Topographic phase simulation is important for deformation estimation in differential synthetic aperture radar (SAR) interferometry. The most commonly used 30 m resolution shuttle radar topography mission (SRTM) digital elevation model (DEM) is usually required to be resampled due to its relatively low resolution (LR) comparing to the high resolution (HR) SAR images. Although the WorldDEM with a 12 m resolution achieves global coverage, it is not available freely. Consequently, it is useful to evaluate the practicability of the super-resolution (SR) from LR SRTM DEMs to HR WorldDEM ones, which has not been investigated. Most existing DEM SR models are trained with synthetic datasets in which the LR DEMs are downsampled from their HR counterparts. However, these models become less effective when applied to real-world scenarios due to the domain gap between the synthetic and real LR DEMs. In this article, we constructed a real-world DEM SR dataset, where the LR and HR DEMs were collected from SRTM and WorldDEM, respectively. An enhanced SR generative adversarial network model was adapted to train on the dataset. Considering that the real LR-HR pairs may suffer from misalignment, we introduced the perceptual loss for better optimizing the model. Moreover, a logarithmic normalization was proposed to compress the wide elevation range and adjust the uneven distribution. We also pretrained the model using natural images since collecting sufficient HR DEMs is costly. Experiments demonstrate that the proposed method achieves near 0.69 dB improvement of peak signal-to-noise ratio. In addition, our method is also validated to improve the topographic phase simulation by 23.42% of MSE.

The understanding of glacier and ice shelf front changes is of paramount importance in analyzing their material balance and their significant contributions to global sea-level variations. Recently, the increasing availability of remote sensing images has provided ample data support for calving front detection. By combining massive amounts of data, deep learning techniques have demonstrated great potential in front extraction, as they can automatically generate task-specific features. In this study, in contrast to the traditional framework based on a pure CNN architecture, we propose a novel calving front detection network, GLA-STDeepLab, by incorporating the Swin transformer module into DeepLabv3+ to enhance the modeling capability of long-range contextual dependencies. We also introduce Global-local attention mechanisms into the module, striving to foster interactions between the corresponding coarse and fine-grained feature maps. Through extensive experimentation on a diverse and challenging SAR database, CaFFe, our network is shown to surpass the current state-of-the-art methods, achieving results with an IoU of 0.94 and a mean distance error (MDE) of 473±34 m. More meaningfully, we extracted the time-series front information of the Amery Ice Shelf from 2015 to 2023 and precisely captured the calving events of the D-28 and D-32 icebergs. The findings demonstrate that our method enables precise monitoring of the dynamic information of glacier and ice shelf fronts, as well as their far-reaching implications for ice sheet mass balance and global climate change. The code used in this study is provided at https://github.com/Tangyu35/Calving-front-detection.

Continuous and large-scale surface deformation monitoring is critical for the comprehension of natural hazards and environmental changes. This can be facilitated by time-series Interferometric Synthetic Aperture Radar (TS-InSAR), which provides unprecedented spatial and temporal resolution. However, the original TS-InSAR measurements, being a superposition of trend, seasonal, and noise signals, often suffer from outlier and annual seasonal variations due to the influences of atmospheric delay, especially in coastal and mountainous areas, resulting in skewed monitoring if neglected. To address these issues, an integration method of variational mode decomposition and gated recurrent unit (VMD-GRU) is proposed in this study to enhance the robustness of continuous large-scale surface deformation monitoring. The VMD decomposes low-frequency trend, specific-frequency seasonal, and high-frequency noise components from the original TS-InSAR data via frequency-domain variational optimization first. Then, by eliminating the seasonal component decomposed by VMD from the original time series, the time series is reconstructed, effectively removing the influence of annual seasonal variations. Subsequently, GRU is utilized to further eradicate noise from the reconstructed time series, mitigating the influence of outliers and noise, thereby yielding a trend component that intuitively reflects surface deformation. Experiments on physical-based synthetic and real-world datasets demonstrate that the proposed VMD-GRU outperforms the existing methods. By introducing the frequency priors, the proposed method significantly enhances the robustness and accuracy of continuous large-scale surface deformation monitoring, providing a more reliable understanding of natural hazards and environmental changes.

Significant progress has occurred in interferometric synthetic aperture radar (InSAR), emerging as a crucial technique for monitoring surface deformation. This evolution is attributed to expanded synthetic aperture radar (SAR) data availability and improved data quality. However, effectively managing and processing SAR big data presents substantial challenges for algorithms and pipelines, especially in large-scale contexts. In this article, we introduce a parallel time-series InSAR processing platform that leverages high-performance computing (HPC) clusters for efficiently managing and processing large-scale SAR data and incorporates graphics processing unit (GPU) acceleration to significantly enhance the speed and efficiency of specific InSAR processing algorithms. Our approach encompasses high-quality data compression, integration of classic InSAR models, and the introduction of a robust distributed scatterer InSAR method for time-series processing. The platform efficiently handles massive data, featuring a parallel optimization tool for acceleration. In addition, it provides web-based two-dimensional (2-D) result visualization and 3-D outcome representation for comprehensive user understanding. To illustrate our platform's capabilities, we applied it to 40 Sentinel-1 SAR data scenes from Tibet (2017–2019). Our data compression technique notably reduces data size, reducing mask data by 87.5% and coherence data to 25% of its original size. Leveraging HPC and GPU, we achieved a 50% reduction in registration computation time. This study offers valuable insights and a comprehensive platform for InSAR practitioners, facilitating calculations and enhancing comprehension of surface deformation processes. Our system's improved processing efficiency, coupled with a variety of InSAR methods, makes it an alternative choice for InSAR data handling and analysis.

Urbanization has led to environmental challenges, with the urban heat island effect being a prominent concern. Understanding the influence of urban environmental characteristics (UECs) on land surface temperature (LST) is essential for addressing this issue and promoting sustainable urban development. The spatiotemporal characteristics and influencing factors of LST have been investigated in past studies, but research that explicitly investigates the key factors and long-term spatial relationships affecting LST in city-scale urban areas is limited. Remote sensing techniques provide valuable insights into LST patterns and the relationship between urban environment and temperature dynamics. We utilized Landsat 8 images to derive the LST and six spectral indices from 2017 to 2022 in Hong Kong, a city characterized by high population density and rapid urban growth. We also acquired land use data to reflect Hong Kong’s dynamic urban landscape. The complex interactions between urban environment and LST were analyzed using various analytical techniques, including slope trend analysis, land use change detection, and correlation analysis. Finally, we constructed a random forest model to assess the importance of each environmental characteristic. Our findings provide three key insights for regions experiencing rapid urbanization. First, the LST showed an increasing trend in Hong Kong from 2017 to 2022, with the annual LST rising from 21.13 °C to 23.46 °C. Second, we identify negative relationships between LST and vegetation (−0.49)/water bodies (−0.49) and a positive correlation between LST and built-up areas (0.56) utilizing Pearson’s correlation. Third, the dominant influence of built-up areas was underscored, contributing as much as 53.4% to elevated LST levels, with specific attention to the substantial reclamation activities in Hong Kong. The insights from this study provide valuable guidance for policymakers, urban planners, and environmental researchers to formulate evidence-based strategies to achieve a resilient, livable urban future.