Mining-induced subsidence poses a serious hazard to the surrounding environment and infrastructure, necessitating the detection of such subsidence for effective disaster mitigation and the safeguarding of local residents. Fucheng 1 is the first high-resolution mini-satellite interferometric Synthetic Aperture Radar (SAR) launched by China in June 2023. In this study, we used Fucheng 1 SAR images to analyze mining-induced subsidence in Karamay by InSAR Stacking and D-InSAR. The findings were compared with Sentinel-1A imagery to evaluate the effectiveness of Fucheng 1 in monitoring subsidence and its interferometric performance. Analysis revealed significant mining-induced subsidence in Karamay, and the results from Fucheng 1 closely corresponded with those from Sentinel-1A, particularly regarding the extent of the subsidence. It is indicated that the precision of Fucheng 1 SAR imagery has reached leading standards. In addition, due to its higher resolution, the maximum detectable deformation gradient (MDDG) of Fucheng 1 is 2.15 times higher than that of Sentinel images. This study provides data support for the monitoring of mining-induced subsidence in the Karamay and give a theoretical basis for the application of Fucheng 1 in the field of Geohazard monitoring.

Fucheng-1 is China’s first commercial synthetic aperture radar (SAR) satellite equipped with interferometric capabilities. Since its launch in 2023, it has demonstrated strong potential across a range of application domains. However, a comprehensive and systematic evaluation of its overall performance, including its time-series monitoring capability, is still lacking. This study applies the Small Baseline Subset (SBAS-InSAR) method to conduct the first systematic processing and evaluation of 22 Fucheng-1 images acquired between 2023 and 2024. A total of 45 potential landslides were identified and subsequently validated through field investigations and UAV-based LiDAR data. Comparative analysis with Sentinel-1 and ALOS-2 indicates that Fucheng-1 demonstrates superior performance in small-scale deformation identification, temporal-variation characterization, and maintaining a high density of coherent pixels. Specifically, in the time-series InSAR-based potential landslide identification, Fucheng-1 identified 13 small-scale potential landslides, whereas Sentinel-1 identified none; the number of identifications is approximately 2.17 times that of ALOS-2. For time-series subsidence monitoring, the deformation magnitudes retrieved from Fucheng-1 are generally larger than those from Sentinel-1, mainly attributable to finer spatial sampling enabled by its higher spatial resolution and a higher maximum detectable deformation gradient. Moreover, as landslide size decreases, the advantages of Fucheng-1 in deformation identification and subsidence estimation become increasingly evident. Interferometric results further show that the number of high-coherence pixels for Fucheng-1 is 7–8 times that of co-temporal Sentinel-1 and 1.1–1.4 times that of ALOS-2, providing more high-quality observations for time-series inversion and thereby supporting a more detailed and spatially continuous reconstruction of deformation fields. Meanwhile, the orbital stability of Fucheng-1 is comparable to that of Sentinel-1, and its maximum detectable deformation gradient in mountainous terrain reaches twice that of Sentinel-1. Overall, this study provides the first systematic validation of the time-series InSAR capability of Fucheng-1 under complex terrain conditions, offering essential support and a solid foundation for the operational deployment of InSAR technologies based on China’s domestic SAR satellite constellation.

Debris-flow hazard assessment is essential for identifying the hazard levels of individual gullies and supporting regional disaster prevention and mitigation. Conventional approaches mainly evaluate potential debris-flow magnitude and intensity based on indicators such as catchment area, historical activity, and relative elevation difference, while dynamic-process information during debris-flow movement is often insufficiently represented. Although process-based hazard assessment has been widely used for individual gullies or specific scenarios, a regional framework that systematically integrates multiple simulated dynamic parameters across numerous gullies remains limited. In this study, the Duoxiongla Valley on the southeastern margin of the Tibetan Plateau was selected as the study area. Based on high-resolution remote sensing data, including 0.5 m orthophoto imagery and 5 m airborne LiDAR data, dynamic numerical simulations were conducted to reproduce debris-flow movement processes. Key parameters, including flow depth, flow velocity, and affected area, were extracted and integrated using the entropy weight method to construct a dynamic characteristics index (DCI). The novelty of this study lies in systematically constructing the DCI for 47 debris-flow gullies and embedding it into a conventional weighted hazard-assessment framework, thereby linking numerical simulation outputs with regional multi-gully hazard classification. The DCI-based hazard-assessment results show that high- and very-high-hazard debris-flow gullies account for 10.64% of the total gullies and are mainly distributed in the middle and upper reaches of the study area. Compared with the traditional assessment method, the introduction of the DCI resulted in hazard-level adjustments for six debris-flow gullies, which were consistent with the actual hazard conditions. These differences mainly reflect the limited ability of conventional indicators to represent debris-flow movement intensity and spatial impact. Specifically, some gullies with large catchment areas exhibit limited spatial expansion due to topographic constraints, whereas some small-catchment gullies may still show high hazard levels due to large elevation differences, high flow velocity, large flow depth, or extensive affected areas. These results demonstrate that integrating multi-parameter dynamic-process information can refine regional debris-flow hazard classification and provide a transferable framework for identifying dynamically hazardous gullies in high-relief mountainous regions.