Geostatistical Analysis and Mitigation of the Atmospheric Phase Screens in Ku-Band Terrestrial Radar Interferometric Observations of an Alpine Glacier

Baffelli, Simone; Frey, Othmar; Hajnsek, Irena

doi:10.1109/tgrs.2020.2976656

Cited by 14 publications

(11 citation statements)

References 87 publications

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“…Monostatic operation mode and the polarimetric calibration of KAPRI (Ku-band Advanced Polarimetric Radar Interferometer)-a real-aperture, frequency-modulated continuous-wave (FMCW) radar-have been introduced in [40], with application to observation of natural environments in [46] and [47]. It was built by Gamma Remote Sensing and is an extension of the GPRI [48]- [51] with fully polarimetric capabilities and a custom hardware extension that allows chirp synchronization.…”

Section: B Kapri: a Real-aperture Polarimetric-interferometric Fmcw R...mentioning

confidence: 99%

“…1) Three-Dimensional Displacement Monitoring: Bistatic interferometric capabilities of KAPRI allow reconstruction of 3-D displacement vector fields [3], [61], with high temporal sampling frequency for monitoring of phenomena that occur both on timescales faster than the ones that can be monitored using satellite-based SAR and longer timescales than those that can be reasonably monitored using airborne instruments. The reduced SNR and wider antenna pattern of the secondary device will result in a reduction of precision by an estimated factor of 4; however, given the excellent resolution and sensitivity of the monostatic GPRI and Ku-band wavelength, the bistatic add-on can still provide submillimeter sensitivity (0.25-mm path length measurement standard deviation at 10 • phase noise standard deviation [48]), as well as providing opportunities for enhancement of atmospheric phase screen compensation models that affect terrestrial radar observations [47].…”

Section: Applicationsmentioning

confidence: 99%

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Calibration and Operation of a Bistatic Real-Aperture Polarimetric-Interferometric Ku-Band Radar

Stefko

Frey

Werner

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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This article presents the bistatic operation mode and the performance analysis of KAPRI, a terrestrial frequency-modulated continuous-wave (FMCW) Ku-band polarimetric radar interferometer capable of acquiring bistatic fullpolarimetric datasets with high spatial and temporal resolution. In the bistatic configuration, the system is composed of two independently operating KAPRI devices, one serving as a primary transmitter and receiver and the other as a secondary receiver. The secondary bistatic dataset is affected by possible offsets between the two devices' reference clocks, as well as distortions arising from the bistatic geometry. To correct for this, we present a two-chirp bistatic FMCW signal model, which accounts for the distortions, and a reference chirp transmission procedure, which allows correcting the clock offsets in the deramped signal time domain. The second challenge of operation of a bistatic polarimetric system is polarimetric calibration since it is not possible to employ purely monostatic targets such as corner reflectors. For this purpose, we developed a novel active calibration device Variable-Signature Polarimetric Active Radar Calibrator (VSPARC), designed for monostatic and bistatic calibration of all polarimetric channels. VSPARC and its associated novel polarimetric calibration method were then used to achieve full calibration of both KAPRI devices with polarimetric phase calibration accuracy of 20 • and 30-dB polarization purity in field conditions. This article thus presents a complete measurement configuration and data processing pipeline necessary for synchronization, coregistration, and polarimetric calibration of bistatic and monostatic datasets acquired by a real-aperture FMCW radar.

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Section: B Kapri: a Real-aperture Polarimetric-interferometric Fmcw R...mentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Calibration and Operation of a Bistatic Real-Aperture Polarimetric-Interferometric Ku-Band Radar

Stefko

Frey

Werner

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…Therefore, the spatial statistics method can be used to extract the horizontal atmospheric turbulence component, which is independent of the terrain. The most common interpolation methods are IDW interpolation [26] and kriging interpolation [27]. The former uses the weighted sum of all known points in space to estimate the values of unknown points, where the weight depends on the reciprocal of the distance.…”

Section: ) Horizontal Atmospheric Turbulencementioning

confidence: 99%

An Improved Method for InSAR Atmospheric Phase Correction in Mountainous Areas

Shi

Peng

Chen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Time series interferometric synthetic aperture radar (TS-InSAR) has been a powerful tool for monitoring land surface deformation in the last two decades. Atmospheric effects cause large-scale delays in InSAR observations, which is one of the difficulties facing deformation calculations from differential InSAR (D-InSAR) and time-series InSAR. Currently, atmospheric delay is derived mainly from auxiliary data from sources such as the global navigation satellite system (GNSS) and moderateresolution imaging spectroradiometry (MODIS), but GNSS data are limited by the sparse distribution of observation stations. MODIS data also may not temporally match SAR image acquisition, which leads to low accuracy in atmospheric phase correction. This paper presents a decomposition method to remove atmospheric delay. We consider the atmospheric phase to be caused by the combined changes in spatial position and elevation. Therefore, quadtree segmentation is applied to divide the topographic units, and we improve the drift function of universal kriging by adding an elevation component. We then interpolate the whole atmospheric phase space from reliable sampling points estimated by the coherence coefficient. Using Sentinel-1 data, we test the proposed method in discriminating and monitoring a mining subsidence area in Shanxi Province and compare the results with the results from interferometric point target analysis (IPTA) and the network-based variance-covariance estimation (NVCE) method. The results demonstrate that the proposed method is superior to existing methods for the detection of deformation inverted from time-series InSAR.

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“…Leveling, GPS, interferometric synthetic aperture radar (InSAR), and other technologies are widely used in mining subsidence monitoring [10,11]. Due to its large coverage, all-weather capability, and high monitoring accuracy [12], InSAR is widely used to measure deformations in glaciers [13,14], volcanoes [15,16], earthquakes [17,18], and other landscapes, as well as urban and general land surface deformations caused by resource exploitation. Scholars worldwide have also tried to apply InSAR technology to the observation of surface deformations caused by mining activities [19,20].…”

Section: Introductionmentioning

confidence: 99%

Position Inversion of Goafs in Deep Coal Seams Based on DS-InSAR Data and the Probability Integral Methods

Zhang

Fan

et al. 2021

Remote Sensing

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The goafs caused by coal mining cause great harm to the surface farmland, buildings, and personal safety. The existing monitoring methods cost a lot of workforce and material resources. Therefore, this paper proposes an inversion approach for establishing the locations of underground goafs and the parameters of the probability integral method (PIM), thus integrating distributed scatter interferometric synthetic aperture radar (DS-InSAR) data and the PIM. Firstly, a large amount of surface deformation observation data above the goaf are obtained by DS-InSAR, and the line-of-sight deformation is regarded as the true value. Secondly, according to the obtained surface deformations, the ranges of eight goaf location parameters and three PIM parameters are set. Thirdly, a correlation function between the surface deformation and the underground goaf location is constructed. Finally, a particle swarm optimization algorithm is used to search for the optimal parameters in the range of the set parameters to meet the requirement for minimum error between the surface deformation calculated by PIM and the line-of-sight deformation obtained by DS-InSAR. These optimal parameters are thus regarded as the real values of the position of the underground goaf and the PIM parameters. The simulation results show that the maximum relative error between the position of the goaf and the PIM parameters is 2.11%. Taking the 93,604 working face of the Zhangshuanglou coal mine in the Peibei mining area as the research object and 12 Sentinel-1A images as the data source, the goaf location and PIM parameters of the working face were successfully inverted. The inversion results show that the maximum relative error in the goaf location parameters was 16.61%, and the maximum relative error in the PIM parameters was 26.67%.

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Geostatistical Analysis and Mitigation of the Atmospheric Phase Screens in Ku-Band Terrestrial Radar Interferometric Observations of an Alpine Glacier

Cited by 14 publications

References 87 publications

Calibration and Operation of a Bistatic Real-Aperture Polarimetric-Interferometric Ku-Band Radar

Calibration and Operation of a Bistatic Real-Aperture Polarimetric-Interferometric Ku-Band Radar

An Improved Method for InSAR Atmospheric Phase Correction in Mountainous Areas

Position Inversion of Goafs in Deep Coal Seams Based on DS-InSAR Data and the Probability Integral Methods

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