Search Results (229)

The comprehensive, accurate, and rapid acquisition of large-scale surface deformation using Interferometric Synthetic Aperture Radar (InSAR) technology provides crucial information support for regional eco-geological safety assessments and the rational development and utilization of groundwater resources. The Linfen-Yuncheng Basin in Shanxi Province is one of China’s historically most frequented regions for geological hazards in plain areas, such as land subsidence and ground fissures. This study employed the coherent point targets based Small Baseline Subset (SBAS) time-series InSAR technique to interpret a dataset of 224 scenes of 5 m resolution RADARSAT-2 satellite SAR images acquired from January 2017 to May 2024. This enabled the acquisition of high-resolution spatiotemporal characteristics of surface deformation in the Linfen-Yuncheng Basin during the monitoring period. The results show that the area with a deformation rate exceeding 5 mm/a in the study area accounts for 12.3% of the total area, among which the subsidence area accounts for 11.1% and the uplift area accounts for 1.2%, indicating that the overall surface is relatively stable. There are four relatively significant local subsidence areas in the study area. The total area with a rate exceeding 30 mm/a is 41.12 km², and the maximum cumulative subsidence is close to 810 mm. By combining high-resolution satellite images and field survey data, it is found that the causes of the four subsidence areas are all the extraction of groundwater for production, living, and agricultural irrigation. This conclusion is further confirmed by comparing the InSAR monitoring results with the groundwater level data of monitoring wells. In addition, on-site investigations reveal that there is a mutually promoting and spatially symbiotic relationship between land subsidence and ground fissures in the study area. The non-uniform subsidence areas monitored by InSAR show significant ground fissure activity characteristics. The InSAR monitoring results can be used to guide the identification and analysis of ground fissure disasters. This study also finds that due to the implementation of surface water supply projects, the demand for groundwater in the study area has been continuously decreasing. The problem of ground water over-extraction has been gradually alleviated, which in turn promotes the continuous recovery of the groundwater level and reduces the development intensity of land subsidence and ground fissures. Full article

Hydrological changes in peatland are directly related to peat condition. Restoration projects typically aim to raise the water table to enhance peat development, support ecology and increase carbon storage. Remote monitoring of peatland hydrology is challenging but advantageous for assessing condition and restoration effectiveness. This study explores how temporal Sentinel-1-derived InSAR coherence relates to ground-based measurements of soil moisture, water level and local precipitation at two sites, near-natural (Munsary) and degraded (Knockfin Heights), in the Flow Country, Scotland, alongside regional Wick weather station precipitation data (2015–2024). Stronger seasonal linear relationships were observed between soil moisture and InSAR coherence in spring/summer (R² reaching 0.83 at Munsary subsite C, p < 0.001), with in-phase cross correlation throughout the year. In contrast, the relationship between water level and InSAR coherence was more complex with an out-of-phase relationship for much of the year and a weaker linear correlation. These relationships varied with peatland condition, strongest at the more intact bog (Munsary). InSAR coherence and precipitation were in-phase, but not linearly correlated, and land use/cover had no significant effect. Outcomes suggest that InSAR coherence could, when combined with other data, assist in mapping soil moisture/water level dynamics in blanket peatlands, and identify the timing of precipitation events in areas with non-frontal rainfall. Full article

The objective of this study is to quantify and characterize ground deformations induced by underground coal mining in the Karaganda coal basin, Kazakhstan, in order to improve the understanding of subsidence processes and their long-term evolution. The SBAS-InSAR method was applied to Sentinel-1 (C-band) and TerraSAR-X (X-band) data from 2019–2021 to estimate the magnitude, extent, and temporal behavior of displacements over the Kostenko, Kuzembayev, Aktasskaya, and Saranskaya mines. The results reveal spatially coherent and progressive deformation, with maximum cumulative LOS displacements exceeding –800 mm in TerraSAR-X data within active longwall mining zones. Time-series analysis confirmed acceleration of displacement during active extraction and its subsequent attenuation after mining ceased. Comparative assessment demonstrated a strong agreement between Sentinel-1 and TerraSAR-X results (r = 0.9628), despite differences in resolution and acquisition geometry, highlighting the robustness of the SBAS-InSAR approach. Analysis of displacement over individual longwalls showed that several panels (3, 5, 8, 15, and 18) already exceeded their projected maximum subsidence values, underlining the necessity of continuous monitoring for ensuring safety. In contrast, other longwalls have not yet reached their maximum deformation, indicating potential for further activity. Overall, this study demonstrates the value of multi-sensor InSAR monitoring for reliable assessment of mining-induced subsidence and for supporting geotechnical risk management in post-industrial regions. Full article

Ground deformation monitoring in mining areas is essential for hazard prevention and environmental protection. Although interferometric synthetic aperture radar (InSAR) provides detailed phase information for accurate deformation measurement, its performance is often compromised in regions experiencing rapid subsidence and strong noise, where phase aliasing and coherence loss lead to significant inaccuracies. To overcome these limitations, this paper proposes JOTGLNet, a guided learning network with joint offset tracking, for multiscale deformation monitoring. This method integrates pixel offset tracking (OT), which robustly captures large-gradient displacements, with interferometric phase data that offers high sensitivity in coherent regions. A dual-path deep learning architecture was designed where the interferometric phase serves as the primary branch and OT features act as complementary information, enhancing the network’s ability to handle varying deformation rates and coherence conditions. Additionally, a novel shape perception loss combining morphological similarity measurement and error learning was introduced to improve geometric fidelity and reduce unbalanced errors across deformation regions. The model was trained on 4000 simulated samples reflecting diverse real-world scenarios and validated on 1100 test samples with a maximum deformation up to 12.6 m, achieving an average prediction error of less than 0.15 m—outperforming state-of-the-art methods whose errors exceeded 0.19 m. Additionally, experiments on five real monitoring datasets further confirmed the superiority and consistency of the proposed approach. Full article

Timely and reliable surface deformation monitoring is critical for hazard prevention and resource management in mining areas. However, traditional Time-Series Interferometric (TSI) Synthetic Aperture Radar techniques often suffer from low coherent point density in mining environments, limiting their effectiveness. To overcome this limitation, we propose an adaptive Polarimetric TSI (PolTSI) method that exploits dual-polarization Sentinel-1 data to achieve more reliable deformation monitoring in complex mining terrains. The method employs a dual-strategy optimization: amplitude dispersion–based optimization for Permanent Scatterer (PS) pixels and minimum mean square error (MMSE)-based polarimetric filtering followed by coherence maximization for Distributed Scatterer (DS) pixels. Experimental results from an open-pit mining area demonstrate that the proposed approach significantly improves phase quality and spatial coverage. In particular, the number of coherent monitoring points increased from 31,183 with conventional TSI to 465,328 using the proposed approach, corresponding to a 1392% improvement. This substantial enhancement confirms the method’s robustness in extracting deformation signals from low-coherence, heterogeneous mining surfaces. As one of the few studies to apply Polarimetric InSAR (Pol-InSAR) in active mining regions, our work demonstrates the underexplored potential of dual-pol SAR data for improving both the spatial density and reliability of time-series deformation mapping. The results provide a solid technical foundation for large-scale, high-precision surface monitoring in complex mining environments. Full article

Using interferometric synthetic aperture radar (InSAR) to observe slow ground deformation can be challenging due to many sources of error, with tropospheric phase delay and unwrapping errors being the most significant. While analytical methods, weather models, and data exist to mitigate tropospheric error, most of these techniques are unsuitable for all InSAR applications (e.g., complex tropospheric mixing in the tropics) or are deficient in spatial or temporal resolution. Likewise, there are methods for removing the unwrapping error, but they cannot resolve the true phase when there is a high prevalence (>40%) of unwrapping error in a set of interferograms. Applying tropospheric delay removal techniques is unnecessary for C-band Sentinel-1 InSAR time series studies, and the effect of unwrapping error can be minimized if the full dataset is utilized. We demonstrate that using interferograms with long temporal baselines (800 days to 1600 days) but very short perpendicular baselines (<5 m) (LTSPB) can lower the velocity detection threshold to 2 mm y⁻¹ to 3 mm y⁻¹ for long-term coherent permanent scatterers. The LTSPB interferograms can measure slow deformation rates because the expected differential phases are larger than those of small baselines and potentially exceed the typical noise amplitude while also reducing the sensitivity of the time series estimation to the noise sources. The method takes advantage of the Sentinel-1 mission length (2016 to present), which, for most regions, can yield up to 300 interferograms that meet the LTSPB baseline criteria. We demonstrate that low velocity detection can be achieved by comparing the expected LTSPB differential phase measurements to synthetic tests and tropospheric delay from the Global Navigation Satellite System. We then characterize the slow (~3 mm/y) ground deformation of the Socorro Magma Body, New Mexico, and the Tampa Bay Area using LTSPB InSAR analysis. The method we describe has implications for simplifying the InSAR time series processing chain and enhancing the velocity detection threshold. Full article

In InSAR processing, optimizing baselines by selecting appropriate interferometric pairs is crucial for ensuring interferogram quality and improving InSAR monitoring accuracy. However, in multi-temporal InSAR processing, the quality of interferometric pairs is constrained by spatiotemporal baseline parameters and surface scattering characteristics. Traditional selection methods, such as those based on average coherence thresholding, consider only a single factor and do not account for the interactions among multiple factors. This study introduces a principal component analysis (PCA) method to comprehensively analyze four factors: temporal baseline, spatial baseline, NDVI difference, and coherence, scientifically setting weights to achieve precise selection of interferometric pairs. Additionally, the GACOS (Generic Atmospheric Correction Online Service) atmospheric correction product is applied to further enhance data quality. Taking the Haikou Phosphate Mine area in Kunming, Yunnan, as the study area, surface deformation information was extracted using the SBAS-InSAR technique, and the spatiotemporal characteristics of subsidence were analyzed. The research results show the following: (1) compared with other methods, the PCA-based interferometric pair optimization method significantly improves the selection performance. The minimum value decreases to 0.248 rad, while the mean and standard deviation are reduced to 1.589 rad and 0.797 rad, respectively, effectively suppressing error fluctuations and enhancing the stability of the inversion; (2) through comparative analysis of the effective pixel ratio and standard deviation of deformation rates, as well as a comprehensive evaluation of the deformation rate probability density function (PDF) distribution, the PCA optimization method maintains a high effective pixel ratio while enhancing sensitivity to surface deformation changes, indicating its advantage in deformation monitoring in complex terrain areas; (3) the combined analysis of spatial autocorrelation (Moran’s I coefficient) and spatial correlation coefficients (Pearson and Spearman) verified the advantages of the PCA optimization method in maintaining spatial structure and result consistency, supporting its ability to achieve higher accuracy and stability in complex surface deformation monitoring. In summary, the PCA-based baseline optimization method significantly improves the accuracy of SBAS-InSAR in surface subsidence monitoring, fully demonstrating its reliability and stability in complex terrain areas, and providing a solid technical support for dynamic monitoring of surface subsidence in mining areas. Full article

For tropical forests characterized by tall and densely packed trees, even long-wavelength SAR signals may fail to achieve full penetration, posing a significant challenge for retrieving sub-canopy terrain using polarimetric interferometric SAR (InSAR)(PolInSAR) techniques. This paper proposes a single-baseline PolInSAR-based correction method for sub-canopy terrain estimation based on a one-dimensional lookup table (LUT) that links forest height to unpenetrated depth. The approach begins by applying an optimal normal matrix approximation to constrain the complex coherence measurements. Subsequently, the difference between the PolInSAR Digital Terrain Model (DTM) derived from the Random Volume over Ground (RVoG) model and the LiDAR DTM is defined as the unpenetrated depth. A nonlinear iterative optimization algorithm is then employed to estimate forest height, from which a fundamental mapping between forest height and unpenetrated depth is established. This mapping can be used to correct the bias in sub-canopy terrain estimation based on the PolInSAR RVoG model, even with only a small amount of sparse LiDAR DTM data. To validate the effectiveness of the method, experiments were conducted using fully polarimetric P-band airborne SAR data acquired by the European Space Agency (ESA) during the AfriSAR campaign over the Mabounie region in Gabon, Africa, in 2016. The experimental results demonstrate that the proposed method effectively mitigates terrain estimation errors caused by insufficient signal penetration or the limitation of single-interferometric geometry. Further analysis reveals that the availability of sufficient and precise forest height data significantly improves sub-canopy terrain accuracy. Compared with LiDAR-derived DTM, the proposed method achieves an average root mean square error (RMSE) of 5.90 m, representing an accuracy improvement of approximately 38.3% over traditional RVoG-derived InSAR DTM retrieval. These findings further confirm that there exist unpenetrated phenomena in single-baseline low-frequency PolInSAR-derived DTMs of tropical forested areas. Nevertheless, when sparse LiDAR topographic data is available, the integration of fully PolInSAR data with LUT-based compensation enables improved sub-canopy terrain retrieval. This provides a promising technical pathway with single-baseline configuration for spaceborne missions, such as ESA’s BIOMASS mission, to estimate sub-canopy terrain in tropical-rainforest regions. Full article

Coal mines play an important role in the global energy supply. Monitoring the displacement of open-pit mines is crucial to preventing geological disasters, such as landslides and surface displacement, caused by high-intensity mining activities. In recent years, multi-temporal Synthetic Aperture Radar Interferometry (InSAR) technology has advanced and become widely used for monitoring the displacement of open-pit mines. However, the scattering characteristics of surfaces in open-pit mining areas are unstable, resulting in few coherence points with uneven distribution. Small BAseline Subset InSAR (SABS-InSAR) technology struggles to extract high-density points and fails to capture the overall displacement trend of the monitoring area. To address these challenges, this study focused on the Shengli West No. 2 open-pit coal mine in eastern Inner Mongolia, China, using 201 Sentinel-1 images collected from 20 May 2017 to 13 April 2024. We applied both SBAS-InSAR and distributed scatterer InSAR (DS-InSAR) methods to investigate the surface displacement and long-term behavior of the open-pit coal mine over the past seven years. The relationship between this displacement and mining activities was analyzed. The results indicate significant land subsidence was observed in reclaimed areas, with rates exceeding 281.2 mm/y. The compaction process of waste materials was the main contributor to land subsidence. Land uplift or horizontal displacement was observed over the areas near the active working parts of the mines. Compared to SBAS-InSAR, DS-InSAR was shown to more effectively capture the spatiotemporal distribution of surface displacement in open-pit coal mines, offering more intuitive, comprehensive, and high-precision monitoring of open-pit coal mines. Full article

The remote sensing community increasingly demands precise ecosystem monitoring, environmental change detection, and natural resource management, particularly in forestry. Key metrics such as biomass and total area index require accurate estimation, necessitating extensive experiments and reliable scattering models. Recent advances in radar interferometry introduce two essential parameters—interferogram phase and correlation coefficient—containing crucial target information. Understanding their relationship to forest biophysical parameters requires analyzing wave interactions with vegetation particles. This study presents a discrete interferometric model for a random medium layer, establishing the link between radar interferometry and forest biophysical properties. Correlation analysis plays a vital role in estimating one variable based on another, reducing uncertainty in random media. The research introduces a novel modeling approach that enhances theoretical foundations and supports empirical studies in the literature. Bridging theoretical analysis and practical observations, this work enhances the precision and applicability of radar interferometry for vegetation monitoring. The findings contribute to improving remote sensing methodologies and expanding their potential in ecological and environmental research. Ultimately, this study advances the use of interferometric models in extracting critical forest parameters with greater accuracy. Full article

Accurately monitoring forest canopy height is crucial for sustainable forest management, particularly in southeastern North Carolina, USA, where dense forests and limited accessibility pose substantial challenges. This study presents an explainable machine learning framework that integrates sparse GEDI LiDAR samples with multi-sensor remote sensing data to improve both the accuracy and interpretability of forest canopy height estimation. This framework incorporates multitemporal optical observations from Sentinel-2; C-band backscatter and InSAR coherence from Sentinel-1; quad-polarization L-Band backscatter and polarimetric decompositions from the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR); texture features from the National Agriculture Imagery Program (NAIP) aerial photography; and topographic data derived from an airborne LiDAR-based digital elevation model. We evaluated four machine learning algorithms, K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and eXtreme gradient boosting (XGB), and found consistent accuracy across all models. Our evaluation highlights our method’s robustness, evidenced by closely matched R² and RMSE values across models: KNN (R² of 0.496, RMSE of 5.13 m), RF (R² of 0.510, RMSE of 5.06 m), SVM (R² of 0.544, RMSE of 4.88 m), and XGB (R² of 0.548, RMSE of 4.85 m). The integration of comprehensive feature sets, as opposed to subsets, yielded better results, underscoring the value of using multisource remotely sensed data. Crucially, SHapley Additive exPlanations (SHAP) revealed the multi-seasonal red-edge spectral bands of Sentinel-2 as dominant predictors across models, while volume scattering from UAVSAR emerged as a key driver in tree-based algorithms. This study underscores the complementary nature of multi-sensor data and highlights the interpretability of our models. By offering spatially continuous, high-quality canopy height estimates, this cost-effective, data-driven approach advances large-scale forest management and environmental monitoring, paving the way for improved decision-making and conservation strategies. Full article

The Afghanistan earthquake of 21 June 2022 ruptured a ~10 km-long fault segment in the North Waziristan–Bannu fault system (NWBFS) located towards the north of the Katawaz Basin. The earthquake was shallow and reportedly caused widespread devastation. In this article, we investigated the long-term, i.e., geological and geomorphological, evidence of deformation along the earthquake segment. For comparison, we also studied the short-term space geodetic and remote sensing results documenting a visible offset between the fault traces. Focusing on the fault modelling and on the published results, it is thus clear that the earthquake rupture did not reach the surface; instead, it stopped in the shallow sub-surface at ~1 km depth. Moreover, the InSAR analyses show some technical issues, such as coherence loss, etc., likely due to severe ground-shaking leaving some gaps in the results; geological and geomorphological evidence complemented this information. As an outcome of this research, we confirmed that InSAR results could generally capture the overall fault geometry at depth, even in cases of blind faulting, whereas the detailed geometry of the tectonic structure, in this case with a right stepping en-echelon pattern, could be successfully captured by combining it with geological and geomorphological approaches and optical remote sensing observations. Accordingly, the right stepping fault generates a restraining bend in the dominantly left-lateral shear zone. Therefore, such fault stepovers are capable of localizing strain and could act as loci for seismic ruptures, bearing strong implications for the seismic hazard assessment of the region, as well as of other strike-slip fault zones. Full article

In recent decades, the interferometric synthetic aperture radar (InSAR) technique has emerged as a powerful tool for monitoring ground subsidence and geohazards. Various satellite SAR systems with different modes, such as Sentinel-1 and Lutan-1, have produced abundant SAR datasets with wide coverage and large historical archives, which have significantly influenced long-term deformation monitoring applications. However, large-scale InSAR data have posed significant challenges to conventional InSAR methods. These issues include the computational burden and storage of multi-temporal InSAR (MT-InSAR) methods, as well as temporal decorrelation for coherent scatterers with long temporal baselines. In this study, we propose a stepwise MT-InSAR with a temporal coherent scatterer method to address these problems. First, a batch sequential method is introduced in the algorithm by grouping the SAR dataset in the time domain based on the average coherence distribution and then applying permanent scatterer interferometry to each temporal subset. Second, a multi-layer network is employed to estimate deformation for partially coherent scatterers using small baseline subset interferograms, with permanent scatterer deformation parameters as the reference. Finally, the final deformation rate and displacement time series were obtained by incorporating all the temporal subsets. The proposed method efficiently generates high-density InSAR deformation measurements for long-time series analysis. The proposed method was validated using 9 years of Sentinel-1 data with 229 SAR images from Jakarta, Indonesia. The deformation results were compared with those of conventional methods and global navigation satellite system data to confirm the effectiveness of the proposed method. Full article

Edgecumbe, a dormant volcano located on Kruzof Island in the southeastern part of Alaska, USA, west of the Sitka Strait, has exhibited increased volcanic activity since 2018. To assess the historical and current intensity of this activity and explore its relationship with seismic events in the surrounding region, this study utilized data from the ERS-1/2, ALOS-1, and Sentinel-1 satellites. The Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) and Small Baseline Subset InSAR (SBAS-InSAR) techniques were employed to obtain surface deformation data spanning nearly 30 years. Based on the acquired deformation field, the point-source Mogi model was applied to invert the position and temporal volume changes in the volcanic source. Then, by integrating seismic activity data from the surrounding area, the correlation between volcanic activity and earthquake occurrences was analyzed. The results indicate the following: (1) the coherence of interferograms is influenced by seasonal variations, with snow accumulation during the winter months negatively impacting interferometric coherence. (2) Between 1992 and 2000, the surface of the volcano remained relatively stable. From 2007 to 2010, the frequency of seismic events increased, leading to significant surface deformation, with the maximum Line-of-Sight (LOS) deformation rate during this period reaching −26 mm/yr. Between 2015 and 2023, the volcano entered a phase of accelerated uplift, with surface deformation rates increasing to 68 mm/yr after August 2018. (3) The inversion results for the period from 2015 to 2023 show that the volcanic source, located at a depth of 5.4 km, experienced expansion in its magma chamber, with a volumetric increase of 57.8 × 10⁶ m³. These inversion results are consistent with surface deformation fields obtained from both ascending and descending orbits, with cumulative LOS displacement reaching approximately 210 mm and 250 mm in the ascending and descending tracks, respectively. (4) Long-term volcanic surface deformation, changes in magma source volume, and seismic activity suggest that the earthquakes occurring after 2018 have facilitated the expansion of the volcanic magma source and intensified surface deformation. The uplift rate around the volcano has significantly increased. Full article

Phase denoising constitutes a critical component of the synthetic aperture radar interferometry (InSAR) processing chain, where noise suppression and detail preservation are two mutually constraining objectives. Recently, deep learning has attracted considerable interest due to its promising performance in the field of image denoising. In this paper, a Neighbor2Neighbor denoising network (NBDNet) is proposed, which is capable of simultaneously estimating phase and coherence in both single-look and multi-look cases. Specifically, repeat-pass PALSAR real interferograms encompassing a diverse range of coherence, fringe density, and terrain features are used as the training dataset, and the novel Neighbor2Neighbor self-supervised training framework is leveraged. The Neighbor2Neighbor framework eliminates the necessity of noise-free labels, simplifying the training process. Furthermore, rich features can be learned directly from real interferograms. In order to validate the denoising capability and generalization ability of the proposed NBDNet, simulated data, repeat-pass data from Sentinel-1 Interferometric Wide (IW) swath mode, and single-pass data from Hongtu-1 stripmap mode are used for phase denoising experiments. The results demonstrate that NBDNet performs well in terms of noise suppression, detail preservation and computation efficiency, validating its potential for high-precision and high-resolution topography reconstruction. Full article