Atmospheric Phase Delay Research Articles

Assessing Slip Rates on the Xianshuihe Fault Using InSAR with Emphasis on Phase Unwrapping Error and Atmospheric Delay Corrections

Located on the southeastern periphery of the Tibetan Plateau, the Xianshuihe fault (XSHF) is an active left-lateral strike-slip fault renowned for its frequent and intensive seismic activities. This highlights the necessity of employing advanced geodetic methodologies to precisely evaluate the fault kinematics and seismic hazard potential along this fault. Among these techniques, interferometric synthetic aperture radar (InSAR) stands out for its high spatial resolution and regular revisit intervals, enabling accurate mapping of interseismic deformation associated with fault motion. However, the precision of InSAR in measuring deformation encounters several challenges, particularly artifacts stemming from phase unwrapping errors and atmospheric phase delays. In this study, we utilize ascending and descending Sentinel-1 InSAR images spanning from January 2017 to January 2023 to drive the line-of-sight (LOS) mean crustal velocities associated with the XSHF with emphasis on phase unwrapping errors and atmospheric delay corrections. Then, the reliability of the derived LOS velocities is assessed using independent observations from the Global Navigation Satellite System (GNSS). The inferred fault slip rate along the XSHF shows significant along-strike variations, gradually decreasing from ~11.1 mm/yr at the Luhuo section to ~6.6 mm/yr at the Kangding section and then sharply increasing to ~13.0 mm/yr towards its eastern terminus at the Moxi section. The fault locking depth shows similar along-strike variations, decreasing from ~19.5 km in the northwestern part to ~4.8 km at the Kangding section, before increasing to 19.6 km at the Moxi segment. Notably, apparent surface fault creeping, characterized by a slip rate of ~2.7 mm/yr, is observed at the Kangding segment, likely resulting from postseismic slip following the 2014 Mw 6.3 Kangding earthquake.

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 Multi-Scale Spatial Difference Approach to Estimating Topography Correlated Atmospheric Delay in Radar Interferograms

The Interferometric Synthetic Aperture Radar (InSAR) has been widely used as a powerful technique for monitoring land surface deformations over the last three decades. InSAR observations can be plagued by atmospheric phase delays; some have a roughly linear relationship with the ground elevation, which can be approximated using a linear model. However, the estimation results of this linear relationship are sometimes affected by phase ramps such as orbital errors, tidal loading, etc. In this study, we present a new approach to estimate the transfer function of vertical stratification phase delays and the transfer function of phase ramps. Our method uses the idea of multi-scale spatial differences to decompose the atmospheric phase delay into the vertical stratification component, phase ramp component, and other features. This decomposition makes the correlation between the vertical stratification phase delays and topography more significant and stable. This can establish the correlation between the different scales and phase ramps. We demonstrate our approach using a synthetic test and two real interferograms. In the synthetic test, the transfer functions estimated by our method were closer to the design values than those estimated by the full interferogram–topography correlation approach and the band-pass filtering approach. In the first real interferogram, out of the 9 sub-regions corrected by the proposed method, 7 sub-regions were outperformed the full interferogram–topography correlation approach, and 8 sub-regions were superior to the band-pass filtering method. Our technique offers a greater correction effect and robustness for coseismic deformation signals in the second real interferogram.

Weather model based atmospheric corrections of Sentinel-1 InSAR deformation data at Turkish volcanoes

SUMMARYOne of the main constraints on the use of satellite radar data for monitoring natural hazards is the existence of atmospheric signals. In particular, volcanic deformation can be difficult to identify because atmospheric phase delays can mask or even mimic ground deformation signals. Eliminating atmospheric signals is particularly crucial for high-relief volcanoes such as Ağrı, Tendürek, Acigöl, Göllüdağ and Hasandağ in the Eastern and Central Anatolia. To overcome the atmospheric effects, we use high-resolution ECMWF weather models coupled with an empirical phase-elevation approach for correcting Sentinel-1 interferograms. We apply these methods to two areas of Turkey, the first of which covers three volcanoes in Central Anatolia (Acigöl, Göllüdağ, Hasandağ) between January 2016 and December 2018 and the second covers two volcanoes in Eastern Anatolia (Ağrı, Tendürek) between September 2016 and December 2018. The reduction in standard deviation (quality factor) is calculated for both ascending and descending tracks and the atmospheric corrections are found to perform better on descending interferograms in both cases. Then, we use a least-squares approach to produce a time-series. For Central Anatolia, we used 416 ascending and 415 descending interferograms to create 144 and 145 cumulative displacement maps, respectively, and for Eastern Anatolia, we used 390 ascending and 380 descending interferograms to produce 137 and 130 cumulative displacement maps, respectively. We find that the temporal standard deviation before atmospheric corrections ranges between 0.9 and 3.7 cm for the five volcanoes in the region and is consistently higher on ascending track data, which is acquired at the end of the day when solar heating is greatest. Atmospheric correction reduces the standard deviation to 0.5–2.5 cm. Residual signals might be due to the ice-cap at Ağrı and agriculture near Acigöl. We conclude that these volcanoes did not experience significant magmatic deformation during this time period, despite the apparent signals visible in individual uncorrected interferograms. We demonstrate that atmospheric corrections are vital when using InSAR for monitoring the deformation of high-relief volcanoes in arid continental climates such as Turkey.

Multi-Temporal InSAR Deformation Monitoring Zongling Landslide Group in Guizhou Province Based on the Adaptive Network Method

Due to the influence of atmospheric phase delays and terrain fluctuation in complex mountainous areas, traditional PS-InSAR technology often fails to select enough measurement points (MPs) and loses effective MPs during phase unwrapping. To solve this problem, this paper proposes an adaptive network construction algorithm, which combines the permanent scatterer (PS) points with the distributed scatterer (DS) points. Firstly, to ensure the extraction quality of the DS points, the covariance matrix of DS points is estimated robustly. Secondly, based on the traditional Delaunay triangulation network, an adaptive network construction method is proposed, which can adaptively increase edge redundancy and network connectivity by considering the edge length, edge coherence, edge number, and spatial distribution. Finally, a total of 31 RADARSAT-2 SAR images that cover the Zongling landslide group in Guizhou Province were used to prove the effectiveness of proposed method. The results show that the quantity of available DS points can be increased by 23.6%, through the robust estimation of the covariance matrix. In addition, it is demonstrated that the proposed network construction algorithm can balance the number, distribution, and quality of edges in the dense and sparse areas of MPs adaptively. This adaptive network construction approach can maintain good connectivity and avoid losing effective MPs to the greatest extent, especially when the scattering points are far away from the reference points. In short, the proposed algorithm improves the number of effective MPs and accuracy of phase unwrapping.

Block Optimization and Outlier Detection in GEO SAR Turbulent Tropospheric Error Compensation

Geosynchronous Synthetic Aperture Radar (GEO SAR) has important potential applications in the fields of atmospheric water cycle and digital weather forecast, and establishing the connection between GEO SAR imaging and atmospheric water vapor inversion is a key step. The turbulent variation of atmospheric phase delay will cause the SAR image defocus, and turbulent tropospheric delay phase estimation based on block autofocus method in low-orbit high resolution space-borne SAR can be used for reference, but this method is very sensitive to the data block selection, modulation frequency (FM) outlier detection and correction. This Letter gives some considerations to these two problems of block selection and outlier correction. Based on the idea of Extreme Learning Machine (ELM), a mathematical model with nonlinearity as the core and linear model as easy to solve is established. When the parameters affecting the turbulent troposphere are known, the model can be directly used to obtain the block optimization selection, namely, the number and size of blocks. When the parameters are unknown, the model can be used to roughly inverse the parameters and provide initial values for iteration. For the second problem, using the prior information of the correlation of the FM estimated by the adjacent sub-blocks, we proposes a Local Outlier Factor (LOF) method to identify the error point by defining the distance threshold. The effectiveness of the proposed algorithm is verified by Sentinel-1 data injected with tropospheric error.

Coseismic deformation and slip distribution of the 2017 Mw 6.3 Jinghe, Xinjiang, western China earthquake based on InSAR observations: a buried reverse event on previously unknown fault

SUMMARY The 2017 Mw 6.3 Jinghe earthquake occurred in the orogenic zone of the North Tianshan mountain range, Xinjiang, western China. No evident surface rupture was identified by field investigation conducted immediately after the earthquake. We investigate the coseismic and post-seismic deformation fields due to the Jinghe event using the C-band Sentinel-1 SAR imagery, and further analyse its causative fault. The Generic Atmospheric Correction Online Service for InSAR (GACOS) model is employed to remove the atmospheric phase delay of multiple InSAR deformation maps. Coseismic deformation fields are resolved by averaging the high quality deformation maps. A nonlinear inversion scheme is used to find the optimized fault geometry in a layered elastic crust. The results imply that the Jinghe earthquake is characterized by thrust faulting, with striking and dipping angles of ∼62° and ∼28°, respectively. Subsequently coseismic slip distribution is estimated using the steepest descent method program, constrained by the derived coseismic deformation fields. The inversion results show that the average slip is ∼0.08 m and the average rake angle is ∼98°. The maximum slip is ∼0.24 m, located at the depth of 12.9 km. The moment magnitude is estimated to be Mw 6.38. The fault geometry is generally consistent with the relocated aftershocks distribution. Both the InSAR-derived deformation field and the aftershock distribution indicate that the Jinghe earthquake is attributed to a previously unknown buried fault beneath the Yongji fold with a strike of 62°. No significant post-seismic deformation is identified in the zone of coseismic deformation. This study shows that the Jinghe earthquake is a typical inland thrust event in the North Tianshan area, which is affected by south to north compression due to the Indian-Eurasian collision.

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.

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.

Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

The interferometric synthetic aperture radar (InSAR) technique is widely utilized to measure ground-surface displacement. One of the main limitations of the measurements is the atmospheric phase delay effects. For satellites with shorter wavelengths, the atmospheric delay mainly consists of the tropospheric delay influenced by temperature, pressure, and water vapor. Tropospheric delay can be calculated using numerical weather prediction (NWP) model at the same moment as synthetic aperture radar (SAR) acquisition. Scientific researchers mainly use ensemble forecasting to produce better forecasts and analyze the uncertainties caused by physic parameterizations. In this study, we simulated the relevant meteorological parameters using the ensemble scheme of the stochastic physic perturbation tendency (SPPT) based on the weather research forecasting (WRF) model, which is one of the most broadly used NWP models. We selected an area in Foshan, Guangdong Province, in the southeast of China, and calculated the corresponding atmospheric delay. InSAR images were computed through data from the Sentinel-1A satellite and mitigated by the ensemble mean of the WRF-SPPT results. The WRF-SPPT method improves the mitigating effect more than WRF simulation without ensemble forecasting. The atmospherically corrected InSAR phases were used in the stacking process to estimate the linear deformation rate in the experimental area. The root mean square errors (RMSE) of the deformation rate without correction, with WRF-only correction, and with WRF-SPPT correction were calculated, indicating that ensemble forecasting can significantly reduce the atmospheric delay in stacking. In addition, the ensemble forecasting based on a combination of initial uncertainties and stochastic physic perturbation tendencies showed better correction performance compared with the ensemble forecasting generated by a set of perturbed initial conditions without considering the model’s uncertainties.

Use of GNSS Tropospheric Delay Measurements for the Parameterization and Validation of WRF High-Resolution Re-Analysis over the Western Gulf of Corinth, Greece: The PaTrop Experiment

In the last thirty years, Synthetic Aperture Radar interferometry (InSAR) and the Global Navigation Satellite System (GNSS) have become fundamental space geodetic techniques for mapping surface deformations due to tectonic movements. One major limiting factor to those techniques is the effect of the troposphere, as surface velocities are of the order of a few mm yr−1, and high accuracy (to mm level) is required. The troposphere introduces a path delay in the microwave signal, which, in the case of GNSS Precise Point Positioning (PPP), can nowadays be partly removed with the use of specialized mapping functions. Moreover, tropospheric stratification and short wavelength spatial turbulences produce an additive noise to the low amplitude ground deformations calculated by the (multitemporal) InSAR methodology. InSAR atmospheric phase delay corrections are much more challenging, as opposed to GNSS PPP, due to the single pass geometry and the gridded nature of the acquired data. Thus, the precise knowledge of the tropospheric parameters along the propagation medium is extremely useful for the estimation and correction of the atmospheric phase delay. In this context, the PaTrop experiment aims to maximize the potential of using a high-resolution Limited-Area Model for the calculation and removal of the tropospheric noise from InSAR data, by following a synergistic approach and integrating all the latest advances in the fields of remote sensing meteorology (GNSS and InSAR) and Numerical Weather Forecasting (WRF). In the first phase of the experiment, presented in the current paper, we investigate the extent to which a high-resolution 1 km WRF weather 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 initially operated with varying parameterization, with GNSS measurements providing a benchmark of real atmospheric conditions. Subsequently, the final WRF daily re-analysis run covers an extended period of one year, based on the optimum model parameterization scheme demonstrated by the parametric analysis. The two datasets (predicted and observed) are compared and statistically evaluated, in order to investigate the extent to which meteorological parameters that affect ZTD can be simulated accurately by the model under different weather conditions. Results demonstrate a strong correlation between predicted and observed ZTDs at the 19 GNSS stations throughout the year (R ranges from 0.91 to 0.93), with an average mean bias (MB) of –19.2 mm, indicating that the model tends to slightly underestimate the tropospheric ZTD as compared to the GNSS derived values. With respect to the seasonal component, model performance is better during the autumn period (October–December), followed by spring (April–June). Setting the acceptable bias range at ±23 mm (equal to the amplitude of one Sentinel-1 C-band phase cycle when projected to the zenithal distance), it is demonstrated that the model produces satisfactory results, with a percentage of ZTD values within the bias margin ranging from 57% in summer to 63% in autumn.

Monitoring Bridge Vibrations Based on GBSAR and Validation by High-Rate GPS Measurements

Ground-Based synthetic aperture radar (GBSAR) is a novel technique for monitoring surface deformation and structural vibrations due to its unique advantages: GBSAR can continually and simultaneously observe a large range of landscapes with a high spatiotemporal resolution. Using GBSAR under the fixed azimuth scanning mode to monitor vibrations is a promising way to monitor a bridge's structural health. Several GBSAR-based bridge vibration experiments have been conducted and verified; however, observation precision tests using high-rate global positioning system (GPS) data are rare, especially for the GAMMA portable radar interferometer II (GPRI-II). In addition, it is complicated to split high-frequency signals into valuable information and undesired noise. Considering these limitations, an on-site experiment using the GPRI-II and high-rate GPS receivers was conducted to evaluate the utility of the GPRI-II. Moreover, a denoising method based on the least-squares adjustment and a triangular chain is proposed under the assumption that the atmospheric phase delay can be eliminated between minuscule time intervals. In addition, an external comparative experiment between the GPRI-II and GPS receivers and an internal comparative experiment was performed to evaluate the effectiveness and reliability, respectively, of the proposed method. The results show that the GPRI-II observation accuracy for permanent scatterers reaches 5-6 millimeters. For the effectiveness of the proposed method, the precision improvement for distributed scatterers is more significant than that for permanent scatterers, and both the internal precision and the external precision have more conspicuous relationships with the deviation of the amplitude than with the coherence.

General Survey of Large-scale Land Subsidence by GACOS-Corrected InSAR Stacking: Case Study in North China Plain

Abstract. Satellite-based InSAR (Interferometric Synthetic Aperture Radar) provides an effective way to measure large-scale land surface motions. Currently, the atmospheric phase delay is one of the most critical issues in InSAR deformation monitoring. Generic Atmospheric Correction Online Service (GACOS) is a free, globally available and easy-to-implement tool to generate high-resolution zenith total delay maps, which could be used for InSAR atmospheric delay correction. The mean velocity could then be estimated by stacking multiple GACOS-corrected interferograms. We applied the proposed GACOS-corrected InSAR stacking method in the North China Plain and analysed its performance. Within the 549 interferograms, more than 85 % gained positive correction performances. The correlation between the phase-dZTD indicator and the performance reached 0.89, demonstrating a significant relationship. Deformation maps revealed by InSAR stacking with and without GACOS corrections showed that GACOS could mainly remove the topography-related and long wavelength signals.

An Accurate Method to Correct Atmospheric Phase Delay for InSAR with the ERA5 Global Atmospheric Model

Differential SAR Interferometry (DInSAR) has proven its unprecedented ability and merits of monitoring ground deformation on a large scale with centimeter to millimeter accuracy. However, atmospheric artifacts due to spatial and temporal variations of the atmospheric state often affect the reliability and accuracy of its results. The commonly-known Atmospheric Phase Screen (APS) appears in the interferograms as ghost fringes not related to either topography or deformation. Atmospheric artifact mitigation remains one of the biggest challenges to be addressed within the DInSAR community. State-of-the-art research works have revealed that atmospheric artifacts can be partially compensated with empirical models, point-wise GPS zenith path delay, and numerical weather prediction models. In this study, we implement an accurate and realistic computing strategy using atmospheric reanalysis ERA5 data to estimate atmospheric artifacts. With this approach, the Line-of-Sight (LOS) path along the satellite trajectory and the monitored points is considered, rather than estimating it from the zenith path delay. Compared with the zenith delay-based method, the key advantage is that it can avoid errors caused by any anisotropic atmospheric phenomena. The accurate method is validated with Sentinel-1 data in three different test sites: Tenerife island (Spain), Almería (Spain), and Crete island (Greece). The effectiveness and performance of the method to remove APS from interferograms is evaluated in the three test sites showing a great improvement with respect to the zenith-based approach.

Advanced Applications of Synthetic Aperture Radar (SAR) Remote Sensing for Detecting Pre- and Syn-eruption Signatures at Mount Sinabung, North Sumatra, Indonesia

DOI:10.17014/ijog.6.2.123-140Mount Sinabung was re-activated at August 28th, 2010 after a long repose interval. The early stage of a phreatic eruption was then followed by magmatic eruptions at September 15th, 2013 for years until now. To understand the ground surface changes accompanying the eruption periods, comprehensive analyses of surface and subsurface data are necessary, especially the condition in pre- and syn-eruption periods. This study is raised to identify ground surface and topographical changes before, intra, and after the eruption periods by analyzing the temporal signature of surface roughness, moisture, and deformation derived from Synthetic Aperture Radar (SAR) data. The time series of SAR backscattering intensity were analyzed prior to and after the early eruption periods to know the lateral ground surface changes including estimated lava dome roughness and surface moisture. Meanwhile, the atmospherically corrected Differential Interferometric SAR (D-InSAR) method was also applied to know the vertical topographical changes prior to the eruptions. The atmospheric correction based on modified Referenced Linear Correlation (mRLC) was applied to each D-InSAR pair to exclude the atmospheric phase delay from the deformation signal. The changes of surface moistures on syn-eruptions were estimated by calculating dielectric constant from SAR polarimetric mode following Dubois model. Twenty-one Phased Array type L-band SAR (PALSAR) data on board Advanced Land Observing Satellite (ALOS) and nine Sentinel-1A SAR data were used in this study with the acquisition date between February 2006 and February 2017. For D-InSAR purposes, the ALOS PALSAR data were paired to generate twenty interferograms. Based on the D-InSAR deformation, three times inflation-deflation periods were observed prior to the early eruption at August 28th 2010. The first and second inflation-deflation periods at the end of 2008 and middle 2009 presented migration of magma batches and dike generations in the deep reservoir. The third inflation-deflation periods in the middle of 2010 served as a precursor signal presenting magma feeding to the shallow reservoir. The summit was inflated about 1.4 cm and followed by the eruptions. The deflation of about 2.3 cm indicated the release pressure and temperature in the shallow reservoir after the early eruption at August 28th, 2010. The last inflation-deflation period was also confirmed by the increase of the lava dome roughness size from 5,121 m2 on July to 6,584 m2 on August. The summit then inflated again about 1.1 cm after the first eruption and followed by unrest periods presented by lava dome growth and destruction at September 15th, 2013. The volcanic products including lava and pyroclastics strongly affected the moisture of surface layer. The volcanic products were observed to reduce the surface moisture within syn-eruption periods. The hot materials are presumed responsible for the evaporation of the surface moisture as well.

An Atmospheric Phase Screen Estimation Strategy Based on Multichromatic Analysis for Differential Interferometric Synthetic Aperture Radar

In synthetic aperture radar (SAR), the separation of the height between the ground subsidence phase components and the atmospheric phase delay mixed in the global SAR interferometry (InSAR) phase information is an issue of primary concern in the remote sensing community. This paper describes a complete procedure to address the challenge to estimate the atmospheric phase screen and to separate the three-phase components by exploiting only one InSAR image couple. This solution has the capability to process persistent scatterers subsidence maps potentially using only two multitemporal InSAR couples observed in any atmospheric condition. The solution is obtained by emulating the atmosphere compensation technique that is largely used by the global positioning system where two frequencies are used in order to estimate and compensate the positioning errors due to atmosphere parameters' variations. A sub-chirping and sub-Doppler algorithm for atmospheric compensation is proposed, which allows the successful separation of the height from the subsidence and the atmosphere parameters from the interferometric phase observed on one InSAR couple. The results are given processing images of two InSAR couples observed by the COSMO-SkyMed satellite system.

Atmospheric Artifacts Correction With a Covariance-Weighted Linear Model Over Mountainous Regions

Mitigating the atmospheric phase delay is one of the largest challenges faced by the differential synthetic aperture radar (SAR) interferometry community. Recently, many publications have studied correcting the stratified tropospheric phase delay by assuming a linear model between them and the topography. However, most of these studies have not considered the effect of turbulent atmospheric artifacts when adjusting the linear model to data. In this paper, we present an improved technique that minimizes the influence of the turbulent atmosphere in the model adjustment. In the proposed algorithm, the model is adjusted to the phase differences of pixels instead of using the unwrapped phase of each pixel. In addition, the different phase differences are weighted as a function of its atmospheric phase screen covariance estimated from an empirical variogram to reduce, in the model adjustment, the impact of pixel pairs with a significant turbulent atmosphere. The good performance of the proposed method has been validated with both the simulated and real Sentinel-1A SAR data in the mountainous area of Tenerife island, Spain.

Monitoring surface changes in discontinuous permafrost terrain using small baseline SAR interferometry, object-based classification, and geological features: a case study from Mayo, Yukon Territory, Canada

Permafrost-induced deformation of ground features is threating infrastructure in northern communities. An understanding of permafrost distribution is therefore critical for sustainable adaptation planning and infrastructure maintenance. Considering the large area underlain by permafrost in the Yukon Territory, there is a need for baseline information to characterize the permafrost in this region. In this study, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique was used to identify areas of ground movement likely caused by changes in permafrost. The DInSAR technique was applied to a series of repeat-pass C-band RADARSAT-2 observations collected in 2015 over the Village of Mayo, in central Yukon Territory, Canada. The conventional DInSAR technique demonstrated that ground deformation could be detected in this area, but the resulting deformation maps contained errors due to a loss of coherence from changes in vegetation and atmospheric phase delay. To address these limitations, the Small BAseline Subset (SBAS) InSAR technique was applied to reduce phase error, thus improving the deformation maps. To understand the relationship between the deformation maps and land cover types, an object-based Random Forest classification was developed to classify the study area into different land cover types. Integration of the InSAR results and the classification map revealed that the built-up class (e.g., airport) was affected by subsidence on the order of −2 to −4 cm. The spatial extent of the surface displacement map obtained using the SBAS InSAR technique was then correlated with the surficial geology map. This revealed that much of the main infrastructure in the Village of Mayo is underlain by interbedded glaciofluvial and glaciolacustrine sediments, the latter of which caused the most damage to human made structures. This study provides a method for permafrost monitoring that builds upon the synergistic use of the SBAS InSAR technique, object-based image analysis, and surficial geology data.

A new approach to selecting coherent pixels for ground-based SAR deformation monitoring

Ground-Based Synthetic Aperture Radar (GBSAR) is a flexible field-based remote sensing technology that, together with interferometric SAR (InSAR), has proven to be a powerful and effective tool for deformation monitoring. The Small Baseline Subset (SBAS) algorithm represents a typical advanced InSAR technique that extracts distributed scatterers from a network of interferograms for the measurement of time series displacement. However, it is well known that coherent points are variable from one interferogram to another, which renders time series analysis complicated. This study therefore proposes an effective approach to selecting coherent pixels from a network of interferograms, aiming to maximize the density of selected pixels and optimize the reliability of GBSAR time series analysis. A pixel is selected for the entire analysis if its coherent phase is capable of forming a full-rank coefficient matrix in the network inversion. A full-rank matrix means the pixel-dependent subset network is connected. Combining with the accurate estimation of coherence and interferometric phase based on sibling pixels identified from non-local windows, the proposed approach enables the selection of not only qualified partially coherent pixels but also persistent scatterers. The proposed approach has been incorporated into a bespoke GBSAR time series analysis chain for deformation monitoring, from which a mean velocity map, displacement time series and atmospheric phase delays can be determined. To validate the approach, experiments on two GBSAR datasets were performed. In both studies, sufficient coherent pixels were selected, suggesting the feasibility of the proposed coherent pixel selection algorithm. Displacement time series at the level of a few sub-millimeters were observed for both datasets, indicating the feasibility of the newly-developed GBSAR time series analysis chain for deformation monitoring, which is believed to lead to a wide range of scientific and practical applications.

An improved atmospheric correction method in repeat-pass InSAR measurements

ABSTRACTAs is well known, the problem of atmospheric phase delays is currently one of the major limitations to high precision repeat-pass interferometric synthetic aperture radar (InSAR) applications. Therefore, accurately mitigating atmospheric phase delays is a key but unresolved problem. In recent decades, many researchers have attempted to mitigate the atmospheric phase delays. A typical method is to calculate the atmospheric phase along radar line of sight direction using precipitable water vapour products. However, this method completely neglects the atmospheric turbulence and heterogeneity in the horizontal direction when the zenith wet delays are converted to slant wet delays, reducing the accuracy of the approach. In this article, we systematically analysed the atmospheric turbulence and heterogeneity in the horizontal direction as well as the vertical stratification of water vapour in the troposphere. From this analysis, we found that the atmospheric turbulence and heterogeneity in the horizontal direction cannot be neglected in estimating atmospheric phase delays. Moreover, the vertical stratification of water vapour and the satellite orbit azimuth can be integrated into the calculation of slant phase delays. We therefore propose an improved atmospheric correction method in repeat-pass InSAR measurements, weighted-sampling radiometer correction. The proposed method was applied to the investigation of ground subsidence in an area of Beijing, China to validate its feasibility and accuracy. The results illustrate that atmospheric phase delays and ground subsidence information can be retrieved more accurately using the proposed method than was obtained using the conventional method.