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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (4) : 55-63     DOI: 10.6046/zrzyyg.2020341
Monitoring of land subsidence in Beijing-Tianjin-Hebei plain during 2016—2018 based on InSAR and Sentinel-1A data
SHI Min1,2,3,4(), GONG Huili1,2,3,4, CHEN Beibei1,2,3,4, GAO Mingliang1,2,3,4, ZHANG Shunkang1,2,3,4
1. Key Laboratory of Mechanism, Prevention and Mitigation of Land Subsidence, Capital Normal University, Beijing 100048, China
2. Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China
3. Base of the State Key Laboratory of Urban Environmental Process and Digital Modeling, Capital Normal University, Beijing 100048, China
4. Key Laboratory of 3D Information Acquisition and Application, Capital Normal University, Beijing 100048, China
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The land subsidence in the Beijing-Tianjin-Hebei (BTH) region has developed the most rapidly and affects the largest area in China and it has become an unnegligible geological problem in the coordinated development of the BTH region. In this study, the multi-track Sentinel-1A data from January 2016 to October 2018 that cover the whole BTH plain was processed using the multi-temporal InSAR (MT-InSAR) technique. After the verification using leveling data and the cross-validation using the data from adjacent tracks, the land subsidence in the BTH region during 2016—2018 were obtained by integrating multi-track SAR data results. The InSAR monitoring results show that the maximum subsidence rate in the BTH region reached 164 mm/a and the land subsidence was widely and unevenly distributed in space in the study area during the monitoring period. According to the analysis of the spatial-temporal change characteristics of the land subsidence in the BTH region, the land subsidence showed an increasing trend in the Tangshan-Qinhuangdao area but stably developed in the remaining areas in the BTH region during 2016—2018. This paper demonstrates that the reliability of the InSAR technique in the monitoring of land subsidence in large regions. The results of this study will provide an important basis for the prevention and mitigation of regional subsidence and will provide a scientific guarantee for the construction of the BTH urban agglomeration.

Keywords land subsidence      Beijing-Tianjin-Hebei plain      InSAR      Sentinel-1A     
ZTFLH:  TP79  
Issue Date: 23 December 2021
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Huili GONG
Beibei CHEN
Mingliang GAO
Shunkang ZHANG
Cite this article:   
Min SHI,Huili GONG,Beibei CHEN, et al. Monitoring of land subsidence in Beijing-Tianjin-Hebei plain during 2016—2018 based on InSAR and Sentinel-1A data[J]. Remote Sensing for Natural Resources, 2021, 33(4): 55-63.
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Fig.1  Location of study area
卫星 Track 数量
40 165 20160107—20181128 升轨 VV IW
142 141 20160114—20181018
69 98 20160109—20181001
Tab.1  Satellite information for the data used in this study
Fig.2  Mean displacement velocities throughout the Beijing-Tianjin-Hebei derived from the Sentinel-1A data by using PS-InSAR from 2016 to 2018
Fig.3  Mean displacement velocities derived by InSAR between 2016 and 2018
Fig.4  Profiles of the InSAR displacement velocities
Fig.5  Accumulative time-series deformation revealed by the InSAR technique
Fig.6  Comparison between the InSAR measurements and levelling data
Track 142 5.5 11.6 0.2 0.97
Track 69 7.2 13.5 1.0 0.98
Tab.2  Comparison of the mean subsidence rate between the Sentinel-1A PS and leveling data
Fig.7  Consistency between the vertical displacement rates derived from different datasets
[1] 郭海朋, 白晋斌, 张有全, 等. 华北平原典型地段地面沉降演化特征与机理研究[J]. 中国地质, 2017,44(6):1115-1127.
[1] Guo H P, Bai J B, Zhang Y Q, et al. The evolution characteristics and mechanism of the land subsidence in typical areas of the North China Plain[J]. Geology in China, 2017,44(6):1115-1127.
[2] 吕潇文, 宋利, 邵兴, 等. 天津市地面沉降监测技术应用及发展建议[J]. 上海国土资源, 2017,38(2):26-30.
[2] Lyu X W, Song L, Shao X, et al. The suggestion and application of land subsidence monitoring in Tianjin[J]. Shanghai Land and Resources, 2017,38(2):26-30.
[3] 李海君, 张耀文, 孟健, 等. 华北平原地面沉降新构造运动影响特征[J]. 能源与环保, 2017,39(6):57-62.
[3] Li H J, Zhang Y W, Meng J, et al. Characteristics on land subsidence in North China Plain within effect of neotectonic movement[J]. China Energy and Environmental Protection, 2017,39(6):57-62.
[4] 杨艳. 京津冀区域地面沉降灾害防治思考[J]. 城市地质, 2015(1):1-7.
[4] Yang Y. Land subsidence disaster prevention and cure in Beijing-Tianjin-Hebei area,China[J]. Urban Geology, 2015(1):1-7.
[5] 朱建军, 李志伟, 胡俊. InSAR变形监测方法与研究进展[J]. 测绘学报, 2017,46(10):1717-1733.
[5] Zhu J J, Li Z W, Hu J. Research progress and methods of InSAR for deformation monitoring[J]. Acta Geodaetica et Cartographica Sinica, 2017,46(10):1717-1733.
[6] 林珲, 马培峰, 王伟玺. 监测城市基础设施健康的星载MT-InSAR方法介绍[J]. 测绘学报, 2017,46(10):1421-1433.
[6] Lin H, Ma P F, Wang W X. Urban infrastructure health monitoring with spaceborne multi-temporal synthetic aperture Radar interferometry[J]. Acta Geodaetica et Cartographica Sinica, 2017,46(10):1421-1433.
[7] Hu B, Wang H S, Sun Y L, et al. Long-term land subsidence monitoring of Beijing (China) using the small baseline subset (SBAS) technique[J]. Remote Sensing, 2014,6(5):3648-3661.
doi: 10.3390/rs6053648 url:
[8] Du Z Y, Ge L L, Ng A, et al. Mapping land subsidence over the eastern Beijing City using satellite Radar interferometry[J]. International Journal of Digital Earth, 2018,11(5):504-519.
doi: 10.1080/17538947.2017.1336651 url:
[9] Gao M L, Gong H L, Chen B B, et al. Regional land subsidence analysis in eastern Beijing Plain by InSAR time series and wavelet transforms[J]. Remote Sensing, 2018,10(3):365.
doi: 10.3390/rs10030365 url:
[10] Hu L Y, Dai K R, Xing C Q, et al. Land subsidence in Beijing and its relationship with geological faults revealed by Sentinel-1 InSAR observations[J]. International Journal of Applied Earth Observation and Geoinformation, 2019,82:101886.
doi: 10.1016/j.jag.2019.05.019 url:
[11] Lyu M Y, Ke Y H, Guo L, et al. Change in regional land subsidence in Beijing after south-to-north water diversion project observed using satellite Radar interferometry[J]. GIScience Remote Sensing, 2019,57:140-156.
doi: 10.1080/15481603.2019.1676973 url:
[12] 雷坤超, 陈蓓蓓, 宫辉力, 等. 基于PS-InSAR技术的天津地面沉降研究[J]. 水文地质工程地质, 2013,40(6):106-111.
[12] Lei K C, Chen B B, Gong H L, et al. Detection of land subsidence in Tianjin based on PS-InSAR technology[J]. Hydrogeology and Engineering Geology, 2013,40(6):106-111.
[13] Luo Q L, Perissin D, Zhang Y Z, et al. L-and X-band multi-temporal InSAR analysis of Tianjin subsidence[J]. Remote Sensing, 2014,6(9):7933-7951.
doi: 10.3390/rs6097933 url:
[14] Liu P, Li Q Q, Li Z H, et al. Anatomy of subsidence in Tianjin from time series InSAR[J]. Remote Sensing, 2016,8(3):266.
doi: 10.3390/rs8030266 url:
[15] Zhang T X, Shen W B, Wu W H, et al. Recent surface deformation in the Tianjin area revealed by Sentinel-1A Data[J]. Remote Sensing, 2019,11(2):130.
doi: 10.3390/rs11020130 url:
[16] 张玲, 葛大庆, 郭小方, 等. 近十年来沧州地区地面沉降演化状况[J]. 上海国土资源, 2014,35(4):72-76,80.
[16] Zhang L, Ge D Q, Guo X F, et al. Land subsidence in Cangzhou over the last decade based on interferometric time series analysis[J]. Shanghai Land and Rescources, 2014,35(4):72-76,80.
[17] Liu X X, Wang Y J, Yan S Y. Ground deformation associated with exploitation of deep groundwater in Cangzhou City measured by multi-sensor synthetic aperture Radar images[J]. Environmental Earth Sciences, 2017,76(1):6.
doi: 10.1007/s12665-016-6311-0 url:
[18] 周洪月, 汪云甲, 闫世勇, 等. 沧州地区地面沉降现状Sentinel-1A/B时序InSAR监测与分析[J]. 测绘通报, 2017(7):89-93.
[18] Zhou H Y, Wang Y J, Yan S Y, et al. Land subsidence monitoring and analyzing of Cangzhou area Sentinel-1A/B based time series InSAR[J]. Bulletin of Surveying and Mapping, 2017(7):89-93.
[19] 周旭, 许才军, 温扬茂. 利用时序InSAR技术分析北京及河北廊坊地面沉降[J]. 测绘科学, 2017,42(7):89-93.
[19] Zhou X, Xu C J, Wen Y M. Land subsidence monitoring of Beijing and Langfang of Hebei Province by time series InSAR[J]. Science of Surveying and Mapping, 2017,42(7):89-93.
[20] 李海君, 张耀文, 杨月巧, 等. 廊坊北三县地区地面沉降时空分布特征与成因分析[J]. 科学技术与工程, 2018,18(11):23-30.
[20] Li H J, Zhang Y W, Yang Y Q, et al. Spatial-temporal distribution characteristics and causation analysis of land subsidence in three northern counties area of Langfang[J]. Science Technology and Engineering, 2018,18(11):23-30.
[21] 马震. 京津冀地区国土资源环境地质条件分析[J]. 中国地质, 2017,44(5):857-873.
[21] Ma Z. The environmental geological conditions of land resources in the Beijing-Tianjin-Hebei region[J]. Geology in China, 2017,44(5):857-873.
[22] 郭海朋, 李文鹏, 王丽亚, 等. 华北平原地下水位驱动下的地面沉降现状与研究展望[J]. 水文地质工程地质, 2021,48(3):162-171.
[22] Guo H P, Li W P, Wang L Y, et al. Present situation and research prospects of the land subsidence driven by groundwater levels in the North China Plain[J]. Hydrogeology and Engineering Geology, 2021,48(3):162-171.
[23] 白泽朝. 天津地区Sentinel-1A雷达影像PSInSAR地面沉降监测[J]. 测绘科学技术学报, 2017,34(3):283-288.
[23] Bai Z C. Subsidence monitoring of Tianjin using PSInSAR technique with Sentinel-1A[J]. Journal of Geomatics Science and Technology, 2017,34(3):283-288.
[24] Perissin D, Wang Z, Lin H. Shanghai subway tunnels and highways monitoring through Cosmo-SkyMed persistent scatterers[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012,73:58-67.
doi: 10.1016/j.isprsjprs.2012.07.002 url:
[25] 郭良迁, 薄万举, 杨国华. 华北地区断裂带的现代形变特征[J]. 大地测量与地球动力学, 2003,23(2):29-36.
[25] Guo L Q, Bo W J, Yang G H. Characteristics of current deformation of fault belts in North China[J]. Journal of Geodesy and Geodynamics, 2003,23(2):29-36.
[26] 张进才, 褚立峰, 肖震, 等. 河北平原地面沉降调查与监测主要进展及成果[J]. 中国地质调查, 2014,1(2):45-50.
[26] Zhang J C, Chu L F, Xiao Z, et al. Main progress and achievements of land subsidence survey and monitoring in Hebei Plain[J]. Geological Survey of China, 2014,1(2):45-50.
[27] 郑玉萍, 韩晔, 王巍, 等. 自然因素对天津市地面沉降影响分析[J]. 中国煤炭地质, 2014(4):36-40.
[27] Zheng Y P, Han Y, Wang W, et al. Analysis of impacts on surface from natural factors in Tianjin Municipality[J]. Coal Geology of China, 2014(4):36-40.
[28] 何庆成, 刘文波, 李志明. 华北平原地面沉降调查与监测[J]. 高校地质学报, 2006,12(2):195-209.
[28] He Q C, Liu W B, Li Z M. Land subsidence survey and monitoring in the North China Plain[J]. Geological Journal of China Universities, 2006,12(2):195-209.
[29] Ge D, Zhang L, Wang Y, et al. Merging multi-track PSI result for land subsidence mapping over very extended area[C]// 2010 IEEE International Geoscience and Remote Sensing Symposium,IEEE, 2010.
[30] 孙赫. 基于PS-InSAR技术的大范围多轨道数据地面沉降监测研究[D]. 西安:长安大学学位论文, 2015.
[30] Sun H. Monitoring large area subsidence with PS-InSAR technique based on multi-track data[D]. Xi'an:Chang'an University, 2015.
[31] 熊思婷, 曾琪明, 焦健, 等. 邻轨PS-InSAR地面沉降结果拼接处理方法与实验[J]. 地球信息科学学报, 2014(5):797-805.
doi: 10.3724/SP.J.1047.2014.00797
[31] Xiong S T, Zeng Q M, Jiao J, et al. Research on connecting PS-InSAR from adjacent tracks for land subsidence monitoring[J]. Journal of Geo-Information Science, 2014(5):797-805.
[32] 张永红, 吴宏安, 康永辉. 京津冀地区1992—2014年三阶段地面沉降InSAR监测[J]. 测绘学报, 2016,45(9):1050-1058.
[32] Zhang Y H, Wu H A, Kang Y H. Ground subsidence over Beijing-Tianjin-Hebei region during three periods of 1992 to 2014 monitored by interferometric SAR[J]. Acta Geodaetica et Cartographica Sinica, 2016,45(9):1050-1058.
[33] 张学东, 葛大庆, 肖斌, 等. 多轨道集成PS-InSAR监测高速公路沿线地面沉降研究——以京沪高速公路(北京—河北)为例[J]. 测绘通报, 2014(10):67-69.
[33] Zhang X D, Ge D Q, Xiao B, et al. Study on multi-track integration PS-InSAR monitoring the land:Taking Jinghu highway (Beijing-Hebei) as an example[J]. Bulletin of Surveying and Mapping, 2014(10):67-69.
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