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

Deadline for manuscript submissions: closed (15 June 2024) | Viewed by 16803

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Special Issue Editors

Dr. Yasser Maghsoudi

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COMET, School of Earth and Environment, University of Leeds, LS2 9JT, UK
Interests: InSAR; geohazards; deformation

Prof. Dr. Mahdi Motagh

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Guest Editor

Department of Geodesy, GFZ German Research Center for Geosciences, Potsdam, and Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, Hannover, Germany
Interests: radar remote sensing for geoscience and engineering applications; multitemporal InSAR time-series techniques; geophysical and numerical modeling of deformation processes
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Dr. Francesca Cigna

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Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Via del Fosso del Cavaliere 100, 00133 Rome, Italy
Interests: landscape evolution; geophysical hazards; archaeology; cultural heritage; remote sensing; earth observation; InSAR; landslides; land subsidence; ground instability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Over the past two decades, SAR/InSAR technology has become a powerful yet inexpensive tool in many remote sensing applications, such as disaster management, damage assessment, permafrost, groundwater and hydrocarbon extraction, natural and anthropogenic hazards, and wetland water level observations.

Nowadays, thanks to a larger number of SAR sensors, including the high-resolution German TerraSAR-X/TanDEM-X, the Italian COSMO-SkyMed First and Second Generation, and the European Commission’s Copernicus Sentinel-1 constellations, a massive volume of high-quality SAR observations with spatial (1–15 m) and temporal resolutions (1–16 days) has become available.

Despite this unique opportunity, the huge amount of SAR data and the associated complexity make the processing of SAR and InSAR data a challenging task.

This Special Issue, “Analysis of SAR/InSAR Data in Geoscience”, invites contributions on reviewing the current progress and highlighting the latest advances in SAR/InSAR processing techniques in various geoscience applications. This provides an outlet for state-of-the-art SAR/InSAR data processing techniques in a broad and diverse range of Earth science processes.

This Special Issue will focus on:

The exploration of new techniques and algorithms as well as the assessment of existing methods for SAR data processing.
Recent advances in SAR/InSAR theory and methodology for deformation monitoring.
Innovative geoscience applications of SAR/InSAR data.
Cloud/grip processing approaches/infrastructure for big data analysis.
Using machine learning and explainable AI in SAR data analysis.

Dr. Yasser Maghsoudi
Prof. Dr. Mahdi Motagh
Dr. Francesca Cigna
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

SAR remote sensing
Interferometric synthetic aperture radar (InSAR)
deformation monitoring
time series analysis
cloud processing

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Published Papers (10 papers)

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Research

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18 pages, 18749 KiB

Open AccessArticle

Nonlinear Evolutionary Pattern Recognition of Land Subsidence in the Beijing Plain

by Mingyuan Lyu, Xiaojuan Li, Yinghai Ke, Jiyi Jiang, Zhenjun Sun, Lin Zhu, Lin Guo, Zhihe Xu, Panke Tang, Huili Gong and Lan Wang

Remote Sens. 2024, 16(15), 2829; https://doi.org/10.3390/rs16152829 - 1 Aug 2024

Viewed by 373

Abstract

Beijing is a city on the North China Plain with severe land subsidence. In recent years, Beijing has implemented effective measures to control land subsidence. Since this implementation, the development of time-series land subsidence in Beijing has slowed and has shown nonlinearity. Most previous studies have focused on the linear evolution of land subsidence; the nonlinear evolutionary patterns of land subsidence require further discussion. Therefore, we aimed to identify the evolution of land subsidence in Beijing, based on Envisat ASAR and Radarsat-2 images from 2003 to 2020, using permanent scatterer interferometric synthetic aperture radar (PS-InSAR) and cubic curve polynomial fitting methods. The dates of the extreme and inflection points were identified from the polynomial coefficients. From 2003 to 2020, the subsidence rate reached 138.55 mm/year, and the area with a subsidence rate > 15 mm/year reached 1688.81 km². The cubic polynomials fit the time-series deformation well, with R² ranging from 0.86 to 0.99 and the RMSE ranging from 1.97 to 60.28 mm. Furthermore, the subsidence rate at 96.64% of permanent scatterer (PS) points first increased and then decreased. The subsidence rate at 86.58% of the PS points began to decrease from 2010 to 2015; whereas the subsidence rate at 30.51% of the PS point reached a maximum between 2015 and 2019 and then decreased. The cumulative settlement continued to increase at 69.49% of the PS points. These findings imply that groundwater levels are highly correlated with the temporal evolution of subsidence in areas with pattern D (Vs+-, S+), with increasing and then decelerating rates and increasing amounts. In regions with a thickness of compressible clay layer over 210 m, subsidence follows pattern E (Vs+, S+), with increasing rates and amounts. Fractures such as the Gaoliying and Sunhe fractures significantly influence the spatial distribution of subsidence patterns, showing distinct differences on either side. Near the Global Resort Station, pattern E (Vs+, S+) intensifies in subsidence, potentially due to factors like land use changes and construction activities. Full article

(This article belongs to the Special Issue Analysis of SAR/InSAR Data in Geoscience)

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19 pages, 8427 KiB

Open AccessArticle

A Persistent Scatterer Point Selection Method for Deformation Monitoring of Under-Construction Cross-Sea Bridges Using Statistical Theory and GMM-EM Algorithm

by Jianyong Li, Zidong Xu, Xuedong Zhang, Weiyu Ma and Shuguang He

Remote Sens. 2024, 16(12), 2197; https://doi.org/10.3390/rs16122197 - 17 Jun 2024

Viewed by 386

Abstract

Using traditional algorithms to identify persistent scatterer (PS) points is challenging during bridge construction because of short-term changes at construction sites, such as earthworks, as well as the erection and dismantling of temporary structures. To address this issue, this study proposes a PS point selection method based on statistical theory and Gaussian Mixture Model-Expectation Maximization (GMM-EM) algorithm. This method adopts amplitude information as an incoherence evaluation indicator. Furthermore, the statistical median of the amplitude dispersion index and amplitude mean is screened twice to extract a set of candidate points, including PS points that exhibit stable backscattering over long durations. Temporal coherence is simultaneously used as the coherence evaluation indicator. Another candidate point set is obtained by extracting high-coherence PS points using the GMM-EM algorithm. These sets of candidate points are then combined to obtain a final PS points set. In the experiment, the deformation monitoring of the under-construction Shenzhen-Zhongshan Cross-Sea Bridge in China was selected as a case study, with 28 Sentinel-1A images used as the data source for PS selection and deformation information extraction. The results show that the proposed method enhanced the density and quality of PS points on the under-construction cross-sea bridge compared to existing PS selection methods, thus offering higher reliability. Deformation analysis further revealed fluctuating deformation trends at characteristic points of the Shenzhen-Zhongshan Cross-Sea Bridge, indicating the occurrence of elastic deformation during its construction. Full article

(This article belongs to the Special Issue Analysis of SAR/InSAR Data in Geoscience)

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18 pages, 7722 KiB

Open AccessArticle

Artisanal Mining River Dredge Detection Using SAR: A Method Comparison

by Marissa A. Alessi, Peter G. Chirico and Marco Millones

Remote Sens. 2023, 15(24), 5701; https://doi.org/10.3390/rs15245701 - 12 Dec 2023

Cited by 1 | Viewed by 1165

Abstract

Challenges exist in monitoring artisanal and small-scale mining (ASM) activities, given their dynamic and often informal nature. ASM takes form through various techniques and scales, including riverine dredging, which often targets the abundant alluvial gold deposits in South America. Remote sensing offers a solution to improve data collection, regulation, and monitoring of the more mobile and elusive ASM activities and their impacts. Mapping ASM riverine dredges using Synthetic Aperture Radar (SAR) is one of the application areas least explored. Three semi-automated detection approaches using Sentinel-1 SAR are compared on their ability to identify dredges with minimal false positives. The methods are: (i) Search for Unidentified Maritime Objects (SUMO), an established method for large ocean ship detection; and two techniques specifically developed for riverine environments that are introduced in this paper: (ii) a local detection method; and (iii) a global threshold method. A visual interpretation of SAR data with the inclusion of optical high-resolution data are used to generate a validation dataset. Results show it is possible to semi-automatically detect riverine dredge using SAR and that a local detection method provides the best balance between sensitivity and precision and has the lowest risk of error. Future improvements may consider further automation, more discriminatory variables, and analyzing the methods in different environments and at higher spatial resolutions. Full article

(This article belongs to the Special Issue Analysis of SAR/InSAR Data in Geoscience)

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19 pages, 14538 KiB

Open AccessArticle

LOS Deformation Correction Method for DInSAR in Mining Areas by Fusing Ground Data without Control Points

by Jingyu Li, Yueguan Yan and Jinchi Cai

Remote Sens. 2023, 15(19), 4862; https://doi.org/10.3390/rs15194862 - 7 Oct 2023

Cited by 2 | Viewed by 1012

Abstract

The traditional leveling, total station, and global navigation satellite system (GNSS) and the new differential interferometric synthetic aperture radar (DInSAR) and terrestrial laser scanning (TLS) systems have their own advantages and limitations in the deformation monitoring of mining areas. It is difficult to obtain accurate deformation information only using single-source measurement data. In this study, we propose an LOS deformation correction method for DInSAR in mining areas by fusing ground data without control points. Based on free space data, small deformations at the edges of mining influence areas accurately obtained using DInSAR. By combining leveling/GNSS and TLS methods, it was possible to obtain large deformations in central areas without the need for control points located outside the mining influence range. For overcoming the non-uniform coordinates of the “space–ground” data and the limited overlap of the effective measurement ranges, the subsidence prediction model was employed to assist in its fusion. In addition, in LOS deformation correction, we retained the non-full cycle phase of DInSAR and replaced the full cycle phase with the one from the data fusion. Engineering experiments have shown that the correction results preserve the differences in the LOS deformations at the edge areas of the mine influence range, and they recover the lost LOS deformations at the center areas. Using the difference in the LOS deformation before and after correction as the verification indicator, the maximum absolute value of the errors after correction was 143 mm, which was approximately 6.4% of the maximum LOS deformation. In addition, there were still two errors that were large (−112 mm and −89 mm, respectively), and the absolute values of errors were not more than 75 mm. For all errors, the mean absolute value was 36 mm. Compared with 399 mm before correction, the error was reduced by 91%. This study provides technical support and theoretical reference for deformation monitoring and control in mining areas. Full article

(This article belongs to the Special Issue Analysis of SAR/InSAR Data in Geoscience)

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23 pages, 24483 KiB

Open AccessArticle

Adaptive High Coherence Temporal Subsets SBAS-InSAR in Tropical Peatlands Degradation Monitoring

by Xiaohan Zheng, Chao Wang, Yixian Tang, Hong Zhang, Tianyang Li, Lichuan Zou and Shaoyang Guan

Remote Sens. 2023, 15(18), 4461; https://doi.org/10.3390/rs15184461 - 11 Sep 2023

Cited by 2 | Viewed by 1151

Abstract

Peatlands in Southeast Asia have been undergoing extensive and rapid degradation in recent years. Interferometric Synthetic Aperture Radar (InSAR) technology has shown excellent performance in monitoring surface deformation. However, due to the characteristics of high vegetation cover and large dynamic changes in peatlands, it is difficult for classical InSAR technology to achieve satisfactory results. Therefore, an adaptive high coherence temporal subsets (HCTSs) small baseline subset (SBAS)-InSAR method is proposed in this paper, which captures the high coherence time range of pixels to establish adaptive temporal subsets and calculates the deformation results in corresponding time intervals, combining with the time-weighted strategy. Ninety Sentinel-1 SAR images (2019–2022) in South Sumatra province were processed based on the proposed method. The results showed that the average deformation rate of peatlands ranged from approximately −567 to 347 mm/year and was affected by fires and the changes in land cover. Besides, the dynamic changes of peatlands’ deformation rate a long time after fires were revealed, and the causes of changes were analyzed. Furthermore, the deformation results of the proposed method observed 2 to 127 times as many measurement points as the SBAS-InSAR method. Pearson’s r (ranged from 0.44 to 0.75) and Root Mean Square Error (ranged from 50 to 75 mm/year) were calculated to verify the reliability of the proposed method. Adaptive HCTSs SBAS-InSAR can be considered an efficient method for peatland degradation monitoring, which provides the foundation for investigating the mechanisms of peatland degradation and monitoring it in broader regions. Full article

(This article belongs to the Special Issue Analysis of SAR/InSAR Data in Geoscience)

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28 pages, 13830 KiB

Open AccessArticle

Evaluation of InSAR Tropospheric Correction by Using Efficient WRF Simulation with ERA5 for Initialization

by Qinghua Liu, Qiming Zeng and Zhiliang Zhang

Remote Sens. 2023, 15(1), 273; https://doi.org/10.3390/rs15010273 - 2 Jan 2023

Cited by 6 | Viewed by 2544

Abstract

The delay caused by the troposphere is one of the major sources of errors limiting the accuracy of InSAR measurements. The tropospheric correction of InSAR measurements is important. The Weather Research and Forecasting (WRF) Model is a state-of-the-art mesoscale numerical weather prediction system designed for atmospheric research applications. It can be applied to InSAR tropospheric correction. Its parameters can be altered according to the requirements of the given application. WRF is usually initialized based on 3 h- or 6 h temporal resolution data in InSAR tropospheric correction studies, a lower temporal resolution compared to ERA5 data. A lower time resolution means a longer integration time for WRF to simulate from the initial time to the target time. Initialization with a higher resolution can shorten the integration time of the simulation theoretically and improve its accuracy. However, an evaluation of the effectiveness of ERA5_WRF for InSAR tropospheric correction is lacking. To evaluate the efficiency of WRF tropospheric correction, we used Reanalysis v5 (ERA5) from the European Centre for Medium-Range Weather Forecasts (ECMWF) for initialization to drive the WRF (ERA5_WRF) for efficient applications in InSAR. Three methods based on global atmospheric models—FNL_WRF (tropospheric correction method based on WRF driven by NCEP FNL), Generic Atmospheric Correction Online Service for InSAR (GACOS), and ERA5—were used to evaluate the corrective effects of ERA5_WRF. The reliability of ERA5_WRF in different scenarios with large tropospheric delay was evaluated from the spatial and temporal perspectives by considering seasonal, topographic, and climatic factors. Its applications in the local space showed that ERA5_WRF could adequately correct tropospheric delay. Benefits include its high-quality data sources and the simulation of WRF, and its application in different seasons had proven superior to other methods in terms of the corrective effects of elevation-related and spatially related delays in summer. By analyzing the data sources and downscaling methods of correction methods and weather conditions of cases, ERA5_WRF had superior performance under the condition of large content and hourly variation of tropospheric delay. Furthermore, WRF showed the potential for tropospheric correction when other higher-quality data appear in the future. Full article

(This article belongs to the Special Issue Analysis of SAR/InSAR Data in Geoscience)

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25 pages, 18521 KiB

Open AccessArticle

Coseismic Deformation and Fault Inversion of the 2017 Jiuzhaigou Ms 7.0 Earthquake: Constraints from Steerable Pyramid and InSAR Observations

by Wenshu Peng, Xuri Huang and Zegen Wang

Remote Sens. 2023, 15(1), 222; https://doi.org/10.3390/rs15010222 - 31 Dec 2022

Cited by 4 | Viewed by 1725

Abstract

The 8 August 2017 Ms 7.0 Jiuzhaigou earthquake was generated in the transition zone between the Tazang fault, Huya fault, and Minjiang fault, all being part of the East Kunlun fault system. In this study, two pairs of SAR (synthetic aperture radar) data from Sentinel-1 satellite were used to derive the surface displacement observations along the satellite line-of-sight (LOS) directions using the differential interferometric SAR (D-InSAR) method. A steerable pyramid filtering method (i.e., a method for a linear multiscale, multidirectional decomposition and filtering technology) was proposed to optimize and enhance the geological features from interferometric image and coseismic deformation field. The 3D deformation was derived under the constraint of the combined D-InSAR and MAI method. The small baseline subset InSAR (SBAS-InSAR) time series method was used to obtain the cumulative deformation across the fault system. Fault slip inversion results from interferogram of InSAR indicate that the 2017 Jiuzhaigou earthquake was dominated by left-lateral slip, the surface movement was dominated by horizontal deformation, the vertical deformation was small, and the coseismic deformation variable in the east–west direction was the largest, with a maximum deformation of 0.2 m to the east and 0.14 m to the west. The maximum slip is about 77 cm, which is located at a depth of 9 km. The moment magnitude obtained by inversion is Mw 6.6, and the seismic fault is the Huya fault. Full article

(This article belongs to the Special Issue Analysis of SAR/InSAR Data in Geoscience)

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20 pages, 5699 KiB

Open AccessArticle

Routine Processing and Automatic Detection of Volcanic Ground Deformation Using Sentinel-1 InSAR Data: Insights from African Volcanoes

by Fabien Albino, Juliet Biggs, Milan Lazecký and Yasser Maghsoudi

Remote Sens. 2022, 14(22), 5703; https://doi.org/10.3390/rs14225703 - 11 Nov 2022

Cited by 9 | Viewed by 2515

Abstract

Since the launch of Sentinel-1 mission, automated processing systems have been developed for near real-time monitoring of ground deformation signals. Here, we perform a regional analysis of 5 years over 64 volcanic centres located along the East African Rift System (EARS). We show that the correction of atmospheric signals for the arid and low-elevation EARS volcanoes is less important than for other volcanic environments. We find that the amplitude of the cumulative displacements exceeds three times the temporal noise of the time series (3

σ

) for 16 of the 64 volcanoes, which includes previously reported deformation signals, and two new ones at Paka and Silali volcanoes. From a 5-year times series, uncertainties in rates of deformation are ∼0.1 cm/yr, whereas uncertainties associated with the choice of reference pixel are typically 0.3–0.6 cm/yr. We fit the time series using simple functional forms and classify seven of the volcano time series as ‘linear’, six as ‘sigmoidal’ and three as ‘hybrid’, enabling us to discriminate between steady deformation and short-term pulses of deformation. This study provides a framework for routine volcano monitoring using InSAR on a continental scale. Here, we focus on Sentinel-1 data from the EARS, but the framework could be expanded to include other satellite systems or global coverage. Full article

(This article belongs to the Special Issue Analysis of SAR/InSAR Data in Geoscience)

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Review

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37 pages, 4141 KiB

Open AccessReview

Error Sources of Interferometric Synthetic Aperture Radar Satellites

by Yen-Yi Wu and Austin Madson

Remote Sens. 2024, 16(2), 354; https://doi.org/10.3390/rs16020354 - 16 Jan 2024

Cited by 1 | Viewed by 2486

Abstract

Interferometric synthetic aperture radar (InSAR) processing techniques have been widely used to derive surface deformation or retrieve terrain elevation. Over the development of the past few decades, most research has mainly focused on its application, new techniques for improved accuracy, or the investigation of a particular error source and its correction method. Therefore, a thorough discussion about each error source and its influence on InSAR-derived products is rarely addressed. Additionally, InSAR is a challenging topic for beginners to learn due to the intricate mathematics and the necessary signal processing knowledge required to grasp the core concepts. This results in the fact that existing papers about InSAR are easy to understand for those with a technical background but difficult for those without. To cope with the two issues, this paper aims to provide an organized, comprehensive, and easily understandable review of the InSAR error budget. In order to assist readers of various backgrounds in comprehending the concepts, we describe the error sources in plain language, use the most fundamental math, offer clear examples, and exhibit numerical and visual comparisons. In this paper, InSAR-related errors are categorized as intrinsic height errors or location-induced errors. Intrinsic height errors are further divided into two subcategories (i.e., systematic and random error). These errors can result in an incorrect number of phase fringes and introduce unwanted phase noise into the output interferograms, respectively. Location-induced errors are the projection errors caused by the slant-ranging attribute of the SAR systems and include foreshortening, layover, and shadow effects. The main focus of this work is on systematic and random error, as well as their effects on InSAR-derived topographic and deformation products. Furthermore, because the effects of systematic and random errors are greatly dependent on radar wavelengths, different bands are utilized for comparison, including L-band, S-band, C-band, and X-band scenarios. As examples, we used the parameters of the upcoming NISAR operation to represent L-band and S-band, ERS-1 and Sentinel-1 to represent C-band, and TerraSAR-X to represent X-band. This paper seeks to bridge this knowledge gap by presenting an approachable exploration of InSAR error sources and their implications. This robust and accessible analysis of the InSAR error budget is especially pertinent as more SAR data products are made available (e.g., NISAR, ICEYE, Capella, Umbra, etc.) and the SAR user-base continues to expand. Finally, a commentary is offered to explore the error sources that were not included in this work, as well as to present our thoughts and conclusions. Full article

(This article belongs to the Special Issue Analysis of SAR/InSAR Data in Geoscience)

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16 pages, 16943 KiB

Open AccessTechnical Note

Spatial-Temporal Changes of Abarkuh Playa Landform from Sentinel-1 Time Series Data

by Sayyed Mohammad Javad Mirzadeh, Shuanggen Jin and Meisam Amani

Remote Sens. 2023, 15(11), 2774; https://doi.org/10.3390/rs15112774 - 26 May 2023

Cited by 1 | Viewed by 1248

Abstract

Playas, as the flattest landforms in semiarid and arid regions, are extremely sensitive to climate changes, such as changes in the hydrologic regime of the landscape. The changes in these landforms due to irrigation, anthropogenic activities, and climate change could be a source of disasters. In this study, we explored the spatial-temporal changes of the Abarkuh Playa in Central Iran using the time series of the Sentinel-1 backscatter dataset in the three scales. Our results showed that the western area of the Abarkuh Playa has been changed to other landforms with different characteristics, which is clear from all backscatter maps. The spatial-temporal analysis of the time series of backscatter data using the independent component analysis and time series of precipitation revealed that the backscatter variations were associated with direct rainfall across the playa and the surface was reacting to changes in the soil moisture content. The results of the power scale showed that the boundary of the playa could successfully be recognized as the oscillating pattern from other landforms in the study area. Moreover, the spatial-temporal analysis of backscatter in the power scale showed that different polarizations could reveal different patterns of surface changes for the playa. Full article

(This article belongs to the Special Issue Analysis of SAR/InSAR Data in Geoscience)

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