j.jobe.2021.103756\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTapete D, Cigna F (2020) Poorly Known 2018 Floods in Bosra UNESCO Site and Sergiopolis in Syria Unveiled from Space Using Sentinel-1/2 and COSMO-Skymed. Sci Rep 10(1):1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-020-69181-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-69181-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThales Alenia Space, Italy M (2023) Italy\u0026rsquo;s New Constellation of Innovative and Flexible Earth Observation Satellites to Monitor Italy and Europe. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.thalesaleniaspace.com/en/press-releases/thales-alenia-space-winscontracts-iride-radar-and-optical-satellites\u003c/span\u003e\u003cspan address=\"https://www.thalesaleniaspace.com/en/press-releases/thales-alenia-space-winscontracts-iride-radar-and-optical-satellites\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe PyAPS software (2026) [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/insarlab/PyAPS\u003c/span\u003e\u003cspan address=\"https://github.com/insarlab/PyAPS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed: February 2nd\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimada M (2013) ALOS-2 science program, \u003cem\u003e2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS\u003c/em\u003e, Melbourne, VIC, Australia, pp. 2400\u0026ndash;2403. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/IGARSS.2013.6723303\u003c/span\u003e\u003cspan address=\"10.1109/IGARSS.2013.6723303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTizzani P et al (February 2013) New insights into the 2012 Emilia (Italy) seismic sequence through advanced numerical modeling of ground deformation InSAR measurements. Geophys Res Lett 40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/grl.50290\u003c/span\u003e\u003cspan address=\"10.1002/grl.50290\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorres R, Snoeij P, Geudtner D, Bibby D, Davidson M, Attema E, Potin P, Rommen B, Floury N, Brown M, Traver IN, Deghaye P, Duesmann B, Rosich B, Miranda N, Bruno C, L\u0026rsquo;Abbate M, Croci R, Pietropaolo A, Huchler M, Rostan F (May 2012) GMES Sentinel-1 mission. Remote Sens Environ 120:9\u0026ndash;24\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUllo SL et al (2019) Application of DInSAR technique to high coherence Sentinel-1 images for dam monitoring and result validation through in situ measurements, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 12, no. 3, pp. 875\u0026ndash;890, Mar\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWerner C, Wegm\u0026uuml;ller U, Strozzi T, Wiesmann A (2003) Interferometric Point Target Analysis for Deformation Mapping, in International Geoscience and Remote Sensing Symposium (IGARSS). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/igarss.2003.1295516\u003c/span\u003e\u003cspan address=\"10.1109/igarss.2003.1295516\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWerninghaus R, Buckreuss S (2010) The TerraSAR-X mission and system design, \u003cem\u003eIEEE Trans. Geosci. Remote Sens.\u003c/em\u003e, vol. 48, no. 2, pp. 606\u0026ndash;614, Feb\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Z, Ma P, Zhang X, Ye G (2024) Efficient management and processing of massive InSAR images using an HPC-based cloud platform. IEEE J Sel Top Appl Earth Observ Remote Sens 17:2866\u0026ndash;2876. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/JSTARS.2023.3349214\u003c/span\u003e\u003cspan address=\"10.1109/JSTARS.2023.3349214\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYague-Martinez N, Cazcarra-Bes V, Farquharson G, Stringham C, De S (2025) Capella Space SAR Interferometry Demonstration: Recent developments and use of Mid-Inclination Orbits, \u003cem\u003eIGARSS 2025\u0026ndash;2025 IEEE International Geoscience and Remote Sensing Symposium\u003c/em\u003e, Brisbane, Australia, pp. 5428\u0026ndash;5432. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/IGARSS55030.2025.11243117\u003c/span\u003e\u003cspan address=\"10.1109/IGARSS55030.2025.11243117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu L, Yang TL, Zhao Q, Liu M, Pepe A (Nov. 2017) The 2015\u0026ndash;2016 ground displacements of the Shanghai coastal area inferred from a combined COSMO-SkyMed/Sentinel-l DInSAR analysis. Remote Sens 9(11):1\u0026ndash;19\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu H, Lan Y, Yuan Z, Xu J, Lee H (2019) Phase Unwrapping in InSAR: A review. IEEE Geosci Remote Sens Mag 7(1):40\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/MGRS.2018.2873644\u003c/span\u003e\u003cspan address=\"10.1109/MGRS.2018.2873644\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu C, Li Z, Penna NT (2020) Triggered afterslip on the southern Hikurangi subduction interface following the 2016 Kaikōura earthquake from InSAR time series with atmospheric corrections. Remote Sens Environ 251:112097\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZebker HA, Rosen PA, Hensley S (1997) Atmospheric effects in interferometric synthetic aperture radar surface deformation and topographic maps. J Geophys Res Solid Earth 102(B4):7547\u0026ndash;7563\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Heresh F, Falk A (2019) Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction, \u003cem\u003eComput. Geosci.\u003c/em\u003e, vol. 133, Art. no. 104331. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31223/osf.io/9sz6m\u003c/span\u003e\u003cspan address=\"10.31223/osf.io/9sz6m\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou L, Yu H, Lan Y, Xing M (2021) Artificial Intelligence in Interferometric Synthetic Aperture Radar Phase Unwrapping: A Review. IEEE Geoscience and Remote Sensing Magazine, vol. 9, no. 2, pp. 10\u0026ndash;28, June 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/MGRS.2021.3065811\u003c/span\u003e\u003cspan address=\"10.1109/MGRS.2021.3065811\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZinno I, Elefante S, Mossucca L, De Luca C, Manunta M, Terzo O, Lanari R, Casu F (2015) A First Assessment of the P-SBAS DInSAR Algorithm Performances Within a Cloud Computing Environment. IEEE J Sel Top Appl Earth Obs Remote Sens 8:4675\u0026ndash;4686\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZinno I, Casu F, De Luca C, Elefante S, Lanari R, Manunta M (2017) A Cloud Computing Solution for the Efficient Implementation of the P-SBAS DInSAR Approach. IEEE J Sel Top Appl Earth Obs Remote Sens 10:802\u0026ndash;817\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZinno I, Bonano M, Buonanno S, Casu F, De Luca C, Manunta M, Manzo M, Lanari R (2018) National scale surface deformation time series generation through advanced DInSAR processing of sentinel-1 data within a cloud computing environment. IEEE Trans Big Data 6(3):558\u0026ndash;571. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/TBDATA.2018.2863558\u003c/span\u003e\u003cspan address=\"10.1109/TBDATA.2018.2863558\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZinno I, Casamento F, Casu F, De Luca C, Lanari R (2022) A Comparative Analysis of Different DInsar Approaches to Filter Out Atmospheric Phase Artifacts in Active Volcano Scenarios Characterized by High Topography Surfaces, IGARSS 2022\u0026ndash;2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, pp. 1099\u0026ndash;1102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/IGARSS46834.2022.9884047\u003c/span\u003e\u003cspan address=\"10.1109/IGARSS46834.2022.9884047\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZinno I, Casamento F, Lanari R (2025) On the Exploitation of the ETAD Product for Filtering Out the Atmospheric Phase Screen From Medium Resolution DInSAR Measurements: An Extensive Performance Analysis. IEEE J Sel Top Appl Earth Observations Remote Sens 18:712\u0026ndash;727. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/JSTARS.2024.3488494\u003c/span\u003e\u003cspan address=\"10.1109/JSTARS.2024.3488494\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"rendiconti-lincei-scienze-fisiche-e-naturali","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lync","sideBox":"Learn more about [Rendiconti Lincei. Scienze Fisiche e Naturali](http://link.springer.com/journal/12208)","snPcode":"12210","submissionUrl":"https://submission.nature.com/new-submission/12210/3","title":"Rendiconti Lincei. Scienze Fisiche e Naturali","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Synthetic Aperture Radar (SAR), DInSAR, Ground displacements, Built-up environment, Deformation monitoring, Sentinel-1, COSMO-SkyMed, SAOCOM-1, ERA5, ETAD, IRIDE, NIMBUS SAR","lastPublishedDoi":"10.21203/rs.3.rs-8904298/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8904298/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe discuss in this work, through the analysis of four selected case studies, some new opportunities for the detection and monitoring of displacements affecting the ground surface and the built-up environment, through Differential SAR Interferometry (DInSAR) techniques. This is done by exploiting the data acquired by currently operating SAR constellations, such as SAOCOM-1 (providing L-band data), Sentinel-1 (providing C-band data), and COSMO-SkyMed (providing X-band data), and by also envisaging those that will be soon available through the forthcoming missions, as for the case of the NIMBUS SAR constellation (that will also provide X-band data).\u003c/p\u003e \u003cp\u003eThe first case study shows how the L-band SAR data, in particular those systematically acquired by the SAOCOM-1 twin systems, allow to retrieve surface displacements in areas characterized by vegetation, where shorter-wavelength sensors have typically limited performance.\u003c/p\u003e \u003cp\u003eThe second experiment is based on the Sentinel-1 C-band DInSAR measurements and permits us to envisage new scenarios for improving surface deformation analysis of very wide areas by exploiting external data, such as the ECMWF ERA5 and the ETAD ones, with the aim of mitigating possible atmospheric artifacts, thus enhancing the retrieved surface displacements accuracy.\u003c/p\u003e \u003cp\u003eThe third experiment highlights how the high resolution SAR data acquired by the X-band sensors of the COSMO-SkyMed constellation permit the investigation of small-scale displacements relevant to single buildings or infrastructures of the built-up environments, even when they are located in areas where extended deformation phenomena occur.\u003c/p\u003e \u003cp\u003eThe last case study explores the potentials of the forthcoming NIMBUS SAR X-band constellation in future DInSAR scenarios, assessing its prospective impact on the displacement monitoring capabilities for the entire Italian territory.\u003c/p\u003e \u003cp\u003eThe final discussion is devoted to summarizing the main findings learned through the presented case studies and to highlighting further possible impacts on the future developments of the DInSAR techniques.\u003c/p\u003e","manuscriptTitle":"Sounding of the Deforming Solid Earth Surface: New Opportunities for Spaceborne Differential SAR Interferometry Techniques to Detect and Monitor Displacements Affecting the Ground and the Built-up Environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 13:18:40","doi":"10.21203/rs.3.rs-8904298/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-03T23:24:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214345172476280650323573985186855840356","date":"2026-03-21T15:35:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-12T17:45:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147389044552958976147336335358774720831","date":"2026-02-26T09:28:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T15:01:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-18T10:08:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T03:19:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Rendiconti Lincei. Scienze Fisiche e Naturali","date":"2026-02-17T20:57:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"rendiconti-lincei-scienze-fisiche-e-naturali","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lync","sideBox":"Learn more about [Rendiconti Lincei. Scienze Fisiche e Naturali](http://link.springer.com/journal/12208)","snPcode":"12210","submissionUrl":"https://submission.nature.com/new-submission/12210/3","title":"Rendiconti Lincei. Scienze Fisiche e Naturali","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f46f243f-99af-47f5-bded-0c23e843a87a","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-27T13:18:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 13:18:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8904298","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8904298","identity":"rs-8904298","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}