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>

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 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 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 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 OPTIMUM CONDITIONS FOR DIFFERENTIAL SAR INTERFEROMETRY TECHNIQUE TO ESTIMATE HIMALAYAN GLACIER VELOCITY

2018 ◽  
Vol IV-5 ◽  
pp. 137-140 ◽  
Author(s):  
B. R. Nela ◽  
G. Singh ◽  
A. V. Kulkarni ◽  
K. Malik
Keyword(s):  
Displacement Component ◽  
Sar Interferometry ◽  
Srtm Dem ◽  
Optimum Conditions ◽  
Topographic Information ◽  
Chhota Shigri Glacier ◽  
Measuring Surface ◽  
L Band ◽  
Glacier Velocity ◽  
Differential Sar Interferometry

<p><strong>Abstract.</strong> Differential SAR Interferometry (DInSAR) is the process of differencing two Interferograms for measuring surface movement with an accuracy of millimeter range. The DInSAR process can be applied to observe glacier movement, earthquake deformations, volcanic activities and rate of subsidence or uplift caused due to the extraction of groundwater or coal. By using single pass interferometry technique we can also generate accurate DEM. In this paper, we are presenting the movement of a Chhota Shigri glacier with the help two pass DInSAR technique and mainly we concentrated on the optimum conditions for estimating glacier movement using DInSAR. We got good coherence and Interferogram fringes for L-band sensor with less temporal baseline. Therefore, we generated glacier velocity using ALOS-2 data with 14 days temporal baseline. Initially, we generated Interferogram (defo-pair) by taking 10th March 2015 image as a master and 24th March 2015 image as a slave. But this generated Interferogram also having topographic information and atmospheric errors with the displacement component. Therefore, we used SRTM DEM for removing topographic information from the Interferogram. Because we are using L-band data, results may not be affected by troposphere. Maximum glacier velocity we observed in the accumulation zone as 7.285<span class="thinspace"></span>cm/day in the month of March and while it’s moving towards ablation zone the glacier velocity is decreasing.</p>

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