Search Results (236)

Although synthetic aperture radar (SAR) performance and polarimetric data quality are closely related, they represent fundamentally different concepts. This paper delineates their distinctions, investigates their interdependence, and introduces a comprehensive set of technical metrics for evaluating radar system performance and assessing polarimetric data quality. Specifically, radar performance is quantified by seven independent parameters, whereas data quality is characterized by a three-component channel imbalance vector and a twelve-element channel crosstalk matrix. The paper details the measurement methods for these parameters and outlines the associated technical requirements, including calibrator specifications and test-site conditions. To improve operational applicability, an approximate method for data quality assessment is proposed, and its associated errors are analyzed. Special attention is given to the $\gamma$ factor, which is highlighted as a critical and irreplaceable indicator of radar performance. Using field data from the GaoFen-3 (GF-3) satellite, the proposed metrics are applied to evaluate both radar performance and data quality. The results provide insights into the polarimetric characteristics of the system and offer practical guidance for the calibration and application of GF-3 polarimetric SAR data. Full article

Recent advances in time-modulated metasurfaces (TMMs) have introduced approaches for controlling target features in radar imaging. These technologies enable dynamic reconstruction of scattering center locations and intensities by flexibly manipulating radar echoes. However, most existing methods focus on amplitude and phase modulation, lacking joint control over the polarimetric scattering characteristics of targets. As a result, the modulated outputs tend to exhibit limited polarimetric diversity and remain strongly tied to the targets’ physical structures. To address this limitation, this paper proposes a modulation method for polarimetric synthetic aperture radar (PolSAR) images based on a flexible metasurface with independently controllable polarizations (FM-ICP). The method independently controls the echo energy distribution in two polarization channels, enabling target representations in PolSAR images to exhibit polarimetric characteristics beyond their physical geometry—for example, rendering a flat plate as a cylinder, or vice versa. In addition, the method can generate synthetic scattering centers with controllable locations and polarimetric properties, which can be precisely tuned via modulation parameters. This work offers a practical approach for target feature manipulation and shows potential in PolSAR image simulation and feature reconstruction. 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

Effective soil salinity monitoring is crucial for sustainable land management in arid regions. Most current studies face limitations by relying solely on single-source data. This study presents a novel three-dimensional (3D) optical-radar feature space model combining Gaofen-3 polarimetric synthetic aperture radar (SAR) and Sentinel-2 multispectral data for China’s Yutian Oasis. The random forest (RF) feature selection algorithm identified three optimal parameters: Huynen_vol (volume scattering component), RVI_Freeman (radar vegetation index), and NDSI (normalized difference salinity index). Based on the interactions of these three optimal features within the 3D feature space, we constructed the Optical-Radar Salinity Inversion Model (ORSIM). Subsequent validation using measured soil electrical conductivity (EC) data (May–June 2023) demonstrated strong model performance, with ORSIM achieving R² = 0.75 and RMSE = 7.57 dS/m. Spatial analysis revealed distinct salinity distribution patterns: (1) Mildly salinized areas clustered in the central oasis region, and (2) severely salinized zones predominated in northern low-lying margins. This spatial heterogeneity strongly correlated with local topography-higher elevation (south) to desert depression (north) gradient. The 3D feature space approach advances soil salinity monitoring by overcoming traditional 2D limitations while providing an accurate, transferable framework for arid ecosystem management. Furthermore, this study significantly expands the application potential of SAR data in soil salinization research. 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

Deep learning has shown significant potential in multi-band Polarimetric Synthetic Aperture Radar (PolSAR) land cover classification. However, the existing methods face two main challenges: accurately modeling the complex nonlinear relationships between multiple bands and balancing classifier parameter efficiency with classification accuracy. To address these challenges, this paper proposes a novel decision-level multi-band fusion framework that leverages the synergistic optimization of the Vision Transformer (ViT) and Kolmogorov–Arnold Network (KAN). This innovative architecture effectively captures global spatial–spectral correlations through ViT’s cross-band self-attention mechanism and achieves parameter-efficient decision-level probability space mapping using KAN’s spline basis functions. The proposed method significantly enhances the model’s generalization capability across different band combinations. The experimental results on the quad-band (P/L/C/X) Hainan PolSAR dataset, acquired by the Aerial Remote Sensing System of the Chinese Academy of Sciences, show that the proposed framework achieves an overall accuracy of 96.24%, outperforming conventional methods in both accuracy and parameter efficiency. These results demonstrate the practical potential of the proposed method for high-performance and efficient multi-band PolSAR land cover classification. Full article

The Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) system is a combination of polarimetric SAR and interferometric SAR, which can simultaneously obtain the power information, polarimetric information, and interferometric information of land cover. Traditional land cover classification methods fail to fully utilize these information types, resulting in limited classification types and low accuracy. This paper proposes a PolInSAR land cover classification method that fuses power information, polarimetric information, and interferometric information, aiming to enrich the classification types and improve the classification accuracy. Firstly, the land cover is divided into strong scattering areas and weak scattering areas by using the power information to avoid the influence of weak scattering areas on the classification results. Then, the weak scattering areas are distinguished into shadows and water bodies by combining the interferometric information and image corners. For the strong scattering areas, the polarimetric information is utilized to distinguish vegetation, buildings, and bare soil. For the vegetation area, the concept of vegetation ground elevation is put forward. By combining with the anisotropy parameter, the vegetation is further subdivided into tall coniferous vegetation, short coniferous vegetation, tall broad-leaved vegetation, and short broad-leaved vegetation. The effectiveness of the method has been verified by the PolInSAR data obtained from the N-SAR system developed by Nanjing Research Institute of Electronics Technology. The overall classification accuracy reaches 90.2%, and the Kappa coefficient is 0.876. Full article

Orientation is an important parameter to identify the working status of a parabolic antenna. Polarimetric inverse synthetic aperture radar (Pol-ISAR) is an effective approach to sense the parabolic antenna, but the imaging process may be seriously deteriorated by the propagation effect under the rainfall condition. This paper proposes a method to compensate for the propagation effect of rainfall for accurately estimating the orientation parameter of a parabolic antenna from Pol-ISAR images. Numerical simulations show that the impact of the propagation effect for the Pol-ISAR imaging of a parabolic antenna is serious, but it can be well compensated for by the method proposed in this paper. Full article

Icebergs pose significant risks to shipping, offshore oil exploration, and underwater pipelines. Detecting and monitoring icebergs in the North Atlantic Ocean, where darkness and cloud cover are frequent, is particularly challenging. Synthetic aperture radar (SAR) serves as a powerful tool to overcome these difficulties. In this paper, we propose a method for automatically detecting and classifying icebergs in various sea conditions using C-band dual-polarimetric images from the RADARSAT Constellation Mission (RCM) collected throughout 2022 and 2023 across different seasons from the east coast of Canada. This method classifies SAR imagery into four distinct classes: open water (OW), which represents areas of water free of icebergs; open water with target (OWT), where icebergs are present within open water; sea ice (SI), consisting of ice-covered regions without any icebergs; and sea ice with target (SIT), where icebergs are embedded within sea ice. Our approach integrates statistical features capturing subtle patterns in RCM imagery with high-dimensional features extracted using a pre-trained Vision Transformer (ViT), further augmented by climate parameters. These features are classified using XGBoost to achieve precise differentiation between these classes. The proposed method achieves a low false positive rate of 1% for each class and a missed detection rate ranging from 0.02% for OWT to 0.04% for SI and SIT, along with an overall accuracy of 96.5% and an area under curve (AUC) value close to 1. Additionally, when the classes were merged for target detection (combining SI with OW and SIT with OWT), the model demonstrated an even higher accuracy of 98.9%. These results highlight the robustness and reliability of our method for large-scale iceberg detection along the east coast of Canada. Full article

Polarimetric decomposition methods are widely used in polarimetric Synthetic Aperture Radar (SAR) data processing for extracting scattering characteristics of targets. However, polarization SAR methods for ship detection still face challenges. The traditional constant false alarm rate (CFAR) detectors face sea clutter modeling and parameter estimation problems in ship detection, which is difficult to adapt to the complex background. In addition, neural network-based detection methods mostly rely on single polarimetric-channel scattering information and fail to fully explore the polarization properties and physical scattering laws of ships. To address these issues, this study constructed two novel characteristics: a helix-scattering enhanced (HSE) local component and a multi-scattering intensity difference (MSID) edge component, which are specifically designed to describe ship scattering characteristics. Based on the characteristic differences of different scattering components in ships, this paper designs a context aggregation network enhanced by local and edge component characteristics to fully utilize the scattering information of polarized SAR data. With the powerful feature extraction capability of a convolutional neural network, the proposed method can significantly enhance the distinction between ships and the sea. Further analysis shows that HSE is able to capture structural information about the target, MSID can increase ship–sea separation capability, and an HV channel retains more detailed information. Compared with other decomposition models, the proposed characteristic combination model performs well in complex backgrounds and can distinguish ship from sea more effectively. The experimental results show that the proposed method achieves a detection precision of 93.6% and a recall rate of 91.5% on a fully polarized SAR dataset, which are better than other popular network algorithms, verifying the reasonableness and superiority of the method. Full article

Quad-polarization synthetic aperture radar (SAR) satellites are important detection tools in Earth observation and remote sensing; in particular, they are of great significance for accurately interpreting radar data and inverting geophysical parameters. Polarimetric calibration is particularly critical to eliminate the effects of distortion in polarized SAR data. The C-SAR/02 satellite launched by China is an important part of the C-band synthetic aperture radar (SAR) constellation, and the quad-polarization strip I (QPSI) is an important imaging mode for its sea–land observation. The relevant research on its polarimetric calibration is still lacking. This study’s polarimetric calibration of C-SAR/02 was performed based on the active radar calibrator (ARC) method using four independently developed L/S/C multi-band ARCs and several trihedral corner reflectors (CRs). The polarimetric calibration distortion matrix varies along the range direction; the polarimetric calibration distortion matrix and polarimetric calibration accuracy along the range direction were analyzed, incorporating the devices in different range directions to calculate the distortion matrix. This approach improved the accuracy of the polarimetric calibration results and the effect of the quantization application of the C-SAR satellites. Moreover, our experimental results indicate that the method presented herein is suitable for the C-SAR/02 satellite and may also be more universally applicable to C-SAR-series satellites. Full article

The characterization of the lunar surface and subsurface through the utilization of synthetic aperture radar data has assumed a pivotal role in the domain of lunar exploration science. This investigation concentrated on the polarimetric analysis aimed at identifying water ice within a specific crater, designated Erlanger, located at the lunar north pole, which is fundamentally a region that is perpetually shaded from solar illumination. The area that is perpetually shaded on the moon is defined as that region that is never exposed to sunlight due to the moon’s slightly tilted rotational axis. These permanently shaded regions serve as cold traps for water molecules. To ascertain the presence of water ice within the designated study area, we conducted an analysis of two datasets from the Chandrayaan mission: Mini-SAR data from Chandrayaan-1 and Dual-Frequency Synthetic Aperture Radar (DFSAR) data from Chandrayaan-2. The polarimetric analysis of the Erlanger Crater, located in a permanently shadowed region of the lunar north pole, utilizes data from the Dual-Frequency Synthetic Aperture Radar (DFSAR) and the Mini-SAR. This study focuses exclusively on the L-band DFSAR data due to the unavailability of S-band data for the Erlanger Crater. The crater, identified by the PSR ID NP_869610_0287570, is of particular interest for its potential water ice deposits. The analysis employs three decomposition models—m-delta, m-chi, and m-alpha—derived from the Mini-SAR data, along with the H-A-Alpha model known as an Eigenvector and Eigenvalue model, applied to the DFSAR data. The H-A-Alpha helps in assessing the entropy and anisotropy of the lunar surface. The results reveal a correlation between the hybrid polarimetric models (m-delta, m-chi, and m-alpha) and fully polarimetric parameters (entropy, anisotropy, and alpha), suggesting that volume scattering predominates inside the crater walls, while surface and double bounce scattering are more prevalent in the right side of the crater wall and surrounding areas. Additionally, the analysis of the circular polarization ratio (CPR) from both datasets suggests the presence of water ice within and around the crater, as values greater than 1 were observed. This finding aligns with other studies indicating that the high CPR values are indicative of ice deposits in the lunar polar regions. The polarimetric analysis of the Erlanger Crater contributes to the understanding of lunar polar regions and highlights the potential for future exploration and resource utilization on the Moon. Full article

Raindrop size distribution (DSD) has an essential effect on rainfall kinetic energy estimation (RKEE) and dual-polarization radar quantitative precipitation estimation (QPE); DSD is a key factor for establishing a dual-polarization radar QPE scheme and RKEE scheme, particularly in mountainous areas. To improve the understanding of seasonal DSD-based RKEE, dual-polarization radar QPE, and the impact of rainfall types and classification methods, we investigated RKEE schemes and dual-polarimetric radar QPE algorithms across seasons and rainfall types based on two classic classification methods (BR09 and BR03) and DSD data from a disdrometer in the Tianshan Mountains during 2020–2022. Two RKEE schemes were established: the rainfall kinetic energy flux–rain rate (KE_time–R) and the rainfall kinetic energy content–mass-weighted mean diameter (KE_mm–D_m). Both showed seasonal variation, whether it was stratiform rainfall or convective rainfall, under BR03 and BR09. Both schemes had excellent performance, especially the KE_mm–D_m relationship across seasons and rainfall types. In addition, four QPE schemes for dual-polarimetric radar—R(K_dp), R(Z_h), R(K_dp,Z_dr), and R(Z_h,Z_dr)—were established, and exhibited characteristics that varied with season and rainfall type. Overall, the performance of the single-parameter algorithms was inferior to that of the double-parameter algorithms, and the performance of the R(Z_h) algorithm was inferior to that of the R(K_dp) algorithm. The results of this study show that it is necessary to consider different rainfall types and seasons, as well as classification methods of rainfall types, when applying RKEE and dual-polarization radar QPE. In this process, choosing a suitable estimator—KE_time(R), KE_mm(D_m), R(K_dp), R(Z_h), R(K_dp,Z_dr), or R(Z_h,Z_dr)—is key to improving the accuracy of estimating the rainfall KE and R. Full article

Forest canopy height data are fundamental parameters of forest structure and are critical for understanding terrestrial carbon stock, global carbon cycle dynamics and forest productivity. To address the limitations of retrieving forest canopy height using conventional PolInSAR-based methods, we proposed a method to estimate forest height by combining single-temporal polarimetric synthetic aperture radar (PolSAR) images with sparse spaceborne LiDAR (forest height) measurements. The core idea of our method is that volume scattering energy variations which are linked to forest canopy height occur during radar acquisition. Specifically, our methodology begins by employing a semi-empirical inversion model directly derived from the random volume over ground (RVoG) formulation to establish the relationship between forest canopy height, volume scattering energy and wave extinction. Subsequently, PolSAR decomposition techniques are used to extract canopy volume scattering energy. Additionally, machine learning is employed to generate a spatially continuous extinction coefficient product, utilizing sparse LiDAR samples for assistance. Finally, with the derived inversion model and the resulting model parameters (i.e., volume scattering power and extinction coefficient), forest canopy height can be estimated. The performance of the proposed forest height inversion method is illustrated with L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope National Park and airborne LiDAR data. Compared to high-accuracy airborne LiDAR data, the obtained forest canopy height from the proposed approach exhibited higher accuracy (R² = 0.92, RMSE = 6.09 m). The results demonstrate the potential and merit of the synergistic combination of PolSAR (volume scattering power) and sparse LiDAR (forest height) measurements for forest height estimation. Additionally, our approach achieves good performance in forest height estimation, with accuracy comparable to that of the multi-baseline PolInSAR-based inversion method (RMSE = 5.80 m), surpassing traditional PolSAR-based methods with an accuracy of 10.86 m. Given the simplicity and efficiency of the proposed method, it has the potential for large-scale forest height estimation applications when only single-temporal dual-polarization acquisitions are available. Full article

Optical remote sensing can effectively capture 2-dimensional (2D) forest information, such as woodland area and percentage forest cover. However, accurately estimating forest vertical-structure relevant parameters such as height using optical images remains challenging, which leads to low accuracy of estimating forest stocks like biomass and carbon stocks. Thus, accurately obtaining vertical structure information of forests has become a significant bottleneck in the application of optical remote sensing to forestry. Microwave remote sensing such as synthetic aperture radar (SAR) and polarimetric SAR provides the capability to penetrate forest canopies with the L-band signal, and is particularly adept at capturing the vertical structure information of forests, which is an alternative ideal remote-sensing data source to overcome the aforementioned limitation. This paper utilizes the Citexs data analysis platform, along with the CNKI and PubMed databases, to investigate the advancements of applying L-band SAR technology to forest canopy penetration and structure-parameter estimation, and provides a comprehensive review based on 58 relevant articles from 1978 to 2024 in the PubMed database. The metrics, including annual publication numbers, countries/regions from which the publications come, institutions, and first authors, with the visualization of results, were utilized to identify development trends. The paper summarizes the state of the art and effectiveness of L-band SAR in addressing the estimation of forest height, moisture, and forest stocks, and also examines the penetration depth of the L-band in forests and highlights key influencing factors. This review identifies existing limitations and suggests research directions in the future and the potential of using L-band SAR technology for forest parameter estimation. Full article