Search Results (291)

Cloud cover can delay landslide detection in optical satellite imagery for weeks, complicating disaster response. Synthetic Aperture Radar (SAR) backscatter imagery, which is widely used for monitoring floods and avalanches, remains underutilised for landslide detection due to a limited understanding of landslide signatures in SAR data. We developed a conceptual model of landslide expression in SAR backscatter (σ°) change images through iterative investigation of over 1000 landslides across 30 diverse study areas. Using multi-temporal composites and dense time series Sentinel-1 C-band SAR data, we identified characteristic patterns linked to land cover, terrain, and landslide material. The results showed either increased or decreased backscatter depending on environmental conditions, with reduced visibility in urban or mixed vegetation areas. Detection was also hindered by geometric distortions and snow cover. The diversity of landslide expression illustrates the need to consider local variability and multi-track (ascending and descending) satellite data in designing representative training datasets for automated detection models. The conceptual model was applied to three recent disaster events using the first post-event Sentinel-1 image, successfully identifying previously unknown landslides before optical imagery became available in two cases. This study provides a theoretical foundation for interpreting landslides in SAR imagery and demonstrates its utility for rapid landslide detection. The findings support further exploration of rapid landslides in SAR backscatter data and future development of automated detection models, offering a valuable tool for disaster response. Full article

Burned area detection plays a critical role in assessing the impact of forest and land fires, particularly in Indonesia, where both peatland and non-peatland areas are increasingly affected. Optical remote sensing has been widely used for this task, but its effectiveness is limited by persistent cloud cover in tropical regions. A Synthetic Aperture Radar (SAR) offers a cloud-independent alternative for burned area mapping. This study investigates the performance of a Stacking Ensemble Neural Network (SENN) model using polarimetric features derived from both C-band (Sentinel 1) and L-band (Advanced Land Observing Satellite—Phased Array L-band Synthetic Aperture Radar (ALOS-2/PALSAR-2)) data. The analysis covers three representative sites in Indonesia: peatland areas in (1) Rokan Hilir, (2) Merauke, and non-peatland areas in (3) Bima and Dompu. Validation is conducted using high-resolution PlanetScope imagery(Planet Labs PBC—San Francisco, California, United States). The results show that the SENN model consistently outperforms conventional artificial neural network (ANN) approaches across most evaluation metrics. L-band SAR data yields a superior performance to the C-band, particularly in peatland areas, with overall accuracy reaching 93–96% and precision between 92 and 100%. The method achieves 76% accuracy and 89% recall in non-peatland regions. Performance is lower in dry, hilly savanna landscapes. These findings demonstrate the effectiveness of the SENN, especially with L-band SAR, in improving burned area detection across diverse land types, supporting more reliable fire monitoring efforts in Indonesia. Full article

Forest fires, intensified by climate change, threaten tropical ecosystems by accelerating biodiversity loss, releasing carbon emissions, and altering hydrological cycles. Continuous detection of fire-induced forest loss is therefore critical. However, commonly used optical-based methods often face limitations, particularly due to cloud cover and coarse spatial resolution. This study explores the use of C-band Sentinel-1 Synthetic Aperture Radar (SAR) time series, combined with Bayesian Online Changepoint Detection (BOCD), for detecting and continuously monitoring fire-induced vegetation loss in forested areas. Three BOCD variants are evaluated: two single-polarization approaches individually using VV and VH reflectivities, and a dual-polarization approach ($pol$-BOCD) integrating both channels. The analysis focuses on a fire-affected area in Baixo Uraim (Paragominas, Brazil), supported by field-validated reference data. BOCD performance is compared against widely used optical products, including MODIS and VIIRS active fire and burned area data, as well as Sentinel-2-based difference Normalized Burn Ratio (dNBR) assessments. Results indicate that $pol$-BOCD achieves spatial accuracy comparable to dNBR (88.2% agreement), while enabling detections within a delay of three Sentinel-1 acquisitions. These findings highlight the potential of SAR-based BOCD for rapid, cloud-independent monitoring. While SAR enables continuous detection regardless of atmospheric conditions, optical imagery remains essential for characterizing the type and severity of change. Full article

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

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

The southwestern mountainous region of China (SMRC), characterized by complex geological environments, experiences frequent landslide disasters that pose significant threats to local residents. This study focuses on the Qijiang District of Chongqing, where we conduct a systematic evaluation of wavelength and observation geometry effects on InSAR-based landslide monitoring. Utilizing multi-sensor SAR imagery (Sentinel-1 C-band, ALOS-2 L-band, and LUTAN-1 L-band) acquired between 2018 and 2025, we integrate time-series InSAR analysis with geological records, high-resolution topographic data, and field investigation findings to assess representative landslide-susceptible zones in the Qijiang District. The results indicate the following: (1) L-band SAR data demonstrates superior monitoring precision compared to C-band SAR data in the SMRC; (2) the combined use of LUTAN-1 ascending/descending orbits significantly improved spatial accuracy and detection completeness in complex landscapes; (3) multi-source data fusion effectively mitigated limitations of single SAR systems, enhancing identification of small- to medium-scale landslides. This study provides critical technical support for multi-source landslide monitoring and early warning systems in Southwest China while demonstrating the applicability of China’s SAR satellites for geohazard applications. Full article

Advanced Synthetic Aperture Radar (SAR) has become an essential modality in remote sensing, offering all-weather capabilities and sensitivity to vegetation biophysical parameters and surface conditions, while effectively complementing optical sensor data. This study evaluates the impact of precipitation on the Normalized Polarimetric Radar Vegetation Index (NPRVI) using dual-polarization Sentinel-1 C-band SAR data from agricultural fields at the Beltsville Agricultural Research Center (BARC). Field-measured precipitation and Global Precipitation Measurement (GPM) precipitation datasets were temporally aligned with Sentinel-1 acquisition times to assess the sensitivity of radar signals to precipitation events. NPRVI exhibited a strong sensitivity to precipitation, particularly within the 1 to 7 h prior to the satellite overpass, even for small amounts of precipitation. A quality assessment (QA) framework was developed to flag and correct precipitation-affected radar observations through interpolation. The adjusted NPRVI values, based on the QA framework using precipitation within a 6 h window, showed strong agreement between field- and GPM-derived data, with an RMSE of 0.09 and a relative RMSE of 19.8%, demonstrating that GPM data can serve as a viable alternative for quality adjustment despite its coarse spatial resolution. The adjusted NPRVI for both soybean and corn fields significantly improved the temporal consistency of the time series and closely followed NDVI trends, while also capturing crop-specific seasonal variations, especially during periods of NDVI saturation or limited variability. These findings underscore the value of the proposed radar-based QA framework in enhancing the interpretability of vegetation dynamics. NPRVI, when adjusted for precipitation effects, can serve as a reliable and complementary tool to optical vegetation indices in agricultural and environmental monitoring. Full article

This study investigates the performance of temporal deep learning models with attention mechanisms for crop classification using Sentinel-1 C-band synthetic aperture radar (C-SAR) data. A time series of 16 scenes, acquired at 12-day intervals from 25 April to 22 October 2024, was used to classify six crop types: beans, beetroot, grassland, maize, potato, and winter wheat. Three temporal models—long short-term memory (LSTM), bidirectional gated recurrent unit (Bi-GRU), and temporal convolutional network (TCN)—were evaluated with and without an attention mechanism. All model configurations achieved accuracies above 83%, demonstrating the potential of Sentinel-1 SAR data for reliable, weather-independent crop classification. The TCN with attention model achieved the highest accuracy of 85.7%, significantly outperforming the baseline. LSTM also showed improved accuracy when combined with attention, whereas Bi-GRU did not benefit from the attention mechanism. These results highlight the effectiveness of combining temporal deep learning models with attention mechanisms to enhance crop classification using Sentinel-1 SAR time-series data. This study further confirms that freely available, regularly acquired Sentinel-1 observations are well-suited for robust crop mapping under diverse environmental conditions. Full article

This study utilizes the unique capabilities of Sentinel-1 C-band synthetic aperture radar (SAR) data to map post-fire burned areas and monitor vegetation recovery in a heath-dominated Queensland National Park. Sentinel-1 SAR data were used due to their cloud-penetrating capability and frequent revisit times. Using Google Earth Engine (GEE), a bitemporal ratio analysis was applied to SAR data from post-fire periods between 2021 and 2023. SAR backscatter changes over time captured fire impacts and subsequent vegetation regrowth. This differentiation was further enhanced with k-means clustering. Validation was supported by Sentinel-2 dNBR and official fire history records. The dNBR provided a quantitative assessment of burn severity and was used alongside the fire history data to evaluate the accuracy of the burned area classification. While Sentinel-2 false-colour composite (FCC) imagery was generated for visualisation and interpretation purposes, the primary validation relied on dNBR and QPWS fire history records. The results highlighted significant vegetation regrowth, with some areas returning to near pre-fire biomass levels by March 2023. This approach demonstrates the sensitivity of Sentinel-1 SAR, especially in VV polarization, for detecting subtle changes in vegetation, providing a cost-effective method for post-fire ecosystem monitoring and informing ecological management strategies amid increasing wildfire events. Full article

Forest age is a key factor in determining the carbon sequestration capacity and trends of forests. Based on the Google Earth Engine platform and using the topographically complex and climatically diverse Southeastern Tibet as the study area, we propose a new method for forest age estimation that integrates multi-source remote-sensing data with machine learning. The study employs the Continuous Degradation Detection (CODED) algorithm combined with spectral unmixing models and Normalized Difference Fraction Index (NDFI) time series analysis to update forest disturbance information and provide annual forest distribution, mapping young forest distribution. For undisturbed forests, we compared 12 machine-learning models and selected the Random Forest model for age prediction. The input variables include multiscale satellite spectral bands (Sentinel-2 MSI, Landsat series, PROBA-V, MOD09A1), vegetation parameter products (canopy height, productivity), data from the Global Ecosystem Dynamics Investigation (GEDI), multi-band SAR data (C/L), vegetation indices (e.g., NDVI, LAI, FPAR), and environmental factors (climate seasonality, topography). The results indicate that the forests in Southeastern Tibet are predominantly overmature (>120 years), accounting for 87% of the total forest cover, while mature (80–120 years), sub-mature (60–80 years), intermediate-aged (40–60 years), and young forests (< 40 years) represent relatively lower proportions at 9%, 1%, 2%, and 1%, respectively. Forest age exhibits a moderate positive correlation with stem biomass (r = 0.54) and leaf-area index (r = 0.53), but weakly negatively correlated with L-band radar backscatter (HV polarization, r = −0.18). Significant differences in reflectance among different age groups are observed in the 500–1000 nm spectral band, with 100 m resolution PROBA-V data being the most suitable for age prediction. The Random Forest model achieved an overall accuracy of 62% on the independent validation set, with canopy height, L-band radar data, and temperature seasonality being the most important predictors. Compared with 11 other machine-learning models, the Random Forest model demonstrated higher accuracy and stability in estimating forest age under complex terrain and cloudy conditions. This study provides an expandable technical framework for forest age estimation in complex terrain areas, which is of significant scientific and practical value for sustainable forest resource management and global forest resource monitoring. Full article

Aurora is caused by the collision of high-energy particles with particles in the Earth’s atmosphere. Recent advances in synthetic aperture radar (SAR) have demonstrated significant potential for ionospheric inversion at various scales, offering new insights into auroral processes. In this study, we present the first reported perturbation of C-band SAR signals induced by auroral activity during the enormous geomagnetic storm of 11 May 2024. The auroral boundaries observed by the Special Sensor Ultraviolet Spectrographic Imager (SSUSI) align closely with Sentinel-1 interferograms, despite the event occurring at mid-latitudes. A novel application of Sentinel-1 is illustrated, namely the inversion of the two-dimensional morphology and boundaries of the aurora at very high resolution and without interference from severe weather. Sentinel-1 promises to become a new method for the detection of precipitation particles, despite operating in the C-band, which is less disturbed by the ionosphere. Interferograms can also capture small-scale sporadic plasma patches associated with aurora. Furthermore, the analysis indicates that distinct polarization channels exhibit varying sensitivity towards auroral boundaries, with cross-polarization displaying heightened responsiveness. Full article

Forested wetlands in temperate mountain ecosystems play a critical role in carbon sequestration and biodiversity maintenance, yet their accurate delineation remains challenging due to spectral similarity with forests and anthropogenic interference. Here, we present an optimized two-stage Random Forest framework integrating 2019–2022 growing season datasets from Sentinel-1 C-SAR, ALOS-2 L-PALSAR, Sentinel-2 MSI, and Landsat-8 TIRS with environmental covariates. The methodology first applied NDBI thresholding (NDBI > 0.12) to exclude 94% of urban/agricultural areas through spectral masking, then implemented an optimized Random Forest classifier (ntree = 1200, mtry = 28) with 10-fold cross-validation, leveraging 42 features including L-band HV backscatter (feature importance = 47), Sentinel-2 SWIR (Band12; importance = 57), and land surface temperature gradients. This study pioneers a 10 m resolution forest swamp map in the Changbai Mountain wetlands, achieving 87.18% overall accuracy (Kappa = 0.84) with strong predictive performance (AUC = 0.89). Forest swamps showed robust classification metrics (PA = 80.37%, UA = 86.87%), driven by L-band SAR’s superior discriminative power (p < 0.05). Quantitative assessment demonstrated that L-band SAR increased classification accuracy in canopy penetration scenarios by 4.2% compared to optical-only approaches, while thermal-IR features reduced confusion with forests. Forested swamps occupied 229.95 km² (9% of protected areas), predominantly in transitional ecotones (720–850 m elevation) between herbaceous wetlands and forest. This study establishes that multi-sensor fusion enables operational wetland monitoring in topographically complex regions, providing a transferable framework for temperate mountain ecosystems. The dataset advances precision conservation strategies for these climate-sensitive habitats, supporting sustainable development goals targets for wetland protection through enhanced machine learning interpretability and anthropogenic interference mitigation. Full article

Cyclical analyses of dynamic changes in urban areas are critical and necessary for policymakers and societies. Remote sensing data processing methods are currently in place to determine the distribution of built-up and sealed areas on global and continental scales. However, there is a lack of research on distinguishing among urban classes at a larger scale for a city or its district. SAR sensors register features of urban areas that, when further processed, such as textures, can help in automatic recognition. We present a novel dataset for urban classification focusing on density analysis. Machine learning methods, including a selection of artificial neural networks and other classifiers, have been used to distinguish among different classes of built-up areas, as defined according to the Urban Atlas database. This dataset was used to establish benchmarks for classification, conduct verification tests, and evaluate accuracy. The C-band of Sentinel-1 images, for the same study areas as ICEYE X-band images and their texture derivatives, were used to classify variants. Better results were obtained using the CNN-based Unet model. The best overall accuracy was 79% for the X-band and 73% for the C-band datasets. The results indicate that the single-polarization X-band is more suitable for this classification despite the presence of more SAR features in the C-band with dual polarization. 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

Dams are crucial for ensuring water and electricity supply, while also providing significant flood protection. Regular monitoring of dam deformations is of vital socio-economic and ecological significance. In Germany, dams must be constructed and operated according to generally accepted rules of engineering. The safety concept for dams based on these rules relies on structural safety, professional operation and maintenance, safety monitoring, and precautionary measures. Rather time-consuming in situ techniques have been employed for these measurements, which permit monitoring deformations with either high spatial or temporal resolution, but not both. As a means of measuring large-scale deformations in the millimeter range, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique of Persistent Scatterer Interferometry (PSI) is already being applied in various fields. However, when considering the operational monitoring of dams using PSI, specific characteristics need to be considered. For example, the geographical location of the dam in space, as well as its shape, size, and land cover. All these factors can affect the visibility of the structure for the use with PSI and, in certain cases, limit the applicability of SAR data. The visibility of dams for PSI monitoring is often limited, particularly in cases where observation is typically not feasible due to factors such as geographical and structural characteristics. While corner reflectors can improve visibility, their large size often makes them unsuitable for dam infrastructure and may raise concerns with heritage protection for listed dams. Addressing these challenges, electronic corner reflectors (ECRs) offer an effective alternative due to their small and compact size. In this study, we analyzed the strategic placement of ECRs on dam structures. We developed a new CR Index, which identifies areas where PSI alone is insufficient due to unfavorable geometric or land use conditions. This index categorizes visibility potential into three classes, presented in a ‘traffic light’ map, and is instrumental in selecting optimal installation sites. We furthermore investigated the signal stability of ECRs over an extended observation period, considering the Amplitude Dispersion Index (ADI). It showed values between 0.1 and 0.4 for many dam structures, which is comparable to normal corner reflectors (CRs), confirming the reliability of these signals for PSI analysis. This work underscores the feasibility of using ECRs to enhance monitoring capabilities at dam infrastructure. Full article