The detection of potential rural mountain landslide displacements using time-series interferometric Synthetic Aperture Radar has been challenged by both atmospheric phase screens and decoherence noise. In this study, we propose the use of a combined distributed scatterer (DS) and the Prophet_ZTD-NEF model to rapidly map the landslide surface displacements in Diqing Tibetan Autonomous Prefecture, China. We conducted tests on 28 full-resolution SENTINEL-1A images to validate the effectiveness of our methods. The conclusions are as follows: (1) Under the same sample conditions, confidence interval estimation demonstrated higher performance in identifying SHPs compared to generalized likelihood ratio test. The density of DS points was approximately eight times and five times higher than persistent scatterer interferometry and small baseline subset methods, respectively. (2) The proposed Prophet_ZTD-NEF model considers the spatial and temporal variability properties of tropospheric delays, and the root mean square error of measured values was approximately 1.19 cm instead of 1.58 cm (PZTD-NEF). (3) The proposed Prophet_ZTD-NEF method reduced the mean standard deviation of the corrected interferograms from 1.88 to 1.62 cm and improved the accuracy of the deformation velocity solution by approximately 8.27% compared to Global Position System (GPS) measurements. Finally, we summarized the driving factors contributing to landslide instability.

InSAR technology effectively monitors urban subsidence and evaluates the stability of infrastructure across extensive regions. Atmospheric tropospheric delay constitutes a significant source of error that adversely impacts the accuracy of InSAR deformation measurements. The meteorological conditions in the highland basin region are complex, and there is a notable deficiency of sufficient sounding balloon observations. This study replaces the sounding balloon data in the power-law model with ERA-5 data (PLE5) to correct the InSAR atmosphere phase delay. This method was tested in Dali utilizing Sentinel-1 data. By comparing its performance against the GACOS model, traditional linear model, and ERA-5 correction, the PLE5 model exhibited the lowest phase standard deviation. This method provides an alternative approach for applying the power-law model in regions with limited sounding balloon data, enhancing the accuracy of InSAR tropospheric delay correction.

Yongshan County in northeastern Yunnan Province is a frequent geological hazard zone. Based on previous detailed geological hazard surveys, the county contains 455 landslide hazard sites, primarily distributed in the western and northern regions. Influenced by multiple factors including rainfall, earthquakes, human activities, and reservoir water storage, it is challenging to evaluate their development using a single indicator. Therefore, there is an urgent need to conduct landslide susceptibility assessments that integrate deformation rate characteristics. However, existing studies in this region have only considered static spatial factors such as slope aspect, elevation, and lithology. Traditional landslide susceptibility assessments often struggle to balance zoning accuracy with timeliness, leading to biased results and limited update efficiency. This study employs SBAS-InSAR technology to capture surface deformation rates and utilizes machine learning models to partition landslide susceptibility distribution maps. It innovatively introduces an RFE-RF-XGBoost model to reduce partitioning errors and enhance the accuracy of landslide susceptibility mapping. Experiments utilized 147 Sentinel-1A and 14 LT-1 scenes. Through five-fold cross-validation, 13 influencing factors were selected. The RFE-RF-XGBoost model was trained via hyperparameter optimization and compared against four conventional models (CatBoost, LightGBM, XGBoost, RF). After validating the predictive performance of different models via ROC curves, the prediction results at each level were analyzed using Accuracy, Precision, Recall, and F1 metrics. Results indicate that all five machine learning models demonstrate effective zoning capabilities. Among them, the RFE-RF-XGBoost model achieves optimal mapping performance. Compared to the other four models, it reduces the proportion of low-risk zones by 2–4% while increasing the proportion of extremely high-risk zones by approximately 2–12%, with an AUC value reaching around 0.95. Field investigations further validated that this approach enhances landslide interpretation accuracy by integrating SBAS-InSAR technology with remote sensing techniques.

Landslide susceptibility mapping (LSM) is widely used for identifying potential landslide-prone areas. However, many existing approaches rely on global models that assume spatial stationarity, which limits their ability to capture spatially heterogeneous relationships in complex mountainous regions. To address this issue, this study improved landslide susceptibility evaluation by accounting for spatial heterogeneity using a Geographically Weighted Random Forest (GWRF) model. By allowing the influence of conditioning factors to vary spatially, the proposed method provides a more adaptive representation of landslide susceptibility compared to conventional global models. The GWRF-based evaluation results were compared with those obtained from Random Forest (RF) and XGBoost models to examine relative performance. The study was conducted in Yingjiang County, a landslide-prone mountainous area, using multiple landslide conditioning factors, including topographic and anthropogenic variables such as slope and distance to roads. Landslide susceptibility maps were generated, and the evaluation results were supported by InSAR-derived deformation data, field investigations, and UAV observations. The results indicate that the GWRF model achieved superior overall susceptibility evaluation performance compared to the RF and XGBoost models, with an AUC value of 0.922. Furthermore, compared to global models, the GWRF model revealed more detailed spatial patterns of landslide susceptibility, particularly in high-susceptibility zones. Areas classified as highly susceptible by the GWRF model also demonstrated greater consistency with observed deformation features. These findings highlight the importance of considering spatial heterogeneity in landslide susceptibility evaluation and demonstrate that the proposed GWRF approach is applicable for regional-scale susceptibility assessment in complex mountainous environments.

This study addresses the stratified and turbulent tropospheric delays that impede interferometric synthetic aperture radar (InSAR) deformation monitoring in alpine canyon regions. We introduce a tropospheric delay model that incorporates time-varying Gaussian coefficients and coupled zenith wet delay (ZWD) by combining diverse multi-source data. This model was incorporated into StaMPS for InSAR processing. Evaluation results demonstrated that (1) the model accurately captured seasonal and diurnal tropospheric variations, achieving a root mean squared error (RMSE) of 2.01 cm relative to the GNSS reference data; (2) the model corrected stratified and turbulent delays and reduced interferometric phase standard deviation (STD) by 9.28% compared to the Generic Atmospheric Correction Online Service (GACOS); and (3) the deformation accuracy improved by 19.07% over GACOS. Discussion results indicate that accounting for time-varying Gaussian coefficients is essential and that coupling ZWD to rectify turbulent delays outperformed the filtering method. The observed negative interferogram corrections result from the random intensity of turbulent delays. These findings confirm the effectiveness of the proposed model for high-precision InSAR deformation monitoring in complex alpine terrains. The proposed model aims to enhance studies of tropospheric delay variations in alpine canyon regions and to mitigate such delays in InSAR-based geological hazard monitoring.

Daguan County, in the Wumeng Mountains of Yunnan Province, Southwest China, is characterized by complex tectonics, steep terrain and highly seasonal rainfall, and is among the counties most severely affected by geological hazards in the province. This study applies distributed-scatterer time-series InSAR (DS-InSAR) to ascending and descending SAR imagery acquired between March 2024 and July 2026 by the L-band LuTan-1 (LT-1) constellation and by C-band Sentinel-1, deriving wide-area line-of-sight (LOS) velocity fields and deformation time series over a monitoring area of about 6.44 km${}^2$ around the county seat. Ascending–descending decomposition and singular spectrum analysis (SSA) separate the deformation at three deforming sites into trend, periodic and residual components, and cross-correlation, lag-response, cumulative regression and wavelet coherence analyses are used to test the temporal association between deformation and meteorological and other external variables. LT-1 shows a clear advantage in vegetated terrain: the ascending stack yields $4.20\times {10}^4$ coherent points per km${}^2$, a 23.74-fold density (2274.0% increase) relative to Sentinel-1 and a 42.22-fold density (4122.3%) within vegetated areas, while the descending stack yields $2.47\times {10}^4$ points per km${}^2$ (10.55-fold, 954.6%; 22.87-fold, 2186.9% in vegetated areas). Because the two stacks also differ in incidence and azimuth angle, acquisition dates, repeat cycle and processing thresholds, this gain is reported as an operational advantage of the L-band mission rather than as an isolated wavelength effect. At significant-deformation points the two missions agree with correlation coefficients of 0.674 and 0.623 and root-mean-square errors of 15.32 and 2.72 mm/yr; this is moderate agreement, and the ascending comparison rests on only 49 matched pairs. Three clusters of significant deformation (H01–H03) were delineated for $\left|v\right|\ge 10$ mm/yr. Two-dimensional decomposition indicates that the resolved motion is dominated by east–west horizontal displacement (mean rates of $-18.48$, $-9.24$ and $-18.21$ mm/yr, peak of $-46.66$ mm/yr at H03), with horizontal-to-vertical trend ratios of 5.30, 1.79 and 4.16; because the north–south component is not resolved, these ratios describe an apparent horizontal dominance whose magnitude depends on the decomposition assumption. SSA reveals cumulative east–west trend displacements of $-61.7$ mm at H01, $-41.8$ mm at H02 and $-90.3$ mm at H03, where the trend rate increases to $-44.5$ mm/yr. At H03 the periodic amplitude of 30.46 mm reproduces most of the apparent rebound present in the raw series, so the rebound should not be read as stabilization; under the adopted decomposition it does not require a reversal of the trend. The east–west periodic component is associated with maximum air temperature (r = 0.364, 0.439 and 0.466) but only weakly with rainfall at zero lag. Following the M 3.9 earthquake of 12 December 2025, trend rates at H01 and H03 increased briefly, and the acceleration at H01 and H02 coincides in time with the period of most intensive urban construction; these coincidences are consistent with, but do not demonstrate, seismic and engineering triggering.