Generation of long-term InSAR ground displacement time-series through a novel multi-sensor data merging technique: The case study of the Shanghai coastal area

Abstract

Ground deformation is one of the most significant challenges faced by many coastal mega-cities, with major societal and economic impacts. In this context, the possibility to monitor the temporal evolution of the ground subsidence processes, which may also last for several years, is of great relevance. This goal can be obtained by applying differential SAR interferometry (DInSAR) techniques to sequences of multiple-satellite synthetic aperture radar (SAR) images. Moreover, the growing availability of large archives of SAR images collected by different SAR instruments nowadays leads to the need of developing new data merging techniques, which may take profit from the complementary information recoverable from every single set of data. In this work, a novel data merging approach for the generation of long-tem ground displacement time-series, based on the use of the modified Quantile-Quantile Adjustment (MQQA) algorithm, is proposed. Specifically, the methodology has successfully been applied to study the long-term evolution of the ground subsidence occurred in the coastal area of Shanghai from February 2007 to April 2017. A cross-comparison analysis between DInSAR and ground truth data has also been carried out, showing that the average root mean square error (RMSE) between the obtained displacement time-series and available ground truth data is of about 3 mm. This outcome confirms the validity of the novel DInSAR-based MQQA combination method.