The grounding line position of glaciers and ice shelves is an essential observation for the study of the Earth's ice sheets. However, in some locations, such as the Antarctic Peninsula, where many grounding lines have not been mapped since the 1990s, remote sensing of grounding line position remains challenging. Here we present a tidal motion offset correlation (TMOC) method for measuring the grounding line position of tidewater glaciers and ice shelves, based on the correlation between tide amplitude and synthetic aperture radar offset tracking measurements. We apply this method to the Antarctic Peninsula Ice Sheet to automatically delineate a new grounding line position for 2019–2020, with near complete coverage along 9300 km of coastline, updating the 20-year-old record. A comparison of the TMOC grounding line to contemporaneous interferometrically measured grounding line position shows the method has a mean seaward offset compared to interferometry of 185 m and a standard deviation of 295 m. Our results show that over the last 24 years there has been grounding line retreat at a number of fast-flowing ice streams on the Antarctic Peninsula, with the most retreat concentrated in the north-eastern sector, where grounding lines have retreated following the collapse of ice shelves. We observe a maximum grounding line retreat since 1996 of 16.3 ± 0.5 km on Hektoria Glacier, with other notable glaciers retreating by 9.3 ± 0.5, 9.1 ± 0.5 and 3.6 ± 0.5 km. Our results document dynamic change on Antarctic Peninsula glaciers and show the importance of using an updated grounding line location to delineate the boundary between floating and grounded ice.

Monitoring surface deformation for highways built on soft clay subgrades is fundamental for understanding the dynamics of the settlement process and preventing the occurrence of safety accidents. Most of the traditional interferometric synthetic aperture radar (InSAR) deformation models for highway monitoring are based on a combination of one or several empirical functions, which ignores the underground mechanism of highway deformation. To overcome this limitation, we proposed an advanced InSAR approach that improved the InSAR deformation model and parameter estimation algorithm for soft clay road deformation monitoring. The improved InSAR deformation model is based on the Poisson curve, which considers the characteristics of the temporal physical deformation evolution of the soft clay. The improved algorithm for the unknown parameter estimation is based on a GARN algorithm, which can solve the parameters with better accuracy by combining the genetic algorithm and the regularized Newton iterative algorithm. To evaluate the better performance for the proposed approach and its feasibility for soft clay highway monitoring, both the simulation and the real data experiments on a soft clay highway in Foshan, China, are performed. The time-series deformation from January 1, 2015 to January 18, 2017, is retrieved, and the temporal deformational characteristics over this area are analyzed, which facilitates a greater understanding of the deformational evolution process of the soft clay highway. The results are verified in terms of the residual high-pass deformation and the final obtained vertical deformation compared with the external leveling measurements, which indicate the greater reliability of our method.

Long-term monitoring of highways in soft soil areas, especially during the postconstruction period, is of great significance to ensure transportation safety and the quality of highway construction. Multitemporal interferometric synthetic aperture radar (MTInSAR) provides an effective tool for soft clay highway monitoring. However, most time-series models used in MTInSAR modeling are empirical mathematical functions, which ignores the physical properties of the observed objects and may limit the accuracy of the retrieved deformation and the understanding of the underground settlement dynamics. We propose a novel InSAR time-series deformation model (namely, NREM) with an emphasis on the rheological mechanisms for soft soil highways and environmental factors (temperature, humidity, and precipitation) to improve the accuracy of the traditional InSAR model and assist in the analyzing the rheological properties of soft soil. The NREM is constructed based on a combination of the seasonal model and the Burgers model introduced from the field of rheology. The primary parameters (i.e., viscosity and elastic modulus) are introduced in the NREM and estimated with the generation of time-series surface deformation. In the real data experiments, two highways are selected as the test areas. The results show that the standard deviations (STDs) of the high-pass deformation, which can reflect the modeling accuracy, derived by the NREM are lower than those of the three traditional models, yielding an improvement of 45% for the Lungui Highway (LH) and 50% for the G1508 Highway (GH). The root mean square errors (RMSEs) for deformation results derived from NREM are estimated to be ±5.1 mm compared with the leveling measurements, which outperforms the traditional models. The obtained rheological parameters can broaden the application of InSAR technology and provide a reference for highway engineering.

Change of supraglacial lakes (SGLs) is an important hydrological activity on the Greenland ice sheet (GrIS), and storage and drainage of SGLs occur throughout the year. However, current studies tend to split SGL changes into melt/non-melt seasons, ignoring the effect of buried lakes in the exploration of drainage, and the existing threshold-based approach to SGL extraction in a synthetic aperture radar (SAR) is influenced by the choice of the study area mask. In this study, a new method (Otsu–Canny–Otsu (OCO)), which accesses the features of SGLs on optical and SAR images objectively, is proposed for full-year SGL extraction with Google Earth Engine (GEE). The SGLs on the Petermann Glacier were monitored well by OCO throughout 2021, including buried lakes and more detailed rapid drainage events. Some SGLs’ extent varied minimally in a year (area varying by 10–25%) while some had very rapid drainage (a rapid drainage event from July 26 to 30). The SGL extraction results were influenced by factors such as the mode of polarization, the surface environment, and the depth of the lake. The OCO method can provide a more comprehensive analysis for SGL changes throughout the year.

The infrastructures built on soft clay in the Dongting Lake area are more prone to settlement and instability due to its significant rheological properties. It is of great importance to conduct a long-term surface deformation monitoring over this area. The most commonly used Synthetic Aperture Radar Interferometry (InSAR) deformation models are based on combinations of one or several pure empirical mathematical functions without considering the physical and mechanical characteristics of the observed objects. In this work, we propose an improved deformation model based on the functional relationship between strain and time in the Maxwell rheological model. The rheological parameters of elastic modulus and viscosity are introduced into a traditional empirical seasonal model. The improved model is applied for the investigation of the spatial-temporal surface evolution over the Dongting Lake area with the Small Baseline Subset InSAR (SBAS-InSAR) technology and TerraSAR-X satellite imagery. With the proposed model, the rheological parameters and the time series deformation are estimated, with the maximum accumulated subsidence estimated as 38 mm. Through the analysis of the generated results, we find that the lower the viscosity and elastic modulus are, the higher the deformation is. Temporally, the overall deformation follows a generally subsiding trend with a seasonal recovery of 5 mm from October 2012 to November 2012 and 12 mm from January 2013 to February 2013. To compensate for the deficiency of the unavailability of external geodetic measurements over this area, three different accuracy indexes (residual phase, temporal coherence, and high-pass deformation) are utilized to evaluate the modeling accuracy. The results of the improved model are also compared to three traditional models (seasonal model, cubic polynomial model, and linear model). The comparison shows that the improved model is highly recommended in this area because of its better accuracy.