Increased Water Content in the Active Layer Revealed by Regional‐Scale InSAR and Independent Component Analysis on the Central Qinghai‐Tibet Plateau

Chen, Jie; Wu, Tonghua; Liu, Lingli; Gong, Wenyu; Zwieback, Simon; Zou, Defu; Wu, Tonghua; Hu, Guojie; Du, Erji; Wu, Xiaodong; Li, Ren

doi:10.1029/2021gl097586

Cited by 13 publications

(6 citation statements)

References 47 publications

(67 reference statements)

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“…However, limitations were reported in these soil moisture products over the permafrost areas on TP, i.e., all products overestimated the surface moisture, with significant errors in the root-zone soil moisture products during thawing-freezing transitional periods. Conversely, InSAR ground deformation data in 2015-2019, were combined with an independent multivariate statistical method [214]. By isolating the cyclic seasonal component of the surface displacements related to active layer freeze and thaw from other signal components, researchers can infer a net increase in the active layer water content of approximately 8 cm equivalent water thickness and widespread intensification of the subsurface water cycle in the central TP.…”

Section: Frozen Ground Hydrologymentioning

confidence: 99%

“…The most frequently used active microwave data are SARs, which have been applied in mapping glaciers [185,282], retrieving debriscovered glaciers [46,51], mapping rock glaciers [283], snow cover [284], lake ice coverage and thickness [152,285]. In addition, InSAR has been widely applied in frozen ground deformation [214,218,286], as well as for an inventory of rock glaciers [133] and rock glacier creep speeds [139]. Moreover, bistatic SAR interferometry has been broadly used in DEM generation [61], glacier surface velocity [80,82], snow depth/SWE [287], and the active layer thickness of permafrost [280].…”

Section: Summary Of Cryosphere Studies From Spacementioning

confidence: 99%

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Remote Sensing and Modeling of the Cryosphere in High Mountain Asia: A Multidisciplinary Review

Ye,

Wang,

Liu

et al. 2024

Remote Sensing

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Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole.

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Section: Frozen Ground Hydrologymentioning

confidence: 99%

Section: Summary Of Cryosphere Studies From Spacementioning

confidence: 99%

Remote Sensing and Modeling of the Cryosphere in High Mountain Asia: A Multidisciplinary Review

Ye,

Wang,

Liu

et al. 2024

Remote Sensing

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“…Once ∆𝑃 arrived at a given threshold of 0.001 and converged, the iterated least squares estimation stopped. The periodic amplitude in a freezing-thawing cycle was assumed as the peak-to-peak amplitude [65], [66]. It is the difference between the maximum uplift and subsidence after removing the linear annual contribution, namely the distance between the positive and negative peaks of:…”

Section: B Estimation Of Linear Velocity and Periodic Amplitude Of Gr...mentioning

confidence: 99%

MT-InSAR Unveils Dynamic Permafrost Disturbances in Hoh Xil (Kekexili) on the Tibetan Plateau Hinterland

Lü

Han

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Hoh Xil is an uninhabited extremity secluded on the Tibetan Plateau hinterland. A complete mapping of ground motion variation in Hoh Xil is essential for in-depth understanding of terrain's responses to climate change on the Tibetan Plateau. However, the inaccessibility and extremely harsh environment impeded extensive field investigations on landform alteration and its formative process. Such difficulty can be resolved by Interferometric Synthetic Aperture Radar (InSAR), which enables a broad detection of subtle permafrost motions at millimeter precision. This study, for the first time, accomplished a Multi-temporal InSAR (MT-InSAR) deformation mapping from 2015 to 2020 in Hoh Xil, with a wide coverage of about 200,000 km 2 . 1,592 Sentinel-1 images were processed based on the small baseline subset (SBAS) technique. The results show that Hoh Xil was experiencing dynamic permafrost disturbances. Thawing permafrost with both the linear subsidence rate higher than 2 mm/yr and the periodic amplitude over 2 mm was primarily detected in areas of flat or gentle slopes. The InSAR cumulative deformation is highly correlated with permafrost thawing depth. Significant lag times were identified between seasonal oscillation of InSAR deformation and land surface temperature (LST). Thermokarst landforms of retrogressive thaw slumps and thermokarst lakes broadly formed and dynamically evolved as a consequence of permafrost degradation. Particularly, widespread thawing permafrost characterized by the spatial clustering of thermokarst lakes appeared to occur in areas adjacent to large lakes. The discovered dynamic permafrost disturbances in Hoh Xil manifested even the secluded Tibetan Plateau hinterland was facing the threat of climate change.

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“…The decomposition methods aim to separate InSAR deformation into multiple components using techniques such as principal component analysis (PCA) or independent component analysis (ICA). These techniques help interpret deformations that originate from distinct physical events tied to different components [33][34][35][36]. Additionally, the decomposition method can reduce the impact of noise, which in turn improves the clarity of the primary signal elements in InSAR deformation [32].…”

Section: Introductionmentioning

confidence: 99%

Exploring the InSAR Deformation Series Using Unsupervised Learning in a Built Environment

Yang,

Li,

Huang

et al. 2024

Remote Sensing

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As a city undergoes large-scale construction and expansion, there is an urgent need to monitor the stability of the ground and infrastructure. The time-series InSAR technique is an effective tool for measuring surface displacements. However, interpreting these displacements in a built environment, where observed displacements consist of mixed signals, poses a challenge. This study uses principal component analysis (PCA) and the k-means clustering method for exploring deformation series within an unsupervised learning context. The PCA method extracts the dominant components in deformation series, whereas the clustering method identifies similar deformation series. This method was tested on Kunming City (KMC) using C-band Sentinel-1, X-band TerraSAR-X, and L-band ALOS-2 PALSAR-2 data acquired between 2017 to 2022. The experiment demonstrated that the suggested unsupervised learning approach can group PS points with similar kinematic characteristics. Five types of deformation kinematic characteristics were discovered in the three SAR datasets: upward, slight upward, stability, slight downward, and downward. According to the results, less than 20% of points exhibit significant motion trends, whereas 50% show small velocity values but still demonstrate movement trends. The remaining 30% are relatively stable. Similar clustering results were obtained from the three datasets using unsupervised methods, highlighting the effectiveness of identifying spatial–temporal patterns over the study area. Moreover, It was found that clustering based on kinematic characteristics enhances the interpretation of InSAR deformation, particularly for points with small deformation velocities. Finally, the significance of PCA decomposition in interpreting InSAR deformation was discussed, as it can better represent series with noise, enabling their accurate identification.

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Increased Water Content in the Active Layer Revealed by Regional‐Scale InSAR and Independent Component Analysis on the Central Qinghai‐Tibet Plateau

Cited by 13 publications

References 47 publications

Remote Sensing and Modeling of the Cryosphere in High Mountain Asia: A Multidisciplinary Review

Remote Sensing and Modeling of the Cryosphere in High Mountain Asia: A Multidisciplinary Review

MT-InSAR Unveils Dynamic Permafrost Disturbances in Hoh Xil (Kekexili) on the Tibetan Plateau Hinterland

Exploring the InSAR Deformation Series Using Unsupervised Learning in a Built Environment

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