The use of satellite imagery for surface deformation monitoring has been steadily increasing. However, the study of extracting deformation slopes from deformation data requires further advancement. This limitation not only poses challenges for subsequent studies but also restricts the potential for deeper exploration and utilization of deformation data. The LT-1 satellite, China’s largest L-band synthetic aperture radar satellite, offers a new perspective for monitoring. In this study, we extracted deformation slopes in Chongqing and its surrounding areas of China based on deformation data generated by LT-1. Twelve factors were selected to analyze their influence on slope deformation, including elevation, topographic position, slope, landcover, soil, lithology, relief, average rainfall intensity, and distances to rivers, roads, railways, and active faults. A total of 5863 deformation slopes were identified, covering an area of 140 km², mainly concentrated in the central part of the study area, with the highest area density reaching 0.22%. Among these factors, average rainfall intensity was found to have the greatest impact on deformation slope. These findings provide valuable information for geological disaster early warning and management in Chongqing and surrounding areas, while also demonstrating the practical value of the LT-1 satellite in deformation monitoring.

The construction of ultra-high voltage (UHV) transmission corridors often spans complex mountainous regions, which are characterized by intricate geographical environments and frequent geological hazards. This study aims to improve deformation monitoring in low-coherence areas of UHV transmission corridors, such as those with dense vegetation, by utilizing Distributed Scatterer Interferometry (DS-InSAR). Using Sentinel-1A data, long-term deformation time series were derived. Compared to traditional time-series InSAR methods, DS-InSAR demonstrated significant improvements across several dimensions. Specifically, phase quality was notably enhanced after optimization using the Phase Linking method, resulting in clearer deformation region boundaries and a significant reduction in noise. Additionally, the number of effective monitoring points increased from 1,481 to 7,698, leading to more densely distributed and reliable deformation results. The annual average deformation rates extracted by traditional time-series InSAR ranged from −146 to 49 mm/year, while those obtained by DS-InSAR ranged from −144 to 74 mm/year. While the overall deformation trends were similar, the traditional time-series InSAR technique slightly overestimated the deformation magnitude. This study provides theoretical support for key technologies in deformation extraction for low-coherence areas in UHV transmission corridor construction.

Against the backdrop of global warming, an intensified hydrological cycle has led to more frequent and more destructive short-duration extreme rainfall events. Rapid and objective delineation of flood inundation extent is therefore critical for emergency response and post-disaster assessment. Taking the extreme rainfall–triggered flood in Miyun District, Beijing, in July 2025 as a case study, this paper develops a machine-learning-based flood inundation mapping workflow through the fusion of optical and SAR remote sensing. Sentinel-1 and Sentinel-2 imagery acquired within 1 month before and 1 month after the flood event were collected, and pixel-wise monthly mean compositing was applied to reduce SAR speckle, cloud contamination, and scene-specific anomalies. Based on the composites, multi-source features—including SAR scattering characteristics, optical spectral information, and DEM-based terrain constraints—were integrated to perform supervised water/non-water classification, and newly inundated areas were extracted via pre-/post-event change detection. In addition, stable water (pixels classified as water in both periods) was identified and removed to avoid counting permanent water bodies such as the Miyun Reservoir as flood extent. High-resolution Jilin-1 optical imagery (1 m) was further incorporated for fine-scale boundary interpretation and auxiliary sample labeling. Results indicate that both SAR and optical data effectively capture flood-driven surface-water expansion, with newly inundated areas mainly distributed in low-lying zones around the reservoir and along both sides of river channels, forming a spatial pattern combining river-aligned belts and locally patchy clusters. Under the monthly compositing scheme, the flood inundation area estimated from Sentinel-2 is 1425 ha, while that from Sentinel-1 is 1284 ha; the optical estimate is 141 ha larger, corresponding to approximately +11.0% (with SAR as the reference). The difference reflects contrasting sensor responses under cloudy/rainy conditions, shallow inundation, and complex scattering environments, as well as the smoothing effect of monthly averaging on peak-flood signals. The proposed multi-source fusion and change-detection workflow provides a methodological reference for rapid flood mapping, damage verification, and flood risk management in Miyun and similar mountainous–river-valley composite regions.

Ultra-high voltage (UHV) transmission lines are key infrastructure ensuring the safe cross-regional transmission of national energy. However, these lines are often located in regions facing multiple disturbances, such as complex terrain, frequent geological disasters, and intense human activities, all of which pose significant threats to the stability of tower foundations. To achieve high-precision deformation monitoring and risk identification for typical mountainous transmission corridors. This study selects the ±800 kV Qishao Line transmission corridor in Badong County, located in the southwestern part of Hubei Province, as the research area. The study integrates two types of C-band SAR data from Sentinel-1A and RADARSAT-2, employing SBAS-InSAR technology to perform time-series deformation analysis from July 2019 to August 2020. Additionally, the Getis-Ord Gi* statistical method is introduced to assess subsidence hot spots. The results show that the annual average deformation in the region, as obtained from Sentinel-1A data, mainly ranges from −10 mm/a to +10 mm/a, indicating a generally stable deformation trend. In contrast, RADARSAT-2, with its high resolution, identifies several local strong subsidence areas, with the maximum annual subsidence rate reaching −95.6 mm/a. These areas are mainly located in geomorphological transition zones such as the northern bank of the Yangtze River and valley intersections. A typical slope area on the northern bank of the Yangtze River shows persistent subsidence bands with an annual average deformation exceeding −76 mm/a, which closely coincides with tower foundation construction areas, steep slopes, and traffic disturbance zones. In the valley-crossing section, significant subsidence hot spots (greater than −20 mm/a) are identified in the mid- and lower slope foot regions, while cold spots are observed in the high-slope areas, with deformations lower than −5 mm/a. Both data types exhibit highly consistent time-series trends at characteristic points, and scatter density map analysis demonstrates a strong linear correlation between them, validating the complementarity and fusion potential of multi-source InSAR data for deformation monitoring in complex mountainous environments. The multi-source InSAR monitoring and spatial aggregation analysis framework developed in this study enables rapid identification of high-risk sections in UHV transmission corridors. It provides high-resolution, quantitative support for slope deformation early warning, tower foundation stability assessment, and operation and maintenance scheduling, offering excellent engineering adaptability and broad applicability.

Soil moisture (SM) governs land–atmosphere exchanges and strongly influences agricultural management and hydrological assessment, yet high-resolution mapping remains challenging due to sensor-specific confounding effects and limited field observations. This study develops a practical workflow for point-scale SM estimation and wall-to-wall mapping by integrating multi-sensor remote sensing predictors with ensemble learning. A compact predictor set was constructed from Sentinel-2 optical indices (MSI and NDWI), Sentinel-1 SAR descriptors (σVV and the polarization ratio σVH/σVV), and topographic information (DEM), collocated with in situ SM measurements along a transect in the study area. Three tree-based regressors—Random Forest, XGBoost, and CatBoost—were trained under an identical feature configuration and evaluated using R², RMSE, and MAE together with predicted–observed diagnostics. A stacking ensemble was then implemented using leakage-controlled K-fold out-of-fold predictions to generate meta-features, with a Decision Tree as the meta-learner tuned via a grid search. Results show that base learners achieve comparable skill (R² ≈ 0.60–0.62; RMSE ≈ 0.038–0.039), while stacking improves test accuracy (RMSE = 0.0346) and provides a stable mapping-ready model. The trained framework was transferred to stacked raster predictors to produce spatially continuous SM maps, revealing coherent moisture heterogeneity across the region.

Soil moisture (SM) governs land–atmosphere exchanges and strongly influences agricultural management and hydrological assessment, yet high-resolution mapping remains challenging due to sensor-specific confounding effects and limited field observations. This study develops a practical workflow for point-scale SM estimation and wall-to-wall mapping by integrating multi-sensor remote sensing predictors with ensemble learning. A compact predictor set was constructed from Sentinel-2 optical indices, Sentinel-1 SAR descriptors (σVV and the polarization ratio σVH/σVV), and topographic information, collocated with in situ SM measurements along a transect in the study area. Three tree-based regressors—Random Forest, XGBoost, and CatBoost—were trained under an identical feature configuration and evaluated using R², Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) together with predicted–observed diagnostics. A stacking ensemble was then implemented using leakage-controlled K-fold out-of-fold predictions to generate meta-features, with a Decision Tree as the meta-learner tuned via a grid search. Results show that base learners achieve comparable skill (R² ≈ 0.60–0.62; RMSE ≈ 0.038–0.039), while stacking improves test accuracy (RMSE = 0.0346) and provides a stable mapping-ready model. The trained framework was transferred to stacked raster predictors to produce spatially continuous SM maps, revealing coherent moisture heterogeneity across the region. Accordingly, the objective of this study is to develop a compact and application-oriented point-to-map workflow for high-resolution soil moisture estimation by integrating Sentinel-1/2-derived predictors with stacking-based model fusion, rather than to propose a new physically based retrieval model.