Repeat-pass Interferometric Synthetic Aperture Research Articles

Radar Interferometric Phase Errors Induced by Faraday Rotation

Ionospheric Faraday rotation distorts satellite radar observations of the Earth’s surface. While its impact on radiometric observables is well understood, the errors in repeat-pass interferometric synthetic aperture radar (InSAR) observations and hence in deformation analysis are largely unknown. Because Faraday rotation cannot rigorously be compensated for in nonquad-pol systems, it is imperative to determine the magnitude and nature of the deformation errors. Focusing on distributed targets at L-band, we assess the errors for a range of land covers using airborne observations with simulated Faraday rotation. We find that the deformation error may reach 2 mm in the copol channels over a solar cycle. It can exceed 5 mm for intense solar maxima. The cross-pol channel is more susceptible to severe errors. We identify the leakage of polarimetric phase contributions into the interferometric phase as a dominant error source. The polarimetric scattering characteristics induce a systematic dependence of the Faraday-induced deformation errors on land cover and topography. Also, their temporal characteristics, with pronounced seasonal and quasi-decadal variability, predispose these systematic errors to be misinterpreted as deformation. While the relatively small magnitude of 1–2 mm is of limited concern in many applications, the persistence on semiannual to multiannual time scales compels attention when long-term deformation is to be estimated with millimetric accuracy. Phase errors induced by uncompensated Faraday rotation constitute a nonnegligible source of bias in interferometric deformation measurements.

A Coherence Improvement Method Based on Sub-Aperture InSAR for Human Activity Detection

Human activity detection plays an important role in social security monitoring. Since human activity is very weak, it is necessary to employ the repeat-pass Interferometric Synthetic Aperture Radar (InSAR) technique to detect the potential activity between two data acquisitions; a high level of coherence is required for detection. With the object of detecting human activity of interest, this paper presents a coherence improvement approach based on sub-aperture InSAR for human activity detection. Different sub-apertures contain different scattering information of the target, as they represent the backscatter of the target from a different range of angles. Integrating corresponding sub-aperture interferometric results can improve the coherence between two complex images compared to the entire synthetic aperture, as well as removing a little disturbance in some circumstances. To validate the method presented in this paper, the actual airborne Ka-band frequency modulated continuous wave (FMCW) InSAR data acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS) are utilized. The experimental results demonstrate that the proposed method can effectively improve the coherence between two complex SAR images and can validly detect human activity of interest.

A Framework for Correcting Ionospheric Artifacts and Atmospheric Effects to Generate High Accuracy InSAR DEM

Repeat-pass interferometric synthetic aperture radar is a well-established technology for generating digital elevation models (DEMs). However, the interferogram usually has ionospheric and atmospheric effects, which reduces the DEM accuracy. In this paper, by introducing dual-polarization interferograms, a new approach is proposed to mitigate the ionospheric and atmospheric errors of the interferometric synthetic aperture radar (InSAR) data. The proposed method consists of two parts. First, the range split-spectrum method is applied to compensate for the ionospheric artifacts. Then, a multiresolution correlation analysis between dual-polarization InSAR interferograms is employed to remove the identical atmospheric phases, since the atmospheric delay is independent of SAR polarizations. The corrected interferogram can be used for DEM extraction. Validation experiments, using the ALOS-1 PALSAR interferometric pairs covering the study areas in Hawaii and Lebanon of the U.S.A., show that the proposed method can effectively reduce the ionospheric artifacts and atmospheric effects, and improve the accuracy of the InSAR-derived DEMs by 64.9% and 31.7% for the study sites in Hawaii and Lebanon of the U.S.A., respectively, compared with traditional correction methods. In addition, the assessment of the resulting DEMs also includes comparisons with the high-precision Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) altimetry data. The results show that the selection of reference data will not affect the validation results.

A Motion Compensation Strategy for Airborne Repeat-Pass SAR Data

Generating accurate repeat-pass interferometric airborne synthetic aperture radar (SAR) products demands the precise compensation of the 3-D motion of the aircraft. This requires to estimate and correct small residual motion errors (RMEs) within the accuracy limits of the sensor navigation subsystem, e.g., using residual motion compensation (MoCo) algorithms. Because of their data-driven nature, the performance of these algorithms severely degrades for heavily decorrelated interferograms. This letter proposes a generic RME estimation and compensation strategy optimally estimating RME in a stack of SAR acquisitions, where some interferometric pairs are strongly affected by decorrelation. The algorithm works even if the whole scene decorrelates, as long as the coherence magnitude is reasonably high over short temporal and spatial baselines. The approach entails correcting the navigation data of each slave image of a one-to-many interferometric network with a cumulative correction. The summation is over the results of a precursory application of a general data-driven residual MoCo algorithm (e.g., multisquint) to interferometric pairs for which the impact of interferometric decorrelation is marginal (i.e., small temporal and/or spatial baselines). Compared to other RME correction strategies, the main appeal of the proposed approach lies in the simplicity of its implementation. The overall methodology is tested on a zero-baseline time series acquired at L-band by the German Aerospace Center’s airborne system F-SAR.

Extraction of digital elevation model from Chinese GF-3 C-band synthetic aperture radar data with a step-wise method focusing on facilitating phase unwrapping

GaoFen-3 (GF-3) is the first fully polarimetric C-band synthetic aperture radar (SAR) satellite in China. Interferometric observation is an important capability of GF-3. However, the long temporal baseline is a great challenge for repeat-pass interferometric synthetic aperture radar (InSAR) processing, especially over mountainous areas. Phase noise and complex topography make phase unwrapping difficult. This paper proposes a step-wise approach for high-resolution InSAR data processing focusing on facilitating phase unwrapping. First, the coarse GF-3 digital elevation model (DEM) is reconstructed by InSAR processing with multilooking of large looks, which is used to suppress phase noise. Second, the coarse GF-3 DEM is used to remove the terrain phase from the InSAR phase with multilooking of small looks, which can reduce phase gradient and improve the accuracy of phase unwrapping. After several iterations, the GF-3 DEM is finally inversed with multilooking of 2 looks, which is of high resolution and high accuracy. The performance of GF-3 C-band InSAR data on the extraction of DEM is examined over a deciduous forested area with the proposed method. The root-mean-square error of the extracted GF-3 DEM is 3.78 m in comparison with the LiDAR DEM.

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.

Interferometric synthetic aperture radar atmospheric correction using a GPS-based iterative tropospheric decomposition model

Atmospheric effects represent one of the major error sources of repeat-pass Interferometric Synthetic Aperture Radar (InSAR), and could mask actual displacements due to tectonic or volcanic deformation. The tropospheric delays vary both vertically and laterally and can be considered as the sum of (i) a vertically stratified component highly correlated with topography and (ii) a turbulent component resulting from turbulent processes in the troposphere varying both in space and time. In this paper, we outline a framework to routinely use pointwise GPS data to reduce tropospheric effects on satellite radar measurements. An Iterative Tropospheric Decomposition (ITD) model is used and further developed to separate tropospheric stratified and turbulent signals and then generate high-resolution correction maps for SAR interferograms. Cross validation is employed to assess the performance of the ITD model and act as an indicator to users of when and where correction is feasible. Tests were carried out to assess the impact of GPS station spacing on the ITD model InSAR correction performance, which provides insights into the trade-off between station spacing and the achievable accuracy. The application of this framework to Sentinel-1A interferograms over the Southern California (USA) and Southern England (UK) regions shows approximately 45–78% of noise reduction even with a sparse (~50–80km station spacing) GPS network and/or with strong and non-random tropospheric turbulence. This is about a 50% greater improvement than previous methods. It is believed that this framework could lead to a generic InSAR atmospheric correction model while incorporating continuous and global tropospheric delay datasets, e.g. numerical weather models.

InSAR Time-Series Estimation of the Ionospheric Phase Delay: An Extension of the Split Range-Spectrum Technique

Repeat pass interferometric synthetic aperture radar (InSAR) observations may be significantly impacted by the propagation delay of the microwave signal through the ionosphere, which is commonly referred to as ionospheric delay. The dispersive character of the ionosphere at microwave frequencies allows one to estimate the ionospheric delay from InSAR data through a split range-spectrum technique. Here, we extend the existing split range-spectrum technique to InSAR time-series. We present an algorithm for estimating a time-series of ionospheric phase delay that is useful for correcting InSAR time-series of ground surface displacement or for evaluating the spatial and temporal variations of the ionosphere’s total electron content (TEC). Experimental results from stacks of L-band SAR data acquired by the ALOS-1 Japanese satellite show significant ionospheric phase delay equivalent to 2 m of the temporal variation of InSAR time-series along 445 km in Chile, a region at low latitudes where large TEC variations are common. The observed delay is significantly smaller, with a maximum of 10 cm over 160 km, in California. The estimation and correction of ionospheric delay reduces the temporal variation of the InSAR time-series to centimeter levels in Chile. The ionospheric delay correction of the InSAR time-series reveals earthquake-induced ground displacement, which otherwise could not be detected. A comparison with independent GPS time-series demonstrates an order of magnitude reduction in the root mean square difference between GPS and InSAR after correcting for ionospheric delay. The results show that the presented algorithm significantly improves the accuracy of InSAR time-series and should become a routine component of InSAR time-series analysis.

Fusion of space-borne multi-baseline and multi-frequency interferometric results based on extended Kalman filter to generate high quality DEMs

Repeat-pass Interferometric Synthetic Aperture Radar (InSAR) is a technique that can be used to generate DEMs. But the accuracy of InSAR is greatly limited by geometrical distortions, atmospheric effect, and decorrelations, particularly in mountainous areas, such as western China where no high quality DEM has so far been accomplished. Since each of InSAR DEMs generated using data of different frequencies and baselines has their own advantages and disadvantages, it is therefore very potential to overcome some of the limitations of InSAR by fusing Multi-baseline and Multi-frequency Interferometric Results (MMIRs). This paper proposed a fusion method based on Extended Kalman Filter (EKF), which takes the InSAR-derived DEMs as states in prediction step and the flattened interferograms as observations in control step to generate the final fused DEM. Before the fusion, detection of layover and shadow regions, low-coherence regions and regions with large height error is carried out because MMIRs in these regions are believed to be unreliable and thereafter are excluded. The whole processing flow is tested with TerraSAR-X and Envisat ASAR datasets. Finally, the fused DEM is validated with ASTER GDEM and national standard DEM of China. The results demonstrate that the proposed method is effective even in low coherence areas.

Efficient mitigation of atmospheric phase effects in repeat-pass InSAR measurements

The problem of atmospheric phase effects is currently one of the most important limiting factors for widespread application of repeat-pass interferometric synthetic aperture radar (InSAR) measurements. Due to the extraordinary complexity of the atmospheric inhomogeneity and turbulence, it is generally difficult to obtain satisfactory mitigation of the atmospheric phase effects in repeat-pass InSAR measurements. In recent years, several methods have been developed for mitigating the atmospheric phase effects. An effective approach is interferogram stacking, which is based on stacking independent interferograms. However, as many as 2n images are required to generate n interferograms and the atmospheric delay errors of the stacked interferogram decrease only with the square root of the number of interferograms in the conventional interferogram stacking method, which is not very efficient. In order to efficiently mitigate the atmospheric phase effects on the stacked interferogram in repeat-pass InSAR measurements, we propose a relay-interferogram stacking method. Compared with the conventional method, this method not only can efficiently mitigate atmospheric phase effects on the stacked interferogram, but also greatly decreases the number of required synthetic aperture radar (SAR) images. The key element is that the first and the last SAR images are selected from the periods of similar meteorological conditions. In addition, we present an application of the approach to the study of ground subsidence in the area around Beijing, China.

Mitigating atmospheric effects in InSAR measurements through high-resolution data assimilation and numerical simulations with a weather prediction model

Repeat-pass spaceborne interferometric synthetic aperture radar (InSAR) is commonly used to measure surface deformation; phase delays due to atmospheric water vapour may have significant impact on the accuracy of these measurements. In recent years, there has been a growing interest in using forecasts and analyses from numerical weather prediction (NWP) models – which can provide good estimates of the atmospheric state – to correct for atmospheric phase delays. In this study, three separate estimates of atmospheric water vapour content from NWP output are used in combination with Environmental Satellite (Envisat) Advanced Synthetic Aperture Radar (ASAR) data over the Pearl River Delta region in South China to mitigate atmospheric distortion. The NWP-based estimates are derived from: (1) interpolation of National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) data; (2) Weather Research and Forecasting (WRF) model simulations initialized with FNL analysis without additional data assimilation; and (3) WRF simulations initialized with a three-dimensional variational (3DVar) data assimilation system that ingests additional meteorological observations. The accuracy of the atmospheric corrections from these different NWP model outputs is further verified quantitatively with precipitable water vapour (PWV) data from several ground-based global positioning system (GPS) stations in Hong Kong. Inter-comparison shows a good agreement between the PWV derived from the WRF-3DVar simulations and the GPS measurements, suggesting that atmospheric correction by convection-permitting WRF simulations initialized with mesoscale data assimilation may effectively mitigate atmospheric distortion in InSAR measurements, especially for coastal areas.

Early melt season velocity fields of Langjökull and Hofsjökull, central Iceland

AbstractWe infer the horizontal velocity fields of the ice caps Langjökull and Hofsjökull, central Iceland, using repeat-pass interferometric synthetic aperture radar (InSAR). NASA’s uninhabited aerial vehicle synthetic aperture radar (UAVSAR) acquired airborne InSAR data from multiple vantage points during the early melt season in June 2012. We develop a Bayesian approach for inferring three-dimensional velocity fields from multiple InSAR acquisitions. The horizontal components generally agree with available GPS measurements wherever ice motion is well constrained by InSAR observations. We provide evidence that changes in volumetric moisture content near the glacier surface induce phase offsets that obfuscate the vertical component of the surface velocity fields, an effect that could manifest itself on any glacier that experiences surface melt. Spatial patterns in the InSAR-derived horizontal speeds are broadly consistent with the results of a simple viscous flow model, and the directionality of the InSAR-derived horizontal flow field is nearly everywhere consistent with the ice surface gradient. Significant differences between the InSAR-derived horizontal speed and the speed predicted by the viscous flow model suggest that basal slip accounts for more than half the observed outlet glacier flow.

Current Development in Airborne Repeat-pass Interferometric Synthetic Aperture Radar

Current Development in Airborne Repeat-pass Interferometric Synthetic Aperture Radar

Time-series deformation monitoring over mining regions with SAR intensity-based offset measurements

Underground mining can induce large vertical displacements that often lead to the loss of coherence in repeat-pass interferometric synthetic aperture radar (SAR) images. Using SAR intensity images, this paper employs the image offset tracking method to map SAR slant range changes due to ground deformation over areas of mining. The rationale of slant range offset measurement with respect to the vertical deformation is analysed and the prerequisite of applying the slant range offset method to monitor vertical deformation is discussed. Results from the slant range offset method are used to produce time-series of cumulative ground displacements via least square estimate. We use six Advanced Land Observation Satellite (ALOS)-Phased Array L-band SAR (PALSAR) images over two coalfields in Inner Mogolia, China, to illustrate the proposed method and its effectiveness. We achieve deformation measurements with a precision of ∼0.2 m, with the maximum vertical displacement over the mining sites reaching ∼4.5 m. Finally, we use time-series results to outline common features identified in mining-induced deformation. Our results are supported by in situ investigations.

Estimation of the Repeat-Pass ALOS PALSAR Interferometric Baseline Through Direct Least-Square Ellipse Fitting

The precise estimation of the baseline is a crucial procedure in repeat-pass interferometric synthetic aperture radar (InSAR) applications. Using the ephemeris of the satellite, a polynomial regression algorithm can fit the satellite orbit at the third or higher order with a main shortcoming that the mutual constraints among the three dimensions defining the orbit are missed. In this paper, a new approach is presented to fit the satellite orbit based on the assumption that the satellite orbit is a 3-D ellipse, which retains the relations among the three dimensions. Considering the complexity of 3-D ellipse parameters estimation, the 3-D orbit is first transformed into three 2-D ellipses. Then, the parameters of these 2-D ellipses are estimated with a direct least-square ellipse fitting method (DLS-EFM). These two orbit fitting algorithms are tested with ten sets of advanced land observation satellite phased array L-band SAR data, which were acquired in north Toronto, Ontario, Canada, from September, 2008 to January, 2009. Moreover, two of them acquired with an adjacent period were chosen to form a repeat-pass InSAR, and the corresponding baseline is calculated with the proposed method as an example. The experimental results show that the error of the satellite position using DLS-EFM is at a submetric level, which is less than one-tenth of that of the polynomial regression algorithm. Consequently, the proposed method is appropriate for the baseline estimation in spaceborne InSAR applications.

Correcting atmospheric effects on InSAR with MERIS water vapour data and elevation-dependent interpolation model

SUMMARY The propagation delay when radar signals travel from the troposphere has been one of the major limitations for the applications of high precision repeat-pass Interferometric Synthetic Aperture Radar (InSAR). In this paper, we first present an elevation-dependent atmospheric correction model for Advanced Synthetic Aperture Radar (ASAR—the instrument aboard the ENVISAT satellite) interferograms with Medium Resolution Imaging Spectrometer (MERIS) integrated water vapour (IWV) data. Then, using four ASAR interferometric pairs over Southern California as examples, we conduct the atmospheric correction experiments with cloud-free MERIS IWV data. The results show that after the correction the rms differences between InSAR and GPS have reduced by 69.6 per cent, 29 per cent, 31.8 per cent and 23.3 per cent, respectively for the four selected interferograms, with an average improvement of 38.4 per cent. Most importantly, after the correction, six distinct deformation areas have been identified, that is, Long Beach–Santa Ana Basin, Pomona–Ontario, San Bernardino and Elsinore basin, with the deformation velocities along the radar line-of-sight (LOS) direction ranging from −20 mm yr−1 to −30 mm yr−1 and on average around −25 mm yr−1, and Santa Fe Springs and Wilmington, with a slightly low deformation rate of about −10 mm yr−1 along LOS. Finally, through the method of stacking, we generate a mean deformation velocity map of Los Angeles over a period of 5 yr. The deformation is quite consistent with the historical deformation of the area. Thus, using the cloud-free MERIS IWV data correcting synchronized ASAR interferograms can significantly reduce the atmospheric effects in the interferograms and further better capture the ground deformation and other geophysical signals.

MERIS Atmospheric Water Vapor Correction Model for Wide Swath Interferometric Synthetic Aperture Radar

A major source of error for repeat-pass interferometric synthetic aperture radar is the phase delay in radio signal propagation through the atmosphere, particularly the part due to tropospheric water vapor. These effects become more significant for ScanSAR observations due to their wider coverage (e.g., 400 km <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\times$</tex></formula> 400 km for ENVISAT Advanced Synthetic Aperture Radar (ASAR) wide swath (WS) mode versus 100 km <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\times$</tex></formula> 100 km for ASAR image mode). In this letter, we demonstrate for the first time that a Medium Resolution Imaging Spectrometer water vapor correction model can significantly reduce atmospheric water vapor effects on ASAR WS interferograms, with the phase variation in non-deforming areas decreasing from 3.8 cm before correction to 0.4 cm after correction.

Observation of atmospheric effects on repeat-pass interferometric synthetic aperture radar over the Kuwait desert

Twenty-eight advanced synthetic aperture radar (ASAR) scenes from the Environmental Satellite (ENVISAT) are analysed to select suitable pairs for generating a digital elevation model (DEM) and displacement maps. For this purpose, the repeat-pass interferometric synthetic aperture radar (InSAR) technique is implemented using GAMMA interferometric modules. The perpendicular component of baseline (B┴) is taken as the criteria for selecting the pairs: 0 < B┴ <100 m for displacement maps and 200 < B┴ < 400 m for the DEM. Though there are many pairs satisfying the above criteria, only four case studies are presented here to illustrate the effects of atmosphere on the DEM and displacement maps over the Kuwait desert climate. In each case study, two examples are selected: one where the atmosphere is a serious problem and another example the atmosphere has no significant problem. The DEM of the Shuttle Radar Topographic Mission (SRTM) is taken as a reference for root mean square (RMS) error estimation in the DEM. The RMS error varies from as low as 2 m to as high as 40 m. Some DEMs showed fringe-like structures resembling precipitable water vapour (PWV) fields. Similarly, the measured displacement values were found to vary randomly from place to place and time to time. The displacement maps showed vertical structures similar to PWV. The DEM was corrected for PWV. The results are encouraging. From this study, it is clear that, even for desert areas, there is a need to look into the effects of PWV on the DEM and displacement maps before the results are used.

Automatic Model-Based Estimation of Boreal Forest Stem Volume From Repeat Pass C-band InSAR Coherence

C-band repeat pass interferometric synthetic aperture radar coherence can provide high-accuracy estimates of boreal forest stem volume in spite of environmental dependence. Typically, the retrieval methods require a data set of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">situ</i> measurements for training the model linking coherence to stem volume. The drawback is the need for such data and the incapacity to take into account spatial variations of environmental conditions. Here, we demonstrate a model training method that does not require reference data. For the investigated case, the relative root mean square error of stem volume is 18% or as good as obtained using the traditional training method with <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">situ</i> data.

Simultaneous Least Squares Adjustment of Multiframe Velocities Derived From Interferometric and Speckle-Tracking Methods

This letter extends the simultaneous least squares adjustment method to the calibration and merging of velocity measurements, which are obtained by applying interferometric and speckle-tracking methods to multiple frames of repeat-pass interferometric synthetic aperture radar (InSAR) data. The underlying observation equations have been derived for velocity control points and tie points. By exploiting a set of tie points, individual frames within a strip or a block can be correlated with each other, and the parameters of calibration models for all individual frames can be simultaneously determined. With Radarsat InSAR data over the Recovery Glacier, Antarctica, we demonstrate that our simultaneous least squares adjustment method has effectively removed velocity discontinuities between adjacent frames and greatly reduced the velocity-control-point requirement.