## 6 Conclusions  
In this study, high-resolution Gaofen-3 SAR images were utilized to measure the land subsidence over the main urban district of Beijing by time series InSAR analysis. Sentinel-1 SAR images were applied to assess the accuracy of subsidence measurements derived from Gaofen-3 SAR images. The subsidence scale and extent are consistent with the detected major subsidence bowls for the two independent SAR datasets, which demonstrates the high accuracy of Gaofen-3 SAR images for measuring the subsidence over the study area. The InSAR results derived from the Gaofen-3 SAR images also revealed the deceleration pattern of the land subsidence over the study area by merging the results derived from the Sentinel-1 SAR images from 2017 to 2020. It is essential for monitoring active subsidence bowls located in the northeastern Chaoyang district.  
Our results have demonstrated the Gaofen-3 SAR images' capability of interferometric analysis over the study area, primarily restricted by a long perpendicular baseline and inaccurate orbit information. The long perpendicular baselines decrease the coherence of the interferograms derived from the Gaofen-3 SAR images, while the artifacts with inaccurate orbit information reduce the quality of the interferograms. Refining and reducing the artifacts related to longer perpendicular baselines and inaccurate orbit information from wrapped interferograms will improve the quality of the interferograms of the Gaofen-3 images and assist us in characterizing and revealing the spatial-temporal subsidence patterns over the study areas.  
Figure 14: Average subsidence velocity maps derived from Sentinel-1 SAR images and Gaofen-3 SAR images between June 2017 and March 2021: (**a**–**c**) indicate the subsidence velocity map derived from Sentinel-1 images during 2017-2020, (**d**) indicates the subsidence velocity map derived from Gaofen-3 images during 2020–2021.  
**Author Contributions:** All the authors participated in editing and reviewing the manuscript. Conceptualization, K.D., T.L. and Y.H.; methodology, Y.H., Z.L. and K.D.; software, Y.H., C.L. and N.W.; data analysis, T.L., Y.H., X.Y. and K.D. Validation, X.S. and X.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is funded by the National Key Research and Development Program of China (Grant No. 2021 YFB3901403), the National Natural Science Foundation of China (Grant No. 41941019, 41801391, 41871373), the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (Grant No. SKLGP2020Z012), Sichuan Province Science Fund for Distinguished Young Scholars (2023 NSFSC1909), the fellowship of China Postdoctoral Science Foundation (Grant No. 2020M673322), the Open Fund of Badong National Observation and Research Station of Geohazards (No. BNORSG-202201), and the Open Research Fund Program of MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area: 20220006.  
**Acknowledgments:** The authors would like to thank [PERSON] from Northwestern Polytechnical University, [PERSON] from the Hebei University of Engineering, [PERSON] from Southern Methodist University, and [PERSON] from the Hunan University of Science and Technology for their help in data processing and analyzing the InSAR results. We also like to thank the European Space Agency for providing the Sentinel-1A data and 1 second Copernicus DEM for free.  
**Conflicts of Interest:** The authors declare no conflict of interest.