Ongoing climate warming and increased human activities have led to significant permafrost degradation on the Qinghai-Tibet Plateau (QTP). Mapping the distribution of active layer thickness (ALT) can provide essential information for understanding this degradation. Over the past decade, InSAR (Interferometric synthetic aperture radar) technology has been utilized to estimate ALT based on remotely-sensed surface deformation information. However, these methods are generally limited by their ability to accurate extract seasonal deformation and model subsurface water content of active layer. In this paper, an ALT inversion method considering both seasonal deformation from InSAR and smoothly multilayer soil moisture from ERA5 is proposed. Firstly, we introduce a ground seasonal deformation extraction model combining RobustSTL and InSAR, and the deformation extraction accuracy by considering the deformation characteristics of permafrost are evaluated, proving the effectiveness of RobustSTL in extracting seasonal deformation of permafrost. Then, using ERA5 soil moisture products, a smoothed multilayer soil moisture model for ALT inversion is established. Finally, integrating the seasonal deformation and multilayer soil moisture, the ALT can be estimated. The proposed model is applied to the Yellow River source region (YRSR) with Sentinel-1A images acquired from 2017 to 2021, and the ALT retrieval accuracy is validated with measured data. Experimental results show that the vertical deformation rate of the study area generally ranges from -30 mm/year to 20 mm/year, with seasonal deformation amplitude ranging from 2 mm to 30 mm. The RobustSTL method has the highest accuracy in extracting seasonal deformation of permafrost, with an RMSE (root mean square error) of 0.69 mm, and is capable of capturing the freeze-thaw characteristics of the active layer. The estimated ALT of the YRSR ranges from 49 cm to 450 cm, with an average value of 145 cm. Compared to the measured data, the proposed method has an average error of 37.5 cm, which represents a 21 % improvement in accuracy over existing methods.

油田注采作业易引发地下储层压力变化,易导致油田地表局部形变并诱发剪切套损,开展油田地表形变监测可以识别油田主要形变区和可能套损区,对油田作业合理规划和油田高质量可持续发展具有重要意义。合成孔径干涉测量(InSAR)技术可实现大面积、高精度的地表形变监测,但传统时序InSAR技术在地表覆盖复杂的油田区面临测量点不足且空间分布不均的问题。以大庆油田为例,针对上述问题以及季节性冻土影响,将周期模型融入DS-InSAR技术开展地表形变监测,并分析地表形变与油田注采量之间的相关性。结果表明:(1)油田区因注采等差异导致其地表形变分布不均匀且呈显著的非线性特征,最大沉降速率约为47 mm/a,最大抬升速率约为45 mm/a;(2)油田区地表形变与油田注采作业高度相关,注液作业使得地下储层压力增大,地表抬升,采油作业使得储层压力减小,造成地表沉降。该研究可为油田注采生产策略优化提供科学数据依据,进一步拓展了InSAR技术在油田区的应用。

【目的】干涉合成孔径雷达测量(InSAR)技术近年来被广泛用于反演活动层厚度（ALT），然而现有研究较少考虑冻融对地表形变和土壤孔隙水热变化的影响，因此，本文构建了考虑土壤水热变化的ALT反演模型。【方法】使用InSAR技术和CNNBiLSTM-AM模型得到地表参数，顾及冻融驱动下活动层的变形和土壤孔隙及水分的变化构建了活动层厚度反演模型。首先，通过SBAS-InSAR技术提取研究区垂直向地表形变。然后，构建CNN-BiLSTM-AM模型，使用卷积神经网络（Convolutional Neural Networks, CNN）对多源遥感数据特征提取，采用双向长短期记忆网络（Bi-directional Long Short-term Memory,BiLSTM）对提取特征进行预测，添加多头自注意力层（Attention Mechanism, AM）提高模型对关键信息的提取，得到多特征约束下的土壤含水量预测值。最后，以垂直向地表形变作为表征活动层的主要参数，构建基于土壤孔隙比和土壤含水量的活动层厚度反演模型，得到兰新高铁冻土区活动层厚度的时空分布。【结果】模型估计值与俄博岭实测数据验证的...

冻土的水-冰相变交替过程会造成水文环境与地表工程的破坏，从而导致路基塌陷、山体滑坡、洪水暴发以及冰川溃决等灾害，智能感知潜在风险对保护冻土区工程建筑具有重要意义。采用2017年01月—2023年04月194景Sentinel-1A 影像，利用SBAS-InSAR技术获取了黄河上游沱沱河盆地冻土区形变结果，冻土地表形变明显且空间分布不均匀，监测时间段内最大形变速率可达13 mm/年。冻土区青藏铁路路基形变呈现“冻胀融沉”的季节性变化，暖季匀速沉降，冷季缓慢抬升，在气候变暖背景下暖季逐渐长于冷季；分别将InSAR监测结果与近7年沱沱河盆地GNSS监测数据对比，两者趋势一致；引入降水和气温因素后发现冻土区形变具有显著聚集特征，在人类活动频繁地区存在较大形变。该研究对冻土防灾减灾、保障人民生命财产安全具有重要意义，为高纬度冻土工程建设提供一定借鉴。

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.

The Qilian Mountains, located on the northeastern edge of the Qinghai-Tibet Plateau, are characterized by unique high-altitude and cold-climate terrain, where permafrost and seasonally frozen ground are extensively distributed. In recent years, with global warming and increasing precipitation on the Qinghai-Tibet Plateau, permafrost degradation has become severe, further exacerbating the fragility of the ecological environment. Therefore, timely research on surface deformation and the freeze-thaw patterns of alpine permafrost in the Qilian Mountains is imperative. This study employs Sentinel-1A SAR data and the SBAS-InSAR technique to monitor surface deformation in the alpine permafrost regions of the Qilian Mountains from 2017 to 2023. A method for spatiotemporal interpolation of ascending and descending orbit results is proposed to calculate two-dimensional surface deformation fields further. Moreover, by constructing a dynamic periodic deformation model, the study more accurately summarizes the regular changes in permafrost freeze-thaw and the trends in seasonal deformation amplitudes. The results indicate that the surface deformation time series in both vertical and east-west directions obtained using this method show significant improvements in accuracy over the initial data, allowing for a more precise reflection of the dynamic processes of surface deformation in the study area. Subsidence is predominant in permafrost areas, while uplift mainly occurs in seasonally frozen ground areas near lakes and streams. The average vertical deformation rate is 1.56 mm/a, with seasonal amplitudes reaching 35 mm. Topographical (elevation; slope gradient; aspect) and climatic factors (temperature; soil moisture; precipitation) play key roles in deformation patterns. The deformation of permafrost follows five distinct phases: summer thawing; warm-season stability; frost heave; winter cooling; and spring thawing. This study enhances our understanding of permafrost deformation characteristics in high-latitude and high-altitude regions, providing a reference for preventing geological disasters in the Qinghai-Tibet Plateau area and offering theoretical guidance for regional ecological environmental protection and infrastructure safety.

地表变形是反映活动层冻融过程的重要特征。为研究地表变形与活动层水热过程的相关性，采用SBAS-InSAR技术对祁连山地区野牛沟多年冻土区近5a的地表变形进行长期连续监测，并基于野外观测数据研究了地表变形与土壤水热过程的关系。结果表明，冻融过程与水力侵蚀作用引起的地表变形最为显著，地表变形表现出明显的季节性特征。冻融过程引起的地表累积变形较小，年际冻胀、融沉幅度约为10～20 mm；水力侵蚀引起的地表累积变形较大，年际地表变形幅度超过50 mm。野外观测数据表明活动层土壤温度具有轻微的下降趋势，负温等温线下探深度增加、历时加长，冻结锋面交汇日逐渐提前。地表变形与土壤温度、土壤湿度具有较好的相关性，在土壤水分富集区相关性更强，相关系数分别为-0.522、-0.415(P<0.001)。土壤水分富集区土壤含水量的变化对地表冻胀、融沉幅度变化的影响也更显著，两者具有良好的线性关系。笔者定量描述了活动层地表变形与土壤水热过程的关系，对大范围活动层冻融参数的监测研究具有参考意义。

Under the interference of climate warming and human engineering activities, the degradation of permafrost causes the frequent occurrence of geological disasters such as uneven foundation settlement and landslides, which brings great challenges to the construction and operational safety of road projects. In this paper, the spatial and temporal evolution of surface deformations along the Beihei Highway was investigated by combining the SBAS-InSAR technique and the surface frost number model after considering the vegetation factor with multi-source remote sensing observation data. After comprehensively considering factors such as climate change, permafrost degradation, anthropogenic disturbance, and vegetation disturbance, the surface uneven settlement and landslide processes were analyzed in conjunction with site surveys and ground data. The results show that the average deformation rate is approximately -16 mm/a over the 22 km of the study area. The rate of surface deformation on the pavement is related to topography, and the rate of surface subsidence on the pavement is more pronounced in areas with high topographic relief and a sunny aspect. Permafrost along the roads in the study area showed an insignificant degradation trend, and at landslides with large surface deformation, permafrost showed a significant degradation trend. Meteorological monitoring data indicate that the annual minimum mean temperature in the study area is increasing rapidly at a rate of 1.266 degrees C/10a during the last 40 years. The occurrence of landslides is associated with precipitation and freeze-thaw cycles. There are interactions between permafrost degradation, landslides, and vegetation degradation, and permafrost and vegetation are important influences on uneven surface settlement. Focusing on the spatial and temporal evolution process of surface deformation in the permafrost zone can help to deeply understand the mechanism of climate change impact on road hazards in the permafrost zone.

Quantifying seasonal deformation is essential for accurately determining the thickness of the active layer and the distribution of water content within it, providing insights into the freeze-thaw dynamics of permafrost environments and their sensitivity to climate change. Due to the limited hydraulic conductivity of the underlying permafrost, the freeze-thaw processes are largely confined to the active layer, allowing for predictable seasonal deformations. This study employed Independent Component Analysis to isolate large-scale seasonal deformation from Interferometric Synthetic Aperture Radar (InSAR) measurements taken from 2016 to 2020 in the Yangtze River Source Region (YRSR) of the Qinghai-Tibet Plateau (QTP), covering 18,500 km2. We developed dedicated machine learning (ML) models that integrate these InSAR-derived measurements with various environmental proxies. By applying these models to the YRSR, we generated a comprehensive, full-coverage deformation map for permafrost terrains, achieving an R2 value of 0.91 and an Root Mean Squared Error of approximately 0.5 cm, thus confirming the model's strong predictability of seasonal deformation in permafrost regions. Deformation magnitude varied from less than 1 cm to over 10 cm. Our analysis suggests that terrain attributes, influenced by climate and soil conditions, are the primary factors driving these deformations. This research provides valuable insights into quantifying permafrost-related seasonal deformation across expansive and rural landscapes. It also aids in assessing subsurface hydrological processes and the resilience and vulnerability of permafrost. The developed ML algorithm, with access to precise environmental data, is capable of forecasting seasonal deformations across the entire QTP and potentially throughout the Arctic. Seasonal ground deformation, including both subsidence and uplift, is common in areas with a layer of ground that freezes and thaws seasonally, underlain by permafrost-a type of ground that remains at or below 0 degrees C for at least 2 years. These deformations are crucial indicators of changes in water content and thickness of this layer, offering insights into the freeze-thaw dynamics of cold environments and their sensitivity to climate change. However, accurately mapping ground deformation over large areas has been challenging. In this study, we developed machine learning (ML) models that use radar remote sensing data, statistical methods, and a set of environmental variables to predict these seasonal ground movements. Our models can accurately forecast seasonal deformation using readily available environmental data. We find that slope of the terrain is the main factor influencing seasonal deformation, with climate and soil conditions also playing significant roles. This research offers new ways to measure and understand ground deformation in remote permafrost regions and demonstrates how ML can be used to predict such deformations on a continental or even global scale large. Our findings provide valuable insights for environmental scientists and could help inform strategies for managing these regions under changing climatic conditions. Our results underscore the predictability of seasonal deformation with high accuracy in permafrost terrains Machine learning models predict full-coverage seasonal deformation with high accuracy (R2 = 0.91, Root Mean Squared Error [RMSE] = 0.5 cm) Seasonal deformation is primarily determined by terrain slope and regulated by climate and soil conditions

冻土活动层厚度（Active Layer Thickness,ALT）的变化是反映青藏高原多年冻土发育情况及其状态的一个重要指标，监测活动层厚度变化对寒区景观稳定发展、碳循环等方面具有非常重要的意义.由于合成孔径雷达干涉测量（Interferometric Synthetic Aperture Radar,InSAR）技术具有大范围、高精度、高时空分辨率等优势，近年来逐渐被用于反演活动层厚度.已有研究中基于InSAR和土壤一维热传导模型的活动层厚度估计方法，没有充分考虑到冻土中土壤水分对流引起的热量传递.因此本文提出了基于InSAR时序形变及土壤热传导-对流模型的活动层厚度估计方法，利用土壤热传导-对流模型建立InSAR探测的最大融沉形变与最高地温之间的滞后时间与活动层厚度之间的相关关系，实现了由滞后时间直接推算大范围、高分辨率的冻土活动层厚度.本文以青藏高原五道梁多年冻土区为例，利用116景Sentinel-1影像图作为实验数据，估计了该地区2017—2020年的平均活动层厚度.结果表明，活动层厚度值范围为0～7.0 m，平均活动层厚度为3.06 m，与已有研究中相近时间段相关成果及...