Search

Spaceborne time series SAR interferometry (TS-InSAR) technology has been widely applied in ground deformation monitoring. The current popular TS-InSAR are carried out mainly on permanent scatterers (PS) or distributed scaterrers (DS), which can map the ground deformation on sparse targets with high or moderate coherence. However, high-precision and high-density deformation monitoring is always restricted by atmospheric artifacts and surface decorrelation, especially over nonurban areas. Here we show that the ground deformation can be precisely mapped with high density by spaceborne InSAR technique. We proposed a full scatterers InSAR (FS-InSAR) methodology, which can significantly improve the quality of the interferograms by applying a dual-scale temporal low-pass filter (DTLF) to separate both atmospheric and noisy phases from deformation phases, without external atmospheric data. Simulation experiments were conducted to determine the small-scale and large-scale window sizes and evaluate the effectiveness of DTLF. Then we applied the FS-InSAR method to the Tianjin-Tangshan region, China using Sentinel-1 data, assessed the regenerated differential low-frequency phases, and validated the results with leveling measurements and groundwater depth data. What's more, detailed ground deformation was retrieved over the Tangshan mining area with an unprecedented density of 98.55%. Our results demonstrate that the FS-InSAR strategy contrasts sharply with the previous PS-InSAR or DS-InSAR methods by simultaneously solving the two bottleneck problems of atmospheric artifacts and decorrelation in most cases except for water bodies or dense vegetations such as rainforest, thereby it is of great importance for future monitoring and understanding the ground deformation to prevent and control geological disasters. • FS-InSAR can effectively remove atmospheric artifacts without external data. • FS-InSAR significantly improves the quality of the interferograms. • FS-InSAR can map ground deformation with an unprecedented density. [ABSTRACT FROM AUTHOR]

Time series synthetic aperture radar (SAR) interferometry (TS-InSAR) has been widely used for monitoring ground deformation. The selection of interferometric image pairs directly affects the quality of the interferograms and the accuracy of deformation monitoring. The Small BAseline Subset (SBAS) method has been widely adopted to form interferograms in TS-InSAR. However, the selection of low-quality interferograms or missed selection of good interferograms due to temporal and/or spatial baselines exceeding the thresholds sometimes occurs in the traditional SBAS method. This letter proposes a new method, namely, the semi-automatic selection of optimum image pairs (SASOIP), based on the interferometric coherence. By quantitatively evaluating the coherence of point targets in a small feature region, the SASOIP results in high-quality interferometric image pairs selected from all possible interferometric combinations for any two SAR images. The SASOIP method can effectively reduce the probability of poor selection and missed selection of quality image pairs. Twenty-nine COSMO-SkyMed SAR images acquired from 27 August 2011, to 29 July 2015, over Tianjin, China, are used to test the proposed algorithm. Validation with 102 levelling data points demonstrates that the accuracy of ground deformation derived from the interferograms by the SASOIP method is 23.13% higher than that based on the SBAS method. [ABSTRACT FROM AUTHOR]

Tianjin, China, has been suggested to have serious ground subsidence due to excessive extraction of groundwater. It is essential to monitor this subsidence, which has potential hazards and risks. Time series InSAR (TS-InSAR), such as small baselines subset (SBAS), is a powerful tool that can monitor ground deformation with high accuracy and at high spatial resolution over a long time interval. However, the high computational complexity may exceed computer memory limit when high-spatial resolution SAR (such as TerraSAR-X, TSX) images are used. In this article, the multi-look approach is introduced to the SBAS tool from StaMPS/MTI (Stanford method for persistent scatter/multi-temporal InSAR) in order to balance the spatial resolution and subsidence information in detection. The looks used for multi-looking are first fixed in terms of the accuracy of deformation and the density of coherent points. Then, the recent subsidence in Tianjin is extracted using multi-looking SBAS based on 48 TSX images acquired from 2009 to 2013. The results are validated by levelling measurements with a root mean square error (RMSE) of 4.7 mm year–1, which demonstrates that SBAS analysis can effectively monitor deformation based on multi-looking TSX acquisitions in the area under investigation. Besides, the results also show that Tianjin has been suffering from subsidence during this period, and there were two separate large subsidence basins located in this study area with more than 500 mm cumulative subsidence. Moreover, the subsidence rate increased after December 2010 in Tianjin. [ABSTRACT FROM AUTHOR]