Search Results (681)

A dual-polarization energy selective surface (ESS) with low insertion loss (IL) and high shielding effectiveness (SE) based on a dual-resonant equivalent circuit topology was proposed for high-intensity radiation field (HIRF) protection in this paper. The design principle was elucidated through an equivalent circuit model and translated into a physical ESS implementation. It consists of two resonant rings, vertically arranged and loaded with diodes, along with two lumped capacitors. Simulation and measurement results demonstrate that the IL is less than 3 dB when in the OFF state in a working frequency band, and the SE exceeds 20 dB when in the ON state. Moreover, the ESS’s dual-polarization, low cost, and easy-to-design characteristics hold great promise for broad applications in protecting communication and radar systems in complex electromagnetic environments. Full article

Breast cancer detection through non-invasive and accurate techniques remains a critical challenge in medical diagnostics. This study introduces a deep learning-based framework that leverages a microwave radar system equipped with an arc-shaped array of six antennas to estimate key tumor parameters, including position, size, and depth. This research begins with the evolutionary design of an ultra-wideband octagram ring patch antenna optimized for enhanced tumor detection sensitivity in directional near-field coupling scenarios. The antenna is fabricated and experimentally evaluated, with its performance validated through S-parameter measurements, far-field radiation characterization, and efficiency analysis to ensure effective signal propagation and interaction with breast tissue. Specific Absorption Rate (SAR) distributions within breast tissues are comprehensively assessed, and power adjustment strategies are implemented to comply with electromagnetic exposure safety limits. The dataset for the deep learning model comprises simulated self and mutual S-parameters capturing tumor-induced variations over a broad frequency spectrum. A core innovation of this work is the development of the Attention-Based Feature Separation (ABFS) model, which dynamically identifies optimal frequency sub-bands and disentangles discriminative features tailored to each tumor parameter. A multi-branch neural network processes these features to achieve precise tumor localization and size estimation. Compared to conventional attention mechanisms, the proposed ABFS architecture demonstrates superior prediction accuracy and interpretability. The proposed approach achieves high estimation accuracy and computational efficiency in simulation studies, underscoring the promise of integrating deep learning with conformal microwave imaging for safe, effective, and non-invasive breast cancer detection. Full article

An EF1 tornado struck Nansha District, Guangzhou, Guangdong, on 28 July 2024. To explore the dynamic and thermodynamic changes during the tornado’s life cycle, high-resolution spatiotemporal data from Foshan’s X-band phased-array radar and the direct wind field synthesis algorithm were used to reconstruct the 3D wind field. The dynamics and 3D structure of the tornado were analysed, with a new parameter, vorticity volume (VV), introduced to study its variation. The observation results indicate that the tornado moved roughly from south to north. During the tornado’s early stage (00:10–00:20 UTC), arc-shaped and annular echoes emerged and positive vorticity increased (peaking at 0.042 s⁻¹). Based on the tornado’s movement direction, the right side of the vortex centre was divergent, while the left side was convergent, whereas the vorticity area and volume continued to grow centrally. During the mature stage (00:23–00:25 UTC), the echo intensity weakened and, at 00:24, the vorticity reached its peak and touched the ground, with the vorticity area and volume also reaching their peaks at the same time. During the dissipation stage (00:25–00:30 UTC), the vorticity and echo features faded and the vorticity area and volume also declined rapidly. The analysis showed that the vorticity volume effectively reflects the tornado’s life cycle, enhancing the understanding of the dynamic and thermodynamic processes during the tornado’s development. Full article

By integrating EarthCARE W-band doppler cloud radar observations with GPM Ku/Ka-band dual-frequency precipitation radar data, this study constructs a novel global “pseudo tripe-frequency” radar coincidence dataset comprising 2886 coincidence events (about one-third of the events detected precipitation), aiming to systematically investigating band-dependent responses to cloud and precipitation structure. Results demonstrate that the W-band is highly sensitive to high-altitude cloud particles and snowfall (reflectivity < 0 dBZ), yet it experiences substantial signal attenuation under heavy precipitation conditions, and with low-altitude reflectivity reductions exceeding 50 dBZ, its probability density distribution is more widespread, with low-altitude peaks increasing first, and then decreasing as precipitation increases. In contrast, the Ku and Ka-band radars maintain relatively stable detection capabilities, with attenuation differences generally within 15 dBZ, but its probability density distribution exhibits multiple peaks. As the precipitation rate increases, the peak value of the dual-frequency ratio (Ka/W) gradually rises from approximately 10 dBZ to 20 dBZ, and can even reach up to 60 dBZ under heavy rainfall conditions. Several cases analyses reveal clear contrasts: In stratiform precipitation regions, W-band radar reflectivity is higher above the melting layer than below, whereas the opposite pattern is observed in the Ku and Ka bands. Doppler velocities exceeding 5 m s⁻¹ and precipitation rates surpassing 30 mm h⁻¹ exhibit strong positive correlations in convection-dominated regimes. Furthermore, the dataset confirms the impact of ice–water cloud phase interactions and terrain-induced precipitation variability, underscoring the complementary strengths of multi-frequency radar observations for capturing diverse precipitation processes. Full article

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

A novel classification algorithm for precipitation types (CP) was developed to address frequent misclassification issues between shallow convection and intense stratiform precipitation using existing methods and to enhance an understanding of their physical characteristics. Based on three-dimensional radar data and temperature fields, CP integrates three approaches: Storm Labeling in Three Dimensions (SLTD), a feature parameter-based algorithm (FP), and an advanced subcategorization method. The algorithm classifies precipitation into ten types: four non-precipitating, three stratiform, and three convective categories. CP was evaluated against traditional methods (SHY and FP) through both qualitative and quantitative analyses for mid-latitude warm-season systems. The CP method demonstrated improved performance, with higher skill scores (e.g., POD: 0.567–0.571) compared to SHY (0.349–0.364) and FP (0.455–0.470). Additionally, comparative analyses of vertical mean profiles of radar reflectivity, dynamical, and microphysical variables confirmed the enhanced capability of CP in distinguishing detailed precipitation structures. Full article

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

Validating the potential of multi-frequency synthetic aperture radar (SAR) data for multi-layer and profile soil moisture (SM) estimation modeling, we conducted an airborne multi-frequency SAR joint observation experiment (AMFSEX) over the Liao River Basin in China. The experiment simultaneously acquired airborne high spatial resolution quad-polarization (quad-pol) SAR data at five frequencies, including the Ka-, X-, C-, S-, and L-band. A preliminary “vegetation–soil” parameter estimation model based on the multi-frequency SAR data was established. Theoretical penetration depths of the multi-frequency SAR data were analyzed using the Dobson empirical model and the Hallikainen modified model. On this basis, a water cloud model (WCM) constrained by multi-polarization weighted and penetration depth weighted parameters was used to analyze the estimation accuracy of the multi-layer and profile SM (0–50 cm depth) under different vegetation types (grassland, farmland, and woodland). Overall, the estimation error (root mean square error, RMSE) of the surface SM (0–5 cm depth) ranged from 0.058 cm³/cm³ to 0.079 cm³/cm³, and increased with radar frequency. For multi-layer and profile SM (3 cm, 5 cm, 10 cm, 20 cm, 30 cm, 40 cm, 50 cm depth), the RMSE ranged from 0.040 cm³/cm³ to 0.069 cm³/cm³. Finally, a multi-input multi-output regression model (Gaussian process regression) was used to simultaneously estimate the multi-layer and profile SM. For surface SM, the overall RMSE was approximately 0.040 cm³/cm³. For multi-layer and profile SM, the overall RMSE ranged from 0.031 cm³/cm³ to 0.064 cm³/cm³. The estimation accuracy achieved by coupling the multi-source data (multi-frequency SAR data, multispectral data, and soil parameters) was superior to that obtained using the SAR data alone. The optimal SM penetration depth varied across different vegetation cover types, generally falling within the range of 10–30 cm, which holds true for both the scattering model and the regression model. This study provides methodological guidance for the development of multi-layer and profile SM estimation models based on the multi-frequency SAR data. Full article

The demand for versatility and finer resolution drives phased array radars to develop towards multi-band operating. However, the bandwidth limitations of conventional electronic devices make multi-band manipulation of frequency and phase rather challenging. This paper introduces a joint phase–frequency distribution manipulation method. By introducing a time delay line after optical pulses, the frequency conversion and phase shift are tightly coupled. Then, the phase–frequency–time mapping for multi-band signals in a single phased array system is established. The generation, transmission, and reception of multi-band signals are simultaneously achieved. Our approach enables multi-band frequency conversion and phase shifting in a single hardware framework, ensuring synchronization and coherence across multiple bands. We experimentally demonstrate the generation, frequency conversion, and phase control of signals across four bands (S, X, Ku, and K). Beamforming and data fusion of four-band linear frequency-modulated signals with a total bandwidth of 4 GHz are achieved, resulting in a four-fold improvement in range resolution. It is also verified that the number of bands and total bandwidth can be further expanded through channel interleaving. Full article

Tropical forests harbor a significant portion of global biodiversity but are increasingly degraded by human activity. Assessing restoration efforts requires the systematic monitoring of tropical ecosystem status and recovery. Satellite-borne synthetic aperture radar (SAR) supports monitoring changes in vegetation structure and is of particular utility in tropical regions where clouds obscure optical satellite observations. To characterize tropical forest recovery in the Lowland Chocó Biodiversity Hotspot of Ecuador, we apply over a decade of dual-polarized (HH + HV) L-band SAR datasets from the Japanese Space Agency’s (JAXA) PALSAR and PALSAR-2 sensors. We assess the complementarity of the dual-polarized imagery with less frequently available fully-polarimetric imagery, particularly in the context of their respective temporal and informational trade-offs. We examine the radar image texture associated with the dual-pol radar vegetation index (DpRVI) to assess the associated determination of forest and nonforest areas in a topographically complex region, and we examine the equivalent performance of texture measures derived from the Freeman–Durden polarimetric radar decomposition classification scheme applied to the fully polarimetric data. The results demonstrate that employing a dual-polarimetric decomposition classification scheme and subsequently deriving the associated gray-level co-occurrence matrix mean from the DpRVI substantially improved the classification accuracy (from 88.2% to 97.2%). Through this workflow, we develop a new metric, the Radar Forest Regeneration Index (RFRI), and apply it to describe a chronosequence of a tropical forest recovering from naturally regenerating pasture and cacao plots. Our findings from the Lowland Chocó region are particularly relevant to the upcoming NASA-ISRO NISAR mission, which will enable the comprehensive characterization of vegetation structural parameters and significantly enhance the monitoring of biodiversity conservation efforts in tropical forest ecosystems. Full article

This work presents a comprehensive technological study of dielectric resonator-based microstrip filters (DRMFs), encompassing the design, fabrication, and rigorous characterization of the $T{E}_{01\delta }$ mode. Through systematic coupling analysis, we demonstrate filters featuring novel input–output coupling techniques and innovative implementations of both transmission zeros (4-2-0 configuration) and equalization zeros (4-0-2 configuration), specifically designed for demanding space and radar receiver applications, while the loaded quality factor ($Q_L$) and insertion loss do not match those of dielectric resonator cavity filters (DRCFs), our solution significantly surpasses conventional microstrip filters (MFs), achieving $Q_L$> 3000 compared to typical $Q_L$≈ 200 for coupled-line MFs in X-band. The fabricated filters exhibit exceptional performance as follows: input reflection ($S_{11}$) below −18 dB (4-2-0) and −16.5 dB (4-0-2), flat transmission response ($S_{21}$), and out-of-band rejection exceeding −30 dB. Mechanical tuning enables precise control of input–output coupling, inter-resonator coupling, cross-coupling, and frequency synthesis, while equalization zeros provide tailored group delay characteristics. This study positions DRMFs as a viable intermediate technology for high-performance RF systems, bridging the gap between conventional solutions. Full article

Semi-Mediterranean (SM) and semi-arid (SA) regions, exemplified by the Kurdo-Zagrosian forests in western Iran and northern Iraq, have experienced frequent wildfires in recent years. This study proposes a modified Non-Negative Matrix Factorization (NMF) method for detecting fire-prone areas using satellite-derived data in SM and SA forests. The performance of the proposed method was then compared with three other already proposed NMF methods: principal component analysis (PCA), K-means, and IsoData. NMF is a factorization method renowned for performing dimensionality reduction and feature extraction. It imposes non-negativity constraints on factor matrices, enhancing interpretability and suitability for analyzing real-world datasets. Sentinel-2 imagery, the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Zagros Grass Index (ZGI) from 2020 were employed as inputs and validated against a post-2020 burned area derived from the Normalized Burned Ratio (NBR) index. The results demonstrate NMF’s effectiveness in identifying fire-prone areas across large geographic extents typical of SM and SA regions. The results also revealed that when the elevation was included, NMF_L1/2-Sparsity offered the best outcome among the used NMF methods. In contrast, the proposed NMF method provided the best results when only Sentinel-2 bands and ZGI were used. Full article

This study provides a comprehensive comparison between Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) measurements through analysis of collocated precipitation at the 19 GHz footprint scale for pixels during hemispheric summer seasons (JJA for Northern Hemisphere and DJF for Southern Hemisphere). Precipitation pixels exceeding 0.2 mm/h are categorized into convective, stratiform, and mixed types based on DPR classifications. While showing generally good agreement in spatial patterns, the GMI and DPR exhibit systematic differences in precipitation intensity measurements. The GMI underestimates convective precipitation intensity by 13.8% but overestimates stratiform precipitation by 12.1% compared to DPR. Mixed precipitation shows the highest occurrence frequency (47.6%) with notable differences between instruments. While measurement differences for convective precipitation have significantly improved from previous Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) estimates (62% to 13.8%), the overall difference has increased (from 2.6% to 12.6%), primarily due to non-convective precipitation. Latitudinal analysis reveals distinct precipitation regimes: tropical regions (below ~30°) produce intense convective precipitation that contributes about 40% of total precipitation despite lower frequency, while mid-latitudes (beyond 30°) shift toward stratiform-dominated regimes where stratiform precipitation accounts for 60–90% of the total. Additionally, geographical variation in GMI-DPR differences shows a see-saw pattern across latitude bands, with opposite signs between tropical and mid-latitude regions for convective and stratiform precipitation types. A fundamental transition in precipitation characteristics occurs between 30° and 40°, reflecting changes in precipitation mechanisms across Earth’s climate zones. Analysis shows that tropical precipitation systems generate approximately three times more precipitation per unit area than mid-latitude regions. Full article

The accurate and timely determination of crop leaf area indices (LAIs) assists in making agricultural decisions. The objective of this study was to estimate crop LAIs using C-band RADARSAT-2 synthetic aperture radar (SAR) datasets and a modified water cloud model (MWCM). The WCM was improved through two steps: (1) constructing a vegetation coverage ratio ($f_v$) using normalized difference vegetation indices calculated from Landsat-8 images and introducing it into the traditional WCM, and (2) incorporating field-collected crop height into the vegetation canopy described in the scattering model. The proposed MWCM parameters were calibrated using an iterative optimization algorithm named the Levenberg–Marquardt (LM) algorithm. The model’s performance before and after improvement was systematically calibrated and validated using field data collected from Yigen Farm (Hulunbuir City, Inner Mongolia Autonomous Region, China). The results show that the MWCM performed better than the original WCM in four polarization channels—HH, VV, HV, and VH—for both wheat and rape oilseed LAI inversion. HH polarization showed the best performance using both the MWCM and WCM for wheat, with R² values of 0.4626 and 0.3327, respectively; meanwhile, for oilseed rape, the R² values were 0.4912 and 0.3128, respectively. The RMSEs of the wheat inversion results were reduced from 1.5227 m²m⁻² to 1.4898 m²m⁻², and those for oilseed rape were reduced from 1.0411 m²m⁻² to 0.7968 m²m⁻². This study proved the feasibility and superiority of the MWCM, which provides new technical support for accurate crop growth monitoring. Full article

To improve the prediction of Typhoon Doksuri (2023), this paper explores how variations in horizontal scale factors used in assimilating radar-derived wind velocities influence the performance of numerical simulations and forecasts. Using the WRF-ARW model in conjunction with the WRF-3DVAR data assimilation system, two assimilation configurations were tested with horizontal length scale factors of 1.0 and 0.25. Results show that a reduced length scale facilitates a more detailed reconstruction of mesoscale features, including the typhoon’s eye and inner-core circulation, leading to improved accuracy in short-term intensity and structure forecasts. The experiment utilizing the 0.25 length scale exhibited a tighter warm core, stronger cyclonic wind bands, and a better representation of the vortex’s three-dimensional structure. However, this configuration also led to growing forecast deviations in the latter stages, likely due to imbalances introduced by excessive localization. In contrast, the 1.0-scale experiment produced smoother but less accurate structures and demonstrated larger track deviations. These findings highlight a key trade-off between localized observational influence and long-term forecast stability. The study underscores the importance of optimizing horizontal scale parameterization in variational assimilation to enhance the forecasting accuracy of high-impact tropical cyclones and offers practical insights for operational forecasting systems in regions frequently affected by typhoon activity. Full article