Synthetic Aperture Radar Interferograms Research Articles

Airborne Platform Three-Dimensional Positioning Method Based on Interferometric Synthetic Aperture Radar Interferogram Matching

As the demand for precise navigation of aircraft increases in modern society, researching high-precision, high-autonomy navigation systems is both theoretically valuable and practically significant. Because the inertial navigation system (INS) has systematic and random errors, its output information diverges. Therefore, it is necessary to combine them with other navigation systems for real-time compensation and correction of these errors. The SAR matching positioning and navigation system uses synthetic aperture radar (SAR) image matching for platform positioning and compensates for the drift caused by errors in the inertial measurement unit (IMU). Images obtained by SAR are matched with digital landmark data, and the platform’s position is calculated based on the SAR imaging geometry. However, SAR matching positioning faces challenges due to seasonal variations in SAR images, the need for typical landmarks for matching, and the lack of elevation information in two-dimensional SAR image matching. This paper proposes an airborne platform positioning method based on interferometric SAR (InSAR) interferogram matching. InSAR interferograms contain terrain elevation information, are less affected by seasonal changes, and provide higher positioning accuracy and robustness. By matching real-time InSAR-processed interferograms with simulated interferograms using a digital elevation model (DEM), three-dimensional position information about the matching points has been obtained. Subsequently, a three-dimensional positioning model for the platform has bene established using the unit line-of-sight vector decomposition method. In actual flight experiments using an FMCW Ku-band Interferometric SAR system, the proposed platform positioning framework demonstrated its ability to achieve precise positioning in the absence of signals from the global navigation satellite system (GNSS).

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

Correcting interferometric synthetic aperture radar (InSAR) interferograms using Global Navigation Satellite System (GNSS) data can effectively improve their accuracy. However, most of the existing correction methods utilize the difference between GNSS and InSAR data for surface fitting; these methods can effectively correct overall long-wavelength errors, but they are insufficient for multiple medium-wavelength errors in localized areas. Based on this, we propose a method for correcting InSAR interferograms using GNSS data and the K-means spatial clustering algorithm, which is capable of obtaining correction information with high accuracy, thus improving the overall and localized area error correction effects and contributing to obtaining high-precision InSAR deformation time series. In an application involving the Central Valley of Southern California (CVSC), the experimental results show that the proposed correction method can effectively compensate for the deficiency of surface fitting in capturing error details and suppress the effect of low-quality interferograms. At the nine GNSS validation sites that are not included in the modeling process, the errors in the ascending track 137A and descending track 144D are mostly less than 15 mm, and the average root mean square error values are 11.8 mm and 8.0 mm, respectively. Overall, the correction method not only realizes effective interferogram error correction, but also has the advantages of high accuracy, high efficiency, ease of promotion, and can effectively address large-scale and high-precision deformation monitoring scenarios.Graphical

LSC-GInSAR: a GNSS-enhanced InSAR approach by using least squares collocation

SUMMARY High quality Interferometric Synthetic Aperture Radar (InSAR) interferograms are essential for determining surface deformation from InSAR time-series. However, InSAR interferograms are usually polluted by spatially correlated errors (SCEs), especially the unmodelled atmospheric phase delays. To mitigate spatially correlated errors and improve the quality of InSAR interferograms, we propose a new approach to incorporate the Global Navigation Satellite System (GNSS) data from continuously operating reference stations for enhancing InSAR interferograms via modelling SCEs as signals and solving the signals together with the systematic parameters using least squares collocation (LSC), which is referred to as the LSC-GInSAR approach. Our improvement for the GInSAR method of Neely et al. can correct more SCEs. The Sentinel-1 data over the southern Central Valley of California, USA, are processed with our LSC-GInSAR approach, which is compared to the GInSAR approach. The performance of the LSC-GInSAR approach is evaluated by external GNSS displacements. The results show that the LSC-GInSAR approach can effectively mitigate medium-to-long-wavelength SCEs. The displacements resolved by LSC-GInSAR are more consistent with the cGNSS observations than those resolved by GInSAR, with an average root mean square improvement of 14.3 per cent. In addition, the LSC-GInSAR approach reduced the average standard deviations of all 276 InSAR interferograms from 14.2 to 11.0 mm compared to that of the GInSAR approach.

A Spectral Model for Multilook InSAR Phase Noise Due to Geometric Decorrelation

We find that the multilook phase noise associated with geometric decorrelation in a synthetic aperture radar interferogram does not follow the spectral shape of the signal in the range dimension because the phase noise arises from the uncorrelated ends of the frequency bands of the single-channel radar data contributing to the interferogram. The phase noise due to geometric decorrelation therefore diminishes much more quickly than the square root of the number of looks in range when the number of looks is large, and it is thus not well characterized by the common practice of computing the multilook phase noise from the Cramer-Rao bound as a function only of the total coherence and the total number of looks. Large discrepancies may result in cases for which the phase noise due to geometric decorrelation dominates, such as when simulating data with no receiver thermal noise. We present here a more accurate model of the multilook phase noise due to geometric decorrelation in addition to thermal noise. Our model depends on the spectra of the individual channels and on the spectrum of the multilook averaging window in range. Use of this model may have bearing on decisions of whether common-band filtering is necessary for particular applications.

Range Spectral Filtering in SAR Interferometry: Methods and Limitations.

A geometrical decorrelation constitutes one of the sources of noise present in Synthetic Aperture Radar (SAR) interferograms. It comes from the different incidence angles of the two images used to form the interferograms, which cause a spectral (frequency) shift between them. A geometrical decorrelation must be compensated by a specific filtering technique known as range filtering, the goal of which is to estimate this spectral displacement and retain only the common parts of the images' spectra, reducing the noise and improving the quality of the interferograms. Multiple range filters have been proposed in the literature. The most widely used methods are an adaptive filter approach, which estimates the spectral shift directly from the data; a method based on orbital information, which assumes a constant-slope (or flat) terrain; and slope-adaptive algorithms, which consider both orbital information and auxiliary topographic data. Their advantages and limitations are analyzed in this manuscript and, additionally, a new, more refined approach is proposed. Its goal is to enhance the filtering process by automatically adapting the filter to all types of surface variations using a multi-scale strategy. A pair of RADARSAT-2 images that mapped the mountainous area around the Etna volcano (Italy) are used for the study. The results show that filtering accuracy is improved with the new method including the steepest areas and vegetation-covered regions in which the performance of the original methods is limited.

Deep Learning for InSAR Phase Filtering: An Optimized Framework for Phase Unwrapping

Interferometric Synthetic Aperture Radar (InSAR) data processing applications, such as deformation monitoring and topographic mapping, require an interferometric phase filtering step. Indeed, the filtering quality significantly impacts the deformation and terrain height estimation accuracy. However, the existing classical and deep learning-based phase filtering methods provide artefacts in the filtered areas where a large amount of noise prevents retrieving the original signal. In this way, we can no longer distinguish the underlying informative signal for the next processing step. This paper proposes a deep convolutional neural network filtering method, developing a novel learning strategy to preserve the initial phase noise input into these crucial areas. Thanks to the encoder–decoder powerful phase feature extraction ability, the network can predict an accurate coherence and filtered interferometric phase, ensuring reliable final results. Furthermore, we also address a novel Synthetic Aperture Radar (SAR) interferograms simulation strategy that, using initial parameters estimated from real SAR images, considers physical behaviors typical of a real acquisition. According to the results achieved on simulated and real InSAR data, the proposed filtering method significantly outperforms the classical and deep learning-based ones.

Automatic delineation of cracks with Sentinel-1 interferometry for monitoring ice shelf damage and calving

Abstract. Monitoring the evolution of ice shelf damage such as crevasses and rifts is important for a better understanding of the mechanisms controlling the breakup of ice shelves and for improving predictions about iceberg calving and ice shelf disintegration. Nowadays, the previously existing observational gap has been reduced by the Copernicus Sentinel-1 synthetic aperture radar (SAR) mission that provides a continuous coverage of the Antarctic margins with a 6 or 12 d repeat period. The unprecedented coverage and temporal sampling enables, for the first time, a year-round systematic monitoring of ice shelf fracturing and iceberg calving, as well as the detection of precursor signs of calving events. In this paper, a novel method based on SAR interferometry is presented for an automatic detection and delineation of active cracks on ice shelves. Propagating cracks cause phase discontinuities that are extracted automatically by applying a Canny edge detection procedure to the spatial phase gradient derived from a SAR interferogram. The potential of the proposed method is demonstrated in the case of Brunt Ice Shelf, Antarctica, using a stack of 6 d repeat-pass Sentinel-1 interferograms acquired between September 2020 and March 2021. The full life cycle of the North Rift is monitored, including the rift detection, its propagation at rates varying between 0.25 and 1.30 km d−1, and the final calving event that gave birth to the iceberg A74 on 26 February 2021. The automatically delineated cracks agree well with the North Rift location in Landsat 8 images and with the eventual location of the ice shelf edge after the iceberg broke off. The strain variations observed in the interferograms are attributed to a rigid-body rotation of the ice about the expanding tip of the North Rift in response to the rifting activity. The extent of the North Rift is captured by SAR interferometry well before it becomes visible in SAR backscatter images and a few days before it could be identified in optical images, hence highlighting the high sensitivity of SAR interferometry to small variations in the ice shelf strain pattern and its potential for detecting early signs of natural calving events, ice shelf fracturing and damage development.

Research on a GB-SAR interferogram filtering method for open-pit mines based on eight-neighbourhood outlier discrimination

Ground-based synthetic aperture radar (GB-SAR) interferograms contain a large amount of phase noise. The existing methods lose effective deformation information to varying degrees upon filtering, which seriously reduces the monitoring accuracy of GB-SAR. In this paper, a GB-SAR interferogram filtering method for open-pit mines is studied. First, permanent scatterer points (PSPs) are extracted through coarse filtering, and the distribution of the noise phase is analysed. Then, an improved Grubbs outlier discrimination criterion is introduced considering the uncertainty of the PSP distribution. A method based on the eight-neighbourhood outlier discrimination (EOD) criterion is proposed to identify noise points. High-coherence points (HCPs) are used to reconstruct the phase of the noise points to filter the GB-SAR interferogram. Finally, the filtering effect is quantitatively evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The superiority of the proposed filtering method in the image displacement region is verified based on simulated and real deformation data. The results show that the proposed method can accurately filter out the phase noise of interferograms and improve the accuracy of GB-SAR for slope monitoring.

A Lightweight Anchor-Free Subsidence Basin Detection Model With Adaptive Sample Assignment in Interferometric Synthetic Aperture Radar Interferogram

The excessive exploitation of coal resources has caused serious land subsidence, which seriously threatens the lives of the residents and the ecological environment in coal mining areas. Therefore, it is of great significance to precisely monitor and analyze the land subsidence in the mining area. To automatically detect the subsidence basins in the mining area from the interferometric synthetic aperture radar (InSAR) interferograms with wide swath, a lightweight model for detecting the subsidence basins with an anchor-free and adaptive sample assignment based on the YOLO V5 network, named Light YOLO-Basin model, is proposed in this paper. First, the depth and width scaling of the convolution layers and the depthwise separable convolution are used to make the model lightweight to reduce the memory consumption of the CSPDarknet53 backbone network. Furthermore, the anchor-free detection box encoding method is used to deal with the inapplicability of the anchor box parameters, and an optimal transport assignment (OTA) adaptive sample assignment method is introduced to solve the difficulty of optimizing the model caused by abandoning the anchor box. To verify the accuracy and reliability of the proposed model, we acquired 62 Sentinel-1A images over Jining and Huaibei coalfield (China) for the training model and experimental verification. In contrast with the original YOLO V5 model, the mean average precision (mAP) value of the Light YOLO-Basin model increases from 45.92 to 55.12%. The lightweight modules of the model sped up the calculation with the one billion floating-point operations (GFLOPs) from 32.81 to 10.07 and reduced the parameters from 207.10 to 40.39 MB. The Light YOLO-Basin model proposed in this paper can effectively recognize and detect the subsidence basins in the mining areas from the InSAR interferograms.

Soil Moisture Estimation Using Atmospherically Corrected C-Band InSAR Data

A methodology to generate calibrated maps of soil moisture from C-band synthetic aperture radar (SAR) images processed by SAR interferometry (InSAR) technique is presented. The proposed methodology uses atmospheric phase delay (APD) maps obtained from a time series of Sentinel-1 interferograms, to disentangle the APD and soil moisture contributions to Sentinel-1 interferograms. We show how the high spatial resolution and short temporal baseline of Sentinel-1 image can help to estimate soil moisture using a daisy chain InSAR processing. The estimated soil moisture maps are compared with in situ data collected by five soil moisture sensors installed in an experimental field, characterized by bare soil, located close to Lisbon, Portugal. Results show that after removing the APD effects in SAR interferogram, there is a correction of the bias in the soil moisture estimation and an improvement in the correlation coefficient with the soil moisture measurements, from 0.38 to 0.78. Soil moisture changes were measured during a sequence of rain events in the winter season. A root-mean-square (rms) error less than 0.04 m³/m³ was found over a variety of meteorological conditions.

On the Phase Nonclosure of Multilook SAR Interferogram Triplets

This work explores the properties characterizing the phase non-closure of multi-look synthetic aperture radar (SAR) interferograms. Specifically, we study the implications of multi-look phase time incongruences on the generation of ground displacement time-series through small baseline (SB) multi-temporal InSAR (Mt-InSAR) methods. Our research clarifies how these phase inconsistencies can propagate through a time-redundant network of SB interferograms and contribute, along with phase unwrapping (PhU) errors, to the quality of the generated ground displacement products. Moreover, we analyze the effects of short-lived phase bias signals that could happen in sequences of short baseline (SB) interferograms and propose a strategy for their mitigation. The developed methods have been tested using both simulated and real SAR data. The latter were collected by the Sentinel-1A/B (C-band) sensors over the study areas of Nevada state, U.S., and Sicily Island, Italy.

An Area-Based Projection Algorithm for SAR Radiometric Terrain Correction and Geocoding

This article describes a projection algorithm between radar and map coordinates based on the representation of radar samples as area elements (AEs) rather than point elements. Each AE on the map grid (geographic grid) is associated with a number of radar grid samples that intersect completely or partially the AE. The association enables the geocoding (i.e., the map projection of radar imagery) with adaptive multilooking, accurately accounting for all radar samples contributing to the geocoded elements according to topography and radar geometry. By using averaging rather than interpolation, the proposed projection does not suffer from interpolation overfitting. The area-based geocoding also enables the generation of the geocoded polarimetric covariance matrix (GCOV) and geocoded synthetic aperture radar (SAR) interferograms with adaptive multilooking. Analogously, the slant-range projection of geocoded data is improved by projecting geographic grid pixels onto the radar grid according to their corresponding location based on the radar geometry without leaving gaps. This approach is used to reduce the computation time of previously published radiometric terrain correction (RTC) algorithms, performing 3.6–6.5 times faster over multilooked data and up to 26.3 times faster over single-look data. We demonstrate the strengths of the proposed area projection (AP) algorithm for RTC and geocoding using Uninhabited Aerial Vehicle SAR (UAVSAR), Sentinel-1B, and ALOS-2/PALSAR-2 data, and evaluate the results in the context of the upcoming NASA-ISRO SAR (NISAR) mission.

Atmospheric Phase Screen Compensation on Wrapped Ground-Based SAR Interferograms

This work proposes a method for estimating and compensating the atmospheric phase screen (APS) in sets of synthetic aperture radar (SAR) interferograms generated with a ground-based SAR (GB-SAR) instrument. We address the presented approach’s physical, statistical, and mathematical framework by discussing its potential and limitations. In contrast with other existing algorithms that estimate the APS from the unwrapped phase signals, our methodology is based on the straightforward analysis of the wrapped phases, directly. Therefore, the method is not affected by any potential phase unwrapping mistake, and it is suitable for multitemporal interferometric SAR (InSAR) applications. The effects of the local topography, the decorrelation noise, and the ground deformation on the APS estimates are deeply studied. Experiments performed on simulated and real GB-SAR InSAR data corroborate the validity of the theory. In particular, the simulated results show that the method is beneficial in zones with medium-to-high topographic slopes (e.g., for Alpine and mountainous regions).

Decorrelating Ambiguities in SAR Interferometry Through Slight PRI Variation

Synthetic aperture radar (SAR) interferometry is a well-established technique for producing high-resolution digital elevation models (DEMs) of the Earth’s surface and measuring displacements on different time scales. Observations of SAR interferograms, however, show that azimuth ambiguities can be coherently imaged and may lead to phase biases and coherence losses that significantly degrade the interferometric performance. Whereas imposing very low ambiguity levels may represent a severe design constraint for a spaceborne SAR system, a slight variation of the pulse repetition interval (PRI) is a new, simple, yet effective technique to decorrelate ambiguities, which in turn reduces the phase biases and coherence losses without substantially affecting the imaged swath width. An additional benefit of the PRI variation is that range ambiguities also become decorrelated. This paper addresses two cases: For the repeat-pass case, slightly different pulse repetition frequencies (PRFs) can be used for the two acquisitions and the minimum required PRF difference can be analytically derived resorting to the power spectral density of the ambiguous signals; For the single-pass case, a slight variation of the PRI during the common acquisition is an effective solution, in case an along-track baseline is present. In particular, a square wave PRI variation scheme outperforms sinusoidal or random ones. Finally, simulations using TanDEM-X data are presented to show the improvement in interferogram and DEM quality resulting from ambiguity decorrelation. This work is relevant for the design of future spaceborne interferometric SAR systems and for the enhanced exploitation of current ones.

A New Likelihood Function for Consistent Phase Series Estimation in Distributed Scatterer Interferometry

The proper use of distributed scatterer (DS) can improve both the density and quality of synthetic aperture radar (SAR) interferometry (InSAR) measurements. A critical step in DS interferometry (DSI) is the restoration of a consistent phase series from SAR interferogram stacks. Most state-of-the-art algorithms adopt an approximate likelihood function to calculate the likelihood by replacing the true coherence matrix with its estimation, more specifically, the sample coherence matrix (SCM). However, this approximation has a drawback in that the coherence estimates are greatly biased when the coherence is low. In this study, we derive a new likelihood function without such an approximation. Accordingly, a DSI framework using this function for phase estimation and point selection is provided. In this framework, the new likelihood function serves as a cost function for phase estimation and a quality measure for DS selection. Its performance is investigated by experiments in a simulation study and a real-world case study using Sentinel-1 data over Shenzhen airport in China. The results reveal that the proposed DSI framework outperforms the existing state-of-the-art approaches in different scenarios, in terms of providing a more accurate estimation and improving DS density and coverage.

New insight into the 24 January 2020, Mw 6.8 Elazığ earthquake (Turkey): An evidence for rupture-parallel pull-apart basin activation along the East Anatolian Fault Zone constrained by Geodetic and Seismological data

In this study, we investigated the main features of the causative fault of the 24 January 2020, Mw 6.8 Elazığ earthquake (Turkey) using seismological and geodetic data sets to provide new insight into the East Anatolian Fault Zone (EAFZ). We first constrained the co-seismic surface deformation and the rupture geometry of the causative fault segment using Interferometric Synthetic Aperture Radar (InSAR) interferograms (Sentinel-1A/B satellites) and teleseismic waveform inversion, respectively. Also, we determined the centroid moment tensor (CMT) solutions of focal mechanisms of the 27 aftershocks using the regional waveform inversion method. Finally, we evaluated the co-seismic slip distribution and the CMT solutions of the causative fault as well as of adjacent segments using the 27 focal solutions of the aftershocks, superimposed on the surface deformation pattern. The CMT solution of the 24 January 2020Elazığ earthquake reveals a pure strike-slip focal mechanism, consistent with the structural pattern and left-lateral motion of the EAFZ. The rupture process of the Elazığ event indicated that the rupture is started at 12 km around the hypocenter, and then propagated bilaterally along the NE-SW but mainly toward the southwest. The rupture slip has initially propagated toward the southwest (first 10 s) and northeast (4 s), and again toward the southwest (9 s). Maximum displacement is calculated as 1.3 m about 20 km southwest of the hypocenter at 6 km depth (centroid depth). The rupture stopped to down-dip around 20 km depth toward the southwest. The distribution of the slip vectors indicates that the rupture continued mostly through a normal oblique movement. Most of the moment release was released SW of the hypocenter and the rupture reached up to around 50 km. The focal mechanisms of analyzed 27 aftershocks show strike-slip, but mostly normal and normal oblique-slip faulting with an orientation of the tensional axes (NNE-SSW), indicating a normal oblique-slip, “transtensional” stress regime, parallel-subparallel to the strike of the EAFZ, consistent with SW-rupture directivity and co- seismic deformation pattern. Finally, based on the co-seismic surface deformation compatible with the distributional pattern of normal focal solutions, normal and normal oblique-slip focals of the aftershocks evidence the rupture-parallel pull-apart basin activation as a segment boundary of the left-lateral strike-slip movement of the EAFZ.

Multifractal Analysis of a Seismic Moment Distribution Obtained From InSAR Inversion.

Interferometric synthetic aperture radar (InSAR) interferograms contain valuable information about the fault systems hidden beneath the surface of the Earth. In a new approach, we aim to fit InSAR ground deformation data using a distribution of multiple seismic point sources whose parameters are found by a genetic algorithm. The resulting source distribution could provide another useful tool in solving the difficult problem of accurately mapping earthquake faults. We apply the algorithm to an ALOS‐2 InSAR interferogram and perform a multifractal analysis on the resulting distribution, finding that it exhibits multifractal properties. We report first results and discuss advantages and disadvantages of this approach.

ON THE FEASIBILITY OF APPLYING ORBITAL CORRECTIONS TO SAOCOM-1 DATA WITH FREE OPEN SOURCE SOFTWARE (FOSS) TO GENERATE DIGITAL SURFACE MODELS: A CASE STUDY IN ARGENTINA

Abstract. In this work we present an orbital correction workflow developed with FOSS tools to compensate for orbital errors present in Synthetic Aperture Radar (SAR) interferograms. The technique is tested in forested areas in Argentina, using full polarimetric images from the argentinean SAR constellation SAOCOM-1 (Satélite Argentino Con Microondas). The results are contrasted with field measurements of canopy height provided by local producers, and the results show that the Root Mean Square Error (RMSE) of the satellite measurements is significantly reduced after the orbital correction. Moreover, forest plantation become more distinguishable in the retrieved Digital Surface Models, especially in those pairs with larger spatial baseline. A section of this article is also dedicated to the discussion on which are the best parameters to run the module, and how different configurations can affect the result.

An adaptive patch-based goldstein filter for interferometric phase denoising

ABSTRACT The Goldstein filter was a well-known approach of synthetic aperture radar interferogram filtering for its simplicity and high efficiency. The filtering performance of Goldstein and its improved filters is mostly affected by several parameters, such as the noise suppression parameter α, the sliding patch size, and the kernel window size. Yet, the fixed parameters will cause the filtering intensity to mismatch the noise levels of different regions. In this paper, we propose an adaptive patch-based Goldstein filter (AP-Goldstein filter), which takes the pseudo-variation coefficient as the measure of patch homogeneity and adaptively selects the patch sizes in different regions of interferogram. This filter controls the noise suppression parameter α by pseudo-coherence, which is a common and effective indicator applied to filter the interferogram phase. Meanwhile, the previously determined patch size of each pixel is used to confirm the kernel window size at the same region. The proposed and other advanced filters were tested on real and simulated interferograms. The qualitative and quantitative comparison of the experimental results shows that this new method can better restrain the phase noise of interferogram while maintaining the edge detailed information and can improve the automation of filtering.

Tropospheric Correction of Sentinel-1 Synthetic Aperture Radar Interferograms Using a High-Resolution Weather Model Validated by GNSS Measurements

Synthetic Aperture Radar Interferometry (InSAR) is a space geodetic technique used for mapping deformations of the Earth’s surface. It has been developed and used increasingly during the last thirty years to measure displacements produced by earthquakes, volcanic activity and other crustal deformations. A limiting factor to this technique is the effect of the troposphere, as spatial and temporal variations in temperature, pressure, and relative humidity introduce significant phase delays in the microwave imagery, thus “masking” surface displacements due to tectonic or other geophysical processes. The use of Numerical Weather Prediction (NWP) models as a tropospheric correction method in InSAR can tackle several of the problems faced with other correction techniques (such as timing, spatial coverage and data availability issues). High-resolution tropospheric modelling is particularly useful in the case of single interferograms, where the removal of the atmospheric phase screen (and especially the highly variable turbulent component) can reveal large-amplitude deformation signals (as in the case of an earthquake). In the western Gulf of Corinth, prominent topography makes the removal of both the stratified and turbulent atmospheric phase screens a challenging task. Here, we investigate the extent to which a high-resolution WRF 1-km re-analysis can produce detailed tropospheric delay maps of the required accuracy by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is operated with varying physical parameterization in order to identify the best configuration, and validated with GNSS zenithal tropospheric delays, providing a benchmark of real atmospheric conditions. We correct sixteen Sentinel-1A interferograms with differential delay maps at the line-of-sight (LOS) produced by WRF re-analysis. In most cases, corrections lead to a decrease in the phase gradient, with average root-mean-square (RMS) and standard deviation (SD) reductions in the wrapped phase of 6.0% and 19.3%, respectively. Results suggest a high potential of the model to reproduce both the long-wavelength stratified atmospheric signal and the short-wave turbulent atmospheric component which are evident in the interferograms.