Glacial lakes (GLs), a vital link between the hydrosphere and the cryosphere, participate in the local hydrological process, and their interannual dynamic evolution is an objective reflection and an indicator of regional climate change. The complex terrain and climatic conditions in mountainous areas where GLs are located make it difficult to employ conventional remote sensing observation means to obtain stable, accurate, and comprehensive observation data. In view of this situation, this study presents an algorithm with a high generalization ability established by optimizing and improving a deep learning (DL) semantic segmentation network model for extracting GL contours from combined synthetic-aperture radar (SAR) amplitude and multispectral imagery data. The aim is to use the high penetrability and all-weather advantages of SAR to reduce the effects of cloud cover as well as to integrate the multiscale and detail-oriented advantages of multispectral data to facilitate accurate, quantitative extraction of GL contours. The accuracy and reliability of the model and algorithm were examined by employing them to extract the contours of GLs in a large region of south-eastern Tibet from Landsat 8 optical remote sensing images and Sentinel-1A amplitude images. In this study, the contours of a total 8262 GLs in south-eastern Tibet were extracted. These GLs were distributed predominantly at altitudes of 4000–5500 m. Only 17.4% of these GLs were greater than 0.1 km² in size, while a large number of small GLs made up the majority. Through analysis and validation, the proposed method was found highly capable of distinguishing rivers and lakes and able to effectively reduce the misidentification and extraction of rivers. With the DL model based on combined optical and SAR images, the intersection-over-union (IoU) score increased by 0.0212 (to 0.6207) on the validation set and by 0.038 (to 0.6397) on the prediction set. These validation data sufficiently demonstrate the efficacy of the model and algorithm. The technical means employed in this study as well as the results and data obtained can provide a reference for research and application expansion in related fields.

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.

Topographic development via paraglacial slope failure (PSF) represents a complex interplay between geological structure, climate, and glacial denudation. Southeastern Tibet has experienced amongst the highest rates of ice mass loss in High Mountain Asia in recent decades, but few studies have focused on the implications of this mass loss on the stability of paraglacial slopes. We used repeat satellite- and unpiloted aerial vehicle (UAV)-derived imagery between 1990 and 2020 as the basis for mapping PSFs from slopes adjacent to Hailuogou Glacier (HLG), a 5 km long monsoon temperate valley glacier in the Mt. Gongga region. We observed recent lowering of the glacier tongue surface at rates of up to 0.88 m a⁻¹ in the period 2000 to 2016, whilst overall paraglacial bare ground area (PBGA) on glacier-adjacent slopes increased from 0.31 ± 0.27 km² in 1990 to 1.38 ± 0.06 km² in 2020. Decadal PBGA expansion rates were ∼ 0.01 km² a⁻¹, 0.02 km² a⁻¹, and 0.08 km² in the periods 1990–2000, 2000–2011, and 2011–2020 respectively, indicating an increasing rate of expansion of PBGA. Three types of PSFs, including rockfalls, sediment-mantled slope slides, and headward gully erosion, were mapped, with a total area of 0.75 ± 0.03 km² in 2020. South-facing valley slopes (true left of the glacier) exhibited more destabilization (56 % of the total PSF area) than north-facing (true right) valley slopes (44 % of the total PSF area). Deformation of sediment-mantled moraine slopes (mean 1.65–2.63 ± 0.04 cm d⁻¹) and an increase in erosion activity in ice-marginal tributary valleys caused by a drop in local base level (gully headward erosion rates are 0.76–3.39 cm d⁻¹) have occurred in tandem with recent glacier downwasting. We also observe deformation of glacier ice, possibly driven by destabilization of lateral moraine, as has been reported in other deglaciating mountain glacier catchments. The formation, evolution, and future trajectory of PSFs at HLG (as well as other monsoon-dominated deglaciating mountain areas) are related to glacial history, including recent rapid downwasting leading to the exposure of steep, unstable bedrock and moraine slopes, and climatic conditions that promote slope instability, such as very high seasonal precipitation and seasonal temperature fluctuations that are conducive to freeze–thaw and ice segregation processes.

As an approach with great potential, the interpretation of space-borne synthetic aperture radar (SAR) images has been applied for monitoring the dynamic evolution of the glacial lakes in recent years. Considering unfavorable factors, such as inherent topography-induced effects and speckle noise in SAR images, it is challenging to accurately map and track the dynamic evolution of the glacial lakes by using multi-temporal SAR images. This paper presents an improved neighborhood-based ratio method utilizing a time series of SAR images to identify the boundaries of the glacial lakes and detect their spatiotemporal changes. The proposed method was applied to monitor the dynamic evolution of the two glacial lakes with periodic water discharge at the terminus of the Gongba Glacier in the southeastern Tibetan Plateau by utilizing 144 Sentinel-1A SAR images collected between October of 2014 and November of 2020. We first generated the reference intensity image (RII) by averaging all the SAR images collected when the water in the glacial lakes was wholly discharged, then calculated the neighborhood-based ratio between RII and each SAR intensity image, and finally identified the boundaries of the glacial lakes by a ratio threshold determined statistically. The time series of areas of the glacial lakes were estimated in this way, and the dates for water recharging and discharging were accordingly determined. The testing results showed that the water of the two glacial lakes began to be recharged in April and reached their peak in August and then remained stable dynamically until they began to shrink in October and were discharged entirely in February of the following year. We observed the expansion process with annual growth rates of 3.19% and 12.63% for these two glacial lakes, respectively, and monitored a glacial lake outburst flood event in July 2018. The validation by comparing with the results derived from Sentinel-2A/B optical images indicates that the accuracy for identifying the boundaries of the glacial lakes with Sentinel-1A SAR images can reach up to 96.49%. Generally, this contribution demonstrates the reliability and precision of SAR images to provide regular updates for the dynamic monitoring of glacial lakes.

Although ground-based synthetic aperture radar (GB-SAR) interferometry has a very high precision with respect to deformation monitoring, it is difficult to match the fan-shaped grid coordinates with the local topography in the geographical space because of the slant range projection imaging mode of the radar. To accurately identify the deformation target and its position, high-accuracy geocoding of the GB-SAR images must be performed to transform them from the two-dimensional plane coordinate system to the three-dimensional (3D) local coordinate system. To overcome difficulties of traditional methods with respect to the selection of control points in GB-SAR images in a complex scattering environment, a high-resolution digital surface model obtained by unmanned aerial vehicle (UAV) aerial photogrammetry was used to establish a high-accuracy GB-SAR coordinate transformation model. An accurate GB-SAR image geocoding method based on solution space search was proposed. Based on this method, three modules are used for geocoding: framework for the unification of coordinate elements, transformation model, and solution space search of the minimum Euclidean distance. By applying this method to the Laoguanjingtai landslide monitoring experiment on Hailuogou Glacier, a subpixel geocoding accuracy was realized. The effectiveness and accuracy of the proposed method were verified by contrastive analysis and error assessment. The method proposed in this study can be applied for accurate 3D interpretation and analysis of the spatiotemporal characteristic in GB-SAR deformation monitoring and should be popularized.