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地球信息科学学报 >

2016 , Vol. 18 >Issue 10: 1418 - 1427

DOI: https://doi.org/10.3724/SP.J.1047.2016.01418

基于SBAS InSAR方法的苏州地区2007-2010年地表形变时空变化研究

  • 朱猛 ,
  • 董少春 , * ,
  • 尹宏伟 ,
  • 黄璐璐
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  • 南京大学地球科学与工程学院,南京,210023
*通讯作者：董少春（1976-）,女,江苏人,副教授,研究方向为地球探测与信息技术。E-mail: dsc@nju.edu.cn

作者简介：朱 猛（1988-）,男,山东人,硕士生,研究方向为InSAR应用研究。E-mail:

收稿日期: 2015-10-07

  要求修回日期: 2016-02-18

  网络出版日期: 2016-10-25

基金资助

国家自然科学基金项目（41372353）

中央高校基本科研业务费专项项目（020614330016）

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Spatial-Temporal Ground Deformation Study of Suzhou Area from 2007 to 2010Based on the SBAS InSAR Method

  • ZHU Meng ,
  • DONG Shaochun , * ,
  • YIN Hongwei ,
  • HUANG Lulu
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  • School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
*Corresponding author: DONG Shaochun, E-mail:

Received date: 2015-10-07

  Request revised date: 2016-02-18

  Online published: 2016-10-25

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《地球信息科学学报》编辑部 所有
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摘要

苏州地区位于中国苏锡常地面沉降带,地面沉降严重影响了该地区的经济社会发展,因此对其进行地面沉降监测具有重要的意义。本文基于SBAS InSAR方法,利用27景ERS-2 SAR数据,反演了苏州地区2007-2010年地表年平均沉降速率分布图和时序沉降变化图,分析了该时间段地表沉降的时空演化特征。结果表明,在整个研究观测期,苏州地区整体呈现出“老区沉降趋缓,新区沉降较快”的特点。老城区（姑苏区及邻近的吴中区）地面沉降趋于缓和,年平均沉降速率在10 mm/a以下,无明显的沉降中心;而相对新发展的区域（相城区、工业园区和吴江区）则表现出明显的沉降特征,沉降速率普遍大于10 mm/a。其中,相城区年平均沉降速率大约为10~20 mm/a,局部地区沉降严重,速率可达或超过20 mm/a;工业园区年平均沉降速率约20 mm/a,最大累计沉降量在50 mm左右。吴江区地面沉降最为严重,表现出面积广、速率大的特点,其年平均沉降速率在 20 mm/a左右,最大累计沉降量可达60 mm以上。

关键词： 地面沉降; 苏州; SBAS; 时空演化特征; 沉降速率

本文引用格式

朱猛 , 董少春 , 尹宏伟 , 黄璐璐 . 基于SBAS InSAR方法的苏州地区2007-2010年地表形变时空变化研究[J]. 地球信息科学学报, 2016 , 18(10) : 1418 -1427 . DOI: 10.3724/SP.J.1047.2016.01418

Abstract

The city of Suzhou is located in the well known Suzhou-Wuxi-Changzhou subsidence zone in China, which suffers from serious land subsidence. Land subsidence occurring in this area can cause conspicuous social and economic lost, thus a proper investigation is necessary for this region. In order to understand the spatial-temporal evolution of land subsidence in Suzhou, we applied the Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) technique to 27 C-band ERS-2 SAR images acquired between 2007 and 2010 by European Space Agency (ESA). The results show that during the observation period, the downtown and uptown areas show a relatively slow subsidence rate, while an exceptionally rapid subsidence is detected in 3 suburb areas. The downtown area such as Gusu district and the uptown area such as Wuzhong district are characterized by their generally low subsidence rates, which are less than 10 mm/a, and there is no subsidence center detected. Land subsidence mainly occurs in Xiangcheng district, Wujiang district and the industrial park, which are the newly developed zones characterized by the subsidence rates of greater than 10 mm/a. We observe the prevalence of ground subsidence phenomena ranging from 10 mm/a up to 20 mm/a in Xiangcheng district, while in certain towns and streets, the subsidence phenomenon is significantly severe with a rate close to 20 mm/a or higher. The maximum accumulative subsidence in the industrial park reaches 50 mm, and the average subsidence rate of this area is approximately 20 mm/a. Rapid subsidence with an average rate being up to 20 mm/a is observed in Wujiang district, which is located in the southern part of Suzhou. The land subsidence in this area is the severest within the whole research area, featured by its large coverage and high subsidence rate, and the maximum accumulative subsidence displacement can reach 60 mm or more.

Key words： land subsidence; Suzhou; SBAS; spatial-temporal evolution; subsidence rate

1 引言

苏州是长江三角洲城市群中的重要城市之一,经济发达,交通便利,人口密度大。自20世纪60年代以来,苏州地区出现了严重的地面沉降现象[1-4],直接影响了城市轨道交通系统（地下隧道、桥梁、高速公路、高速铁路等）、地下管网（城市供水、供气等）等大型工程的安全,使城市洪涝灾害加剧,防洪、排涝工程效能下降,不仅增加了维护成本和不安全因素,更对人们的生产、生活造成严重威胁[5-6]。因此,对苏州地区的地表形变进行高精度的动态监测,及时了解地面沉降的现状和发展趋势,是对地面沉降的防治治理提供决策依据的重要举措。
合成孔径雷达差分干涉测量技术（Differential Interferometric Synthetic Aperture Radar,DInSAR）具有精度高、覆盖范围大、全天候、全天时以及不需要进入形变区域进行直接测量等优点,广泛应用于全球及区域性地形测图和地表形变的监测中[7]。相对于全球定位系统和经典大地测量手段而言, DInSAR技术在测量精度、作业条件、工作成本等方面具有传统监测方式无法比拟的优势和极大的应用潜力[8-11],而在常规DInSAR技术基础上发展起来的小基线集方法（Small Baseline Subset,SBAS）,克服了常规DInSAR时间失相干、几何失相干以及大气异常引起的相位误差,实现了高精度的长时间缓慢地表形变反演,该技术已经成为国际地面沉降监测研究中广泛应用的可靠技术方法[12]。
苏州地面沉降一直深受重视,特别是1999年开始依托中国地质调查局的国家级地调项目,系统的调查监测与深入的分析研究延续至今[13]。随着DInSAR技术越来越广泛的应用于地面沉降的监测,王超等[2]、吴涛等[3]、汤益先等[14]、朱叶飞等[15]利用DInSAR及其相关技术研究了苏州地区地面沉降的时空变化,查明了沉降较为严重的区域。此外,张云、施小清等从地下水的角度以及数字模拟方法研究了苏州地区的地面沉降现象,揭示了地下水位变化与该地区地面沉降的关系[16-17]。但是,采用SBAS InSAR技术监测苏州地区地表形变时空演化特征的研究工作尚不多见。因此,本文采用SBAS方法对苏州地区2007-2010年的地表形变时空分布和演化规律进行研究,为掌握苏州地区地面沉降特征提供依据。

2 研究区概况

苏州位于长江三角洲中部、江苏省东南部,地处119°55′~121°20′ E,30°47′~32°02′ N之间（图1）。东临上海,南连浙江,西抱太湖,北依长江,总面积8488.42 km2。全市地势低平,境内河流纵横,湖泊众多。
Fig. 1 Landsat-8 true color composite image ofSuzhou and its surroundings

图1 苏州及其周边地区Landsat-8 真彩色合成影像图

苏州地区地面沉降出现在20世纪60年代,70年代开始日趋严重,出现了较明显的沉降,市区沉降速率达到40~45 mm/a,市郊沉降速率约为 20~30 mm/a,城区最大累计沉降量达1680 mm[18]。20世纪80年代至90年代初,由于快速经济发展和大规模城市化建设,地面沉降加剧。20世纪90年代中后期,随着政府对地下水开采的管制,市区地面沉降速率开始变缓,沉降现象由市区开始向周边县市扩散,沉降面积明显扩大并且形成了新的沉降中心[19]。2000年以来,苏州市开展了地面沉降的防治工作,地下水开采得到了有效控制,地面沉降速率大大降低,部分地区甚至出现了抬升[20]。

3 研究方法

本文采用Berardino于2002年提出的SBAS方法[21],利用欧洲空间局（European Space Agency,ESA）2007-2010年获取的研究区ERS-2单视复数（Single Look Complex,SLC）图像对苏州地区进行了地表形变反演。该方法利用时间基线和空间基线都较短的SAR数据集形成干涉像对。干涉像对经过解缠、滤波、偏移量估算等去除轨道误差、长波噪声及地形残差相位,在高相干点上建立方程组,通过奇异值分解（Singular Value Decomposition,SVD）方法,求得每个高相干点上的形变速率估算,通过时间域和空间域滤波,将残余相位中的大气相位和非线性形变相位分离出来,得到覆盖整个观测时间的平均形变速率和累积形变量。
假设有N景同一传感器获取的覆盖同一地区的SAR影像,在短基线条件下,可获得M对差分干涉组合,对这M对差分干涉组合进行常规D-InSAR处理,可获取M幅解缠后的差分干涉图。M与N的关系满足式(1)。
N + 1 2 ≤ M ≤ N ( N + 1 2 ) (1)
第i幅差分干涉图中任意像元（x,y）处得到的干涉相位可以表示成式（2）[22]。
φ obs , i x , y = φ topo , i x , y + φ defo , i x , y + φ atm , i x , y + φ noise , i ( x , y ) (2)
式中：i代表第i幅差分干涉图,i∈[1, M]; φ topo代表地形相位; φ defo代表LOS（Line-of-sight）向形变相位; φ atm代表大气噪声相位; φ noise代表仪器热噪声等其他相位。为了得到形变相位,上述地形相位,大气延迟相位及仪器热噪声等相位分量,可通过滤波的方法去除。
基于SBAS方法的地表形变时间序列计算可以由式（3）-（5）表示。
A V los = φ obs (3)
V los = A + φ obs (4)
d los i + 1 = d los i + V los i + 1 Δ t i + 1 (5)
式中：A为一个M×N的时间矩阵[23];Vlos代表LOS向的形变速率; φ obs 代表常规D-InSAR处理后,经过时域、空域滤波后的形变相位值; d los i 代表第i时刻地表LOS向的形变量,对于矩阵A进行奇异值分解（SVD）处理后得到的结果用A+表示,再由式（5）得到Vlos的最小二乘解。

4 数据处理

4.1 数据

本文共采用ESA提供的2007年10月至2010年10月的27景ERS-2降轨数据进行了SBAS计算,具体参数如表1所示。SBAS方法的详细原理和实现过程参见文献[21]、[24]。本文仅介绍数据处理的基本流程。
Tab. 1 List of ERS-2 data parameters

表1 ERS-2数据参数表

传感器 轨道号 极化方式 入射角 顶角 升降轨 时间跨度 N M
ERS-2 275 VV 23.2° -166.2° 降轨 2007-10-14-2010-10-03 27 43

注：N表示SLC影像数量;M表示干涉像对的数量

4.2 数据处理

利用ESA提供的精密星历轨道数据对ERS-2的SAR数据进行轨道参数校正,然后对ERS-2的SAR数据进行标准差分干涉处理,具体步骤包括：① 选择2008年11月2日的影像作为主影像,对所有SLC数据进行配准;② 设定时间基线为400 d,空间基线为400 m,多视比例设为1:6,生成干涉图集（图2）;③ 采用90 m分辨率的SRTM DEM数据去除地形相位;④ 进行自适应滤波;⑤ 用最小费用流（Minimum Cost Flow,MCF）方法进行相位解缠; ⑥ 对解缠结果中由于失相干而造成的数据空洞采用16×16的窗口进行插值填补小的空洞;⑦ 最后进行地理编码。
Fig. 2 Distribution of spatial and temporal baselines

图2 时空基线配对图

经过上述DInSAR处理后共获得了43对差分干涉对,利用这些差分干涉像对进行SBAS InSAR计算。本文采用的SBAS InSAR算法由Samsonov[24]提供。首先,对差分干涉像对进行二维空间域高通滤波,用以去除长波噪声;然后,进行配准和地形校正;接着,对经过配准和地形校正的差分干涉像对进行二维空间域低通滤波及一维时间域滤波,分别去除噪声相位和地形残差相位;最后,对滤波后的干涉像对进行LOS向时间序列形变速率的计算。完整的数据处理流程如图3所示。
Fig. 3 Data processing flowchart

图3 数据处理流程图

4.3 数据处理结果

首先假设所有干涉图的平均形变量为0,进行第一次SBAS计算,得到初始苏州地区年平均地面形变速率图。之后,在初始年平均地面形变速率图的稳定区域内选取一个稳定点作为参照点,标记为R,再次进行SBAS运算,获得2007-2010年苏州地区LOS向年平均地面形变速率图（图4）。参照点R选在虎丘山附近,此处基岩出露,地表稳定性好。
Fig. 4 Line-of-sight average deformation rate map ofSuzhou between 2007 and 2010

图4 苏州地区LOS向2007-2010年平均沉降速率图

5 结果分析

经滤波处理后获得研究区整体年平均形变 速率（图4）。本文探测到的最大年沉降速率超过50 mm/a,出现在极少的区域,位于图4中西南部及西北部。西北部属无锡市辖区,不在本次研究的范围之内;西南部位于太湖沿岸,年平均沉降速率约为30 mm/a。从图4可看出,2007-2010年,研究区整体地面沉降速率分布在±15 mm/a,少部分区域达到或超过±20 mm/a。苏州地区整体地面沉降空间分布特征可概括为“老区沉降趋缓,新区沉降较快”。沉降区主要分布在相城区、工业园区和吴江区这3个新发展的区域,沉降速率在20 mm/a左右。而苏州老城区—姑苏区在整个观测期间地面沉降趋于缓和,最大沉降速率不超过10 mm/a。吴中区紧邻老城区的部分也表现出沉降趋缓的地表状态,但在与沉降区相邻的部分,则表现出明显的沉降特征。研究区整体时间序列如图5所示。选取观测起始时刻2007年10月14日为参照基准,假设此时全区形变量为0。由图5可看出,2007-2010年底,相城区地表累计形变量由-10 mm沉降至-50 mm左右;工业园区大部地区由20 mm发展至-30 mm,少数区域由50 mm沉降至10 mm,后抬升至40 mm左右;吴江区大部地面由25 mm沉降至-30 mm左右;姑苏区及与之邻近的吴中区一带地表由10 mm沉降至-10 mm左右。相城区平均沉降速率图和累计形变时间序列图分别如图6、图7所示。姑苏、吴中、吴江区及工业园区的年平均形变速率图以及累积形变时间序列图分别如图8、9 所示。
Fig. 5 Time series of the accumulative displacement of Suzhou from 2007 to 2010

图5 2007-2010年苏州地区地表累计形变量时间序列变化图

Fig. 6 Line-of-sight average deformation ratemap of Xiangcheng district

图6 相城区LOS向年平均形变速率图

5.1 相城区沉降时空分布特征分析

相城区是苏州北部新城区,以产业经济和交通运输业为主,是重要的交通物流枢纽。从图6可看出,全区地面沉降现象较为明显,年平均沉降速率达到10~20 mm/a,在渭塘镇、黄埭镇、北桥街道、元和街道及黄桥街道（图6的黄色边框）均探测到了明显的沉降,无明显抬升。其中,渭塘镇和北桥街道沉降速率较大,达到或超过20 mm/a,黄埭镇、黄桥街道及元和街道一带年平均沉降速率在10 mm/a 左右。
Fig. 7 Time series of the accumulative displacement of Xiangcheng district from 2007 to 2010

图7 2007-2010年相城区地表累计形变量时间序列变化图

相城区地面累计形变量时间序列变化如图7 所示。选取2007年10月14日为参照基准,假设此时刻全区形变量为零,从图7可看出,自2007-2010年的整个观测期间,相城区发生地面沉降的区域不断扩大,累计沉降量明显逐年增大。2007年底,该区几乎不存在累积沉降量大于50 mm的地区,沉降主要集中在相城区的北部和西南角,而到了2010年底,累积沉降量达到50 mm左右的区域已明显扩大。值得注意的是,该区东南角小部分区域自2007年底就出现抬升现象,抬升部分的面积呈逐渐缩小趋势,累积抬升量也逐渐减小。在选取的5处明显沉降区域中,北桥街道沉降最为严重,在2007-2010年,累计沉降量超过50 mm,年平均沉降速率达到或超过20 mm/a,2007-2009年,累计沉降量增长较缓慢,2009年后半年至2010年观测结束阶段,累计沉降量迅速增大,覆盖的面积也快速扩大。渭塘镇是相城区内另一处沉降较为严重的区域,最大累计沉降量达到约40 mm,年平均沉降速率约为20 mm/a。黄埭镇、黄桥街道及元和街道沉降趋势基本相当,3年内累计沉降量接近30 mm,平均沉降速率约 为10 mm/a。

5.2 工业园区沉降时空分布特征分析

苏州工业园区位于老城区东部,以高新技术产业和综合商务发展为主,是国家级经济技术开发区。如图8所示,该地区在观测期间出现了大面积明显的沉降。其中,胜浦镇、唯亭镇沉降速率较大,达到20 mm/a;斜塘镇沉降速率稍小,在10 mm/a左右。由时间序列（图9）可以看出,该地区在观测一开始的2007年已表现为抬升,累计抬升至30 mm左右,2007-2008年,地表基本稳定,累计形变量无大的变化;2009-2010年,该地区出现了明显的地面沉降现象,累计沉降量逐渐加大,沉降面积也不断扩大,最大累计形变量接近-50 mm;2010年末,该区地面又开始回弹,主要分布在斜塘镇一带。
Fig. 8 Line-of-sight average deformation rate map of Gusu, Wuzhong, Wujiang district and the industrial park

图8 姑苏、吴中、吴江区及工业园区LOS向年平均沉降速率图

5.3 吴江区沉降时空分布特征分析

吴江区是以生态旅游业和现代服务业发展为主的南部新城区。观测期间该地区沉降现象较为突出,具有范围广、速率大的特点。由图8可看出,大部地区超过10 mm/a,最大可达到20 mm/a以上。在时间序列上（图9）,吴江地区地表累计沉降量不断加大,2007年观测开始时,全区地表累计形变量为10 mm左右,几乎不见沉降区域,而到了2010年底,该区地表已经出现明显的沉降,大部份地区累计形变量在-30 mm左右,局部地区累计形变量超过-50 mm,几乎不存在抬升的区域。

5.4 姑苏区、吴中区沉降时空分布特征分析

姑苏区是苏州老城区,区内文物遗迹遍布,是国家历史文化名城。从图8可看出,在整个观测期间（2007-2010年）该区大部分地区地面沉降趋缓,局部地区甚至出现小幅抬升,抬升速率约为 10 mm/a,老沉降漏斗齐门至火车站一带地面沉降速率不足10 mm/a。在观前街一带存在5 mm/a左右的沉降区域,面积较小且没有形成沉降中心。姑苏区东侧东环高架附近探测到8~10 mm/a的小范围沉降区域,这一带交通线路密集,常台高速苏州段及东内环高速路2条交通干道紧密相邻,局部沉降现象高于周边地区。图9展示的累积形变时间序列显示2007年10月至2009年8月期间,该区地表几乎处于稳定状态,2009年底至2010年10月,观前街一带地表出现下沉,最大沉降量20 mm左右。姑苏区东部交通干道密集区域出现了微量的沉降,3年内累计形变量不超过-20 mm。
Fig. 9 Time series of the accumulation displacement of Gusu, Wuzhong, Wujiang district and the industrial park

图9 2007-2010年姑苏、吴江、吴中区及工业园区地表累计形变量时间序列变化图

吴中区在与老城区临近的部分整个观测期间地表形变特征与姑苏区相当,未探测到大面积的明显沉降现象;在与工业园区、吴江区邻接处则沉降明显,年平均沉降速率在10~20 mm/a,沉降范围也逐年呈小幅扩大的趋势。

6 结论

本文基于SBAS InSAR方法利用27景ERS-2 SAR数据对苏州地区2007-2010年地表形变进行了监测,分析了苏州地区2007-2010年地表形变的时空分布和变化特征,主要结论如下：
（1）在整个观测期间,苏州地区整体呈现“老区沉降趋缓,新区沉降较快”的特征。老城区姑苏区及邻近的吴中区一带,地表形变年平均速率约为 ±10 mm/a;3个新城区（相城区、吴江区、工业园区）地面沉降速率相对较快,范围也在逐年扩大,沉降趋势不容忽视。
（2）相城区出现了比较明显的地面沉降现象。其中,北桥街道和渭塘镇沉降速率较大,达到或超过20 mm/a;黄埭镇、黄桥街道和元和街道年平均沉降速率在10 mm/a左右。
（3）苏州工业园区整体地面沉降速率在20 mm/a左右。其中,胜浦镇、唯亭镇和斜塘镇沉降现象比较突出。
（4）吴江区地面沉降现象最为突出,表现为范围大、速率快的特点。大部分地区沉降速率都达到约20 mm/a,最大累积沉降量可达60 mm以上。
致谢：本文ERS-2 SLC数据以及精密轨道数据都得到了欧洲空间局（ESA）的支持（29330）。

The authors have declared that no competing interests exist.

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[ Wu J Q, Wu S L, Min W, et al.Review on recent progress of land subsidence control in Suzhou-Wuxi-Changzhou area[J]. Journal of Geology, 2014,38(2):319-323. ]

[20]
朱叶飞,陈火根,张登明,等.基于PS-InSAR的1995-2000年苏州地面沉降监测[J].地球科学进展,2010,25(4):428-434.<p>&nbsp;永久散射体(PS)技术在传统差分干涉测量(D-InSAR)中引入时间维,分析长时间内保持稳定的像元集相位变化,获得毫米级的地面测量精度,但是该技术要求处理的范围较小。采用分块处理的方法，通过PS差分干涉测量处理，得到1995&mdash;2000年苏州地区地面沉降场的测量值。地面水准测量数据的验证分析表明，雷达差分干涉测量精度可达5mm(以水准测量代表地面形变的真实情况)，基于分块处理的PS-InSAR技术在进行城市地面沉降监测和时空演化特征研究中具有很大的优势。<br /></p>

[ Zhu Y F, Chen H G, Zhang D M, et al.Subsidence of Suzhou area from 1995 to 2000 detected by persistent scatterers for SAR interferometry technique[J]. Advances N Earth Science, 2010,25(4):428-434. ]

[21]
Berardino P, Fornaro G, Lanari R, et al.A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002,40(11):2375-2383.We present a new differential synthetic aperture radar (SAR) interferometry algorithm for monitoring the temporal evolution of surface deformations. The presented technique is based on an appropriate combination of differential interferograms produced by data pairs characterized by a small orbital separation (baseline) in order to limit the spatial decorrelation phenomena. The application of the singular value decomposition method allows us to easily "link" independent SAR acquisition data sets, separated by large baselines, thus increasing the observation temporal sampling rate. The availability of both spatial and temporal information in the processed data is used to identify and filter out atmospheric phase artifacts. We present results obtained on the data acquired from 1992 to 2000 by the European Remote Sensing satellites and relative to the Campi Flegrei caldera and to the city of Naples, Italy that demonstrate the capability of the proposed approach to follow the dynamics of the detected deformations.

DOI

[22]
Ferretti A, Prati C, Rocca F.Permanent scatterers in SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001,39(1):8-20.Differential SAR interferometry measurements provide a unique tool for low-cost, large-coverage surface deformations monitoring. Limitations are essentially due to temporal decorrelation and atmospheric inhomogeneities. Though temporal decorrelation and atmospheric disturbances strongly affect interferogram quality, reliable deformation measurements can be obtained in a multi-image framework on a small subset of image pixels, corresponding to stable areas. These points, hereafter called Permanent Scatterers, can be used as a `natural GPS network' to monitor terrain motion, analyzing the phase history of each one. In this paper, results obtained using 45 ERS SAR images gathered over the Italian town of Camaiore (within a time span of more than 6 years and a range of normal baseline of more than 2000 m) are presented. The area is of high geophysical interest because it is known to be unstable. A subterranean cavity collapsed in October 1995 causing the ruin of several houses in that location. Time series analysis of the phase values showed the presence of precursors three months before the collapse.

DOI

[23]
Samsonov S.Topographic correction for ALOS PALSAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010,48(7):3020-3027.L-band synthetic aperture radar (SAR) interferometry is very successful for mapping ground deformation in densely vegetated regions. However, due to its larger wavelength, the capacity to detect slow deformation over a short period of time is limited. Stacking and small baseline subset (SBAS) techniques are routinely used to produce time series of deformation and average deformation rates by reducing the contribution of topographic and atmospheric noise. For large sets of images that are presently available from C-band European Remote Sensing Satellites (ERS-1/2) and Environmental Satellite (ENVISAT), the standard stacking and SBAS algorithms are accurate. However, the same algorithms are often inaccurate when used for processing of interferograms from L-band Advanced Land Observing Satellite Phased Array type L-band SAR (ALOS PALSAR). This happens because only a limited number of interferograms is acquired and also because of large spatial baselines often correlated with the time of acquisition. In this paper two techniques are suggested that can be used for removing the residual topographic component from stacking and SBAS results, thereby increasing their accuracy.

DOI

[24]
Samsonov S, van der Kooij M, Tiampo K. A simultaneous inversion for deformation rates and topographic errors of DInSAR data utilizing linear least square inversion technique[J]. Computer & Geosciences, 2011,37:1083-1091.

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