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.

受全球气候变暖影响，青藏高原冻土退化和地表失稳问题不断加剧，对基础设施的建设维护和区域社会经济发展造成阻碍。近年来，SBAS-InSAR技术已在冻土地表形变监测中得到广泛应用，但由于青藏高原部分地区存在较为严重的失相干现象，导致形变监测结果出现空间不连续性，进而无法获得全面且精细的监测结果。针对上述问题，本文提出了一种联合机器学习与SBAS-InSAR的冻土形变监测方法，选取西藏阿里门士乡为研究区域，使用2020年1月7日至2021年6月6日共43景Sentinel-1A降轨影像数据提取地表形变信息；综合多类环境因子数据生成训练集后，引入机器学习模型拟合SBAS-InSAR监测结果与环境因子之间的内在关系，从而获取研究区连续形变速率图。结果表明，联合随机森林模型与SBAS-InSAR的方法效果最优，通过该方法对冻土形变缺失区域进行插值能极大提高原有SBAS-InSAR方法的监测覆盖率，其插值结果平均误差和均方根误差分别为0.459和0.739 mm/a。

受全球气候变暖影响，青藏高原冻土退化和地表失稳问题不断加剧，对基础设施的建设维护和区域社会经济发展造成阻碍。近年来，SBAS-InSAR技术已在冻土地表形变监测中得到广泛应用，但由于青藏高原部分地区存在较为严重的失相干现象，导致形变监测结果出现空间不连续性，进而无法获得全面且精细的监测结果。针对上述问题，本文提出了一种联合机器学习与SBAS-InSAR的冻土形变监测方法，选取西藏阿里门士乡为研究区域，使用2020年1月7日至2021年6月6日共43景Sentinel-1A降轨影像数据提取地表形变信息；综合多类环境因子数据生成训练集后，引入机器学习模型拟合SBAS-InSAR监测结果与环境因子之间的内在关系，从而获取研究区连续形变速率图。结果表明，联合随机森林模型与SBAS-InSAR的方法效果最优，通过该方法对冻土形变缺失区域进行插值能极大提高原有SBAS-InSAR方法的监测覆盖率，其插值结果平均误差和均方根误差分别为0.459和0.739 mm/a。

受全球气候变暖影响，青藏高原冻土退化和地表失稳问题不断加剧，对基础设施的建设维护和区域社会经济发展造成阻碍。近年来，SBAS-InSAR技术已在冻土地表形变监测中得到广泛应用，但由于青藏高原部分地区存在较为严重的失相干现象，导致形变监测结果出现空间不连续性，进而无法获得全面且精细的监测结果。针对上述问题，本文提出了一种联合机器学习与SBAS-InSAR的冻土形变监测方法，选取西藏阿里门士乡为研究区域，使用2020年1月7日至2021年6月6日共43景Sentinel-1A降轨影像数据提取地表形变信息；综合多类环境因子数据生成训练集后，引入机器学习模型拟合SBAS-InSAR监测结果与环境因子之间的内在关系，从而获取研究区连续形变速率图。结果表明，联合随机森林模型与SBAS-InSAR的方法效果最优，通过该方法对冻土形变缺失区域进行插值能极大提高原有SBAS-InSAR方法的监测覆盖率，其插值结果平均误差和均方根误差分别为0.459和0.739 mm/a。

针对仅利用单轨道SAR卫星只能获取地表沿着雷达视线向(LOS)的变形，而无法准确描述多年冻土垂直向的季节性冻胀和融沉的问题，本文利用短基线干涉测量(SBAS-InSAR)技术，并联合地表二维形变解算模型和时间序列分解模型，获得了青藏廊道唐古拉山至楚玛尔河路段2020年6月—2023年6月的垂向形变时间序列及其季节性形变幅度，分析季节性形变时空分布特征及其对气候变化的响应。研究结果表明，研究区的垂向形变速率为-41～32 mm/a,东西向形变速率为-33～34 mm/a,季节性形变幅度为0～41 mm;垂向形变较大的路段集中在五道梁、北麓河、风火山、乌丽、沱沱河及通天河等地，主要以沉降为主，形变速率超过了-15 mm/a,相应地，这些区域的季节性形变也较大，形变幅度超过15 mm;不同地表覆盖类型季节性形变差异明显，高寒草甸区季节性形变幅度高于高寒荒漠与河漫滩区；地表温度和降水是影响冻土区季节性形变的主要外部因素，其造成的季节性形变时滞2～3个月。

针对仅利用单轨道SAR卫星只能获取地表沿着雷达视线向(LOS)的变形，而无法准确描述多年冻土垂直向的季节性冻胀和融沉的问题，本文利用短基线干涉测量(SBAS-InSAR)技术，并联合地表二维形变解算模型和时间序列分解模型，获得了青藏廊道唐古拉山至楚玛尔河路段2020年6月—2023年6月的垂向形变时间序列及其季节性形变幅度，分析季节性形变时空分布特征及其对气候变化的响应。研究结果表明，研究区的垂向形变速率为-41～32 mm/a,东西向形变速率为-33～34 mm/a,季节性形变幅度为0～41 mm;垂向形变较大的路段集中在五道梁、北麓河、风火山、乌丽、沱沱河及通天河等地，主要以沉降为主，形变速率超过了-15 mm/a,相应地，这些区域的季节性形变也较大，形变幅度超过15 mm;不同地表覆盖类型季节性形变差异明显，高寒草甸区季节性形变幅度高于高寒荒漠与河漫滩区；地表温度和降水是影响冻土区季节性形变的主要外部因素，其造成的季节性形变时滞2～3个月。

针对仅利用单轨道SAR卫星只能获取地表沿着雷达视线向(LOS)的变形，而无法准确描述多年冻土垂直向的季节性冻胀和融沉的问题，本文利用短基线干涉测量(SBAS-InSAR)技术，并联合地表二维形变解算模型和时间序列分解模型，获得了青藏廊道唐古拉山至楚玛尔河路段2020年6月—2023年6月的垂向形变时间序列及其季节性形变幅度，分析季节性形变时空分布特征及其对气候变化的响应。研究结果表明，研究区的垂向形变速率为-41～32 mm/a,东西向形变速率为-33～34 mm/a,季节性形变幅度为0～41 mm;垂向形变较大的路段集中在五道梁、北麓河、风火山、乌丽、沱沱河及通天河等地，主要以沉降为主，形变速率超过了-15 mm/a,相应地，这些区域的季节性形变也较大，形变幅度超过15 mm;不同地表覆盖类型季节性形变差异明显，高寒草甸区季节性形变幅度高于高寒荒漠与河漫滩区；地表温度和降水是影响冻土区季节性形变的主要外部因素，其造成的季节性形变时滞2～3个月。

The Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) system is a combination of polarimetric SAR and interferometric SAR, which can simultaneously obtain the power information, polarimetric information, and interferometric information of land cover. Traditional land cover classification methods fail to fully utilize these information types, resulting in limited classification types and low accuracy. This paper proposes a PolInSAR land cover classification method that fuses power information, polarimetric information, and interferometric information, aiming to enrich the classification types and improve the classification accuracy. Firstly, the land cover is divided into strong scattering areas and weak scattering areas by using the power information to avoid the influence of weak scattering areas on the classification results. Then, the weak scattering areas are distinguished into shadows and water bodies by combining the interferometric information and image corners. For the strong scattering areas, the polarimetric information is utilized to distinguish vegetation, buildings, and bare soil. For the vegetation area, the concept of vegetation ground elevation is put forward. By combining with the anisotropy parameter, the vegetation is further subdivided into tall coniferous vegetation, short coniferous vegetation, tall broad-leaved vegetation, and short broad-leaved vegetation. The effectiveness of the method has been verified by the PolInSAR data obtained from the N-SAR system developed by Nanjing Research Institute of Electronics Technology. The overall classification accuracy reaches 90.2%, and the Kappa coefficient is 0.876.

Srinagar city is located in the heart of the Kashmir valley of the northwest Himalaya and is the largest urban center in the seismically active region. As yet, no direct deformation measurement or observation of any kind has been made in Srinagar and the surrounding areas using InSAR. We detect and quantify the ground deformation in the city's western flank using the InSAR time series. Stanford Method for Persistent Scatterer (StaMPS) is employed to process Sentinel-1A radar images acquired between 2015 and 2022 for ascending (161 scenes) and 2020 to 2022 for descending track (31 scenes). Generated velocity fields were decomposed into vertical rate maps, revealing a deformation of 17 mm year(-1) for ascending and 19 mm year(-1) for descending track. Time series analysis exhibits an identical deformation rate for both tracks on concurrent dates. Time-series GPS data was employed to validate the outcomes of our InSAR analysis. A field survey conducted in the main zone of deformation revealed extensive damage to structures in the form of wide cracks. Such cracks develop in older infrastructure (similar to 8 years) due to cumulative ground deformation over several years. Geotechnical investigation and strength calculation on a 30-m borehole of the subsiding region shows a vertical domination of high void, floodplain soils, with appreciable amounts of decomposed organic matter and lower shear strength parameters that are prone to volume reduction and particle rearrangement upon wetting and loading. The overall relevance of this study is in detecting and quantifying such subsidence in the Kashmir basin using SAR remote sensing. We also seek to establish a linkage of this deformation with the local stratum to allow for more consideration and efficient planning of civil infrastructure in the subsidence-prone regions of the citified zone and appropriate management of the subsidence-induced risk.

石冰川是发育于冰缘环境中的一种特殊地貌，其内部冻结冰在气候暖化背景下是高寒山区重要的淡水资源.本文利用时序In SAR提取了川西高原大雪山地区1280个活动石冰川的表面年平均运动速率，然后采用耦合表面运动速率和和动力学模型的反演方法定量估计了这些石冰川的含冰量和储水当量.编目清单显示大雪山石冰川主要分布在海拔4300～4900 m之间，面积介于0.004～1.5 km2之间，厚度主要分布在6～32 m之间.研究区石冰川沿坡向最大年运动速率约为125 cm·a-1，所有石冰川年均形变速率平均约27 cm·a-1；研究区所有石冰川含冰量位于57%～74%之间（平均值为70.1%），对应的总水储量约为2.884 km3.与传统基于“面积-体积”含冰量经验估计方法相比，发现传统方法仅适宜于小面积石冰川的含冰量估算.此外，研究区内石冰川数目约是冰川的10倍，但石冰川与冰川的水储量比值为1:2.7.本研究为进一步探究大雪山地区石冰川的水文效应提供了关键数据资料，同时为高寒环境广泛发育的石冰川水储量估计提供了可行...