The Three Gorges hydropower station in China creates a large reservoir by diverting water from the Yangtze River, increasing the risk of geological disasters, especially massive landslides along the reservoir shoreline. To mitigate these risks, improving geological monitoring and early warning systems is crucial. Interferometric Synthetic Aperture Radar (InSAR) is widely used to monitor reservoir bank landslides. However, its potential in early warning systems is limited due to temporal resolution constraints, preventing timely warnings. To address this, we propose integrating daily hydrological data (precipitation and water level observations) with historical InSAR deformation sequences using our deep learning-based multivariate united state estimation network, “MUSEnet.” This approach generates customized daily landslide deformation products for high-risk areas, greatly enhancing early warning capabilities by providing timely and accurate information on landslide occurrence and magnitude. We validated our method using 161 Sentinel-1 A images of the Xinpu landslide in the Three Gorges Reservoir area. Through statistical analysis, we identified different degrees of influence from rainfall and reservoir water level on the deformation of the Xinpu landslide at various locations. Additionally, we observed distinct lag times between deformation and corresponding rainfall and reservoir water level events. By utilizing deep learning, our method estimates nonlinear states by considering hysteresis and intelligently accounts for the impact of rainfall and reservoir water level, resulting in more accurate estimations compared to traditional models.

Landslides are the most serious geological disaster in our country, causing economic losses. Because they go undetected, a large number of landslides that have caused disasters are not in the catalogue. At present, Interferometric Synthetic Aperture Radar (InSAR) has been widely used in the identification of landslides. However, it is time-consuming, inefficient, etc., to survey landslides throughout our large country. In the context of massive SAR data, this problem is more obvious. Therefore, based on the current technique of using differential interferogram phase gradient stacking to avoid phase unwrapping errors, a landslide phase gradient dataset has been constructed. To validate the dataset’s effectiveness and applicability, deep learning methods were introduced, applying the dataset to four networks: U-Net, Attention-Unet, Bisenet v2, and Deeplab v3. The results indicate that the phase gradient dataset performs well across different models, with the Attention-Unet network demonstrating the best performance. Specifically, the precision, recall, and accuracy on the test dataset were 0.8771, 0.8712, and 0.9834, respectively, and the accuracy on the validation dataset was 0.8523. Finally, in this paper, the model is applied to landslide identification in Gansu Province, China, during 2022-2023, and a total of 1882 landslides are found. These landslides are mainly concentrated in the south of Gansu Province, where the terrain is relatively undulating. The results show that this method can quickly and accurately realize landslide automatic identification in a wide area and provide technical support for large-scale landslide disaster surveys.

Landslides are geological events that frequently cause major disasters. Research on landslides is essential, but current studies mostly use historical landslide data and do not reflect dynamic, real-time research results. In this study, landslide deformations and land-use changes were used to analyze the landslide distribution in Fengjie County and Wushan County in the Three Gorges Reservoir Area (TGRA) by using interferometric and polarimetric SAR. In this study, the mean annual rate of landslide deformations was obtained using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) for the ALOS-2 (2014–2019) data. Land-use changes were based on the 2007 and 2017 land-use results from dual-polarization ALOS-1 and ALOS-2 data, respectively. To address the problem of classification accuracy reduction caused by geometric distortion in mountainous areas, we first used texture maps and pseudocolor maps synthesized with dual-polarization intensity maps to perform classification with random forest (RF), and then we used coherence and slope maps to run the K-Means algorithm (KMA). We named this the secondary classification method. It is an improvement on the single classification method, exhibiting a 94% classification accuracy, especially in rugged areas. Combined with land-use changes, GIS spatial analysis was used to analyze the spatial distribution of landslides, and it was found that the landslide rate was significantly correlated with the type after change, with a correlation coefficient of 0.7. In addition, land-use types associated with human activities, such as cultivated vegetation, were more likely to cause landslide deformation, which can be used to guide local land-use planning.

Multiple-aperture interferometric synthetic aperture radar (MAI) technology can obtain the deformation of the surface along the azimuth direction, which makes up for the limitations of traditional InSAR technology. However, the MAI measurement is vulnerable to the ionosphere, which makes the ionospheric delay mixed with the surface deformation, resulting in a seriously reduction of the accuracy of the azimuth measurement. In this paper, a method for correcting the azimuth ionospheric delay phase is proposed based on the relationship between the ionospheric delay phase and the group phase delay offset, termed by azimuth group phase delay (AGPD) method. This approach accommodates large-scale deformation fields, facilitating a more comprehensive acquisition of ionospheric information. This method is firstly employed to reconstruct the coseismic deformation field associated with the 2021 Maduo earthquake. After ionospheric correction, the root mean square errors (RMSEs) between GNSS and MAI measurements decreases from 0.08 m to 0.04 m. Then, the results from the Alaska case demonstrate the method’s ability in the identification of intricate ionospheric stripe patterns. Comparative analysis against the existing azimuth ionospheric error correction methods indicates a significant improvement of above 50%.

Traditional Selection of Homogeneous Pixels (SHP) methods insufficiently utilize polarization information and rely on extensive statistical computations from large datasets. When applied to mountainous or densely vegetated regions with low coherence, these methods are susceptible to noise interference, which reduces the interpretation reliability. In this study, we propose ViTPolSHP———the Vision Transformer(ViT)-based Polarimetric Synthetic Aperture Radar (PolSAR) Selection of Homogeneous Pixels algorithm for selecting homogeneous pixels in multi-temporal PolSAR imagery. The ViTPolSHP algorithm leverages the temporal average Yamaguchi four-component decomposition features for classification and integrates the terrain characteristics and geometric distortions to derive homogeneous pixel sets even with limited datasets, presenting a promising solution for SAR/InSAR data processing in complex mountainous terrains.