Mapping Complete Three-Dimensional Ice Velocities by Integrating Multi-Baseline and Multi-Aperture InSAR Measurements: A Case Study of the Grove Mountains Area, East Antarctic

Zheng, Wanji; Hu, Jun; Liu, Jihong; Sun, Qiliang; Li, Zhiwei; Zhu, Jianjun; Wu, Lixin

doi:10.3390/rs13040643

Cited by 10 publications

(6 citation statements)

References 42 publications

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“…As for DEM error, it is an undesired component for deformation estimation. However, if the DEM error can be precisely estimated, it can be added back to the existing DEM data to generate a more accurate version of DEM data [25,26]. The DEM error-related interferometric phase is proportional to the spatial baseline of the interferogram [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Liu

et al. 2021

Remote Sensing

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The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series.

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Section: Introductionmentioning

confidence: 99%

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Liu

et al. 2021

Remote Sensing

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“…According to the derived complete 3‐D movements, the sliding direction of the Xinpu landslide complex is obtained and shown in Figure 5, indicating obvious north‐westward movements. The amplitude of deformation can present the distribution of the movement (Zheng et al., 2021). XET slope is the primary deformation area with a maximum amplitude of approximately 370 mm/yr, and there are few significant deformations in the SET and DP slopes.…”

Section: Resultsmentioning

confidence: 99%

Enhanced Kinematic Inversion of 3‐D Displacements, Geometry, and Hydraulic Properties of a North‐South Slow‐Moving Landslide in Three Gorges Reservoir

Zheng

Lü

et al. 2023

JGR Solid Earth

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Complete three‐dimensional (3‐D) movements of slow‐moving landslides are critical to enhancing the understanding of the landslide mechanism. Multi‐source synthetic aperture radar (SAR) observations provide an opportunity to derive 3‐D movements. However, deriving the complete 3‐D movements faces potential challenges of incoherent phases and an ill‐posed inverse problem, which may result in incomplete and inaccurate results, especially for slopes facing north/south. Here, we propose a topography‐constrained strain model, which exploits the spatial relationship of 3‐D deformations between neighboring points as well as the assumption of the surface parallel flow of landslide, to derive complete 3‐D movements. Both synthetic and real datasets over the north‐south Xinpu landslide complex are utilized, to assess if the implemented method can overcome the ill‐posed condition and retrieve the complete 3‐D movement field. With the multi‐source SAR datasets, the performance of various datasets and the potential of NISAR in deriving time series and 3‐D movements are assessed. Based on the derived complete 3‐D movements and long‐term InSAR measurements, the landslide metrics, including elementary parameters of landslide geometry, spatial‐temporal patterns of movement, thickness, and hydraulic diffusivity, are derived to reveal that (a) the thickest landslide mass concentrates in the toe of the landslide, and (b) the effects of precipitation are more significant than those of the water level fluctuation in the Xinpu landslide complex, Three Gorges Reservoir areas.

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“…The Chinese Antarctic research team conducted GPS observations of the Grove Mountains from 17 January 2006 to 31 January 2006 and obtained high-accuracy GPS ice velocity data. To validate the abilities of the ionospheric correction for InSAR-based ice-motion measurements, we compared the InSAR-based ice velocity with GPS-based ice velocity [16,47]. Seven GPS measurements were used for the validation.…”

Section: Comparison With Gpsmentioning

confidence: 99%

“…The third factor is the different times of observation between the InSAR and GPS. However, Envisat ASAR images (C-band) with DInSAR and offset-tracking methods have proved that ice velocities for the years 2006, 2007, and 2009 had no obvious interannual changes in the Grove Mountains [16,47]. Therefore, the difference between the InSAR and GPS caused by the different times of observation is very small [16].…”

Section: Comparison With Gpsmentioning

confidence: 99%

Ionospheric Correction of L-Band SAR Interferometry for Accurate Ice-Motion Measurements: A Case Study in the Grove Mountains Area, East Antarctica

Wang

et al. 2022

Remote Sensing

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Ice motion is an essential element for accurately evaluating glacier mass balance. Interferometric synthetic aperture radar (InSAR) has been widely applied for monitoring ice motion with high precision and wide coverage in the Antarctic. However, the ionospheric effects can significantly impact InSAR-based ice-motion measurements. At low radar frequencies in particular, the ionospheric effects have been regarded as a serious source of noise in L-band SAR data. The split-spectrum method (SSM) is commonly used for correcting the ionospheric effects of the InSAR technique. However, it requires spatial filtering with the relatively large factors used to scale the sub-bands’ interferograms, which often results in an unwrapped phase error. In this paper, a reformulation of the split-spectrum method (RSSM) is introduced to correct the ionospheric effects in the Grove Mountains of East Antarctica, which have slow ice flow and frequent ionosphere changes. The results show that RSSM can effectively correct the ionospheric effects of InSAR-based ice-motion measurements. To evaluate the ability of ionospheric correction using RSSM, the result of ionospheric correction derived from SSM is compared with the results of RSSM. In addition, ionosphere-corrected ice motion is also compared with GPS and MEaSUREs. The results show that the ionosphere-corrected ice velocities are in good agreement with GPS observations and MEaSUREs. The average ice velocity from the InSAR time series is compared to that from MEaSUREs, and the average ionosphere-corrected ice velocity error reduces 43.9% in SSM and 51.1% in RSSM, respectively. The ionosphere-corrected ice velocity error is the most significant, reducing 86.9% in SSM and 90.4% in RSSM from 1 November 2007 to 19 December 2007. The results show that the ability of RSSM to correct ionospheric effects is slightly better than that of SSM. Therefore, we deduce that the RSSM offers a feasible way to correct ionospheric effects in InSAR-based ice-motion measurements in Antarctica.

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Mapping Complete Three-Dimensional Ice Velocities by Integrating Multi-Baseline and Multi-Aperture InSAR Measurements: A Case Study of the Grove Mountains Area, East Antarctic

Cited by 10 publications

References 42 publications

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Enhanced Kinematic Inversion of 3‐D Displacements, Geometry, and Hydraulic Properties of a North‐South Slow‐Moving Landslide in Three Gorges Reservoir

Ionospheric Correction of L-Band SAR Interferometry for Accurate Ice-Motion Measurements: A Case Study in the Grove Mountains Area, East Antarctica

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