Hydrological Influences on Landslide Dynamics in the Three Gorges Reservoir Area: An SBAS-InSAR Study in Yunyang County, Chongqing

Hydrological Influences on Landslide Dynamics in the Three Gorges Reservoir Area: An SBAS-InSAR Study in Yunyang County, Chongqing | 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 Hydrological Influences on Landslide Dynamics in the Three Gorges Reservoir Area: An SBAS-InSAR Study in Yunyang County, Chongqing jinhu Cui, Yuxiang Tao, Pinglang Kou, Zhao Jin, Yijian Huang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4247951/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Aug, 2024 Read the published version in Environmental Earth Sciences → Version 1 posted 7 You are reading this latest preprint version Abstract Landslide hazards pose a significant threat to lives and infrastructure, especially in mountainous regions like the Three Gorges Reservoir area. While the mechanisms driving landslide initiation and progression in reservoir environments are not fully understood. This study aimed to leverage the capabilities of Sentinel-1 satellite imagery and the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to detect and monitor potential landslide deformations in Yunyang County, Chongqing, China. We utilized Sentinel-1 data acquired between January 1, 2020, and December 28, 2022, to generate deformation velocity maps. Twelve potential landslides were identified, primarily concentrated near residential areas and along the Yangtze River. Precipitation emerged as the primary driver of surface deformation and landslide initiation, with potential landslides in residential vicinities and along the river exhibiting significantly higher deformation rates during the wet season compared to the dry season. These sites are susceptible to slope failures and geological disasters upon reaching critical antecedent rainfall thresholds, highlighting the necessity for continuous monitoring. Other potential landslides maintained consistent deformation rates across seasons but experienced brief accelerations following heavy precipitation events. Notably, potential landslides adjacent to the Yangtze River experienced accelerated deformation during periods of significant river water level reductions, suggesting that the river's cyclical water level fluctuations influence slope stability. The study demonstrated the effectiveness of SBAS-InSAR in detecting millimetric deformations in incipient landslides, a crucial step in averting landslide disasters and ensuring public safety. Potential Lanslides Sentinel-1A SBAS-InSAR Precipitation Yangtze River Water Level Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1 Introduction Landslides, a critical geohazard, annually affect millions and engender significant socioeconomic impacts (Petley 2012 ). Slope failures arise from diverse triggers: river erosion (Liu et al. 2021 ), groundwater flux (Wang et al. 2020 ), rainfall (Finlay et al. 1997 ), seismicity (Li et al. 2016 ), and anthropogenic excavation (Pan et al. 2022 ). These elements precipitate abrupt, calamitous earth movements, imperiling lives and assets in upland locales, notably where human expansion and environmental flux intensify (Froude and Petley 2018 ). Therefore, the prognostication and cartography of potential landslide zones are vital for disaster prophylaxis and attenuation. Conventional landslide deformation detection predominantly employs total stations and Global Positioning System (GPS) for monitoring purposes (Bentley et al. 2023 ; Ma et al. 2022 ). Total stations, however, demand extensive time and labor, rendering them unsuitable for complex terrains (Casagli et al. 2023 ). GPS, while more versatile and user-friendly than total stations and leveling devices, leverages high-precision field surveying to precisely delineate landslide geometries, facilitating the observation and analysis of landslide dynamics (Guerriero et al. 2019 ). Nonetheless, GPS encounters obstacles such as elevated costs, underutilized reference stations, and dependency on singular reference points (Wang et al. 2022 ), which hinder its widespread adoption for proactive landslide surveillance. Synthetic Aperture Radar Interferometry (InSAR) represents a transformative approach for acquiring large-scale deformation data from satellite observations, transcending the constraints of conventional landslide surveillance methodologies that are labor-intensive and restricted in scope (Xu et al. 2022 ). Illustratively, InSAR has been adeptly utilized to track the deformation dynamics of a landslide proximal to Yizi Village, China, in the context of the Xiluodu Reservoir's impoundment in 2019 (Li et al. 2019 ). Notwithstanding, InSAR's efficacy is moderated by temporal, spatial, and atmospheric considerations, and its application is confined to regions characterized by sparse vegetation (Devaraj et al. 2022 ). To surmount the limitations of traditional landslide monitoring, researchers have innovated sophisticated InSAR methodologies, notably the Permanent Scatterers Technique (PS-InSAR) and the Small Baseline Subset (SBAS) approach. PS-InSAR, which targets stable reference points, has refined the landslide catalog in Italy's Tuscany region, pinpointing 672 active landslides (Rosi et al. 2018 ). Despite its utility, PS-InSAR is hampered by the irregular distribution of scatterers and constrained processing capabilities (Crosetto et al. 2016 ). Conversely, SBAS-InSAR ameliorates spatial decorrelation and diminishes topographic and atmospheric distortions (Berardino et al. 2002 ). Its applications span earthquake detection (Huang et al. 2016 ), ground subsidence analysis (Xu et al. 2022 ), and landslide surveillance (Xiao et al. 2022 ). Notably, SBAS-InSAR has yielded encouraging outcomes in identifying incipient landslides within vegetated terrains, as evidenced in Badong County's ecological buffer zone within China's Three Gorges Reservoir (Dong et al. 2023 ) and along the national highway in Wenchuan County (Zhang et al. 2021 ), underscoring its efficacy in challenging contexts. Chongqing, located in southwestern China, epitomizes a region prone to frequent and devastating landslide events (Kwong et al. 2004 ). A notable incident occurred on July 25, 2020, when intense rainfall induced a landslide in Liu Jing Village, Tudixiang, Wulong District, obstructing the Yan Cang River and forming a barrier lake, imperiling 152 households and 520 residents (Chen et al. 2021 ). Furthermore, on January 17, 2001, a landslide at Wufeng Mountain in Yunyang County inflicted substantial damage on roughly 30,000 m² of the Yangtze River's protective forest and demolished 16 homes, half of which were completely razed (Su et al. 2002 ). Presently, landslide deformation detection in this area predominantly relies on manual field inspections, a method that significantly hampers the efficiency and precision of hazard identification. This study harnessed Sentinel-1 satellite imagery from January 1, 2020, to December 28, 2022, applying the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to surveil deformations across Yunyang County, Chongqing. The study aimed to: (1) delineate areas at risk of landslides within Yunyang County, (2) discern deformation patterns of emblematic landslides and prognosticate their potential reoccurrence, and (3) explore the interplay between landslide evolution and variables such as precipitation and Yangtze River water levels. 2 Study area and data 2.1 Study area Yunyang County, nestled in the Three Gorges Reservoir's heartland within Chongqing Municipality, spans roughly 3649 km² and rises to an average elevation of 731 m. Characterized by precipitous slopes and distinct ridge-valley topography (Guo et al. 2019 ), the county's intricate geology comprises prevalent purple soil, shale, Jurassic sandstone, and mudstone (Zhang et al. 2022 ), all prone to hydro-gravitational erosion, precipitating landslides. The county's subtropical monsoon climate yields copious rainfall year-round, with July and August marking peak precipitation periods, aligning with heightened landslide and debris flow incidents. Annual rainfall for 2020, 2021, and 2022 was recorded at 1584.67 mm, 1534.88 mm, and 1070.16 mm, respectively, showcasing a seasonal disparity (Fig. 2 ). The mean summer rainfall from 2020 to 2022 constituted 46.73% of the yearly total at 652.73 mm, while winter's contribution was a mere 9.07% at 126.79 mm. Spring and autumn presented comparable figures, with 346.59 mm and 276.41 mm, respectively. The county's robust fluvial network, anchored by the Yangtze River and supplemented by tributaries like Modaoxi, Changtan, Pengxi, and Tangxi Rivers, alongside myriad streams, forms an intricate runoff matrix (Fig. 2 ). Notably, the Yangtze's water level undergoes marked cyclical shifts (He et al. 2020 ), potentially subjecting landslide masses to recurrent stress cycles, thus instigating slope failures. 2.2 Data The study area, veiled by dense vegetation, presents formidable challenges in the precise detection and assessment of landslides over temporal scales. To surmount this, we leveraged 80 ascending orbit images from the C-band Sentinel-1A satellite, spanning January 1, 2020, to December 28, 2022 ( https://scihub.copernicus.eu/ ). The SAR images were strategically cropped to align with the study area's dimensions, enhancing computational efficiency. Key SAR data parameters are encapsulated in Table 1 . Terrain-related parameters were derived using the 30-meter resolution digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM) ( http://www.gscloud.cn/search ), which aided in negating topographic influences during InSAR processing and in discerning overlapping and shadowed zones. Complementing this, rainfall metrics and Yangtze River water level data were procured for the corresponding period in Yunyang County, Chongqing, facilitating an analysis of the interplay between precipitation and landslide kinetics ( https://www . xihe-energy. com/). Table 1 Basic information of Sentinel-1A images Satellite Sentinel-1A Orbital direction Ascending Temporal coverage 2020. 1. 1-2022. 12. 28 Type SLC Image mode IW Band C-band Wavelength 5. 6cm Resolution 5×20m Average angle of incidence 35. 51° Polarization VV SLC stands for single-view complex, IW stands for strip scan mode, and VV stands for polarization mode as vertical polarization. 3 Methodology 3.1 SBAS-InSAR method The Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) is a time-series analytical modality apt for distributed targets. It fundamentally operates on the pairwise interferometric processing of imagery with minimal temporal and spatial baselines to fabricate interferograms. These are then subjected to multi-look processing to attenuate phase noise. Addressing the ill-posed equations due to sparse observations, singular value decomposition is employed to resolve the deformation phase equations at each point, thereby deducing elevation discrepancies and deformation velocities. Atmospheric phase and nonlinear deformation are subsequently inferred from the residual phase, culminating in a temporal deformation sequence for the specified duration (Li et al. 2022 ). This technique organizes N + 1 Synthetic Aperture Radar (SAR) Single-Look Complex (SLC) images of a region in sequential order (t0, t1,..., tv). The registered images are partitioned into subsets based on their temporal and spatial baselines, aiming to minimize baseline distances within subsets and maximize those between them. Differential interferometric processing of images across subsets ensues, yielding M interferograms, where M adheres to the stipulated condition (Chen et al. 2020 ): $$\text{ }\frac{\text{N+1}}{\text{2}}\text{ ≤ M ≤ }\frac{\text{N}\left(\text{N+1}\right)}{\text{2}}\text{ }\text{ }\text{ }\text{(1)}$$ And the composition of the interference phase of an pixel x in the jth interference pair \({\text{φ}}_{\text{int, x, j}}\) is $$\begin{array}{r}{\text{φ}}_{\text{int , x, j}}\text{=}{\text{φ}}_{\text{topo , x, j}}\text{+}{\text{φ}}_{\text{def, x, j}}\text{+}\\ {\text{φ}}_{\text{flat, x, j}}\text{+}{\text{φ}}_{\text{atm, x, j}}\text{+}{\text{φ}}_{\text{noise , x, j}}\text{+}{\text{φ}}_{\text{orb , x, j}}\end{array} \text{(2)}$$ Of which: $$\begin{array}{c} {\text{φ}}_{\text{topo , x, j}}\text{=}-\frac{\text{4π}{\text{B}}_{\perp }\text{h}}{\text{λRsin}\text{ }\text{θ}} \text{(3)} \\ {\text{φ}}_{\text{flat , x, j}}\text{=}-\frac{\text{4π}{\text{B}}_{\perp }}{\text{λ}} \text{(4)}\end{array}$$ Equation (2) represents the interferometric phase composition of image j (generated by two SAR images at times t A and t B , given that t A < t B ) within the pixel element at azimuth x and j distance from the satellite x, j. \({\text{φ}}_{\text{topo, x, j}}\) is the terrain phase. \({\text{φ}}_{\text{def, x, j}}\) is the deformation phase. \({\text{φ}}_{\text{flat, x, j}}\) is the flat earth phase. \({\text{φ}}_{\text{atm, x, j}}\) is the atmospheric delay phase. \({\text{φ}}_{\text{noise , x, j}}\) is the system thermal noise; \({\text{φ}}_{\text{orb, x, j}}\) is the phase due to orbital error. \({\text{B}}_{\perp }\) is the vertical baseline length; h is the elevation error; \(\text{λ}\) is the radar wavelength. \(\text{R}\) is the line-of-sight slope distance. \(\text{θ}\) is the angle of incidence. The differential interferometric phase model \(\text{ }{\text{φ}}_{\text{dif, x, j}}\) of pixel x is obtained after removing the topographic phase and flat earth effect by differential processing with DEM: $${\text{φ}}_{\text{dif, x, j}}={\text{φ}}_{\text{def , x, j}}+{\text{φ}}_{\text{atm, x, j}}+{\text{φ}}_{\text{noise , x, j}}+{\text{φ}}_{\text{ε, x, j}} \text{(5)}$$ Of which, $${\text{φ}}_{\text{ε, x, j}}=-\frac{\text{4π}{\text{B}}_{\perp }\text{ε}}{\text{λRsin}\text{ }\text{θ}} \text{(6)}$$ In the Eq. (6): \({\text{φ}}_{\text{dif, x, j}}\) is the differential interference phase. \({\text{φ}}_{\text{ε, x, j}}\) is the residual topographic phase due to the DEM error. ε is the DEM error. The deformation phase \({\text{φ}}_{\text{def, x, j}}\) consists of nonlinear deformation phase \({\text{φ}}_{\text{nonlinear , x, j}}\) and linear deformation phase \({\text{φ}}_{\text{linear , x, j}}\) . $${\text{φ}}_{\text{linear , x, j}}=-\frac{\text{4π}}{\text{λ}}\text{vT}\text{ }\text{(7)}\text{ }$$ $${ \phi }_{\text{def }, x, j}=-\frac{\text{4π}}{\text{λ}}\text{vT}+{\text{φ}}_{\text{nonlinear , x, j}} \text{(8) }$$ In the Eq. (7), v represents the average strain rate within the monitoring time span, and T represents the time span. The differential interference phase model \({\text{φ}}_{\text{def , x, j}}\) can be rewritten as: $$\begin{array}{c}\text{ }\\ {\text{φ}}_{\text{dif, x, j}}=-\frac{\text{4π}}{\text{λ}}\text{vT}-\frac{\text{4π}{\text{B}}_{\perp }\text{ε}}{\text{λRsin}\text{ }\text{θ}}+{\text{φ}}_{\text{res, x, j}} \text{(9)} \end{array}$$ In the Eq. (9), \({\text{φ}}_{\text{res , x, j}}\) represents the residual differential phase, which consists of nonlinear deformation, system thermal noise, and atmospheric delay phase: \({\text{φ}}_{\text{res , x, j}}={\text{φ}}_{\text{nonlinear , x, j}}+{\text{φ}}_{\text{noise , x, j}}+{\text{φ}}_{\text{atm, x, j}}\) . \({\text{φ}}_{\text{dif, x, j}}\) can be obtained based on the phase information of manually selected high-coherence control points. The strain rate v is determined using the least squares adjustment method. \({\text{φ}}_{\text{res, x, j}}\) is obtained by solving the single-point strain phase through singular value decomposition. Finally, the deformation time series within the observation time period is obtained. 3.2 InSAR process Figure 3 illustrates the flowchart of the data processing conducted in this study. The main steps are as follows: Step1 To proficiently process the voluminous Synthetic Aperture Radar (SAR) data amassed, this research utilized the semi-automated SBAS module within SARscape software. Preprocessing of SAR imagery is a prerequisite for interferometry; thus, the original SAR images were initially cropped according to the geographic coordinate system, ensuring the Digital Elevation Model (DEM) coverage supersedes the study area, thereby streamlining data processing. Before differential interferometry, image data underwent pairing and linkage, forming a connectivity graph (Fig. 4 ). Adhering to the Sentinel-1A satellite sensor specifications and empirical insights, we established a spatial baseline threshold of 2% and a temporal baseline of 90 days to preclude extensive temporal disparities between images. This protocol yielded 277 pairs of high-caliber interferometric data points. Step2 : In the post-processing phase of Synthetic Aperture Radar (SAR) imagery, interferometric analysis is paramount. Utilizing ENVI SARscape software, this process encompasses interferogram synthesis, orbital adjustment, re-referencing, filtration, and phase unwrapping. To curtail noise in interferometry and phase unwrapping, we adopted a multi-look ratio of 3:1 in interferogram production. The Goldstein filtering algorithm was then employed to refine the SAR images. Given the extensive vegetative cover in Yunyang County, the Minimum Cost Flow (MCF) algorithm was selected for phase unwrapping due to its stability in expansive, low-coherence zones. A threshold of 0.2 was instituted for phase unwrapping to efficaciously dampen phase noise. Step3 In our methodology, Ground Control Points (GCPs) were pivotal in rectifying residual terrain phase and satellite orbit discrepancies. The GCPs were judiciously situated in locales exhibiting prime unwrapped phase quality, strategically distanced from any deformation zones. For this analysis, GCPs were situated within level residential sectors, ensuring the integrity of the data correction process. Step4 : In the final analytical phase, we conducted dual inversions to ascertain and excise the residual constant phase and phase gradient post-phase unwrapping. The initial inversion utilized a linear model to gauge the deformation velocity and residual topography. The subsequent inversion, predicated on the initial deformation rate calculations, incorporated filtering (spatial filter window: 1600 m; temporal filter window: 365 days) to expunge atmospheric phase interference and diminish atmospheric delay impacts. Post atmospheric delay phase removal, we derived the aggregate deformation for the triennial span (2020–2022) across Yunyang County. The deformation data were then geocoded, converting SAR-derived deformation metrics into geographic coordinate-based deformation figures. 4 Results 4.1 Landslides Identification Result Utilizing Sentinel-1's ascending orbit data, an annual displacement rate map was constructed in the line-of-sight (LOS) direction, identifying 12 potential landslides with significant displacements in Yunyang County (Fig. 5 a). The color-coded displacements indicate ground movements relative to the satellite: red (negative values) signals the ground receding, while blue (positive values) represents an approach. The landslide boundaries were delineated by integrating InSAR deformation rate maps, Google Earth imagery, and landslide morphology (Fig. 6 ). Empirical evidence, such as substantial surface displacements, fractures, or gullies, was prominent in most slopes, including locations H2 (108°51'51.30"E, 30°48'38.35"N), H3 (109°00'34.52"E, 30°43'48.77"N), H4 (109°02'17.19"E, 30°47'14.41"N), with the maximal cumulative deformation reaching − 134 mm (Fig. 5 b). Government and production units have recognized some of the detected landslides as high-risk areas, such as H5 (109°00'03.08"E, 30°42'57.55"N) and H6 (108°48'36.97"E, 30°56'08.84"N). Moreover, several landslides situated along the Yangtze River, including H9 (109°02'16.79"E, 30°56'30.41"N) and H10 (109°06'42.91"E, 30°56'46.06"N), pose potential threats to the river channel, should they collapse. 4.2 Typical Landslides Analysis 4.2.1 Deformation zone H1 The deformation zone H1, situated roughly 1000 m southeast of Longjiao Town in Yunyang County, spans an estimated length of 497 m, a width of 154 m, and encompasses an area of about 61,785 m 2 (Fig. 7 a). For the temporal displacement analysis, five pivotal points—P1, P2, P3, P4, and P5—were selected on the potential landslide mass H1. P1, perched atop the slope, and P4, adjacent to the landslide's left boundary, exhibited relatively minor deformations, with cumulative displacements of -27.20 mm and − 30.50 mm, respectively. In contrast, P2 in the landslide's core, P3 at the leading edge, and P5 near the right margin, displayed pronounced acceleration patterns. The cumulative deformations for P2, P3, and P5 were significantly greater, registering at -99.30 mm, -89.30 mm, and − 98.40 mm, respectively. The analysis revealed that the H1 landslide zone underwent substantial deformation, with the rear of the mass exhibiting higher deformation rates than the front, indicative of a convergent sliding motion. The deformation phases of the landslide throughout the monitoring period can be categorized into four distinct stages: a phase of uniform deformation from January 1, 2020, to July 15, 2020; an initial acceleration phase from July 16, 2020, to September 1, 2021; a secondary acceleration phase from December 1, 2021, to June 1, 2022; culminating in a phase of abrupt acceleration commencing on June 2, 2022. Examining P2, the uniform deformation phase exhibited an average deformation velocity of 4.00 mm month − 1 , resulting in a cumulative displacement of about 30.00 mm. This period coincided with a cumulative rainfall of 967.50 mm, averaging 4.90 mm daily. The initial acceleration phase displayed an increase in average deformation velocity to 4.80 mm month − 1 , and a cumulative displacement of 60.00 mm, with the cumulative rainfall reaching 1853.18 mm and a daily average of 4.49 mm. A further increase in deformation velocity to 6.86 mm month − 1 was observed in the second acceleration phase, with a cumulative displacement of 48.00 mm amidst a cumulative rainfall of 494.81 mm, averaging 2.70 mm daily. Notably, a rainfall event from June 2, 2022, to July 22, 2022, resulted in the highest rainfall of the year (261.79 mm), triggering a sudden acceleration of the H1 potential landslide. These observations affirm a strong correlation between landslide deformation velocity and both cumulative and average daily rainfall. Deformation velocity was markedly higher in the rainy season compared to non-rainy periods. Additionally, each acceleration phase of the H1 potential landslide was preceded by a concentrated period of rainfall within the year, signifying the role of rainfall in landslide instability. To enhance the assessment of the spatial deformation progression of the H1 potential landslide, our study concentrated on the cumulative Line-of-Sight (LOS) displacement map, capturing the landslide's dynamic temporal evolution (Fig. 8 ). The data revealed that deformation initiated in the central rear slope and progressively advanced forward, while the left-front slope exhibited stability, with negligible downslope movement. The InSAR-derived relative cumulative LOS displacements proved instrumental in delineating the spatiotemporal development of the landslide, providing critical insights for crafting bespoke monitoring and early warning strategies to accommodate varying landslide evolution trajectories. 4.2.2 Deformation zone H2 The H2 deformation zone, situated in Yunfeng Township and bisected by the S305 Provincial Road, extends over an area of approximately 759,665 m 2 , with dimensions of roughly 1224 m by 830 m (Fig. 9 a). Within this landslide-prone expanse, five strategic points—P4 and P5 at the slope's base and left edge, and P1, P2, and P3 along the incline's summit and midsection—were analyzed for displacement over time. P4 and P5 demonstrated stability, with cumulative deformations of -26.91 mm and − 26.30 mm, respectively. Conversely, P1, P2, and P3 exhibited notable deformations, with a linear acceleration pattern culminating in a maximum cumulative deformation of -83.01 mm, indicative of significant geodynamic activity within the landslide mass. Throughout the monitoring interval, the H2 potential landslide's deformation manifested in two distinct phases: an initial uniform deformation stage spanning from January 1, 2020, to July 15, 2020, followed by an emergent acceleration phase commencing on July 16, 2020. This biphasic pattern underscores the dynamic nature of the landslide's movement and the critical importance of temporal analysis in understanding landslide behavior. Examining P1, the uniform deformation phase exhibited an average deformation velocity of 2.67 mm month − 1 , corresponding to a cumulative displacement of approximately 20.00 mm. This period coincided with a cumulative rainfall of 967.50 mm, averaging 4.90 mm daily. As the phase transitioned into acceleration, the deformation velocity slightly decreased to 2.46 mm month − 1 , but the cumulative displacement increased to 70.00 mm. Concurrently, the cumulative rainfall escalated to 3245.20 mm, with a daily average of 3.60 mm. The deformation trajectory and rate of P1 closely parallel those of P2 and P3, suggesting rapid deformation in the central region of the potential H2 landslide. 4.2.3 Deformation zone H4 The H4 deformation zone, situated in Shanshuping, encompasses a residential slope area spanning approximately 1224 m in length, 830 m in width, and covering an area of approximately 759665 m² (Fig. 9 c). Employing a similar analysis approach, we selected five key points within the potential H4 landslide mass, ordered in descending elevation, to examine temporal displacement patterns. P1 is positioned at the landslide's upper edge, while P2, P3, and P4 are located within the residential area mid-slope. P5 represents the cumulative deformation at the slope's right leading edge. P1, located at the slope's crest, exhibits a cumulative deformation of -17.90 mm, a stark difference compared to other sample points. Mid-slope points, P2, P3, and P4, showcase considerable deformations, characterized by a linear acceleration trend, with cumulative deformation values of -72.10 mm, -75.50 mm, and − 56.41 mm, respectively. P5, situated at the foot of the slope, displays a cumulative deformation of -72.31 mm, averaging an annual deformation rate of approximately − 24.10 mm. Throughout the observation period, the deformation events of the H4 potential landslide can be broadly categorized into two stages: the uniform deformation stage (January 1, 2020, to September 1, 2020) and the onset of acceleration (beginning September 2, 2020). Examining P2 during the uniform deformation phase, the average deformation velocity was 1.67 mm month − 1 , corresponding to a cumulative displacement of approximately 15.00 mm. This phase recorded a cumulative rainfall of 1277.89 mm, averaging 5.22 mm daily. As the stage transitioned into acceleration, the deformation velocity increased to 2.24 mm month − 1 , and the cumulative displacement rose to 60.05 mm. Concurrently, the cumulative rainfall escalated to 2934.79 mm, averaging 3.45 mm daily. P1, situated at the landslide's upper edge with dense vegetation, displayed no significant deformation. In contrast, P2, P3, P4, and P5, affected by human activities, demonstrated noticeable deformation variations compared to P1. 4.2.4 Deformation zone H8 The H8 deformation zone is situated on the Yangtze River bank, spanning approximately 1000 m in length, 940 m in width, and covering approximately 851961 m² (Fig. 10 a). To analyze the displacement variations within the potential H8 landslide, we selected two key points: P1, mid-slope, and P2, at the right front edge (Fig. 10 b). P1 showcases an average deformation rate of approximately − 23.31 mm year − 1 , accumulating a total deformation of -70.61 mm, and exhibits a distinct deformation acceleration trend. In contrast, P2 displays an average deformation rate of approximately − 7.76 mm year − 1 , with a cumulative deformation of -23.31 mm. Deformation results distinctly show that the deformation velocity at the H8 landslide's front edge considerably exceeds that at the rear. Throughout the observation period, the H8 landslide can be segmented into four stages: uniform deformation (January 1, 2020, to June 16, 2020), first acceleration (June 17, 2020, to October 3, 2020), second acceleration (April 13, 2021, to November 3, 2021), and sudden acceleration (March 15, 2022, to June 7, 2022). Examining P2 during the uniform deformation stage, the average deformation rate is 0.77 mm mon − 1 , tallying a cumulative deformation of approximately 5.01 mm. The cumulative rainfall stands at 539.75 mm, averaging 3.21 mm daily, with the Yangtze River's water level dropping by 24.84 m. At the onset of the first acceleration stage, the deformation rate elevates to 5.57 mm mon − 1 , and the cumulative deformation rises to approximately 19.50 mm. The cumulative rainfall is 1221.98 mm, averaging 4.48 mm daily, with the river's water level escalating by 21.66 m. During the second acceleration stage, the deformation rate is 6.04 mm mon − 1 , with a cumulative deformation of 39.40 mm. The cumulative rainfall is 1221.92 mm, averaging 5.96 mm daily. Notably, in the first half of the second acceleration stage (April 13, 2021, to August 11, 2021), the river's water level falls by 16.72 m, a low level persisting for three months. In the latter half, the water level surges by 28.14 m. During the sudden acceleration stage, the deformation rate is 6.69 mm, with a cumulative deformation of 23.40 mm. The cumulative rainfall is 441.51 mm, averaging 5.19 mm daily, with the river's water level decreasing by 18.5 m. A strong correlation is evident between landslide deformation velocity and both cumulative and average daily rainfall. Deformation velocity during the rainy season markedly surpasses that during the non-rainy season. Beyond rainfall's influence, the Yangtze River's water level decrease also crucially impacts the H8 potential landslide's deformation. Following the three heavy rainfall events from 2020 to 2022, clear acceleration processes were observed in the H8 deformation zone. 5 Discussion Most of the potential landslides identified in the study area are primarily reactivations of large-scale ancient landslides, often attributed to past seismic events and fault activities (Zhang et al. 2015 ). While these ancient landslides typically experience exceedingly slow creep deformation influenced by river erosion, human activities, and long-term gravitational effects, intense rainfall can abruptly reactivate them (Zhang et al. 2015 ). 5.1 The Analysis of the relationship between potential landslide deformation and rainfall Landslide occurrences result from the synergistic action of numerous influences, with precipitation being a primary trigger. On one hand, rainfall can induce water-soil interactions that reduce the soil mass's cohesion and frictional forces (Yue et al. 2018 ). Concurrently, surface runoff generated by rainfall can alter the original slope surface, inducing instability (Bogaard and Greco 2018 ). Additionally, the infiltration of substantial rainfall can saturate soil layers, augmenting the landslide mass's weight and potentially causing landslide disasters (Ray and Jacobs 2007 ). In essence, two prerequisites exist for rainfall-induced landslides: prolonged, continuous rainfall and sufficiently high rainfall intensity. Rainfall time series analysis indicates that when monthly cumulative rainfall exceeds 100 mm, subsidence tends to occur in the landslide mass's middle and rear parts, typically in peak landslide and debris flow periods of July and August. During these months, accelerated subsidence was observed in the rear and middle sections of both H1 and H8 potential landslides. This suggests that July and August's rainfall intensity surpassed these landslides' surface infiltration rate (Fig. 11 ), transforming heavy rainfall into surface runoff and eroding the slope (Kanungo and Sharma 2014 ). H1 potential landslide exhibited continuous subsidence around 3 to 6 months post-heavy rainfall in August, likely due to the soil's poor permeability hindering timely evaporation and discharge of shallow soil moisture caused by rainfall. This moisture outflow can generate pore water pressure, inducing instability (Shuqiang et al. 2014 ). While H2 and H4 potential landslides' deformation rates show no significant rainy vs. non-rainy season difference, slight acceleration occurs during the 2021–2022 summer months (June to September). Unlike H1 and H8, these accelerations don't result in extensive deformation, and overall deformation rates maintain linear time correlation. Following the rainy season, when Yunyang County's precipitation significantly decreases and slopes' shallow soil moisture evaporates, the four potential landslides' deformation exhibits minor seasonal stability fluctuations, including small-scale settlement or uplift, typically between January-April and September-December. 5.2 Precipitation correlation analysis A typical Pearson correlation analysis between rainfall and typical landslides in Yunyang County revealed that, except for H4 and H8 potential landslides, deformation in the central and toe regions of the other two landslides demonstrated certain rainfall correlation (Fig. 12 ). Moreover, deformation between the front, middle, and rear regions of individual potential landslides exhibited strong correlation. Specifically, significant rainfall correlation was noted in the H1 potential landslide's rear region and left margin, and the H2 potential landslide's middle-front region. Although the H4 landslide's rear region and the H8 landslide's middle region showed some rainfall correlation, overall correlation was relatively low, with these areas remaining relatively stable. 5.3 Influence of Yangtze River water level on slope stability The fluctuating water levels of the Three Gorges Reservoir are known to compromise slope stability, as evidenced by the pronounced decline in the Yangtze River's water level during the latter stages of the H8 landslide's acceleration (Cao et al. 2021 ). This phenomenon impacts slope stability through several mechanisms. A lower water level translates to diminished horizontal water pressure at the landslide's base, reducing sliding resistance. Concurrently, the upper soil mass, unburdened by its own weight, may exert increased pressure on the lower strata, undermining stability (Wang et al. 2021 ). Furthermore, a receding water level reshapes the pore water pressure distribution within the landslide, altering the soil's effective stress distribution and, by extension, its stability (Hou et al. 2022 ). Exposure of the landslide's surface to atmospheric conditions due to water level reduction also heightens vulnerability to weather elements like rainfall, potentially intensifying landslide activity (Zhao et al. 2017 ). Notably, in rare instances, rising water levels can also precipitate landslides if the Yangtze River's force surpasses the slope's erosion resistance threshold, directly triggering a landslide event. 5.4 Limitations of SBAS-InSAR Geometry in Complex Mountainous Terrain Our study area is situated in mountainous terrain, where significant geometric distortions are encountered particularly when utilizing InSAR technique with SAR imagery. This is primarily attributed to the inherent side-view geometry of SAR. In regions characterized by steep mountain ranges and deep valleys, this challenge becomes more pronounced. To quantify these distortions, we conducted an analysis of the geometric parameters of Sentinel-1 ascending orbit images and SRTM DEM (Dai et al. 2021 ). Our research findings indicate that, for ascending orbit images, the proportions of Feasible, Layover, and Shadow regions are 92.97%, 6.63%, and 0.4% respectively (Fig. 13 a). It is noteworthy that, among the potential landslide areas detected, the majority are distributed within the Feasible range, with only a few areas exhibiting Layover and Shadow, such as the H7 potential landslide area. This finding provides support for the validity of SBAS-InSAR data. However, the deformation velocity maps derived from the SBAS-InSAR technique still contain numerous blank areas (Fig. 5 a), mainly due to inherent limitations in the Sentinel-1A sensor (Yang et al. 2023 ). In areas with high vegetation coverage, the C-band's wavelength (approximately 5.6 centimeters) cannot penetrate, resulting in inconsistencies in the deformation velocity maps (Xiao et al. 2022 ). The absence of InSAR data in these areas complicates reasonable surface susceptibility category assessment. To mitigate this, L-band SAR sensors, with wavelengths ranging from 30 to 15 centimeters, can enhance radar penetration (Liu et al. 2023 ). Therefore, L-band SAR offers improved applicability in densely vegetated areas by reducing vegetation-induced temporal correlation effects. However, due to the greater cost of collecting L-band SAR data and its longer revisit period compared to C-band Sentinel-1A data, its sensitivity to surface deformation is lower. As a result, L-band SAR data was excluded from this experiment. 6 Conclusions Leveraging Sentinel-1 satellite imagery and SBAS-InSAR methodology, this study pinpointed 12 nascent landslides, predominantly situated near residential zones and along the Yangtze River's trajectory. Precipitation emerged as the principal agent of surface deformation and landslide genesis in Yunyang County. Notably, potential landslides within residential vicinities and flanking the Yangtze River manifested markedly elevated deformation rates during the wet season versus the dry season. These sites are prone to slope failures and geological calamities upon reaching critical antecedent rainfall levels, underscoring the imperative for ongoing surveillance. While other potential landslides maintained consistent deformation rates across seasons, they underwent brief accelerations post-heavy precipitation. A subsequent correlation analysis between rainfall and four selected landslides corroborated a definitive link between their deformation rates and rainfall volume. Furthermore, potential landslides abutting the Yangtze River bank experienced accelerated deformation concurrent with significant river water level reductions, indicating that the river's cyclical water level fluctuations bear implications for slope stability. In summation, SBAS-InSAR technology proves instrumental in detecting millimetric deformations in incipient landslides, a crucial step in averting landslide disasters and ensuring public safety. Declarations Acknowledgements This work was supported by the Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJQN202100624), and the Opening fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology) (SKLGP2023K008). 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Cite Share Download PDF Status: Published Journal Publication published 05 Aug, 2024 Read the published version in Environmental Earth Sciences → Version 1 posted Editorial decision: Revision requested 30 Jun, 2024 Reviews received at journal 27 Jun, 2024 Reviewers agreed at journal 27 May, 2024 Reviewers invited by journal 25 May, 2024 Submission checks completed at journal 13 Apr, 2024 Editor assigned by journal 13 Apr, 2024 First submitted to journal 10 Apr, 2024 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. 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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-4247951","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290727951,"identity":"e53a8e4b-cff4-45a5-94b1-79dee5b7f881","order_by":0,"name":"jinhu Cui","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"jinhu","middleName":"","lastName":"Cui","suffix":""},{"id":290727952,"identity":"8382fe36-23c6-4e9e-8d96-3d2e20cc084b","order_by":1,"name":"Yuxiang Tao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYPACCQSTn5n58AN8annQtUhItrOlGRChBck+g/M8ChLYlMKAPfvhY9K8bRby5vwLGD+8bbtTZ3yYh8GAocYmGqctPGlp0jxnJAx3znjALDm37ZmE2WHeAw8YjqXlNuB0WI6ZNE+FBOOGGwfYmHnbDgO18CUYMDYcxq2F/w1Qi4GEPVyLcTOQi1eLBMSWxA3nGyBaDJgJabnxLNlyzhmJ5A03GJgl55x7JjnjMDCQE/D4hb0/+eCNt211thvOH2D88KbsDj9//+HDDz7U2ODUAgQskFiQyP8ADIwDELEE3MpBgPkDmOI/wIDQMgpGwSgYBaMACQAAteJVgXgK/OoAAAAASUVORK5CYII=","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Yuxiang","middleName":"","lastName":"Tao","suffix":""},{"id":290727953,"identity":"0b4fb2e6-8e07-4513-a874-dce0941cb849","order_by":2,"name":"Pinglang Kou","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Pinglang","middleName":"","lastName":"Kou","suffix":""},{"id":290727954,"identity":"9aef9271-3074-46ae-b798-5d89c8e1c74c","order_by":3,"name":"Zhao Jin","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Jin","suffix":""},{"id":290727955,"identity":"6c87a779-5319-4189-9b4f-6efc2c51f77d","order_by":4,"name":"Yijian Huang","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Yijian","middleName":"","lastName":"Huang","suffix":""},{"id":290727956,"identity":"1109785f-5539-4bdb-a1e8-53583b9044aa","order_by":5,"name":"Jinlai Zhang","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Jinlai","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-04-10 14:33:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4247951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4247951/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12665-024-11770-4","type":"published","date":"2024-08-05T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54820562,"identity":"5e2a411f-5988-45fc-b8c3-7718df1ce9eb","added_by":"auto","created_at":"2024-04-17 08:39:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2368293,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area. Location of the study area. (a) Location of the study area in China; (b) DEM of Chongqing city and the relative position of the Yunyang within Chongqing city are marked by red boxes; (c) Basic geographic information of Yunyang, The red five-pointed star represents the urban center of Yunyang County.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4247951/v1/cc00fab215445df45194101c.png"},{"id":54820564,"identity":"437155fa-c74c-46dd-a88e-56752350b936","added_by":"auto","created_at":"2024-04-17 08:39:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60639,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly average Yangtze water level and precipitation from 2020 to 2022.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4247951/v1/9e70c265e1d4fac5352e6325.png"},{"id":54820566,"identity":"75b86e5d-1c14-48c1-861d-b35272cc5e04","added_by":"auto","created_at":"2024-04-17 08:39:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":284160,"visible":true,"origin":"","legend":"\u003cp\u003eProcessing workflow of SBAS-InSAR method.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4247951/v1/701e4297c7fbe68cca75c6b6.png"},{"id":54820560,"identity":"e959f529-cf5c-4c88-8763-0c95f052447c","added_by":"auto","created_at":"2024-04-17 08:39:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":175071,"visible":true,"origin":"","legend":"\u003cp\u003eImage pair combinations of the Sentinel-1.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4247951/v1/01f1a4b1a14d3b5fe2365892.png"},{"id":54820565,"identity":"d5ea5c19-a11d-448e-a716-83847f5c049f","added_by":"auto","created_at":"2024-04-17 08:39:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":31666531,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual average deformation velocity of the detected potential landslides. (a) Deformation velocity result of the ascending Sentinel-1 images; (b) Cumulative deformation result of the detected potential landslides; (c) Temporal deformation sequence results of the detected potential landslides.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4247951/v1/df7f4f864a231efa737536cf.png"},{"id":54820563,"identity":"1494118d-3369-4e54-a0d7-9620e17beefc","added_by":"auto","created_at":"2024-04-17 08:39:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":60501077,"visible":true,"origin":"","legend":"\u003cp\u003eShows the optical image of the detected H1-H12 potential landslides and the corresponding deformation rate.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-4247951/v1/2cb0e5b5937fe05c7ff370c3.png"},{"id":54820569,"identity":"1911fcab-eec4-43e3-a408-17fbb881a2db","added_by":"auto","created_at":"2024-04-17 08:39:51","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3494890,"visible":true,"origin":"","legend":"\u003cp\u003eInSAR results of the deformation zone H1. (a) Deformation velocity map of deformation zone H1; (b) Illustration of the excavated toe of the slope in 2016; (c) Illustration of the slope in 2021; (d) Potential runoff area at the toe of the slope in 2016; (e) Illustration of the hollowed-out area on the slope in 2021; (f) Satellite flight angle and looking direction; (g) Time series curve of cumulative deformation of P1, P2 , P3, P4 , P5 and daily precipitation in deformation zone H1.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-4247951/v1/b9d289e3b159a9678142ac39.png"},{"id":54820567,"identity":"dc5bf122-1ab9-4824-b606-c6060c2e4da5","added_by":"auto","created_at":"2024-04-17 08:39:50","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":15236689,"visible":true,"origin":"","legend":"\u003cp\u003eTime series analysis of deformation zone H1. (a) Cumulative displacement maps of deformation zone H1 from March 1, 2020 to December 28, 2022; (b) Mean value graph of deformation zone H1; (c) The deformation-displacement distribution associated with the DEM\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-4247951/v1/556c2a0a58dbb7de3110768f.png"},{"id":54820572,"identity":"10484ee3-8de2-4e13-8b0d-36eb41fe775f","added_by":"auto","created_at":"2024-04-17 08:39:51","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":5378584,"visible":true,"origin":"","legend":"\u003cp\u003eInSAR results of deformation zone H2 and deformation zone H4. (a) Deformation velocity map of deformation zone H2; (b) Time series curve of Cumulative deformation of P1, P2 , P3, P4 , P5 and daily precipitation in deformation zone H2; (c) Deformation velocity map of deformation zone H4; (d) Time series curve of Cumulative deformation of P1, P2 , P3, P4 , P5 and daily precipitation in deformation zone H4.\u003c/p\u003e","description":"","filename":"Fig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-4247951/v1/d24e6c22401d1163a3bbdb44.png"},{"id":54820571,"identity":"3b15e59c-fa55-4b95-82ed-4a30543eeb88","added_by":"auto","created_at":"2024-04-17 08:39:51","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1854845,"visible":true,"origin":"","legend":"\u003cp\u003eInSAR results of deformation zone H8. (a) Deformation velocity map of deformation zone H8; (b) Cumulative deformation variable distribution of deformation zone H8 , box chart represents deformation rate of deformation zone H8; (c) Time series curve of cumulative deformation of P1 and P2 and precipitation in deformation zone H8 with Yangtze water level; (d), (e), (f) represent the relationship between three phases of heavy daily precipitation and cumulative deformation of P1 and P2 in the deformation zone H8, respectively.\u003c/p\u003e","description":"","filename":"Fig.10.png","url":"https://assets-eu.researchsquare.com/files/rs-4247951/v1/747a308415825ec2110e8a74.png"},{"id":54820568,"identity":"c417a332-f286-4f1c-a2f9-26f6ad7cadea","added_by":"auto","created_at":"2024-04-17 08:39:50","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":157415,"visible":true,"origin":"","legend":"\u003cp\u003ePotentially active landslide creep processes. (a) Original stabilization of the potential landslide; (b) Rainfall and falling water levels causing significant creep of potential landslides.\u003c/p\u003e","description":"","filename":"Fig.11.png","url":"https://assets-eu.researchsquare.com/files/rs-4247951/v1/fd0db074b7a1ee225998bca2.png"},{"id":54820561,"identity":"1f3a3b19-1017-4ba9-9cc8-45512b4eb246","added_by":"auto","created_at":"2024-04-17 08:39:50","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":224442,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation coefficient matrix for the characteristic points (P1-P5) of potential landslides and rainfall. The symbol with * indicates a significant correlation.\u003c/p\u003e","description":"","filename":"Fig.12.png","url":"https://assets-eu.researchsquare.com/files/rs-4247951/v1/0286bf555e4ba6e050496582.png"},{"id":54820559,"identity":"cd5c3d53-f555-4d8a-953a-d247166e72dc","added_by":"auto","created_at":"2024-04-17 08:39:49","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":443403,"visible":true,"origin":"","legend":"\u003cp\u003eGeometrical distortions (Feasible, layover, and shadow) and feasible areas. (a) Feasibility Analysis of Yunyang County; (b), (c), (d),(e), (f), (g), (h), (i), (j), (k), (l), (m) are feasibility Analysis of Potential Landslide Areas H1-H12 repectively.\u003c/p\u003e","description":"","filename":"Fig.13.png","url":"https://assets-eu.researchsquare.com/files/rs-4247951/v1/d406d9062f4c5d5f3e25f274.png"},{"id":62298584,"identity":"376a2b36-9c2d-43c4-88e1-1a78d9499fd1","added_by":"auto","created_at":"2024-08-12 16:14:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":263380718,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4247951/v1/7bf9034d-2c96-447b-8a7f-2eb4e396d22b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hydrological Influences on Landslide Dynamics in the Three Gorges Reservoir Area: An SBAS-InSAR Study in Yunyang County, Chongqing","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLandslides, a critical geohazard, annually affect millions and engender significant socioeconomic impacts (Petley \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Slope failures arise from diverse triggers: river erosion (Liu et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), groundwater flux (Wang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), rainfall (Finlay et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), seismicity (Li et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and anthropogenic excavation (Pan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These elements precipitate abrupt, calamitous earth movements, imperiling lives and assets in upland locales, notably where human expansion and environmental flux intensify (Froude and Petley \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, the prognostication and cartography of potential landslide zones are vital for disaster prophylaxis and attenuation.\u003c/p\u003e \u003cp\u003eConventional landslide deformation detection predominantly employs total stations and Global Positioning System (GPS) for monitoring purposes (Bentley et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ma et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Total stations, however, demand extensive time and labor, rendering them unsuitable for complex terrains (Casagli et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). GPS, while more versatile and user-friendly than total stations and leveling devices, leverages high-precision field surveying to precisely delineate landslide geometries, facilitating the observation and analysis of landslide dynamics (Guerriero et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nonetheless, GPS encounters obstacles such as elevated costs, underutilized reference stations, and dependency on singular reference points (Wang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which hinder its widespread adoption for proactive landslide surveillance.\u003c/p\u003e \u003cp\u003eSynthetic Aperture Radar Interferometry (InSAR) represents a transformative approach for acquiring large-scale deformation data from satellite observations, transcending the constraints of conventional landslide surveillance methodologies that are labor-intensive and restricted in scope (Xu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Illustratively, InSAR has been adeptly utilized to track the deformation dynamics of a landslide proximal to Yizi Village, China, in the context of the Xiluodu Reservoir's impoundment in 2019 (Li et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Notwithstanding, InSAR's efficacy is moderated by temporal, spatial, and atmospheric considerations, and its application is confined to regions characterized by sparse vegetation (Devaraj et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo surmount the limitations of traditional landslide monitoring, researchers have innovated sophisticated InSAR methodologies, notably the Permanent Scatterers Technique (PS-InSAR) and the Small Baseline Subset (SBAS) approach. PS-InSAR, which targets stable reference points, has refined the landslide catalog in Italy's Tuscany region, pinpointing 672 active landslides (Rosi et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Despite its utility, PS-InSAR is hampered by the irregular distribution of scatterers and constrained processing capabilities (Crosetto et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Conversely, SBAS-InSAR ameliorates spatial decorrelation and diminishes topographic and atmospheric distortions (Berardino et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Its applications span earthquake detection (Huang et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), ground subsidence analysis (Xu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and landslide surveillance (Xiao et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notably, SBAS-InSAR has yielded encouraging outcomes in identifying incipient landslides within vegetated terrains, as evidenced in Badong County's ecological buffer zone within China's Three Gorges Reservoir (Dong et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and along the national highway in Wenchuan County (Zhang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), underscoring its efficacy in challenging contexts.\u003c/p\u003e \u003cp\u003eChongqing, located in southwestern China, epitomizes a region prone to frequent and devastating landslide events (Kwong et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). A notable incident occurred on July 25, 2020, when intense rainfall induced a landslide in Liu Jing Village, Tudixiang, Wulong District, obstructing the Yan Cang River and forming a barrier lake, imperiling 152 households and 520 residents (Chen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, on January 17, 2001, a landslide at Wufeng Mountain in Yunyang County inflicted substantial damage on roughly 30,000 m\u0026sup2; of the Yangtze River's protective forest and demolished 16 homes, half of which were completely razed (Su et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Presently, landslide deformation detection in this area predominantly relies on manual field inspections, a method that significantly hampers the efficiency and precision of hazard identification.\u003c/p\u003e \u003cp\u003eThis study harnessed Sentinel-1 satellite imagery from January 1, 2020, to December 28, 2022, applying the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to surveil deformations across Yunyang County, Chongqing. The study aimed to: (1) delineate areas at risk of landslides within Yunyang County, (2) discern deformation patterns of emblematic landslides and prognosticate their potential reoccurrence, and (3) explore the interplay between landslide evolution and variables such as precipitation and Yangtze River water levels.\u003c/p\u003e"},{"header":"2 Study area and data","content":"\n\u003ch3\u003e2.1 Study area\u003c/h3\u003e\n\u003cp\u003eYunyang County, nestled in the Three Gorges Reservoir's heartland within Chongqing Municipality, spans roughly 3649 km\u0026sup2; and rises to an average elevation of 731 m. Characterized by precipitous slopes and distinct ridge-valley topography (Guo et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the county's intricate geology comprises prevalent purple soil, shale, Jurassic sandstone, and mudstone (Zhang et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), all prone to hydro-gravitational erosion, precipitating landslides. The county's subtropical monsoon climate yields copious rainfall year-round, with July and August marking peak precipitation periods, aligning with heightened landslide and debris flow incidents. Annual rainfall for 2020, 2021, and 2022 was recorded at 1584.67 mm, 1534.88 mm, and 1070.16 mm, respectively, showcasing a seasonal disparity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The mean summer rainfall from 2020 to 2022 constituted 46.73% of the yearly total at 652.73 mm, while winter's contribution was a mere 9.07% at 126.79 mm. Spring and autumn presented comparable figures, with 346.59 mm and 276.41 mm, respectively. The county's robust fluvial network, anchored by the Yangtze River and supplemented by tributaries like Modaoxi, Changtan, Pengxi, and Tangxi Rivers, alongside myriad streams, forms an intricate runoff matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, the Yangtze's water level undergoes marked cyclical shifts (He et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), potentially subjecting landslide masses to recurrent stress cycles, thus instigating slope failures.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e2.2 Data\u003c/h3\u003e\n\u003cp\u003eThe study area, veiled by dense vegetation, presents formidable challenges in the precise detection and assessment of landslides over temporal scales. To surmount this, we leveraged 80 ascending orbit images from the C-band Sentinel-1A satellite, spanning January 1, 2020, to December 28, 2022 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scihub.copernicus.eu/\u003c/span\u003e\u003cspan address=\"https://scihub.copernicus.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The SAR images were strategically cropped to align with the study area's dimensions, enhancing computational efficiency. Key SAR data parameters are encapsulated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Terrain-related parameters were derived using the 30-meter resolution digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gscloud.cn/search\u003c/span\u003e\u003cspan address=\"http://www.gscloud.cn/search\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which aided in negating topographic influences during InSAR processing and in discerning overlapping and shadowed zones. Complementing this, rainfall metrics and Yangtze River water level data were procured for the corresponding period in Yunyang County, Chongqing, facilitating an analysis of the interplay between precipitation and landslide kinetics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www\u003c/span\u003e\u003cspan address=\"https://www\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. xihe-energy. com/).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic information of Sentinel-1A images\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatellite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentinel-1A\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrbital direction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscending\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporal coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020. 1. 1-2022. 12. 28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImage mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIW\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC-band\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWavelength\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5. 6cm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResolution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026times;20m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage angle of incidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35. 51\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolarization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSLC stands for single-view complex, IW stands for strip scan mode, and VV stands for polarization mode as vertical polarization.\u003c/p\u003e"},{"header":"3 Methodology","content":"\n\u003ch3\u003e3.1 SBAS-InSAR method\u003c/h3\u003e\n\u003cp\u003eThe Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) is a time-series analytical modality apt for distributed targets. It fundamentally operates on the pairwise interferometric processing of imagery with minimal temporal and spatial baselines to fabricate interferograms. These are then subjected to multi-look processing to attenuate phase noise. Addressing the ill-posed equations due to sparse observations, singular value decomposition is employed to resolve the deformation phase equations at each point, thereby deducing elevation discrepancies and deformation velocities. Atmospheric phase and nonlinear deformation are subsequently inferred from the residual phase, culminating in a temporal deformation sequence for the specified duration (Li et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This technique organizes N\u0026thinsp;+\u0026thinsp;1 Synthetic Aperture Radar (SAR) Single-Look Complex (SLC) images of a region in sequential order (t0, t1,..., tv). The registered images are partitioned into subsets based on their temporal and spatial baselines, aiming to minimize baseline distances within subsets and maximize those between them. Differential interferometric processing of images across subsets ensues, yielding M interferograms, where M adheres to the stipulated condition (Chen et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{ }\\frac{\\text{N+1}}{\\text{2}}\\text{ \u0026le; M \u0026le; }\\frac{\\text{N}\\left(\\text{N+1}\\right)}{\\text{2}}\\text{ }\\text{ }\\text{ }\\text{(1)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAnd the composition of the interference phase of an pixel x in the jth interference pair \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{int, x, j}}\\)\u003c/span\u003e\u003c/span\u003e is\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\begin{array}{r}{\\text{\u0026phi;}}_{\\text{int , x, j}}\\text{=}{\\text{\u0026phi;}}_{\\text{topo , x, j}}\\text{+}{\\text{\u0026phi;}}_{\\text{def, x, j}}\\text{+}\\\\ {\\text{\u0026phi;}}_{\\text{flat, x, j}}\\text{+}{\\text{\u0026phi;}}_{\\text{atm, x, j}}\\text{+}{\\text{\u0026phi;}}_{\\text{noise , x, j}}\\text{+}{\\text{\u0026phi;}}_{\\text{orb , x, j}}\\end{array} \\text{(2)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOf which:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c} {\\text{\u0026phi;}}_{\\text{topo , x, j}}\\text{=}-\\frac{\\text{4\u0026pi;}{\\text{B}}_{\\perp }\\text{h}}{\\text{\u0026lambda;Rsin}\\text{ }\\text{\u0026theta;}} \\text{(3)} \\\\ {\\text{\u0026phi;}}_{\\text{flat , x, j}}\\text{=}-\\frac{\\text{4\u0026pi;}{\\text{B}}_{\\perp }}{\\text{\u0026lambda;}} \\text{(4)}\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEquation (2) represents the interferometric phase composition of image j (generated by two SAR images at times t\u003csub\u003eA\u003c/sub\u003e and t\u003csub\u003eB\u003c/sub\u003e, given that t\u003csub\u003eA\u003c/sub\u003e \u0026lt; t\u003csub\u003eB\u003c/sub\u003e) within the pixel element at azimuth x and j distance from the satellite x, j. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{topo, x, j}}\\)\u003c/span\u003e\u003c/span\u003eis the terrain phase. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{def, x, j}}\\)\u003c/span\u003e\u003c/span\u003eis the deformation phase. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{flat, x, j}}\\)\u003c/span\u003e\u003c/span\u003eis the flat earth phase. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{atm, x, j}}\\)\u003c/span\u003e\u003c/span\u003eis the atmospheric delay phase. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{noise , x, j}}\\)\u003c/span\u003e\u003c/span\u003eis the system thermal noise; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{orb, x, j}}\\)\u003c/span\u003e\u003c/span\u003eis the phase due to orbital error. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{B}}_{\\perp }\\)\u003c/span\u003e\u003c/span\u003e is the vertical baseline length; h is the elevation error; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{\u0026lambda;}\\)\u003c/span\u003e\u003c/span\u003e is the radar wavelength. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{R}\\)\u003c/span\u003e\u003c/span\u003e is the line-of-sight slope distance. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{\u0026theta;}\\)\u003c/span\u003e\u003c/span\u003e is the angle of incidence. The differential interferometric phase model\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{ }{\\text{\u0026phi;}}_{\\text{dif, x, j}}\\)\u003c/span\u003e\u003c/span\u003e of pixel x is obtained after removing the topographic phase and flat earth effect by differential processing with DEM:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$${\\text{\u0026phi;}}_{\\text{dif, x, j}}={\\text{\u0026phi;}}_{\\text{def , x, j}}+{\\text{\u0026phi;}}_{\\text{atm, x, j}}+{\\text{\u0026phi;}}_{\\text{noise , x, j}}+{\\text{\u0026phi;}}_{\\text{\u0026epsilon;, x, j}} \\text{(5)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOf which,\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$${\\text{\u0026phi;}}_{\\text{\u0026epsilon;, x, j}}=-\\frac{\\text{4\u0026pi;}{\\text{B}}_{\\perp }\\text{\u0026epsilon;}}{\\text{\u0026lambda;Rsin}\\text{ }\\text{\u0026theta;}} \\text{(6)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the Eq.\u0026nbsp;(6): \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{dif, x, j}}\\)\u003c/span\u003e\u003c/span\u003e is the differential interference phase. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{\u0026epsilon;, x, j}}\\)\u003c/span\u003e\u003c/span\u003eis the residual topographic phase due to the DEM error. ε is the DEM error. The deformation phase \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{def, x, j}}\\)\u003c/span\u003e\u003c/span\u003e consists of nonlinear deformation phase \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{nonlinear , x, j}}\\)\u003c/span\u003e\u003c/span\u003e and linear deformation phase \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{linear , x, j}}\\)\u003c/span\u003e\u003c/span\u003e .\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$${\\text{\u0026phi;}}_{\\text{linear , x, j}}=-\\frac{\\text{4\u0026pi;}}{\\text{\u0026lambda;}}\\text{vT}\\text{ }\\text{(7)}\\text{ }$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$${ \\phi }_{\\text{def }, x, j}=-\\frac{\\text{4\u0026pi;}}{\\text{\u0026lambda;}}\\text{vT}+{\\text{\u0026phi;}}_{\\text{nonlinear , x, j}} \\text{(8) }$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the Eq.\u0026nbsp;(7), v represents the average strain rate within the monitoring time span, and T represents the time span. The differential interference phase model \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{def , x, j}}\\)\u003c/span\u003e\u003c/span\u003e can be rewritten as:\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}\\text{ }\\\\ {\\text{\u0026phi;}}_{\\text{dif, x, j}}=-\\frac{\\text{4\u0026pi;}}{\\text{\u0026lambda;}}\\text{vT}-\\frac{\\text{4\u0026pi;}{\\text{B}}_{\\perp }\\text{\u0026epsilon;}}{\\text{\u0026lambda;Rsin}\\text{ }\\text{\u0026theta;}}+{\\text{\u0026phi;}}_{\\text{res, x, j}} \\text{(9)} \\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the Eq.\u0026nbsp;(9), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{res , x, j}}\\)\u003c/span\u003e\u003c/span\u003e represents the residual differential phase, which consists of nonlinear deformation, system thermal noise, and atmospheric delay phase: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{res , x, j}}={\\text{\u0026phi;}}_{\\text{nonlinear , x, j}}+{\\text{\u0026phi;}}_{\\text{noise , x, j}}+{\\text{\u0026phi;}}_{\\text{atm, x, j}}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{dif, x, j}}\\)\u003c/span\u003e \u003c/span\u003ecan be obtained based on the phase information of manually selected high-coherence control points. The strain rate v is determined using the least squares adjustment method. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{\u0026phi;}}_{\\text{res, x, j}}\\)\u003c/span\u003e\u003c/span\u003e is obtained by solving the single-point strain phase through singular value decomposition. Finally, the deformation time series within the observation time period is obtained.\u003c/p\u003e\n\u003ch3\u003e3.2 InSAR process\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the flowchart of the data processing conducted in this study. The main steps are as follows:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStep1\u003c/strong\u003e \u003cp\u003eTo proficiently process the voluminous Synthetic Aperture Radar (SAR) data amassed, this research utilized the semi-automated SBAS module within SARscape software. Preprocessing of SAR imagery is a prerequisite for interferometry; thus, the original SAR images were initially cropped according to the geographic coordinate system, ensuring the Digital Elevation Model (DEM) coverage supersedes the study area, thereby streamlining data processing. Before differential interferometry, image data underwent pairing and linkage, forming a connectivity graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Adhering to the Sentinel-1A satellite sensor specifications and empirical insights, we established a spatial baseline threshold of 2% and a temporal baseline of 90 days to preclude extensive temporal disparities between images. This protocol yielded 277 pairs of high-caliber interferometric data points.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStep2\u003c/b\u003e: In the post-processing phase of Synthetic Aperture Radar (SAR) imagery, interferometric analysis is paramount. Utilizing ENVI SARscape software, this process encompasses interferogram synthesis, orbital adjustment, re-referencing, filtration, and phase unwrapping. To curtail noise in interferometry and phase unwrapping, we adopted a multi-look ratio of 3:1 in interferogram production. The Goldstein filtering algorithm was then employed to refine the SAR images. Given the extensive vegetative cover in Yunyang County, the Minimum Cost Flow (MCF) algorithm was selected for phase unwrapping due to its stability in expansive, low-coherence zones. A threshold of 0.2 was instituted for phase unwrapping to efficaciously dampen phase noise.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStep3\u003c/strong\u003e \u003cp\u003eIn our methodology, Ground Control Points (GCPs) were pivotal in rectifying residual terrain phase and satellite orbit discrepancies. The GCPs were judiciously situated in locales exhibiting prime unwrapped phase quality, strategically distanced from any deformation zones. For this analysis, GCPs were situated within level residential sectors, ensuring the integrity of the data correction process.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStep4\u003c/b\u003e: In the final analytical phase, we conducted dual inversions to ascertain and excise the residual constant phase and phase gradient post-phase unwrapping. The initial inversion utilized a linear model to gauge the deformation velocity and residual topography. The subsequent inversion, predicated on the initial deformation rate calculations, incorporated filtering (spatial filter window: 1600 m; temporal filter window: 365 days) to expunge atmospheric phase interference and diminish atmospheric delay impacts. Post atmospheric delay phase removal, we derived the aggregate deformation for the triennial span (2020\u0026ndash;2022) across Yunyang County. The deformation data were then geocoded, converting SAR-derived deformation metrics into geographic coordinate-based deformation figures.\u003c/p\u003e"},{"header":"4 Results","content":"\n\u003ch3\u003e4.1 Landslides Identification Result\u003c/h3\u003e\n\u003cp\u003eUtilizing Sentinel-1's ascending orbit data, an annual displacement rate map was constructed in the line-of-sight (LOS) direction, identifying 12 potential landslides with significant displacements in Yunyang County (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The color-coded displacements indicate ground movements relative to the satellite: red (negative values) signals the ground receding, while blue (positive values) represents an approach. The landslide boundaries were delineated by integrating InSAR deformation rate maps, Google Earth imagery, and landslide morphology (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Empirical evidence, such as substantial surface displacements, fractures, or gullies, was prominent in most slopes, including locations H2 (108\u0026deg;51'51.30\"E, 30\u0026deg;48'38.35\"N), H3 (109\u0026deg;00'34.52\"E, 30\u0026deg;43'48.77\"N), H4 (109\u0026deg;02'17.19\"E, 30\u0026deg;47'14.41\"N), with the maximal cumulative deformation reaching \u0026minus;\u0026thinsp;134 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Government and production units have recognized some of the detected landslides as high-risk areas, such as H5 (109\u0026deg;00'03.08\"E, 30\u0026deg;42'57.55\"N) and H6 (108\u0026deg;48'36.97\"E, 30\u0026deg;56'08.84\"N). Moreover, several landslides situated along the Yangtze River, including H9 (109\u0026deg;02'16.79\"E, 30\u0026deg;56'30.41\"N) and H10 (109\u0026deg;06'42.91\"E, 30\u0026deg;56'46.06\"N), pose potential threats to the river channel, should they collapse.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e4.2 Typical Landslides Analysis\u003c/h3\u003e\n\n\u003ch3\u003e4.2.1 Deformation zone H1\u003c/h3\u003e\n\u003cp\u003eThe deformation zone H1, situated roughly 1000 m southeast of Longjiao Town in Yunyang County, spans an estimated length of 497 m, a width of 154 m, and encompasses an area of about 61,785 m\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). For the temporal displacement analysis, five pivotal points\u0026mdash;P1, P2, P3, P4, and P5\u0026mdash;were selected on the potential landslide mass H1. P1, perched atop the slope, and P4, adjacent to the landslide's left boundary, exhibited relatively minor deformations, with cumulative displacements of -27.20 mm and \u0026minus;\u0026thinsp;30.50 mm, respectively. In contrast, P2 in the landslide's core, P3 at the leading edge, and P5 near the right margin, displayed pronounced acceleration patterns. The cumulative deformations for P2, P3, and P5 were significantly greater, registering at -99.30 mm, -89.30 mm, and \u0026minus;\u0026thinsp;98.40 mm, respectively.\u003c/p\u003e \u003cp\u003eThe analysis revealed that the H1 landslide zone underwent substantial deformation, with the rear of the mass exhibiting higher deformation rates than the front, indicative of a convergent sliding motion. The deformation phases of the landslide throughout the monitoring period can be categorized into four distinct stages: a phase of uniform deformation from January 1, 2020, to July 15, 2020; an initial acceleration phase from July 16, 2020, to September 1, 2021; a secondary acceleration phase from December 1, 2021, to June 1, 2022; culminating in a phase of abrupt acceleration commencing on June 2, 2022.\u003c/p\u003e \u003cp\u003eExamining P2, the uniform deformation phase exhibited an average deformation velocity of 4.00 mm month\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, resulting in a cumulative displacement of about 30.00 mm. This period coincided with a cumulative rainfall of 967.50 mm, averaging 4.90 mm daily. The initial acceleration phase displayed an increase in average deformation velocity to 4.80 mm month\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and a cumulative displacement of 60.00 mm, with the cumulative rainfall reaching 1853.18 mm and a daily average of 4.49 mm. A further increase in deformation velocity to 6.86 mm month\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was observed in the second acceleration phase, with a cumulative displacement of 48.00 mm amidst a cumulative rainfall of 494.81 mm, averaging 2.70 mm daily. Notably, a rainfall event from June 2, 2022, to July 22, 2022, resulted in the highest rainfall of the year (261.79 mm), triggering a sudden acceleration of the H1 potential landslide. These observations affirm a strong correlation between landslide deformation velocity and both cumulative and average daily rainfall. Deformation velocity was markedly higher in the rainy season compared to non-rainy periods. Additionally, each acceleration phase of the H1 potential landslide was preceded by a concentrated period of rainfall within the year, signifying the role of rainfall in landslide instability.\u003c/p\u003e \u003cp\u003eTo enhance the assessment of the spatial deformation progression of the H1 potential landslide, our study concentrated on the cumulative Line-of-Sight (LOS) displacement map, capturing the landslide's dynamic temporal evolution (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The data revealed that deformation initiated in the central rear slope and progressively advanced forward, while the left-front slope exhibited stability, with negligible downslope movement. The InSAR-derived relative cumulative LOS displacements proved instrumental in delineating the spatiotemporal development of the landslide, providing critical insights for crafting bespoke monitoring and early warning strategies to accommodate varying landslide evolution trajectories.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e4.2.2 Deformation zone H2\u003c/h3\u003e\n\u003cp\u003eThe H2 deformation zone, situated in Yunfeng Township and bisected by the S305 Provincial Road, extends over an area of approximately 759,665 m\u003csup\u003e2\u003c/sup\u003e, with dimensions of roughly 1224 m by 830 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea). Within this landslide-prone expanse, five strategic points\u0026mdash;P4 and P5 at the slope's base and left edge, and P1, P2, and P3 along the incline's summit and midsection\u0026mdash;were analyzed for displacement over time. P4 and P5 demonstrated stability, with cumulative deformations of -26.91 mm and \u0026minus;\u0026thinsp;26.30 mm, respectively. Conversely, P1, P2, and P3 exhibited notable deformations, with a linear acceleration pattern culminating in a maximum cumulative deformation of -83.01 mm, indicative of significant geodynamic activity within the landslide mass.\u003c/p\u003e \u003cp\u003eThroughout the monitoring interval, the H2 potential landslide's deformation manifested in two distinct phases: an initial uniform deformation stage spanning from January 1, 2020, to July 15, 2020, followed by an emergent acceleration phase commencing on July 16, 2020. This biphasic pattern underscores the dynamic nature of the landslide's movement and the critical importance of temporal analysis in understanding landslide behavior.\u003c/p\u003e \u003cp\u003eExamining P1, the uniform deformation phase exhibited an average deformation velocity of 2.67 mm month\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, corresponding to a cumulative displacement of approximately 20.00 mm. This period coincided with a cumulative rainfall of 967.50 mm, averaging 4.90 mm daily. As the phase transitioned into acceleration, the deformation velocity slightly decreased to 2.46 mm month\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, but the cumulative displacement increased to 70.00 mm. Concurrently, the cumulative rainfall escalated to 3245.20 mm, with a daily average of 3.60 mm. The deformation trajectory and rate of P1 closely parallel those of P2 and P3, suggesting rapid deformation in the central region of the potential H2 landslide.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e4.2.3 Deformation zone H4\u003c/h3\u003e\n\u003cp\u003eThe H4 deformation zone, situated in Shanshuping, encompasses a residential slope area spanning approximately 1224 m in length, 830 m in width, and covering an area of approximately 759665 m\u0026sup2; (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec). Employing a similar analysis approach, we selected five key points within the potential H4 landslide mass, ordered in descending elevation, to examine temporal displacement patterns. P1 is positioned at the landslide's upper edge, while P2, P3, and P4 are located within the residential area mid-slope. P5 represents the cumulative deformation at the slope's right leading edge.\u003c/p\u003e \u003cp\u003eP1, located at the slope's crest, exhibits a cumulative deformation of -17.90 mm, a stark difference compared to other sample points. Mid-slope points, P2, P3, and P4, showcase considerable deformations, characterized by a linear acceleration trend, with cumulative deformation values of -72.10 mm, -75.50 mm, and \u0026minus;\u0026thinsp;56.41 mm, respectively. P5, situated at the foot of the slope, displays a cumulative deformation of -72.31 mm, averaging an annual deformation rate of approximately \u0026minus;\u0026thinsp;24.10 mm.\u003c/p\u003e \u003cp\u003eThroughout the observation period, the deformation events of the H4 potential landslide can be broadly categorized into two stages: the uniform deformation stage (January 1, 2020, to September 1, 2020) and the onset of acceleration (beginning September 2, 2020).\u003c/p\u003e \u003cp\u003eExamining P2 during the uniform deformation phase, the average deformation velocity was 1.67 mm month\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, corresponding to a cumulative displacement of approximately 15.00 mm. This phase recorded a cumulative rainfall of 1277.89 mm, averaging 5.22 mm daily. As the stage transitioned into acceleration, the deformation velocity increased to 2.24 mm month\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and the cumulative displacement rose to 60.05 mm. Concurrently, the cumulative rainfall escalated to 2934.79 mm, averaging 3.45 mm daily. P1, situated at the landslide's upper edge with dense vegetation, displayed no significant deformation. In contrast, P2, P3, P4, and P5, affected by human activities, demonstrated noticeable deformation variations compared to P1.\u003c/p\u003e\n\u003ch3\u003e4.2.4 Deformation zone H8\u003c/h3\u003e\n\u003cp\u003eThe H8 deformation zone is situated on the Yangtze River bank, spanning approximately 1000 m in length, 940 m in width, and covering approximately 851961 m\u0026sup2; (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea). To analyze the displacement variations within the potential H8 landslide, we selected two key points: P1, mid-slope, and P2, at the right front edge (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb). P1 showcases an average deformation rate of approximately \u0026minus;\u0026thinsp;23.31 mm year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, accumulating a total deformation of -70.61 mm, and exhibits a distinct deformation acceleration trend. In contrast, P2 displays an average deformation rate of approximately \u0026minus;\u0026thinsp;7.76 mm year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a cumulative deformation of -23.31 mm.\u003c/p\u003e \u003cp\u003eDeformation results distinctly show that the deformation velocity at the H8 landslide's front edge considerably exceeds that at the rear. Throughout the observation period, the H8 landslide can be segmented into four stages: uniform deformation (January 1, 2020, to June 16, 2020), first acceleration (June 17, 2020, to October 3, 2020), second acceleration (April 13, 2021, to November 3, 2021), and sudden acceleration (March 15, 2022, to June 7, 2022).\u003c/p\u003e \u003cp\u003eExamining P2 during the uniform deformation stage, the average deformation rate is 0.77 mm mon\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, tallying a cumulative deformation of approximately 5.01 mm. The cumulative rainfall stands at 539.75 mm, averaging 3.21 mm daily, with the Yangtze River's water level dropping by 24.84 m. At the onset of the first acceleration stage, the deformation rate elevates to 5.57 mm mon\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and the cumulative deformation rises to approximately 19.50 mm. The cumulative rainfall is 1221.98 mm, averaging 4.48 mm daily, with the river's water level escalating by 21.66 m. During the second acceleration stage, the deformation rate is 6.04 mm mon\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with a cumulative deformation of 39.40 mm. The cumulative rainfall is 1221.92 mm, averaging 5.96 mm daily. Notably, in the first half of the second acceleration stage (April 13, 2021, to August 11, 2021), the river's water level falls by 16.72 m, a low level persisting for three months. In the latter half, the water level surges by 28.14 m. During the sudden acceleration stage, the deformation rate is 6.69 mm, with a cumulative deformation of 23.40 mm. The cumulative rainfall is 441.51 mm, averaging 5.19 mm daily, with the river's water level decreasing by 18.5 m.\u003c/p\u003e \u003cp\u003eA strong correlation is evident between landslide deformation velocity and both cumulative and average daily rainfall. Deformation velocity during the rainy season markedly surpasses that during the non-rainy season. Beyond rainfall's influence, the Yangtze River's water level decrease also crucially impacts the H8 potential landslide's deformation. Following the three heavy rainfall events from 2020 to 2022, clear acceleration processes were observed in the H8 deformation zone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5 Discussion","content":"\u003cp\u003eMost of the potential landslides identified in the study area are primarily reactivations of large-scale ancient landslides, often attributed to past seismic events and fault activities (Zhang et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While these ancient landslides typically experience exceedingly slow creep deformation influenced by river erosion, human activities, and long-term gravitational effects, intense rainfall can abruptly reactivate them (Zhang et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e5.1 The Analysis of the relationship between potential landslide deformation and rainfall\u003c/h3\u003e\n\u003cp\u003eLandslide occurrences result from the synergistic action of numerous influences, with precipitation being a primary trigger. On one hand, rainfall can induce water-soil interactions that reduce the soil mass's cohesion and frictional forces (Yue et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Concurrently, surface runoff generated by rainfall can alter the original slope surface, inducing instability (Bogaard and Greco \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, the infiltration of substantial rainfall can saturate soil layers, augmenting the landslide mass's weight and potentially causing landslide disasters (Ray and Jacobs \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In essence, two prerequisites exist for rainfall-induced landslides: prolonged, continuous rainfall and sufficiently high rainfall intensity.\u003c/p\u003e \u003cp\u003eRainfall time series analysis indicates that when monthly cumulative rainfall exceeds 100 mm, subsidence tends to occur in the landslide mass's middle and rear parts, typically in peak landslide and debris flow periods of July and August. During these months, accelerated subsidence was observed in the rear and middle sections of both H1 and H8 potential landslides. This suggests that July and August's rainfall intensity surpassed these landslides' surface infiltration rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e), transforming heavy rainfall into surface runoff and eroding the slope (Kanungo and Sharma \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). H1 potential landslide exhibited continuous subsidence around 3 to 6 months post-heavy rainfall in August, likely due to the soil's poor permeability hindering timely evaporation and discharge of shallow soil moisture caused by rainfall. This moisture outflow can generate pore water pressure, inducing instability (Shuqiang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). While H2 and H4 potential landslides' deformation rates show no significant rainy vs. non-rainy season difference, slight acceleration occurs during the 2021\u0026ndash;2022 summer months (June to September). Unlike H1 and H8, these accelerations don't result in extensive deformation, and overall deformation rates maintain linear time correlation. Following the rainy season, when Yunyang County's precipitation significantly decreases and slopes' shallow soil moisture evaporates, the four potential landslides' deformation exhibits minor seasonal stability fluctuations, including small-scale settlement or uplift, typically between January-April and September-December.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e5.2 Precipitation correlation analysis\u003c/h3\u003e\n\u003cp\u003eA typical Pearson correlation analysis between rainfall and typical landslides in Yunyang County revealed that, except for H4 and H8 potential landslides, deformation in the central and toe regions of the other two landslides demonstrated certain rainfall correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). Moreover, deformation between the front, middle, and rear regions of individual potential landslides exhibited strong correlation. Specifically, significant rainfall correlation was noted in the H1 potential landslide's rear region and left margin, and the H2 potential landslide's middle-front region. Although the H4 landslide's rear region and the H8 landslide's middle region showed some rainfall correlation, overall correlation was relatively low, with these areas remaining relatively stable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e5.3 Influence of Yangtze River water level on slope stability\u003c/h3\u003e\n\u003cp\u003eThe fluctuating water levels of the Three Gorges Reservoir are known to compromise slope stability, as evidenced by the pronounced decline in the Yangtze River's water level during the latter stages of the H8 landslide's acceleration (Cao et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This phenomenon impacts slope stability through several mechanisms. A lower water level translates to diminished horizontal water pressure at the landslide's base, reducing sliding resistance. Concurrently, the upper soil mass, unburdened by its own weight, may exert increased pressure on the lower strata, undermining stability (Wang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, a receding water level reshapes the pore water pressure distribution within the landslide, altering the soil's effective stress distribution and, by extension, its stability (Hou et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Exposure of the landslide's surface to atmospheric conditions due to water level reduction also heightens vulnerability to weather elements like rainfall, potentially intensifying landslide activity (Zhao et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Notably, in rare instances, rising water levels can also precipitate landslides if the Yangtze River's force surpasses the slope's erosion resistance threshold, directly triggering a landslide event.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Limitations of SBAS-InSAR Geometry in Complex Mountainous Terrain\u003c/h2\u003e \u003cp\u003eOur study area is situated in mountainous terrain, where significant geometric distortions are encountered particularly when utilizing InSAR technique with SAR imagery. This is primarily attributed to the inherent side-view geometry of SAR. In regions characterized by steep mountain ranges and deep valleys, this challenge becomes more pronounced. To quantify these distortions, we conducted an analysis of the geometric parameters of Sentinel-1 ascending orbit images and SRTM DEM (Dai et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our research findings indicate that, for ascending orbit images, the proportions of Feasible, Layover, and Shadow regions are 92.97%, 6.63%, and 0.4% respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ea). It is noteworthy that, among the potential landslide areas detected, the majority are distributed within the Feasible range, with only a few areas exhibiting Layover and Shadow, such as the H7 potential landslide area. This finding provides support for the validity of SBAS-InSAR data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, the deformation velocity maps derived from the SBAS-InSAR technique still contain numerous blank areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), mainly due to inherent limitations in the Sentinel-1A sensor (Yang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In areas with high vegetation coverage, the C-band's wavelength (approximately 5.6 centimeters) cannot penetrate, resulting in inconsistencies in the deformation velocity maps (Xiao et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The absence of InSAR data in these areas complicates reasonable surface susceptibility category assessment. To mitigate this, L-band SAR sensors, with wavelengths ranging from 30 to 15 centimeters, can enhance radar penetration (Liu et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, L-band SAR offers improved applicability in densely vegetated areas by reducing vegetation-induced temporal correlation effects. However, due to the greater cost of collecting L-band SAR data and its longer revisit period compared to C-band Sentinel-1A data, its sensitivity to surface deformation is lower. As a result, L-band SAR data was excluded from this experiment.\u003c/p\u003e \u003c/div\u003e"},{"header":"6 Conclusions","content":"\u003cp\u003eLeveraging Sentinel-1 satellite imagery and SBAS-InSAR methodology, this study pinpointed 12 nascent landslides, predominantly situated near residential zones and along the Yangtze River's trajectory. Precipitation emerged as the principal agent of surface deformation and landslide genesis in Yunyang County. Notably, potential landslides within residential vicinities and flanking the Yangtze River manifested markedly elevated deformation rates during the wet season versus the dry season. These sites are prone to slope failures and geological calamities upon reaching critical antecedent rainfall levels, underscoring the imperative for ongoing surveillance. While other potential landslides maintained consistent deformation rates across seasons, they underwent brief accelerations post-heavy precipitation. A subsequent correlation analysis between rainfall and four selected landslides corroborated a definitive link between their deformation rates and rainfall volume. Furthermore, potential landslides abutting the Yangtze River bank experienced accelerated deformation concurrent with significant river water level reductions, indicating that the river's cyclical water level fluctuations bear implications for slope stability. In summation, SBAS-InSAR technology proves instrumental in detecting millimetric deformations in incipient landslides, a crucial step in averting landslide disasters and ensuring public safety.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJQN202100624), \u0026nbsp;and the Opening fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology) (SKLGP2023K008).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJinhu Cui: Methodology, Software, Writing \u0026ndash;original draft. Yuxiang Tao: Visualization, Methodology, Writing \u0026ndash; review \u0026amp; editing. Pinglang Kou: Conceptualization, Writing \u0026ndash; review \u0026amp; editing. Zhao Jin: Investigation. Jinlai Zhang: Data curation, Software. 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Geofluids. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2017/3724867\u003c/span\u003e\u003cspan address=\"10.1155/2017/3724867\" 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":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Potential Lanslides, Sentinel-1A, SBAS-InSAR, Precipitation, Yangtze River Water Level","lastPublishedDoi":"10.21203/rs.3.rs-4247951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4247951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLandslide hazards pose a significant threat to lives and infrastructure, especially in mountainous regions like the Three Gorges Reservoir area. While the mechanisms driving landslide initiation and progression in reservoir environments are not fully understood. This study aimed to leverage the capabilities of Sentinel-1 satellite imagery and the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to detect and monitor potential landslide deformations in Yunyang County, Chongqing, China. We utilized Sentinel-1 data acquired between January 1, 2020, and December 28, 2022, to generate deformation velocity maps. Twelve potential landslides were identified, primarily concentrated near residential areas and along the Yangtze River. Precipitation emerged as the primary driver of surface deformation and landslide initiation, with potential landslides in residential vicinities and along the river exhibiting significantly higher deformation rates during the wet season compared to the dry season. These sites are susceptible to slope failures and geological disasters upon reaching critical antecedent rainfall thresholds, highlighting the necessity for continuous monitoring. Other potential landslides maintained consistent deformation rates across seasons but experienced brief accelerations following heavy precipitation events. Notably, potential landslides adjacent to the Yangtze River experienced accelerated deformation during periods of significant river water level reductions, suggesting that the river's cyclical water level fluctuations influence slope stability. The study demonstrated the effectiveness of SBAS-InSAR in detecting millimetric deformations in incipient landslides, a crucial step in averting landslide disasters and ensuring public safety.\u003c/p\u003e","manuscriptTitle":"Hydrological Influences on Landslide Dynamics in the Three Gorges Reservoir Area: An SBAS-InSAR Study in Yunyang County, Chongqing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-17 08:39:44","doi":"10.21203/rs.3.rs-4247951/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-30T09:01:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-27T11:08:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293612035233975209948592126305248595329","date":"2024-05-27T07:37:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-25T23:17:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-13T04:37:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-13T04:37:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Earth Sciences","date":"2024-04-10T14:31:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5d6bcac2-734e-4547-9a0e-ee33ee894e05","owner":[],"postedDate":"April 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-12T16:03:42+00:00","versionOfRecord":{"articleIdentity":"rs-4247951","link":"https://doi.org/10.1007/s12665-024-11770-4","journal":{"identity":"environmental-earth-sciences","isVorOnly":false,"title":"Environmental Earth Sciences"},"publishedOn":"2024-08-05 15:57:43","publishedOnDateReadable":"August 5th, 2024"},"versionCreatedAt":"2024-04-17 08:39:44","video":"","vorDoi":"10.1007/s12665-024-11770-4","vorDoiUrl":"https://doi.org/10.1007/s12665-024-11770-4","workflowStages":[]},"version":"v1","identity":"rs-4247951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4247951","identity":"rs-4247951","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}