Abstract
Time series interferometric synthetic aperture radar (TS-InSAR) has been a powerful tool for monitoring land surface deformation over 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 and time-series InSAR. Currently, the atmospheric delay is derived mainly from auxiliary data from sources such as the global navigation satellite system (GNSS) and moderate-resolution imaging spectroradiometry (MODIS), but GNSS data are limited by the sparse distribution of observation stations. MODIS data may also not temporally match SAR image acquisition, which leads to low accuracy in atmospheric phase correction. This article presents a decomposition method to remove the 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 and the network-based variance-covariance estimation method. The results demonstrate that the proposed method is superior to existing methods for the detection of deformation inverted from TS-InSAR.
Highlights
Interferometric synthetic aperture radar (InSAR) is a new technology for monitoring surface deformation that processes two complex images obtained by a satellite passing a single location at two moments in time to calculate the phase change caused by deformation; in this way, the characteristics of surface deformation can be analyzed
GAMMAatmmod1 refers to the single patch of the linear atmospheric phase model with the digital elevation model (DEM), GAMMAatmmod2 refers to the adaptive 2-dimensional atmospheric phase model, the initial phase refers to the unwrapped phase after extracting the topographic phase and flattened phase, and network-based variance-covariance estimation (NVCE) is the method proposed by Li et al and Cao et al The results show that the proposed method has a lower mean value and standard deviation, indicating that the proposed method is beneficial for atmospheric phase correction in TS-InSAR
This paper fully considers the characteristics of the troposphere and proposes a method combining quadtree segmentation with universal kriging for tropospheric correction under the TS-InSAR framework
Summary
Interferometric synthetic aperture radar (InSAR) is a new technology for monitoring surface deformation that processes two complex images obtained by a satellite passing a single location at two moments in time to calculate the phase change caused by deformation; in this way, the characteristics of surface deformation can be analyzed. InSAR has been successfully used in many fields to study phenomena such as landslides, ground subsidence, and earthquakes; in addition, this technique is regarded as an effective method for the (a) (b). Many scholars have explored tropospheric correction methods in interferometry. H. Su are at the School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
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