Interferograms with short wavelength (e.g., X-band) are usually prone to temporal decorrelation in permafrost regions, leading to the unavailability of sufficient high-coherence interferograms for performing conventional time series InSAR analysis. This paper proposes the utilization of temporary scatterers for the stacking InSAR method, thus enabling extraction of subsidence in a permafrost region with limited SAR images and limited high-coherence interferograms. Such method is termed as the temporary scatterers stacking InSAR (TSS-InSAR). Taking the Gonghe-Yushu highway (about 30 km), part of G214 National Highway in Qinghai province (in a permafrost region), as a case study, this TSS-InSAR approach was demonstrated in detail and implemented. With 10 TerraSAR-X images acquired during the period from May 2015 to August 2015, the subsidence along this highway was extracted. In this case the lack of a consistent number of SAR acquisitions limits the possibility to perform other conventional time series InSAR analysis. The results show that the middle part of this highway is in the thermokarst and seasonal frozen soil area, and its accumulated subsidence reach up to 10 cm in 110 days. The thawing phenomena is still the main reason for the instability of highway. The results demonstrate that the TSS-InSAR method can effectively extract the subsidence information in a challenging scenario with limited X-band SAR images and limited high-coherence interferograms, where other time series InSAR-based techniques cannot be applied in a simple way.

Clouds seriously limit the application of optical remote sensing images. In this paper, we remove clouds from satellite images using a novel method that considers ground surface reflections and cloud top reflections as a linear mixture of image elements from the perspective of image superposition. We use a two-step convolutional neural network to extract the transparency information of clouds and then recover the ground surface information of thin cloud regions. Given the poor balance of the generated samples, this paper also improves the binary Tversky loss function and applies it on multi-classification tasks. The model was validated on the simulated dataset and ALCD dataset, respectively. The results show that this model outperformed other control group experiments in cloud detection and removal. The model better locates the clouds in images with cloud matting, which is built based on cloud detection. In addition, the model successfully recovers the surface information of the thin cloud region when thick and thin clouds coexist, and it does not damage the original image’s information.

Thaw consolidation of degrading permafrost is a serious hazard to the safety and operation of infrastructure. Monitoring thermal changes in the active layer (AL), the proportion of the soil above permafrost that thaws and freezes periodically, is critical to understanding the conditions of the top layer above the permafrost and regulating the construction, operation, and maintenance of facilities. However, this is a very challenging task using ground-based methods such as ground-penetrating radar (GPR) or temperature sensors. This study explores the integration of interferometric measurements from high-resolution X-band Synthetic Aperture Radar (SAR) images and volumetric water content (VWC) data from SoilGrids to quantify detailed spatial variations in active layer thickness (ALT) in Iqaluit, the territorial capital of Nunavut in Canada. A total of 21 SAR images from COSMO Sky-Med (CSK) were first analyzed using the freely connected network interferometric synthetic aperture radar (FCNInSAR) method to map spatial and temporal variations in ground surface subsidence in the study area. Subsequently, we built an ALT retrieval model by introducing the thaw settlement coefficient, which takes soil properties and saturation state into account. The subsidence measurements from InSAR were then integrated with VWC extracted from the SoilGrids database to estimate changes in ALT. For validation, we conducted a comparison between estimated ALTs and in situ measurements in the airport sector. The InSAR survey identifies several sites of ground deformation at Iqaluit, subsiding at rates exceeding 80 mm/year. The subsidence rate changes along the runway coincide with frost cracks and ice-wedge furrows. The obtained ALTs, ranging from 0 to 5 m, vary significantly in different sediments. Maximum ALTs are found for rock areas, while shallow ALTs are distributed in the till blanket (Tb), the intertidal (Mi) sediments, and the alluvial flood plain (Afp) sediment units. The intersection of taxiway and runway has an AL thicker than other parts in the glaciomarine deltaic (GMd) sediments. Our study suggests that combining high-resolution SAR imagery with VWC data can provide more comprehensive ALT knowledge for hazard prevention and infrastructure operation in the permafrost zone.

Severe subsidence poses a significant threat to coastal river deltas, bringing substantial hazards. The Yellow River Delta, being the youngest of such deltas, is particularly vulnerable due to its unstable sedimentary structure. It has experienced significant subsidence as a result of frequent human activities. The InSAR technology facilitates large-area, long-term deformation monitoring of the Yellow River Delta region. Current research has identified that subsidence in the Yellow River Delta is primarily influenced by groundwater extraction and oil exploitation. However, there is a lack of comprehensive study on the spatiotemporal patterns of subsidence induced by different human activities. To acquire extensive and long-term surface subsidence information in the Yellow River Delta region and analyze its causes, this study utilizes 92 ascending Sentinel-1 satellite images and 79 descending Sentinel-1 satellite images. Applying the SBAS-DInSAR technique, the research investigates ground subsidence in the area from January 2019 to April 2022. The study includes an integration of interferometric fringes and DInSAR deformation monitoring results, along with ascending and descending orbit deformation rates for internal accuracy cross-checking. Additionally, it examines the spatiotemporal evolution characteristics and causes in different human-impacted areas. The results indicate that the deformation rates from ascending and descending orbits show high consistency, with a correlation coefficient exceeding 0.8. Significant subsidence areas in the Yellow River Delta are concentrated in the eastern coastal regions, with the maximum subsidence rate reaching approximately -255 mm/year. The primary human-induced factors contributing to subsidence include the extraction of underground brine, oil, and groundwater. Additionally, natural factors like temperature, precipitation, and evaporation also impact subsidence. Notably, the deformation rate changes induced by precipitation exhibit a delayed response. Human activities, with their varying types and intensities, impart distinct temporal characteristics to subsidence. In areas like wetlands, urban regions, and farmlands, deformation is influenced by changes in groundwater levels, resulting in smaller deformation magnitudes and fluctuating deformation rates affected by variations in rainfall, temperature, and evaporation. Oil extraction deformation exhibits long-term change characteristics, influenced by the volume of oil extracted and water injection; from January 2019 to July 2020, a subsidence trend was observed, followed by a slow rebound after July 2020. Subsidence induced by groundwater extraction is characterized by rapid and stable deformation rates. Temporally, significant linear rate subsidence occurred from January to August 2019, with varying degrees of rebound from June 2019 to March 2020, followed by a return to subsidence. Influenced by heavy rainfall, there is a minor rebound each year from July to August.