An Improved Method for InSAR Atmospheric Phase Correction in Mountainous Areas
Mengyao Shi, Junhuan Peng, Xue Chen, Yueze Zheng, Honglei Yang, Yuhan Su, Wang Guoya, Wenwen Wang
Abstract
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 moderate-resolution imaging spectroradiometer (MODIS), but GNSS data are limited by the sparse distribution of observation stations. MODIS data 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. 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)the Generic Atmospheric Correction Online Service (GACOS)the network-based variance-covariance estimation (NVCE) method and other atmospheric phase correction models in GAMMA software. The results demonstrate that the proposed method is superior to existing methods for the detection of deformation and the mitigation of atmosphere effects.