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Volume 45 Issue 6
Jun.  2020
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LI Peng, GAO Mengyao, LI Zhenhong, WANG Houjie. Evaluation of Wide-Swath InSAR Tropospheric Delay Estimation Methods over the Altyn Tagh Fault[J]. Geomatics and Information Science of Wuhan University, 2020, 45(6): 879-887. doi: 10.13203/j.whugis20190236
Citation: LI Peng, GAO Mengyao, LI Zhenhong, WANG Houjie. Evaluation of Wide-Swath InSAR Tropospheric Delay Estimation Methods over the Altyn Tagh Fault[J]. Geomatics and Information Science of Wuhan University, 2020, 45(6): 879-887. doi: 10.13203/j.whugis20190236

Evaluation of Wide-Swath InSAR Tropospheric Delay Estimation Methods over the Altyn Tagh Fault

doi: 10.13203/j.whugis20190236
Funds:  The National Natural Science Foundation of China (41806108); the National Key Research and Development Program of China (2017YFE0133500, 2016YFA0600903); Shandong Provincial Natural Science Foundation (ZR2016DB30); China Postdoctoral Science Foundation (2016M592248); the Fundamental Research Funds for the Central Universities (201713039); Qingdao Indigenous Innovation Program (16‐5‐1‐25‐jch); Qingdao Postdoctoral Application Research Project.
More Information
  • Author Bio:

    LI Peng, PhD, lecturer, specializes in radar interferometry and its applications.pengli@ouc.edu.cn

  • Corresponding author: LI Zhenhong, PhD, professor. E-mail:Zhenhong.Li@newcastle.ac.uk
  • Received Date: 2019-11-21
  • Publish Date: 2020-06-05
  • In recent years, wide-swath (WS) interferometric synthetic aperture radar (InSAR) technique that has the potential to produce continental-scale maps has been widely used in geological disaster survey and crustal deformation monitoring. However, the impact of tropospheric delay greatly limits its accuracy in mapping small amounts of ground deformation over large spatial areas. Three common used methods, that is ECMWF (European Centre for Medium-Range Weather Forecasts), GACOS (generic atmospheric correction online service for InSAR) and topography-correlated linear relationship, are evaluated to investigate their statistical performance with WS InSAR time series derived from Envisat ASAR ScanSAR and Sentinel-1 TOPOSAR modes over the western segment of the Altyn Tagh Fault. The results show that the GACOS correction method is superior to the other two methods and performs best in capturing both topography-correlated and turbulent mixing tropospheric delays. For Envisat ASAR and Sentinel-1 datasets, the mean reduction of phase standard deviation after GACOS correction can reach 68.1% and 54.5% respectively. The linear correction method can perform relatively well in large-scale areas with rough topography when vertical atmospheric stratification dominates the tropospheric delay. Due to a lack of ground meteorological observation, ECMWF products with limited spatial and temporal resolution cannot accurately reveal the local details. As a fast, robust and effective online service for tropospheric delay estimation and correction, GACOS products can provide critical and reliable support for global InSAR users in large-scale geological disaster applications.
  • [1] 李鹏, 李振洪, 李陶, 等. 宽幅InSAR大地测量学与大尺度形变监测方法[J]. 武汉大学学报·信息科学版, 2017, 42(9):1195-1202 doi:  10.13203/j.whugis20150587

    Li Peng, Li Zhenhong, Li Tao, et al. Wide-Swath InSAR Geodesy and Its Applications to Large-Scale Deformation Monitoring[J]. Geomatics and Information Science of Wuhan University, 2017, 42(9):1195-1202 doi:  10.13203/j.whugis20150587
    [2] 李振洪, 宋闯, 余琛, 等. 卫星雷达遥感在滑坡灾害探测和监测中的应用:挑战与对策[J]. 武汉大学学报·信息科学版, 2019, 44(7):967-979 doi:  10.13203/j.whugis20190098

    Li Zhenhong, Song Chuang, Yu Chen, et al. Application of Satellite Radar Remote Sensing to Landslide Detection and Monitoring:Challenges and Solutions[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7):967-979 doi:  10.13203/j.whugis20190098
    [3] 李振洪, 李鹏, 丁咚, 等. 全球高分辨率数字高程模型研究进展与展望[J]. 武汉大学学报·信息科学版, 2018, 43(12):1927-1942 doi:  10.13203/j.whugis20180295

    Li Zhenhong, Li Peng, Ding Dong, et al. Research Progress of Global High Resolution Digital Elevation Models[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12):1927-1942 doi:  10.13203/j.whugis20180295
    [4] Li Z, Cao Y, Wei J, et al. Time-Series InSAR Ground Deformation Monitoring:Atmospheric Delay Modeling and Estimating[J]. Earth-Science Reviews, 2019, 192:258-284 doi:  10.1016/j.earscirev.2019.03.008
    [5] Dong J, Zhang L, Liao M, et al. Improved Correction of Seasonal Tropospheric Delay in InSAR Observations for Landslide Deformation Monitoring[J]. Remote Sensing of Environment, 2019, 233:111370 doi:  10.1016/j.rse.2019.111370
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    [8] Li Z H, Pasquali P, Cantone A, et al. MERIS Atmospheric Water Vapor Correction Model for Wide Swath Interferometric Synthetic Aperture Radar[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(2):257-261 doi:  10.1109/LGRS.2011.2166053
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    [10] 宋小刚, 李德仁, 廖明生, 等. 基于GPS观测量的InSAR干涉图中对流层改正方法及其论证[J]. 武汉大学学报·信息科学版, 2008, 33(3):233-236 http://ch.whu.edu.cn/article/id/1561

    Song Xiaogang, Li Deren, Liao Mingsheng, et al. A Method to Correct Tropospheric Delay in SAR Interferometry from GPS Observations[J]. Geomatics and Information Science of Wuhan University, 2008, 33(3):233-236 http://ch.whu.edu.cn/article/id/1561
    [11] Li Z H, Fielding E J, Cross P, et al. Advanced InSAR Atmospheric Correction:MERIS/MODIS Combination and Stacked Water Vapour Models[J]. International Journal of Remote Sensing, 2009, 30(13):3343-3363 doi:  10.1080/01431160802562172
    [12] Parker A L, Biggs J, Walters R J, et al. Systematic Assessment of Atmospheric Uncertainties for InSAR Data at Volcanic Arcs Using Large-Scale Atmospheric Models:Application to the Cascade Volcanoes, United States[J]. Remote Sensing of Environment, 2015, 170:102-114 doi:  10.1016/j.rse.2015.09.003
    [13] Yu C, Li Z, Penna N T, et al. Generic Atmospheric Correction Model for Interferometric Synthetic Aperture Radar Observations[J]. Journal of Geophysical Research:Solid Earth, 2018, 123(10):9202-9222 doi:  10.1029/2017JB015305
    [14] Yu C, Li Z, Penna N T. Interferometric Synthetic Aperture Radar Atmospheric Correction Using a GPS-Based Iterative Tropospheric Decomposition Model[J]. Remote Sensing of Environment, 2018, 204:109-121 doi:  10.1016/j.rse.2017.10.038
    [15] Elliott J R, Biggs J, Parsons B, et al. InSAR Slip Rate Determination on the Altyn Tagh Fault, Northern Tibet, in the Presence of Topographically Correlated Atmospheric Delays[J]. Geophysical Research Letters, 2008, 35(L12309):1-5 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1029/2008GL033659
    [16] Doin M P, Lasserre C, Peltzer G, et al. Corrections of Stratified Tropospheric Delays in SAR Interferometry:Validation with Global Atmospheric Models[J]. Journal of Applied Geophysics, 2009, 69(1):35-50 doi:  10.1016/j.jappgeo.2009.03.010
    [17] Jolivet R, Grandin R, Lasserre C, et al. Systematic InSAR Tropospheric Phase Delay Corrections from Global Meteorological Reanalysis Data[J]. Geophysical Research Letters, 2011, 38(17):L17311 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0224700399/
    [18] Yu C, Penna N T, Li Z. Generation of Real-Time Mode High-Resolution Water Vapor Fields from GPS Observations[J]. Journal of Geophysical Research:Atmospheres, 2017, 122(3):2008-2025 doi:  10.1002/2016JD025753
    [19] Delacourt C. Tropospheric Correction of SAR Interferograms with Strong Topography:Application to Etna[J]. Geophysical Research Letters, 1998, 25(15):2849-2852 doi:  10.1029/98GL02112
    [20] Cavalié O, Doin M P, Lasserre C, et al. Ground Motion Measurement in the Lake Mead Area, Nevada, by Differential Synthetic Aperture Radar Interferometry Time Series Analysis:Probing the Lithosphere Rheological Structure[J]. Journal of Geophysical Research:Solid Earth, 2007, 112(B3):403 https://www.researchgate.net/publication/238013428_Ground_Motion_Measurement_in_the_Lake_Mead_Area_Nevada_USA_by_DinSAR_Time_Series_Analysis_Probing_of_the_Lithosphere_Rheological_Structure
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Evaluation of Wide-Swath InSAR Tropospheric Delay Estimation Methods over the Altyn Tagh Fault

doi: 10.13203/j.whugis20190236
Funds:  The National Natural Science Foundation of China (41806108); the National Key Research and Development Program of China (2017YFE0133500, 2016YFA0600903); Shandong Provincial Natural Science Foundation (ZR2016DB30); China Postdoctoral Science Foundation (2016M592248); the Fundamental Research Funds for the Central Universities (201713039); Qingdao Indigenous Innovation Program (16‐5‐1‐25‐jch); Qingdao Postdoctoral Application Research Project.

Abstract: In recent years, wide-swath (WS) interferometric synthetic aperture radar (InSAR) technique that has the potential to produce continental-scale maps has been widely used in geological disaster survey and crustal deformation monitoring. However, the impact of tropospheric delay greatly limits its accuracy in mapping small amounts of ground deformation over large spatial areas. Three common used methods, that is ECMWF (European Centre for Medium-Range Weather Forecasts), GACOS (generic atmospheric correction online service for InSAR) and topography-correlated linear relationship, are evaluated to investigate their statistical performance with WS InSAR time series derived from Envisat ASAR ScanSAR and Sentinel-1 TOPOSAR modes over the western segment of the Altyn Tagh Fault. The results show that the GACOS correction method is superior to the other two methods and performs best in capturing both topography-correlated and turbulent mixing tropospheric delays. For Envisat ASAR and Sentinel-1 datasets, the mean reduction of phase standard deviation after GACOS correction can reach 68.1% and 54.5% respectively. The linear correction method can perform relatively well in large-scale areas with rough topography when vertical atmospheric stratification dominates the tropospheric delay. Due to a lack of ground meteorological observation, ECMWF products with limited spatial and temporal resolution cannot accurately reveal the local details. As a fast, robust and effective online service for tropospheric delay estimation and correction, GACOS products can provide critical and reliable support for global InSAR users in large-scale geological disaster applications.

LI Peng, GAO Mengyao, LI Zhenhong, WANG Houjie. Evaluation of Wide-Swath InSAR Tropospheric Delay Estimation Methods over the Altyn Tagh Fault[J]. Geomatics and Information Science of Wuhan University, 2020, 45(6): 879-887. doi: 10.13203/j.whugis20190236
Citation: LI Peng, GAO Mengyao, LI Zhenhong, WANG Houjie. Evaluation of Wide-Swath InSAR Tropospheric Delay Estimation Methods over the Altyn Tagh Fault[J]. Geomatics and Information Science of Wuhan University, 2020, 45(6): 879-887. doi: 10.13203/j.whugis20190236
  • 合成孔径雷达(synthetic aperture radar, SAR)卫星在天线发射微波信号与接收地面散射回波的过程中由于受到各种因素的影响,大气温度、压力和水汽含量等条件极易发生变化,因此微波信号穿过大气介质时会发生不同程度的雷达信号相位延迟,即大气延迟效应,其通常包括对流层延迟与电离层延迟,本文仅讨论对流层大气延迟。

    对流层延迟是对流层大气参数(如压强、温度和水汽含量)变化引起的相位延迟,水汽含量变化会显著影响对流层延迟。宽幅(wide swath, WS)SAR模式(幅宽可达400 km)已成功用于获取全球数字高程模型(digital elevation model, DEM)与地震形变监测等方面,但大气延迟效应严重制约了星载重复轨道宽幅合成孔径雷达干涉测量(interferometric synthetic aperture radar, InSAR)技术获取地表高程或探测大范围、缓慢构造形变信号的精度[1-3]

    国内外已有许多学者研究了干涉图对流层延迟的量化、估计与改正方法[4-11],包括:(1)通过叠加、平均或者时空滤波干涉图时间序列方法剔除具有时空随机分布性质的湍流相位延迟;(2)根据地形与相位间的经验关系使用线性或者二次多项式模型估计地形相关的分层对流层延迟;(3)利用地面气象观测数据或其他外部数据源研究干涉图对流层延迟相位的估计与改正方法,但此类方法通常受限于外部辅助数据的时空分辨率、光学遥感数据与SAR观测的非同步性、云覆盖等因素。Li等[4]系统总结了时序InSAR中性大气延迟的时空特性与大气随机模型估计。假定大气湍流延迟模式是时空随机的,InSAR时序分析方法通过叠加、平均或时空自适应滤波干涉图序列的方式构建观测量协方差矩阵,以用来探测点散射体(如永久散射体方法)或面状散射体(如小基线集方法),或者联合提取两种散射体,从而将形变信号与随机噪声分离。

    通过同化地表与卫星资料,全球大气模型数值产品可以提供地表及不同高度的大气物理量(如温度、湿度、压强等),近年来,欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts, ECMWF)提供的全球中等分辨率数值大气再分析产品(ECMWF reanalysis, ERA)被广泛用于某些地区单个干涉图及时序InSAR的分层对流层延迟估计与改正[4-7, 12]。已有研究表明,数值大气模型可用于改正山区地形相关的分层对流层相位延迟[6-7, 12],但是通常基于条带模式数据研究滑坡、火山等小范围区域。

    Yu等[13]基于迭代对流层分解模型融合连续GPS对流层估计与高分辨率ECMWF资料[14]开发了一种全球覆盖、全天时、全天候、近实时的InSAR通用型大气改正在线服务(generic atmospheric correction online service for InSAR, GACOS),结果表明,该方法可以有效估计与改正全球多个地区Sentinel-1干涉图的相位标准差与位移中误差(约1 cm),目前全球已有大量InSAR用户在线申请与使用该数据源。Dong等[5]针对地形陡峭区域的地形相关对流层分层延迟对InSAR形变时间序列引入的季节性震荡偏差分别提出了迭代线性模型方法与多种全球气象模型融合方法,结果表明,在中国西南潜在滑坡地区,Sentinel-1的改正效果优于35%。

    Bekaert等[6]以墨西哥、意大利等地区的Envisat ASAR与TerraSAR-X条带模式解缠干涉图时间序列为例,比较了中等分辨率成像光谱仪观测(medium resolution imaging spectrometer,MERIS)与MODIS(moderate resolution imaging spectroradiometer)、数值天气模型ERA-Interim与WRF(weather research and forecasting model)、经验干涉相位线性与幂律方法,验证了白天、无云、数据源观测可获取时MERIS方法的改正效果最好[11]。若对流层延迟与地形强相关时,经验相位方法的改正效果优于天气模型;若存在对流层湍流与动态局部天气时,天气模型具有最好的潜在性能,但并未发现高分辨率局部天气模型的性能显著优于全球再分析天气模型。

    由于不同对流层延迟方法都有各自的局限性,并非总是对同一对流层延迟分量敏感,难以做到在任何地区、任何时间均能一致、有效地削弱对流层延迟。本文利用覆盖阿尔金断裂带西段的两类宽幅InSAR数据(Envisat ASAR与Sentinel-1)为例,定量分析3种对流层延迟改正方法(地形线性相关经验方法、ECMWF大气再分析产品、GACOS大气改正在线服务产品)的性能与可靠性。

  • 图 1所示,阿尔金断裂带西段位于33°~38°N和79°~85°E之间,地形起伏强烈,自塔里木盆地向南至昆仑山高程差异可达5 000 m。自北向南随着海拔的增高,塔里木暖温带荒漠逐渐过渡为高山荒漠,年均降水量也随之增加(40~300 mm)。在海拔5600~5900 m雪线以上为永久冰川,形成了现代高山冰川作用中心,冰川面积达到3000 km2以上。0 ℃年均等温线与海拔4000 m左右的等高线基本一致。

    Figure 1.  SAR Data Coverage in the Western Segment of the Altyn Tagh Fault

    2008-03-20 UTC(universal time coordinated)22:33新疆于田县以南发生了Mw7.1地震,位于阿尔金断裂带、昆仑断裂带与康西瓦断裂带交界处,是青藏高原北部有仪器记录以来的最大正断层地震。由于大部分地区属于高寒、高海拔无人区,缺少地面全球导航卫星系统(global navigation satellite system, GNSS)等常规大地测量观测,其现今地球动力学机制仍然存在争议[15]

    本文采用Envisat ASAR ScanSAR和Sentinel-1 IWS(interferometric wide swath)两类数据,其中,Envisat ASAR宽幅降轨434轨道35幅干涉图,SAR影像时间范围为2008-04—2010-10,影像获取时间为04:38,空间分辨率约160 m,垂直基线范围为0~117 m,时间基线范围为35~665 d;Sentinel-1数据降轨165轨道35幅干涉图,SAR影像时间范围为2016-09—2018-05,影像获取时间为00:30,空间分辨率约110 m,垂直基线范围为0~122 m,时间基线范围为6~150 d。

  • 对流层大气物理参数的时空变化会导致InSAR信号发生延迟,主要的估计与改正方法可分为两类:(1)外部数据校正法:利用外部观测数据生成对流层延迟相位来改正InSAR干涉图相位,例如GNSS、ECMWF、GACOS、MERIS、MODIS等。(2)经验方法:以干涉图相位为基础建立对流层延迟与地表高程的函数关系,例如线性关系、幂律关系、小波变换等高程-相位关系。本文采用以ECMWF、GACOS为代表的外部数据校正法和以线性关系为代表的经验方法评估宽幅InSAR对流层延迟相位改正的性能,流程图见图 2

    Figure 2.  Flowchart of Tropospheric Delay Correction Method

  • 2006年,ECMWF发布了ERA-Interim(ERA-I)全球天气再分析数据集,水平分辨率约79 km(0.75°格网),垂向分辨率为60级,对1979年以来的全球大气数据进行再分析,能够每天4次(UTC 00:00、06:00、12:00、18:00)提供大气温度、湿度、风向、水汽偏压强、沿37个大气压强级别的地球重力位高度等。

    Doin等[16]假定雷达影像存在均匀高程-相位关系,验证了基于全球大气模型对线性高程-相位关系进行先验估计的有效性。Jolivet等[17]通过在ERA-I格网节点高程进行空间插值,估计干涉图对应的SAR影像获取时间每个像素的大气相位贡献,得到了对流层相位延迟。

  • GACOS改正方法利用迭代对流层分解模型从对流层总延迟中分离地形相关的垂直分层延迟分量和湍流混合延迟分量,生成高空间分辨天顶总延迟图,用于InSAR大气延迟校正[18]

    GACOS官方网站(http://ceg-research.ncl.ac.uk/v2/gacos/)可以提供SAR影像获取时间对应的对流层延迟产品,其数据源包括:(1)最高分辨率ECMWF近实时数值天气模型,水平分辨率约9 km,0.125°×0.125°格网,垂向分辨率为137级,时间分辨率为1 h;(2)空间分辨率90 m的SRTM (shuttle radar topography mission)与ASTER GDEM(advanced spaceborne thermal emission and reflection radiometer global digital elevation model)。

  • 对流层延迟主要由大气水汽分布造成,其中大气垂直分层效应引入高程相关的延迟相位,湍流效应引入局部延迟相位。与湍流混合延迟不同,垂直分层效应被认为是在特定区域和时间范围内的静态分层。若地表高程发生变化,垂直分层对流层延迟随着水汽含量、压强与温度比值的增加而增加[16]。水汽含量会随海拔高度的升高呈指数下降,理论延迟曲线是海拔高度的指数函数[19]。若干涉图主要受静态对流层延迟的影响,其延迟信号为主辅SAR影像对应延迟曲线的差值,对所得指数函数进行泰勒展开并忽略二阶及高阶项,可以得到简单线性关系如下[9]

    式中,,{\phi _C}为单幅干涉图相位整体偏移量;K为高程(h)与对流层相位延迟($ {\rm{\Delta }}{\phi _{{\rm{trop}}}}$)之间的传递函数。

    利用地形相关的线性模型改正对流层延迟适用于多数天气条件,但反向或非单调对流层等特殊情况除外。由于高程-相位关系可能与高程-变形关系存在局部权衡,因此传递函数K最好通过全局线性回归确定[20]

  • 本文分别对Envisat ASAR和Sentinel-1数据提供的35幅干涉图进行3种对流层延迟校正方法的评估与验证(图 2)。其中,ECMWF校正流程包括:(1)根据雷达影像获取时间,提取ECMWF提供的温度、气压、湿度等相应气象参数;(2)结合研究区域DEM和SAR影像入射角,计算单个雷达影像的逐像元对流层延迟;(3)根据干涉日期进行差分,最终获取ECMWF对流层延迟图。

    同样,根据SAR影像获取时间,通过时空差分两幅大气延迟图像获得GACOS对流层差分延迟。通过对干涉相位和高程全局回归分析可得,两者存在一定的线性关系,通过拟合延迟相位与地面高程的线性关系,得到干涉图地形线性相关对流层延迟相位图。

    图 3图 4分别给出了Envisat ASAR(影像获取时间2008-06-26—2008-07-31)和Sentinel-1(影像获取时间2016-09-24—2017-02-09)干涉图与3种校正方法的效果。整体趋势而言,干涉图相位空间分布与研究区域的地形起伏存在较大的相关性,说明大气垂直分层延迟占主导地位,3种估计方法对应的对流层延迟相位均表现出一定的地形相关性。需要注意的是,由图 3图 4的红色实线圈区域可明显看出,ECMWF与GACOS方法均存在与局部地形不相关的信号。

    Figure 3.  Correction Effects of Envisat ASAR Wide‐Swath InSAR Interferograms by Three Tropospheric Delay Methods

    Figure 4.  Correction Effects of Sentinel‐1 Wide‐Swath InSAR Interferograms by Three Tropospheric Delay Methods

  • 本文采用相位标准差衡量对流层延迟估计与改正前后的效果[7, 12],结果如图 5表 1所示,可以看出:(1)对于Envisat ASAR干涉图,在去除轨道平面前,线性改正、ECMWF改正与GACOS改正后分别对应35幅、19幅、31幅干涉图标准差削减;而去除轨道平面后,3种方法改正后的干涉图相位标准差均明显降低。其中,GACOS改正方法去除轨道平面后对应的相位标准差均值最低(8.6 mm),改正前后相位标准差的削减程度最大(68.1%)。(2)对于Sentinel-1干涉图,在去除轨道平面前,线性改正、ECMWF改正与GACOS改正后分别对应35幅、21幅、32幅干涉图标准差削减;而去除轨道平面后,3种方法改正后的干涉图相位标准差均有所降低。其中,GACOS改正方法去除轨道平面后对应的相位标准差均值最低(6.0 mm),改正前后相位标准差的削减程度最大(54.5%)。

    数据源 统计指标 改正前 线性改正 线性改正+去除轨道平面 ECMWF改正 ECMWF改正+去除轨道平面 GACOS改正 GACOS改正+去除轨道平面
    Envisat ASAR干涉图 相位标准差均值/mm 27.0 23.6 12.9 26.9 11.0 23.0 8.6
    改正前后相位标准差均值的削减量/% 12.6 52.2 0.4 59.3 14.8 68.1
    Sentine-1 干涉图 相位标准差均值/mm 13.2 9.1 6.3 13.1 8.8 9.4 6.0
    改正前后相位标准差均值的削减量/% 31.1 52.3 0.8 33.3 28.8 54.5

    Table 1.  Statistics Results of Different Tropospheric Correction Methods

    Figure 5.  Phase Standard Deviation Reduction for Envisat ASAR and Sentinel‐1 Interferograms Before and After Tropospheric Correction

    对于3种对流层延迟改正方法,将去除轨道平面前后的Envisat ASAR与Sentinel-1干涉图相位标准差的变化情况进行两两比较,结果如图 6所示,每幅子图右上象限表示两种改正方法均有改善效果,左下象限表示两者均没有改善效果。从图 6可以看出:(1)对于Envisat ASAR干涉图而言,ECMWF与GACOS改正方法均优于线性改正方法,而GACOS改正则优于ECMWF改正方法。(2)对于Sentinel-1干涉图,线性改正方法与GACOS改正方法均优于ECMWF改正方法,而GACOS则优于线性改正方法。

    Figure 6.  Correlation Maps of Phase Standard Deviation Change for Envisat ASAR and Sentinel‐1 Interferograms Before and After Orbital Ramp Removal

  • 1)干涉图时间序列没有包含Mw>5等地震震级较大的形变信号,可以认为干涉图相位是大气延迟、地形误差、时空失相干等斑点噪声相位的叠加,其中对流层延迟相位量级最大。表 1给出了改正前后的相位标准差均值和削减百分比,反映了不同改正方法对宽幅InSAR大气延迟相位的敏感性。Envisat宽幅InSAR干涉图改正前的相位标准差均值较大,其主要受到ASAR传感器硬件成像性能的影响,相位信噪比相对Sentinel-1 TOPS(terrain observation by progressive scans)模式较差。随着全球变暖趋势显著,该区域陡峭地形、冰雪与冻土周期性冻融作用、季节性河流冲刷、夏季低矮植被等条件变化均会对干涉图相干性产生影响。

    2)ECMWF产品通过数据同化技术融合了地面观测、气球、雷达、卫星等气象观测大气状态,经模式大气状态初始场和边界条件优化和调整,模式预报更加接近真实大气。当垂直分层延迟在大气延迟中占主导地位时,对流层延迟与地形呈强相关性,而湍流混合延迟与局部地形不相关。大气对流过程会引起局部湍流,进而改变大气条件,使得依赖地面气象观测的ECMWF产品获取的大气相位与地形不相关。受极端地理与气候条件影响,该地区缺少地面气象观测,会导致数值天气预报产品时空分辨率和模型精度受限,进而影响到校正对流层延迟的精度,例如ERA-Interim产品的时空分辨率相对粗糙,难以反映局部细节,同时与SAR影像获取时间也未必完全吻合,因此难以精确反映真实大气条件。

    3)GACOS产品融合了高空间分辨率的ECMWF产品大气物理参数,利用迭代对流层分解模型从对流层总延迟中分离与地形相关的垂直分层延迟和湍流混合延迟分量,能够捕捉中长波段以及与地形相关的中短波段的大气延迟信号。线性模型改正方法采用简单线性关系拟合与地形相关的对流层延迟,无法体现湍流大气特征及影响。对于地形起伏较大的大范围研究区域,当大气垂直分层效应占据大尺度对流层延迟的主导地位时,该方法可以表现出较好的改正效果。

  • 近5年来,随着Sentinel-1、ALOS-2 PALSAR等可用于干涉测量的星载宽幅SAR数据的不断获取与积累,宽幅InSAR技术在大范围地质灾害普查、地壳形变监测等领域逐渐得到普及。大气对流层延迟是对宽幅InSAR形变监测影响最显著的误差源之一,但专门针对宽幅InSAR对流层延迟改正方法的定量评估工作仍然相对薄弱。

    本文以覆盖青藏高原北部阿尔金断裂带西段的两类宽幅InSAR干涉图时间序列为例,评估了地形相关线性模型、ECMWF与GACOS这3类方法对大尺度对流层延迟的改正效果。结果表明,GACOS产品能够有效估计并改正与地形相关的垂直分层延迟及小尺度大气信号,改正后的Envisat ASAR与Sentinel-1宽幅InSAR干涉图相位标准差均值削减量分别可达68.1%和54.5%,均优于ECMWF和地形相关线性改正方法。作为一种快速、稳健、有效的对流层延迟在线估计与改正服务,GACOS产品能够为国内外InSAR用户开展宽幅InSAR大范围地质灾害监测等应用提供关键、可靠的支持。

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