An Improved Method for InSAR Atmospheric Phase Correction in Mountainous Areas

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Yueze Zheng¹  Guoya Wang²  Mengyao Shi³  Honglei Yang³  Xue Chen³  Yuhan Su³  Junhuan Peng³  Wenwen Wang⁴ 

[1] Beijing Institute of Surveying and Mapping, Beijing, China;Geological Hazards Prevention Institute, Gansu Academy of Sciences, Lanzhou, China;School of Land Science and Technology, China University of Geosciences, Beijing, China;Shanxi Key Laboratory of Resources, Environment and Disaster Monitoring, Jinzhong, China;

关键词: Atmospheric phase correction;    interferometric synthetic aperture radar (InSAR);    kriging interpolation method;   

DOI  :  10.1109/JSTARS.2021.3113619

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown