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SAR Interferometry: Methods and Applications for Earth Science and Environmental Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 28374

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Dr. Pasquale Imperatore

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National Research Council of Italy (CNR), Institute for Electromagnetic Sensing of the Environment (IREA), Naples, Italy
Interests: SAR processing; SAR interferometry; SAR calibration; SAR modeling; electromagnetic scattering; random layered media; parallel algorithms; high performance computing
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Dr. Eugenio Sansosti

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National Research Council of Italy (CNR), Institute for Electromagnetic Sensing of the Environment (IREA), Via Diocleziano 328, 80124 Napoli, Italy
Interests: remote sensing; Synthetic Aperture Radar; InSAR; ground deformation; radar signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Earth changes constantly over a wide range of temporal and spatial scales as a result of natural processes and direct human activities. Within this scenario, Synthetic Aperture Radar (SAR) Interferometry is a mature technology that provides a unique way to resolve spatial and temporal characteristics of the Earth’s surface deformation, with application to a plethora of natural and anthropogenic processes.

In the last decades, Earth Observation platforms with enhanced SAR sensors have rapidly evolved, thus being able to acquire information in different portions of the microwave spectrum, with improved spatial and temporal coverage. At the same time, refined interferometric SAR (InSAR) processing methodologies are able to provide a wealth of information of interest for a broader science community.

This special issue aims at highlighting recent advancements, developments and applications in InSAR methodologies, including applications to geoscience investigations and environmental monitoring. We solicit papers describing challenging conceptual and practical problems for Earth observation and monitoring.

Potential topics include (but are not limited to) the following:

Innovative InSAR algorithms and processing methods
Phase unwrapping and multi-channel phase unwrapping methods
Processing techniques for mitigating atmospheric artefacts
SAR interferometry with New-Generation and Forthcoming Spaceborne Sensors
Airborne and Ground-Based SAR interferometry
Advances in understanding of ground deformation processes (both natural and anthropogenic)
InSAR applications to geomorphology, hydrology, glaciology and geophysical processes
Deformation monitoring of Infrastructures (e.g. buildings, dams, engineered slopes, bridges)
Parallel algorithms and High-Performance Computing architectures for SAR data processing

Dr. Pasquale Imperatore
Dr. Eugenio Sansosti
Guest Editors

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Keywords

Synthetic Aperture Radar (SAR)
InSAR
SAR processing
Phase unwrapping
Ground deformation
High resolution InSAR techniques
Mapping ground deformation
Geophysical processes

Published Papers (8 papers)

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Research

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16 pages, 4282 KiB

Open AccessArticle

Statistically-Based Trend Analysis of MTInSAR Displacement Time Series

by Fabio Bovenga, Guido Pasquariello and Alberto Refice

Remote Sens. 2021, 13(12), 2302; https://doi.org/10.3390/rs13122302 - 11 Jun 2021

Cited by 10 | Viewed by 2556

Abstract

Current multi-temporal interferometric Synthetic Aperture Radar (MTInSAR) datasets cover long time periods with regular temporal sampling. This allows high-rate and non-linear trends to be observed, which typically characterize pre-failure warning signals. In order to fully exploit the content of MTInSAR products, methods are needed for the automatic identification of relevant changes along displacement time series and the classification of the targets on the ground according to their kinematic regime. This work reviews some of the classical procedures for model ranking, based on statistical indices, which are applied to the characterization of MTInSAR displacement time series, and introduces a new quality index based on the Fisher distribution. Then, we propose a procedure to recognize automatically the minimum number of parameters needed to model a given time series reliably within a predefined confidence level. The method, though general, is explored here for polynomial models, which can be used in particular to approximate satisfactorily and with computational efficiency the piecewise linear trends that are generally used to model warning signals preceding the failure of natural and artificial structures. The algorithm performance is evaluated under simulated scenarios. Finally, the proposed procedure is also demonstrated on displacement time series derived by the processing of Sentinel-1 data. Full article

(This article belongs to the Special Issue SAR Interferometry: Methods and Applications for Earth Science and Environmental Monitoring)

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19 pages, 32130 KiB

Open AccessArticle

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

by Jun Hu, Qiaoqiao Ge, Jihong Liu, Wenyan Yang, Zhigui Du and Lehe He

Remote Sens. 2021, 13(10), 2006; https://doi.org/10.3390/rs13102006 - 20 May 2021

Cited by 6 | Viewed by 2814

Abstract

The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series. Full article

(This article belongs to the Special Issue SAR Interferometry: Methods and Applications for Earth Science and Environmental Monitoring)

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22 pages, 3230 KiB

Open AccessArticle

Multithreading Based Parallel Processing for Image Geometric Coregistration in SAR Interferometry

by Pasquale Imperatore and Eugenio Sansosti

Remote Sens. 2021, 13(10), 1963; https://doi.org/10.3390/rs13101963 - 18 May 2021

Cited by 4 | Viewed by 2580

Abstract

Within the framework of multi-temporal Synthetic Aperture Radar (SAR) interferometric processing, image coregistration is a fundamental operation that might be extremely time-consuming. This paper explores the possibility of addressing fast and accurate SAR image geometric coregistration, with sub-pixel accuracy and in the presence of a complex 3-D object scene, by exploiting the parallelism offered by shared-memory architectures. An efficient and scalable processor is proposed by designing a parallel algorithm incorporating thread-level parallelism for solving the inherent computationally intensive problem. The adopted functional scheme is first mathematically framed and then investigated in detail in terms of its computational structures. Subsequently, a parallel version of the algorithm is designed, according to a fork-join model, by suitably taking into account the granularity of the decomposition, load-balancing, and different scheduling strategies. The developed parallel algorithm implements parallelism at the thread-level by using OpenMP (Open Multi-Processing) and it is specifically targeted at shared-memory multiprocessors. The parallel performance of the implemented multithreading-based SAR image coregistration prototype processor is experimentally investigated and quantitatively assessed by processing high-resolution X-band COSMO-SkyMed SAR data and using two different multicore architectures. The effectiveness of the developed multithreaded prototype solution in fully benefitting from the computing power offered by multicore processors has successfully been demonstrated via a suitable experimental performance analysis conducted in terms of parallel speedup and efficiency. The demonstrated scalable performance and portability of the developed parallel processor confirm its potential for operational use in the interferometric SAR data processing at large scales. Full article

(This article belongs to the Special Issue SAR Interferometry: Methods and Applications for Earth Science and Environmental Monitoring)

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12 pages, 401 KiB

Open AccessArticle

Explanation of InSAR Phase Disturbances by Seasonal Characteristics of Soil and Vegetation

by Rogier Westerhoff and Moira Steyn-Ross

Remote Sens. 2020, 12(18), 3029; https://doi.org/10.3390/rs12183029 - 17 Sep 2020

Cited by 5 | Viewed by 3871

Abstract

Seasonal phase disturbances in satellite Interferometric Synthetic Aperture Radar (InSAR) measurements have been reported in other studies to suggest sub-centimetre land surface terrain motion. These have been interpreted in various ways because they correlate with multiple other (sub-)seasonal signatures of, e.g., clay swelling/shrinkage and groundwater level. Recent microwave radar studies mention the occurrence of phase disturbances in different soil types and soil moisture. This study further explored this topic by modeling phase disturbances caused by both soil and vegetation surface characteristics and aimed to interpret what their possible effects on InSAR-interpreted terrain motion is. Our models, based on fundamental microwave reflection and transmission theory, found phase disturbances caused by seasonal variation of soil and vegetation that have the same magnitude as interpreted seasonal land movement in earlier InSAR studies. We showed that small, temporal differences in soil moisture and vegetation can lead to relatively large phase disturbances in InSAR measurements. These disturbances are a result of waves having to comply with boundary conditions at the interface between media with different dielectric properties. The findings of this study explain the seasonal variations found in other InSAR studies and will therefore bring new insights and alternative explanations to help improve interpretation of InSAR-derived seasonal terrain motion. Full article

(This article belongs to the Special Issue SAR Interferometry: Methods and Applications for Earth Science and Environmental Monitoring)

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20 pages, 7457 KiB

Open AccessArticle

Integrating RELAX with PS-InSAR Technique to Improve Identification of Persistent Scatterers for Land Subsidence Monitoring

by Di Zhou, Anita Simic-Milas, Jie Yu, Lin Zhu, Beibei Chen and Nijiati Muhetaer

Remote Sens. 2020, 12(17), 2730; https://doi.org/10.3390/rs12172730 - 24 Aug 2020

Cited by 6 | Viewed by 3255

Abstract

Identifying Persistent Scatterers (PSs) is one of the key processing steps of the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique. The number, density, and reliability of identified PSs directly affect the monitoring accuracy of land subsidence, especially in higher density urban environments. As a result of the side-looking viewing geometry of SAR, the layover effect poses a major challenge to the PS identification. This research proposes joint modeling of the PS-InSAR technique and RELAX algorithm for SAR tomography (PS-InSAR+RELAX) to detect single and double scatterers and to improve the identification and reliability of PSs. It has been demonstrated that RELAX improves separation of the scatterers when compared to two other spectral analysis methods for SAR tomography, Beam-Forming (BF) and Singular Value Decomposition (SVD). RELAX exhibits the least noise when the number of baseline changes from 15 to 30, and it can separate the scatterers at a lower Normal-Slant-Range (NSR) height than the two other methods. As RELAX can better identify, separate, and then filter out layover scatterers, the number and density of PSs identified by PS-InSAR+RELAX is reduced and visually simplified, suggesting that the method can effectively reduce the influence of the layover effect on the PS identification. Also, the PSs identified by PS-InSAR+RELAX are more coherent than those identified by the traditional PS-InSAR technique. The proposed technique has been applied to Sentinel-1A data acquired from 2014 to 2016, to monitor land subsidence in the city of Beijing, China. When evaluated against the leveling measurements, PS-InSAR+RELAX performs better than the traditional PS-InSAR technique, with the correlation coefficients (r) of r = 0.98 and r = 0.95, respectively. Full article

(This article belongs to the Special Issue SAR Interferometry: Methods and Applications for Earth Science and Environmental Monitoring)

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14 pages, 4735 KiB

Open AccessArticle

A Framework for Correcting Ionospheric Artifacts and Atmospheric Effects to Generate High Accuracy InSAR DEM

by Zhiwei Liu, Cui Zhou, Haiqiang Fu, Jianjun Zhu and Tingying Zuo

Remote Sens. 2020, 12(2), 318; https://doi.org/10.3390/rs12020318 - 18 Jan 2020

Cited by 7 | Viewed by 3122

Abstract

Repeat-pass interferometric synthetic aperture radar is a well-established technology for generating digital elevation models (DEMs). However, the interferogram usually has ionospheric and atmospheric effects, which reduces the DEM accuracy. In this paper, by introducing dual-polarization interferograms, a new approach is proposed to mitigate the ionospheric and atmospheric errors of the interferometric synthetic aperture radar (InSAR) data. The proposed method consists of two parts. First, the range split-spectrum method is applied to compensate for the ionospheric artifacts. Then, a multiresolution correlation analysis between dual-polarization InSAR interferograms is employed to remove the identical atmospheric phases, since the atmospheric delay is independent of SAR polarizations. The corrected interferogram can be used for DEM extraction. Validation experiments, using the ALOS-1 PALSAR interferometric pairs covering the study areas in Hawaii and Lebanon of the U.S.A., show that the proposed method can effectively reduce the ionospheric artifacts and atmospheric effects, and improve the accuracy of the InSAR-derived DEMs by 64.9% and 31.7% for the study sites in Hawaii and Lebanon of the U.S.A., respectively, compared with traditional correction methods. In addition, the assessment of the resulting DEMs also includes comparisons with the high-precision Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) altimetry data. The results show that the selection of reference data will not affect the validation results. Full article

(This article belongs to the Special Issue SAR Interferometry: Methods and Applications for Earth Science and Environmental Monitoring)

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Review

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35 pages, 6512 KiB

Open AccessReview

High Performance Computing in Satellite SAR Interferometry: A Critical Perspective

by Pasquale Imperatore, Antonio Pepe and Eugenio Sansosti

Remote Sens. 2021, 13(23), 4756; https://doi.org/10.3390/rs13234756 - 24 Nov 2021

Cited by 11 | Viewed by 5051

Abstract

Synthetic aperture radar (SAR) interferometry has rapidly evolved in the last decade and can be considered today as a mature technology, which incorporates computationally intensive and data-intensive tasks. In this paper, a perspective on the state-of-the-art of high performance computing (HPC) methodologies applied to spaceborne SAR interferometry (InSAR) is presented, and the different parallel algorithms for interferometric processing of SAR data are critically discussed at different levels. Emphasis is placed on the key processing steps, which typically occur in the interferometric techniques, categorized according to their computational relevance. Existing implementations of the different InSAR stages using diverse parallel strategies and architectures are examined and their performance discussed. Furthermore, some InSAR computational schemes selected in the literature are analyzed at the level of the entire processing chain, thus emphasizing their potentialities and limitations. Therefore, the survey focuses on the inherent computational approaches enabling large-scale interferometric SAR processing, thus offering insight into some open issues, and outlining future trends in the field. Full article

(This article belongs to the Special Issue SAR Interferometry: Methods and Applications for Earth Science and Environmental Monitoring)

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Other

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11 pages, 40437 KiB

Open AccessLetter

Estimation of Deformation Intensity above a Flooded Potash Mine Near Berezniki (Perm Krai, Russia) with SAR Interferometry

by Sergey Samsonov and Alexandr Baryakh

Remote Sens. 2020, 12(19), 3215; https://doi.org/10.3390/rs12193215 - 02 Oct 2020

Cited by 14 | Viewed by 3251

Abstract

In this study we used RADARSAT-2 and Sentinel-1 Synthetic Aperture Radar data for measuring subsidence above a flooded potash mine, which is almost entirely located within the city of Berezniki (Perm Krai, Russia), population 150,000. This area has experienced very fast subsidence since October 2006 when the integrity of the Berezniki-1 mine was compromised, resulting in water intrusion, subsequent flooding and closure of the mine. Due to the ongoing dissolution of carnallite, subsidence in this region is expected to continue in the foreseeable future. In addition to rapid subsidence, at least five sinkholes have formed in the region, with the largest being 440 × 320 m. We observed ground subsidence during the period October 2011–April 2014 (RADARSAT-2) with a vertical rate up to 14 cm/year and horizontal rate up to 10 cm/year; during the period July 2016–June 2020 (Sentinel-1) with a vertical rate up to 17 cm/year. Our results were validated by precise leveling, with a coefficient of correlation of 0.75. Subsidence faster than 17 cm/year observed by precise leveling was not resolvable with Differential Interferometric Synthetic Aperture Radar (DInSAR). Our results show the complementary nature of ground-based and space-borne measurement techniques. The precise leveling captures subsidence along profile lines with high precision but lower temporal resolution, while DInSAR captures subsidence with high spatial and temporal resolutions but with lower precision. DInSAR is also significantly affected by decorrelation outside of urban areas. An important advantage of our methodology is the ability to measure the horizontal east component of the ground deformation when both, ascending and descending, data are available. This measurement directly characterizes the level of anthropogenic load on buildings and infrastructure. We recommend continuing monitoring subsidence using both measurement techniques, which can also be complemented by continuous Global Navigation Satellite System (GNSS). Full article

(This article belongs to the Special Issue SAR Interferometry: Methods and Applications for Earth Science and Environmental Monitoring)

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