A Case Study on the Correction of Atmospheric Phases for SAR Tomography in Mountainous Regions

Siddique, Muhammad Adnan; Strozzi, Tazio; Hajnsek, Irena; Frey, Othmar

doi:10.1109/tgrs.2018.2855101

Cited by 13 publications

(9 citation statements)

References 79 publications

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“…PSI attempts to identify long-term temporally coherent targets for which the deformation phase can be separated from other sources of interferometric phase such as the atmosphere-induced phases and phase contributions induced by initially unknown residual topographic height differences with respect to a reference digital elevation model. In our work, we use an interferometric stack that was processed using the Interferometric Point Target Analysis (IPTA) module of the GAMMA software [8,43,44].…”

Section: Persistent Scatterer Interferometrymentioning

confidence: 99%

“…We assume that the atmospheric phases are spatially correlated to a certain extent while being uncorrelated from one pass to the next (given several weeks of separation among the repeat passes). The atmospheric phases estimated for the PS are low-pass filtered and spatially interpolated over the scene for example with a kriging interpolator [5,8]. The PS candidate list is iteratively refined using least-squares regression with quality control at each iteration [41].…”

Section: Persistent Scatterer Interferometrymentioning

confidence: 99%

“…In this technique, the so-called persistent scatterers (PS), i.e., pixels that exhibit long-term temporal phase coherence, are identified in the imaged area. It has been shown in earlier works that the atmospheric phases can be effectively isolated at PS locations by using appropriate spatio-temporal filtering of the interferometric phases [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Tropospheric Path Delay Estimates from GNSS and Space-Borne SAR Interferometry in Alpine Conditions

et al. 2019

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We compare tropospheric delays from Global Navigation Satellite Systems (GNSS) and Synthetic Aperture Radar (SAR) Interferometry (InSAR) in a challenging mountainous environment in the Swiss Alps, where strong spatial variations of the local tropospheric conditions are often observed. Tropospheric delays are usually considered to be an error for both GNSS and InSAR, and are typically removed. However, recently these delays are also recognized as a signal of interest, for example for assimilation into numerical weather models or climate studies. The GNSS and InSAR are techniques of complementary nature, as one has sparse spatial but high temporal resolution, and the other very dense spatial coverage but repeat pass of only a few days. This raises expectations for a combination of these techniques. For this purpose, a comprehensive comparison between the techniques must be first performed. Due to the relative nature of InSAR estimates, we compare the difference slant tropospheric delays ( d S T D ) retrieved from GNSS with the d S T D s estimated using Persistent Scatterer Interferometry (PSI) of 32 COSMO-SkyMed SAR images taken in a snow-free period from June to October between 2008 and 2013. The GNSS estimates calculated at permanent geodetic stations are interpolated to the locations of persistent scatterers using an in-house developed least-squares collocation software COMEDIE. The Pearson’s correlation coefficient between InSAR and GNSS estimates averaged over all acquisitions is equal to 0.64 and larger than 0.8 for approximately half of the layers. Better agreement is obtained mainly for days with high variability of the troposphere (relative to the tropospheric conditions at the time of the reference acquisition), expressed as standard deviations of the GNSS-based d S T D s. On the other hand, the most common feature for the days with poor agreement is represented by very stable, almost constant GNSS estimates. In addition, there is a weak correlation between the agreement and the water vapor values in the area, as well as with the number of stations in the closest vicinity of the study area. Adding low-cost L-1 only GPS stations located within the area of the study increases the biases for most of the dates, but the standard deviations between InSAR and GNSS decrease for the limited area with low-cost stations.

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Section: Persistent Scatterer Interferometrymentioning

confidence: 99%

Section: Persistent Scatterer Interferometrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Tropospheric Path Delay Estimates from GNSS and Space-Borne SAR Interferometry in Alpine Conditions

et al. 2019

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“…Further contributions (Li et al, 2009;Puysségur et al, 2007) have investigated the calibration of atmospheric effects in InSAR based on external sources such as Medium Resolution Imaging Spectrometer (MERIS) or Moderate resolution Imaging Spectroradiometer (MODIS) or by integrating numerical weather prediction (NWP) models into InSAR applications such as Hobiger et al (2010), Kinoshita et al (2013), and Mateus et al (2013). In more recent research (Bekaert, Walters, et al, 2015;Siddique et al, 2019;Wicks et al, 2002), the InSAR tropospheric delays are interpolated over the entire footprint by modeling their spatial dependence.…”

Section: Introductionmentioning

confidence: 99%

A collocation framework to retrieve tropospheric delays from a combination of GNSS and InSAR

et al. 2020

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High spatio-temporal variability of atmospheric water vapor affects microwave signals of Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). A better knowledge of the distribution of water vapor improves both GNSS-and InSAR-derived data products. In this work, we present a collocation framework to combine and retrieve zenith and (relative) slant tropospheric delays. GNSS and InSAR meteorological products are combined aiming at a better retrieval of the atmospheric water vapor. We investigate the combination approach with synthetic and real data acquired in the Alpine region of Switzerland. Based on a closed-loop validation with simulated delays, a few mm accuracy is achieved for the GNSS-InSAR combination in terms of retrieved ZTDs. Furthermore, when real delays are collocated, the combination results are more congruent with InSAR computed products. This research is a contribution to improve the spatio-temporal mapping of tropospheric delays by combining GNSS-derived and InSAR-derived delays.

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“…For example, in persistent scatterer interferometry (PSI), the goal is to identify coherent targets for which the atmosphereinduced phase can be isolated from other phase components, mainly residual topography and deformation. Different methods, such as linear regression (Wicks et al 2002), linear or power law (Bekaert et al 2015a, b) or kriging (Siddique et al 2018), have been suggested to model the spatial dependence of tropospheric delays and interpolate them over the entire scene. However, finding the coherent points can be very chal-lenging in non-urban areas, such as mountainous regions.…”

Section: Introductionmentioning

confidence: 99%

High-resolution models of tropospheric delays and refractivity based on GNSS and numerical weather prediction data for alpine regions in Switzerland

Wilgan

Geiger

2018

J Geod

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The tropospheric delay of a microwave signal affects all space geodetic techniques. One possibility of modeling the delay is by introducing tropospheric models from external data sources. In this study, we present high-resolution models of tropospheric total refractivity and zenith total delay (ZTD) for the alpine area in Switzerland. The troposphere models are based on different combinations of data sources, including numerical weather prediction (NWP) model COSMO-1 with high spatial resolution of 1.1 km × 1.1 km, GNSS data from permanent geodetic stations and GPS L1-only data from low-cost permanent stations. The tropospheric parameters are interpolated to the arbitrary locations by the least-squares collocation method using the in-house developed software package COMEDIE (Collocation of Meteorological Data for Interpretation and Estimation of Tropospheric Pathdelays). The first goal of this study is to validate the obtained models with the reference radiosonde and GNSS data to show the improvement w.r.t. the previous studies that used lower resolution input data. In case of total refractivity, the profiles reconstructed from COSMO-1 model show the best agreement with the reference radiosonde measurements, with an average bias of 1.1 ppm (0.6% of the total refractivity value along a vertical profile) and standard deviation of 2.6 ppm (1.6%) averaged from the whole profile. The radiosondes are assimilated into COSMO-1 model; thus, a high correlation is expected, and this comparison is not independent. In case of ZTD, the GNSS-based model shows the highest agreement with the reference GNSS data, with an average bias of 0.2 mm (0.01%) and standard deviation of 4.3 mm (0.2%). For COSMObased model, the agreement is also very high, especially compared to our previous studies with lower resolution NWPs. The average bias is equal to − 2.5 mm (0.1%) with standard deviation of 9.2 mm (0.5%). The second goal of this study is to test the feasibility of calculating high-resolution troposphere models over a limited area from coarser data sets. We calculate the ZTD models with spatial resolution of 20 m for a test area in Matter Valley. We include the information from the low-cost GPS stations (X-Sense), to also assess the performance and future usability of such stations. We validate the models based on three data sources w.r.t. the reference GNSS data. For the station located inside the area of the study, the models have an agreement of few mm with the reference data. For stations located further away from the study area, the agreement for X-Sense is smaller, but the standard deviations of residuals are still below 15 mm. We consider also another factor of evaluating the high-resolution models, i.e., spatial variability of the data. For designing a GNSS network, also for the tropospheric estimates, the height variability of the network may be as important as the horizontal distribution. The GNSS-based models are built from the coarsest network; thus, their variability is the lowest. The variability of X-Sense-based stations...

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A Case Study on the Correction of Atmospheric Phases for SAR Tomography in Mountainous Regions

Cited by 13 publications

References 79 publications

Comparison of Tropospheric Path Delay Estimates from GNSS and Space-Borne SAR Interferometry in Alpine Conditions

Comparison of Tropospheric Path Delay Estimates from GNSS and Space-Borne SAR Interferometry in Alpine Conditions

A collocation framework to retrieve tropospheric delays from a combination of GNSS and InSAR

High-resolution models of tropospheric delays and refractivity based on GNSS and numerical weather prediction data for alpine regions in Switzerland

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