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Article(id=1322506736452358326, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322506567065383670, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2024.08.384, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1723392000000, receivedDateStr=2024-08-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793239776957, onlineDateStr=2026-10-29, pubDate=1752508800000, pubDateStr=2025-07-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793239776957, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793239776957, creator=13701087609, updateTime=1793239776957, updator=13701087609, issue=Issue{id=1322506567065383670, tenantId=1146029695717560320, journalId=1281212831689347082, year='2025', volume='45', issue='7', pageStart='661', pageEnd='770', issueExtLink='null', onlineDate='null', pubDate='1752508800000', pubDateStr='2025-07-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793239736572, creator='13701087609', updateTime=1793239736572, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=695, endPage=698, ext={EN=ArticleExt(id=1322506736691433655, articleId=1322506736452358326, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=Atmospheric Residual Effects and Their Correction of Time Series InSAR Deformation, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Time series InSAR deformation may be affected by the atmospheric residual effects caused by seasonal differences and weather changes(such as precipitation and snowfall), thereby reducing its accuracy. Taking Tianjin city as an example, we conduct research on the atmospheric residual effects and their correction of time series InSAR deformation. First, the SBAS-InSAR technique was used to process Sentinel-1A images to obtain time series InSAR deformation. Then, the time series InSAR deformation was compared with precise leveling measurement results, and the atmospheric residual effects in time series InSAR deformation were analyzed combining with seasonal and weather change characteristics. Finally, an atmospheric residual error model was constructed based on ERA5 PWV data, which was used to remove the atmospheric residual effect errors to optimize the time series InSAR deformation. The results show that time series InSAR deformation has atmospheric residual effect errors, especially those caused by weather changes such as precipitation, which is more pronounced and affect the accuracy of time series InSAR deformation. There is a significant negative correlation between the difference of monthly time series InSAR deformation and monthly precise leveling deformation and the PWV changes in the same period. The atmospheric residual effect correction model constructed using the linear regression method can effectively improve the accuracy of time series InSAR deformation.

, authors=Yong WANG1, Haozhe CHI1, Yanping LIU2, Yubo LIU1, authorsList=Yong WANG, Haozhe CHI, Yanping LIU, Yubo LIU, authorCompany=null, correspAuthors=null, authorNote=

WANG Yong, PhD, professor, majors in GNSS meteorology, E-mail:

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时序InSAR形变可能受到季节差异、天气变化(如降水、降雪等)引起的大气残余效应的影响，从而使其精度降低。本文以天津市为例，开展时序InSAR形变大气残余效应及校正研究。首先，利用SBAS-InSAR技术处理Sentinel-1A影像，获取时序InSAR形变结果；然后，将时序InSAR形变与精密水准测量结果进行对比，并结合季节和天气变化特征，分析时序InSAR形变中的大气残余效应；最后，基于ERA5水汽资料构建大气残余误差模型，并利用该模型去除大气残余效应误差，提高时序InSAR形变精度。结果表明，时序InSAR形变存在大气残余效应误差，尤其是降水天气变化引起的大气残余效应较为明显，从而影响时序InSAR形变精度；时序InSAR月形变量与精密水准月形变量的差值和同一时间段内的水汽变化量之间存在显著的负相关关系；通过线性回归方法构建的大气残余效应校正模型能够有效改善时序InSAR形变精度。

, authors=王勇1, 迟皓哲1, 刘严萍2, 刘宇博1, authorsList=王勇, 迟皓哲, 刘严萍, 刘宇博, authorCompany=null, correspAuthors=null, authorNote=

王勇，博士，教授，主要研究方向为GNSS气象学，E-mail:

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WANG Yong, PhD, professor, majors in GNSS meteorology, E-mail:

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WANG Yong, PhD, professor, majors in GNSS meteorology, E-mail:

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王勇，博士，教授，主要研究方向为GNSS气象学，E-mail:

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王勇，博士，教授，主要研究方向为GNSS气象学，E-mail:

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journalId=1281212831689347082, articleId=1322506736452358326, language=CN, orderNo=4, keyword=ERA5水汽), Keyword(id=1322506744882909413, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506736452358326, language=CN, orderNo=5, keyword=校正模型)], refs=[Reference(id=1322506746862620917, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506736452358326, doi=null, pmid=null, pmcid=null, year=2023, volume=285, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=1, rfOrder=0, authorNames=Liang H Y, Zhang L, Lu Z, journalName=Remote Sensing of Environment, refType=null, unstructuredReference=Liang H Y,Zhang L,Lu Z,et al.Correction of Spatially Varying Stratified Atmospheric Delays in Multitemporal InSAR[J].Remote Sensing of Environment,2023,285, articleTitle=Correction of Spatially Varying Stratified Atmospheric Delays in Multitemporal InSAR, refAbstract=null), Reference(id=1322506746933924086, tenantId=1146029695717560320, 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figureFileBig=2Cw1qkF6gfoibf7Ja9jNyw==, tableContent=null), ArticleFig(id=1322506746187337966, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506736452358326, language=CN, label=图4, caption=校正前后InSAR形变与水准形变比较, figureFileSmall=oCMAEsrLaA83RzOW8T7gwQ==, figureFileBig=2Cw1qkF6gfoibf7Ja9jNyw==, tableContent=null), ArticleFig(id=1322506746279612655, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506736452358326, language=EN, label=Tab. 1, caption=

RMSE of monthly InSAR deformation and monthly leveling deformation under normal weather and precipitation conditions

, figureFileSmall=null, figureFileBig=null, tableContent=
形变比较点储运2号楼-1储运2号楼-3储运2号楼-5储运2号楼-7
正常天气1.91.71.61.6
降水情况4.24.04.03.1
), ArticleFig(id=1322506746376081648, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506736452358326, language=CN, label=表1, caption=

正常天气与降水情况下的InSAR月形变与水准月形变的均方根误差

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形变比较点储运2号楼-1储运2号楼-3储运2号楼-5储运2号楼-7
正常天气1.91.71.61.6
降水情况4.24.04.03.1
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时序InSAR形变大气残余效应及其校正研究
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王勇 1 , 迟皓哲 1 , 刘严萍 2 , 刘宇博 1
大地测量与地球动力学 | 现代大地测量 2025,45(7): 695-698
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大地测量与地球动力学 |现代大地测量 2025 , 45 (7) : 695 -698
时序InSAR形变大气残余效应及其校正研究
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王勇1 , 迟皓哲1, 刘严萍2, 刘宇博1
作者信息
  • 1 天津城建大学地质与测绘学院, 天津市津静路26号, 300384
  • 2 天津城建大学经济与管理学院, 天津市津静路26号, 300384
作者简介:

王勇，博士，教授，主要研究方向为GNSS气象学，E-mail:

Atmospheric Residual Effects and Their Correction of Time Series InSAR Deformation
Yong WANG1 , Haozhe CHI1, Yanping LIU2, Yubo LIU1
Affiliations
  • 1 School of Geology and Geomatics, Tianjin Chengjian University, 26 Jinjing Road, Tianjin 300384, China
  • 2 School of Economics and Management, Tianjin Chengjian University, 26 Jinjing Road, Tianjin 300384, China

    • WANG Yong, PhD, professor, majors in GNSS meteorology, E-mail:

出版时间: 2025-07-15 doi: 10.14075/j.jgg.2024.08.384
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时序InSAR形变可能受到季节差异、天气变化(如降水、降雪等)引起的大气残余效应的影响，从而使其精度降低。本文以天津市为例，开展时序InSAR形变大气残余效应及校正研究。首先，利用SBAS-InSAR技术处理Sentinel-1A影像，获取时序InSAR形变结果；然后，将时序InSAR形变与精密水准测量结果进行对比，并结合季节和天气变化特征，分析时序InSAR形变中的大气残余效应；最后，基于ERA5水汽资料构建大气残余误差模型，并利用该模型去除大气残余效应误差，提高时序InSAR形变精度。结果表明，时序InSAR形变存在大气残余效应误差，尤其是降水天气变化引起的大气残余效应较为明显，从而影响时序InSAR形变精度；时序InSAR月形变量与精密水准月形变量的差值和同一时间段内的水汽变化量之间存在显著的负相关关系；通过线性回归方法构建的大气残余效应校正模型能够有效改善时序InSAR形变精度。

时序InSAR  /  Sentinel-1A  /  大气残余效应  /  ERA5水汽  /  校正模型

Time series InSAR deformation may be affected by the atmospheric residual effects caused by seasonal differences and weather changes(such as precipitation and snowfall), thereby reducing its accuracy. Taking Tianjin city as an example, we conduct research on the atmospheric residual effects and their correction of time series InSAR deformation. First, the SBAS-InSAR technique was used to process Sentinel-1A images to obtain time series InSAR deformation. Then, the time series InSAR deformation was compared with precise leveling measurement results, and the atmospheric residual effects in time series InSAR deformation were analyzed combining with seasonal and weather change characteristics. Finally, an atmospheric residual error model was constructed based on ERA5 PWV data, which was used to remove the atmospheric residual effect errors to optimize the time series InSAR deformation. The results show that time series InSAR deformation has atmospheric residual effect errors, especially those caused by weather changes such as precipitation, which is more pronounced and affect the accuracy of time series InSAR deformation. There is a significant negative correlation between the difference of monthly time series InSAR deformation and monthly precise leveling deformation and the PWV changes in the same period. The atmospheric residual effect correction model constructed using the linear regression method can effectively improve the accuracy of time series InSAR deformation.

time series InSAR  /  Sentinel-1A  /  atmospheric residual effects  /  ERA5 PWV  /  correction model
王勇, 迟皓哲, 刘严萍, 刘宇博. 时序InSAR形变大气残余效应及其校正研究. 大地测量与地球动力学, 2025 , 45 (7) : 695 -698 . DOI: 10.14075/j.jgg.2024.08.384
Yong WANG, Haozhe CHI, Yanping LIU, Yubo LIU. Atmospheric Residual Effects and Their Correction of Time Series InSAR Deformation[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (7) : 695 -698 . DOI: 10.14075/j.jgg.2024.08.384
正文
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获取高精度的时序InSAR形变，不仅能掌握区域地面沉降动态，还能为地面沉降灾害防治以及城市规划提供参考。大气延迟是影响时序InSAR精度的主要误差源之一。大气的不均匀性使得相位-高程依赖关系在空间和时间上存在差异，Liang等[1]将对流层延迟、变形和可能的地形误差整合到一个联合模型中，实现了对流层延迟的精确分离。部分学者[2-3]分别使用ERA5、NCEP气象再分析资料校正InSAR干涉图大气延迟，有效削弱了大气延迟对干涉测量结果的影响。基于通用型大气校正在线服务(GACOS)，Xiao等[4]和杨文韬等[5]分别对中国东部两个典型地形、小尺度区域(山东省济南市某矿区)进行改正效果评估，结果表明，经过GACOS大气校正可去除大气噪声相位，对于小区域长时间形变监测具有一定的适用性。Wang等[6]选择合适的MP方案增强WRF(weather research and forecast) 模型对ZTD的模拟精度。祝杰等[7]利用MERIS近红外水汽产品去除北京地区地面沉降InSAR监测中的大气延迟相位，有效削弱了干涉图中的大气延迟相位误差。Guo等[8]使用ERA5资料消除对流层延迟带来的时间振荡偏差，并利用昆明连续运行参考站系统(CORS)对该方法的位移结果进行验证。Lu等[9]提出一种基于GNSS ZTD及其水平梯度构建大气校正模型的增强方法，解决了大气延迟空间变异导致的校正效果不一致的问题。Wang等[10]在局部滑动窗口内进行多时相建模，可为大面积InSAR滑坡识别和监测提供精确的非均匀大气校正。
时序InSAR分析方法假设大气延迟在时间上是不相关的，进而采用时间维-空间维滤波方法分离形变相位和大气延迟相位。但大气水汽的季节性变化使得大气延迟在时间上存在相关性，不满足大气延迟在时间上不相关的假设条件。基于以上分析，时序InSAR形变可能受到季节差异、天气变化(降水、降雪等) 引起的大气残余效应的影响，从而使其精度降低。水准测量是获取高程(沉降)精度最高的一种测量方法，将时序InSAR形变与水准测量结果进行对比，可分析天气变化引起的大气残余误差的影响，再结合水汽资料构建大气残余误差模型，有助于提高天气变化情况下时序InSAR形变精度。本文以天津市为例，开展时序InSAR形变大气残余误差分析，依据ERA5水汽资料构建大气残余误差模型，利用该模型去除大气残余误差，提高时序InSAR形变精度。
1 时序InSAR形变获取
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以天津市为例，研究数据为欧空局Sentinel-1A卫星C波段升轨影像数据，成像模式为干涉宽幅模式，极化方式为垂直极化，影像分辨率为5 m×20 m。数据量为101景，时间跨度为2016-09-29—2023-11-16。卫星轨道数据为欧空局公布的精密定轨星历数据，采用30 m分辨率的SRTM1数字高程模型作为外部辅助DEM数据，使用GACOS数据(http://www.gacos.net/)校正大气延迟误差。选取2020-05-23为主影像，设置时空基线阈值，其他影像与该主影像进行配对，共获得100对干涉像对的时空基线。采用SARscape软件和SBAS-InSAR技术对Sentinel-1A影像进行处理，获得时序InSAR形变。
2 InSAR形变大气残余效应分析
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时序InSAR形变可能受到季节差异、天气变化(降水、降雪等)引起的大气延迟残余误差的影响，从而使其精度降低。选取研究区某建筑及周边区域开展精密水准测量，与时序InSAR形变进行比较，分析时序InSAR形变大气残余误差。选择某建筑2号楼周边的4个监测点，以2020-05-23的InSAR形变值为起始值，用2020-06-28及以后的每月InSAR形变值减去起始值，得到2020-05 —2022-10的InSAR累积形变量。通过比较2020—2022年年积日(2020-01-01为起始日)的时序InSAR累积形变量与水准累积形变量(图 1为其中2个监测点的比较)，分析大气残余效应对时序InSAR形变的影响。
由图 1可知，时序InSAR形变值与水准值的变化规律较为一致，但在第422天(2021-03-19)、第518天(2021-06-23)、第554天(2021-07-29)、第614天(2021-09-27)差异较大，最大可达7 mm，其他时间两者的差值为1~3 mm。造成累积形变值差异较大的原因，可能是由于获取SAR影像的时间段存在降水情况或获取的2景SAR影像间存在季节性差异。查阅历史天气可知，在第422天、第518天、第554天、第614天前后获取SAR影像时均发生降水，由此可认为降水所产生的大气残余效应会对InSAR地表形变监测产生较大影响。此外，季节性差异也可能引起大气残余效应，从而影响InSAR地表形变精度。为进一步分析由于降水所引起的大气残余效应对InSAR地表形变监测精度的影响，将相邻2个月份的InSAR形变值相减，得到每月的InSAR形变量，以此去除季节性差异造成的大气残余效应，并与水准月形变量进行对比(图 2)。
由图 2可知，时序InSAR月形变与水准月形变接近，但在第422天、第518天、第554天、第614天差异较大(以上时间段存在降水过程)，其差值为5~6 mm。为了验证正常天气情况下InSAR地表形变监测精度，将上述InSAR月形变量去除在降水时获取的InSAR形变量，并与水准数据进行对比。分别计算各点在正常天气和降水情况下的InSAR月形变量与水准月形变量的均方根误差(RMSE)，结果如表 1(单位mm)所示。
由表 1可知，降水对InSAR形变有影响。降水使得获取2景SAR影像期间大气中的水汽值差异较大，引起大气残余误差，导致InSAR地表形变监测精度降低。
3 基于ERA5水汽资料的时序InSAR形变大气残余效应校正模型
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降水引起的大气残余效应会影响时序InSAR形变精度，因此有必要开展时序InSAR形变大气残余效应校正研究。本文选取与SAR影像数据时间一致的ERA5水汽资料开展校正研究。选择发生降水过程的SAR影像时间以及相邻影像时间，将InSAR月形变量与水准月形变量作差(可认为是降水天气过程引起的大气残余效应)，同时将2期SAR影像时间对应的ERA5水汽作差，获得水汽变化量(ΔPWV)，对比分析形变量差值与水汽变化量(图 3)。
由图 3可知，4个监测点的InSAR月形变量与水准月形变量的差值大致呈现“M”形，而相同时间段的水汽变化量呈现“W”形。计算形变量差值与相同时间段水汽变化量的相关性，相关系数为-0.975，显著性检验值小于0.001。该结果说明可采用线性回归方法，通过水汽变化量(ΔPWV)建立关于InSAR月形变量与水准月形变量差值的大气残余效应校正模型，减弱或消除由于降水所产生的大气残余效应，从而提高降水天气状况下InSAR地表形变监测精度。选择某建筑2号楼-1、2号楼-3、2号楼-7的InSAR月形变量与水准月形变量的差值和水汽变化量建立大气残余效应校正模型，该建筑2号楼-5的数据用来检验模型精度。建立的大气残余效应校正模型为：
$y=-0.211 x-0.055$
式中，y为InSAR月形变量与水准月形变量的差值；x为水汽变化量(ΔPWV)。
将未参与建模的2号楼-5监测点降水时获取的InSAR形变值减去利用该模型得到的校正后InSAR月形变量与水准月形变量的差值，得到校正后的InSAR月形变值，并再次与水准月形变量进行对比(图 4)，计算其RMSE，校正前后的RMSE分别为4.0 mm和1.2 mm。结合图 4可知，基于ERA5水汽资料构建时序InSAR形变大气残余效应校正模型，能有效降低因降水引起的大气残余效应，提高时序InSAR地表形变监测精度(形变精度优于2 mm)。
4 结语
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本文以天津市为例，开展时序InSAR形变大气残余效应误差分析，依据ERA5水汽资料构建大气残余误差校正模型，利用该模型去除大气残余效应误差，提高时序InSAR形变精度。结论如下：
1) 时序InSAR形变存在大气残余效应误差，大气残余效应包括季节影响和天气变化影响。采用时序InSAR月形变可以去除季节残余效应的影响。降水天气变化引起的大气残余效应较为明显，会影响时序InSAR形变精度。
2) InSAR月形变量与水准月形变量差值的变化趋势和相同时间段水汽变化量的变化趋势相反，相关系数为-0.975，两者存在显著负相关特性。利用线性回归方法构建的大气残余效应校正模型，能有效改善时序InSAR形变精度(精度优于2 mm)。
由于水准数据的限制，模型需要更长时间或更多的点进行可靠性验证。或者针对GNSS连续观测数据，构建基于GNSS的大气残余效应模型，提高模型的普适性。下一步将选取更多的水准结果以及研究区连续GNSS坐标时序开展大气残余效应分析和建模研究。
基金
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  • 天津市科技计划(21KPHDRC00070)
  • 国家级大学生创新创业训练计划(202310792005)
文献
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2025年第45卷第7期
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doi: 10.14075/j.jgg.2024.08.384
  • 接收时间：2024-08-12
  • 首发时间：2026-10-29
  • 出版时间：2025-07-15
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  • 收稿日期：2024-08-12
基金
Science and Technology Planning of Tianjin Municipality(21KPHDRC00070)
天津市科技计划(21KPHDRC00070)
National College Students' Innovation and Entrepreneurship Training Program(202310792005)
国家级大学生创新创业训练计划(202310792005)
作者信息
    1 天津城建大学地质与测绘学院, 天津市津静路26号, 300384
    2 天津城建大学经济与管理学院, 天津市津静路26号, 300384
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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