Search Results (2,749)

Optical fiber radiation sensing probes made using inorganic scintillator materials have notable advantages in achieving high spatial resolution and building sensing arrays due to their small size and excellent linearity, serving as a key tool for dose measurement in precision radiotherapy. This study establishes a theoretical model for scintillator luminescence coupling into optical fibers, and derives a fluorescence intensity calculation formula based on the fiber’s numerical aperture and fluorescence self-absorption. The light intensity response to scintillator length for different absorption coefficients is established based on numerical simulation, providing a nonlinear fitting equation, resulting in a novel “effective length of scintillator” concept. Five probes with scintillator lengths of 0.2 mm, 0.5 mm, 1.0 mm, 1.5 mm, and 2.0 mm were prepared in the laboratory using a 3:1 mass ratio mixture of UV-setting epoxy and Gd₂O₂S:Tb powder. Tests in a clinical radiation delivery setting showed good agreement between experimental data and theory, confirming optimum effective length of the scintillator as 0.62 mm. This study indicates that inorganic scintillators for end-constructed probes do need not need to be excessively long. Analyzing the effective length can reduce scintillator usage, simplify fabrication and processing, and enhance the probe’s spatial resolution without decreasing the signal-to-noise ratio, thus offering new insights for optimizing optical fiber radiation probes. Full article

Water pollution is a growing concern for aquatic ecosystems worldwide, with threats like plastic waste, nutrient pollution, and oil spills harming biodiversity and impacting human health, fisheries, and local economies. Traditional methods of monitoring water quality, such as ground sampling, are often limited in how frequently and widely they can collect data. Satellite imagery is a potent tool in offering broader and more consistent coverage. This review explores how Multispectral Imagery (MSI) and Synthetic Aperture Radar (SAR), including polarimetric SAR (PolSAR), are utilised to monitor harmful algal blooms (HABs) and other types of aquatic pollution. It looks at recent advancements in satellite sensor technologies, highlights the value of combining different data sources (like MSI and SAR), and discusses the growing use of artificial intelligence for analysing satellite data. Real-world examples from places like Lake Erie, Vembanad Lake in India, and Korea’s coastal waters show how satellite tools such as the Geostationary Ocean Colour Imager (GOCI) and Environmental Sample Processor (ESP) are being used to track seasonal changes in water quality and support early warning systems. While satellite monitoring still faces challenges like interference from clouds or water turbidity, continued progress in sensor design, data fusion, and policy support is helping make remote sensing a key part of managing water health. Full article

Spaceborne Synthetic Aperture Radar (SAR) can be applied for monitoring tropical cyclones (TCs), but co-polarized C-band SAR suffers from signal saturation such that it is improper for high wind-speed conditions. In contrast, cross-polarized SAR data does not suffer from this issue, but the retrieval algorithm needs more deliberation. Previously, many geophysical model functions (GMFs) have been developed using cross-polarized data, which obtain wind speeds using the complex relationships described by radar backscatter, incidence angle, wind direction, and radar look direction. In this regard, the rapid development of artificial intelligence technology has provided versatile machine learning methods for such a nonlinear inversion problem. In this study, we comprehensively compare the wind-speed retrieval performance of several models including Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN), which were developed based on spatio-temporal matching and correlation analysis of stepped frequency microwave radiometer (SFMR) and dual-polarized Sentinel-1 SAR data after noise removal. A data set with ~2800 samples is generated during TCs for training and validating the inversion model. The generalization ability of different models is tested by the reserved independent data. When using similar parameters with GMFs, RF inversion has the highest accuracy with a Root Mean Square Error (RMSE) of 3.40 m/s and correlation coefficient of 0.94. Furthermore, considering that the sea surface temperature is a crucial factor for generating TCs and influencing ocean backscattering, its effects on the proposed RF model are also explored, the results of which show improved wind-speed retrieval performances. Full article

An effective understanding of sediment deposition and erosion in river basins, particularly floodplains, is critical for modeling geomorphic evolution, managing flood risks, and maintaining ecological integrity. However, most related studies have been limited to hydraulic or hydrodynamic modeling approaches. Therefore, this study integrated Sentinel-1 differential interferometric synthetic aperture radar (DInSAR) coherence, Sentinel-2 normalized difference vegetation index, and soil surface moisture index data with one-dimensional hydraulic modeling to assess flood-induced sediment deposition and erosion in the Gamcheon River basin under non-flood, short flood, and long flood scenarios. The DInSAR deformation analysis revealed a clear pattern of upstream erosion and downstream deposition during flood events, indicating a total depositional uplift of 0.33 m during the long flood scenario but dominant erosion with a total measured surface lowering of −2.03 m during the non-flood scenario. These results were highly consistent with the predictions from the hydraulic model and supported by the hysteresis curves for in situ suspended sediment concentration. The findings of this study demonstrate the effectiveness of the proposed integrated approach for quantifying floodplain sediment dynamics, offering particular application value in data-scarce or inaccessible floodplains. Furthermore, the proposed approach provides practical insights into sediment management, flood risk assessment, and ecosystem restoration efforts. Full article

Soil moisture (SM) is crucial for ecosystems and agriculture. Since the root systems of plants absorb water at different depths with different intensities, monitoring multi-layer SM can better respond to the water demand of plants and offer a crucial technical backing for drought monitoring and precision irrigation. Synthetic aperture radar (SAR) and multispectral (MS) have been widely used in SM estimation; however, their combined application for multi-layer SM profiling remains underexplored. Existing research based on these two data types has primarily focused on surface soil moisture (SSM), with limited investigation into estimating SM at deeper or varying depths. Therefore, the aims of this research are to integrate Sentinel-1 SAR and Sentinel-2 MS data and employ machine learning algorithms to estimate multi-layer SM in the Shandian River Basin. The results showed that (1) MS + SAR-based SM estimation significantly outperformed single-source data (MS or SAR alone). Specifically, MS data performed better in the root-zone estimation, while SAR data showed superior performance in SSM estimation. (2) The BKA-CNN estimation accuracy significantly outperformed RF and XGBoost. The results of its five-fold cross-validation are as follows: R² = 0.768 ± 0.011 at 3 cm, R² = 0.777 ± 0.013 at 5 cm, R² = 0.799 ± 0.011 at 10 cm, R² = 0.792 ± 0.01 at 20 cm, and R² = 0.782 ± 0.011 at 50 cm. (3) The BKA-CNN model performed better in grassland than in farmland. These findings indicate that the BKA-CNN model proposed in this study effectively improves the estimation precision of multi-layer SM by fusing SAR and MS data, demonstrating considerable generalization ability and robustness. It holds potential application value in ecological protection and agricultural water resource management. Full article

In recent years, deep learning (DL)-based synthetic aperture radar (SAR) ship detection has made significant strides. However, many existing DL-based SAR ship detection methods treat sea regions and land regions equally, failing to be fully aware of the differences between the two regions during training and testing. This oversight may prevent the network’s attention from fully concentrating on valuable regions (i.e., sea regions and ship regions), thereby adversely affecting overall detection accuracy. To address these issues, we propose the Sea–Land Aware Net (SLA-Net), which introduces a novel SLA Hierarchical Attention mechanism to gradually focus the network’s attention on sea and ship regions across different stages. Specifically, SLA-Net instantiates the SLA Hierarchical Attention mechanism through three components: the SLA Sea-Attention Backbone, which incorporates sea attention in the feature extraction stage; the SLA Ship-Attention FPN, which implements ship attention in the feature fusion stage; and the SLA Ship-Attention Detection Heads, which enforce ship attention in the detection refinement stage. Moreover, to tackle the lack of sea–land priors in the community working on DL-based SAR ship detection, we introduce the sea–land segmentation dataset for SSDD (SL-SSDD). Built upon the well-established SAR ship detection dataset (SSDD), it serves as a synergistic dataset for ship detection when used in conjunction with SSDD. Quantitative experimental results on SSDD and generalization results on HRSID and LS-SSDD demonstrate that SLA-Net achieves superior SAR ship detection performance compared to other methods. Furthermore, SL-SSDD, which contains sea–land segmentation information, can provide a new perspective for the community working on DL-based SAR ship detection. Full article

Multimodal remote sensing image registration faces severe challenges due to geometric and radiometric differences, particularly between optical and synthetic aperture radar (SAR) images. These inherent disparities make extracting highly repeatable cross-modal feature points difficult. Current methods typically rely on image intensity extreme responses or network regression without keypoint supervision for feature point detection. Moreover, they not only lack explicit keypoint annotations as supervision signals but also fail to establish a clear and consistent definition of what constitutes a reliable feature point in cross-modal scenarios. To overcome this limitation, we propose PLISA—a novel heterogeneous image registration method. PLISA integrates two core components: an automated pseudo-labeling module (APLM) and a pseudo-twin interaction network (PTIF). The APLM introduces an innovative labeling strategy that explicitly defines keypoints as corner points, thereby generating consistent pseudo-labels for dual-modality images and effectively mitigating the instability caused by the absence of supervised keypoint annotations. These pseudo-labels subsequently train the PTIF, which adopts a pseudo-twin architecture incorporating a cross-modal interactive attention (CIA) module to effectively reconcile cross-modal commonalities and distinctive characteristics. Evaluations on the SEN1-2 dataset and OSdataset demonstrate PLISA’s state-of-the-art cross-modal feature point repeatability while maintaining robust registration accuracy across a range of challenging conditions, including rotations, scale variations, and SAR-specific speckle noise. Full article

Urban land subsidence, a globally prevalent environmental problem and geohazard triggered by rapid urbanization, threatens ecological security and socioeconomic stability. Chongqing City in southwestern China, recognized as the world’s largest mountainous city, has encountered land subsidence challenges exacerbated by accelerated urban construction. This study proposes an effective method for extracting urbanization intensity by integrating Sentinel-1, Sentinel-2, and its derived synthetic aperture radar and spectral indices features, combined with texture features. The small baseline subset interferometric synthetic aperture radar technique was employed to monitor land subsidence in Chongqing between 2018 and 2024. Furthermore, the relationships among urbanization intensity, metro construction, groundwater dynamics, and land subsidence were systematically analyzed. Finally, geographical detector and multiscale geographically weighted regression models were employed to explore the interactive effects of anthropogenic, topographic, geological-tectonic, climatic, and land surface characteristic factors contributing to land subsidence. The findings reveal that (1) the method proposed in this paper can effectively extract urbanization intensity and provide an important approach to analyze the influence of urbanization on land subsidence. (2) Land subsidence along newly opened metro lines was more pronounced than along existing lines. The shorter the interval between metro construction completion and the start of operation, the greater the subsidence observed within the first 3 months of operation, which indicates that this interval influences land subsidence. (3) Overall, groundwater dynamics and land subsidence showed a clear correlation from June 2022 to June 2023, a phenomenon largely caused by the extreme summer high temperatures of 2022, triggering reduced precipitation and a notable groundwater decline. Beyond this period, however, only a weak correlation was observed between groundwater fluctuations and land subsidence trends, indicating that other factors likely dominated subsidence dynamics. (4) The anthropogenic factors have a higher relative influence on land subsidence than other drivers. In terms of q-value, the top six factors are road network density > precipitation > elevation > enhanced normalized difference impervious surface index > population density > nighttime light, while distance to fault exhibits the least explanatory power. Given Chongqing’s exemplary status as a mountainous city, this study offers a foundational reference for subsequent quantitative analyses of land subsidence and its drivers in other mountainous cities worldwide. Full article

Accurate monitoring of settlement in high-speed railway embankments is critical for operational safety and long-term serviceability. This study investigates the applicability of Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) for quantifying millimeter-scale deformations and emphasizes how surrounding environmental factors influence measurement accuracy. Using 29 TerraSAR-X images acquired between 2016 and 2018, PS-InSAR-derived settlements were compared with precise leveling survey data across twelve representative embankment sections of the Honam High-Speed Railway in South Korea. Temporal and spatial discrepancies between the two datasets were harmonized through preprocessing, allowing robust accuracy assessment using mean absolute error (MAE) and standard deviation (SD). Results demonstrate that PS-InSAR reliably captures settlement trends, with MAE ranging from 1.7 to 4.2 mm across different scenes. However, significant variability in accuracy was observed depending on local land-cover composition. Correlation analysis revealed that vegetation-dominated areas, such as agricultural and forest land, reduce persistent scatterer density and increase measurement variability, whereas high-reflectivity surfaces, including transportation facilities and buildings, enhance measurement stability and precision. These findings confirm that environmental conditions are decisive factors in determining the performance of PS-InSAR. The study highlights the necessity of integrating site-specific land-cover information when designing and interpreting satellite-based monitoring strategies for railway infrastructure management. Full article

The pollen morphological characteristics of 16 pummelos and 4 other citrus germplasms from Vietnam, Thailand, and China were observed using scanning electron microscopy (SEM). Observations included equatorial view, polar view, and exine ornamentation. The results showed that the pollen of all tested materials was monads, prolate, or subprolate. The average polar axis length of the pollen ranged from 29.121 to 37.043 µm, and that of the equatorial axis ranged from 19.861 to 25.911 µm. A t-test revealed that the polar axis of pollen from Chinese pummelo germplasms was significantly longer than that of varieties from Southeast Asia, indicating certain geographical differentiation. The apertures were all colporate type, predominantly with four to five colpi (75% of grains); only four colpi were observed in the remainder (25%). The pollen exine ornamentation of pummelo germplasms was perforated, whereas that of other citrus types was reticulate. Pummelos exhibited a smaller pori diameter (0.264–0.673 µm) and wider distance between pori (0.581–1.118 µm), while other citrus species had larger lumina (1.253–1.684 µm) and narrower muri (0.443–0.664 µm). Principal component analysis (PCA) and cluster analysis were performed based on pollen traits, and two principal component factors were extracted. The pummelo germplasms were divided into two subgroups: sweet pummelo and red pummelo, which demonstrated a correlation among their pollen morphology, flesh color, and flesh flavor. The phenotypic diversity of pollen among different pummelo germplasms may provide a valuable auxiliary reference for the identification and systematic classification of pummelos. Full article

We propose using Synthetic Aperture Radar (SAR) images as real-time images and road vector images as reference images for matching navigation, and propose a Siamese U-Net dual-task supervised network for solving the problem, called SUDS. Unlike existing methods of heterogenous image matching, which extract common features and eliminate saliency differences for matching, we exploit the advantages of the vector images themselves to reduce the matching difficulty from the reference image selection. Firstly, we extract the common road features between SAR images and road vector images using a weight-sharing U-Net feature extraction network. Then, we propose to weight the sum of segmentation loss and matching loss as the network loss to optimize the feature extraction efficiency from both segmentation and matching perspectives. We prepare a specialized SAR-VEC dataset for experiments. Experiments show that the method is able to obtain high matching correctness, with 80.2% correctness within 5 pixels of matching error and 91.0% correctness within 10 pixels of matching error. Compared to existing methods, this method is able to identify the differences in similar roads and better eliminate the influence of imaging interference in SAR images on the matching results, obtaining more accurate matching results with better robustness. And we explore the effect of different weighting parameters $\beta$ on the matching accuracy, and the best matching results are obtained when $\beta =0.8$. Full article

Over the last decade, L-band synthetic aperture radar (SAR) satellite data has become more widely available globally, providing new opportunities for biodiversity and ecosystem services (BES) monitoring. To better understand these opportunities, we conducted a systematic scoping review of articles that utilized L-band synthetic aperture radar (SAR) satellite data for BES monitoring. We found that the data have mainly been analyzed using image classification and regression methods, with classification methods attempting to understand how the extent, spatial distribution, and/or changes in different types of land use/land cover affect BES, and regression methods attempting to generate spatially explicit maps of important BES-related indicators like species richness or vegetation above-ground biomass. Random forest classification and regression algorithms, in particular, were used frequently and found to be promising in many recent studies. Deep learning algorithms, while also promising, have seen relatively little usage thus far. PALSAR-1/-2 annual mosaic data was by far the most frequently used dataset. Although free, this data is limited by its low temporal resolution. To help overcome this and other limitations of the existing L-band SAR datasets, 64% of studies combined them with other types of remote sensing data (most commonly, optical multispectral data). Study sites were mainly subnational in scale and located in countries with high species richness. Future research opportunities include investigating the benefits of new free, high temporal resolution L-band SAR datasets (e.g., PALSAR-2 ScanSAR data) and the potential of combining L-band SAR with new sources of SAR data (e.g., P-band SAR data from the “Biomass” satellite) and further exploring the potential of deep learning techniques. Full article

Multi-view inverse synthetic aperture radar (ISAR) image sequences provide multi-dimensional observation information about space targets, enabling precise attitude estimation that is fundamental to both non-cooperative target monitoring and critical space operations including active debris removal and space collision avoidance. However, directly utilizing all images within an ISAR sequence for attitude estimation can result in a substantial data preprocessing workload and reduced algorithm efficiency. Given the inherent overlap and redundancy in the target information provided by these ISAR images, this paper proposes a novel space target attitude estimation method based on the selection of multi-view ISAR image sequences. The proposed method begins by establishing an ISAR imaging projection model, then characterizing the target information differences through variations in imaging plane normal, and proposing an image selection method based on the uniform sampling across elevation and azimuth angles of the imaging plane normal. On this basis, the method utilizes a high-resolution network (HRNet) to extract the feature points of typical components of the space target. This method enables simultaneous feature point extraction and matching association within ISAR images. The attitude estimation problem is subsequently modeled as an unconstrained optimization problem. Finally, the particle swarm optimization (PSO) algorithm is employed to solve this optimization problem, thereby achieving accurate attitude estimation of the space target. Experimental results demonstrate that the proposed methodology effectively filters image data, significantly reducing the number of images required while maintaining high attitude estimation accuracy. The method provides a more informative sequence than conventional selection strategies, and the tailored HRNet + PSO estimator resists performance degradation in sparse-data conditions, thereby ensuring robust overall performance. Full article

Flat-panel-type light field displays can generate three-dimensional images viewable without glasses; however, they often suffer from a narrow viewing zone, low light efficiency, low resolution, and moiré artifacts. Previously, flat-panel-type light field displays with a near-virtual-image mode were proposed, comprising a lens array and an aperture array; these displays offered an enhanced viewing zone, increased light efficiency, and improved resolution. In this study, a moiré reduction technique is proposed for near-virtual-image-mode light field displays. In this configuration, moiré artifacts arise from the periodic deformation of virtual subpixel images seen through the lens array, caused by the nonrectangular subpixel structures for the R, G, and B colors of the display panel. To suppress the differences in subpixel shapes, the aperture shapes in the aperture array were modified from straight to zigzag shapes. Zigzag-shaped slits were designed, and their effectiveness in reducing moiré artifacts was evaluated using a diffraction-based moiré analysis technique. Experimental results demonstrated a lower moiré contrast with the designed zigzag slit than with the conventional straight slit, confirming the effectiveness of the proposed technique. Full article

The seepage characteristics and heat transfer efficiency in rough fractures are indispensable for assessing the lifetime and production performance of geothermal reservoirs. In this study, a two-dimensional rough rock fracture model with different secondary roughness is developed using the wavelet analysis method to simulate the coupled flow and heat transfer process under multiscale roughness based on two theories: local thermal equilibrium (LTE) and local thermal nonequilibrium (LTNE). The simulation results show that the primary roughness controls the flow behavior in the main flow zone in the fracture, which determines the overall temperature distribution and large-scale heat transfer trend. Meanwhile, the nonlinear flow behaviors induced by the secondary roughness significantly influence heat transfer performance: the secondary roughness usually leads to the formation of more small-scale eddies near the fracture walls, increasing flow instability, and these changes profoundly affect the local water temperature distribution and heat transfer coefficient in the fracture–matrix system. The eddy aperture and eddy area fraction are proposed for analyzing the effect of nonlinear flow behavior on heat transfer. The eddy area fraction significantly and positively correlates with the overall heat transfer coefficient. Meanwhile, the overall heat transfer coefficient increases by about 3% to 10% for eddy area fractions of 0.3% to 3%. As the eddy aperture increases, fluid mixing is enhanced, leading to a rise in the magnitude of the local heat transfer coefficient. Finally, the roughness characterization was decomposed into primary roughness root mean square and secondary roughness standard deviation, and for the first time, an empirical correlation was established between multiscale roughness, flow velocity, and the overall heat transfer coefficient. Full article