13 citations as recorded by crossref.

1. A Workflow Based on SNAP–StaMPS Open-Source Tools and GNSS Data for PSI-Based Ground Deformation Using Dual-Orbit Sentinel-1 Data: Accuracy Assessment with Error Propagation Analysis F. Mancini et al. 10.3390/rs13040753
2. Contribution of anthropogenic consolidation processes to subsidence phenomena from multi-temporal DInSAR: a GIS-based approach F. Grassi et al. 10.1080/15481603.2022.2143683
3. Review on the Use of Satellite-Based Radar Interferometry for Monitoring Mining Subsidence in Urban Areas and Demographic Indicators Assessment A. Radutu & M. Vlad-Sandru 10.2478/minrv-2023-0004
4. 3D geological modelling of the Bologna urban area (Italy) S. Giacomelli et al. 10.1016/j.enggeo.2023.107242
5. Mapping of Mean Deformation Rates Based on APS-Corrected InSAR Data Using Unsupervised Clustering Algorithms M. Khalili et al. 10.3390/rs15020529
6. Sentinel-1 Data for Underground Processes Recognition in Bucharest City, Romania A. Radutu et al. 10.3390/rs12244054
7. Technical Challenges for Multi-Temporal and Multi-Sensor Image Processing Surveyed by UAV for Mapping and Monitoring in Precision Agriculture A. Lambertini et al. 10.3390/rs14194954
8. Different processes affecting long-term Ravenna coastal drainage basins (Italy): implications for water management L. Soboyejo et al. 10.1007/s12665-021-09774-5
9. Review of Works Combining GNSS and InSAR in Europe M. Del Soldato et al. 10.3390/rs13091684
10. Analysis of the Periodic Component of Vertical Land Motion in the Po Delta (Northern Italy) by GNSS and Hydrological Data E. Vitagliano et al. 10.3390/rs14051126
11. Long-term spatiotemporal evolution of land subsidence in the urban area of Bologna, Italy A. Zuccarini et al. 10.1007/s10064-023-03517-5
12. The Need for a Standardized Methodology for Quantitative Assessment of Natural and Anthropogenic Land Subsidence: The Agosta (Italy) Gas Field Case V. Comerci & E. Vittori 10.3390/rs11101178
13. Poroelasticity and Fluid Flow Modeling for the 2012 Emilia-Romagna Earthquakes: Hints from GPS and InSAR Data M. Nespoli et al. 10.1155/2018/4160570