Trends in Satellite Earth Observation for Permafrost Related Analyses—A Review

Identifying spatiotemporal pattern and trend prediction of land subsidence in Zhengzhou combining MT-InSAR, XGBoost and hydrogeological analysis

Zhengzhou city (China) experienced relatively significant land deformation following the July 20, 2021, extreme rainstorm (7·20 event). This study jointly utilised Multi-temporal synthetic aperture radar interferometry (MT-InSAR), eXtreme Gradient Boosting (XGBoost), and hydrogeological analysis to quantitatively assess the extent and trends, as well as the causes of land deformation before and after the 7·20 event in Zhengzhou city. The findings detected three major subsidence zones and two uplift zones within the city. The most significant subsidence occurred in the northern part of Zhongmu (- 28 mm/year), the northwest of Xingyang (- 16 mm/year), and the western region of Gongyi (- 6 mm/year). Conversely, a notable uplift was observed in the central city district (13 mm/year) and Xinzheng Airport (12 mm/year). The accuracy assessment of in-situ measurements (GNSS and levelling) yielded an overall root-mean-square error (RMSE) of 2.2 mm/year and an R-square of 0.948. Subsequently, the feature evaluation results based on the XGBoost method suggest that road density and precipitation are the dominant factors affecting land deformation in the entire study area or in the subsidence and uplift zones individually. Nevertheless, the other five factors (groundwater storage, soil type, soil thickness, NDVI, and slope) also act on land deformation individually and are intricately intertwined with each other. Furthermore, hydrogeological analysis from six groundwater wells reveals a synchronous relationship between groundwater level decline and land subsidence. The building load analysis shows a significant correlation between build-up density and subsidence rates, especially for those severe subsidence areas, with the maximum correlation coefficient reaching 0.6312. Finally, the geographic patterns analysis of post-event demonstrated a northeastward trend in land deformation, with a gradual reduction of deformation impact from 2018 to 2022.

Spatial distribution of permafrost degradation and its impact on vegetation phenology from 2000 to 2020

As global temperatures rise, permafrost is degraded. Permafrost degradation alters vegetation phenology and community composition, thereby affecting local and regional ecosystems. The Xing'an Mountains, located on the southern edge of the Eurasian permafrost region, are very sensitive to the impact of permafrost degradation on ecosystems. Climate change has direct effects on permafrost and vegetation growth, and analysis of the indirect effects of permafrost degradation on vegetation phenology based on the normalized difference vegetation index (NDVI) can explain the internal impact mechanisms of ecosystem components. Based on the temperature at the top of permafrost (TTOP) model, which was used to simulate the spatial distribution of permafrost areas in the Xing'an Mountains from 2000 to 2020, the areas of the three permafrost types showed a decreasing trend. The mean annual surface temperature (MAST) increased significantly at a rate of 0.008 °C year-1 from 2000 to 2020, and the southern boundary of the permafrost region moved north by 0.1-1 degrees. The average NDVI value of the permafrost region increased significantly in 8.34 % of the region. The significant correlations between NDVI and permafrost degradation, temperature and precipitation in the permafrost degradation region were 92.06 % (80.19 % positive, 11.87 % negative), 50.37 % (42.72 % positive, 7.65 % negative), and 81.59 % (36.25 % positive, 45.34 % negative), and were mainly distributed along the southern boundary of the permafrost region. A significance test of phenology in the Xing'an Mountains showed that the end of the growing season (EOS) and the length of the growing season (GLS) were significantly delayed and prolonged in the southern sparse island permafrost region. Sensitivity analysis showed that permafrost degradation was the main factor that affected the start of the growing season (SOS) and GLS. When the effects of temperature, precipitation, and sunshine duration were excluded, the regions with a significant positive correlation between permafrost degradation and SOS (20.96 %) and GLS (28.55 %) were located in both continuous and discontinuous permafrost regions. The regions with a significant negative correlation between permafrost degradation and SOS (21.11 %) and GLS (8.98 %) were mainly distributed on the southern edge of the island permafrost region. In summary, the NDVI changed significantly in the southern boundary of the permafrost region, and this change was mainly attributed to permafrost degradation.

Evaluating Permafrost Degradation in the Tuotuo River Basin by MT-InSAR and LSTM Methods

Permafrost degradation can significantly affect vegetation, infrastructure, and sustainable development on the Qinghai-Tibet Plateau (QTP). The permafrost on the QTP faces a risk of widespread degradation due to climate change and ecosystem disturbances; thus, monitoring its changes is critical. In this study, we conducted a permafrost surface deformation prediction over the Tuotuo River tributary watershed in the southwestern part of the QTP using the Long Short-Term Memory model (LSTM). The LSTM model was applied to the deformation information derived from a time series of Multi-Temporal Interferometry Synthetic Aperture Radar (MT-InSAR). First, we designed a quadtree segmentation-based Small BAseline Subset (SBAS) to monitor the seasonal permafrost deformation from March 2017 to April 2022. Then, the types of frozen soil were classified using the spatio-temporal deformation information and the temperature at the top of the permafrost. Finally, the time-series deformation trends of different types of permafrost were predicted using the LSTM model. The results showed that the deformation rates in the Tuotuo River Basin ranged between -80 to 60 mm/yr. Permafrost, seasonally frozen ground, and potentially degraded permafrost covered 7572.23, 900.87, and 921.70 km², respectively. The LSTM model achieved high precision for frozen soil deformation prediction at the point scale, with a root mean square error of 4.457 mm and mean absolute error of 3.421 mm. The results demonstrated that deformation monitoring and prediction using MT-InSAR technology integrated with the LSTM model can be used to accurately identify types of permafrost over a large region and quantitatively evaluate its degradation trends.

Automated Extraction of Annual Erosion Rates for Arctic Permafrost Coasts Using Sentinel-1, Deep Learning, and Change Vector Analysis

Arctic permafrost coasts become increasingly vulnerable due to environmental drivers such as the reduced sea-ice extent and duration as well as the thawing of permafrost itself. A continuous quantification of the erosion process on large to circum-Arctic scales is required to fully assess the extent and understand the consequences of eroding permafrost coastlines. This study presents a novel approach to quantify annual Arctic coastal erosion and build-up rates based on Sentinel-1 (S1) Synthetic Aperture RADAR (SAR) backscatter data, in combination with Deep Learning (DL) and Change Vector Analysis (CVA). The methodology includes the generation of a high-quality Arctic coastline product via DL, which acted as a reference for quantifying coastal erosion and build-up rates from annual median and standard deviation (sd) backscatter images via CVA. The analysis was applied on ten test sites distributed across the Arctic and covering about 1038 km of coastline. Results revealed maximum erosion rates of up to 160 m for some areas and an average erosion rate of 4.37 m across all test sites within a three-year temporal window from 2017 to 2020. The observed erosion rates within the framework of this study agree with findings published in the previous literature. The proposed methods and data can be applied on large scales and, prospectively, even for the entire Arctic. The generated products may be used for quantifying the loss of frozen ground, estimating the release of stored organic material, and can act as a basis for further related studies in Arctic coastal environments.

Characteristics of Freeze–Thaw Cycles in an Endorheic Basin on the Qinghai-Tibet Plateau Based on SBAS-InSAR Technology

The freeze–thaw (F-T) cycle of the active layer (AL) causes the “frost heave and thaw settlement” deformation of the terrain surface. Accurately identifying its amplitude and time characteristics is important for climate, hydrology, and ecology research in permafrost regions. We used Sentinel-1 SAR data and small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain the characteristics of F-T cycles in the Zonag Lake-Yanhu Lake permafrost-affected endorheic basin on the Qinghai-Tibet Plateau from 2017 to 2019. The results show that the seasonal deformation amplitude (SDA) in the study area mainly ranges from 0 to 60 mm, with an average value of 19 mm. The date of maximum frost heave (MFH) occurred between November 27th and March 21st of the following year, averaged in date of the year (DOY) 37. The maximum thaw settlement (MTS) occurred between July 25th and September 21st, averaged in DOY 225. The thawing duration is the thawing process lasting about 193 days. The spatial distribution differences in SDA, the date of MFH, and the date of MTS are relatively significant, but there is no apparent spatial difference in thawing duration. Although the SDA in the study area is mainly affected by the thermal state of permafrost, it still has the most apparent relationship with vegetation cover, the soil water content in AL, and active layer thickness. SDA has an apparent negative and positive correlation with the date of MFH and the date of MTS. In addition, due to the influence of soil texture and seasonal rivers, the seasonal deformation characteristics of the alluvial-diluvial area are different from those of the surrounding areas. This study provides a method for analyzing the F-T cycle of the AL using multi-temporal InSAR technology.

An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends

As satellite observation technology develops and the number of Earth observation (EO) satellites increases, satellite observations have become essential to developments in the understanding of the Earth and its environment. However, the current impacts to the remote sensing community of different EO satellite data and possible future trends of EO satellite data applications have not been systematically examined. In this paper, we review the impacts of and future trends in the use of EO satellite data based on an analysis of data from 15 EO satellites whose data are widely used. Articles that reference EO satellite missions included in the Web of Science core collection for 2020 were analyzed using scientometric analysis and meta-analysis. We found the following: (1) the number of publications and citations referencing EO satellites is increasing exponentially; however, the number of articles referencing AVHRR, SPOT, and TerraSAR is tending to decrease; (2) papers related to EO satellites are concentrated in a small number of journals: 43.79% of the articles that were reviewed were published in only 13 journals; and (3) remote sensing impact factor (RSIF), a new impact index, was constructed to measure the impacts of EO satellites and to predict future trends in applications of their data. Landsat, Sentinel, MODIS, Gaofen, and WorldView were found to be the most significant current EO satellite missions and MODIS data to have the widest range of applications. Over the next five years (2021–2025), it is expected that Sentinel will become the satellite mission with the greatest influence.

Detecting Rock Glacier Displacement in the Central Himalayas Using Multi-Temporal InSAR

Rock glaciers represent typical periglacial landscapes and are distributed widely in alpine mountain environments. Rock glacier activity represents a critical indicator of water reserves state, permafrost distribution, and landslide disaster susceptibility. The dynamics of rock glacier activity in alpine periglacial environments are poorly quantified, especially in the central Himalayas. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been shown to be a useful technique for rock glacier deformation detection. In this study, we developed a multi-baseline persistent scatterer (PS) and distributed scatterer (DS) combined MT-InSAR method to monitor the activity of rock glaciers in the central Himalayas. In periglacial landforms, the application of the PS interferometry (PSI) method is restricted by insufficient PS due to large temporal baseline intervals and temporal decorrelation, which hinder comprehensive measurements of rock glaciers. Thus, we first evaluated the rock glacier interferometric coherence of all possible interferometric combinations and determined a multi-baseline network based on rock glacier coherence; then, we constructed a Delaunay triangulation network (DTN) by exploiting both PS and DS points. To improve the robustness of deformation parameters estimation in the DTN, we combined the Nelder–Mead algorithm with the M-estimator method to estimate the deformation rate variation at the arcs of the DTN and introduced a ridge-estimator-based weighted least square (WLR) method for the inversion of the deformation rate from the deformation rate variation. We applied our method to Sentinel-1A ascending and descending geometry data (May 2018 to January 2019) and obtained measurements of rock glacier deformation for 4327 rock glaciers over the central Himalayas, at least more than 15% detecting with single geometry data. The line-of-sight (LOS) deformation of rock glaciers in the central Himalayas ranged from −150 mm to 150 mm. We classified the active deformation area (ADA) of all individual rock glaciers with the threshold determined by the standard deviation of the deformation map. The results show that 49% of the detected rock glaciers (monitoring rate greater than 30%) are highly active, with an ADA ratio greater than 10%. After projecting the LOS deformation to the steep slope direction and classifying the rock glacier activity following the IPA Action Group guideline, 12% of the identified rock glaciers were classified as active and 86% were classified as transitional. This research is the first multi-baseline, PS, and DS network-based MT-InSAR method applied to detecting large-scale rock glaciers activity.

Seasonal InSAR Displacements Documenting the Active Layer Freeze and Thaw Progression in Central-Western Spitsbergen, Svalbard

In permafrost areas, the active layer undergoes seasonal frost heave and thaw subsidence caused by ice formation and melting. The amplitude and timing of the ground displacement cycles depend on the climatic and ground conditions. Here we used Sentinel-1 Synthetic Aperture Radar Interferometry (InSAR) to document the seasonal displacement progression in three regions of Svalbard. We retrieved June–November 2017 time series and identified thaw subsidence maxima and their timing. InSAR measurements were compared with a composite index model based on ground surface temperature. Cyclic seasonal patterns are identified in all areas, but the timing of the displacement progression varies. The subsidence maxima occurred later on the warm western coast (Kapp Linné and Ny-Ålesund) compared to the colder interior (Adventdalen). The composite index model is generally able to explain the observed patterns. In Adventdalen, the model matches the InSAR time series at the location of the borehole. In Kapp Linné and Ny-Ålesund, larger deviations are found at the pixel-scale, but km or regional averaging improves the fit. The study highlights the potential for further development of regional InSAR products to represent the cyclic displacements in permafrost areas and infer the active layer thermal dynamics.