The Qinghai-Tibet Plateau (QTP) stores a significant amount of organic carbon in permafrost regions, and the temporal dynamic changes under permafrost degradation remain uncertain. In this study, integrating on-site and multi-source remote sensing data, we proposed a dual-input small-sample deep learning framework for estimating the soil organic carbon (SOC) density and storage at the depth of 0-3m in permafrost regions based on attention mechanisms and deep learning (DL) methods. Our model achieved an improvement of 10.6% and 22.9% in the accuracy of SOC estimation compared to previous studies in the shallow (0-30 cm) and deep (0-100 cm) layers of permafrost regions, respectively. The SOC storage over permafrost regions to the depth of 3m were 14.27 ± 4.38 Pg and 12.26 ± 2.02 Pg in 2005 and 2020 respectively, suggesting a release of 2.01 ± 0.48 Pg of SOC over the past 15 years. The carbon release intensities per unit area in thermokarst lakes and retrogressive thaw slumps are approximately 24.6 times and 21.0 times higher than the mean value of whole permafrost regions, respectively. The factor analysis revealed that precipitation, NDVI (Normalized Difference Vegetation Index), and MAGT (Mean Annual Ground Temperature) are the primary controlling factors when estimating the shallow layers (0-30 cm). In contrast, at deeper layers, the spatial distribution of shallower SOC, soil water content, and DEM possess greater weight. This finding is crucial for modeling SOC storage and its dynamics in the permafrost regions on the QTP.

Coal mining activities can induce ground subsidence, collapse, and even surface fissures, posing a severe threat to human safety. In this article, a novel method integrating interferometric synthetic aperture radar (InSAR) and convolutional neural networks (CNNs) was proposed for automated subsidence funnel identification. Initially, a hybrid InSAR dataset was constructed by combining real samples from mining areas obtained through interferometric processing with simulated samples synthesized using the probability integral method, Polin noise, and complex Gaussian white noise. Subsequently, on the basis of the YOLOv8 algorithm, the adaptive detection head dynamic head (Dyhead) based on attention mechanism and the regression box loss function Wise-intersection over union (WIoU) that can improve the problem of uneven sample difficulty were introduced, resulting in the proposed Improved-YOLOv8 model. Trained on the hybrid dataset, it significantly improved detection accuracy compared to five base models, achieving AP50, AP75, and AP50-95 of 92.0%, 60.0%, and 54.1% respectively. Further experiments and analyses indicate that the trained Improved-YOLOv8 model exhibits satisfactory applicability and accuracy for different surface types and other satellite datasets, and performs well in subsidence funnels detection task covering the entire Shanxi. Therefore, the proposed method shows significant application potential in determining the location distribution of subsidence funnels over wide areas, regularly updating data and monitoring geological disasters in mining areas.

With the global warming, thaw slump activity has increased in permafrost regions of Qinghai-Tibet Plateau (QTP), which influence the stability of human infrastructure and carbon cycling. However, the intrinsic dynamic process of surface displacement of the retrogression thaw slump (RTS) is still less understood. Here, we employed the spaceborne interferometric synthetic aperture radar (InSAR) based on L-band ALOS-2 PLASAR2 data acquired from December 2015 to May 2022 to monitor the surface subsidence trends of thaw slump-derived thermokarst in permafrost terrain of Qinghai-Tibet Plateau (QTP). The InSAR analysis reveals the thermokarst subsidence of -81~46 mm/year between 2015 and 2022. A large number of RTS were distributed in the slopes, with the large annual average sedimentation rates. Besides, the time-series seasonal deformation reveals that during the period from January 2019 to March 2019, with the mean temperature below 0 °C , RTS’ deformation still represents large seasonal subsidence, which indicates the intrinsic pattern of surface displacement of the thaw slump is not simple cold-season freeze heaving.

Accurately predicting large-scale surface displacements is a vital task in the prevention and control of geological hazards in mining areas. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) faces challenges in capturing significant deformations, and implementing extensive global navigation satellite system (GNSS) monitoring can only obtain a single point of deformation. To combine the strengths of both techniques, we proposed a novel approach named the LL-SIG (Integrating single SBAS-InSAR pair, GNSS and Log-logistic model), which leverages the understanding of mining subsidence patterns and integrates single-orbit InSAR data with limited GNSS data to achieve overall and high-gradient three-dimensional (3-D) deformation monitoring and prediction in mining areas. Firstly, we employed empirical Bayesian kriging interpolation to integrate GNSS and SBAS-InSAR data at a consistent time interval. This allowed us to obtain an overall large-gradient deformation pattern of the mining area based on its subsidence characteristics. Subsequently, we combined the Log-logistic model with the SIP (single InSAR pair) model to calculate the 3-D deformation resulting from mining activities at any moment. The proposed method was tested in the Fenxi Ruitai mine. The root mean square error (RMSE) of the fitted deformation in the line-of-sight (LOS) direction was within 10% of the maximum deformation. Compared to the Logistic model and Gompertz model, the Log-logistic model show superior accuracy and efficiency in mine deformation prediction under the same testing conditions. The RMSE of deformation for this method was 3.20 cm, 5.85 cm, and 3.73 cm in the vertical, northward, and eastward directions, respectively.

Three operational long-term land surface temperature (LST) products from Landsat series are available to the community until now, i.e., U.S. Geological Survey (USGS) LST, Instituto Português do Mar e da Atmosfera (IPMA) LST, and China University of Geosciences (CUG) LST. A comprehensive assessment of these LST products is essential for their subsequent applications (APPs) in energy, water, and carbon cycle modeling. In this study, an evaluation of these three Landsat LST products was performed using in situ LST measurements from five networks [surface radiation budget (SURFRAD), atmospheric radiation measurement (ARM), Heihe watershed allied telemetry experimental research (HiWATER), baseline surface radiation network (BSRN), and National Data Buoy Center (NDBC)] for the period of 2009–2019. Results reveal that the overall accuracies of CUG LST with bias [root-mean-square error (RMSE)] of 0.54 K (2.19 K) and IPMA LST with bias (RMSE) of 0.59 K (2.34 K) are marginally superior to USGS LST with bias (RMSE) of 0.96 K (2.51 K). The RMSE of USGS LST is about 0.3 K less than IPMA/CUG LST at water surface sites and is about 0.4 K higher than IPMA/CUG LST at cropland and shrubland sites. As for tundra, grassland, and forest sites, the RMSEs of three Landsat LST products are similar, and the RMSE difference among three Landsat LST products is < 0.18 K. Considering the close emissivity estimates over water surface in these three LST data, USGS LST has a better performance in atmospheric correction over water surface compared with IPMA/CUG LST. For land surface sites, the RMSE of LST increases initially and then decreases with land surface emissivity (LSE) for three Landsat LST products. This indicates that the emissivity correction has a large uncertainty for moderately vegetated surface with emissivity ranging from 0.970 to 0.980. Underestimated emissivity for USGS LST at vegetated sites leads to overestimation of LST, which could have led to the higher bias and RMSE compared with IPMA/CUG LST. For the LST retrievals for the three different sensors [i.e., Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and thermal infrared sensor (TIRS)] onboard the Landsat satellite series, the accuracies are consistent and comparable, which is beneficial for providing long-term and coherent LST.

The freeze/thaw (F/T) state of the soil is an essential indicator for permafrost monitoring. However, current soil F/T products with a coarse spatial resolution (>1 km) have limited their use on a fine scale. In this letter, a new approach integrating two microwave sensors [i.e., Sentinel-1 and advanced microwave scanning radiometer 2 (AMSR-2)] is developed to identify the soil F/T state at a spatial resolution of 10 m in the Qinghai-Tibet engineering corridor (QTEC). Using a linear regression model to integrate the coarse AMSR-2 data with the finer Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), the frozen frequency product at a 1-km resolution can be obtained. Then, the high-spatial-resolution F/T map based on Sentinel-1 synthetic aperture radar (SAR) time-series images can be produced using the threshold extracted from each pixel of frozen-frequency products. We tested soil F/T results via both visual and quantitative evaluations. The overall accuracy of the 10-m soil F/T map achieves 84.63% and 77.09% for ascending and descending orbits based on four meteorological stations, respectively.

With the global climate changing and increasing of anthropogenic activity, the dynamics permafrost environment of Qinghai-Tibet Plateau (QTP) is becoming fragile. As one of the most important infrastructures in the QTP, the Qinghai-Tibet Power Transmission Line (QTPTL) has been constructed since 2011. In some section, the stability of QTPTL has been damaged due to the harsh climate and the effect of freezing and thawing of permafrost. In this paper, structural health and stability of QTPTL are evaluated using time-series InSAR with TerraSAR X-band data. The structure feature of the transmission line tower in high resolution SAR image are analyzed. Then the deformation velocity, height and thermal dilation of the QTPTL is retrieved.

In order to detect power transmission tower efficiently in synthetic aperture radar (SAR) images, a new constant false alarm rate (CFAR) detection algorithm based on integrated superpixel window and adaptive statistical model has been proposed. Firstly, the SEEDS algorithm is used to extract superpixels from the SAR image, and the segmented objects are merged into CFAR detection windows. Subsequently, the power transmission towers are detected by the CFAR algorithm with the adaptive statistical model in each window. The experiment results using UAVSAR images prove that the proposed algorithm can achieve good detection performance and suppress false alarms at the same time.

Global change has significant impact on permafrost region in the Tibet Plateau. Soil moisture of permafrost is the important factor influencing the energy flux, ecosystem and hydrologic process. Synthetic aperture radar (SAR) provides us a powerful tool to monitor the soil moisture. In this study, 19 scenes of German TerraSAR-X data are used to retrieve the soil moisture in Beiluhe, Northern Tibet. The field campaign was performed on Aug. 12, 2015 and Mar. 8-13, 2016 during the TerraSAR-X overflight to acquire in situ soil parameters, together with the temperature and precipitation data from the weather station. Two approaches are proposed, one is based on time series observations, and the other is based on AIEM. Promising results are obtained.

Qinghai-Tibet Plateau (QTP) is often affected by climate change and anthropogenic activity. In this study, permafrost surface deformation is detected by time series InSAR method using high resolution TerraSAR-X images. In particularly, a sinusoidal function model is adopted in the parameter retrieving step for seasonal deformation extraction. The backscattering (σ°) of the main ground target types, such as alpine meadow, gross desert, mountainous slope, and railway, have been extracted. The evolutions of deformation and σ° of the main typical ground targets have been analyzed. Experimental result shows that σ° in alpine meadow areas increases about 10 dB from thawing season to frozen season. Experimental result shows that most area undergoes obvious displacement with the range from −15 mm/year to 15 mm/year.

Permafrost is sensitive to climate change and anthropogenic activities. The interferometric synthetic aperture radar (InSAR) is a new technology allowing us to explore the interaction between the permafrost change and human infrastructures. In this paper, the deformation of the Qinghai-Tibet Railway (QTR) in Beiluhe of the Tibet Plateau (TP) between Jun. and Dec. 2014 is detected using the new mode TerraSAR-X interferometry. In the summer thawing season, the cross-profiles of the railway show the asymmetric “W” pattern settlement, with the local deformation maxima at the transition zones between the embankment slopes and the natural meadow. In contrast, the cross-profiles of the QTR have inverse heaving pattern in the freezing season. Differential settlement occurs along the QTR, probably due to its underlying permafrost conditions. Displacements of the embankments with different proactive cooling measures along the experimental embankment segment are derived, showing their cooling performance.

In order to monitor the ground deformation due to coal mining, a new time-series InSAR technique combining PSs and DSs is presented in this paper. Firstly DSs are efficiently identified using classified information and statistical characteristics. Then a two-scale network is introduced into traditional PSI to deal with PSs and DSs. The proposed method is performed to investigate the subsidence of Huainan City, Anhui province (China) during the time of 2012-2013 using 14 scenes of Radarsat-2 images. Experimental results show that the proposed method can ease the estimation complexity and significantly increase the spatial density of measurement points, which can provide more detailed deformation information. The proposed method brings practical applications for non-urban area deformation monitoring.