InSAR-Based Deformation Monitoring of High-Fill Engineered Landslides: A Case Study at Panzhihua Airport, Southwest China

InSAR-Based Deformation Monitoring of High-Fill Engineered Landslides: A Case Study at Panzhihua Airport, Southwest China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article InSAR-Based Deformation Monitoring of High-Fill Engineered Landslides: A Case Study at Panzhihua Airport, Southwest China Yangwei Yu, Mengshi Yang, Menghua Li, Cheng Huang, Zhifang Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6628868/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Feb, 2026 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract High-fill engineered landslides challenge infrastructure safety in mountainous regions, particularly where geological settings interact with human activity. This study investigated a landslide at Panzhihua Airport, Southwest China, using multi-track Sentinel-1 data with a Time-Series InSAR framework. This methodology innovatively combines multi-orbit LOS (Line of Sight) deformation retrieval through InSAR processing, 2D deformation vector decomposition (vertical + slope-parallel directions), and spatiotemporal correlation analysis with geological structure and rainfall patterns. Results show: (i) In the airport area, the average LOS deformation rates for the runway and surrounding buildings are 3.58 mm/year and − 1.37 mm/year, indicating stability, while the landslide area to the northeast decreases to -37.07 mm/year and − 12.75 mm/year, suggesting active sliding. (ii) Deformation in the landslide area was spatially heterogeneous, with vertical settlement (max. 45.3 mm/year) at the rear, and creeping (max. 53.61 mm/year) at the front of the fill body. (iii) The displacement time series revealed a clear correlation between vertical deformation and rainfall. During the first two uplift cycles, the expansion volume showed a strong correlation with rainfall, with a Pearson correlation coefficient (PCC) value exceeding 0.9 in some regions. These findings provide insights for managing secondary deformation risks on large-scale fill slopes for early warning system development in similar geo-engineered environments. TS-InSAR high-fill landslide Spatiotemporal evolution airport Creep mechanism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction China's transportation infrastructure has achieved significant advancements, with extensive construction of airports, railways, and expressways implemented in the central and western regions. (Feng et al. 2024 ). Nevertheless, the Midwestern region predominantly features rugged topography and geographically constrained zones, necessitating extensive cut-and-fill engineering operations during aviation infrastructure implementation.(Wang et al. 2018a ; He et al. 2023 ; Wen et al. 2024 ). This has resulted in the formation of numerous fill slopes around airports in many regions, with fill heights ranging from tens to hundreds of meters (Yang et al. 2016 ; Liao et al.2021; Mei et al. 2022 ). The geotechnical stabilization of high-fill slope is paramount for ensuring aviation infrastructure resilience. Monitoring investigations (Zhao et al., 2019 ; Li et al., 2022 ; Bao et al., 2024 ) have consistently identified creeping deformation patterns in these constructed slopes. Although such gradual displacements seldom pose immediate risks to human safety, progressive ground movement induces cumulative deterioration of structural foundations and ancillary facilities. Critically, sustained creep behavior has been recognized as a potential precursor to abrupt geohazards (Li et al 2021 ; Li et al.2024). The Panzhihua Airport landslide represents a paradigm of geotechnical failures impacting aviation infrastructure. Extensive earthwork operations during construction, including mass cut-and-fill activities, generated multiple engineered embankments along the airfield boundary, with vertical accumulations reaching 128 meters (Sun et al.2015). The engineered slopes manifested recurrent slope instability events throughout their construction and service periods, culminating in two discrete failure episodes (3 October 2009 and 25 June 2011) within the northeast quadrant of the aviation complex. This geotechnical disruption necessitated full operational suspension of the facility for 24 consecutive months. (Ruan et al.2013). A phased remediation program was implemented from 2011 to 2017 under governmental oversight, involving deployment of an integrated stabilization system with anti-slide piles, shear-resistant keys, and reinforced retaining structures (Yang and Cheng, 2024 ). Post-intervention monitoring between 2016 and 2017 revealed persistent slope displacement phenomena, manifesting as progressive surface subsidence and material disaggregation. (Li et al.2019). The earthwork operations predominantly employed locally sourced geomaterials, coinciding with the pervasive distribution of expansive clay deposits within the Panzhihua Basin (Cun et al., 2011 ). These hydro-active clays exhibit cyclic volumetric changes through moisture absorption-desorption mechanisms. Geotechnical investigations confirm that slopes incorporating such expansive matrices are prone to accelerated shear strength degradation and differential displacement (Hou et al., 2013 ). Post-failure analysis of the Panzhihua Airport landslide identified moderate expansivity in the embankment clay strata (Gong et al., 2007 ). Consequently, aviation infrastructure safety remains persistently influenced by dual geohazard factors—steep engineered slopes interacting with semi-expansive clays—necessitating continuous deformation pattern analysis for operational risk mitigation. Synthetic Aperture Radar Interferometry (InSAR) has undergone rapid technological advancement in recent years. This electromagnetic wave-based geospatial observation technique enables measurement processes unaffected by temporal or meteorological constraints. The advent of Time-Series InSAR (TS-InSAR) methodologies has established a systematic framework for persistent ground deformation monitoring, with proven efficacy in slope displacement tracking and precursor identification (Shi et al.2015; Dai et al.2016. Shi et al.2017; Dong et al.2018a; Dong et al.2018b;Duan et al.2023; Carlà et al.2018; Cohen-Waeber et al.2018; Chen et al.2021; Yan et al.2023; Yang et al.2020 ). The synergistic integration of multi-track SAR datasets significantly enhances landslide displacement characterization, enabling simultaneous acquisition of 2D and 3D deformation metrics. (Li et al. 2019 a; Samsonov et al.2020; Liu et al. 2021 ; Li et al. 2023 ). Leveraging SAR constellations' systematic revisit capabilities, TS-InSAR demonstrates unparalleled proficiency in detecting cyclic displacement patterns, especially within hydro-responsive geotechnical matrices (Cook et al., 2022 ; Zhu et al., 2022 ; Zhao et al., 2024 ). The Panzhihua case study represents a prototypical engineered slope system combining substantial fill geometry (vertical relief > 100m) with semi-expansive clay lithology. Post-2017 remediation, however, scholarly focus on this site's spatiotemporal deformation behavior and underlying geomechanical drivers has markedly diminished. This investigation developed an advanced multi-track SAR data integration framework to elucidate the spatiotemporal dynamics of the Panzhihua Airport slope system - a quintessential representation of large-scale anthropogenic slope instability in China's southwestern orogenic belt. By integrating historical geospatial records (2000–2017 construction phase optical imagery) with multi-platform Sentinel-1 SAR acquisitions (2018–2023 ascending/descending orbits), we implemented a Time-series InSAR protocol for millimeter-scale displacement monitoring. The methodology encompassed: (1) derivation of LOS deformation vectors through persistent scatterer analysis; (2) two-dimensional displacement field decomposition using orbital geometry constraints; (3) mechanistic correlation modeling between rainfall patterns and clay swell potential. The outcomes of this investigation provide critical operational insights for implementing effective deformation surveillance and stability evaluation protocols in analogous high-fill slope environments. 2. Study area 2.1. Overview of the airport project Panzhihua is located in the southern part of Sichuan Province in China (Fig. 1). The region has a subtropical monsoon climate, with distinct dry and wet seasons, and abundant rainfall in the summer, with annual rainfall ranging from 760 to 1200 mm, mainly from June to October. Panzhihua Airport is a highland airport located approximately 9 km from the city center of Panzhihua. It was built in 2000, and opened to traffic in 2003. The altitude of the runway is 1976 meters, near the top of a mountain, and the airport is surrounded by a number of overhanging areas, which is known as an "aircraft carrier airport.” The airport was built on the southeast side of a shaped ridge on a parapet slope with poor geology and a rugged terrain. A large number of excavations and fills were carried out during the construction process, and the amount of soil and rock was over 58 million cubic meters, forming an embankment more than 3,600 m in length, with the highest point of the embankment reaching 128 m. The large amount of embankment fill poses a safety threat to the airport and causes a number of engineering landslides during the construction and operation of the project (Li et al.2012). 2.2. Geology and geomorphology of landslide areas The landslide was located on the northeast side of the airport runway (Figure.2a). This is one of the most dangerous hazards. The composite landslide structure extends 1,600 m longitudinally with 200–400 m transverse dimensions, encompassing approximately 5.1×10⁶ m³ of displaced material (average thickness: 10–25 m; Li et al., 2013 ). The slip mass contains semi-expansive clay lithologies exhibiting poorly consolidated trailing-edge morphology, having undergone recurrent translational displacement vectors (Gong et al., 2007 ). Kinematic analysis reveals a primary failure azimuth of 125° with translational displacement magnitudes ranging 100–300 m (Li et al., 2021 ).. The landslide comprises two distinct geomorphic units (Fig. 2b). The upper section is a fill-body landslide formed during airport construction, while the lower section constitutes the pre-existing Yujiaping landslide (Li et al., 2012 ). The fill-body landslide primarily consists of anthropogenic materials including gravelly soils and silty clays (containing sandstone and mudstone fragments), with thickness ranging 12–25 m. Its profile features steeper gradients (8°-13°) at the rear section transitioning to gentler slopes mid-slope. This zone was identified as the active sliding area.The Yujiaping landslide contains natural gravelly clay deposits (10–20 m thick) with developed ground fractures and slope angles of 5°-25°, functioning as a passive sliding zone. The bedrock comprises alternating Permian-Triassic sandstone (permeable) and mudstone (impermeable) strata, exhibiting contrasting geomechanical properties between sliding surfaces. Groundwater levels measured 5–10 m below surface from slope center to front, demonstrating significant hydrogeological influence on slope stability through softening effects. 2.3. Historical events of the slide. Panzhihua Airport has experienced recurrent landslides and deformations of varying scales during its construction and operational phases (Li et al., 2012 ). These events critically compromised aviation safety and resulted in significant socioeconomic losses (Wu et al., 2019 ). Historical analysis of landslide-induced deformations identified two major failure events on 3 October 2009 and 15 July 2011 (Fig. 3), triggered by seismic activity and intense rainfall. The high-fill slope failures propagated downward, overriding the pre-existing Yujiaping landslide system, ultimately necessitating full airport closure for nearly two years. Operational normalcy was restored in 2013 following completion of targeted remediation measures (Yin et al., 2023 ).. Following recurrent landslide events, governmental agencies implemented a phased mitigation program establishing an integrated stabilization system incorporating prestressed anchor cables, anti-slide piles, and reinforced retaining walls. Post-remediation monitoring revealed persistent slope displacement manifested through progressive surface cracking and differential settlement, culminating in renewed deformation episodes during 2016–2017 (Li et al., 2021 ). The slope system continues to exhibit gradual creep-type displacements, necessitating continuous deformation surveillance to ensure operational safety. 3. Study data and methodology 3.1. Study data This investigation utilized multi-source datasets including: (1) Sentinel-1 SAR imagery (European Space Agency); (2) 12.5-m resolution ALOS PALSAR DEM; (3) precipitation records (NOAA database: https://www.ncei.noaa.gov/data/global-summary-of-the-day/archive/ ); (4) POD precise orbit ephemerides; and (5) multi-temporal optical imagery (Google Earth). Figure 4 illustrates the methodological framework. The analytical protocol comprised three phases: initial processing of raw SAR datasets to extract displacement metrics, followed by two-dimensional decomposition to derive vertically-oriented deformation rates and slope-parallel displacement vectors. Finally, the derived deformation metrics were systematically analyzed to identify potential geomechanical and hydrological triggering mechanisms. Sentinel-1 satellite data from the European Space Agency's Copernicus Earth observation program were acquired and processed, utilizing C-band synthetic aperture radar (SAR) with a 12-day orbital revisit cycle. Ascending (January 2018-May 2023) and descending (June 2018-May 2023) orbit datasets in Interferometric Wide (IW) swath mode were processed to retrieve line-of-sight (LOS) deformation vectors. All geospatial data were georeferenced to the WGS84 ellipsoidal coordinate system. Detailed sensor parameters and acquisition geometries are provided in Table 1 . Table 1 Main parameters of the SAR image. Parameters Sentinel-1 Orbit direction Ascending Descending Coverage time January 2018 to May 2023 June 2018 to May 2023 Number of data 163 134 Revisit time(day) 12 12 Wavelength(cm) 5.6 5.6 Path 26 135 Frame 83 503 incidence angle(°) 36.7024 32.8845 3.2. Data processing of InSAR Surface deformation monitoring within the study area was implemented through the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) methodology (Berardino et al., 2002 ). This technique constructs interferometric pairs by applying spatiotemporal baseline constraints, followed by deformation velocity field retrieval through singular value decomposition (SVD) optimization under minimum-norm regularization criteria (Zhu et al., 2021 ). Interferometric processing was systematically conducted according to the established workflow. The master images for ascending and descending orbits were selected as 21 March 2020 and 16 March 2020, respectively, to ensure accurate DEM co-registration. Temporal and perpendicular baselines were constrained to 48 days and 300 meters based on empirical thresholds, generating 483 ascending and 388 descending interferograms. Coregistration of slave images was performed using conventional methods, followed by orbital phase correction with precision orbit ephemerides (Wen et al., 2024 ). Topographic phases were removed using external DEM data, with multi-looking and adaptive filtering applied to suppress noise. The interferometric phase (φ_i) for any pixel is expressed as: $$\:\:\:\:\:\:\:\:\:\:\:\:\delta\:{\phi\:}_{i}\left(r,x\right)=\phi\:\left({t}_{A},r,x\right)-\phi\:\left({t}_{B},r,x\right)\approx\:\frac{4\pi\:}{\lambda\:}\left[d\left({t}_{A},r,x\right)-d\left({t}_{B},r,x\right)\right]\:\:\:\:\:\:\:\:\:\left(1\right)$$ where φ(t A ,r, x) and φ(t B , r, x) represent the phases of the pixel at moments t A and t B , λ is the radar wavelength, and d represents the deformation of the pixel along the radar line-of-sight (LOS). Then, the temporal deformation of each pixel is calculated, and phase Eq. ( 2 ) exists in the interferogram, which can be further simplified to Eq. ( 3 ): $$\:\delta\:{\phi\:}_{j}=\phi\:{(t}_{{IM}_{i}})-\phi\:{(t}_{{IS}_{i}})$$ 2 In Eq. ( 2 ), \(\:{t}_{{IM}_{i}}\) and \(\:{t}_{{IS}_{i}}\:\) represent the time series of the master and secondary images, respectively: The equation can be rewritten in matrix form as follows: $$\:Bv=\delta\:\phi\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$ 3 Where B represents an M × N matrix and v is the deformation rate for each time period to be solved. expressed in terms of the phase, and δφ represents the differential interferometric phase of all interferograms. To obtain a unique solution to Eq. ( 3 ), the singular value decomposition (Berardino et al.2002) is used for the decomposition of the singular values of matrix B, which represents the rate of the phase transition and thus the time series of the deformation. 3.3. Two-dimensional deformation decomposition Conventional single-track InSAR monitoring yields unidimensional deformation measurements along the radar line-of-sight (LOS) direction, representing vector projections of actual slope movements (Liu et al., 2021 ). However, landslide kinematics typically involve three-dimensional displacement components. The critical sliding interface between the destabilized mass and underlying bedrock induces slope-parallel shear deformation and vertical displacement components. Recent advances in multi-platform SAR acquisitions enable synergistic integration of ascending/descending datasets, permitting decomposition of two-dimensional displacement fields through geometric inversion models. This methodology effectively resolves the slope-aligned deformation vector (parallel to dominant movement direction) and vertical displacement component, significantly enhancing kinematic interpretation accuracy: $$\:\left(\begin{array}{c}{D}_{as}\\\:{D}_{vs}\end{array}\right)={\left(\frac{\text{c}os\beta\:cos{\theta\:}_{A}\:\:\:\:-sin{\theta\:}_{A}\text{c}\text{o}\text{s}(\delta\:-({\alpha\:}_{A}-\frac{3\pi\:}{2}\left)\right)}{\text{c}os\beta\:cos{\theta\:}_{D}\:\:\:\:-sin{\theta\:}_{D}\text{c}\text{o}\text{s}(\delta\:-({\alpha\:}_{D}-\frac{3\pi\:}{2}\left)\right)}\right)}^{-1}\left(\begin{array}{c}{D}_{LOS-a}\\\:{D}_{LOS-d}\end{array}\right)$$ 4 In Eq. ( 4 ), β represents the slope (replaced by the average slope calculated by the DEM), θ denotes the satellite incidence angle, δ signifies the landslide azimuth angle, and α denotes the satellite flight direction. In accordance with this model, data regarding landslide deformation in both the vertical and lateral directions were extracted. We interpolated the deformation time series generated from the ascending and descending data to complete the time-domain alignment of the two datasets (Fig. 5), and then obtained the deformation time series in the vertical direction and along the slope. To investigate the effect of rainfall on landslide deformation, the rainfall model divided the data into two types of time windows (Table 2 ): rise window (TWR) and subsidence window (TWS). We calculated the absolute displacement vertically at twenty day intervals, still using cumulative rainfall data for the same period(the data were obtained through the implementation of the inverse distance weighting interpolation method.). We examined the controlling role of rainfall in landslide movement by calculating Pearson’s correlation coefficient (PCC). Table 2 Time windows and time scale. Time windows Time Scale Time windows Time Scale TWR1 2019.5.15-2019.7.14 TWS1 2020.7.15-2020.5.11 TWR2 2020.5.12-2020.8.25 TWS2 2020.8.26-2020.5.14 TWR3 2021.5.15-2021.6.27 TWS3 2021.6.28–222.3.16 TWR4 2022.3.17-2022.6.23 TWS4 2022.6.24-2023.4.11 4. Results 4.1. InSAR results of the airport area. Figure 6 displays the InSAR-derived displacement rates for both ascending and descending orbits during the monitoring period. Discrepancies in detected deformation patterns stem from inherent differences in satellite viewing geometries between orbit configurations. LOS displacement values are defined as follows: negative values represent surface movement away from the satellite, while positive values indicate movement toward the satellite. Ascending orbit observations reveal significant deformation contrast across the runway corridor. The western sector, characterized by dense airport infrastructure with extensive paved surfaces (including localized stabilized zones at the southern runway extremity), exhibits minimal displacement. Descending orbit data confirm this spatial heterogeneity, recording average LOS deformation rates of 3.58 mm/yr (toward satellite) and − 1.37 mm/yr (away from satellite).In contrast, the eastern sector—comprising large-scale engineered fills and natural slopes—demonstrates widespread deformation anomalies. Pronounced instability is observed northeast of the runway, spatially consistent with the studied fill landslide, where peak displacement rates reach 47.31 mm/yr (ascending) and 34.36 mm/yr (descending) in satellite-away directions. Ground validation confirms measurement reliability and persistent slope activity.Descending orbit analysis further delineates three geomechanically distinct zones: the northeastern landslide sector exhibiting high-magnitude fill displacements, the mid-eastern slope area displaying progressive translational movement, and the southern steep slopes manifesting differential settlement. 4.2. Two-dimensional displacement of landslide. Integrating ascending and descending orbit datasets enabled two-dimensional deformation characterization of the landslide body. Applying a 5-meter spatial coherence threshold for homologous point selection, the 2D displacement field was reconstructed through geometric decomposition (Fig. 7). Deformation sign conventions were defined as: positive slope-parallel values indicating downslope movement along the failure surface, and positive vertical values representing surface heave relative to the local topographic datum. The results demonstrate that the vertical deformation rate within the landslide area exhibits a range of -45.3 mm/year to 15.4 mm/year, while the deformation rate along the slope direction displays a range of -18.5 mm/year to 53.64 mm/year. The prevailing mode of movement at the trailing edge of the fill landslide is vertical subsidence, which represents the area with the highest rate of subsidence and is accompanied by a tendency to advance along the slope. The deformation in both dimensions in the central region of the fill body landslide was relatively stable with minor vertical settlement and forward sliding along the slope. In addition, there are localized areas where no deformation occurs in either direction. The deformation of the front of the fill landslide was relatively slight in the vertical direction; however, there was an extensive forward movement along the slope. In this area, the dominant deformation trend was slipwise, with deformation occurring in the form of a slope. The magnitude of deformation was observed to decrease in the region of the landslide that has been in existence for a longer period of time. The rate of deformation exhibits a notable increase in the central region of the area under consideration. In contrast, the front edge demonstrates a higher degree of stability. 4.3. Temporal patterns of landslide movement. As illustrated in Fig. 7, the deformation rate was measured along the vertical landslide and in the direction of its propagation, encompassing both the fill body landslide area and the old landslide area. The measured results are indicative of the spatial distribution trend of landslide deformation rates. However, it should be noted that these values correspond to the average annual deformation rates over the study period and may differ from the actual state of landslide motion. Therefore, further research in the form of time-series deformation analyses is required. Four representative points were selected for plotting the time series of the deformation, the results of which are shown in Fig. 8 . In the vertical direction, the overall performance shows a decreasing trend, with a smaller degree of decrease at point P2, whereas point P1, which is located at the rear edge of the landslide, shows a drastic decreasing trend. Furthermore, all points demonstrated distinct seasonal variations that appeared to be significantly correlated with precipitation. The correlation between these variables is examined in Section 5.2 . The deformation time series along the landslide direction exhibited no discernible seasonal trend, with points P1 and P3 demonstrating substantial forward slips, point P4 exhibiting minimal movement, and point P2 demonstrating stability with negligible sliding. 5. Discussion 5.1. Characteristics of the spatial distribution of landslide regional deformation and the controlling role of geological conditions. The rate of deformation and its spatial distribution can, to some extent, reflect the state of landslide movement. At the trailing edge of the fill-body landslide, intense deformation occurred both vertically and along the landslide direction. The deformation rates in the vertical direction and along the slope were − 22.99 mm/year and 13.07 mm/year, respectively, with a maximum settlement rate of -45.3 mm/year in the vertical direction. This indicates that the trailing edge is the most threatened area, driving the development of landslides and reflecting the properties of the active slip zone in the fill area. In the central part of the fill body landslide zone, the vertical deformation is negligible (the average rate is -4.56 mm/year.) In some areas, the deformation is reduced to such an extent that it is absent. The slope-wise deformation was also reduced (the average rate was 8.59 mm/year.) with displacement in the opposite direction in some areas. By the leading edge of the fill body landslide, the rate of vertical and slope deformation rebounded relative to the central part of the fill area (The average rate is -8.59 mm/year and 27.23 mm/year, respectively), and the slope deformation in this part was the largest(53.64 mm/year), reflecting the advancing trend of the landslide. In the area of the old landslide, the overall deformation rate is minimal (The average rate is -7.73 mm/year and 7.33 mm/year, respectively)), indicative of the passive slip zone property of the aforementioned landslide. Geological hazards are the result of a variety of geological factors, including topography, geological structure, ecological environment, meteorology and hydrology, geotechnical properties of landslides and landslide zones, earthquakes, and human activity (Chen et al.2019). It is hypothesized that the spatial distribution characteristics of deformation rates are influenced by topography and human engineering activities, as evidenced by the geological data of the landslide at the Panzhihua Airport. Geologic conditions are an important factor in controlling landslide movements (Ering P and Badu G.L.S 2016). The construction process involves a substantial amount of cut-and-fill work, resulting in the creation of numerous steep slopes near the airport runway. The presence of a significant number of critical surfaces on these slopes, which are subject to the influence of gravity, poses a substantial safety hazard. The trailing edge of the landslide in this study was located within the area of the original high-fill slopes, with slopes ranging from 30° to 35°. The topography, characterized by a steep incline, diminished the resistance of the slide bed, thereby facilitating landslide movement and inducing substantial deformation at the trailing edge of the landslide. The topography of the remaining landslide region underwent moderation in its characteristics, accompanied by a decline in the rate of deformation. Furthermore, the fill body landslide is composed of a comparatively loose artificial fill, which exhibits diminished physical-mechanical strength compared to the original clay in the area of the former landslide. Consequently, the deformation rate of the fill-body landslide generally exceeded that of the old landslide. Continued deformation of landslides may lead to reactivation of previously dormant landslides; therefore, continuous deformation monitoring is required to ensure safe operation of airports. Anthropogenic activities also had a considerable impact on landslide movement, especially in the context of this study. Our findings revealed that the central segment of the landslide exhibited a distinct movement tendency compared with the front and back segments. This discrepancy can be attributed to the impact of human activities, as the relevant authorities have implemented comprehensive support measures, including the installation of anti-slip piles, keys, and other structures within the affected area. This results in an enhancement of the local slip resistance and a substantial improvement in landslide stability. Consequently, in this area, the vertical and slope movements of the landslide were significantly mitigated, and certain regions exhibited an opposing movement trend from other areas. This phenomenon may be attributed to the presence of support works that impede the landslide body at the trailing edge and contribute to the accumulation of landslide materials. This observation lends further credence to the efficacy of supportive work. 5.2. Rainfall and expansion of the soil. Rainfall is also a significant contributing factor in triggering landslides (Gui M.W and Wu Y.M 2014; Zhou et al. 2022 ). Rainfall-induced landslides are the most pervasive globally, with seasonal precipitation and sudden meteorological events frequently precipitating a precipitous surge in pore water pressure and a concomitant diminution in the mechanical properties of rocks and soils (Yang et al. 2023 ), This is accompanied by a series of chemical processes that change the structure of the minerals that make up the rocks and soils, resulting in slopes. Because of the distinct dry and wet seasons in the region, there are frequent rainstorms in summer and little dry precipitation in winter, resulting in large variations in landslide water content and groundwater levels throughout the year. Infiltrating rainfall causes the mechanical properties of the rocks and soils that make up the sliding body to deteriorate, a significant reduction in shear strength, increased sliding body weight, and softening of the sliding surface, which significantly reduces the sliding force of the sliding bed, thereby inducing sliding. The landslide body composition is characterized by the presence of moderately expansive soils, which exhibit a tendency to expand in the presence of water and contract in the absence of water. The study area exhibits a clear distinction between dry and rainy seasons, which must be considered when examining the impact of precipitation on landslides. The revisit period of Sentinel-1 is only 12 days, which is very useful for analyzing the response mechanism between landslide movement and precipitation. To match the satellite monitoring period, we collected daily rainfall data for Panzhihua. A joint analysis of the rainfall data with the interpolated two-dimensional displacement time series (Figure. 8) revealed no significant correlation between displacement in the horizontal direction and daily rainfall. However, a significant correlation was observed between the displacement in the vertical direction and daily rainfall. Significant vertical uplift occurred in the landslide area during the rainy season, whereas intense subsidence occurred during the dry season. The results of the deformation time series demonstrated that this seasonal variation also occurs annually. Concurrently, the ongoing displacement along the slope precipitates a general decline in the vertical displacement of the landslide as the continuous slope displacement diminishes the degree of uplift and augments the degree of subsidence. To further ascertain the relationship between rainfall and landslide soil expansion, we employed Pearson correlation coefficient (PCC) analysis. Two types of time windows were established: rising and subsidence. The absolute displacement within each window was computed and the correlation coefficient between the two was calculated. The results are shown in Figs. 9 and 10. The results show that most points have strong correlations between the shape variables and precipitation within the rising window, especially within the TWR1 and TWR2 windows, with some points having correlations of 0.9 or higher. Within the TWR3 and TWR4 windows, most points showed either no correlation or a positive correlation. Within the subsidence window, the correlation coefficients between the shape variables and precipitation at most points decreased significantly, and tended to be uncorrelated or negatively correlated. We conclude that rainfall has a controlling effect on landslide movement, especially within the rising window, which may originate from the expansion of the soil, whereas no significant control was found for the sinking window. Although the surface uplift resulting from soil expansion caused an apparent increase in elevation, the overall trend of the landslide movement remained downward. However, the rate of this downward movement appears to exhibit a lag. This finding is consistent with those of numerous other scholars in the field(Sun et al.2015; Li et al.2023). A potential causal factor for this phenomenon may be the infiltration of rainfall, and there is a strong correlation between the rate of rainfall infiltration and rainfall duration and intensity, as well as the thickness and permeability of the landslide. It can be reasonably deduced that the rate of rainfall infiltration varied depending on the prevailing conditions. Consequently, the time required for rainwater to reach the sliding surface depends on these conditions. Furthermore, the diffusion of pore pressure through the sliding body following heavy precipitation requires a significant timeframe (Zhao et al. 2012 ). This may explain the discrepancy between our monitoring results and observed rainfall and the lack of an Significant correlation between displacement and rainfall within the subsidence window. 6. Conclusion This study presents a comprehensive InSAR analysis of deformation mechanisms governing a high-fill engineered landslide system at Panzhihua International Airport, Southwest China, spanning 5.5 years (January 2018-May 2023). Through the integrated processing of multi-track Sentinel-1 datasets (163 ascending and 134 descending acquisitions) and innovative 2D deformation modeling, the findings of this study are summarized below. The airport's operational core (runways, terminal) exhibits millimeter-scale stability (The average LOS deformation rate in this region is 3.58 mm/yr for ascending orbits and − 1.37 mm/yr for descending orbits.) Validation of the effectiveness of engineering remediation. In contrast, peripheral fill slopes demonstrate spatially heterogeneous movements, with the northeast landslide complex showing peak deformation rates. The average deformation rate in the LOS direction for the ascending track at the trailing edge of the fill landslide immediately adjacent to the flight area decreases sharply to -37.07 mm/year, and for the descending track, to -12.75 mm/year. This dichotomy highlights the critical influence of material heterogeneity on the long-term slope performance. 2D deformation vector analysis reveals a tripartite movement mechanism: Rear scarp: Vertical compaction-dominated subsidence (45.3 mm/yr max, 22.99 mm/yr mean) in thick unsaturated fills; Mid-slope: More stable transitional shear zones (The average rate of deformation is -4.56 mm/year in the vertical direction and 8.59 mm/year along the slope.) with coupled vertical/slope parallel displacements for reinforcement work.; Toe region: Strike-slip dominated creep along pre-existing discontinuities (53.64 mm/yr max, 27.23 mm/yr mean). Notably, the original bedrock landslide area maintains stability (The average rate of deformation is -7.73 mm/year in the vertical direction and 7.33 mm/year along the slope.), underscoring the rheology of the fill material as the primary driver of deformation. Time-series deformation analysis revealed distinct seasonal cyclicity in the vertical displacements, exhibiting coupling with rainfall patterns. The uplift-settlement movement occurred reciprocally with the changes in rainfall. The correlation coefficient (PCC value in the bulge area was greater than 0.9.) analysis was conducted to examine the relationship between the deformation and rainfall. The analysis revealed a discernible controlling effect of rainfall on the uplift cycle. A thorough evaluation revealed that slope, rainfall, and human engineering activities were pivotal in the regulation of landslide movement. Declarations Funding: This work was financially supported by the National Natural Science Foundation of China [No.42474040,42101450], Yunnan Fundamental Research Projects [No. 202301AT070145], and the Project “Yunnan Revitalization Talent Support Program.” We thank the European Space Agency (ESA) for providing the Sentinel-1/2 dataset under the framework of the Sino-EU Dragon Project [ID 95473]. Author contributions Yangwei Yu proposed the idea, and drafted the manuscript, Mengshi Yang carried on the experiments and conducted the data analysis. Menghua Li revised the intellectual content and manuscript structure. Cheng Huang performed the visualization and interpretation of results. Zhifang Zhao supervised the research, reviewed the manuscript, and approved the final version for publication. All authors agree to be accountable for all aspects of the work. Disclosure of interest No potential conflict of interest was reported by the author(s). Date availability statement (DAS) The data that support the findings of this study are available from the corresponding author, upon reasonable request. References Bao H, Liu L, Lan H, Peng J, Yan C, Tang M, Guo G, Zheng H (2024) Evolution of high-filling loess slope under long-term seasonal fluctuation of groundwater. CATENA 238:107898. https://doi.org/10.1016/j.catena.2024.107898 Berardino P, Fornaro G, Lanari R, Sansosti E (2002) A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans Geosci Remote Sens 40:2375–2383. https://doi.org/10.1109/TGRS.2002.803792 Carlà T, Farina P, Intrieri E, Ketizmen H, Casagli N (2018) Integration of ground-based radar and satellite InSAR data for the analysis of an unexpected slope failure in an open-pit mine. Eng Geol 235:39–52. https://doi.org/10.1016/j.enggeo.2018.01.021 Chen L, Zhao C, Li B, He K, Ren C, Liu X, Liu D (2021) Deformation monitoring and failure mode research of mining-induced Jianshanying landslide in karst mountain area, China with ALOS/PALSAR-2 images. Landslides 18:2739–2750. https://doi.org/10.1007/s10346-021-016786 Chen Y, Chang K, Wang S, Huang J, Yu C, Tu J, Chu H, Liu C (2019) Controls of preferential orientation of earthquake- and rainfall‐triggered landslides in Taiwan’s orogenic mountain belt. Earth Surf Processes Landf 44:1661–1674. https://doi.org/10.1002/esp.4601 Cohen-Waeber J, Bürgmann R, Chaussard E, Giannico C, Ferretti A (2018) Spatiotemporal Patterns of Precipitation‐Modulated Landslide Deformation From Independent Component Analysis of InSAR Time Series. Geophys Res Lett 45:1878–1887. https://doi.org/10.1002/2017GL075950 Cook ME, Brook MS, Hamling IJ, Cave M, Tunnicliffe JF, Holley R, Alama DJ (2022) Engineering geomorphological and InSAR investigation of an urban landslide. Gisborne New Z Landslides 19:2423–2437. https://doi.org/10.1007/s10346-022-01938-z Cun C, Bao F, Zhao Y, Xia Y, Zhang L (2011) ENGINEERING PROPERTIES OF EXPANSIVE SOIL FORMED DURING GLACIAL PERIOD AT PANZHIHUA AREA. Journal of Engineering Geology.1004–9665/2011-19(2)-254–208. (in Chinese). Dai K, Li Z, Tomás R, Liu G, Yu B, Wang X, Cheng H, Chen J, Stockamp J (2016) Monitoring activity at the Daguangbao mega-landslide (China) using Sentinel-1 TOPS time series interferometry. Remote Sens Environ 186:501–513. https://doi.org/10.1016/j.rse.2016.09.009 Dong J, Liao M, Xu Q, Zhang L, Tang M, Gong J (2018a) Detection and displacement characterization of landslides using multi-temporal satellite SAR interferometry: A case study of Danba County in the Dadu River Basin. Eng Geol 240:95–109. https://doi.org/10.1016/j.enggeo.2018.04.015 Dong J, Zhang L, Tang M, Liao M, Xu Q, Gong J, Ao M (2018b) Mapping landslide surface displacements with time series SAR interferometry by combining persistent and distributed scatterers: A case study of Jiaju landslide in Danba, China. Remote Sens Environ 205:180–198. https://doi.org/10.1016/j.rse.2017.11.022 Duan H, Li Y, Jiang H, Li Q, Jiang W, Tian Y, Zhang J (2023) Retrospective monitoring of slope failure event of tailings dam using InSAR time-series observations. Nat Hazards 117, 2375–2391 (2023). https://doi.org/10.1007/s11069-023-05946-7 Ering P, Babu GLS (2016) Probabilistic back analysis of rainfall induced landslide- A case study of Malin landslide, India. Eng Geol 208:154–164. https://doi.org/10.1016/j.enggeo.2016.05.002 Fan X, Xu Q, Scaringi G, Dai L, Li W, Dong X, Zhu X, Pei X, Dai K, Havenith H-B (2017) Failure mechanism and kinematics of the deadly June 24th 2017 Xinmo landslide, Maoxian, Sichuan, China. Landslides 14:2129–2146. https://doi.org/10.1007/s10346-017-0907-7 Feng X, Yao Y, Zhang J, Zhang X (2024), Stability analysis of high-fill slopes at mountain airports. J Beijing Univ Aeronaut Astronaut (in Chinese) (001), 046 Gong Z, Li T, Gong X, Li Y (2007) Study on Landslide Remediation Measures in the North East Corner of Panzhihua Airport. J Eng Geol 15(2):7 (in Chinese) Gui M-W, Wu Y-M (2014) Failure of soil under water infiltration condition. Eng Geol 181:124–141. https://doi.org/10.1016/j.enggeo.2014.07.005 He L, Qian J, Zhao Y, Li Z (2023) Study on stability analysis method of high fill slope of Wushan Shennv Peak Airport. Journal Of Hefei University Of Technology(Natural Science)(Chinese). 1003–5060(2023)05-0646-07 Hou T, Xu G, Shen Y, Wu Z, Zhang N, Wang R (2013) Formation mechanism and stability analysis of the Houba expansive soil landslide. Eng Geol 161:34–43. https://doi.org/10.1016/j.enggeo.2013.04.010 LI J, Zhang J, Yuan Y, Chen P, Liu T, Pu W (2019) Study on multi-stage sliding mechanism of high-fill slope: 12~#Landslides, for example of Panzhihua Airport. National Engineering Geology Annual Conference Li J, Fan C, Zhao K, Zhang Z, Duan P (2024) Landslide displacement prediction using time series InSAR with combined LSTM and TCN: application to the Xiao Andong landslide, Yunnan Province, China. https://doi.org/10.1007/s11069-024-06937-y . Nat Hazards Li K, Zhao X, Xiao D (2021) Acid rain: an unsuspected factor predisposing Panzhihua airport landslide, China. Environ Sci Pollut Res 28:36753–36764. https://doi.org/10.1007/s11356-021-13308-8 Li M, Zhang L, Shi X, Liao M, Yang M (2019a) Monitoring active motion of the Guobu landslide near the Laxiwa Hydropower Station in China by time-series point-like targets offset tracking. Remote Sens Environ 221:80–93. https://doi.org/10.1016/j.rse.2018.11.006 Li M, Zhang L, Yang M, Liao M (2023) Complex surface displacements of the Nanyu landslide in Zhouqu, China revealed by multi-platform InSAR observations. Eng Geol 317:107069. https://doi.org/10.1016/j.enggeo.2023.107069 Li T, Liu J, Ren Y, Xue D, Chen M (2012) Sliding Mechanism Of High-fill Slope With Pre-reinforced Piles At Panzhihua Airport. J Eng Geol 20(5):0723–0709 (in Chinese) Li T, Tian X, Han W, Ren Y, He Y, Wei Y (2013) Centrifugal model tests on sliding failure of a pile-stabilized high fill slope. Rock Soil Mech. 24 11 (in Chinese) 10.16285/j.rsm.2013.11.003 Li Y, Jiang W, Zhang J( (2022) A time series processing chain for geological disasters based on a GPU-assisted sentinel-1 InSAR processor. Nat Hazards 111:803–815. https://doi.org/10.1007/s11069-021-05079-9 Li Z, Sun L, Liu L, Guan C (2022) Research on safety evaluation method of high-fill slope considering heterogeneity and anisotropy. Bull Eng Geol Environ 81:275. https://doi.org/10.1007/s10064-022-02766-0 Liao H, Xv C, Yang Y (2021) Optimal design of the high geogrid-reinforced slope at the airfield of an airport. Hydrogeol Eng Geol 1000–3665 (in Chinese). 10.16030/j.cnki.issn.1000-3665.202012015 Liu X, Zhao C, Zhang Q, Yin Y, Lu Z, Samsonov S, Yang C, Wang M, Tomás R (2021) Three-dimensional and long-term landslide displacement estimation by fusing C- and L-band SAR observations: A case study in Gongjue County, Tibet, China. Remote Sens Environ 267:112745. https://doi.org/10.1016/j.rse.2021.112745 Mei Y, Zhou D, Hu C, Wang X, Zhang Y, Xiao N, Shi W (2022) Study on Deformation Characteristics of Loess Ultrahigh-Fill Slope Based on Large-Scale Undisturbed Soil Centrifugal Model Tests. Front Earth Sci 10:848542. https://doi.org/10.3389/feart.2022.848542 Ruan X, Huang S, Wang J, Wei J (2013) Analysis of the causes of landslide at Panzhihua Airport and treatment countermeasures. Sichuan Archit 5:3 (in Chinese) Samsonov S, Dille A, Dewitte O, Kervyn F, d’Oreye N (2020) Satellite interferometry for mapping surface deformation time series in one, two and three dimensions: A new method illustrated on a slow-moving landslide. Eng Geol 266:105471. https://doi.org/10.1016/j.enggeo.2019.105471 Shi X, Zhang L, Balz T, Liao M (2015) Landslide deformation monitoring using point-like target offset tracking with multi-mode high-resolution TerraSAR-X data. ISPRS J Photogrammetry Remote Sens 105:128–140. https://doi.org/10.1016/j.isprsjprs.2015.03.017 Shi X, Zhang L, Tang M, Li M, Liao M (2017) Investigating a reservoir bank slope displacement history with multi-frequency satellite SAR data. Landslides 14:1961–1973. https://doi.org/10.1007/s10346-017-0846-3 Sun Q, Zhang L, Ding XL, Hu J, Li ZW, Zhu JJ (2015) Slope deformation prior to Zhouqu, China landslide from InSAR time series analysis. Remote Sens Environ 156:45–57. https://doi.org/10.1016/j.rse.2014.09.029 Sun Z (2015) The Factors of Airport High Embankment Slope Stability Analysis. Chengdu University of Technology Wang J, Xu Y, Ma Y, Qiao S, Feng K (2018a) Study on the deformation and failure modes of filling slope in loess filling engineering: a case study at a loess mountain airport. Landslides 15:2423–2435. https://doi.org/10.1007/s10346-018-1046-5 Wen M, Yang M, Zhao X, Zhao Z (2024) Postconstruction Deformation Characteristics of High-Fill Foundations of Kunming Changshui International Airport Using Time-Series InSAR Technology. IEEE J Sel Top Appl Earth Observations Remote Sens 17:206–218. https://doi.org/10.1109/JSTARS.2023.3328321 Wu H, Pai L, Lai. T, Zhang J, Zhang Y, Li Y (2019) Study on Optimization of Cooperative Performance of Pile-Anchor-Reinforced Soil Composite Structure of High-fill Slope in Mountain Airport. Chin J Rock Mechan Eng 7:14 (in Chinese) Yan Y, Guo C, Li C, Yuan H, Qiu Z (2023) The Creep-Sliding Deformation Mechanism of the Jiaju Ancient Landslide in the Upstream of Dadu River, Tibetan Plateau, China. Remote Sens 15:592. https://doi.org/10.3390/rs15030592 Yang H, Liu J, Peng J, Wang J, Zhao B, Zhang B (2020) A method for GB-InSAR temporal analysis considering the atmospheric correlation in time series. Nat Hazards 104, 1465–1480 (2020). https://doi.org/10.1007/s11069-020-04228-w Yang H, Song K, Chen L, Qu L (2023) Hysteresis effect and seasonal step-like creep deformation of the Jiuxianping landslide in the Three Gorges Reservoir region. Eng Geol 317:107089. https://doi.org/10.1016/j.enggeo.2023.107089 Yang X, Zhu Y, Zhou Y, Yang X, Shi Z (2016) Time-space monitoring and stability analysis of high fill slope slip process at a airport in mountain region. Chin J Rock Mechan Eng :1000–6915 Yang Z, Cheng X (2024) High fill slope collapse: Stability evaluation based on finite element limit analyses. Transp Geotechnics 44:101156. https://doi.org/10.1016/j.trgeo.2023.101156 Yin Y, Shi X, Zhong J, Gao H, Dai K (2023) Monitoring 2D Deformation of High Slope in Panzhihua Airport by Joint Lifting Rail Sentinel-1A. GEOMATICS ༆ SPATIAL INFORMATION TECHNOLOGY;46, No,12. (in Chinese). Zhao C, Lu Z, Zhang Q, De La Fuente J (2012) Large-area landslide detection and monitoring with ALOS/PALSAR imagery data over Northern California and Southern Oregon, USA. Remote Sens Environ 124:348–359. https://doi.org/10.1016/j.rse.2012.05.025 Zhao G, Yang Y, Zhang H, Zhang G (2019) A case study integrating field measurements and numerical analysis of high-fill slope stabilized with cast-in-place piles in Yunnan. China Eng Geol 253:160–170. https://doi.org/10.1016/j.enggeo.2019.03.005 Zhao Y, Zhang R, Xing X, Ling S, Zheng G (2024) Deformation Instability Characteristics of Expansive Soil Slope Based on SBAS-InSAR Technique. J Highway Transp Res Dev. (08),22–30 Zhou C, Huang W, Ai D, Xu H, Yuan J, Kou L, Luo X (2022) Catastrophic landslide triggered by extreme rainfall in Chongqing, China: July 13, 2020, Niuerwan landslide. Landslides 19:2397–2407. https://doi.org/10.1007/s10346-022-01911-w Zhu W, Dou H, Yin N, Cheng Y, Zhang S, Zhang Q (2022) Deformation characteristics analysis of the expansive soil slope by integrating of InSAR and SSA techniques: a case study of the South-to-North Water Diversion Project. Acta Geod et Cattographic Sinica 51(10):2083–2092. 10.119947/j.AGCS.22022.20220357 Zhu Y, Qiu H, Yang D, Liu Z, Ma S, Pei Y, He J, Du C, Sun H (2021) Pre- and post-failure spatiotemporal evolution of loess landslides: a case study of the Jiangou landslide in Ledu. China Landslides 18:3475–3484. https://doi.org/10.1007/s10346-021-01714-5 Cite Share Download PDF Status: Published Journal Publication published 25 Feb, 2026 Read the published version in Natural Hazards → Version 1 posted Reviewers agreed at journal 23 Jun, 2025 Reviewers invited by journal 16 Jun, 2025 Editor invited by journal 06 Jun, 2025 Editor assigned by journal 09 May, 2025 First submitted to journal 09 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6628868","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471927443,"identity":"201d47cd-fb39-4ff3-b82b-2e5a55f5e406","order_by":0,"name":"Yangwei Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYLCCBAMJOX5m5sMPiNfyocDGWLKdLc2AaB2MMz6kJRqc51GQIEq5wY0cA2Yeg8MJxod5GAwYamyiidaSZ3aY98ADhmNpuQ2EteRuAGkpNjvMl2DA2HCYeC2Jm5t5DCSI1sI4wyAtEaSROC2SZ95/YPhgYGMscRgYyAnE+IXveFoCQ8IfYFT2Hz784EONDWEtChcS2H/AeQmElIOAfP8BYpSNglEwCkbBiAYAqcRA8xLItDkAAAAASUVORK5CYII=","orcid":"","institution":"Yunnan University","correspondingAuthor":true,"prefix":"","firstName":"Yangwei","middleName":"","lastName":"Yu","suffix":""},{"id":471927444,"identity":"1529f41e-e34a-4cf2-afae-54bb98ef9c96","order_by":1,"name":"Mengshi Yang","email":"","orcid":"https://orcid.org/0000-0003-1449-3494","institution":"Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Mengshi","middleName":"","lastName":"Yang","suffix":""},{"id":471927445,"identity":"a6cb8593-ee8e-405c-a095-a3ec9bdee573","order_by":2,"name":"Menghua Li","email":"","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Menghua","middleName":"","lastName":"Li","suffix":""},{"id":471927446,"identity":"27ecc107-9c4f-49d9-ac9d-9f9b46917f17","order_by":3,"name":"Cheng Huang","email":"","orcid":"","institution":"Yunnan Institute of Geological Environment Monitoring","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Huang","suffix":""},{"id":471927447,"identity":"eba70603-4249-4983-a001-4564f5b8857c","order_by":4,"name":"Zhifang Zhao","email":"","orcid":"","institution":"Yunnan University","correspondingAuthor":false,"prefix":"","firstName":"Zhifang","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-05-09 12:56:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6628868/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6628868/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11069-026-07991-4","type":"published","date":"2026-02-25T15:59:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85346668,"identity":"972b36df-8d7e-435a-8323-ae5f1139f99e","added_by":"auto","created_at":"2025-06-25 02:13:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":122050,"visible":true,"origin":"","legend":"\u003cp\u003e1 Study area location and SAR image coverage.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6628868/v1/000f4bddf55d0813cf07626b.jpg"},{"id":85345727,"identity":"b32639a1-c096-4e07-b7b7-0e7216265eef","added_by":"auto","created_at":"2025-06-25 02:05:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":997684,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Relationship between the location of the airfield and the slope; (b）Geological profile of the main sliding surface （Adapted from Li et al,2012）\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6628868/v1/e533b9d1dcb31f94df85a858.png"},{"id":85345684,"identity":"d2e69e71-13e3-4071-80a3-64d4d284e4b7","added_by":"auto","created_at":"2025-06-25 02:04:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":743615,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical image of landslide movement (from Geogle Earth image).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6628868/v1/e8c971c7da9ea054eb518daf.png"},{"id":85345745,"identity":"c2b505d6-64a6-4f2d-87d7-bb4315884df2","added_by":"auto","created_at":"2025-06-25 02:05:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":501141,"visible":true,"origin":"","legend":"\u003cp\u003eTechnology roadmap for this study.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6628868/v1/ef9fc17957fcffa95969eda8.png"},{"id":85347611,"identity":"f6652ad0-14e3-4c6c-a8cc-d0b94220d9ce","added_by":"auto","created_at":"2025-06-25 02:21:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":315102,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation of the principle of time series interpolation.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6628868/v1/8ff348b38bb08be7e9a63270.png"},{"id":85347609,"identity":"46e53ce5-a077-4cb9-9641-54754d7bfc2b","added_by":"auto","created_at":"2025-06-25 02:21:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1547571,"visible":true,"origin":"","legend":"\u003cp\u003eLOS deformation rate in the airport area obtained by InSAR monitoring. (a) indicates ascending orbit monitoring data;(b) indicates descending orbit monitoring data.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6628868/v1/b1f1c1f29fc9358880c33673.png"},{"id":85345691,"identity":"6758de5a-a57b-4429-9b6c-145298db827d","added_by":"auto","created_at":"2025-06-25 02:05:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":569633,"visible":true,"origin":"","legend":"\u003cp\u003eA two-dimensional decomposition of landslide regional deformation is presented herein. (a) indicates vertical direction, upward is positive, downward is negative; (b) indicates along the slope, forward along the direction of the main slide is positive, vice versa is negative.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6628868/v1/f3ae69bc5c68584f25b345f6.png"},{"id":85347610,"identity":"26555e7d-ae71-441c-88e8-c3e55b54c151","added_by":"auto","created_at":"2025-06-25 02:21:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":253170,"visible":true,"origin":"","legend":"\u003cp\u003eVertical and slope deformation rates of landslide areas solved from combined ascending-descending data.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6628868/v1/675e99e87817f77ba1dc2db3.png"},{"id":85346673,"identity":"275e69e5-7d10-4efb-9e56-bd12c62999ac","added_by":"auto","created_at":"2025-06-25 02:13:01","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1014336,"visible":true,"origin":"","legend":"\u003cp\u003ePCC values of absolute displacements within the rose window versus precipitation over the same period, a-d represent TWR1-TWR4, respectively.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6628868/v1/6f1f0f8b768c1eeabe97e649.png"},{"id":85346671,"identity":"657fe2f8-077c-4590-a786-58ff357b3f77","added_by":"auto","created_at":"2025-06-25 02:13:00","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":818387,"visible":true,"origin":"","legend":"\u003cp\u003ePCC values of absolute displacements within the subsidence window versus precipitation over the same period, a-d represent TWS1-TWS4, respectively.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6628868/v1/a5123c222030096846b7cd48.png"},{"id":103765637,"identity":"53bab404-d4df-465d-bc1f-b967a5285bbc","added_by":"auto","created_at":"2026-03-02 16:06:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8094820,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6628868/v1/a471e073-160a-4ff0-b021-a2c487da7fd0.pdf"}],"financialInterests":"","formattedTitle":"InSAR-Based Deformation Monitoring of High-Fill Engineered Landslides: A Case Study at Panzhihua Airport, Southwest China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChina's transportation infrastructure has achieved significant advancements, with extensive construction of airports, railways, and expressways implemented in the central and western regions. (Feng et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, the Midwestern region predominantly features rugged topography and geographically constrained zones, necessitating extensive cut-and-fill engineering operations during aviation infrastructure implementation.(Wang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e; He et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wen et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This has resulted in the formation of numerous fill slopes around airports in many regions, with fill heights ranging from tens to hundreds of meters (Yang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liao et al.2021; Mei et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The geotechnical stabilization of high-fill slope is paramount for ensuring aviation infrastructure resilience. Monitoring investigations (Zhao et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bao et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have consistently identified creeping deformation patterns in these constructed slopes. Although such gradual displacements seldom pose immediate risks to human safety, progressive ground movement induces cumulative deterioration of structural foundations and ancillary facilities. Critically, sustained creep behavior has been recognized as a potential precursor to abrupt geohazards (Li et al \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al.2024).\u003c/p\u003e \u003cp\u003eThe Panzhihua Airport landslide represents a paradigm of geotechnical failures impacting aviation infrastructure. Extensive earthwork operations during construction, including mass cut-and-fill activities, generated multiple engineered embankments along the airfield boundary, with vertical accumulations reaching 128 meters (Sun et al.2015). The engineered slopes manifested recurrent slope instability events throughout their construction and service periods, culminating in two discrete failure episodes (3 October 2009 and 25 June 2011) within the northeast quadrant of the aviation complex. This geotechnical disruption necessitated full operational suspension of the facility for 24 consecutive months. (Ruan et al.2013). A phased remediation program was implemented from 2011 to 2017 under governmental oversight, involving deployment of an integrated stabilization system with anti-slide piles, shear-resistant keys, and reinforced retaining structures (Yang and Cheng, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Post-intervention monitoring between 2016 and 2017 revealed persistent slope displacement phenomena, manifesting as progressive surface subsidence and material disaggregation. (Li et al.2019). The earthwork operations predominantly employed locally sourced geomaterials, coinciding with the pervasive distribution of expansive clay deposits within the Panzhihua Basin (Cun et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These hydro-active clays exhibit cyclic volumetric changes through moisture absorption-desorption mechanisms. Geotechnical investigations confirm that slopes incorporating such expansive matrices are prone to accelerated shear strength degradation and differential displacement (Hou et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Post-failure analysis of the Panzhihua Airport landslide identified moderate expansivity in the embankment clay strata (Gong et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Consequently, aviation infrastructure safety remains persistently influenced by dual geohazard factors\u0026mdash;steep engineered slopes interacting with semi-expansive clays\u0026mdash;necessitating continuous deformation pattern analysis for operational risk mitigation.\u003c/p\u003e \u003cp\u003eSynthetic Aperture Radar Interferometry (InSAR) has undergone rapid technological advancement in recent years. This electromagnetic wave-based geospatial observation technique enables measurement processes unaffected by temporal or meteorological constraints. The advent of Time-Series InSAR (TS-InSAR) methodologies has established a systematic framework for persistent ground deformation monitoring, with proven efficacy in slope displacement tracking and precursor identification (Shi et al.2015; Dai et al.2016. Shi et al.2017; Dong et al.2018a; Dong et al.2018b;Duan et al.2023; Carl\u0026agrave; et al.2018; Cohen-Waeber et al.2018; Chen et al.2021; Yan et al.2023; Yang et al.2020 ). The synergistic integration of multi-track SAR datasets significantly enhances landslide displacement characterization, enabling simultaneous acquisition of 2D and 3D deformation metrics. (Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003ea; Samsonov et al.2020; Liu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Leveraging SAR constellations' systematic revisit capabilities, TS-InSAR demonstrates unparalleled proficiency in detecting cyclic displacement patterns, especially within hydro-responsive geotechnical matrices (Cook et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Panzhihua case study represents a prototypical engineered slope system combining substantial fill geometry (vertical relief\u0026thinsp;\u0026gt;\u0026thinsp;100m) with semi-expansive clay lithology. Post-2017 remediation, however, scholarly focus on this site's spatiotemporal deformation behavior and underlying geomechanical drivers has markedly diminished.\u003c/p\u003e \u003cp\u003eThis investigation developed an advanced multi-track SAR data integration framework to elucidate the spatiotemporal dynamics of the Panzhihua Airport slope system - a quintessential representation of large-scale anthropogenic slope instability in China's southwestern orogenic belt. By integrating historical geospatial records (2000\u0026ndash;2017 construction phase optical imagery) with multi-platform Sentinel-1 SAR acquisitions (2018\u0026ndash;2023 ascending/descending orbits), we implemented a Time-series InSAR protocol for millimeter-scale displacement monitoring. The methodology encompassed: (1) derivation of LOS deformation vectors through persistent scatterer analysis; (2) two-dimensional displacement field decomposition using orbital geometry constraints; (3) mechanistic correlation modeling between rainfall patterns and clay swell potential. The outcomes of this investigation provide critical operational insights for implementing effective deformation surveillance and stability evaluation protocols in analogous high-fill slope environments.\u003c/p\u003e"},{"header":"2. Study area","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e\u003cstrong\u003e2.1. Overview of the airport project\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePanzhihua is located in the southern part of Sichuan Province in China (Fig.\u0026nbsp;1). The region has a subtropical monsoon climate, with distinct dry and wet seasons, and abundant rainfall in the summer, with annual rainfall ranging from 760 to 1200 mm, mainly from June to October. Panzhihua Airport is a highland airport located approximately 9 km from the city center of Panzhihua. It was built in 2000, and opened to traffic in 2003. The altitude of the runway is 1976 meters, near the top of a mountain, and the airport is surrounded by a number of overhanging areas, which is known as an \"aircraft carrier airport.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eThe airport was built on the southeast side of a shaped ridge on a parapet slope with poor geology and a rugged terrain. A large number of excavations and fills were carried out during the construction process, and the amount of soil and rock was over 58\u0026nbsp;million cubic meters, forming an embankment more than 3,600 m in length, with the highest point of the embankment reaching 128 m. The large amount of embankment fill poses a safety threat to the airport and causes a number of engineering landslides during the construction and operation of the project (Li et al.2012).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e\u003cstrong\u003e2.2. Geology and geomorphology of landslide areas\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe landslide was located on the northeast side of the airport runway (Figure.2a). This is one of the most dangerous hazards. The composite landslide structure extends 1,600 m longitudinally with 200\u0026ndash;400 m transverse dimensions, encompassing approximately 5.1\u0026times;10⁶ m\u0026sup3; of displaced material (average thickness: 10\u0026ndash;25 m; Li et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). The slip mass contains semi-expansive clay lithologies exhibiting poorly consolidated trailing-edge morphology, having undergone recurrent translational displacement vectors (Gong et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Kinematic analysis reveals a primary failure azimuth of 125\u0026deg; with translational displacement magnitudes ranging 100\u0026ndash;300 m (Li et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e)..\u003c/p\u003e\n\u003cp\u003eThe landslide comprises two distinct geomorphic units (Fig.\u0026nbsp;2b). The upper section is a fill-body landslide formed during airport construction, while the lower section constitutes the pre-existing Yujiaping landslide (Li et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). The fill-body landslide primarily consists of anthropogenic materials including gravelly soils and silty clays (containing sandstone and mudstone fragments), with thickness ranging 12\u0026ndash;25 m. Its profile features steeper gradients (8\u0026deg;-13\u0026deg;) at the rear section transitioning to gentler slopes mid-slope. This zone was identified as the active sliding area.The Yujiaping landslide contains natural gravelly clay deposits (10\u0026ndash;20 m thick) with developed ground fractures and slope angles of 5\u0026deg;-25\u0026deg;, functioning as a passive sliding zone. The bedrock comprises alternating Permian-Triassic sandstone (permeable) and mudstone (impermeable) strata, exhibiting contrasting geomechanical properties between sliding surfaces. Groundwater levels measured 5\u0026ndash;10 m below surface from slope center to front, demonstrating significant hydrogeological influence on slope stability through softening effects.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e\u003cstrong\u003e2.3. Historical events of the slide.\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003ePanzhihua Airport has experienced recurrent landslides and deformations of varying scales during its construction and operational phases (Li et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). These events critically compromised aviation safety and resulted in significant socioeconomic losses (Wu et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Historical analysis of landslide-induced deformations identified two major failure events on 3 October 2009 and 15 July 2011 (Fig.\u0026nbsp;3), triggered by seismic activity and intense rainfall. The high-fill slope failures propagated downward, overriding the pre-existing Yujiaping landslide system, ultimately necessitating full airport closure for nearly two years. Operational normalcy was restored in 2013 following completion of targeted remediation measures (Yin et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e)..\u003c/p\u003e\n\u003cp\u003eFollowing recurrent landslide events, governmental agencies implemented a phased mitigation program establishing an integrated stabilization system incorporating prestressed anchor cables, anti-slide piles, and reinforced retaining walls. Post-remediation monitoring revealed persistent slope displacement manifested through progressive surface cracking and differential settlement, culminating in renewed deformation episodes during 2016\u0026ndash;2017 (Li et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The slope system continues to exhibit gradual creep-type displacements, necessitating continuous deformation surveillance to ensure operational safety.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Study data and methodology","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e3.1. Study data\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eThis investigation utilized multi-source datasets including: (1) Sentinel-1 SAR imagery (European Space Agency); (2) 12.5-m resolution ALOS PALSAR DEM; (3) precipitation records (NOAA database: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncei.noaa.gov/data/global-summary-of-the-day/archive/\u003c/span\u003e\u003c/span\u003e); (4) POD precise orbit ephemerides; and (5) multi-temporal optical imagery (Google Earth). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the methodological framework. The analytical protocol comprised three phases: initial processing of raw SAR datasets to extract displacement metrics, followed by two-dimensional decomposition to derive vertically-oriented deformation rates and slope-parallel displacement vectors. Finally, the derived deformation metrics were systematically analyzed to identify potential geomechanical and hydrological triggering mechanisms.\u003c/p\u003e\n \u003cp\u003eSentinel-1 satellite data from the European Space Agency\u0026apos;s Copernicus Earth observation program were acquired and processed, utilizing C-band synthetic aperture radar (SAR) with a 12-day orbital revisit cycle. Ascending (January 2018-May 2023) and descending (June 2018-May 2023) orbit datasets in Interferometric Wide (IW) swath mode were processed to retrieve line-of-sight (LOS) deformation vectors. All geospatial data were georeferenced to the WGS84 ellipsoidal coordinate system. Detailed sensor parameters and acquisition geometries are provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMain parameters of the SAR image.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSentinel-1\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrbit direction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAscending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDescending\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoverage time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJanuary 2018 to May 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJune 2018 to May 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRevisit time(day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWavelength(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrame\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eincidence angle(\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.7024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.8845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e3.2. Data processing of InSAR\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eSurface deformation monitoring within the study area was implemented through the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) methodology (Berardino et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). This technique constructs interferometric pairs by applying spatiotemporal baseline constraints, followed by deformation velocity field retrieval through singular value decomposition (SVD) optimization under minimum-norm regularization criteria (Zhu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eInterferometric processing was systematically conducted according to the established workflow. The master images for ascending and descending orbits were selected as 21 March 2020 and 16 March 2020, respectively, to ensure accurate DEM co-registration. Temporal and perpendicular baselines were constrained to 48 days and 300 meters based on empirical thresholds, generating 483 ascending and 388 descending interferograms. Coregistration of slave images was performed using conventional methods, followed by orbital phase correction with precision orbit ephemerides (Wen et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Topographic phases were removed using external DEM data, with multi-looking and adaptive filtering applied to suppress noise. The interferometric phase (\u0026phi;_i) for any pixel is expressed as:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\delta\\:{\\phi\\:}_{i}\\left(r,x\\right)=\\phi\\:\\left({t}_{A},r,x\\right)-\\phi\\:\\left({t}_{B},r,x\\right)\\approx\\:\\frac{4\\pi\\:}{\\lambda\\:}\\left[d\\left({t}_{A},r,x\\right)-d\\left({t}_{B},r,x\\right)\\right]\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u0026phi;(t\u003csub\u003eA\u003c/sub\u003e ,r, x) and \u0026phi;(t\u003csub\u003eB\u003c/sub\u003e, r, x) represent the phases of the pixel at moments t\u003csub\u003eA\u003c/sub\u003e and t\u003csub\u003eB\u003c/sub\u003e, \u0026lambda; is the radar wavelength, and d represents the deformation of the pixel along the radar line-of-sight (LOS). Then, the temporal deformation of each pixel is calculated, and phase Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) exists in the interferogram, which can be further simplified to Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e):\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$\\:\\delta\\:{\\phi\\:}_{j}=\\phi\\:{(t}_{{IM}_{i}})-\\phi\\:{(t}_{{IS}_{i}})$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}_{{IM}_{i}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{t}_{{IS}_{i}}\\:\\)\u003c/span\u003e\u003c/span\u003erepresent the time series of the master and secondary images, respectively: The equation can be rewritten in matrix form as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$$\\:Bv=\\delta\\:\\phi\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere B represents an M \u0026times; N matrix and v is the deformation rate for each time period to be solved. expressed in terms of the phase, and \u0026delta;\u0026phi; represents the differential interferometric phase of all interferograms. To obtain a unique solution to Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), the singular value decomposition (Berardino et al.2002) is used for the decomposition of the singular values of matrix B, which represents the rate of the phase transition and thus the time series of the deformation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cstrong\u003e3.3. Two-dimensional deformation decomposition\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eConventional single-track InSAR monitoring yields unidimensional deformation measurements along the radar line-of-sight (LOS) direction, representing vector projections of actual slope movements (Liu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, landslide kinematics typically involve three-dimensional displacement components. The critical sliding interface between the destabilized mass and underlying bedrock induces slope-parallel shear deformation and vertical displacement components. Recent advances in multi-platform SAR acquisitions enable synergistic integration of ascending/descending datasets, permitting decomposition of two-dimensional displacement fields through geometric inversion models. This methodology effectively resolves the slope-aligned deformation vector (parallel to dominant movement direction) and vertical displacement component, significantly enhancing kinematic interpretation accuracy:\u003c/p\u003e\n \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equ3\" class=\"mathdisplay\"\u003e$$\\:\\left(\\begin{array}{c}{D}_{as}\\\\\\:{D}_{vs}\\end{array}\\right)={\\left(\\frac{\\text{c}os\\beta\\:cos{\\theta\\:}_{A}\\:\\:\\:\\:-sin{\\theta\\:}_{A}\\text{c}\\text{o}\\text{s}(\\delta\\:-({\\alpha\\:}_{A}-\\frac{3\\pi\\:}{2}\\left)\\right)}{\\text{c}os\\beta\\:cos{\\theta\\:}_{D}\\:\\:\\:\\:-sin{\\theta\\:}_{D}\\text{c}\\text{o}\\text{s}(\\delta\\:-({\\alpha\\:}_{D}-\\frac{3\\pi\\:}{2}\\left)\\right)}\\right)}^{-1}\\left(\\begin{array}{c}{D}_{LOS-a}\\\\\\:{D}_{LOS-d}\\end{array}\\right)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), \u0026beta; represents the slope (replaced by the average slope calculated by the DEM), \u0026theta; denotes the satellite incidence angle, \u0026delta; signifies the landslide azimuth angle, and \u0026alpha; denotes the satellite flight direction. In accordance with this model, data regarding landslide deformation in both the vertical and lateral directions were extracted. We interpolated the deformation time series generated from the ascending and descending data to complete the time-domain alignment of the two datasets (Fig.\u0026nbsp;5), and then obtained the deformation time series in the vertical direction and along the slope.\u003c/p\u003e\n \u003cp\u003eTo investigate the effect of rainfall on landslide deformation, the rainfall model divided the data into two types of time windows (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e): rise window (TWR) and subsidence window (TWS). We calculated the absolute displacement vertically at twenty day intervals, still using cumulative rainfall data for the same period(the data were obtained through the implementation of the inverse distance weighting interpolation method.). We examined the controlling role of rainfall in landslide movement by calculating Pearson\u0026rsquo;s correlation coefficient (PCC).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTime windows and time scale.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime windows\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime Scale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime windows\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime Scale\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTWR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019.5.15-2019.7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTWS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020.7.15-2020.5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTWR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2020.5.12-2020.8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTWS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2020.8.26-2020.5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTWR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2021.5.15-2021.6.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTWS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2021.6.28\u0026ndash;222.3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTWR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022.3.17-2022.6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTWS4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2022.6.24-2023.4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e\u003cstrong\u003e4.1. InSAR results of the airport area.\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eFigure 6 displays the InSAR-derived displacement rates for both ascending and descending orbits during the monitoring period. Discrepancies in detected deformation patterns stem from inherent differences in satellite viewing geometries between orbit configurations. LOS displacement values are defined as follows: negative values represent surface movement away from the satellite, while positive values indicate movement toward the satellite.\u003c/p\u003e\n\u003cp\u003eAscending orbit observations reveal significant deformation contrast across the runway corridor. The western sector, characterized by dense airport infrastructure with extensive paved surfaces (including localized stabilized zones at the southern runway extremity), exhibits minimal displacement. Descending orbit data confirm this spatial heterogeneity, recording average LOS deformation rates of 3.58 mm/yr (toward satellite) and \u0026minus;\u0026thinsp;1.37 mm/yr (away from satellite).In contrast, the eastern sector\u0026mdash;comprising large-scale engineered fills and natural slopes\u0026mdash;demonstrates widespread deformation anomalies. Pronounced instability is observed northeast of the runway, spatially consistent with the studied fill landslide, where peak displacement rates reach 47.31 mm/yr (ascending) and 34.36 mm/yr (descending) in satellite-away directions. Ground validation confirms measurement reliability and persistent slope activity.Descending orbit analysis further delineates three geomechanically distinct zones: the northeastern landslide sector exhibiting high-magnitude fill displacements, the mid-eastern slope area displaying progressive translational movement, and the southern steep slopes manifesting differential settlement.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e\u003cstrong\u003e4.2. Two-dimensional displacement of landslide.\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eIntegrating ascending and descending orbit datasets enabled two-dimensional deformation characterization of the landslide body. Applying a 5-meter spatial coherence threshold for homologous point selection, the 2D displacement field was reconstructed through geometric decomposition (Fig.\u0026nbsp;7). Deformation sign conventions were defined as: positive slope-parallel values indicating downslope movement along the failure surface, and positive vertical values representing surface heave relative to the local topographic datum.\u003c/p\u003e\n\u003cp\u003eThe results demonstrate that the vertical deformation rate within the landslide area exhibits a range of -45.3 mm/year to 15.4 mm/year, while the deformation rate along the slope direction displays a range of -18.5 mm/year to 53.64 mm/year. The prevailing mode of movement at the trailing edge of the fill landslide is vertical subsidence, which represents the area with the highest rate of subsidence and is accompanied by a tendency to advance along the slope. The deformation in both dimensions in the central region of the fill body landslide was relatively stable with minor vertical settlement and forward sliding along the slope. In addition, there are localized areas where no deformation occurs in either direction. The deformation of the front of the fill landslide was relatively slight in the vertical direction; however, there was an extensive forward movement along the slope. In this area, the dominant deformation trend was slipwise, with deformation occurring in the form of a slope. The magnitude of deformation was observed to decrease in the region of the landslide that has been in existence for a longer period of time. The rate of deformation exhibits a notable increase in the central region of the area under consideration. In contrast, the front edge demonstrates a higher degree of stability.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e\u003cstrong\u003e4.3. Temporal patterns of landslide movement.\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAs illustrated in Fig.\u0026nbsp;7, the deformation rate was measured along the vertical landslide and in the direction of its propagation, encompassing both the fill body landslide area and the old landslide area. The measured results are indicative of the spatial distribution trend of landslide deformation rates. However, it should be noted that these values correspond to the average annual deformation rates over the study period and may differ from the actual state of landslide motion. Therefore, further research in the form of time-series deformation analyses is required.\u003c/p\u003e\n\u003cp\u003eFour representative points were selected for plotting the time series of the deformation, the results of which are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. In the vertical direction, the overall performance shows a decreasing trend, with a smaller degree of decrease at point P2, whereas point P1, which is located at the rear edge of the landslide, shows a drastic decreasing trend. Furthermore, all points demonstrated distinct seasonal variations that appeared to be significantly correlated with precipitation. The correlation between these variables is examined in Section \u003cspan class=\"InternalRef\"\u003e5.2\u003c/span\u003e. The deformation time series along the landslide direction exhibited no discernible seasonal trend, with points P1 and P3 demonstrating substantial forward slips, point P4 exhibiting minimal movement, and point P2 demonstrating stability with negligible sliding.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003e\u003cstrong\u003e5.1. Characteristics of the spatial distribution of landslide regional deformation and the controlling role of geological conditions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rate of deformation and its spatial distribution can, to some extent, reflect the state of landslide movement. At the trailing edge of the fill-body landslide, intense deformation occurred both vertically and along the landslide direction. The deformation rates in the vertical direction and along the slope were \u0026minus;\u0026thinsp;22.99 mm/year and 13.07 mm/year, respectively, with a maximum settlement rate of -45.3 mm/year in the vertical direction. This indicates that the trailing edge is the most threatened area, driving the development of landslides and reflecting the properties of the active slip zone in the fill area. In the central part of the fill body landslide zone, the vertical deformation is negligible (the average rate is -4.56 mm/year.) In some areas, the deformation is reduced to such an extent that it is absent. The slope-wise deformation was also reduced (the average rate was 8.59 mm/year.) with displacement in the opposite direction in some areas. By the leading edge of the fill body landslide, the rate of vertical and slope deformation rebounded relative to the central part of the fill area (The average rate is -8.59 mm/year and 27.23 mm/year, respectively), and the slope deformation in this part was the largest(53.64 mm/year), reflecting the advancing trend of the landslide. In the area of the old landslide, the overall deformation rate is minimal (The average rate is -7.73 mm/year and 7.33 mm/year, respectively)), indicative of the passive slip zone property of the aforementioned landslide.\u003c/p\u003e\n\u003cp\u003eGeological hazards are the result of a variety of geological factors, including topography, geological structure, ecological environment, meteorology and hydrology, geotechnical properties of landslides and landslide zones, earthquakes, and human activity (Chen et al.2019). It is hypothesized that the spatial distribution characteristics of deformation rates are influenced by topography and human engineering activities, as evidenced by the geological data of the landslide at the Panzhihua Airport.\u003c/p\u003e\n\u003cp\u003eGeologic conditions are an important factor in controlling landslide movements (Ering P and Badu G.L.S 2016). The construction process involves a substantial amount of cut-and-fill work, resulting in the creation of numerous steep slopes near the airport runway. The presence of a significant number of critical surfaces on these slopes, which are subject to the influence of gravity, poses a substantial safety hazard. The trailing edge of the landslide in this study was located within the area of the original high-fill slopes, with slopes ranging from 30\u0026deg; to 35\u0026deg;. The topography, characterized by a steep incline, diminished the resistance of the slide bed, thereby facilitating landslide movement and inducing substantial deformation at the trailing edge of the landslide. The topography of the remaining landslide region underwent moderation in its characteristics, accompanied by a decline in the rate of deformation. Furthermore, the fill body landslide is composed of a comparatively loose artificial fill, which exhibits diminished physical-mechanical strength compared to the original clay in the area of the former landslide. Consequently, the deformation rate of the fill-body landslide generally exceeded that of the old landslide. Continued deformation of landslides may lead to reactivation of previously dormant landslides; therefore, continuous deformation monitoring is required to ensure safe operation of airports.\u003c/p\u003e\n\u003cp\u003eAnthropogenic activities also had a considerable impact on landslide movement, especially in the context of this study. Our findings revealed that the central segment of the landslide exhibited a distinct movement tendency compared with the front and back segments. This discrepancy can be attributed to the impact of human activities, as the relevant authorities have implemented comprehensive support measures, including the installation of anti-slip piles, keys, and other structures within the affected area. This results in an enhancement of the local slip resistance and a substantial improvement in landslide stability. Consequently, in this area, the vertical and slope movements of the landslide were significantly mitigated, and certain regions exhibited an opposing movement trend from other areas. This phenomenon may be attributed to the presence of support works that impede the landslide body at the trailing edge and contribute to the accumulation of landslide materials. This observation lends further credence to the efficacy of supportive work.\u003c/p\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e\u003cstrong\u003e5.2. Rainfall and expansion of the soil.\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eRainfall is also a significant contributing factor in triggering landslides (Gui M.W and Wu Y.M 2014; Zhou et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Rainfall-induced landslides are the most pervasive globally, with seasonal precipitation and sudden meteorological events frequently precipitating a precipitous surge in pore water pressure and a concomitant diminution in the mechanical properties of rocks and soils (Yang et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), This is accompanied by a series of chemical processes that change the structure of the minerals that make up the rocks and soils, resulting in slopes. Because of the distinct dry and wet seasons in the region, there are frequent rainstorms in summer and little dry precipitation in winter, resulting in large variations in landslide water content and groundwater levels throughout the year. Infiltrating rainfall causes the mechanical properties of the rocks and soils that make up the sliding body to deteriorate, a significant reduction in shear strength, increased sliding body weight, and softening of the sliding surface, which significantly reduces the sliding force of the sliding bed, thereby inducing sliding. The landslide body composition is characterized by the presence of moderately expansive soils, which exhibit a tendency to expand in the presence of water and contract in the absence of water. The study area exhibits a clear distinction between dry and rainy seasons, which must be considered when examining the impact of precipitation on landslides.\u003c/p\u003e\n\u003cp\u003eThe revisit period of Sentinel-1 is only 12 days, which is very useful for analyzing the response mechanism between landslide movement and precipitation. To match the satellite monitoring period, we collected daily rainfall data for Panzhihua. A joint analysis of the rainfall data with the interpolated two-dimensional displacement time series (Figure. 8) revealed no significant correlation between displacement in the horizontal direction and daily rainfall. However, a significant correlation was observed between the displacement in the vertical direction and daily rainfall. Significant vertical uplift occurred in the landslide area during the rainy season, whereas intense subsidence occurred during the dry season. The results of the deformation time series demonstrated that this seasonal variation also occurs annually. Concurrently, the ongoing displacement along the slope precipitates a general decline in the vertical displacement of the landslide as the continuous slope displacement diminishes the degree of uplift and augments the degree of subsidence.\u003c/p\u003e\n\u003cp\u003eTo further ascertain the relationship between rainfall and landslide soil expansion, we employed Pearson correlation coefficient (PCC) analysis. Two types of time windows were established: rising and subsidence. The absolute displacement within each window was computed and the correlation coefficient between the two was calculated. The results are shown in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e and 10. The results show that most points have strong correlations between the shape variables and precipitation within the rising window, especially within the TWR1 and TWR2 windows, with some points having correlations of 0.9 or higher. Within the TWR3 and TWR4 windows, most points showed either no correlation or a positive correlation. Within the subsidence window, the correlation coefficients between the shape variables and precipitation at most points decreased significantly, and tended to be uncorrelated or negatively correlated. We conclude that rainfall has a controlling effect on landslide movement, especially within the rising window, which may originate from the expansion of the soil, whereas no significant control was found for the sinking window.\u003c/p\u003e\n\u003cp\u003eAlthough the surface uplift resulting from soil expansion caused an apparent increase in elevation, the overall trend of the landslide movement remained downward. However, the rate of this downward movement appears to exhibit a lag. This finding is consistent with those of numerous other scholars in the field(Sun et al.2015; Li et al.2023). A potential causal factor for this phenomenon may be the infiltration of rainfall, and there is a strong correlation between the rate of rainfall infiltration and rainfall duration and intensity, as well as the thickness and permeability of the landslide. It can be reasonably deduced that the rate of rainfall infiltration varied depending on the prevailing conditions. Consequently, the time required for rainwater to reach the sliding surface depends on these conditions. Furthermore, the diffusion of pore pressure through the sliding body following heavy precipitation requires a significant timeframe (Zhao et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). This may explain the discrepancy between our monitoring results and observed rainfall and the lack of an Significant correlation between displacement and rainfall within the subsidence window.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study presents a comprehensive InSAR analysis of deformation mechanisms governing a high-fill engineered landslide system at Panzhihua International Airport, Southwest China, spanning 5.5 years (January 2018-May 2023). Through the integrated processing of multi-track Sentinel-1 datasets (163 ascending and 134 descending acquisitions) and innovative 2D deformation modeling, the findings of this study are summarized below.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe airport's operational core (runways, terminal) exhibits millimeter-scale stability (The average LOS deformation rate in this region is 3.58 mm/yr for ascending orbits and \u0026minus;\u0026thinsp;1.37 mm/yr for descending orbits.) Validation of the effectiveness of engineering remediation. In contrast, peripheral fill slopes demonstrate spatially heterogeneous movements, with the northeast landslide complex showing peak deformation rates. The average deformation rate in the LOS direction for the ascending track at the trailing edge of the fill landslide immediately adjacent to the flight area decreases sharply to -37.07 mm/year, and for the descending track, to -12.75 mm/year. This dichotomy highlights the critical influence of material heterogeneity on the long-term slope performance.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e2D deformation vector analysis reveals a tripartite movement mechanism: Rear scarp: Vertical compaction-dominated subsidence (45.3 mm/yr max, 22.99 mm/yr mean) in thick unsaturated fills; Mid-slope: More stable transitional shear zones (The average rate of deformation is -4.56 mm/year in the vertical direction and 8.59 mm/year along the slope.) with coupled vertical/slope parallel displacements for reinforcement work.; Toe region: Strike-slip dominated creep along pre-existing discontinuities (53.64 mm/yr max, 27.23 mm/yr mean). Notably, the original bedrock landslide area maintains stability (The average rate of deformation is -7.73 mm/year in the vertical direction and 7.33 mm/year along the slope.), underscoring the rheology of the fill material as the primary driver of deformation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTime-series deformation analysis revealed distinct seasonal cyclicity in the vertical displacements, exhibiting coupling with rainfall patterns. The uplift-settlement movement occurred reciprocally with the changes in rainfall. The correlation coefficient (PCC value in the bulge area was greater than 0.9.) analysis was conducted to examine the relationship between the deformation and rainfall. The analysis revealed a discernible controlling effect of rainfall on the uplift cycle. A thorough evaluation revealed that slope, rainfall, and human engineering activities were pivotal in the regulation of landslide movement.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the National Natural Science Foundation of China [No.42474040,42101450], Yunnan Fundamental Research Projects [No. 202301AT070145], and the Project “Yunnan Revitalization Talent Support Program.” \u0026nbsp;We thank the European Space Agency (ESA) for providing the Sentinel-1/2 dataset under the framework of the Sino-EU Dragon Project [ID 95473].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYangwei Yu proposed the idea, and drafted the manuscript, Mengshi Yang carried on the experiments and conducted the data analysis. Menghua Li revised the intellectual content and manuscript structure. Cheng Huang performed the visualization and interpretation of results. Zhifang Zhao supervised the research, reviewed the manuscript, and approved the final version for publication. All authors agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the author(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDate availability statement (DAS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBao H, Liu L, Lan H, Peng J, Yan C, Tang M, Guo G, Zheng H (2024) Evolution of high-filling loess slope under long-term seasonal fluctuation of groundwater. CATENA 238:107898. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.catena.2024.107898\u003c/span\u003e\u003cspan address=\"10.1016/j.catena.2024.107898\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerardino P, Fornaro G, Lanari R, Sansosti E (2002) A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans Geosci Remote Sens 40:2375\u0026ndash;2383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/TGRS.2002.803792\u003c/span\u003e\u003cspan address=\"10.1109/TGRS.2002.803792\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarl\u0026agrave; T, Farina P, Intrieri E, Ketizmen H, Casagli N (2018) Integration of ground-based radar and satellite InSAR data for the analysis of an unexpected slope failure in an open-pit mine. Eng Geol 235:39\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enggeo.2018.01.021\u003c/span\u003e\u003cspan address=\"10.1016/j.enggeo.2018.01.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Zhao C, Li B, He K, Ren C, Liu X, Liu D (2021) Deformation monitoring and failure mode research of mining-induced Jianshanying landslide in karst mountain area, China with ALOS/PALSAR-2 images. Landslides 18:2739\u0026ndash;2750. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10346-021-016786\u003c/span\u003e\u003cspan address=\"10.1007/s10346-021-016786\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Chang K, Wang S, Huang J, Yu C, Tu J, Chu H, Liu C (2019) Controls of preferential orientation of earthquake- and rainfall‐triggered landslides in Taiwan\u0026rsquo;s orogenic mountain belt. Earth Surf Processes Landf 44:1661\u0026ndash;1674. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/esp.4601\u003c/span\u003e\u003cspan address=\"10.1002/esp.4601\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen-Waeber J, B\u0026uuml;rgmann R, Chaussard E, Giannico C, Ferretti A (2018) Spatiotemporal Patterns of Precipitation‐Modulated Landslide Deformation From Independent Component Analysis of InSAR Time Series. Geophys Res Lett 45:1878\u0026ndash;1887. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/2017GL075950\u003c/span\u003e\u003cspan address=\"10.1002/2017GL075950\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCook ME, Brook MS, Hamling IJ, Cave M, Tunnicliffe JF, Holley R, Alama DJ (2022) Engineering geomorphological and InSAR investigation of an urban landslide. Gisborne New Z Landslides 19:2423\u0026ndash;2437. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10346-022-01938-z\u003c/span\u003e\u003cspan address=\"10.1007/s10346-022-01938-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCun C, Bao F, Zhao Y, Xia Y, Zhang L (2011) ENGINEERING PROPERTIES OF EXPANSIVE SOIL FORMED DURING GLACIAL PERIOD AT PANZHIHUA AREA. Journal of Engineering Geology.1004\u0026ndash;9665/2011-19(2)-254\u0026ndash;208. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai K, Li Z, Tom\u0026aacute;s R, Liu G, Yu B, Wang X, Cheng H, Chen J, Stockamp J (2016) Monitoring activity at the Daguangbao mega-landslide (China) using Sentinel-1 TOPS time series interferometry. Remote Sens Environ 186:501\u0026ndash;513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2016.09.009\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2016.09.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong J, Liao M, Xu Q, Zhang L, Tang M, Gong J (2018a) Detection and displacement characterization of landslides using multi-temporal satellite SAR interferometry: A case study of Danba County in the Dadu River Basin. Eng Geol 240:95\u0026ndash;109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enggeo.2018.04.015\u003c/span\u003e\u003cspan address=\"10.1016/j.enggeo.2018.04.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong J, Zhang L, Tang M, Liao M, Xu Q, Gong J, Ao M (2018b) Mapping landslide surface displacements with time series SAR interferometry by combining persistent and distributed scatterers: A case study of Jiaju landslide in Danba, China. Remote Sens Environ 205:180\u0026ndash;198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2017.11.022\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2017.11.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuan H, Li Y, Jiang H, Li Q, Jiang W, Tian Y, Zhang J (2023) Retrospective monitoring of slope failure event of tailings dam using InSAR time-series observations. \u003cem\u003eNat Hazards\u003c/em\u003e 117, 2375\u0026ndash;2391 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-023-05946-7\u003c/span\u003e\u003cspan address=\"10.1007/s11069-023-05946-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEring P, Babu GLS (2016) Probabilistic back analysis of rainfall induced landslide- A case study of Malin landslide, India. Eng Geol 208:154\u0026ndash;164. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enggeo.2016.05.002\u003c/span\u003e\u003cspan address=\"10.1016/j.enggeo.2016.05.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan X, Xu Q, Scaringi G, Dai L, Li W, Dong X, Zhu X, Pei X, Dai K, Havenith H-B (2017) Failure mechanism and kinematics of the deadly June 24th 2017 Xinmo landslide, Maoxian, Sichuan, China. Landslides 14:2129\u0026ndash;2146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10346-017-0907-7\u003c/span\u003e\u003cspan address=\"10.1007/s10346-017-0907-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng X, Yao Y, Zhang J, Zhang X (2024), Stability analysis of high-fill slopes at mountain airports. J Beijing Univ Aeronaut Astronaut (in Chinese) (001), 046\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong Z, Li T, Gong X, Li Y (2007) Study on Landslide Remediation Measures in the North East Corner of Panzhihua Airport. J Eng Geol 15(2):7 (in Chinese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGui M-W, Wu Y-M (2014) Failure of soil under water infiltration condition. Eng Geol 181:124\u0026ndash;141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enggeo.2014.07.005\u003c/span\u003e\u003cspan address=\"10.1016/j.enggeo.2014.07.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe L, Qian J, Zhao Y, Li Z (2023) Study on stability analysis method of high fill slope of Wushan Shennv Peak Airport. Journal Of Hefei University Of Technology(Natural Science)(Chinese). 1003\u0026ndash;5060(2023)05-0646-07\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou T, Xu G, Shen Y, Wu Z, Zhang N, Wang R (2013) Formation mechanism and stability analysis of the Houba expansive soil landslide. Eng Geol 161:34\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enggeo.2013.04.010\u003c/span\u003e\u003cspan address=\"10.1016/j.enggeo.2013.04.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLI J, Zhang J, Yuan Y, Chen P, Liu T, Pu W (2019) Study on multi-stage sliding mechanism of high-fill slope: 12~#Landslides, for example of Panzhihua Airport. National Engineering Geology Annual Conference\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Fan C, Zhao K, Zhang Z, Duan P (2024) Landslide displacement prediction using time series InSAR with combined LSTM and TCN: application to the Xiao Andong landslide, Yunnan Province, China. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-024-06937-y\u003c/span\u003e\u003cspan address=\"10.1007/s11069-024-06937-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Nat Hazards\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi K, Zhao X, Xiao D (2021) Acid rain: an unsuspected factor predisposing Panzhihua airport landslide, China. Environ Sci Pollut Res 28:36753\u0026ndash;36764. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-021-13308-8\u003c/span\u003e\u003cspan address=\"10.1007/s11356-021-13308-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M, Zhang L, Shi X, Liao M, Yang M (2019a) Monitoring active motion of the Guobu landslide near the Laxiwa Hydropower Station in China by time-series point-like targets offset tracking. Remote Sens Environ 221:80\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2018.11.006\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2018.11.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M, Zhang L, Yang M, Liao M (2023) Complex surface displacements of the Nanyu landslide in Zhouqu, China revealed by multi-platform InSAR observations. Eng Geol 317:107069. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enggeo.2023.107069\u003c/span\u003e\u003cspan address=\"10.1016/j.enggeo.2023.107069\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, Liu J, Ren Y, Xue D, Chen M (2012) Sliding Mechanism Of High-fill Slope With Pre-reinforced Piles At Panzhihua Airport. J Eng Geol 20(5):0723\u0026ndash;0709 (in Chinese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, Tian X, Han W, Ren Y, He Y, Wei Y (2013) Centrifugal model tests on sliding failure of a pile-stabilized high fill slope. Rock Soil Mech. 24 11 (in Chinese) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.16285/j.rsm.2013.11.003\u003c/span\u003e\u003cspan address=\"10.16285/j.rsm.2013.11.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Jiang W, Zhang J( (2022) A time series processing chain for geological disasters based on a GPU-assisted sentinel-1 InSAR processor. Nat Hazards 111:803\u0026ndash;815. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-021-05079-9\u003c/span\u003e\u003cspan address=\"10.1007/s11069-021-05079-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z, Sun L, Liu L, Guan C (2022) Research on safety evaluation method of high-fill slope considering heterogeneity and anisotropy. Bull Eng Geol Environ 81:275. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10064-022-02766-0\u003c/span\u003e\u003cspan address=\"10.1007/s10064-022-02766-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiao H, Xv C, Yang Y (2021) Optimal design of the high geogrid-reinforced slope at the airfield of an airport. Hydrogeol Eng Geol 1000\u0026ndash;3665 (in Chinese). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.16030/j.cnki.issn.1000-3665.202012015\u003c/span\u003e\u003cspan address=\"10.16030/j.cnki.issn.1000-3665.202012015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Zhao C, Zhang Q, Yin Y, Lu Z, Samsonov S, Yang C, Wang M, Tom\u0026aacute;s R (2021) Three-dimensional and long-term landslide displacement estimation by fusing C- and L-band SAR observations: A case study in Gongjue County, Tibet, China. Remote Sens Environ 267:112745. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2021.112745\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2021.112745\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMei Y, Zhou D, Hu C, Wang X, Zhang Y, Xiao N, Shi W (2022) Study on Deformation Characteristics of Loess Ultrahigh-Fill Slope Based on Large-Scale Undisturbed Soil Centrifugal Model Tests. Front Earth Sci 10:848542. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/feart.2022.848542\u003c/span\u003e\u003cspan address=\"10.3389/feart.2022.848542\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuan X, Huang S, Wang J, Wei J (2013) Analysis of the causes of landslide at Panzhihua Airport and treatment countermeasures. Sichuan Archit 5:3 (in Chinese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamsonov S, Dille A, Dewitte O, Kervyn F, d\u0026rsquo;Oreye N (2020) Satellite interferometry for mapping surface deformation time series in one, two and three dimensions: A new method illustrated on a slow-moving landslide. Eng Geol 266:105471. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enggeo.2019.105471\u003c/span\u003e\u003cspan address=\"10.1016/j.enggeo.2019.105471\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi X, Zhang L, Balz T, Liao M (2015) Landslide deformation monitoring using point-like target offset tracking with multi-mode high-resolution TerraSAR-X data. ISPRS J Photogrammetry Remote Sens 105:128\u0026ndash;140. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.isprsjprs.2015.03.017\u003c/span\u003e\u003cspan address=\"10.1016/j.isprsjprs.2015.03.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi X, Zhang L, Tang M, Li M, Liao M (2017) Investigating a reservoir bank slope displacement history with multi-frequency satellite SAR data. Landslides 14:1961\u0026ndash;1973. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10346-017-0846-3\u003c/span\u003e\u003cspan address=\"10.1007/s10346-017-0846-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Q, Zhang L, Ding XL, Hu J, Li ZW, Zhu JJ (2015) Slope deformation prior to Zhouqu, China landslide from InSAR time series analysis. Remote Sens Environ 156:45\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2014.09.029\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2014.09.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Z (2015) The Factors of Airport High Embankment Slope Stability Analysis. Chengdu University of Technology\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Xu Y, Ma Y, Qiao S, Feng K (2018a) Study on the deformation and failure modes of filling slope in loess filling engineering: a case study at a loess mountain airport. Landslides 15:2423\u0026ndash;2435. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10346-018-1046-5\u003c/span\u003e\u003cspan address=\"10.1007/s10346-018-1046-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen M, Yang M, Zhao X, Zhao Z (2024) Postconstruction Deformation Characteristics of High-Fill Foundations of Kunming Changshui International Airport Using Time-Series InSAR Technology. IEEE J Sel Top Appl Earth Observations Remote Sens 17:206\u0026ndash;218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/JSTARS.2023.3328321\u003c/span\u003e\u003cspan address=\"10.1109/JSTARS.2023.3328321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu H, Pai L, Lai. T, Zhang J, Zhang Y, Li Y (2019) Study on Optimization of Cooperative Performance of Pile-Anchor-Reinforced Soil Composite Structure of High-fill Slope in Mountain Airport. Chin J Rock Mechan Eng 7:14 (in Chinese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan Y, Guo C, Li C, Yuan H, Qiu Z (2023) The Creep-Sliding Deformation Mechanism of the Jiaju Ancient Landslide in the Upstream of Dadu River, Tibetan Plateau, China. Remote Sens 15:592. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs15030592\u003c/span\u003e\u003cspan address=\"10.3390/rs15030592\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang H, Liu J, Peng J, Wang J, Zhao B, Zhang B (2020) A method for GB-InSAR temporal analysis considering the atmospheric correlation in time series. \u003cem\u003eNat Hazards\u003c/em\u003e 104, 1465\u0026ndash;1480 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11069-020-04228-w\u003c/span\u003e\u003cspan address=\"10.1007/s11069-020-04228-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang H, Song K, Chen L, Qu L (2023) Hysteresis effect and seasonal step-like creep deformation of the Jiuxianping landslide in the Three Gorges Reservoir region. Eng Geol 317:107089. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enggeo.2023.107089\u003c/span\u003e\u003cspan address=\"10.1016/j.enggeo.2023.107089\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang X, Zhu Y, Zhou Y, Yang X, Shi Z (2016) Time-space monitoring and stability analysis of high fill slope slip process at a airport in mountain region. Chin J Rock Mechan Eng :1000\u0026ndash;6915\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Z, Cheng X (2024) High fill slope collapse: Stability evaluation based on finite element limit analyses. Transp Geotechnics 44:101156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.trgeo.2023.101156\u003c/span\u003e\u003cspan address=\"10.1016/j.trgeo.2023.101156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin Y, Shi X, Zhong J, Gao H, Dai K (2023) Monitoring 2D Deformation of High Slope in Panzhihua Airport by Joint Lifting Rail Sentinel-1A. GEOMATICS ༆ SPATIAL INFORMATION TECHNOLOGY;46, No,12. (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao C, Lu Z, Zhang Q, De La Fuente J (2012) Large-area landslide detection and monitoring with ALOS/PALSAR imagery data over Northern California and Southern Oregon, USA. Remote Sens Environ 124:348\u0026ndash;359. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2012.05.025\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2012.05.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao G, Yang Y, Zhang H, Zhang G (2019) A case study integrating field measurements and numerical analysis of high-fill slope stabilized with cast-in-place piles in Yunnan. China Eng Geol 253:160\u0026ndash;170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enggeo.2019.03.005\u003c/span\u003e\u003cspan address=\"10.1016/j.enggeo.2019.03.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Zhang R, Xing X, Ling S, Zheng G (2024) Deformation Instability Characteristics of Expansive Soil Slope Based on SBAS-InSAR Technique. J Highway Transp Res Dev. (08),22\u0026ndash;30\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou C, Huang W, Ai D, Xu H, Yuan J, Kou L, Luo X (2022) Catastrophic landslide triggered by extreme rainfall in Chongqing, China: July 13, 2020, Niuerwan landslide. Landslides 19:2397\u0026ndash;2407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10346-022-01911-w\u003c/span\u003e\u003cspan address=\"10.1007/s10346-022-01911-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu W, Dou H, Yin N, Cheng Y, Zhang S, Zhang Q (2022) Deformation characteristics analysis of the expansive soil slope by integrating of InSAR and SSA techniques: a case study of the South-to-North Water Diversion Project. Acta Geod et Cattographic Sinica 51(10):2083\u0026ndash;2092. 10.119947/j.AGCS.22022.20220357\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Y, Qiu H, Yang D, Liu Z, Ma S, Pei Y, He J, Du C, Sun H (2021) Pre- and post-failure spatiotemporal evolution of loess landslides: a case study of the Jiangou landslide in Ledu. China Landslides 18:3475\u0026ndash;3484. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10346-021-01714-5\u003c/span\u003e\u003cspan address=\"10.1007/s10346-021-01714-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"TS-InSAR, high-fill landslide, Spatiotemporal evolution, airport, Creep mechanism","lastPublishedDoi":"10.21203/rs.3.rs-6628868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6628868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh-fill engineered landslides challenge infrastructure safety in mountainous regions, particularly where geological settings interact with human activity. This study investigated a landslide at Panzhihua Airport, Southwest China, using multi-track Sentinel-1 data with a Time-Series InSAR framework. This methodology innovatively combines multi-orbit LOS (Line of Sight) deformation retrieval through InSAR processing, 2D deformation vector decomposition (vertical\u0026thinsp;+\u0026thinsp;slope-parallel directions), and spatiotemporal correlation analysis with geological structure and rainfall patterns. Results show: (i) In the airport area, the average LOS deformation rates for the runway and surrounding buildings are 3.58 mm/year and \u0026minus;\u0026thinsp;1.37 mm/year, indicating stability, while the landslide area to the northeast decreases to -37.07 mm/year and \u0026minus;\u0026thinsp;12.75 mm/year, suggesting active sliding. (ii) Deformation in the landslide area was spatially heterogeneous, with vertical settlement (max. 45.3 mm/year) at the rear, and creeping (max. 53.61 mm/year) at the front of the fill body. (iii) The displacement time series revealed a clear correlation between vertical deformation and rainfall. During the first two uplift cycles, the expansion volume showed a strong correlation with rainfall, with a Pearson correlation coefficient (PCC) value exceeding 0.9 in some regions. These findings provide insights for managing secondary deformation risks on large-scale fill slopes for early warning system development in similar geo-engineered environments.\u003c/p\u003e","manuscriptTitle":"InSAR-Based Deformation Monitoring of High-Fill Engineered Landslides: A Case Study at Panzhihua Airport, Southwest China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 02:04:54","doi":"10.21203/rs.3.rs-6628868/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-06-23T11:49:10+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-16T11:19:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Natural Hazards","date":"2025-06-06T16:28:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-10T02:59:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2025-05-09T08:55:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e49bf399-dc4d-40bf-8683-956a4b81ccf7","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:03:11+00:00","versionOfRecord":{"articleIdentity":"rs-6628868","link":"https://doi.org/10.1007/s11069-026-07991-4","journal":{"identity":"natural-hazards","isVorOnly":false,"title":"Natural Hazards"},"publishedOn":"2026-02-25 15:59:25","publishedOnDateReadable":"February 25th, 2026"},"versionCreatedAt":"2025-06-25 02:04:54","video":"","vorDoi":"10.1007/s11069-026-07991-4","vorDoiUrl":"https://doi.org/10.1007/s11069-026-07991-4","workflowStages":[]},"version":"v1","identity":"rs-6628868","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6628868","identity":"rs-6628868","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}