Temporal Decorrelation Research Articles

PolInSAR coherence-based decomposition modeling for scattering characterization: A case study in Uttarakhand, India

Polarimetric decomposition models such as Freeman-Durden’s three-component and Yamaguchi’s four-component models were used to extract the scattering mechanisms. In a few cases, both the models have shown overestimation of the volume scattering from the highly-dense building instead of even-bounce scattering. Deorientation process helped to reduce the overestimation of the volume scattering but didn’t accomplish completely. This paper addressed this issue by the insertion of PolInSAR coherence in the existing decomposition models. The coherence varies with features based on its temporal and volume decorrelation, hence forest results in low coherence compared to permanent scatterers helps to characterize the man-made and natural features. This paper experimented with a proposed model on the RADARSAT-2 dataset of Uttarakhand as a case study. Volume scattering was modified and extracted based on different coherence parameter assumptions. Finally, the first optimal band with spatial and temporal baseline coefficients as 1 and 0.6 thresholds gave a more reliable outcome. For qualitative analysis, the scattering mechanisms of features from different models were compared. In the proposed model, closely spaced buildings exhibited 54% and 26% dominance in the even and odd-bounce scattering while volume scattering reduced to 18%. Whereas Freeman-Durden and Yamaguchi’s model has shown dominance in volume nearly 98% and 50% while even and odd-bounce scattering were less than 1% in Freeman-Durden model and 39% and 11% in Yamaguchi’s model. Forest shows dominance in the volume scattering higher than 60% in the Freeman-Durden and the proposed models whereas 35% in the Yamaguchi’s model. Therefore, the proposed methodology successfully fused polarimetry and interferometry to overcome the ambiguity in scattering.

Forest Height Estimation Using MultiBaseline Low-Frequency PolInSAR Data Affected by Temporal Decorrelation

For repeat-pass interferometric systems, temporal decorrelation (TD) is inevitable and cannot be ignored, and can lead to significant bias in the forest height estimation. The TD random volume over ground (TD + RVoG) model has been found to be a reasonable way to describe the scattering process over forest areas. In this letter, based on the TD + RVoG model, a new forest height estimation method is proposed for use with multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) data. First, the correlation between the ground-to-volume ratios (GVRs) associated with the different polarizations is parameterized according to the geometric interpretation of the RVoG model. An interferometric pair that is assumed to have no TD is then selected based on the eccentricity of the polarimetric coherence region, and the other interferometric pairs are fitted by the TD + RVoG model. E-synthetic aperture radar (E-SAR) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> -band PolInSAR data sets affected by TD are used to prove the effectiveness of the proposed method. The experimental results show that the forest height results are improved by 25.90% when compared to the RVoG-based method.

Snow depth retrieval in North-Western Himalayan region using pursuit-monostatic TanDEM-X datasets applying polarimetric synthetic aperture radar interferometry based inversion Modelling

ABSTRACT Synthetic Aperture Radar (SAR) remote sensing is a state-of-the-art tool for snow monitoring and snow parameters estimation. SAR remote sensing-based techniques, such as interferometric SAR (InSAR) and Polarimetric SAR (PolSAR) have already proven useful in the estimation of geophysical parameters of snow. InSAR-based techniques utilize interferometric phase information from repeat-pass datasets for snow parameters retrieval. During the monitoring of snow, the large temporal gap between the repeat passes results in the temporal decorrelation in the snowpack, which leads to the loss of interferometric coherence. Hence, there is a need for a technique for snow parameters estimation, which can work with zero temporal baseline datasets. This study works on the development of a Polarimetric SAR Interferometry (PolInSAR) based modelling approach for snow-depth estimation using TerraSAR-X/TanDEM-X datasets acquired in the pursuit monostatic mode (temporal baseline = 10 seconds). The study area of this work is the Manali region of Himachal Pradesh situated in the Beas basin. Multi-temporal analysis of the snow-depth variation is executed utilizing the two pursuit-monostatic TanDEM-X interferometric quad-pol dataset pairs of the dates 21 January 2015 and 22 January 2015. In this study, the PolInSAR-based Coherence Amplitude Inversion modelling approach is used for the snow-depth retrieval. The magnitude of the complex interferometric coherence is used during the modelling implementation. The snow extinction coefficient is estimated and used as an input during PolInSAR modelling. Further, a comparison of the calculated volume coherence magnitude and the observed volume coherence magnitude is done during model implementation for the snow-depth estimation. The snow depth is estimated at a resolution of 15 m × 15 m in range and azimuth directions respectively. The estimated snow depth for both the dates shows a precise correlation with the ground datasets. The rise in model retrieved snow-depth value from 0.84 m to 1.24 m is observed during the period. The retrieved results were validated using the ground data of snow depth from the Automatic weather station (AWS) of Snow and Avalanche Study Establishment (SASE), Defence Research and Development Organization (DRDO), and Indian Institute of Remote Sensing (IIRS) installed in the Dhundi region of the study area for same dates.

A Novel Four-Stage Method for Vegetation Height Estimation with Repeat-Pass PolInSAR Data via Temporal Decorrelation Adaptive Estimation and Distance Transformation

Vegetation height estimation plays a pivotal role in forest mapping, which significantly promotes the study of environment and climate. This paper develops a general forest structure model for vegetation height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR) data. In simple terms, the temporal decorrelation factor of the random volume over ground model with volumetric temporal decorrelation (RVoG-vtd) is first modeled by random motions of forest scatterers to solve the problem of ambiguity. Then, a novel four-stage algorithm is proposed to improve accuracy in forest height estimation. In particular, to compensate for the temporal decorrelation mainly caused by changes between multiple observations, one procedure of temporal decorrelation adaptive estimation via Expectation-Maximum (EM) algorithm is added into the novel method. On the other hand, to extract the features of amplitude and phase more effectively, in the proposed method, we also convert Euclidean distance to a generalized distance for the first time. Assessments of different algorithms are given based on the repeat-pass PolInSAR data of Gabon Lope Park acquired in AfriSAR campaign of German Aerospace Center (DLR). The experimental results show that the proposed method presents a significant improvement of vegetation height estimation accuracy with a root mean square error (RMSE) of 6.23 m and a bias of 1.28 m against LiDAR heights, compared to the results of the three-stage method (RMSE: 8.69 m, bias: 4.81 m) and the previous four-stage method (RMSE: 7.72 m, bias: −2.87 m).

TanDEM-X: 10 Years of Formation Flying Bistatic SAR Interferometry

On June 21, 2010, the TanDEM-X mission was launched and opened a new era in spaceborne radar remote sensing. The first formation flying radar system was built by extending the TerraSAR-X synthetic aperture radar (SAR) mission by a second, TerraSAR-X-like satellite TanDEM-X. The resulting large single-pass SAR interferometer features flexible baseline selection, enabling the acquisition of highly accurate cross-track interferograms not impacted by temporal decorrelation and atmospheric disturbances. The primary objective of the mission was the generation of a global Digital Elevation Model (DEM) with unprecedented accuracy (12-m horizontal resolution and 2-m relative height accuracy). The main mission phase for DEM data acquisition has been finished in 2014; the processing of the global TanDEM-X DEM was concluded in September 2016. The final DEM product is well within specifications and features an extremely low percentage of void areas. It is of fundamental importance for a wide range of commercial and scientific applications. But the scientific exploitation of TanDEM-X is not limited to the DEM. TanDEM-X has unique capabilities, including along-track interferometry, and new bistatic and multistatic SAR techniques, that support numerous secondary mission objectives. Indeed, some of these experiments were directly performed during the DEM acquisition phase, when suitable satellite formation geometries were available. Moreover, regular acquisitions over selected super test sites enabled multitemporal analyses. A dedicated science phase after the DEM acquisitions included up to 4 km cross-track baselines, operation in the so-called Dual-Receive Antenna mode, as well as a period in pursuit monostatic flight formation. Comparisons of the TanDEM-X DEM with that of SRTM, or among multitemporal TanDEM-X data, revealed dramatic, ongoing, changes in Earth's topography, especially over ice and forests. In the last 3.5 years the mission has further acquired data for a global change layer showing the height changes relative to the first global DEM dataset. The so-called “Change DEM” is planned for release in 2021. Despite being well beyond their design lifetime, both satellites are still fully functional and have enough consumables for several additional years. Therefore, bistatic operations continue with a focus on changes in the cryosphere, biosphere, and densely populated urban areas.

Assessment of subsidence phenomena for the Thatta, Jamshoro Districts, Southern Pakistan

The focus of this study is the monitoring and analysis of the most recent crustal deformation phenomena in Southern Pakistan. For subsidence evaluation, the longer temporal monitoring is recommended; therefore, the time period chosen for this measurement is between two separate 2005 to 2008 and 2014 to 2018 time series analysis. Remote sensing, synthetic aperture radar interferometry techniques (InSAR) technique is employed for the understanding of localized phenomena of crustal deformation in spatio-temporal coverage. Thatta and Jamshoro districts of the Southern Pakistan region are selected as areas of interest (AOI). These districts are notable for the subsidence phenomena in Pakistan. ENVISAT and central sub swath of Sentinal-1A satellites and InSAR datasets are utilized for quantification of subsidence rate of the area of interest because of the accuracy of their sensors up to millimeters. These satellite missions have widespread spatial coverage, continuous temporal sampling, and free data accessibility due to open data policy of European Space Agency. These two different satellite datasets for different periods of acquisitions provided the comparison of the subsidence rate between temporal periods of these two satellite imageries for area of interest. The long-term data monitoring enables to understand temporal-spatial characteristic to the AOI and understanding of the localized trend. Computation of localized subsidence pattern from ENIVSAT and central sub swath IW2 of Sentinel-1A had been carried out from period Jan 2005 to Dec 2009 with 32 acquisitions and from April 2014 to Nov 2018 with 49 imagery acquisitions, respectively, for ascending orbit. For this Line of Sight (LOS) displacement measurement, InSAR time-series analysis is utilized with the application of the small baseline subset (SBAS) interferogram technique. SBAS time series analysis emerged as strong algorithm with capability to reduce temporal decorrelation for deformation investigation within last couple of years. A total of 53 interferograms from ENVISAT and 141 interferograms from Sentinel-1A are formed. Relative temporal baseline positions are of ± 300 m for ENVISAT and ± 150 m for Sentinel-1A with respect to master acquisition. Subsidence of average 4 mm per year is measured along the cross section line from ENVISAT in the area of interest with total subsidence of 24 mm over a period of 6 years. During the Sential-1A acquisition period, the subsidence rate is the same but the area of subsidence is smaller in size as compared with the previous period of monitoring with ENVISAT. Maximum subsidence noted for the period from May 2005 to Aug 2006. It was the same period when over pumping of water was carried out in the area of interest. The larger deformation zone is due to the intense extraction of subsurface freshwater, because the subsidence correlated with the data of tube wells provided by the country water management agency. The data showed the increase in numbers of tube wells installed in the locality over the first phase study time period with extraction of water up to 7000 ha per year (Development Statistics of Sindh 2001-2016) with depletion of groundwater table up to 122 m from the surface.

Monthly Deforestation Monitoring with Sentinel-1 Multi-temporal Signatures and InSAR Coherences

&lt;p class="p1"&gt;Sentinel-1 interferometric time-series allow for the accurate retrieval of the target’s temporal decorrelation and, therefore, the inversion of land cover information and its temporal monitoring. This paper describes the development of an observation scenario for monitoring monthly deforestation over the Amazon rainforest, which relies on the use of radar for overcoming the physical limitations of optical sensors caused by the presence of cloud coverage. Specifically, we implement a classification scheme that exploits multi-temporal SAR features, such as backscatter, spatial textures, and interferometric parameters, to map forested areas. Distinct forest maps are generated for consecutive months and further processed to detect deforestation phenomena and map clear-cuts evolution. The obtained results are validated by selecting cloud-free Sentinel-2 multispectral data on the selected area and acquired during the same observation time.&lt;/p&gt;

Multi-channel space-time decorrelation analysis method based on sea clutter

In sea clutter background, spaceborne or airborne surveillance radar with a high-speed moving platform is confronted with the problem of clutter spreading. This poses a considerable challenge for maritime radar systems when detecting slowly moving targets of low signal-to-noise ratio. However, the space-time decorrelation characteristics caused by the internal motions of sea clutter under different sea conditions are mostly neglected in the current detecting techniques, which go against the reliable parameter selection in the clutter suppression process. An innovative multi-channel space-time decorrelation analysis method based on sea clutter is proposed. First, the channel decorrelation factors caused by the channel amplitude-phase errors and the noise are calculated by fusing the calibration signal with echo data. Then, the temporal decorrelation curve caused by the internal motions of sea clutter is obtained by fitting the peak points of multi-channel correlation coefficient curve based on the multi-channel phase center. Finally, the independent spatial decorrelation curve is derived through calculating the multi-channel correlation coefficients of different range bins. Due to the utilization of space-time characteristics decomposition method for sea clutter, the space-time coupling problem can be solved effectively. The experimental results based on the measured data prove the effectiveness of the proposed method.

Detection and Monitoring of Post-Earthquake Landslides in Jiuzhaigou Using Radar Remote Sensing

Objectives On 8th August 2017, a catastrophic earthquake of Ms 7.0 struck the County of Jiuzhaigou in Sichuan Province, China. Because of its high magnitude and shallow epicenter, the earthquake caused grave casualties and property losses. Furthermore, the earthquake triggered numerous secondary mountain disasters such as rockfall and landslide. We use the synthetic aperture radar interferometry (InSAR) technique to detect post-earthquake landslides in Jiuzhaigou area. Methods Firstly, we carry out a quick detection across wide area using differential InSAR (DInSAR) technique with 6 ALOS-2 PALSAR-2 ascending images. DInSAR technique can observe the subtle deformation of landslide surface during the period between two SAR observations, so the active deforming landslides can be efficiently distinguished from stable areas. Nevertheless, the performance of DInSAR is often limited by temporal and spatial decorrelations induced by dense vegetation coverage, etc. To retrieve the temporal evolution of these landslides, detailed monitoring of specific landslides is carried out using time-series InSAR technique with 112 Sentinel-1 ascending and descending images. Time-series InSAR technique can overcome the limitation of traditional DInSAR technique in spatial-temporal decorrelation, and improve the deformation measurement accuracy. Therefore, the combination of DInSAR and time-series InSAR is adopted to accurately monitor single landslide, and efficiently detect landslides in Jiuzhaigou area. Results The results show that there are 13 landslides that can be detected by InSAR analyses in Jiuzhaigou area, including 7 landslides close to residential areas. The time series displacements retrieved indicate that several landslides have long-term linear deformations after the 2017 earthquake. These landslides severely threaten people's life and property and need to be monitored regularly. Finally, the reliability of detection results based on InSAR technology is verified by a field survey. Conclusions The results can provide key supports for the geohazard mitigation and prevention in the Jiuzhaigou scenic area. We indicate that a strategy which combines DInSAR technique with time-series InSAR can efficiently and accurately detect and monitor landslides over a wide area.

Synthetic aperture radar phase unwrapping using region-growing with polynomial-based phase prediction

Phase unwrapping for interferometric synthetic aperture radar (InSAR) remains a challenge due to the presence of speckle noise and temporal decorrelation in many interferograms. This paper proposes a polynomial-based region-growing phase unwrapping (PBRGPU) approach that is built on the region-growing phase unwrapping (RGPU) approach in Xu and Cumming (Xu and Cumming. 1996. A region growing algorithm for insar phase unwrapping. IGARSS 96. International Geoscience and Remote Sensing Symposium. 31–31 May 1996. Lincoln, NE, USA. doi: 10.1109/igarss.1996.516883 ). The proposed approach iteratively performs phase unwrapping at the edges of multiple seeded regions using a least-squares polynomial phase prediction, which allows for the use of statistically rigorous quality assurance to remove low quality pixels from further processing. Here, a user-specified statistical confidence interval is more intuitive to users than the threshold parameters used by other algorithms. The proposed approach is currently the only phase unwrapping approach to take this strategy with its quality assurance. The proposed approach was found to improve upon the solution quality of the RGPU approach, in some cases achieving a tenfold decrease in root-mean-square error for simulated data. The PBRGPU approach performed well when applied to RADARSAT-2 data collected over Polar Bear Provincial Park (Ontario, Canada). The PBRGPU solutions were consistently on par with or superior to those generated by SNAPHU in terms of accuracy. While the PBRGPU approach does lag behind SNAPHU in terms of the domain of the solution, with SNAPHU unwrapping a significantly larger portion of the interferogram in all test cases, this issue could readily be mitigated through post-processing of the unwrapped interferogram. The proposed approach provides a solid foundation for region-growing algorithms that adapt to local noise levels and integrate all available information rather than relying on preprocessing strategies.

Long-Term Remote Monitoring of Ground Deformation Using Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR): Applications and Insights into Geotechnical Engineering Practices

Development of synthetic aperture radar (SAR) technology and the dedicated suite of processing tools have aided the evolution of remote sensing techniques for various Earth Observation (EO) applications. Interferometric SAR (InSAR) is a relatively new geodetic technique which provides high-speed and reliable geographic, geologic, and hazards information allowing the prognosis of future environmental and urban planning. In this study, we explored the applicability of two differential interferometry techniques, conventional and advanced differential InSAR (A-DInSAR), for topographic mapping and long-term geotechnical monitoring by exploiting satellite data, particularly Sentinel-1 SAR data, which is publicly shared. We specifically used the open-source tools of SeNtinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS) for interferometric data processing to implement A-DInSAR. This study presents various applications, which include generation of a digital elevation model (DEM), mapping of seismically induced displacement and associated damages, and detection and long-term monitoring of tunneling-induced ground deformation and rainfall-induced landslide. Geometric and temporal decorrelations posed challenges and limitations in the successful implementation of Sentinel-1 SAR interferometry specifically in vegetated areas. The presented results proved the validity and reliability of the exploited SAR data and InSAR techniques for addressing geotechnical engineering related problems.

Investigation on Mining Subsidence Based on Sentinel-1A Data by SBAS-InSAR technology—Case Study of Ningdong Coalfield, China

Ground deformation characterization was difficult to obtain over large spatial areas before the invention of the Satellite radar interferometry (InSAR) technique. Especially underground mining in the Loess Plateau of China, it causes large-scale ground damage within a short period of time. A small baseline subset (SBAS) algorithm can overcome some limitations of InSAR technology, such as temporal decorrelation, spatial decorrelation, and atmospheric delay. In this study, SBAS-InSAR technology was applied to process 19 scenes of Sentinel-1A data in Ningdong Coalfield, China. We investigated and analyzed the mining subsidence status from March 2015 to June 2016. There are 6 ground deformation areas in the cumulative subsidence maps, and the maximum cumulative subsidence value is -178cm distributed in the Renjiazhuang mining area during this period. The deformation rate map shows that the maximum deformation rate was -117cm/year. GPS data above the working tunnel was collected in six mining areas in Shigouyi. The subsidence value of SBAS data is consistent with GPS observation station data. The results reveal the evolution process of subsidence in mining subsidence and are helpful to the early warning of the mine disaster.

Real-time control of radiofrequency ablation by three-dimensional echo decorrelation imaging

Feasibility for real-time control of radiofrequency ablation (RFA) by three-dimensional (3-D) echo decorrelation imaging, with potential application to liver cancer treatment, is investigated. Ex vivo bovine liver is treated using a clinical RFA system (RITA 1500X generator with StarBurst probe, Angiodynamics). During ablation, pairs of echo volumes from a Siemens SC2000 scanner with a 4Z1c matrix array probe (inter-frame time 18 ms) are recorded as in-phase and quadrature (IQ) data. 3-D echo decorrelation maps are computed from these volume pairs every 11 s. Cumulative echo decorrelation, defined as the temporal maximum decorrelation for each voxel, is spatially averaged within a spherical region of interest (ROI) centered at the RF needle tip. Once the spatially averaged cumulative decorrelation within the spherical ROI exceeds a predefined threshold, ablation is halted by communication with the RF generator via a pneumatic switch connected to a microcontroller circuit. The control threshold and ROI were determined by empirically evaluating predictive performance of decorrelation in preliminary uncontrolled ablation trials. Controlled and uncontrolled ablation are compared based on correspondence of sectioned tissue histology to targeted ablation zones, receiver operating characteristic curve analysis of local ablation prediction by echo decorrelation, treatment duration, and ablation rate.

Gabor mode enrichment in large eddy simulations of turbulent flow

Abstract

First assessment of the interferometric capabilities of SAOCOM-1A: New results over the Domuyo Volcano, Neuquén Argentina

Differential Interferometric Synthetic Aperture Radar (DInSAR) has been used for measuring ground deformations with high both spatial and temporal resolutions. The effectiveness of this technique has been extensively proved using mainly C and X band because of the availability of SAR platforms operating in these frequencies. In vegetated areas, L-band SAR is more adequate because of their less sensitivity to temporal decorrelation. This work presents a review of the characteristics of the L-band Argentinian satellite SAOCOM-1A and its potential in deformation monitoring. In order to show it, we processed a dataset acquired over the Domuyo Volcano (Neuquén, Argentina). Time series and mean deformation maps are validated against those computed using a Sentinel-1 dataset spanning the same time period. Results show an inflation pattern of 6 cm between August 2019 and May 2020. As expected and despising the scarce availability of SAOCOM scenes, its mean velocity map is admittedly more coherent in comparison with the Sentinel-1. Thus, we demonstrate, for the first time, the computation of deformation time series using SAOCOM data.

How Long Is Too Long? Variogram Analysis of AERONET Data to Aid Aerosol Validation and Intercomparison Studies

AbstractGeophysical data sets derived from satellite sensors, ground/airborne instrumentation, and computational models are often compared against each other. A common example is the validation of satellite aerosol optical depth (AOD) retrievals against measurements from Aerosol Robotic Network (AERONET) Sun photometers. Spatiotemporal mismatch between data set sampling means that uncaptured variation in the underlying geophysical field introduces apparent disagreement into such comparisons, known as representation or collocation matchup uncertainty. This study uses variogram analysis of AERONET data to estimate temporal mismatch uncertainties and decorrelation time scales for the global AERONET record. As well as total AOD, the fine‐ and coarse‐mode AODs, Ångström Exponent (AE), and fine‐mode fraction (FMF) of AOD are analyzed. Globally, a time difference of 30 min typically induces from 0.011–0.035 variation in AOD. For total, fine, and coarse AODs the typical time to decorrelation is around 2–10 days. For AE and FMF it is 3–33 days; that is, aerosol systems often persist significantly longer than individual events in them. Biomass burning regions tend to show the largest and fastest subdaily AOD variability and also longest times to decorrelation. Some sites show significant season‐to‐season variations in behavior. These results can be used to inform site‐specific time collocation thresholds for aerosol validation analyses and account for temporal variation when estimating data set uncertainty. They also have implications for comparisons between different satellite products or models, data aggregation, and time series analyses. Results are provided on a site‐by‐site basis to facilitate use by other researchers.

COMPARISON OF PSI AND DINSAR APPROACH FOR THE SUBSIDENCE MONITORING CAUSED BY COAL MINING EXPLOITATION

Abstract. Underground coal exploitation has its reflection in ground movements such as subsidence, sinking or shaking. These cause buildings and infrastructure damage, therefore it is important to measure the magnitude of deformation. Last decades, Differential Interferometric Synthetic Aperture Radar (DInSAR) captured considerable attention as a tool for deformation monitoring. The results of conventional DInSAR, which utilizes two SAR images, are degraded due to atmospheric, topographic and orbital errors. To overcome these limitations, various stacking-based methods have been introduced. Therefore, the goal of presented study is to compare Persistent Scatterer Interferometry (PSI) as stacking-based method with classical DInSAR for monitoring of subsidence caused by underground coal exploitation. Deformations in the areas of active mining exploitation are characterised typically by rapid non-linear movement. The comparison has been performed for the area of active exploitation in Rydułtowy mine located in Upper Silesian Coal Basin (USCB) in Poland. Results from two separate PSI and DInSAR processing portray similar deformation pattern over the study area. Unfortunately, due to the temporal decorrelation, PSI clearly demonstrate smaller information coverage in respect to DInSAR results. Additionally, due to the applied linear deformation model, PSI failed in displacement estimation with magnitude higher than 12 cm. In contrast, DInSAR thanks 6-day temporal baseline and no assumption for a deformation model, was able to capture the maximum magnitude of subsidence reaching 86 cm/year. However, these results are affected by atmospheric artefacts which in presented case study can reach even 14 cm/year. To achieve few cm level of accuracy and to estimate high deformation magnitude such as in presented study case (1m/year), integrated use of both InSAR techniques seems to be the reasonable solution.

InSAR Decorrelation at X-Band From the Joint TanDEM-X/PAZ Constellation

Decorrelation phenomena are always present in synthetic aperture radar interferometry (InSAR). While this implies a certain level of signal degradation, decorrelation is also a characteristic of the type of imaged target itself and can, therefore, be seen as a source of information. In this letter, we investigate InSAR decorrelation effects at the X-band by fitting volume and temporal decorrelation trends using the unique combination of data provided by the TanDEM-X (TDX) and PAZ spaceborne missions. The innovative use of this constellation allows for the acquisition of both single- and repeat-pass data at short revisit times. The concurrent availability of simultaneous acquisitions and the fine temporal resolution makes this constellation the ideal observation scenario for the study of decorrelation phenomena. Overall, we analyze five test sites, characterized by the presence of different land cover classes, and for each of them, we provide volume and temporal decorrelation fitting parameters. The performed analysis gives a first insight on the potential of combining bistatic and repeat-pass InSAR acquisitions also in view of future spaceborne constellations, which could benefit from the TDX/PAZ experience.

A method for GB-InSAR temporal analysis considering the atmospheric correlation in time series

GB-InSAR, with high time-spatial resolution and high accuracy, shows great potential in landslide monitoring. However, the accuracy of GB-InSAR is usually reduced by the atmospheric disturbance and temporal decorrelation. PS-InSAR technology can solve those problems well and get accurate deformation information, so it has been widely adopted in space-borne SAR. For the atmospheric correction, PS-InSAR assumes that the atmospheric phase is only strongly correlated in the space domain. But for GB-InSAR, atmospheric phase shows correlation in both the time and space domains, because of the short time interval. Therefore, the calculation of linear velocity will be affected by the atmospheric disturbance when the PS-InSAR technology is applied to the GB-SAR data. To solve this problem, a PS-InSAR strategy for GB-SAR data considering the atmospheric disturbance in the time domain is proposed. The proposed method uses the differential interferograms interfered by nearby SLCs to ensure the high coherence of interferometric phase and reduces the impact of atmospheric disturbance. The coherence is used for PS selection besides the amplitude deviation index, which can increase the density of PS points. Furthermore, a method for atmospheric correction based on the wrapped phase is presented. Thus, the linear velocity is computed based on the interferogram without most atmospheric disturbance. The validation using the data of the open pit in Malanzhuang, Hebei, China, shows that the proposed method can get a deformation monitoring accuracy of sub-millimeter.

Vegetated Target Decorrelation in SAR and Interferometry: Models, Simulation, and Performance Evaluation

The paper addresses the temporal stability of distributed targets, particularly referring to vegetation, to evaluate the degradation affecting synthetic aperture radar (SAR) imaging and repeat-pass interferometry, and provide efficient SAR simulation schemes for generating big dataset from wide areas. The models that are mostly adopted in literature are critically reviewed, and aim to study decorrelation in a range of time (from hours to days), of interest for long-term SAR, such as ground-based or geosynchronous, or repeat-pass SAR interferometry. It is shown that none of them explicitly account for a decorrelation occurring in the short-term. An explanation is provided, and a novel temporal decorrelation model is proposed to account for that fast decorrelation. A formal method is developed to evaluate the performance of SAR focusing, and interferometry on a homogenous, stationary scene, in terms of Signal-to-Clutter Ratio (SCR), and interferometric coherence. Finally, an efficient implementation of an SAR simulator capable of handling the realistic case of heterogeneous decorrelation over a wide area is discussed. Examples are given by assuming two geostationary SAR missions in C and X band.