Conference Agenda

Persistent Scatterer Interferometry (PSI) is a consolidated active remote sensing technique to measure and monitor land deformation. The technique has experienced an intense development in the last 25 years. PSI techniques use large stacks of SAR images that cover a given observation period. The outcome of any PSI processing is a cloud of geocoded measurement points that contain the estimated deformation time series over the observation period. If the analysed area is wide, the corresponding point cloud can be huge. In these cases, the potential users often experience problem in analysing such huge point clouds, and this can limit the PSI exploitation. In this paper we present a set of products that address specific application needs or that offer higher-level products with respect to the standard PSI products, which can facilitate the interpretation and exploitation of the PSI results.

Interferometric SAR (InSAR) is a well-established remote sensing technique to measure and monitor land deformation. We focus in this paper on Persistent Scatterer Interferometry (PSI) techniques based on large stacks of SAR images. Several PSI approached have been proposed in the last three decades, see for a review Crosetto et al. (2016). In this paper, we describe an approach the is based on the direct integration of the interferometric phases (back-to-back approach).

The Persistent Scatterer Interferometry (PSI) is a multi-temporal InSAR approach that allows to monitor displacement time series of the Earth's surface. The method identifies and analyzes Persistent Scatterers (PSs) which are phase stable scattering points which dominate the backscatter of their resolution cell. Standard PSI techniques only identify and analyze PSs which are coherent throughout the whole considered SAR time series. However, PSs can fade, appear or be occluded during the time series, forming so called Temporary PSs (TPSs), which should be integrated into the PSI to establish optimal measurement point networks. Previous research has proposed methods to integrate such TPSs into the PSI, however these were exclusively evaluated for construction-related TPSs. In this work, we evaluate the performance of a TPS integration method in handling recurringly occured PSs, and compare the performance of individual components of the algorithm against alternative methods. We evaluate the methods using simulated TPSs with temporal and spatial baseline settings taken from real Sentinel-1 data stacks. Furthermore, we present and discuss the application of the methods to a Sentinel-1 data stack acquired over the Oetztal Alps, which are seasonally covered by snow. We show that the integration of ROPSs significantly increases the measurement pixel density at many locations across the study area, compared to results from the European Ground Motion Service. Even if most of the ROPS did not have identified coherent segments in each covered summer with the current analysis algorithm, their integration leads to a significant information gain compared to standard PSI approaches.

Deformation monitoring in open‑pit mining and tailings‑dam operations is critical for operational safety, yet conventional in situ geodetic techniques provide only sparse, point‑based measurements. InSAR offers many displacement measurements, but its operational uptake is limited by complex workflows and the difficulty of interpreting analysis‑ready products such as EGMS. In cold regions, seasonal data gaps can introduce phase‑unwrapping artefacts that appear as winter‑only displacement offsets of approximately half the Sentinel‑1 wavelength. We propose a semi‑automated workflow to post‑process EGMS displacement time series, including pre‑filtering to identify and remove points affected by phase‑unwrapping errors and subsequent time‑series clustering in either a reduced‑dimensional representation or the full feature space. Cluster selection is automated using heuristic criteria and a custom metric based on temporal homogeneity and consistency. The findings show that the semi‑automatically detected clusters are plausible with regards to a visual interpretation of the EGMS data. The workflow supports improved interpretation of EGMS time series and avoids hard‑coded thresholds or reliance on velocity‑based estimates.

The Inglewood Oil Field, located in the Los Angeles Basin, California, is a major urban hydrocarbon production site with a documented history of ground deformation linked to oil extraction. To assess ongoing deformation and validate previous monitoring results, Interferometric Synthetic Aperture Radar (InSAR) analysis was conducted using Sentinel-1 SAR data processed through the Alaska Satellite Facility’s HyP3 platform and the Miami InSAR Time-series software in Python (MintPy). The study analysed ascending and descending datasets acquired between 2020 and 2025 to derive high-resolution deformation time series and velocity maps. Results reveal a localized deformation pattern characterized by low-magnitude vertical motion, with maximum uplift and subsidence rates of approximately +0.8 cm/yr and –1.6 cm/yr, respectively. Minor horizontal displacements (±1.0 cm/yr) suggest limited lateral strain associated with reservoir compaction and stress redistribution. Compared with previous assessments conducted up to 2024, the current findings indicate a marked reduction in deformation magnitude, implying progressive stabilization of reservoir pressure and improved subsurface management. These results demonstrate the effectiveness of InSAR for long-term monitoring of urban oilfields, providing critical insights into the behaviour and contributing to risk mitigation in densely populated environments.

Ground deformation caused by excessive groundwater extraction has become a major environmental concern in agricultural regions worldwide. Interferometric Synthetic Aperture Radar (InSAR) enables large-scale monitoring of ground deformation. However, its performance often decreases in low-coherence areas affected by vegetation growth and irrigation. In this study, we conducted a comparative evaluation of three multi-temporal SBAS-InSAR processing frameworks, MintPy, LiCSBAS, and SARvey, to assess their consistency in monitoring ground deformation across Golestan Province, Iran, using Sentinel-1 data acquired between 2014 and 2024. The analysis included deformation velocity fields, cross-sectional profiles, and time-series displacements, which were compared with temperature and precipitation variations. All three frameworks identified a pronounced deformation zone in the Gorgan Plain, with maximum line-of-sight deformation rates up to 13 cm/year. Quantitative comparisons showed strong correlations among the frameworks (r = 0.80 to 0.89), confirming their mutual reliability even under low coherence conditions. The time-series analysis revealed clear seasonal deformation patterns, with summer subsidence and winter uplift closely related to hydroclimatic fluctuations. Overall, this study demonstrates that multi-temporal SBAS-InSAR approaches can provide consistent and physically meaningful deformation estimates in challenging agricultural environments, offering valuable insights for subsidence monitoring and water resource management.