scholarly journals Study of Systematic Bias in Measuring Surface Deformation with SAR Interferometry

2020 ◽  
Author(s):  
Homa Ansari ◽  
Francesco De Zan ◽  
Alessandro Parizzi
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Sar Interferometry ◽  
Systematic Bias ◽  
Earth Surface ◽  
Phase Component ◽  
Measuring Surface ◽  
Deformation Velocity ◽  
Temporal Decorrelation ◽  
Efficient Processing

<div>This paper investigates the presence of a new interferometric signal in multilooked Synthetic Aperture Radar (SAR) interferograms which cannot be attributed to atmospheric or earth surface topography changes. The observed signal is short-lived and decays with temporal baseline; however, it is distinct from the stochastic noise usually attributed to temporal decorrelation. The presence of such fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. <br></div><div>The contribution of the mentioned phase component is quantitatively assessed. For short temporal baseline interferograms, we quantify the phase contribution to be in the regime of 5 rad at C-band. The biasing impact on deformation signal retrieval is further evaluated. As an example, exploiting a subset of short temporal baseline interferograms which connects each acquisition with the successive 5 in the time series, a significant bias of -6.5 mm/yr is observed in the estimation of deformation velocity from a four-year Sentinel-1 data stack. A practical solution for mitigation of this physical fading signal is further discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease to -0.24 mm/yr for the Sentinel-1 time series.</div>Based on these analyses, we put forward our recommendations for efficient and accurate deformation signal retrieval from large stacks of multilooked interferograms.

scholarly journals Study of Systematic Bias in Measuring Surface Deformation with SAR Interferometry

2020 ◽  
Author(s):  
Homa Ansari ◽  
Francesco De Zan ◽  
Alessandro Parizzi
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Sar Interferometry ◽  
Systematic Bias ◽  
Earth Surface ◽  
Phase Component ◽  
Measuring Surface ◽  
Deformation Velocity ◽  
Temporal Decorrelation ◽  
Efficient Processing

<div>This paper investigates the presence of a new interferometric signal in multilooked Synthetic Aperture Radar (SAR) interferograms which cannot be attributed to atmospheric or earth surface topography changes. The observed signal is short-lived and decays with temporal baseline; however, it is distinct from the stochastic noise usually attributed to temporal decorrelation. The presence of such fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. <br></div><div>The contribution of the mentioned phase component is quantitatively assessed. For short temporal baseline interferograms, we quantify the phase contribution to be in the regime of 5 rad at C-band. The biasing impact on deformation signal retrieval is further evaluated. As an example, exploiting a subset of short temporal baseline interferograms which connects each acquisition with the successive 5 in the time series, a significant bias of -6.5 mm/yr is observed in the estimation of deformation velocity from a four-year Sentinel-1 data stack. A practical solution for mitigation of this physical fading signal is further discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease to -0.24 mm/yr for the Sentinel-1 time series.</div>Based on these analyses, we put forward our recommendations for efficient and accurate deformation signal retrieval from large stacks of multilooked interferograms.

scholarly journals Comment on “Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry” by Ansari et al. (2021)

2021 ◽  
Author(s):  
Claudio De Luca ◽  
Francesco Casu ◽  
Michele Manunta ◽  
Giovanni Onorato ◽  
Riccardo Lanari
Keyword(s):  
Surface Deformation ◽  
Sar Interferometry ◽  
Systematic Bias ◽  
Phase Errors ◽  
Measuring Surface ◽  
Systematic Biases ◽  
Small Baseline ◽  
Differential Sar Interferometry ◽  
Long Baselines ◽  
Baseline Approach

<p>In a recent publication Ansari et al. (2021) [1] claim (see, in particular, the Discussion and Recommendation Section in their article) that the advanced differential SAR interferometry (InSAR) algorithms for surface deformation retrieval, based on the small baseline approach, are affected by systematic biases in the generated InSAR products. Therefore, to avoid such biases, they recommend a strategy primarily focused on excluding “the short temporal baseline interferograms and using long baselines to decrease the overall phase errors”. In particular, among various techniques, Ansari et al. (2021) [1] identify the solution presented by Manunta et al. (2019) [2] as a small baseline advanced InSAR processing approach where the presence of the above-mentioned biases (referred to as a fading signal) compromises the accuracy of the retrieved InSAR deformation products. We show that the claim of Ansari et al. (2021) [1] is not correct (at least) for what concerns the mentioned approach discussed by Manunta et al. (2019) [2]. In particular, by processing the Sentinel-1 dataset relevant to the same area in Sicily (southern Italy) investigated by Ansari et al. (2021) [1], we demonstrate that the generated InSAR products do not show any significant bias.</p>

scholarly journals Comment on “Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry” by Ansari et al. (2021)

2021 ◽  
Author(s):  
Claudio De Luca ◽  
Francesco Casu ◽  
Michele Manunta ◽  
Giovanni Onorato ◽  
Riccardo Lanari
Keyword(s):  
Surface Deformation ◽  
Sar Interferometry ◽  
Systematic Bias ◽  
Phase Errors ◽  
Measuring Surface ◽  
Systematic Biases ◽  
Small Baseline ◽  
Differential Sar Interferometry ◽  
Long Baselines ◽  
Baseline Approach

<p>In a recent publication Ansari et al. (2021) [1] claim (see, in particular, the Discussion and Recommendation Section in their article) that the advanced differential SAR interferometry (InSAR) algorithms for surface deformation retrieval, based on the small baseline approach, are affected by systematic biases in the generated InSAR products. Therefore, to avoid such biases, they recommend a strategy primarily focused on excluding “the short temporal baseline interferograms and using long baselines to decrease the overall phase errors”. In particular, among various techniques, Ansari et al. (2021) [1] identify the solution presented by Manunta et al. (2019) [2] as a small baseline advanced InSAR processing approach where the presence of the above-mentioned biases (referred to as a fading signal) compromises the accuracy of the retrieved InSAR deformation products. We show that the claim of Ansari et al. (2021) [1] is not correct (at least) for what concerns the mentioned approach discussed by Manunta et al. (2019) [2]. In particular, by processing the Sentinel-1 dataset relevant to the same area in Sicily (southern Italy) investigated by Ansari et al. (2021) [1], we demonstrate that the generated InSAR products do not show any significant bias.</p>

TIME-SERIES SAR INTERFEROMETRY ANALYSIS OF SURFACE DEFORMATION AT MT. BROMO INDONESIA

2019 ◽  
Vol 3 ◽  
pp. 771
Author(s):  
Arliandy Pratama Arbad ◽  
Wataru Takeuchi ◽  
Yosuke Yosuke ◽  
Mutiara Jamilah ◽  
Achmad Ardy
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Sar Interferometry ◽  
Time Interval ◽  
Infrastructure Management ◽  
Alos Palsar ◽  
Volcanic System ◽  
Deformation Velocity ◽  
Small Baseline Subset ◽  
Small Baseline

One of the most active volcanoes in Indonesia is Mt. Bromo, volcanic activities at Mt. Bromo has been recorded in 1775. We observe the surface deformation of the Mt. Bromo which located at eastern Java Indonesia area that includes neighborhood volcanic system on TNBTS (Taman Nasional Bukit Tengger Semeru). Recently, remote sensing has played as an important role to observe volcano behavior. We apply the SAR Interferometry (InSAR) algorithm referred to as Small Baseline Subset (SBAS) approach that allows us to generate mean deformation velocity maps and displacement time series for the studied area. The common SBAS technique, the set of interferometric phase observations writes as a linear combination of individual SAR scene phase values for each pixel independently. Particularly, the proposed analysis is based on 22 SAR data acquired by the ALOS/PALSAR sensors during the 2007–2017 time interval. A fewer studies have been able to show capability of InSAR analysis for investigating cycle of volcano especially of Mt. Bromo which characterized eruption stratovolcano in ranging one to five years. The results expected in this work represent an advancement of previous InSAR studies of the area that are mostly focused on the deformation affecting the caldera. According to the result, we expected this study could implement on risk management or infrastructure management.

scholarly journals Comments on ``Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry''

2021 ◽  
pp. 1-5
Author(s):  
Claudio De Luca ◽  
Francesco Casu ◽  
Michele Manunta ◽  
Giovanni Onorato ◽  
Riccardo Lanari
Keyword(s):  
Surface Deformation ◽  
Sar Interferometry ◽  
Systematic Bias ◽  
Measuring Surface

scholarly journals LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor

2020 ◽  
Vol 12 (3) ◽  
pp. 424 ◽  
Author(s):  
Yu Morishita ◽  
Milan Lazecky ◽  
Tim Wright ◽  
Jonathan Weiss ◽  
John Elliott ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Open Source ◽  
Large Scale ◽  
Surface Deformation ◽  
Atmospheric Correction ◽  
Sar Interferometry ◽  
Processing Scheme ◽  
Series Analysis ◽  
Analysis Package

For the past five years, the 2-satellite Sentinel-1 constellation has provided abundant and useful Synthetic Aperture Radar (SAR) data, which have the potential to reveal global ground surface deformation at high spatial and temporal resolutions. However, for most users, fully exploiting the large amount of associated data is challenging, especially over wide areas. To help address this challenge, we have developed LiCSBAS, an open-source SAR interferometry (InSAR) time series analysis package that integrates with the automated Sentinel-1 InSAR processor (LiCSAR). LiCSBAS utilizes freely available LiCSAR products, and users can save processing time and disk space while obtaining the results of InSAR time series analysis. In the LiCSBAS processing scheme, interferograms with many unwrapping errors are automatically identified by loop closure and removed. Reliable time series and velocities are derived with the aid of masking using several noise indices. The easy implementation of atmospheric corrections to reduce noise is achieved with the Generic Atmospheric Correction Online Service for InSAR (GACOS). Using case studies in southern Tohoku and the Echigo Plain, Japan, we demonstrate that LiCSBAS applied to LiCSAR products can detect both large-scale (>100 km) and localized (~km) relative displacements with an accuracy of <1 cm/epoch and ~2 mm/yr. We detect displacements with different temporal characteristics, including linear, periodic, and episodic, in Niigata, Ojiya, and Sanjo City, respectively. LiCSBAS and LiCSAR products facilitate greater exploitation of globally available and abundant SAR datasets and enhance their applications for scientific research and societal benefit.

scholarly journals Time-Series InSAR Analysis of the Small Baseline Subset to Estimate Surface Deformation at Mt. Bromo Indonesia

Teknik ◽  
2019 ◽  
Vol 39 (2) ◽  
pp. 126
Author(s):  
Arliandy Pratama Arbad ◽  
Wataru Takeuchi ◽  
Yosuke Aoki ◽  
Achmad Ardy ◽  
Mutiara Jamilah
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Sar Interferometry ◽  
Alos Palsar ◽  
Small Baseline Subset ◽  
Sar Data ◽  
Small Baseline

Penginderaan jauh kini memainkan peranan penting dalam pengamatan perilaku gunung api. Penelitian ini bertujuan untuk mengamati deformasi permukaan Gunung Bromo, yang terletak di Jawa bagian Timur, Indonesia, yang masuk dalam rangkaian sistem volkanik di Taman Nasional Bukit Tengger Semeru (TNBTS). Penggunaan algoritma SAR Interferometry (InSAR) yang disebut sebagai pendekatan Small Baseline Subset (SBAS) memungkinkan perancangan peta kecepatan deformasi rata-rata dan and peta time series displacement di wilayah kajian. Teknik SBAS yang biasa menghasilkan rangkaian observasi tahap interferometrik. Ini tercatat sebagai kombinasi linear dari nilai fase SAR  scene untuk setiap pixel secara tersendiri. Analisis yang dilakukan terutama berdasarkan 22 data SAR data yang diperoleh melalui sensor ALOS/PALSAR selama kurun waktu 2007–2011. Beberapa penelitian menunjukkan bahwa kemampuan analisis InSAR dalam menyelidiki siklus gunung api, terutama Gunung Bromo yang memiliki karakteristik erupsi stratovolcano dalam satu hingga lima tahun. Analisis hasil memperlihatkan adanya kemajuan dari kajian sebelumnya akan InSAR wilayah tersebut, yang lebih fokus  kepada deformasi yang berpengaruh kepada kaldera. Hal ini menunjukkan bahwa penelitian ini bisa diimplementasikan pada manajemen risiko atau manajemen infrastruktur

scholarly journals Analyzing an InSAR short-term systematic phase bias with regards to soil moisture and landcover

2021 ◽  
Author(s):  
Paloma Saporta ◽  
Giorgio Gomba ◽  
Francesco De Zan
Keyword(s):  
Soil Moisture ◽  
Surface Deformation ◽  
Phase Error ◽  
Region Of Interest ◽  
Sar Interferometry ◽  
Systematic Bias ◽  
Short Term ◽  
Sample Covariance ◽  
Phase Bias ◽  
Eigen Decomposition

&lt;p&gt;This work investigates a systematic phase bias affecting Synthetic Aperture Radar interferograms, in particular at short-term, causing biases in displacement velocity estimates that can reach several mm per year ([1]).&lt;br&gt;The analysis relies on the processing of a stack of Single Look Complex SAR images; in our case, the stack consists in 184 Sentinel-1 images acquired regularly between 2014 and 2018 and covering the Eastern part of Sicily. A reference phase history is derived using the EMI method (Eigen-decomposition-based Maximum-likelihood estimator of Interferometric phase), which takes advantage of the full sample covariance matrix built out of all the SAR acquisitions at a given pixel. This phase history has been shown to be equivalent to a persistent scatterer&amp;#8217;s phase history over our region of interest. We use it to calibrate the direct multilooked interferograms built out of consecutive acquisitions. The short-term phase bias signal thus obtained is analyzed in time and space, making use in addition of ASCAT soil moisture variations and landcover information from the CORINE dataset.&lt;br&gt;We observe that for certain land classes, the high-frequency part of the signal is correlated with soil moisture variations in both dry and wet seasons. The low-pass trend exhibits strongly seasonal variations, with maxima of comparable value in spring (April-May) of each year. Areas with similar landcover types (forests, vegetated areas, agricultural areas) witness similar phase biases behavior, indicating a physical contribution associated with vegetation effects.&lt;br&gt;By investigating the behavior of the bias, this study contributes towards a future mitigation of this phase error in deformation estimates, or the exploitation of the bias itself as a physically relevant signal.&lt;/p&gt;&lt;p&gt;[1] H. Ansari, F. De Zan and A. Parizzi, &quot;Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry,&quot; in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3003421.&lt;/p&gt;

scholarly journals LAND SUBSIDENCE MONITORING USING PS-InSAR TECHNIQUE FOR L-BAND SAR DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 995-997 ◽  
Author(s):  
S. Thapa ◽  
R. S. Chatterjee ◽  
K. B. Singh ◽  
D. Kumar
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Land Surface ◽  
Surface Deformation ◽  
Underground Mining ◽  
Ground Surface ◽  
Sar Interferometry ◽  
Persistent Scatterer Interferometry ◽  
Long Time ◽  
Subsidence Monitoring

Differential SAR-Interferometry (D-InSAR) is one of the potential source to measure land surface motion induced due to underground coal mining. However, this technique has many limitation such as atmospheric in homogeneities, spatial de-correlation, and temporal decorrelation. Persistent Scatterer Interferometry synthetic aperture radar (PS-InSAR) belongs to a family of time series InSAR technique, which utilizes the properties of some of the stable natural and anthropogenic targets which remain coherent over long time period. In this study PS-InSAR technique has been used to monitor land subsidence over selected location of Jharia Coal field which has been correlated with the ground levelling measurement. This time series deformation observed using PS InSAR helped us to understand the nature of the ground surface deformation due to underground mining activity.

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