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Satellite-based interferometric synthetic aperture radar (InSAR) is an invaluable technique in the detection and monitoring of changes on the surface of the earth. Its high spatial coverage, weather friendly and remote nature are among the advantages of the tool. The multi-temporal differential InSAR (DInSAR) methods in particular estimate the spatio-temporal evolution of deformation by incorporating information from multiple SAR images. Moreover, opportunities from the DInSAR techniques are accompanied by challenges that affect the final outputs. Resolving the inherent ambiguities of interferometric phases, especially in areas with a high spatio-temporal deformation gradient, represents the main challenge. This brings the necessity of quality indices as important DInSAR data processing tools in achieving ultimate processing outcomes. Often such indices are not provided with the deformation products. In this work, we propose four scores associated with (i) measurement points, (ii) dates of time series, (iii) interferograms and (iv) images involved in the processing. These scores are derived from a redundant set of interferograms and are calculated based on the consistency of the unwrapped interferometric phases in the frame of a least-squares adjustment. The scores reflect the occurrence of phase unwrapping errors and represent valuable input for the analysis and exploitation of the DInSAR results. The proposed tools were tested on 432,311 points, 1795 interferograms and 263 Sentinel-1 single look complex images by employing the small baseline technique in the PSI processing chain, PSIG of the geomatics division of the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC). The results illustrate the importance of the scores—mainly in the interpretation of the DInSAR outputs. This research was partially funded by the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), Generalitat de Catalunya—through a grant for the recruitment of early-stage research staff (Ref: 2021FI_B2 00186).

Satellite-based interferometric synthetic aperture radar (InSAR) is an invaluable technique in the detection and monitoring of changes on the surface of the earth. Its high spatial coverage, weather friendly and remote nature are among the advantages of the tool. The multi-temporal differential InSAR (DInSAR) methods in particular estimate the spatio-temporal evolution of deformation by incorporating information from multiple SAR images. Moreover, opportunities from the DIn-SAR techniques are accompanied by challenges that affect the final outputs. Resolving the inherent ambiguities of interferometric phases, especially in areas with a high spatio-temporal deformation gradient, represents the main challenge. This brings the necessity of quality indices as important DInSAR data processing tools in achieving ultimate processing outcomes. Often such indices are not provided with the deformation products. In this work, we propose four scores associated with (i) measurement points, (ii) dates of time series, (iii) interferograms and (iv) images involved in the processing. These scores are derived from a redundant set of interferograms and are calculated based on the consistency of the unwrapped interferometric phases in the frame of a least-squares adjustment. The scores reflect the occurrence of phase unwrapping errors and represent valuable input for the analysis and exploitation of the DInSAR results. The proposed tools were tested on 432,311 points, 1795 interferograms and 263 Sentinel-1 single look complex images by employing the small baseline technique in the PSI processing chain, PSIG of the geomatics division of the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC). The results illustrate the importance of the scores—mainly in the interpretation of the DInSAR outputs. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Geoscience and Remote Sensing Society (GRSS);The Institute of Electrical and Electronics Engineers (IEEE), 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 -- 22 July 2018 through 27 July 2018 -- -- 141934, Multi-temporal differential synthetic aperture radar interferometry techniques are well-developed and beneficial tools for deformation analysis using remotely sensed images. The primary objective of the study is to investigate the capability and the performance of the new generation SAR system Sentinel-1 data to detect and monitor surface deformation. We combined open source tools of Sentinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS) for data processing. The results highlight the recent surface displacement rates over the Istanbul city. The deformation rates reach up to approximately 8 mm/yr over a 4 year period. © 2018 IEEE.

Wide-swath satellite missions with short revisit times, such as Sentinel-1 and NISAR, provide an unprecedented wealth of interferometric time series and open new opportunities for systematic monitoring of the Earth surface. The process-ing of the emerging Big Data with the state-of-the-art InSAR time series analysis techniques is, however, computa-tionally challenging. A new demand has emerged for the analysis of the fast growing data volumes specifically for systematic near real-time (NRT) monitoring of the Earth surface. We have addressed this demand by the proposal of two efficient alternative estimators for NRT processing of the emerging Big Data in [1, 2]. In this contribution, a hybrid approach based on the proposed estimators is introduced and applied in efficient wide area processing of two-year archive of Sentinel-1 data over eastern part of the Trans-Mexican Volcanic Belt.

Wide-swath satellite missions with short revisit times, such as Sentinel-1 and the planned NISAR and Tandem-L, provide an unprecedented wealth of interferometric time series and open new opportunities for systematic monitoring of the Earth surface. The processing of the emerging Big Data with the state-of-the-art InSAR time series analysis techniques is, however, challenging. This contribution introduces a novel approach, named Sequential Estimator, for efficient estimation of the interferometric phase from the long InSAR time series. The algorithm uses recursive estimation and analysis of the data covariance matrix via division of the data into small batches, followed by compression of the data batches. From each compressed data batch artificial interferograms are formed, resulting in a strong data reduction. This scheme avoids the necessity of re-processing the entire data stack at the face of each new acquisition. It is shown that the proposed estimator introduces negligible degradation compared to the Cramer-Rao Lower Bound. The estimator may therefore be adapted for high-precision Near-Real-Time processing of InSAR and accommodate the conversion of InSAR from an off-line to a monitoring geodetic tool. The performance of the Sequential Estimator is compared to the state-of-the-art techniques via simulations and application to Sentinel-1 data.

We studied the Yellowstone caldera geological unrest between 1977 and 2010 by investigating temporal changes in differential Interferometric Synthetic Aperture Radar (InSAR), precise spirit leveling and gravity measurements. The analysis of the 1992–2010 displacement time series, retrieved by applying the SBAS InSAR technique, allowed the identification of three areas of deformation: (i) the Mallard Lake (ML) and Sour Creek (SC) resurgent domes, (ii) a region close to the Northern Caldera Rim (NCR), and (iii) the eastern Snake River Plain (SRP). While the eastern SRP shows a signal related to tectonic deformation, the other two regions are influenced by the caldera unrest. We removed the tectonic signal from the InSAR displacements, and we modeled the InSAR, leveling, and gravity measurements to retrieve the best fitting source parameters. Our findings confirmed the existence of different distinct sources, beneath the brittle-ductile transition zone, which have been intermittently active during the last three decades. Moreover, we interpreted our results in the light of existing seismic tomography studies. Concerning the SC dome, we highlighted the role of hydrothermal fluids as the driving force behind the 1977–1983 uplift; since 1983–1993 the deformation source transformed into a deeper one with a higher magmatic component. Furthermore, our results support the magmatic nature of the deformation source beneath ML dome for the overall investigated period. Finally, the uplift at NCR is interpreted as magma accumulation, while its subsidence could either be the result of fluids migration outside the caldera or the gravitational adjustment of the source from a spherical to a sill-like geometry.

Subsidence is a major consequence of underground mining. Over 1000 underground coal mines are operating in the United States and affect a surface area of more than 1 million hectares. Surface subsidence associated with underground coal mining is expected to remain a major environmental and engineering issue. The comprehensive subsidence engineering and management to characterize the nature, extent, and magnitude of expected and actual surface displacements are developed while simultaneously identifying subsidence impacts with high risk levels and/or severe consequences. This is a difficult task and one that is currently limited by shortcoming in data collection and assessment. Differential Interferometric Synthetic Aperture Radar (DInSAR) is a growing technology in surface displacement monitoring and has the potential to improve the efficiency and accuracy of subsidence monitoring by significantly increasing the quantity and accuracy of measurement data.