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Remote Sensing
Volume 16
Issue 18
10.3390/rs16183515
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Article

SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation Monitoring

by
Zhigang Yu
1,
Guanghui Zhang
1,*,
Guoman Huang
2,
Chunquan Cheng
2,
Zhuopu Zhang
1 and
Chenxi Zhang
1
1
College of Resources, Shandong University of Science and Technology, Taian 271000, China
2
Chinese Academy of Surveying and Mapping, Beijing 100830, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(18), 3515; https://doi.org/10.3390/rs16183515
Submission received: 23 August 2024 / Revised: 19 September 2024 / Accepted: 20 September 2024 / Published: 22 September 2024
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Abstract

SBAS-InSAR technology is effective in obtaining surface deformation information and is widely used in monitoring landslides and mining subsidence. However, SBAS-InSAR technology is susceptible to various errors, including atmospheric, orbital, and phase unwrapping errors. These multiple errors pose significant challenges to precise deformation monitoring over large areas. This paper examines the spatial characteristics of these errors and introduces a spatially constrained SBAS-InSAR method, termed SSBAS-InSAR, which enhances the accuracy of wide-area surface deformation monitoring. The method employs multiple stable ground points to create a control network that limits the propagation of multiple types of errors in the interferometric unwrapped data, thereby reducing the impact of long-wavelength signals on local deformation measurements. The proposed method was applied to Sentinel-1 data from parts of Jining, China. The results indicate that, compared to the traditional SBAS-InSAR method, the SSBAS-InSAR method significantly reduced phase closure errors, deformation rate standard deviations, and phase residues, improved temporal coherence, and provided a clearer representation of deformation in time-series curves. This is crucial for studying surface deformation trends and patterns and for preventing related disasters.
Keywords: SBAS-InSAR; deformation monitoring; control network; spatial constraints; error propagation SBAS-InSAR; deformation monitoring; control network; spatial constraints; error propagation

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MDPI and ACS Style

Yu, Z.; Zhang, G.; Huang, G.; Cheng, C.; Zhang, Z.; Zhang, C. SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation Monitoring. Remote Sens. 2024, 16, 3515. https://doi.org/10.3390/rs16183515

AMA Style

Yu Z, Zhang G, Huang G, Cheng C, Zhang Z, Zhang C. SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation Monitoring. Remote Sensing. 2024; 16(18):3515. https://doi.org/10.3390/rs16183515

Chicago/Turabian Style

Yu, Zhigang, Guanghui Zhang, Guoman Huang, Chunquan Cheng, Zhuopu Zhang, and Chenxi Zhang. 2024. "SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation Monitoring" Remote Sensing 16, no. 18: 3515. https://doi.org/10.3390/rs16183515

APA Style

Yu, Z., Zhang, G., Huang, G., Cheng, C., Zhang, Z., & Zhang, C. (2024). SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation Monitoring. Remote Sensing, 16(18), 3515. https://doi.org/10.3390/rs16183515

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