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Surface deformation monitoring of Shanghai based on ENVISAT ASAR and Sentinel-1A data

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Abstract

During urbanization, different dimensions of the expansion of construction land causes different degrees of surface deformation. Based on the C-band ENVISAT ASAR data (December 2004 to September 2010) and Sentinel-1A data (March 2015 to April 2017), the small baseline subset interferometric synthetic aperture radar (SBAS InSAR) method was used to monitor the spatial and temporal variations of surface deformation in Shanghai, China. The results showed that widespread uneven subsidence occurred in Shanghai from December 2004 to April 2017. A transition from urban areas toward the suburbs appeared in the spatial distribution, in which the cumulative deformation in the urban areas has the characteristics of seasonal fluctuation, which shows the alternation of subsidence and rebound. In addition, the deformation characteristics of different types of construction land with the same geological conditions were compared, which showed that residential land had the least cumulative subsidence and clear seasonal fluctuations, industrial land had the greatest cumulative subsidence, and transportation land had greater subsidence during the construction period but tended to become stable after being put into use. This suggests that the deformation characteristics of Shanghai are changing, and the type of construction land is also an important factor in the deformation process.

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Acknowledgements

This work is supported financially by National Natural Science Foundation of China (No. 41830105) and also funded by the International Scholar Exchange Fellowship (ISEF) program at KFAS ( Korean Foundation of Advanced Studies). SAR data of Envisat ASAR and Sentinel-1A are courtesy of the European Space Agency (ESA). Thanks to Shanghai Institute of Geological Survey for providing level measurement data.

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Correspondence to Chang-Qing Ke.

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Yao, G., Ke, CQ., Zhang, J. et al. Surface deformation monitoring of Shanghai based on ENVISAT ASAR and Sentinel-1A data. Environ Earth Sci 78, 225 (2019). https://doi.org/10.1007/s12665-019-8226-z

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  • DOI: https://doi.org/10.1007/s12665-019-8226-z

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