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

Abstract

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.

SBAS-InSAR-Based Analysis of Surface Deformation in the Eastern Tianshan Mountains, China

“…This effort demonstrated the outstanding measurement capability of SBAS-InSAR in this region and obtained credible results. Based on this work, the parameters involved in the SBAS-InSAR processing for this paper were consistent with their settings in the literature (Mora et al, 2002;Hu et al, 2021a;Du et al, 2021b;Kursah et al, 2021), ensuring the reliability of the results, and therefore it was unnecessary to verify the accuracy of the measurement results in this study. This paper focuses on obtaining surface settlement information through SBAS-InSAR processing and analyzing settlement or uplift characteristics to understand the deformation features of the study area over a 3-year period, which will provide basic data for regional development policy formulation.…”

“…Moreover, it costs less time and money than manual measurements with comparable precision. Differential interferometric synthetic aperture radar (D-InSAR) (Massonnet and Feigl, 1998;Hanssen, 2001;Simons and Rosen, 2007;Lu and Dzurisin, 2014) has been developed based on InSAR technology, and multi-temporal InSAR (MT-InSAR) (Pepe and Calò, 2017;Zhu et al, 2019;Gatsios et al, 2020;Shahzad et al, 2020) including persistent scatterer InSAR (PS-InSAR) (Ferretti et al, 2001;Colesanti et al, 2003;Zhang et al, 2013;Li et al, 2020;Ge et al, 2021), small baseline subset InSAR (SBAS-InSAR) (Ferretti et al, 2001;Mora et al, 2002;Colesanti et al, 2003;Hu et al, 2021a;Du et al, 2021b), and distributed scatterer InSAR (DS-InSAR) ( Zhu et al, 2018;He and Zhao, 2020;Du et al, 2021d;Hu et al, 2021a) have been developed based on D-InSAR. Their practical scope varies, and each has its own advantages and disadvantages.…”

Mitigating Atmospheric Delays in InSAR Time Series: The DetrendInSAR Method and Its Validation

“…InSAR deformation signals typically change slowly in both space and time, except for cases involving sudden deformation events such as earthquakes (e.g., Guglielmino et al., 2011; J. Wang et al., 2019). With increasing SAR image acquisition frequency of modern SAR satellite systems, the temporal evolution of most deformation processes can be represented by relatively simple time‐dependent polynomials or other functions (Hetland et al., 2012; Hu et al., 2021). In addition, within areas of limited spatial extent (e.g., 2 × 2 km 2 in H. Liang et al., 2019), the stratified atmospheric delays can be regarded as uniform and modeled by a DEM‐dependent linear function.…”

“…Since the orbital control of the new generation of SAR satellites is generally within 200 m, the DEM‐error‐related phase is negligible compared with the atmospheric delays (Text S1 in Supporting Information S1), and we thus do not model it in the DetrendInSAR method. For older SAR satellite data, an adaptive deformation model can be used for estimating DEM errors after the DetrendInSAR procedure (Hu et al., 2021).…”

Determination of periodic deformation from InSAR results using the FFT time series analysis method in Gediz Graben

“…Therefore, while estimating the velocities of PS points, it is necessary to distinguish the periodic and trend components and then estimate the velocity values free from periodic effects. There are a limited number of studies using different deformation models with different methods in monitoring deformations with the PSInSAR method (Li et al 2015 ; Morishita and Hanssen 2015 ; Wang et al 2017 ; Ilieva et al 2019 ; Bao et al 2021 ; Hu et al 2021 ). However, these studies did not use the FFT time series analysis method.…”