Existing studies have shown that satellite synthetic aperture radar (SAR) interferometry has two apparent drawbacks, i.e., temporal decorrelation and atmospheric contamination, in the application of deformation mapping. It is however possible to improve deformation analysis by tracking some natural or man-made objects with steady radar reflectivity, i.e., permanent scatterers (PS), in the frame of time series of SAR images acquired over the same area. For detecting land subsidence in Shanghai, China, this paper presents an attempt to explore an approach of PS-neighborhood networking SAR interferometry. With use of 26 ERS-1/2 SAR images acquired 1992 through 2002 over Shanghai, the analysis of subsiding process in time and space is performed on the basis of a strong network which is formed by connecting neighboring PSs according to a distance threshold. The linear and nonlinear subsidence, atmospheric effects as well as topographic errors can be separated effectively in this way. The subsidence velocity field in 10 years over Shanghai is also derived. It was found that the annual subsidence rates in the study area range from -2.1 to -0.6 cm/yr, and the averaged subsidence rate reaches -1.1 cm/yr.

Shadows are a special distortion in synthetic aperture radar (SAR) imaging. They often hamper proper image understanding and target recognition but also offer useful information, and therefore, the statistical modeling of SAR image shadows is imperative. In this endeavor, we systematically deduced the statistical models of shadows in multimodal SAR images, including single-look intensity and amplitude images and multilook intensity and amplitude images in a real domain and complex domain, respectively. In particular, for the filtered SAR image shadow, we introduced the generalized extreme value (GEV) distribution to characterize its statistics. We carried out an experiment on shadows in a real SAR image and conducted chi-square goodness-of-fit tests on the deduced models. Furthermore, we compared the deduced statistical models of shadows with state-of-the-art statistical models of SAR imagery. Finally, suggestions are given for selecting the optimal statistical model of shadows in multimodal SAR images.

Early and accurate identification of landslide-prone areas is critical for monitoring and early-warning systems, forming the foundation of disaster prevention and mitigation. However, current landslide susceptibility assessment methods often rely on arbitrary probability classification thresholds, leading to subjective and regionally non-adaptive results that neglect low-susceptibility areas, thereby limiting their practical utility in disaster management. To address these limitations, this study proposes a novel method for identifying landslide-prone areas by integrating multi-factor analysis with adaptive probability thresholds. The methodology combines landslide catalog data with key landslide influencing factors, including geology, topography, precipitation, surface deformation, and human activities. The gradient boosting decision tree (GBDT) algorithm is employed to estimate landslide susceptibility probabilities, while an adaptive threshold criterion—based on minimizing the Jensen–Shannon (JS) divergences weighted sum between landslide-prone areas and positive samples—is established to objectively classify regions. Validation experiments were conducted in the northern Tongren region of the Longwu River Basin, Qinghai Province, China. Historical landslides (February 2016–June 2017) were used for model training, and subsequent landslides (June 2017–November 2022) served as validation data. The results demonstrate exceptional performance: the susceptibility model achieved an AUC value of 0.99, with 94.07% accuracy in classifying landslides positive samples. Furthermore, 77.78% of post-2017 landslides occurred within the identified prone areas, yielding a 22.22% omission rate. These findings highlight the method’s ability to dynamically adapt to regional characteristics, balance sensitivity and specificity, and provide actionable insights for landslide risk management.