{"title":"用于 SBAS-InSAR 的湍流大气相位校正","authors":"Meng Duan, Zhiwei Li, Bing Xu, Weiping Jiang, Yunmeng Cao, Ying Xiong, Jianchao Wei","doi":"10.1007/s00190-024-01892-9","DOIUrl":null,"url":null,"abstract":"<p>The atmospheric phase, which is the sum of vertical stratification and turbulent atmospheric phase, is a major challenge currently faced by small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) measurements. Many previous studies have demonstrated that the former can be separated from the interferogram by establishing a functional model between it and the topography. Due to the high variability of the turbulent atmospheric phase (TAP) in the space and time domains, however, the TAP is difficult to model and remove. Recently, many stochastic models have been developed to reduce the influence of the TAP in SBAS-InSAR. To avoid the rank deficient in stochastic model method, we present a correction method using network-based variance estimation, interferogram stacking and ordinary kriging interpolation (NIO). There are three main steps in the proposed algorithm to ensure the accuracy of the correction result: (1) adaptively identify and select sufficient good-quality interferograms that contain less turbulent atmospheric noise to participate in deformation calculation; (2) further select the short temporal baseline interferogram and mask the corresponding deformation location to avoid the effect of deformation; and 3) take advantage of ordinary kriging interpolation to reduce the effects of TAP from the selected good-quality interferograms. The performance of the proposed method has been validated with a set of simulations and real Sentinel-1A SAR data in Southern California, USA.</p>","PeriodicalId":54822,"journal":{"name":"Journal of Geodesy","volume":"18 1","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Turbulent atmospheric phase correction for SBAS-InSAR\",\"authors\":\"Meng Duan, Zhiwei Li, Bing Xu, Weiping Jiang, Yunmeng Cao, Ying Xiong, Jianchao Wei\",\"doi\":\"10.1007/s00190-024-01892-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The atmospheric phase, which is the sum of vertical stratification and turbulent atmospheric phase, is a major challenge currently faced by small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) measurements. Many previous studies have demonstrated that the former can be separated from the interferogram by establishing a functional model between it and the topography. Due to the high variability of the turbulent atmospheric phase (TAP) in the space and time domains, however, the TAP is difficult to model and remove. Recently, many stochastic models have been developed to reduce the influence of the TAP in SBAS-InSAR. To avoid the rank deficient in stochastic model method, we present a correction method using network-based variance estimation, interferogram stacking and ordinary kriging interpolation (NIO). There are three main steps in the proposed algorithm to ensure the accuracy of the correction result: (1) adaptively identify and select sufficient good-quality interferograms that contain less turbulent atmospheric noise to participate in deformation calculation; (2) further select the short temporal baseline interferogram and mask the corresponding deformation location to avoid the effect of deformation; and 3) take advantage of ordinary kriging interpolation to reduce the effects of TAP from the selected good-quality interferograms. The performance of the proposed method has been validated with a set of simulations and real Sentinel-1A SAR data in Southern California, USA.</p>\",\"PeriodicalId\":54822,\"journal\":{\"name\":\"Journal of Geodesy\",\"volume\":\"18 1\",\"pages\":\"\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Geodesy\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s00190-024-01892-9\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Geodesy","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s00190-024-01892-9","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0
摘要
大气相位是垂直分层和湍流大气相位的总和,是小基线子集干涉合成孔径雷达(SBAS-InSAR)测量目前面临的主要挑战。以往的许多研究表明,通过建立干涉图与地形之间的函数模型,可以将前者从干涉图中分离出来。然而,由于湍流大气相位(TAP)在空间和时间域的高度可变性,TAP 难以建模和去除。最近,人们开发了许多随机模型来减少 SBAS-InSAR 中湍流大气相的影响。为了避免随机模型方法的秩缺陷,我们提出了一种使用基于网络的方差估计、干涉图堆叠和普通克里金插值(NIO)的修正方法。为确保校正结果的准确性,所提出的算法主要有三个步骤:(1)自适应识别并选择足够多的包含较少湍流大气噪声的优质干涉图参与形变计算;(2)进一步选择短时基线干涉图并屏蔽相应的形变位置,以避免形变的影响;以及(3)利用普通克里格插值来减少所选优质干涉图的 TAP 影响。通过一组模拟和美国南加州的 Sentinel-1A SAR 真实数据,验证了所提方法的性能。
Turbulent atmospheric phase correction for SBAS-InSAR
The atmospheric phase, which is the sum of vertical stratification and turbulent atmospheric phase, is a major challenge currently faced by small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) measurements. Many previous studies have demonstrated that the former can be separated from the interferogram by establishing a functional model between it and the topography. Due to the high variability of the turbulent atmospheric phase (TAP) in the space and time domains, however, the TAP is difficult to model and remove. Recently, many stochastic models have been developed to reduce the influence of the TAP in SBAS-InSAR. To avoid the rank deficient in stochastic model method, we present a correction method using network-based variance estimation, interferogram stacking and ordinary kriging interpolation (NIO). There are three main steps in the proposed algorithm to ensure the accuracy of the correction result: (1) adaptively identify and select sufficient good-quality interferograms that contain less turbulent atmospheric noise to participate in deformation calculation; (2) further select the short temporal baseline interferogram and mask the corresponding deformation location to avoid the effect of deformation; and 3) take advantage of ordinary kriging interpolation to reduce the effects of TAP from the selected good-quality interferograms. The performance of the proposed method has been validated with a set of simulations and real Sentinel-1A SAR data in Southern California, USA.
期刊介绍:
The Journal of Geodesy is an international journal concerned with the study of scientific problems of geodesy and related interdisciplinary sciences. Peer-reviewed papers are published on theoretical or modeling studies, and on results of experiments and interpretations. Besides original research papers, the journal includes commissioned review papers on topical subjects and special issues arising from chosen scientific symposia or workshops. The journal covers the whole range of geodetic science and reports on theoretical and applied studies in research areas such as:
-Positioning
-Reference frame
-Geodetic networks
-Modeling and quality control
-Space geodesy
-Remote sensing
-Gravity fields
-Geodynamics