Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (2,025)

Search Parameters:
Keywords = Sentinel-1 SAR

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
48 pages, 16562 KiB  
Article
Dense Matching with Low Computational Complexity for   Disparity Estimation in the Radargrammetric Approach of SAR Intensity Images
by Hamid Jannati, Mohammad Javad Valadan Zoej, Ebrahim Ghaderpour and Paolo Mazzanti
Remote Sens. 2025, 17(15), 2693; https://doi.org/10.3390/rs17152693 (registering DOI) - 3 Aug 2025
Abstract
Synthetic Aperture Radar (SAR) images and optical imagery have high potential for extracting digital elevation models (DEMs). The two main approaches for deriving elevation models from SAR data are interferometry (InSAR) and radargrammetry. Adapted from photogrammetric principles, radargrammetry relies on disparity model estimation [...] Read more.
Synthetic Aperture Radar (SAR) images and optical imagery have high potential for extracting digital elevation models (DEMs). The two main approaches for deriving elevation models from SAR data are interferometry (InSAR) and radargrammetry. Adapted from photogrammetric principles, radargrammetry relies on disparity model estimation as its core component. Matching strategies in radargrammetry typically follow local, global, or semi-global methodologies. Local methods, while having higher accuracy, especially in low-texture SAR images, require larger kernel sizes, leading to quadratic computational complexity. Conversely, global and semi-global models produce more consistent and higher-quality disparity maps but are computationally more intensive than local methods with small kernels and require more memory (RAM). In this study, inspired by the advantages of local matching algorithms, a computationally efficient and novel model is proposed for extracting corresponding pixels in SAR-intensity stereo images. To enhance accuracy, the proposed two-stage algorithm operates without an image pyramid structure. Notably, unlike traditional local and global models, the computational complexity of the proposed approach remains stable as the input size or kernel dimensions increase while memory consumption stays low. Compared to a pyramid-based local normalized cross-correlation (NCC) algorithm and adaptive semi-global matching (SGM) models, the proposed method maintains good accuracy comparable to adaptive SGM while reducing processing time by up to 50% relative to pyramid SGM and achieving a 35-fold speedup over the local NCC algorithm with an optimal kernel size. Validated on a Sentinel-1 stereo pair with a 10 m ground-pixel size, the proposed algorithm yields a DEM with an average accuracy of 34.1 m. Full article
29 pages, 9514 KiB  
Article
Kennaugh Elements Allow Early Detection of Bark Beetle Infestation in Temperate Forests Using Sentinel-1 Data
by Christine Hechtl, Sarah Hauser, Andreas Schmitt, Marco Heurich and Anna Wendleder
Forests 2025, 16(8), 1272; https://doi.org/10.3390/f16081272 (registering DOI) - 3 Aug 2025
Abstract
Climate change is generally having a negative impact on forest health by inducing drought stress and favouring the spread of pest species, such as bark beetles. The terrestrial monitoring of bark beetle infestation is very time-consuming, especially in the early stages, and therefore [...] Read more.
Climate change is generally having a negative impact on forest health by inducing drought stress and favouring the spread of pest species, such as bark beetles. The terrestrial monitoring of bark beetle infestation is very time-consuming, especially in the early stages, and therefore not feasible for extensive areas, emphasising the need for a comprehensive approach based on remote sensing. Although numerous studies have researched the use of optical data for this task, radar data remains comparatively underexplored. Therefore, this study uses the weekly and cloud-free acquisitions of Sentinel-1 in the Bavarian Forest National Park. Time series analysis within a Multi-SAR framework using Random Forest enables the monitoring of moisture content loss and, consequently, the assessment of tree vitality, which is crucial for the detection of stress conditions conducive to bark beetle outbreaks. High accuracies are achieved in predicting future bark beetle infestation (R2 of 0.83–0.89). These results demonstrate that forest vitality trends ranging from healthy to bark beetle-affected states can be mapped, supporting early intervention strategies. The standard deviation of 0.44 to 0.76 years indicates that the model deviates on average by half a year, mainly due to the uncertainty in the reference data. This temporal uncertainty is acceptable, as half a year provides a sufficient window to identify stressed forest areas and implement targeted management actions before bark beetle damage occurs. The successful application of this technique to extensive test sites in the state of North Rhine-Westphalia proves its transferability. For the first time, the results clearly demonstrate the expected relationship between radar backscatter expressed in the Kennaugh elements K0 and K1 and bark beetle infestation, thereby providing an opportunity for the continuous and cost-effective monitoring of forest health from space. Full article
(This article belongs to the Section Forest Health)
Show Figures

Figure 1

22 pages, 3267 KiB  
Article
Identifying Deformation Drivers in Dam Segments Using Combined X- and C-Band PS Time Series
by Jonas Ziemer, Jannik Jänichen, Gideon Stein, Natascha Liedel, Carolin Wicker, Katja Last, Joachim Denzler, Christiane Schmullius, Maha Shadaydeh and Clémence Dubois
Remote Sens. 2025, 17(15), 2629; https://doi.org/10.3390/rs17152629 - 29 Jul 2025
Viewed by 216
Abstract
Dams play a vital role in securing water and electricity supplies for households and industry, and they contribute significantly to flood protection. Regular monitoring of dam deformations holds fundamental socio-economic and ecological importance. Traditionally, this has relied on time-consuming in situ techniques that [...] Read more.
Dams play a vital role in securing water and electricity supplies for households and industry, and they contribute significantly to flood protection. Regular monitoring of dam deformations holds fundamental socio-economic and ecological importance. Traditionally, this has relied on time-consuming in situ techniques that offer either high spatial or temporal resolution. Persistent Scatterer Interferometry (PSI) addresses these limitations, enabling high-resolution monitoring in both domains. Sensors such as TerraSAR-X (TSX) and Sentinel-1 (S-1) have proven effective for deformation analysis with millimeter accuracy. Combining TSX and S-1 datasets enhances monitoring capabilities by leveraging the high spatial resolution of TSX with the broad coverage of S-1. This improves monitoring by increasing PS point density, reducing revisit intervals, and facilitating the detection of environmental deformation drivers. This study aims to investigate two objectives: first, we evaluate the benefits of a spatially and temporally densified PS time series derived from TSX and S-1 data for detecting radial deformations in individual dam segments. To support this, we developed the TSX2StaMPS toolbox, integrated into the updated snap2stamps workflow for generating single-master interferogram stacks using TSX data. Second, we identify deformation drivers using water level and temperature as exogenous variables. The five-year study period (2017–2022) was conducted on a gravity dam in North Rhine-Westphalia, Germany, which was divided into logically connected segments. The results were compared to in situ data obtained from pendulum measurements. Linear models demonstrated a fair agreement between the combined time series and the pendulum data (R2 = 0.5; MAE = 2.3 mm). Temperature was identified as the primary long-term driver of periodic deformations of the gravity dam. Following the filling of the reservoir, the variance in the PS data increased from 0.9 mm to 3.9 mm in RMSE, suggesting that water level changes are more responsible for short-term variations in the SAR signal. Upon full impoundment, the mean deformation amplitude decreased by approximately 1.7 mm toward the downstream side of the dam, which was attributed to the higher water pressure. The last five meters of water level rise resulted in higher feature importance due to interaction effects with temperature. The study concludes that integrating multiple PS datasets for dam monitoring is beneficial particularly for dams where few PS points can be identified using one sensor or where pendulum systems are not installed. Identifying the drivers of deformation is feasible and can be incorporated into existing monitoring frameworks. Full article
(This article belongs to the Special Issue Dam Stability Monitoring with Satellite Geodesy II)
Show Figures

Figure 1

23 pages, 2129 KiB  
Article
GIS-Based Flood Susceptibility Mapping Using AHP in the Urban Amazon: A Case Study of Ananindeua, Brazil
by Lianne Pimenta, Lia Duarte, Ana Cláudia Teodoro, Norma Beltrão, Dênis Gomes and Renata Oliveira
Land 2025, 14(8), 1543; https://doi.org/10.3390/land14081543 - 27 Jul 2025
Viewed by 394
Abstract
Flood susceptibility mapping is essential for urban planning and disaster risk management, especially in rapidly urbanizing areas exposed to extreme rainfall events. This study applies an integrated approach combining Geographic Information Systems (GIS), map algebra, and the Analytic Hierarchy Process (AHP) to assess [...] Read more.
Flood susceptibility mapping is essential for urban planning and disaster risk management, especially in rapidly urbanizing areas exposed to extreme rainfall events. This study applies an integrated approach combining Geographic Information Systems (GIS), map algebra, and the Analytic Hierarchy Process (AHP) to assess flood-prone zones in Ananindeua, Pará, Brazil. Five geoenvironmental criteria—rainfall, land use and land cover (LULC), slope, soil type, and drainage density—were selected and weighted using AHP to generate a composite flood susceptibility index. The results identified rainfall and slope as the most influential criteria, with both contributing to over 184 km2 of high-susceptibility area. Spatial patterns showed that flood-prone zones are concentrated in flat urban areas with high drainage density and extensive impermeable surfaces. CHIRPS rainfall data were validated using Pearson’s correlation (r = 0.83) and the Nash–Sutcliffe efficiency (NS = 0.97), confirming the reliability of the precipitation input. The final susceptibility map, categorized into low, medium, and high classes, was validated using flood events derived from Sentinel-1 SAR data (2019–2025), of which 97.2% occurred in medium- or high-susceptibility zones. These findings demonstrate the model’s strong predictive performance and highlight the role of unplanned urban expansion, land cover changes, and inadequate drainage in increasing flood risk. Although specific to Ananindeua, the proposed methodology can be adapted to other urban areas in Brazil, provided local conditions and data availability are considered. Full article
(This article belongs to the Section Land Use, Impact Assessment and Sustainability)
Show Figures

Figure 1

22 pages, 1954 KiB  
Article
Pre-Evaluation of Wave Energy Converter Deployment in the Baltic Sea Through Site Limitations Using CMEMS Hindcast, Sentinel-1, and Wave Buoy Data
by Nikon Vidjajev, Sander Rikka and Victor Alari
Energies 2025, 18(14), 3843; https://doi.org/10.3390/en18143843 - 19 Jul 2025
Viewed by 755
Abstract
This study assesses the wave energy potential and spectral variability in the Väinameri—a semi-sheltered, island-filtered basin on Estonia’s west coast—by combining six months of high-resolution in situ wave spectra with deep learning-enhanced satellite retrievals. Directional spectra were recorded at Rohuküla Harbor using a [...] Read more.
This study assesses the wave energy potential and spectral variability in the Väinameri—a semi-sheltered, island-filtered basin on Estonia’s west coast—by combining six months of high-resolution in situ wave spectra with deep learning-enhanced satellite retrievals. Directional spectra were recorded at Rohuküla Harbor using a wave-following LainePoiss buoy from June to December 2024. In parallel, one-dimensional wave spectra were reconstructed from Sentinel-1 SAR imagery using a long short-term memory (LSTM) neural network trained on more than 71,000 collocations with NORA3 WAM hindcasts. Spectral pairs matched within a ±1 h window exhibited strong agreement in the dominant 0.2–0.4 Hz frequency band, while systematic underestimation at higher frequencies reflected both the radar resolution limits and the short-period, wind–sea-dominated nature of the Baltic Sea. Our results confirm that LSTM-enhanced SAR retrievals enable robust bulk and spectral wave characterizations in data-sparse nearshore regions, and offer a practical basis for the site evaluation, device tuning, and survivability testing of pilot-scale wave energy converters under both typical and storm-driven forcing conditions. Full article
(This article belongs to the Special Issue New Advances in Wave Energy Conversion)
Show Figures

Figure 1

23 pages, 2695 KiB  
Article
Estimation of Subtropical Forest Aboveground Biomass Using Active and Passive Sentinel Data with Canopy Height
by Yi Wu, Yu Chen, Chunhong Tian, Ting Yun and Mingyang Li
Remote Sens. 2025, 17(14), 2509; https://doi.org/10.3390/rs17142509 - 18 Jul 2025
Viewed by 362
Abstract
Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest [...] Read more.
Forest biomass is closely related to carbon sequestration capacity and can reflect the level of forest management. This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. In addition, a canopy height model, constructed from a digital surface model (DSM) derived from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) and an ICESat-2-corrected SRTM DEM, is incorporated to quantify its impact on the accuracy of AGB estimation. The results indicate the following: (1) The incorporation of multi-source remote sensing data significantly improves the accuracy of AGB estimation, among which the RF model performs the best (R2 = 0.69, RMSE = 24.26 t·ha−1) compared with the single-source model. (2) The canopy height model (CHM) obtained from InSAR-LiDAR effectively alleviates the signal saturation effect of optical and SAR data in high-biomass areas (>200 t·ha−1). When FCH is added to the RF model combined with multi-source remote sensing data, the R2 of the AGB estimation model is improved to 0.74. (3) In 2018, AGB in Chenzhou City shows clear spatial heterogeneity, with a mean of 51.87 t·ha−1. Biomass increases from the western hilly part (32.15–68.43 t·ha−1) to the eastern mountainous area (89.72–256.41 t·ha−1), peaking in Dongjiang Lake National Forest Park (256.41 t·ha−1). This study proposes a comprehensive feature integration framework that combines red-edge spectral indices for capturing vegetation physiological status, SAR-derived texture metrics for assessing canopy structural heterogeneity, and canopy height metrics to characterize forest three-dimensional structure. This integrated approach enables the robust and accurate monitoring of carbon storage in subtropical forests. Full article
(This article belongs to the Collection Feature Paper Special Issue on Forest Remote Sensing)
Show Figures

Figure 1

19 pages, 7782 KiB  
Article
Two Novel Multidimensional Data Analysis Approaches Using InSAR Products for Landslide Prone Areas
by Hamit Beran Gunce and Bekir Taner San
Appl. Sci. 2025, 15(14), 8024; https://doi.org/10.3390/app15148024 - 18 Jul 2025
Viewed by 229
Abstract
Successfully detecting ground deformation, especially landslides, using InSAR has not always been possible. Improvements to existing InSAR tools are needed to address this issue. This study develops and evaluates two novel approaches that use multidimensional InSAR products to detect surface displacements in the [...] Read more.
Successfully detecting ground deformation, especially landslides, using InSAR has not always been possible. Improvements to existing InSAR tools are needed to address this issue. This study develops and evaluates two novel approaches that use multidimensional InSAR products to detect surface displacements in the landslide-prone region of Büyükalan, Antalya. Multi-temporal InSAR analysis of Sentinel-1 data (2015–2020) is performed using LiCSAR–LiCSBAS, followed by two novel approaches: multi-dimensional InSAR research and analysis (MIRA) and Crosta’s InSAR application (InCROSS). Cumulative LOS velocity maps reveal deformation rates of −1.1 cm/year to 1.0 cm/year for descending tracks and −3.8 cm/year to 3.8 cm/year for ascending tracks. Vertical displacements range from −1.9 cm/year to 2.3 cm/year and east–west components from −2.8 cm/year to 2.9 cm/year. MIRA uses an n-Dimensional Visualizer and SVM classifier to identify deformation clusters, and InCROSS applies PCA to enhance deformation features. MIRA increases the deformation detection capacity compared to conventional InSAR products, and InCROSS integrates these products. A comparison of the results reveals 80.48% consistency between them. Overall, the integration of InSAR with statistical and multidimensional analysis significantly enhances the detection and interpretation of ground deformation patterns in landslide-prone areas. Full article
Show Figures

Figure 1

16 pages, 3372 KiB  
Article
Monitoring the Time-Lagged Response of Land Subsidence to Groundwater Fluctuations via InSAR and Distributed Fiber-Optic Strain Sensing
by Qing He, Hehe Liu, Lu Wei, Jing Ding, Heling Sun and Zhen Zhang
Appl. Sci. 2025, 15(14), 7991; https://doi.org/10.3390/app15147991 - 17 Jul 2025
Viewed by 285
Abstract
Understanding the time-lagged response of land subsidence to groundwater level fluctuations and subsurface strain variations is crucial for uncovering its underlying mechanisms and enhancing disaster early warning capabilities. This study focuses on Dangshan County, Anhui Province, China, and systematically analyzes the spatio-temporal evolution [...] Read more.
Understanding the time-lagged response of land subsidence to groundwater level fluctuations and subsurface strain variations is crucial for uncovering its underlying mechanisms and enhancing disaster early warning capabilities. This study focuses on Dangshan County, Anhui Province, China, and systematically analyzes the spatio-temporal evolution of land subsidence from 2018 to 2024. A total of 207 Sentinel-1 SAR images were first processed using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to generate high-resolution surface deformation time series. Subsequently, the seasonal-trend decomposition using the LOESS (STL) model was applied to extract annual cyclic deformation components from the InSAR-derived time series. To quantitatively assess the delayed response of land subsidence to groundwater level changes and subsurface strain evolution, time-lagged cross-correlation (TLCC) analysis was performed between surface deformation and both groundwater level data and distributed fiber-optic strain measurements within the 5–50 m depth interval. The strain data was collected using a borehole-based automated distributed fiber-optic sensing system. The results indicate that land subsidence is primarily concentrated in the urban core, with annual cyclic amplitudes ranging from 10 to 18 mm and peak values reaching 22 mm. The timing of surface rebound shows spatial variability, typically occurring in mid-February in residential areas and mid-May in agricultural zones. The analysis reveals that surface deformation lags behind groundwater fluctuations by approximately 2 to 3 months, depending on local hydrogeological conditions, while subsurface strain changes generally lead surface subsidence by about 3 months. These findings demonstrate the strong predictive potential of distributed fiber-optic sensing in capturing precursory deformation signals and underscore the importance of integrating InSAR, hydrological, and geotechnical data for advancing the understanding of subsidence mechanisms and improving monitoring and mitigation efforts. Full article
Show Figures

Figure 1

27 pages, 7109 KiB  
Article
The Long-Term Surface Deformation Monitoring and Prediction of Hutubi Gas Storage Reservoir in Xinjiang Based on InSAR and the GWO-VMD-GRU Model
by Wang Huang, Wei Liao, Jie Li, Xuejun Qiao, Sulitan Yusan, Abudutayier Yasen, Xinlu Li and Shijie Zhang
Remote Sens. 2025, 17(14), 2480; https://doi.org/10.3390/rs17142480 - 17 Jul 2025
Viewed by 332
Abstract
Natural gas storage is an effective solution to address the energy supply–demand imbalance, and underground gas storage (UGS) is a primary method for storing natural gas. The overarching goal of this study is to monitor and analyze surface deformation at the Hutubi underground [...] Read more.
Natural gas storage is an effective solution to address the energy supply–demand imbalance, and underground gas storage (UGS) is a primary method for storing natural gas. The overarching goal of this study is to monitor and analyze surface deformation at the Hutubi underground gas storage facility in Xinjiang, China, which is the largest gas storage facility in the country. This research aims to ensure the stable and efficient operation of the facility through long-term monitoring, using remote sensing data and advanced modeling techniques. The study employs the SBAS-InSAR method, leveraging Synthetic Aperture Radar (SAR) data from the TerraSAR and Sentinel-1 sensors to observe displacement time series from 2013 to 2024. The data is processed through wavelet transformation for denoising, followed by the application of a Gray Wolf Optimization (GWO) algorithm combined with Variational Mode Decomposition (VMD) to decompose both surface deformation and gas pressure data. The key focus is the development of a high-precision predictive model using a Gated Recurrent Unit (GRU) network, referred to as GWO-VMD-GRU, to accurately predict surface deformation. The results show periodic surface uplift and subsidence at the facility, with a notable net uplift. During the period from August 2013 to March 2015, the maximum uplift rate was 6 mm/year, while from January 2015 to December 2024, it increased to 12 mm/year. The surface deformation correlates with gas injection and extraction periods, indicating periodic variations. The accuracy of the InSAR-derived displacement data is validated through high-precision GNSS data. The GWO-VMD-GRU model demonstrates strong predictive performance with a coefficient of determination (R2) greater than 0.98 for the gas well test points. This study provides a valuable reference for the future safe operation and management of underground gas storage facilities, demonstrating significant contributions to both scientific understanding and practical applications in underground gas storage management. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Land Subsidence Monitoring)
Show Figures

Figure 1

28 pages, 8088 KiB  
Article
Multi-Band Differential SAR Interferometry for Snow Water Equivalent Retrieval over Alpine Mountains
by Fabio Bovenga, Antonella Belmonte, Alberto Refice and Ilenia Argentiero
Remote Sens. 2025, 17(14), 2479; https://doi.org/10.3390/rs17142479 - 17 Jul 2025
Viewed by 278
Abstract
Snow water equivalent (SWE) can be estimated using Differential SAR Interferometry (DInSAR), which captures changes in snow depth and density between two SAR acquisitions. However, challenges arise due to SAR signal penetration into the snowpack and the intrinsic limitations of DInSAR measurements. This [...] Read more.
Snow water equivalent (SWE) can be estimated using Differential SAR Interferometry (DInSAR), which captures changes in snow depth and density between two SAR acquisitions. However, challenges arise due to SAR signal penetration into the snowpack and the intrinsic limitations of DInSAR measurements. This study addresses these issues and explores the use of multi-band SAR data to derive SWE maps in alpine regions characterized by steep terrain, small spatial extent, and a potentially heterogeneous snowpack. We first conducted a performance analysis to assess SWE estimation precision and the maximum unambiguous SWE variation, considering incidence angle, wavelength, and coherence. Based on these results, we selected C-band Sentinel-1 and L-band SAOCOM data acquired over alpine areas and applied tailored DInSAR processing. Atmospheric artifacts were corrected using zenith total delay maps from the GACOS service. Additionally, sensitivity maps were generated for each interferometric pair to identify pixels suitable for reliable SWE estimation. A comparative analysis of the C- and L-band results revealed several critical issues, including significant atmospheric artifacts, phase decorrelation, and phase unwrapping errors, which impact SWE retrieval accuracy. A comparison between our Sentinel-1-based SWE estimations and independent measurements over an instrumented site shows results fairly in line with previous works exploiting C-band data, with an RSME in the order of a few tens of mm. Full article
(This article belongs to the Special Issue Understanding Snow Hydrology Through Remote Sensing Technologies)
Show Figures

Figure 1

24 pages, 26359 KiB  
Article
Evaluating the Interferometric Performance of China’s Dual-Star SAR Satellite Constellation in Large Deformation Scenarios: A Case Study in the Jinchuan Mining Area, Gansu
by Zixuan Ge, Wenhao Wu, Jiyuan Hu, Nijiati Muhetaer, Peijie Zhu, Jie Guo, Zhihui Li, Gonghai Zhang, Yuxing Bai and Weijia Ren
Remote Sens. 2025, 17(14), 2451; https://doi.org/10.3390/rs17142451 - 15 Jul 2025
Viewed by 332
Abstract
Mining activities can trigger geological disasters, including slope instability and surface subsidence, posing a serious threat to the surrounding environment and miners’ safety. Consequently, the development of reasonable, effective, and rapid deformation monitoring methods in mining areas is essential. Traditional synthetic aperture radar(SAR) [...] Read more.
Mining activities can trigger geological disasters, including slope instability and surface subsidence, posing a serious threat to the surrounding environment and miners’ safety. Consequently, the development of reasonable, effective, and rapid deformation monitoring methods in mining areas is essential. Traditional synthetic aperture radar(SAR) satellites are often limited by their revisiting period and image resolution, leading to unwrapping errors and decorrelation issues in the central mining area, which pose challenges in deformation monitoring in mining areas. In this study, persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology is used to monitor and analyze surface deformation of the Jinchuan mining area in Jinchang City, based on SAR images from the small satellites “Fucheng-1” and “Shenqi”, launched by the Tianyi Research Institute in Hunan Province, China. Notably, the dual-star constellation offers high-resolution SAR data with a spatial resolution of up to 3 m and a minimum revisit period of 4 days. We also assessed the stability of the dual-star interferometric capability, imaging quality, and time-series monitoring capability of the “Fucheng-1” and “Shenqi” satellites and performed a comparison with the time-series results from Sentinel-1A. The results show that the phase difference (SPD) and phase standard deviation (PSD) mean values for the “Fucheng-1” and “Shenqi” interferograms show improvements of 21.47% and 35.47%, respectively, compared to Sentinel-1A interferograms. Additionally, the processing results of the dual-satellite constellation exhibit spatial distribution characteristics highly consistent with those of Sentinel-1A, while demonstrating relatively better detail representation capabilities at certain measurement points. In the context of rapid deformation monitoring in mining areas, they show a higher revisit frequency and spatial resolution, demonstrating high practical value. Full article
Show Figures

Figure 1

19 pages, 2494 KiB  
Article
Assessing Forest Structure and Biomass with Multi-Sensor Remote Sensing: Insights from Mediterranean and Temperate Forests
by Maria Cristina Mihai, Sofia Miguel, Ignacio Borlaf-Mena, Julián Tijerín-Triviño and Mihai Tanase
Forests 2025, 16(7), 1164; https://doi.org/10.3390/f16071164 - 15 Jul 2025
Viewed by 370
Abstract
Forests provide habitat for diverse species and play a key role in mitigating climate change. Remote sensing enables efficient monitoring of many forest attributes across vast areas, thus supporting effective and efficient management strategies. This study aimed to identify an effective combination of [...] Read more.
Forests provide habitat for diverse species and play a key role in mitigating climate change. Remote sensing enables efficient monitoring of many forest attributes across vast areas, thus supporting effective and efficient management strategies. This study aimed to identify an effective combination of remote sensing sensors for estimating biophysical variables in Mediterranean and temperate forests that can be easily translated into an operational context. Aboveground biomass (AGB), canopy height (CH), and forest canopy cover (FCC) were estimated using a combination of optical (Sentinel-2, Landsat) and radar sensors (Sentinel-1 and TerraSAR-X/TanDEM-X), along with records of past forest disturbances and topography-related variables. As a reference, lidar-derived AGB, CH, and FCC were used. Model performance was assessed not only with standard approaches such as out-of-bag sampling but also with completely independent lidar-derived reference datasets, thus enabling evaluation of the model’s temporal inference capacity. In Mediterranean forests, models based on optical imagery outperformed the radar-enhanced models when estimating FCC and CH, with elevation and spectral indices being key predictors of forest structure. In contrast, in denser temperate forests, radar data (especially X-band relative heights) were crucial for estimating CH and AGB. Incorporating past disturbance data further improved model accuracy in these denser ecosystems. Overall, this study underscores the value of integrating multi-source remote sensing data while highlighting the limitations of temporal extrapolation. The presented methodology can be adapted to enhance forest variable estimation across many forest ecosystems. Full article
Show Figures

Figure 1

16 pages, 57657 KiB  
Article
InSAR Inversion of the Source Mechanism of the 23 January 2024 Xinjiang Wushi Mw7.0 Earthquake
by Mingyang Jin, Yongsheng Li and Yujiang Li
Remote Sens. 2025, 17(14), 2435; https://doi.org/10.3390/rs17142435 - 14 Jul 2025
Viewed by 268
Abstract
The Mw7.0 earthquake that occurred on 23 January 2024, in Wushi County, Xinjiang, China, was centered on the Maidan fault, located at the rear edge of the Kalpin reverse-thrust system in the southwestern Tianshan Mountains, at a depth of 13 km. [...] Read more.
The Mw7.0 earthquake that occurred on 23 January 2024, in Wushi County, Xinjiang, China, was centered on the Maidan fault, located at the rear edge of the Kalpin reverse-thrust system in the southwestern Tianshan Mountains, at a depth of 13 km. This event caused significant surface deformation and triggered a series of secondary geologic hazards. In this study, data from two satellites, Sentinel-1A and LuTan-1, were combined to obtain the coseismic deformation field of the earthquake. The two-step inversion method was applied to determine the geometrical parameters and slip characteristics of the mainshock fault. The results indicate that the seismicity is primarily driven by reverse faulting, with a contribution from sinistral strike–slip faulting, and the maximum dip–slip displacement is 4.2 m. Additionally, an aftershock of magnitude 5.7 occurring on January 30 was identified in the LT-1 data. This aftershock was controlled by a reverse fault dipping opposite to the mainshock fault, and its maximum slip is 0.65 m. Analysis of the Coulomb stress triggering effect suggests that the Wushi earthquake may have induced the aftershock. Full article
Show Figures

Figure 1

21 pages, 15482 KiB  
Article
InSAR Detection of Slow Ground Deformation: Taking Advantage of Sentinel-1 Time Series Length in Reducing Error Sources
by Machel Higgins and Shimon Wdowinski
Remote Sens. 2025, 17(14), 2420; https://doi.org/10.3390/rs17142420 - 12 Jul 2025
Viewed by 321
Abstract
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, [...] Read more.
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
Show Figures

Graphical abstract

28 pages, 8538 KiB  
Article
Deep-Learning Integration of CNN–Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
by Abbas Mohammed Noori, Abdul Razzak T. Ziboon and Amjed N. AL-Hameedawi
Appl. Sci. 2025, 15(14), 7770; https://doi.org/10.3390/app15147770 - 10 Jul 2025
Viewed by 572
Abstract
Flash floods are natural disasters that have significant impacts on human life and economic damage. The detection of flash floods using remote-sensing techniques provides essential data for subsequent flood-risk assessment through the preparation of flood inventory samples. In this research, a new deep-learning [...] Read more.
Flash floods are natural disasters that have significant impacts on human life and economic damage. The detection of flash floods using remote-sensing techniques provides essential data for subsequent flood-risk assessment through the preparation of flood inventory samples. In this research, a new deep-learning approach for bi-temporal flash-flood detection in Synthetic Aperture Radar (SAR) is proposed. It combines a U-Net convolutional network with a Transformer model using a compact Convolutional Tokenizer (CCT) to improve the efficiency of long-range dependency learning. The hybrid model, namely CCT-U-ViT, naturally combines the spatial feature extraction of U-Net and the global context capability of Transformer. The model significantly reduces the number of basic blocks as it uses the CCT tokenizer instead of conventional Vision Transformer tokenization, which makes it the right fit for small flood detection datasets. This model improves flood boundary delineation by involving local spatial patterns and global contextual relations. However, the method is based on Sentinel-1 SAR images and focuses on Erbil, Iraq, which experienced an extreme flash flood in December 2021. The experimental comparison results show that the proposed CCT-U-ViT outperforms multiple baseline models, such as conventional CNNs, U-Net, and Vision Transformer, obtaining an impressive overall accuracy of 91.24%. Furthermore, the model obtains better precision and recall with an F1-score of 91.21% and mIoU of 83.83%. Qualitative results demonstrate that CCT-U-ViT can effectively preserve the flood boundaries with higher precision and less salt-and-pepper noise compared with the state-of-the-art approaches. This study underscores the significance of hybrid deep-learning models in enhancing the precision of flood detection with SAR data, providing valuable insights for the advancement of real-time flood monitoring and risk management systems. Full article
Show Figures

Figure 1

Back to TopTop