Multi-orbit InSAR discrepancy analysis of landslide hazard identification results

doi:10.13474/j.cnki.11-2246.2026.0113

Abstract

Abstract: The incidence angles and directions of SAR satellites with different orbits exhibit significant variations,leading to substantial discrepancies in acquired topographic deformation data.Previous studies have predominantly focused on integrating multi-orbit InSAR for landslide hazard identification,yet few have systematically analyzed the causes of discrepancies among results from different orbits.This study selects the Jiuzhaigou county area as the test site,utilizing Sentinel-1 images from three orbits (January 2020 to June 2024) to derive surface deformation via SBAS-InSAR,interpret landslide hazards,and compare the discrepancies in hazard identification across orbits.By evaluating metrics including the number of identified landslides,deformation distribution,time-series deformation,and slope aspect deformation recovery rates,the results reveal the following.Ascending orbit 55 identified 4 landslides,ascending orbit 128 detected 3,and descending orbit 62 detected 1.The deformation zones identified by ascending orbit 55 generally exhibited larger spatial coverage,higher deformation rates,and greater cumulative deformation compared to ascending orbit 128,though their overall time-series deformation trends are broadly consistent.The average slope aspect deformation recovery rates are 72.69%for ascending orbit 55 and 68.79% for ascending orbit 128.These findings provide technical insights for optimizing multi-orbit InSAR applications in landslide hazard detection.

Key words: InSAR, multi-orbit, landslide, deformation distribution, deformation recovery rate, incidence angle

CLC Number:

P237

SU Weijiang, SUN Liucun, XIAO Wenxing, GAO Yongqiang, HE Wen, LIAO Dan, XIE Mingli. Multi-orbit InSAR discrepancy analysis of landslide hazard identification results[J]. Bulletin of Surveying and Mapping, 2026, 0(1): 78-84.

References

[1] TU Kuan,YE Shirong,ZOU Jingui,et al.InSAR displacement with high-resolution optical remote sensing for the early detection and deformation analysis of active landslides in the upper Yellow River[J].Water,2023,15(4): 769.
[2] YE Kai,WANG Zhe,WANG Ting,et al.Deformation monitoring and analysis of baige landslide (China) based on the fusion monitoring of multi-orbit time-series InSAR technology[J].Sensors,2024,24(20): 6760.
[3] 李晓恩,周亮,苏奋振,等.InSAR技术在滑坡灾害中的应用研究进展[J].遥感学报,2021,25(2): 614-629.
[4] LI Xing,JÓNSSON S,CAO Yunmeng.Interseismic deformation from Sentinel-1 burst-overlap interferometry: application to the southern Dead Sea fault[J].Geophysical Research Letters,2021,48(16): e2021GL093481.
[5] WEI Lianhuan,FENG Qiuyue,LIU Feiyue,et al.Precise topographic model assisted slope displacement retrieval from small baseline subsets results: case study over a high and steep mining slope[J].Sensors,2020,20(22): 6674.
[6] ZHAO C,LU Z,ZHANG Q.Impact of topography and orbital parameters on the performance of InSAR in mountainous regions[J].IEEE Transactions on Geoscience and Remote Sensing,2018,56(2): 710-723.
[7] XIA Ye.Synthetic aperture radar interferometry[M]//Sciences of Geodesy-I.Berlin,Heidelberg: Springer,2010: 415-474.
[8] HU J,LI Z W,DING X L,et al.Resolving three-dimensional surface displacements from InSAR measurements: a review[J].Earth-Science Reviews,2014,133: 1-17.
[9] HU J,LI Z,DING X,et al.Multi-track InSAR fusion for 3D deformation mapping of landslides in complex terrain[J].Remote Sensing of Environment,2021,263: 112541.
[10] LIU G,JIA H,ZHANG R,et al.Complementarity of ascending and descending ALOS PALSAR data for landslide detection in the Wenchuan earthquake region[J].IEEE Transactions on Geoscience and Remote Sensing,2018,56(5): 2846-2858.
[11] ZHAO Chaoying,KANG Ya,ZHANG Qin,et al.Landslide identification and monitoring along the Jinsha River Catchment (wudongde reservoir area),China,using the InSAR method[J].Remote Sensing,2018,10(7): 993.
[12] HE Liming,PEI Panke,ZHANG Xiangning,et al.Sensitivity evaluation of time series InSAR monitoring results for landslide detection[J].Remote Sensing,2023,15(15): 3906.
[13] 高志良,解明礼,巨能攀,等.堆积层滑坡多源遥感动态演变特征分析研究[J].武汉大学学报(信息科学版),2024,49(8): 1482-1491.
[14] 何佳阳,巨能攀,解明礼,等.高山峡谷地区地质灾害隐患InSAR识别技术对比[J].地球科学,2023,48(11): 4295-4310.
[15] LU Huiyan,LI Weile,XU Qiang,et al.Active landslide detection using integrated remote sensing technologies for a wide region and multiple stages: a case study in southwestern China[J].Science of the Total Environment,2024,931: 172709.
[16] 吴琼,葛大庆,于峻川,等.广域滑坡灾害隐患InSAR显著性形变区深度学习识别技术[J].测绘学报,2022,51(10): 2046-2055.
[17] 廖明生,董杰,李梦华,等.雷达遥感滑坡隐患识别与形变监测[J].遥感学报,2021,25(1): 332-341.
[18] 蔡杰华,张路,董杰,等.九寨沟震后滑坡隐患雷达遥感早期识别与形变监测[J].武汉大学学报(信息科学版),2020,45(11): 1707-1716.
[19] YU Chen,LI Zhenhong,PENNA N T,et al.Generic atmospheric correction model for interferometric synthetic aperture radar observations[J].Journal of Geophysical Research: Solid Earth,2018,123(10):9202-9222.