Advanced GNSS and InSAR Technologies for Geoscience Applications

Topic Information

Dear Colleagues,

Accurately determining the three-dimensional coordinates and dynamic changes in the Earth's surface is a fundamental task in modern geodesy and Earth sciences. Over the past several decades, the Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) have emerged as cornerstone space-geodetic techniques, revolutionizing our ability to observe the Earth system. GNSS provides high-precision, high-temporal-resolution measurements of discrete points, making it indispensable for monitoring plate tectonics, crustal deformation, and maintaining the global terrestrial reference frame. In parallel, InSAR offers unparalleled capabilities for mapping surface deformation over wide areas with high spatial resolution and all-weather coverage, proving particularly powerful for studying large-scale phenomena like volcanic activity, land subsidence, and glacial motion.

Nevertheless, each technique possesses inherent limitations. GNSS provides high-fidelity time series at discrete points but suffers from sparse spatial sampling. Conversely, InSAR captures spatially dense deformation fields but is limited by lower temporal resolution and susceptibility to significant atmospheric artifacts. This Topic is dedicated to showcasing the latest advancements in the theories, innovative algorithms, and novel applications of GNSS and InSAR, both individually and in combination. We welcome contributions that explore new data processing methods, as well as comprehensive studies focusing on the deep fusion of these techniques to enhance measurement accuracy and geophysical interpretation. We particularly encourage submissions addressing cross-disciplinary challenges in precise coordinate determination, atmospheric delay modeling, multi-scale deformation monitoring, and geological hazard assessment.

Topics of interest include, but are not limited to, the following:

• Novel theories, models, and algorithms for GNSS and InSAR data fusion.
• Advanced modeling and correction of atmospheric delays (tropospheric and ionospheric).
• Unification of geodetic reference frames and establishment of high-precision deformation datums.
• Time-series InSAR analysis constrained or integrated with GNSS data for improved deformation retrieval.
• Monitoring multi-scale surface deformation (e.g., tectonic motion, volcanic activity, land subsidence).
• Applications in monitoring and early warning of geological hazards (e.g., landslides, earthquakes, glacial dynamics).
• Opportunities and challenges presented by next-generation GNSS and SAR satellite constellations.
• Application of Artificial Intelligence (AI) and big data techniques in GNSS/InSAR data processing.

Dr. Guo Chen
Dr. Wei Tang
Dr. Ling Huang
Dr. Amir Allahvirdi-Zadeh
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Geomatics geomatics	3.7	4.6	2021	21.6 Days	CHF 1200	Submit
Geosciences geosciences	2.3	4.4	2011	22.7 Days	CHF 1800	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit
Technologies technologies	5.2	6.7	2013	17 Days	CHF 1800	Submit

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Published Papers (6 papers)

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Open AccessArticle

Modeling Long-Term Postseismic Deformation Following the 2020 Mw 7.0 Samos Earthquake Using Campaign and Continuous GNSS Observations

by Halil İbrahim Solak, İbrahim Tiryakioğlu, Cemil Gezgin, Kayhan Aladoğan, Sefa Yalvaç, Bahadır Aktuğ, Cemal Özer Yiğit, Ergin Dönmez, Ertuğrul Demirelli, Eda Esma Eyübagil, Ece Bengünaz Çakanşimşek Ünlükaya, Furkan Şahiner, Muhiddin Can Yıldırım and Vahap Engin Gülal

Sensors 2026, 26(17), 5609; https://doi.org/10.3390/s26175609 - 3 Sep 2026

Viewed by 451

Abstract

Postseismic deformation provides fundamental insights into earthquake-cycle processes, lithospheric rheology, and stress redistribution following large earthquakes. Although the 2020 Mw 7.0 Samos earthquake has been extensively investigated in terms of coseismic deformation and early postseismic behavior, the long-term evolution of deformation following the [...] Read more.

Postseismic deformation provides fundamental insights into earthquake-cycle processes, lithospheric rheology, and stress redistribution following large earthquakes. Although the 2020 Mw 7.0 Samos earthquake has been extensively investigated in terms of coseismic deformation and early postseismic behavior, the long-term evolution of deformation following the event remains poorly constrained. This study characterizes the long-term spatiotemporal evolution of postseismic deformation associated with the 2020 Mw 7.0 Samos earthquake using combined campaign and continuous GNSS observations. A total of 18 GNSS stations were analyzed over an approximately 4.5-year period following the earthquake. Pre-earthquake GNSS velocities were incorporated as prior constraints, while postseismic deformation was modeled using linear, logarithmic, exponential, and combined logarithmic–exponential functions. The preferred model for each station component was identified using the corrected Akaike Information Criterion (AICc), and model-selection robustness was evaluated through 1000 Monte Carlo observation–perturbation simulations. The results reveal a spatially heterogeneous postseismic deformation field with station-dependent temporal behavior. Among the nonlinear solutions passing the Monte Carlo and goodness-of-fit criteria, the seven single-process LOG and EXP solutions yielded characteristic relaxation times ranging from 182.6 to 730.5 days, with a median of 438.3 days. The two LOGEXP solutions additionally contained a logarithmic timescale of 109.6 days and exponential timescales of 292.2–438.3 days. These findings demonstrate that campaign GNSS observations, when integrated with continuous GNSS data and an objective statistical framework, can provide meaningful constraints on the long-term evolution of postseismic deformation despite sparse temporal sampling. More broadly, the results emphasize the strongly time-dependent nature of postseismic deformation and the critical role of observation timing in capturing its spatiotemporal evolution. Full article

(This article belongs to the Topic Advanced GNSS and InSAR Technologies for Geoscience Applications)

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19 pages, 20663 KB  

Open AccessArticle

Monitoring and Prediction of Ground Deformation Using InSAR and Machine Learning Approaches in Tianjin City, China

by Jinjie Miao, Rally Kimpese Talong, Minsen Wang, Ying Zhang, Dong Du, Hongwei Liu, Yihang Gao, Yaonan Bai and Wei Liu

Remote Sens. 2026, 18(14), 2294; https://doi.org/10.3390/rs18142294 - 9 Jul 2026

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Abstract

Ground deformation is a hazardous geological phenomenon. In this study, the small baseline subset (SBAS) with the coherence baseline interferometric technique was employed to derive historical ground deformation in Tianjin City, Northern China, between 2019 and 2024. Using InSAR-derived datasets for training and [...] Read more.

Ground deformation is a hazardous geological phenomenon. In this study, the small baseline subset (SBAS) with the coherence baseline interferometric technique was employed to derive historical ground deformation in Tianjin City, Northern China, between 2019 and 2024. Using InSAR-derived datasets for training and validation, three machine learning architectures, namely two-dimensional convolutional long short-term memory (ConvLSTM2D), hybrid convolutional neural network–long short-term memory (hybrid CNN-LSTM), and hybrid convolutional neural network–bidirectional long short-term memory (hybrid CNN-BiLSTM), were developed to further analyze ground deformation and make future predictions. It was found that from SBAS-InSAR, the deformation rates for the whole Dongli District, Tianjin, ranged from −40.98 to 27.18 mm/year, with a mean of −2.41 mm/year from 2019 to 2024. Model performance was evaluated using held-out validation samples derived from the InSAR deformation dataset. The ConvLSTM2D model achieved the best performance, with an R² value of 0.99 and root mean squared error (RMSE) of 1.37 mm, compared with the hybrid CNN-LSTM (R² = 0.99, RMSE = 2.16 mm) and hybrid CNN-BiLSTM (R² = 0.99, RMSE = 2.19 mm). This optimized ConvLSTM2D model was applied to estimate the predictions of the ground deformation rate with −43.71 mm/year in the high-deformation zone between 2025 and 2028. These findings predict a continuing trend of land instability, highlighting the necessity for urgent geohazard mitigation and urban planning strategies in the affected regions. Full article

(This article belongs to the Topic Advanced GNSS and InSAR Technologies for Geoscience Applications)

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30 pages, 11915 KB  

Open AccessArticle

GeoSlide-XMamba: A Spectral-Topographic Boundary-Aware State-Space Network for Landslide Semantic Segmentation

by Yi Tang, Fei Zhao, Guojian Feng, Hongwen Yang, Luhao Gao, Lin Zheng and Weixia Zhou

Sensors 2026, 26(13), 4146; https://doi.org/10.3390/s26134146 - 1 Jul 2026

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Abstract

Rapid and reliable landslide mapping from satellite observations is essential for hazard assessment, emergency response, and reservoir-area risk management, yet automatic segmentation remains challenging in mountainous regions because landslide scars are spectrally heterogeneous, terrain-constrained, morphologically irregular, and frequently confused with other exposed surfaces. [...] Read more.

Rapid and reliable landslide mapping from satellite observations is essential for hazard assessment, emergency response, and reservoir-area risk management, yet automatic segmentation remains challenging in mountainous regions because landslide scars are spectrally heterogeneous, terrain-constrained, morphologically irregular, and frequently confused with other exposed surfaces. This study proposes GeoSlide-XMamba, a terrain-conditioned spectral-topographic boundary-aware state-space network for pixel-wise landslide semantic segmentation. The model first separates Sentinel-2 spectral bands and DEM/slope-derived topographic layers into modality-specific branches, integrates them through spectral-topographic adaptive fusion (STAF++), and then performs terrain-conditioned selective state-space scanning in the XMamba bottleneck. Unlike direct token concatenation, the proposed bottleneck uses terrain descriptors to dynamically weight directional selective scan branches so that long-range feature propagation is guided by slope-related morphology. Boundary-aware decoding, signed-distance supervision, and hard-negative mining are further introduced to improve inventory-oriented geometric quality and suppress common false positives. Experiments were conducted on the Landslide4Sense benchmark using 14-channel multispectral-topographic inputs. Among the compared methods, GeoSlide-XMamba achieved the highest validation performance under a unified five-seed protocol, with precision = 0.729, recall = 0.626, F1-score = 0.673, IoU = 0.507, kappa = 0.666, Boundary-F1 = 0.466, and HD95 = 3.45 pixels. Five-seed experiments produced F1 = 0.673 ± 0.003, IoU = 0.507 ± 0.002, Boundary-F1 = 0.466 ± 0.002, and HD95 = 3.45 ± 0.13 pixels, with a 95% CI of [0.670, 0.676] for F1. Relative to the strong 14-channel concatenation baseline, the proposed model improves mean F1 by 0.045 and reduces HD95 by 1.42 pixels. Expanded qualitative inference on Jinsha River patches indicates that the learned spectral-topographic representation transfers plausibly to high-relief reservoir-canyon terrain. These results show that terrain-conditioned state-space modeling can improve both segmentation accuracy and boundary geometry for remote sensing landslide mapping. Full article

(This article belongs to the Topic Advanced GNSS and InSAR Technologies for Geoscience Applications)

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21 pages, 11253 KB  

Open AccessArticle

A Method for Enhancing the Positioning Performance of PPP-B2b by Integrating Galileo Observation

by Xuena Shang, Liwenle Liu, Yilong Yuan, Mengxiang Tong, Qianqian He and Xiaopeng Gong

Sensors 2026, 26(10), 3073; https://doi.org/10.3390/s26103073 - 13 May 2026

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Abstract

The BeiDou-3 (BDS-3) Precise Point Positioning service (PPP-B2b) can realize decimeter-level positioning by broadcasting satellite orbit, clock offset, and code bias corrections via GEO satellites, enabling PPP without reliance on ground communication networks. However, the current PPP-B2b service only provides corrections for BDS-3 [...] Read more.

The BeiDou-3 (BDS-3) Precise Point Positioning service (PPP-B2b) can realize decimeter-level positioning by broadcasting satellite orbit, clock offset, and code bias corrections via GEO satellites, enabling PPP without reliance on ground communication networks. However, the current PPP-B2b service only provides corrections for BDS-3 and GPS satellites, which limits the number of available satellites and may affect positioning performance in challenging environments. To further enhance the positioning performance, we propose to incorporate Galileo observation into the PPP-B2b positioning. A PPP model integrating PPP-B2b service and broadcast ephemeris was established. First, the accuracy of the Galileo broadcast ephemeris was evaluated using precise orbit and clock products as references. The results show that the mean signal-in-space range error (SISRE) standard deviation of Galileo broadcast ephemeris is 0.30, which is only a little worse than that of GPS from PPP-B2b service. Then, the positioning experiments were conducted under different elevation cutoff angles. The experiments were conducted using data from 94 reference stations in China over a 7-day period. The results demonstrate that the inclusion of Galileo satellites significantly increases the number of visible satellites and improves satellite geometry. Compared with the BDS-3/GPS dual-system PPP solution, the BDS-3/GPS/Galileo triple-system PPP solution reduces the horizontal convergence time by approximately 13.70–16.67% and the vertical convergence time by about 18.75–20.00% under cutoff angles from 7° to 30° based on the 68th percentile statistics. The 95th percentile results further confirm the advantage of the triple-system solution under a more stringent statistical criterion. Where convergence is achieved, the triple-system solution reduces the horizontal convergence time by approximately 6.0–7.3% and the vertical convergence time by about 15.3–26.0%. Moreover, the triple-system solution exhibits a smaller re-convergence jump under abnormal observation conditions. In addition, under high elevation cutoff conditions, the introduction of Galileo satellites effectively improves PPP availability, thereby enhancing the continuity and robustness of PPP. These results indicate that incorporating Galileo observation within the PPP-B2b framework can effectively improve PPP performance and provide a simple and practical approach for high-precision real-time positioning. Full article

(This article belongs to the Topic Advanced GNSS and InSAR Technologies for Geoscience Applications)

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32 pages, 135570 KB  

Open AccessArticle

Sentinel-1 Consecutive Interferogram Stacking Approach (CISA) for High-Resolution and Near-Real-Time Ground Subsidence Mapping

by Sajid Hussain, Fei Liu, Bin Pan, Rui Xu, Zeeshan Afzal, Wajid Hussain, Yucheng Pan and Heping Li

Remote Sens. 2026, 18(10), 1486; https://doi.org/10.3390/rs18101486 - 9 May 2026

Cited by 1 | Viewed by 734

Abstract

Interferometric Synthetic Aperture Radar (InSAR) is crucial for monitoring ground displacement, particularly in Pakistan’s capital area, where urban expansion and active geotectonics converge. This study introduces the Consecutive Interferogram Stacking Approach (CISA), a processing framework optimized for near-real-time deformation monitoring using full-resolution Sentinel-1 [...] Read more.

Interferometric Synthetic Aperture Radar (InSAR) is crucial for monitoring ground displacement, particularly in Pakistan’s capital area, where urban expansion and active geotectonics converge. This study introduces the Consecutive Interferogram Stacking Approach (CISA), a processing framework optimized for near-real-time deformation monitoring using full-resolution Sentinel-1 data from adjacent acquisition pairs. Unlike conventional InSAR techniques that rely on spatial multilooking to suppress phase noise—which sacrifices spatial resolution for computational efficiency—CISA preserves native resolution through sequential interferogram stacking, accepting that short-interval interferograms retain geophysical phase instabilities (including fading signals) inherent to scatterer decorrelation. By minimizing temporal decorrelation through consecutive pairing, CISA enhances interferogram coherence (6–14% improvement) and reduces Root Mean Square Error (RMSE) by approximately 25% compared to conventional multilooked time series, while enabling the computational efficiency critical for operational applications. The framework’s incremental architecture allows velocity updates within hours of new image acquisition—requiring only single interferogram addition rather than complete network reprocessing—making it suitable for rapid-response hazard assessment where latency constraints outweigh the need for long-baseline phase filtering. CISA reveals spatiotemporal subsidence patterns potentially reflecting the influence of fault zone geometry, groundwater fluctuation, and urbanization, with full-resolution analysis delineating linear deformation patterns spatially consistent with blind fault traces through multi-directional displacement modeling. These findings demonstrate that operational monitoring of geohazards can be achieved through strategic trade-offs between processing latency and geophysical noise suppression, providing actionable intelligence for infrastructure risk management in tectonically active urban environments. Full article

(This article belongs to the Topic Advanced GNSS and InSAR Technologies for Geoscience Applications)

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24 pages, 9702 KB  

Open AccessArticle

Geodetic Constraints on Segment-Scale Slip Rates and Interseismic Coupling Along the Havran–Balıkesir Fault Zone, NW Anatolia, Türkiye

by İbrahim Tiryakioğlu, Halil İbrahim Solak, Ali Özkan, Cemil Gezgin, Eda Esma Eyübagil, Ece Bengünaz Çakanşimşek Ünlükaya, Kayhan Aladoğan, Çağlar Özkaymak, Mehmet Ali Uğur, Hasan Hakan Yavaşoğlu, Cemal Özer Yiğit, Bahadır Aktuğ and Vahap Engin Gülal

Sensors 2026, 26(8), 2539; https://doi.org/10.3390/s26082539 - 20 Apr 2026

Cited by 1 | Viewed by 821

Abstract

This study presents a new high-resolution GNSS-derived velocity field and the first internally consistent, segment-resolved block model for the Havran–Balıkesir Fault Zone (HBFZ) in western Anatolia. Inversion of the GNSS velocity field was performed using a dense network of 77 sites within a [...] Read more.

This study presents a new high-resolution GNSS-derived velocity field and the first internally consistent, segment-resolved block model for the Havran–Balıkesir Fault Zone (HBFZ) in western Anatolia. Inversion of the GNSS velocity field was performed using a dense network of 77 sites within a 3D elastic half-space framework to estimate fault slip rates and interseismic coupling. The results reveal that the HBFZ behaves as a kinematically heterogeneous fault system, with deformation systematically partitioned along strike. Block-modeling results indicate pronounced along-strike variations in interseismic coupling and slip-deficit accumulation. While the westernmost Havran segment is weakly coupled and accommodates limited accumulation, the Turplu and Gökçeyazı segments emerge as major strain-accumulation zones with high and laterally continuous slip-deficit rates. In particular, the Gökçeyazı segment exhibits slip-deficit rates of ~4–6 mm/yr and nearly two millennia of seismic quiescence, implying the potential for a future large-magnitude earthquake (Mw ~7.1–7.3). The strong agreement between GNSS-derived deformation patterns and independent geological and paleoseismological constraints suggests that this segment is currently in an advanced stage of the seismic cycle. These findings highlight the importance of segment-scale geodetic observations for seismic hazard assessment in northwestern Anatolia. Full article

(This article belongs to the Topic Advanced GNSS and InSAR Technologies for Geoscience Applications)

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