Ionospheric Phase Delay Correction for Time Series Multiple-Aperture InSAR Constrained by Polynomial Deformation Model

被引:5
作者
Mao, Wenfei [1 ]
Wang, Xiaowen [2 ]
Liu, Guoxiang [2 ]
Ma, Peifeng [1 ]
Zhang, Rui [2 ]
Ma, Zhangfeng [3 ]
Tang, Jun [4 ]
Lin, Hui [5 ,6 ]
机构
[1] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Hong Kong, Peoples R China
[2] Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Dept Surveying & Geoinformat, Chengdu 610031, Peoples R China
[3] Hohai Univ, Sch Earth Sci & Engn, Nanjing 211100, Peoples R China
[4] Kunming Univ Sci & Technol, Inst Fac Land Resources Engn, Kunming 650093, Peoples R China
[5] Jiangxi Normal Univ, UNESCO Int Ctr Nat & Cultural Heritage Space Techn, Nanchang, Peoples R China
[6] Jiangxi Normal Univ, Nanchang Sub Ctr, Nanchang 330022, Peoples R China
基金
中国国家自然科学基金;
关键词
Ionospheric delay correction; linear and logarithmic function constraint; multiple-aperture InSAR (MAI); time series; SAR; EXTRACTION;
D O I
10.1109/LGRS.2023.3281343
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
As a supplement to time-series interferometric synthetic aperture radar (TS-InSAR), time-series multiple-aperture InSAR (TS-MAI) can measure the spatiotemporal changes in SAR along-track surface deformation. TS-MAI is often applied with low-frequency SAR data (e.g., L-band data) due to its ability to retain high interferometric coherence. However, the low-frequency SAR signal is vulnerable to ionospheric delays, which can significantly degrade the measurement accuracy of TS-MAI. This letter presents an approach to correct the ionospheric errors in TS-MAI. A polynomial cubic model is employed to constrain the ground deformation, which is then incorporated into the observation model for effectively separating the deformation signal and the ionospheric delays. The proposed method is tested using the L-band ALOS-1 PALSAR-1 datasets covering the Tocopilla area in Chile between November 2007 and March 2011. The correction performance and accuracy of the proposed method are demonstrated by comparing the range split-spectrum interferometry (RSSI)-based method and the local GPS data, respectively. The root mean square error (RMSE) improvement rates between TS-MAI and GPS are 72.17% for the SRGD site and 84.51% for the VLZL site, and their correlation coefficients increase from 0.23 and 0.50 to 0.52 and 0.61 after the correction.
引用
收藏
页数:5
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