SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation Monitoring
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...
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MDPI AG
2024-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/18/3515 |
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| author | Zhigang Yu Guanghui Zhang Guoman Huang Chunquan Cheng Zhuopu Zhang Chenxi Zhang |
| author_facet | Zhigang Yu Guanghui Zhang Guoman Huang Chunquan Cheng Zhuopu Zhang Chenxi Zhang |
| author_sort | Zhigang Yu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e77f64dbf0ec448db0c78f5cc7e8b6f9 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-e77f64dbf0ec448db0c78f5cc7e8b6f92025-08-20T01:55:49ZengMDPI AGRemote Sensing2072-42922024-09-011618351510.3390/rs16183515SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation MonitoringZhigang Yu0Guanghui Zhang1Guoman Huang2Chunquan Cheng3Zhuopu Zhang4Chenxi Zhang5College of Resources, Shandong University of Science and Technology, Taian 271000, ChinaCollege of Resources, Shandong University of Science and Technology, Taian 271000, ChinaChinese Academy of Surveying and Mapping, Beijing 100830, ChinaChinese Academy of Surveying and Mapping, Beijing 100830, ChinaCollege of Resources, Shandong University of Science and Technology, Taian 271000, ChinaCollege of Resources, Shandong University of Science and Technology, Taian 271000, ChinaSBAS-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.https://www.mdpi.com/2072-4292/16/18/3515SBAS-InSARdeformation monitoringcontrol networkspatial constraintserror propagation |
| spellingShingle | Zhigang Yu Guanghui Zhang Guoman Huang Chunquan Cheng Zhuopu Zhang Chenxi Zhang SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation Monitoring Remote Sensing SBAS-InSAR deformation monitoring control network spatial constraints error propagation |
| title | SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation Monitoring |
| title_full | SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation Monitoring |
| title_fullStr | SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation Monitoring |
| title_full_unstemmed | SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation Monitoring |
| title_short | SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation Monitoring |
| title_sort | ssbas insar a spatially constrained small baseline subset insar technique for refined time series deformation monitoring |
| topic | SBAS-InSAR deformation monitoring control network spatial constraints error propagation |
| url | https://www.mdpi.com/2072-4292/16/18/3515 |
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