Abstract
The spatio-temporal random effects (STRE) model is a classic dynamic filtering model, which can be used to fuse GNSS and InSAR deformation data. The STRE model uses a certain time span of high spatial resolution Interferometric Synthetic Aperture Radar (InSAR) time series data to establish a spatial model and then obtain a deformation result with high spatio-temporal resolution through the state transition equation recursively in time domain. Combined with the Kalman filter, the STRE model is continuously updated and modified in time domain to obtain higher accuracy result. However, it relies heavily on the prior information and personal experience to establish an accurate spatial model. To the authors' knowledge, there are no publications which use the STRE model with multiple sets of different deformation monitoring data to verify its applicability and reliability. Here, we propose an improved STRE model to automatically establish accurate spatial model to improve the STRE model, then apply it to the fusion of GNSS and InSAR deformation data in the San Francisco Bay Area covering approximately 6000 km2 and in Southern California covering approximately 100,000 km2. Our experimental results show that the improved STRE model can achieve good fusion effects in both study areas. For internal inspection, the average error RMS values in the two regions are 0.13 mm and 0.06 mm for InSAR and 2.4 and 2.8 mm for GNSS, respectively; for Jackknife cross-validation, the average error RMS values are 6.0 and 1.3 mm for InSAR and 4.3 and 4.8 mm for GNSS in the two regions, respectively. We find that the deformation rate calculated from the fusion results is highly consistent with the existing studies, the significant difference in the deformation rate on both sides of the major faults in the two regions can be clearly seen, and the area with abnormal deformation rate corresponds well to the actual situation. These results indicate that the improved STRE model can reduce the reliance on prior information and personal experience, realize the effective fusion of GNSS and InSAR deformation data in different regions, and also has the advantages of high accuracy and strong applicability.
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The Sentinel-1A/B standardized interference products of the San Francisco Bay Area are available in the ARIA, https://aria.jpl.nasa.gov/products. The InSAR time series of Southern California are provided by Xiaohua Xu, https://topex.ucsd.edu/gmtsar/insargen/. The GNSS data is obtained from the Nevada Geodesy Laboratory, http://geodesy.unr.edu.
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Acknowledgements
The authors thank Peiliang Xu, the Assistant Editor-in-Chief and two anonymous reviewer for their constructive comments that helped improve the manuscript. This work was funded by the National Key R&D Program of China (2019YFC1509205), the National Natural Science Foundation of China (No. 42174023), the National Natural Science Foundation of China (No. 42174053), the Natural Science Foundation of Hunan Province, China (No. 2021JJ30805), the Fundamental Research Funds for the Central Universities of Central South University (No. 2020zzts175) and the Postgraduate Scientific Research Innovation Project of Hunan Province (No. CX20200231). We would like to thank the ARIA for providing the Sentinel-1A/B standardized interference products and MintPy software, thank the Nevada Geodesy Laboratory for providing the GNSS data. We would also like to thank Neely for his help on the CSBAS method and Xiaohua Xu for providing the InSAR time series data of Southern California. Maps and plots in the paper were made using the Generic Mapping Tools and MATLAB.
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WD and WX provided the initial idea and designed the experiments for this study; HY carried out the designed experiments and wrote the paper; LX processed and analyzed the InSAR data of the San Francisco Bay Area; WD and WX contributed to the final manuscript with suggestions and corrections.
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Yan, H., Dai, W., Xie, L. et al. Fusion of GNSS and InSAR time series using the improved STRE model: applications to the San Francisco Bay Area and Southern California. J Geod 96, 47 (2022). https://doi.org/10.1007/s00190-022-01636-7
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DOI: https://doi.org/10.1007/s00190-022-01636-7