Atmospheric delay has a profound impact on synthetic aperture radar (SAR) interferometry, inducing a spatial signal that significantly devaluates interferometric products. While the wide-scale variability of the atmosphere can be adequately modeled with global or regional weather models, it is especially the turbulent and convective part of the atmosphere at smaller spatial and temporal scales that is typically poorly captured. Due to the high resolution and precision of InSAR, there is a need for a realistic modeling of the 3-D distribution of turbulent refractivity in the boundary layer. This would enable assessment of the impact of a temporal or spatial model misalignment on the interferometric products, and contribute to studying the impact for future SAR missions. Here we test the feasibility of an advanced large Eddy simulation (LES) model to simulate a time-series refractivity distribution with a high spatio-temporal resolution to show the spatio-temporal variability of the troposphere on short time scales. We found for a fair-weather situation that the LES model produces realistic atmospheric simulations that match stochastically with results found in interferometric studies and that tropospheric delay variation leads to significant phase gradients within several minutes. This implies that even when using an (unrealistic) perfect weather model with resolutions similar to the SAR image, realizations that are several minutes apart from the time of the SAR acquisition will lead to significant phase errors. We propose the use of LES models as a realistic instrument to perform InSAR quality-assessments and for the development and simulation for future missions.@en

Synthetic aperture radar (SAR) missions with short repeat times enable opportunities for near real-time deformation monitoring. Traditional multitemporal interferometric SAR (MT-InSAR) is able to monitor long-term and periodic deformation with high precision by time-series analysis. However, as time series lengthen, it is time-consuming to update the current results by reprocessing the whole dataset. Additionally, the number of coherent scatterers varies over time due to disappearing and emerging scatterers due to inevitable changes in surface scattering, and potential deformation anomalies require changes in the prevailing deformation model. Here, we propose a novel method to analyze InSAR time series recursively and detect both significant changes in scattering as well as deformation anomalies based on the new acquisitions. Sequential change detection is developed to identify temporary coherent scatterers (TCSs) using amplitude time series. Based on the predicted phase residuals, scatterers with abnormal deformation displacements are identified by a generalized ratio test, while the parameters of stable scatterers are updated using Kalman filtering. The quality of the anomaly detection is assessed based on the detectability power and the minimum detectable deformation. This facilitates (near) real-time data processing and decreases the false alarm likelihood. Experimental results show that the technique can be used for the real-time evaluation of deformation risks.

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Atmospheric delay induces spatial phase errors and decorrelation in synthetic aperture radar (SAR) interferometry, especially in extreme weather conditions. For SAR missions, the atmosphere is considered to be spatio-temporally frozen during the aperture integration time, which is correct for Low Earth Orbit (LEO) SAR systems. However, this assumption may be inappropriate for Geosynchronous Earth Orbit (GEO) SAR since it can deploy a much longer integration time. Here we simulate a sequence of refractivity distributions with a high spatio-temporal resolution to analyze the spatio-temporal variable troposphere. The impacts of both frozen flow shift and turbulent delay in the time series in-terferograms are obtained, showing that tropospheric delay varies rapidly and may lead to phase decorrelaton within a few minutes.@en

Multi-temporal interferometric synthetic apertureradar (MT-InSAR) is used for many applications in earthobservation. Most MT-InSAR methods select scatterers with highcoherence throughout the entire time series. However, as timeseries lengthen, inevitable changes in surface scattering leadto decorrelation, which systematically decreases the number ofcoherent scatterers. Here, we propose a novel method to detectand process temporary coherent scatterers (TCS) by subsequentlyanalyzing the amplitude and the interferometric phase. Twohypothesis tests are developed for amplitude analysis in order toidentify the moments of appearing and/or disappearing coherentscatterers. Based on the amplitude analysis, the parametersof interest are then estimated using the interferometric phase.An optimized adaptive temporal subset approach is proposed toimprove the precision of the estimated parameters. If the scatter-ers are not evenly distributed over the area, a secondary (support)network is designed to improve the spatial point distribution.The main advantage of this method is the reliable extraction ofa subset of time series without using any contextual information.Experimental results show that the TCSs significantly increasethe number of observations for displacement monitoring andimprove the change detection capability in urban constructionareas.@en

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) can be applied to monitor the structural health of infrastructure such as railways, bridges, and highways. However, for the successful interpretation of the observed deformation within a structure, or between structures, it is imperative to associate a radar scatterer unambiguously with an actual physical object. Unfortunately, the limited positioning accuracy of the radar scatterers hampers this attribution, which limits the applicability of MT-InSAR. In this study, we propose an approach for health monitoring of railway system combining MT-InSAR and LiDAR (laser scanning) data. An amplitude-augmented interferometric processing approach is applied to extract continuously coherent scatterers (CCS) and temporary coherent scatterers (TCS), and estimate the parameters of interest. Based on the 3D confidence ellipsoid and a decorrelation transformation, all radar scatterers are linked to points in the point cloud and their coordinates are corrected as well. Additionally, several quality metrics defined using both the covariance matrix and the radar geometry are introduced to evaluate the results. Experimental results show that most radar scatterers match well with laser points and that LiDAR data are valuable as auxiliary data to classify the radar scatterers.@en