Interferometric Synthetic Aperture Radar Processing Research Articles

Fast InSAR Time-Series Analysis Method in a Full-Resolution SAR Coordinate System: A Case Study of the Yellow River Delta

Ground deformation is a major determinant of delta sustainability. Sentinel-1 Terrain Observation by Progressive Scans (TOPS) data are widely used in interferometric synthetic aperture radar (InSAR) applications to monitor ground subsidence. Due to the unparalleled mapping coverage and considerable data volume requirements, high-performance computing resources including graphics processing units (GPUs) are employed in state-of-the-art methodologies. This paper presents a fast InSAR time-series processing approach targeting Sentinel-1 TOPS images to process massive data with higher efficiency and resolution. We employed a GPU-assisted InSAR processing method to accelerate data processing. Statistically homogeneous pixel selection (SHPS) filtering was used to reduce noise and detect features in scenes with minimal image resolution loss. Compared to the commonly used InSAR processing software, the proposed method significantly improved the Sentinel-1 TOPS data processing efficiency. The feasibility of the method was investigated by mapping the surface deformation over the Yellow River Delta using SAR datasets acquired between January 2021 and February 2022. The findings indicate that several events of significant subsidence have occurred in the study area. Combined with the geological environment, underground brine and hydrocarbon extraction as well as sediment consolidation and compaction contribute to land subsidence in the Yellow River Delta.

Use of InSAR data for measuring land subsidence induced by groundwater withdrawal and climate change in Ardabil Plain, Iran

The Ardabil plain, with an approximate area of 1097.2 km2 in northwestern Iran, has experienced land subsidence due to intensive groundwater withdrawal and long seasons of drought in recent years. Different techniques have been used to investigate and evaluate subsidence in this region including: Global Positioning Systems (GPS), Levelling, and Geotechnical methods. These methods are typically expensive, time-consuming, and identify only a small fraction of the areas prone to subsidence. This study employs an Interferometric Synthetic Aperture Radar (InSAR) technique to measure the long-term subsidence of the plain. An open-source SAR interferometry time series analysis package, LiCSBAS, that integrates with the automated Sentinel-1 InSAR processor (COMET-LiCSAR) is used to analyze Sentinel-1 satellite images from October 2014 to January 2021. Processing of Sentinel-1 images shows that the Ardabil plain has been facing rapid subsidence due to groundwater pumping and reduced rainfall, especially between May 2018 to January 2019. The maximum subsidence rate was 45 mm/yr, measured at the southeastern part of the plain. While providing significant advantages (less processing time and disk space) over other InSAR processing packages, implementation of the LiCSBAS processing package and its accuracy for land subsidence measurements at different scales needs further evaluation. This study provides a procedure for evaluating its efficiency and accuracy for land subsidence measurements by comparing its measurements with the results of the GMTSAR and geotechnical numerical modeling. The results of geotechnical numerical modeling showed land subsidence with an average annual rate of 38 mm between 2006 and 2020, which was close to measurements using the InSAR technique. Comparison of the subsidence measurements of the Ardabil plain using the LiCSBAS package with results obtained from other techniques shows that LiCSBAS is able to accurately detect land deformation at large scales (~ km). However, they may not be optimized for more local deformations such as infrastructure monitoring.

Multi-resolution dictionary-based sparse coding method for interferometric phase image denoising

Interferometric synthetic aperture radar (InSAR) is widely used in acquisition of digital elevation data as well as the settlement of Earth surface. Researching on the processing of space-born InSAR is of great importance in improving the accuracy of sensing data. The phase denoising method based on multi-resolution dictionary sparse coding (SC) is proposed. First, noisy blocks are extracted from the complex valued interferometric image. Then, the dictionary is trained by online method, which uses a portion of elements in the training set to update the dictionary column by column. And the trained dictionary is used to sparsely represent the noisy blocks by greedy method to denoise the image patches with different resolutions. The whole image of a given resolution is formed by combining the patches in the same resolution together. Finally, the denoising results of different resolutions are merged based on hypothesis testing and the phase estimations according to each pixels are computed. The experiments prove that the proposed denoising method based on multi-resolution SC achieves higher peak signal to noise ratio than traditional SC method.

A new algorithm for landslide dynamic monitoring with high temporal resolution by Kalman filter integration of multiplatform time-series InSAR processing

• Provide a new scheme for landslide dynamic monitoring and prediction. • Integrate multiplatform time-series InSAR processing by Kalman filter. • Compensate the deficiency of the space-borne InSAR in landslide temporal resolution. Free and open access to the earth observation synthetic aperture radar (SAR) satellite images has enabled the implementation of landslide time-series monitoring. However, with the restriction of the low revisit period of satellite, it is difficult to meet the requirements of landslide dynamic monitoring with high temporal resolution. Moreover, with the multiplication of SAR satellite platforms, a corresponding data integration algorithm is needed to obtain the complementary displacement information from every single observation orbit. In this work, an integrated algorithm for landslide multi-source displacement optimization estimation based on Kalman filter (KF) is proposed to improve the temporal resolution and realize the dynamic monitoring and prediction of landslide movement. Specifically, the landslide migration coordinate system is established firstly, and the interferometric SAR (InSAR) displacement results in line of sight (LOS) are projected to the downslope direction. Then, with the introduction of the acceleration variable into the process covariance matrix of prediction model and the observation noise variance weight determination, the downslope displacements of multiplatform InSAR observations are dynamically integrated into a unified time series by KF dynamic prediction and correction. So as to achieve the high temporal resolution monitoring of landslide. For validation purpose, the Baige landslide in Tibet, China is selected as the test area, and 55 Sentinel-1A ascending images, 42 Sentinel-1A descending images, and 10 ALOS-2 PALSAR-2 images collected over this area from May 2017 to April 2019 are used to estimate the high temporal time series. The temporal resolution of landslide monitoring is successfully improved from 12 days with a single orbit to the shortest 1 day and repeatedly 2–5 days with multiple platforms, and the prediction of subsequent displacement is also realized. Prospectively, with the continuous multiplication of the InSAR satellite platforms, this proposed algorithm can provide high temporal monitoring data for landslide and better assist relevant emergency response, which is necessary for the dynamic monitoring and early warning of landslide.

A Multi-Temporal Small Baseline Interferometry Procedure Applied to Mining-Induced Deformation Monitoring

This work addresses a methodology based on the interferometric synthetic aperture radar (InSAR) applied to analyze and monitor ground-motion phenomena induced by underground mining activities in the Legnica-Glogow copper district, south-western Poland. The adopted technique employs an InSAR processing chain that exploits a stack of Sentinel-1 synthetic aperture radar (SAR) images using a small baseline multitemporal approach. Interferograms with small temporal baselines are first selected, then their network is optimized and reduced to eliminate noisy data, in order to mitigate the effect of decorrelation sources related to seasonal phenomena, i.e., snow and vegetation growth, and to the radar acquisition geometry. The atmospheric disturbance is mitigated using a spatio-temporal filter based on the nonequispaced fast Fourier transform. The estimated displacement maps and time series show the effect of both linear and impulsive ground motion and are validated against global navigation satellite system (GNSS) measurements. In this context, a significant threat to the built environment is represented by seismic tremors triggered by underground mining activities, which are analyzed using the proposed method to integrate the information gathered by in situ seismometer devices.

Deep-Learning-Based Phase Discontinuity Prediction for 2-D Phase Unwrapping of SAR Interferograms

Phase unwrapping is a critical step of interferometric synthetic aperture radar processing, and its accuracy directly determines the reliability of subsequent applications. Many phase unwrapping methods have been proposed, most of which assume that the phase has spatial continuity, while decorrelation noise and aliasing fringes invalidate the assumptions, resulting in poor performance of these methods. To obtain more reliable unwrapping results, in this article, a deep convolutional neural network, called a discontinuity estimation network (DENet), is proposed for predicting the probabilities of phase discontinuities in interferograms. The main advantages of DENet are: 1) using branching structure to extract detailed and high-level features separately and retain details while making full use of contextual information; 2) using multichannel input, including interferogram, range/azimuthal phase gradients, and residues map, to provide effective guidance for discontinuity prediction; and 3) using a single network to estimate phase discontinuities in both range and azimuth directions simultaneously. To train the network, a dataset simulation strategy is proposed to generate enough training samples. The strategy considers a variety of phase components, such as terrain-related phase, random deformation, atmospheric turbulence, and noise. The phase discontinuity estimated by DENet is then converted to costs in the minimum cost flow (MCF) solver of the statistical-cost, network-flow algorithm for phase unwrapping (SNAPHU) to obtain the final unwrapped phase. Based on validations of simulated and real interferograms, the proposed method exhibits excellent performance compared to traditional and deep learning unwrapping methods. The proposed method can effectively unwrap large-scale, low-quality interferograms, which is expected to significantly improve the accuracy of synthetic aperture radar interferometry (InSAR) applications.

Atmospheric phase delay correction of PS-InSAR to Monitor Land Subsidence in Surabaya

Atmospheric phase delay is one of the most significant errors limiting the accuracy of Interferometric Synthetic Aperture Radar (InSAR) results. In this research, we used the Generic Atmospheric Correction Online Service for InSAR (GACOS) data to correct the tropospheric delay modeling from the persistent scatterers’ InSAR monitoring. Eighty-one (81) Sentinel-1A images and tropospheric delay maps from GACOS monitored land subsidence in Surabaya city between 2017 and 2019. InSAR processing was carried out using the GMTSAR software, continued with StaMPS and TRAIN, which were used to correct the tropospheric delay of PSInSAR-derived deformation measurements. The results before and after the atmospheric phase delay correction using GACOS were confirmed and analyzed in the main subsidence area. The findings of the experiments reveal that the atmospheric phase affects the mean LOS velocity results to some extent. The average difference between PS-InSAR before and after tropospheric correction is 1.734 mm/year with a standard deviation of 0.550 mm/year. The significance test of the two variables, 95%, showed that the tropospheric correction with GACOS data could affect the PS-InSAR results. Furthermore, GACOS correction may increase the error at some points, which could be due to its turbulence data’s low accuracy.

High Performance Computing in Satellite SAR Interferometry: A Critical Perspective

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.

Precise Relative Orbit Determination for Chinese TH-2 Satellite Formation Using Onboard GPS and BDS2 Observations

TH-2 is China’s first short-range satellite formation system used to realize interferometric synthetic aperture radar (InSAR) technology. In order to achieve the mission goal of InSAR processing, the relative orbit must be determined with high accuracy. In this study, the precise relative orbit determination (PROD) for TH-2 based on global positioning system (GPS), second-generation BeiDou navagation satellite system (BDS2), and GPS + BDS2 observations was performed. First, the performance of onboard GPS and BDS2 measurements were assessed by analyzing the available data, code multipath errors and noise levels of carrier phase observations. The differences between the National University of Defense Technology (NDT) and the Xi’an Research Institute of Surveying and Mapping (CHS) baseline solutions exhibited an RMS of 1.48 mm outside maneuver periods. The GPS-based orbit was used as a reference orbit to evaluate the BDS2-based orbit and the GPS + BDS2-based orbit. It is the first time BDS2 has been applied to the PROD of low Earth orbit (LEO) satellite formation. The results showed that the root mean square (RMS) of difference between the PROD results using GPS and BDS2 measurements in 3D components was 2.89 mm in the Asia-Pacific region. We assigned different weights to geostationary Earth orbit (GEO) satellites to illustrate the impact of GEO satellites on PROD, and the accuracy of PROD was improved to 7.08 mm with the GEO weighting strategy. Finally, relative orbits were derived from the combined GPS and BDS2 data. When BDS2 was added on the basis of GPS, the average number of visible navigation satellites from TH-2A and TH-2B improved from 7.5 to 9.5. The RMS of the difference between the GPS + BDS2-based orbit and the GPS-based orbit was about 1.2 mm in 3D. The overlap comparison results showed that the combined orbit consistencies were below 1 mm in the radial (R), along-track (T), and cross-track (N) directions. Furthermore, when BDS2 co-worked with GPS, the average of the ambiguity dilution of precision (ADOP) reduced from 0.160 cycle to 0.153 cycle, which was about a 4.4% reduction. The experimental results indicate that millimeter-level PROD results for TH-2 satellite formation can be obtained by using onboard GPS and BDS2 observations, and multi-GNSS can further improve the accuracy and reliability of PROD.

Three- and Four-Dimensional Topographic Measurement and Validation

This paper reports on the activities carried out in the context of “Dragon project 32278: Three- and Four-Dimensional Topographic Measurement and Validation”. The research work was split into three subprojects and encompassed several activities to deliver accurate characterization of targets on land surfaces and deepen the current knowledge on the exploitation of Synthetic Aperture Radar (SAR) data. The goal of Subproject 1 was to validate topographic mapping accuracy of various ESA, TPM, and Chinese satellite system on test sites in the EU and China; define and improve validation methodologies for topographic mapping; and develop and setup test sites for the validation of different surface motion estimation techniques. Subproject 2 focused on the specific case of spatially and temporally decorrelating targets by using multi-baseline interferometric (InSAR) and tomographic (TomoSAR) SAR processing. Research on InSAR led to the development of robust retrieval techniques to estimate target displacement over time. Research on TomoSAR was focused on testing or defining new processing methods for high-resolution 3D imaging of the interior of forests and glaciers and the characterization of their temporal behavior. Subproject 3 was focused on near-real-time motion estimation, considering efficient algorithms for the digestion of new acquisitions and for changes in problem parameterization.

REGRESSION ANALYSIS OF ERRORS OF SAR-BASED DEMS AND CONTROLLING FACTORS

Abstract. Interferometric Synthetic Aperture Radar (InSAR) has been well developed for several decades and is known for its powerful capability of retrieving three-dimensional ground information from SAR imagery. One of the most important application of InSAR technique is topographic mapping. The technique is limited when confronting certain poor conditions which lead to low coherence. In this research, we aim at investigating the relationship between SAR-based digital elevation models (DEMs) and related factors that contribute to the error budget by conducting a linear regression analysis. The surface deformation in line of sight (LOS) direction and the amount of integral refractivity change over two acquisition events are considered as two related factors. Eight pairs of Sentinel-1 images were selected to conduct InSAR processing over Chaiyi City of Taiwan, and SNAP software was used to generate SAR-based DEMs. The coherence mask was applied during the InSAR workflow in order to alleviate unwrapping error. The result has shown that the coherence thresholds help to improve the accuracy by up to 52.61%. Since some large errors were observed from the resulting InSAR-DEMs, these points were removed based on standard error. In regression analysis, there were 15 set of data, categorized by different coherence threshold and data removal standard, to test the model. As the result has shown, when the coherence threshold is 0.3 and the points were filtered with half standard error, the R2 can achieve 0.85. However, the rest of the dataset did not produce desirable results. In our discussion, we have provided several reasons which might have contributed to this outcome.

Land Subsidence in Wuhan Revealed Using a Non-Linear PSInSAR Approach with Long Time Series of COSMO-SkyMed SAR Data

Wuhan is an important city in central China, with a rapid development that has led to increasingly serious land subsidence over the last decades. Most of the existing Interferometric Synthetic Aperture Radar (InSAR) subsidence monitoring studies in Wuhan are either short-term investigations—and thus can only detect this process within limited time periods—or combinations of different Synthetic Aperture Radar (SAR) datasets with temporal gaps in between. To overcome these constraints, we exploited nearly 300 high-resolution COSMO-SkyMed StripMap HIMAGE scenes acquired between 2012 and 2019 to monitor the long-term subsidence process affecting Wuhan and to reveal its spatiotemporal variations. The results from the Persistent Scatterer Interferometric SAR (PSInSAR) processing highlight several clearly observable subsidence zones. Three of them (i.e., Houhu, Xinrong, and Guanggu) are affected by serious subsidence rates and non-linear temporal behavior, and are investigated in this paper in more detail. The subsidence in Houhu is caused by soft soil consolidation and compression. Soil mechanics are therefore used to estimate when the subsidence is expected to finish and to calculate the degree of consolidation for each year. The COSMO-SkyMed PSInSAR results indicate that the area has entered the late stage of consolidation and compression and is gradually stabilizing. The subsidence curve found for the area around Xinrong shows that the construction of an underground tract of the subway Line 21 caused large-scale settlement in this area. The temporal granularity of the PSInSAR time series also allows precise detection of a rebound phase following a major flooding event in 2016. In the southern industrial park of Guanggu, newly detected subsidence was found. The combination of the subsidence curve with an optical time-series image analysis indicates that urban construction is the main trigger of deformation in this area. While this study unveils previously unknown characters of land subsidence in Wuhan and clarifies the relationship with the urban causative factors, it also proves the benefits of non-linear PSInSAR in the analysis of the temporal evolution of such processes in dynamic and expanding cities.

Sentinel-1 Big Data Processing with P-SBAS InSAR in the Geohazards Exploitation Platform: An Experiment on Coastal Land Subsidence and Landslides in Italy

The growing volume of synthetic aperture radar (SAR) imagery acquired by satellite constellations creates novel opportunities and opens new challenges for interferometric SAR (InSAR) applications to observe Earth’s surface processes and geohazards. In this paper, the Parallel Small BAseline Subset (P-SBAS) advanced InSAR processing chain running on the Geohazards Exploitation Platform (GEP) is trialed to process two unprecedentedly big stacks of Copernicus Sentinel-1 C-band SAR images acquired in 2014–2020 over a coastal study area in southern Italy, including 296 and 283 scenes in ascending and descending mode, respectively. Each stack was processed in the GEP in less than 3 days, from input SAR data retrieval via repositories, up to generation of the output P-SBAS datasets of coherent targets and their displacement histories. Use-cases of long-term monitoring of land subsidence at the Capo Colonna promontory (up −2.3 cm/year vertical and −1.0 cm/year east–west rate), slow-moving landslides and erosion landforms, and deformation at modern coastal protection infrastructure in the city of Crotone are used to: (i) showcase the type and precision of deformation products outputting from P-SBAS processing of big data, and the derivable key information to support value-adding and geological interpretation; and (ii) discuss potential and challenges of big data processing using cloud/grid infrastructure.

Accuracy of a Model-Free Algorithm for Temporal InSAR Tropospheric Correction

Atmospheric propagational phase variations are the dominant source of error for InSAR (interferometric synthetic aperture radar) time series analysis, generally exceeding uncertainties from poor signal to noise ratio or signal correlation. The spatial properties of these errors have been well studied, but, to date, their temporal dependence and correction have received much less attention. Here, we present an evaluation of the magnitude of tropospheric artifacts in derived time series after compensation using an algorithm that requires only the InSAR data. The level of artifact reduction equals or exceeds that from many weather model-based methods, while avoiding the need to globally access fine-scale atmosphere parameters at all times. Our method consists of identifying all points in an InSAR stack with consistently high correlation and computing, and then removing, a fit of the phase at each of these points with respect to elevation. A comparison with GPS truth yields a reduction of three, from a rms misfit of 5–6 to ~2 cm over time. This algorithm can be readily incorporated into InSAR processing flows without the need for outside information.

Sentinel1の干渉SAR処理による2020年８月ベイルート爆発事故の被害調査

Sentinel1の干渉SAR処理による2020年８月ベイルート爆発事故の被害調査

Exploring PAZ co-polarimetric SAR data for surface movement mapping and scattering characterization

In this contribution, we investigate PAZ co-polarimetric SAR data applicability for surface movement mapping and scattering characterization. PAZ simultaneously collects SAR imagery in both VV and HH channels. Using a small stack of PAZ data, we apply the real-valued impulse response function correlation to identify constantly coherent scatterers (CCS), separately in VV and HH, in the course of time series InSAR (Interferometric SAR) processing. The proposed method has an advantage to selecting the CCS with minimal incoherent scatterer inclusion and exact radar location, which can eventually lead to the precise deformation time series estimations of all CCS, and a high-precision surface movement map. Moreover, we apply the co-polarimetric phase difference (CPD) method to classify the CCS in terms of scattering mechanisms which provides a new attribute to every individual CCS. We recognize the sibling pairs by both thresholding the spatial distance between any two CCS observed separately in VV and HH, and using common scattering characteristic as a new criterion. The deformation estimates of sibling pairs are used to reduce the biases in the deformation estimates of every ground target. The proposed methods are demonstrated in a test site, in the northern part of the Netherlands, using 10 co-polarimetric SAR data acquired between September 2019 and April 2020. The results show that 83.5% sibling pairs behave a linear deformation trend over time, and that the other pairs show a correlation between their deformation and temperature, and the sibling pairs with the surface, dihedral, volume scattering mechanisms account for 62%,12% and 26%, respectively. We conclude that by combining data from VV and HH polarization as siblings, PAZ co-polarimetric SAR data are highly suited to map surface changes and characterize surface features.

A Fully Automatic Algorithm for Editing the TanDEM-X Global DEM

The spaceborne mission TanDEM-X successfully acquired and processed a global Digital Elevation Model (DEM) from interferometric bistatic SAR data at X band. The product has been delivered in 2016 and is characterized by an unprecedented vertical accuracy. It is provided at 12 m, 30 m, and 90 m sampling and can be accessed by the scientific community via a standard announcement of opportunity process and the submission of a scientific proposal. The 90 m version is freely available for scientific purposes. The DEM is unedited, which means that it is the pure result of the interferometric SAR processing and subsequent mosaicking. Residual gaps, resulting, e.g., from unprocessable data, are still present and water surfaces appear noisy. This paper reports on the algorithms developed at DLR’s Microwaves and Radar Institute for a fully automatic editing of the global TanDEM-X DEM comprising gap filling and water editing. The result is a new global gap-free DEM product at 30 m sampling, which can be used for a large variety of scientific applications. It also serves as a reference for processing the upcoming TanDEM-X Change DEM layer.

Accuracy Assessment of Digital Elevation Models Produced From Different Geomatics Data

Digital Elevation Models (DEM) are now being used in several geospatial applications. DEMs play an important role in the preliminary surveys for constructing dams and reservoirs, highways, canals, and projects in which earth work is essential. In many remote sensing applications, DEMs have become a significant tool for InSAR (Interferometric Synthetic Aperture Radar) processing, ground cover classification and images ortho-rectification. In this study, the accuracy of DEMs obtained from ALOS V1.1, ASTER V2, SRTM V3 and other obtained from a pair of Pleiades high-resolution (PHR) 1B satellites in a study area were evaluated after comparing them with high accuracy GNSS/RTK checkpoints. The SRTM3, ALOS V1.1, ASTER V2 DEM revealed a Root Mean Square Error (RMSE) of 2.234m, 0.838m, and 15.116m respectively; while the DEM which is produced from a 0.5m resolution of Pleiades 0.5m shows an RMSE of 0.642m. The correct bias Linear transformation algorithm was used and the RMSE results were: SRTM V3 (1.319m), ALOS V1.1 (0.830m), ASTER V2 (3.815m), and PHR (0.433m). The results showed that the ALOS V1.1 model is the most accurate of the open source models followed by the SRTM V3 model and then followed by ASTER V2. The results obtained from a pair by Pleiades high-resolution (PHR) 1B satellites show a higher accuracy than the results obtained from the open source models.

Multi-Temporal InSAR Parallel Processing for Sentinel-1 Large-Scale Surface Deformation Mapping

Interferometric synthetic aperture radar (InSAR) has achieved great success in various geodetic applications, and its potential for ground deformation measurements on the large scale has attracted increasingly more attention in recent years. The increasing number of synthetic aperture radar (SAR) satellite systems have steadily provided a large amount of SAR data. Among these systems, the Sentinel-1 mission can be considered a milestone in the development of InSAR techniques, offering new opportunities to monitor global surface deformation with high precision, due to its wide coverage, short revisit time, and free access. However, conventional InSAR techniques have encountered great challenges in large-scale InSAR processing over wide areas because of the large computational burden and complexity. In this work, we present a novel parallel computing-based coherent scatterer InSAR (P-CSInSAR) method for automatic and efficient generation of deformation results from Sentinel-1 raw data. To achieve high parallelization performance for the overall InSAR processing chain, parallelization strategies at different levels have been adopted in the P-CSInSAR method, which has been fully addressed in this work. To evaluate the efficiency and accuracy of the proposed method, P-CSInSAR has been tested on the North China Plain regions with three adjacent frames of Sentinel-1 images, and the deformation results have been validated by GPS measurements. The experimental results confirm the effectiveness of the proposed parallel computing-based P-CSInSAR method. The proposed method can also play an important role in exploiting Sentinel-1 InSAR big data for disaster prevention and reduction.

Polarimetric Behavior for the Derivation of Sea Ice Topographic Height From TanDEM-X Interferometric SAR Data

Single-pass interferometric synthetic aperture radar (InSAR) is an effective technique for sea ice topographic retrieval despite the inherent dynamics of sea ice. However, the penetration of microwaves into snow-covered thick ice and the achievable height sensitivity for tens-of-centimeters thin ice are two major issues, which limit the accuracy of InSAR-derived sea ice topography. Polarimetry provides scattering information concerning the sea ice properties and has the potential, in combination with interferometry, to achieve an accurate reconstruction of a sea ice digital elevation model (DEM). This article studies the relation between polarimetric signatures and sea ice topography, and explores the possibility to compensate the penetration bias by merging copolar coherence into InSAR processing. The newly generated topographic map has a root-mean-square error under 0.3 m. For thin ice below 1 m, a positive relation between copolar phase φ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coPol</sub> and surface height is observed, suggesting that φ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coPol</sub> can effectively characterize thin sea ice topography. For thick ice with ridges, the maximum polarimetric phase difference Δφ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maxPol</sub> reveals a particular shape of the coherence region, which can be interpreted as oriented volume scattering. It suggests that the model-based approach using polarimetric SAR interferometry assuming an oriented volume scattering model is promising in measuring the scattering centers in thick and deformed sea ice. The study of polarimetric behavior for the InSAR DEM is, therefore, a step forward toward accurate modeling of sea ice topography from polarimetric single-pass InSAR data.