Radar Polarimetry Research Articles

Calibration of Dual-Polarised Antennas for Air-Coupled Ground Penetrating Radar Applications

Radar polarimetry is a technique that can be used to enhance target detection, identification and classification; however, the quality of these measurements can be significantly influenced by the characteristics of the radar antenna. For an accurate and reliable system, the calibration of the antenna is vitally important to mitigate these effects. This study presents a methodology to calibrate Ultra-Wideband (UWB) dual-polarised antennas in the near-field using a thin elongated metallic cylinder as the calibration object. The calibration process involves measuring the scattering matrix of the metallic cylinder as it is rotated, in this case producing 100 distinct scattering matrices from which the calibration parameters are derived, facilitating a robust and stable solution. The calibration procedure was tested and validated using a Vector Network Analyser (VNA) and two quad-ridged antennas, which presented different performance levels. The calibration methodology demonstrated notable improvements, aligning the performance of both functioning and under-performing antennas to equivalent specifications. Mid-band validation measurements indicated minimal co-polar channel imbalance (<0.3 dB), low phase error (<0.8°) and improved cross-polar isolation (≈48 dB).

Raindrop size distribution (DSD) retrieval from polarimetric radar observations using neural networks

As a key component of rainfall estimation, the understanding of raindrop size distribution (DSD) is a long-standing goal in meteorology and hydrology. Given that weather radar can observe the precipitation microphysics over large spatial and temporal scales, it has been broadly applied in DSD estimation. Traditional polynomial regression algorithms that correlate DSD parameters and radar signatures are still widely applied due to their simple structure and acceptable accuracy. This study proposes a new DSD retrieval model using dual-polarization radar observations based on long short-term memory (LSTM) network techniques. Three schemes able to retrieve the parameters of a normalized gamma DSD (LSTM-D0, LSTM-Nw, and LSTM-μ) are proposed with different combinations of polarimetric radar measurement inputs. All LSTM estimators exhibit better performance than the polynomial regression method. The Nash-Sutcliffe efficiency coefficient for estimates of drop median diameter (D0) and intercept parameter (Nw) increases from 0.93 and 0.70 to 0.95 and 0.93 respectively at Chilbolton station. Poor estimates of the shape parameter (μ) using the polynomial regression estimator complicate real applications, whereas the remarkable improvement of LSTM model estimation facilitates practical applications. The temporal and spatial predictability is then estimated to investigate long-term estimator performance for various radars, or at least for all radar pixels of a single radar. The predictability, measured by the Nash coefficient, increases temporally by 0.08, 0.31, and 0.39 and spatially by 0.03, 0.19, and 0.23 for the parameters D0, log10Nw, and μ respectively. This study contributes to improving quantitative precipitation estimates from radar polarimetry, enabling a better understanding of precipitation microphysics.

Calculation of snow cover characteristics using the method of radar polarimetry

Calculation of snow cover characteristics using the method of radar polarimetry

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

Abstract. The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectra. We propose a novel approach to retrieve snowfall properties by combining the latter two techniques, while relaxing some assumptions on, e.g., beam alignment and non-turbulent atmosphere. The method relies on a two-step deep-learning framework inspired from data compression techniques: an encoder model maps a high-dimensional signal to a low-dimensional latent space, while the decoder reconstructs the original signal from this latent space. Here, Doppler spectrograms at two frequencies constitute the high-dimensional input, while the latent features are constrained to represent the snowfall properties of interest. The decoder network is first trained to emulate Doppler spectra from a set of microphysical variables, using simulations from the Passive and Active Microwave radiative TRAnsfer model (PAMTRA) as training data. In a second step, the encoder network learns the inverse mapping, from real measured dual-frequency spectrograms to the microphysical latent space; in doing so, it leverages with a convolutional structure the spatial consistency of the measurements to mitigate the ill-posedness of the problem. The method was implemented on X- and W-band data from the ICE GENESIS campaign that took place in the Swiss Jura Mountains in January 2021. An in-depth assessment of the retrieval accuracy was performed through comparisons with colocated aircraft in situ measurements collected during three precipitation events. The agreement is overall good and opens up possibilities for acute characterization of snowfall microphysics on larger datasets. A discussion of the sensitivity and limitations of the method is also conducted. The main contribution of this work is, on the one hand, the theoretical framework itself, which can be applied to other remote-sensing retrieval applications and is thus possibly of interest to a broad audience across atmospheric sciences. On the other hand, the seven retrieved microphysical descriptors provide relevant insights into snowfall processes.

Estimativa da área afetada por inundações através de polarimetria com imagens Sentinel 1A Synthetic Aperture Radar (SAR) na bacia do rio Frío (Costa Rica)

Synthetic Aperture Radar (SAR) polarimetry is a remote sensing technique known for being applied to monitoring of land surface and water bodies. Polarimetry aims at charactering the land surface through the analysis of its response under different combinations of transmitting and receiving an- tennas polarization, called polarimetric channels. The use of cross-polarization (vertical/horizontal) proved to be efficient for estimating the extent of the floods between July 22nd and July 30th in the Frío river basin (Costa Rica). To that end, Sentinel-1 images from the European Space Agency have been used, with a workflow process and RGB composition that allowed to draw a clear distinction between flooded and permanent water bodies. The use of optical imagery for flood mapping is limit- ed by weather conditions, specially by the presence of clouds. SAR measurements are independent of daytime and weather conditions, providing valuable information for monitoring flood events. Furthermore, flood extent is a critical factor for the calibration and validation of hydraulic models (Horritt, 2006). Flood extent can be also used for damage assessment and risk management and can be of benefit to rescuers during flooding (Corbley, 1999).

Surface scattering model for dual-polarization planetary radars

This work presents a surface scattering model to interpret radar polarimetry derived from dual-polarization planetary radar systems, such as Arecibo Observatory and the Goldstone Solar System Radar. This model divides surface scattering contributions within a radar echo into quasi-specular and diffuse components, and further divides the quasi-specular component into single- and double-bounce scattering. In particular, the increase in the degree of linear polarization resulting from dihedral double-bounce scattering is emphasized, in addition to the increase in the circular polarization ratio. This mechanism is illustrated in radar observations of young craters on the Moon by the Mini-RF radar on board the Lunar Reconnaissance Orbiter. Arecibo radar observations of 8 near-Earth asteroids are compared to this model and the implications for the physical properties of these objects are discussed. This work provides new insight in interpreting radar polarimetry from dual-polarization planetary radar systems and is broadly applicable to all solar system targets.

Polarimetric Weather Radar: Overview of principles and applications

We introduce radar polarimetry, which generally is a less widely known concept of radar engineering, and explain the principles and applications of polarimetric Doppler weather radars. For example, a polarimetric radar can distinguish among precipitation particles of different shapes, compositions, and orientations. A relatively simple electromagnetic idea resulted in the 2012 upgrade of the National Weather Service (NWS) network of 160 high-resolution Doppler weather radars in the United States, with dual-polarization technology. We describe the details of the Colorado State University (CSU)–CHILL research radar, featuring exceptional polarization purity and dual-frequency operation, and its setup and role in winter field experiments in Colorado.

Don’t Forget to Water Your Antennas! [Editor’s Comments

Everyone with just a bit of a green thumb will tell you that water can do miracles for a well-maintained lawn. Sometimes, however, water can also do great things for antennas. Please check out the article by Kong et al. focusing on a frequency-selective structure that can be controlled using water channels. The article starts with an introduction that reviews the concept of frequency-selective surfaces. Then it turns to the new design concept, providing both analytic and numerical evidence in support of the new idea. Finally, the contribution is enriched with experimental results confirming the performance of the technology, showing its practical interest. This issue of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Antennas and Propagation Magazine</i> also features many other interesting articles. Surender et al. presents a nice review of rectennas for applications in wireless technologies. The work starts with a rich bibliographic analysis. It then reviews the primary elements of a rectenna system, examining the main solutions available in literature. Finally, attention is turned to applications, with a detailed list of relevant scenarios analyzed in large detail. The article by Notaros <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ˇ</small> is an excellent review of polarimetric weather radars. The article starts by illustrating the principles of radar polarimetry, then it proceeds with technical analyses of specific devices, and finally the contribution turns to applications, showing the data richness that can be provided by polarimetry and its applicability to weather analysis and forecasts. Finally, the article by Loghmannia and Manteghi presents a broadband strategy for small-antenna matching by an amplifier. After a bibliographic review and a section summarizing the relevant background material, the article reviews the principles of broadband parametric matching and presents an antenna design enriched with validating simulations. Finally, a prototype with experimental validations is presented showing achievable performance of the technology.

Physical Characterization of 2015 JD1: A Possibly Inhomogeneous Near-Earth Asteroid

The surfaces of airless bodies such as asteroids are exposed to many phenomena that can alter their physical properties. Bennu, the target of the OSIRIS-REx mission, has demonstrated how complex the surface of a small body can be. In 2019 November, the potentially hazardous asteroid 2015 JD1 experienced a close approach of 0.033 1 au from the Earth. We present results of the physical characterization of 2015 JD1 based on ground-based radar, spectroscopy, and photometric observations acquired during 2019 November. Radar polarimetry measurements from the Arecibo Observatory indicate a morphologically complex surface. The delay-Doppler images reveal a contact binary asteroid with an estimated visible extent of ∼150 m. Our observations suggest that 2015 JD1 is an E-type asteroid with a surface composition similar to aubrites, a class of differentiated enstatite meteorites. The dynamical properties of 2015 JD1 suggest that it came from the ν 6 resonance with Jupiter, and spectral comparison with major E-type bodies suggests that it may have been derived from a parental body similar to the progenitor of the E-type (64) Angelina. Significantly, we find rotational spectral variation across the surface of 2015 JD1 from the red to blue spectral slope. Our compositional analysis suggests that the spectral slope variation could be due to the lack of iron and sulfides in one area of the surface of 2015 JD1 and/or differences in grain sizes.

Theory of Radar Polarimetric Interferometry and Its Application to the Retrieval of Sea Ice Elevation in the Western Weddell Sea, Antarctic

AbstractSea ice elevation plays a crucial role in sea ice dynamic processes driven by winds and waves, for which satellite radar remote sensing becomes indispensable to monitor snow‐covered sea ice across the vast polar regions regardless of darkness and clouds. To measure sea ice elevation, a theory of polarimetric interferometry for both monostatic and bistatic radars is developed based on analytic solutions of Maxwell's equations, accounting for realistic and complicated properties of snow, sea ice, and seawater. This analytic method inherently preserves phase information that is imperative for radar polarimetry and interferometry. Among a multitude of complex radar coefficients in the general polarimetric interferometric covariance matrix, the symmetry group theory is utilized to identify and select appropriate terms pertaining to the retrieval of sea ice elevation while avoiding radar parameters that may inadvertently introduce non‐uniqueness and excessive uncertainty. Theoretical calculations compare well with field observations for rough and old sea ice encountered in the Operation‐IceBridge and TanDEM‐X Antarctic Science Campaign over the Western Weddell Sea. The results show that the magnitude of the coefficient of normalized correlation between co‐polarized horizontal and co‐polarized vertical radar returns is inversely related to sea ice elevation, while the associate phase term is nonlinear and noisy and should be excluded. From these analyses, a protocol is set up to measure Antarctic sea ice elevation to be presented in the next companion paper.

Polarization Estimation with a Single Vector Sensor for Radar Detection

Target detection using radar has important applications in military and civilian fields. Aimed at targets containing interference, radar polarimetry can facilitate discrimination between the target and interference. Since existing methods require the utilization of interference signals without targets in advance, they have a poor effect on interference with variable polarization. To solve this problem, this paper proposes a novel synchronous method to estimate the parameters of interference. First, we introduce a definition of the pulse compression signal-to-noise ratio, and prove that it is the polarization invariant in the virtual polarization adaptation. Then, for signals containing a target, interference, and noise, we propose a novel synchronous estimation method. Subsequently, we propose the two-dimensional golden selected method to further optimize the method with minimum calculation, and prove that the method presented in this paper is convergent and globally optimal. Finally, we analyze the presented method from three aspects: robustness, complexity, and applicability; the results of which demonstrate the efficacy of the method presented in this paper.

Multi-temporal phenological indices derived from time series Sentinel-1 images to country-wide crop classification

Crop classification is a crucial prerequisite for the collection of agricultural statistics, efficient crop management, biodiversity control, the design of agricultural policy, and food security. Crops are characterized by significant change during the growing season, and this information can be used to improve classification accuracy. However, capturing variation in vegetation cover requires a reliable source of valid data. Sentinel-1 radar images are a good candidate, as they supply information about Earth’s surface every six days, independent of weather and light conditions. In this paper, we present a method for crop classification based on radar polarimetry. We propose a set of multi-temporal indices derived from time series Sentinel-1 images that aim to characterize crop phenology. A big data, object-oriented classification technique is developed and tested on 16 crop types for the whole of Poland. Our analysis found that overall accuracy varied (regionally) from 86.36 to 89.13% in 2019, and from 85.95 to 89.81% in 2020. F1 scores for individual crops varied from 0.73 to 0.99, and the use of our multi-temporal phenological indices increased F1 scores by about 0.14 compared to calculations using only basic parameters. Results obtained for the whole country demonstrate the efficacy of the method and its resistance to environmental conditions.

Null-Pol Response Pattern in Polarimetric Rotation Domain: Characterization and Application

Man-made targets with various orientations usually exhibit scattering diversity which makes polarimetric radar target recognition difficult. The recently developed polarimetric rotation domain techniques provide solutions to understand and utilize target scattering diversity. Meanwhile, the Null-Pol theory is the important component of target optimal polarization in radar polarimetry. This work first extends the Null-Pol theory into the polarimetric rotation domain and discloses the different scattering responses from canonical man-made structures. In this vein, the Null-Pol response pattern interpretation tool is established and a set of Null-Pol features are extracted and characterized. Then, a man-made structure identification approach is proposed based on selected features derived from the Null-Pol response pattern. Comparison studies are carried out with simulated datasets of canonical structures considering polarimetric measurement errors, simulated, and measured data of unmanned aerial vehicles (UAVs). Compared with the Cameron decomposition, the proposed method achieves more accurate and stable performance.

Statistical Comparison of Melting Iceberg Backscatter Embedded in Sea Ice and Open Water Using RADARSAT-2 Images of the Newfoundland Sea

This article investigates and compares polarimetric signatures of icebergs embedded in sea ice and icebergs in open water. The main objective is to study on the backscatter properties of melting iceberg and to check on whether there is any distinguishable property in them in the case of different background clutter conditions (i.e. sea ice and open water). This study results will improve the potential of iceberg detection using radar polarimetry. RADARSAT-2 images have been used for the analysis acquired over locations near the coastline (approximately 3–35 km) of the island of Newfoundland. For analysis, polarimetry parameters, such as co-(HH) and cross-(HV) polarization and several popular decomposition techniques, specifically Pauli, Freeman–Durden, Yamaguchi, Cloud–Pottier, and van Zyl, have been used to determine the polarimetric signatures of icebergs and sea ice. The statistical hypothesis T-test has been applied to achieve a precise comparison among backscatters from different icebergs groups. Statistical results tend to show a dominant surface scattering mechanism for icebergs in all types of clutter conditions. Moreover, icebergs in open water produce larger volume scatter than icebergs in sea ice, whereas icebergs in sea ice produce larger surface scatter than icebergs in open water.

Scattering Purity and Complexity in Radar Polarimetry

The generalized degree of polarimetric purity is a vital descriptor widely studied and interpreted for electromagnetic wave characterization. It is invariant under the rotation of the reference frame. In this work, we first propose an alternate expression of this purity measure using the mean <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(m)$ </tex-math></inline-formula> and standard deviation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(s)$ </tex-math></inline-formula> of the real positive eigenvalues of a Hermitian positive semidefinite matrix. We then use this expression to propose a polarimetric scattering purity and scattering complexity measure. To obtain these expressions, we use certain inequalities on the bounds of the condition number for Hermitian positive definite matrices defined in terms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> . The polarimetric scattering purity parameter characterizes the overall polarization structure in the scattered wave. In contrast, the polarimetric scattering complexity parameter describes the mixture of orthogonal polarized pure components in the scattered wave. First, we demonstrate the two proposed measures by analyzing two cases: 1) multiple scattering and 2) a mixture of canonical targets. Then, we utilize full-polarimetric C- and L-band synthetic aperture radar (SAR) data to describe the variation of these measures over various land cover classes. We compare their spatial variations over the ocean surface, built-up areas, and vegetation. We observe notable contrasts in the purity and the complexity parameters over a diverse mixture of targets in the scene. Finally, we critically interpret the variation of the two measures over the temporal scene of rice crop acquired by C-band full-polarimetric SAR data. These analyses affirm the importance of these measures for explicit target characterization. The open-source version of the code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Subho07/scattering-purity-</uri> and-complexity

Three-Dimension Polarimetric Correlation Pattern Interpretation Tool and its Application

Polarimetric radar can acquire complete polarization information and is widely used in many applications. However, target orientation relative to the radar line of sight usually exhibits significant influences on the scattering mechanisms. Recently, such target scattering diversity has been successfully characterized and utilized with the polarimetric rotation domain interpretation techniques. In radar polarimetry, target scattering responses are affected by both polarization orientation angle and polarization ellipticity angle. In this vein, this work aims at exploring and utilizing the complete target scattering diversity by extending polarimetric rotation domain techniques to the polarization ellipticity angle dimension. The main idea is to develop a three-dimension polarimetric correlation pattern (3-D PCP) interpretation tool, which can visualize and exhibit targets’ polarimetric rotation domain properties in terms of both the polarimetric orientation and ellipticity angles. Then, a set of global, local, and mutual polarimetric features are proposed to characterize the responses of a 3-D PCP interpretation tool. Especially, the curvatures in differential geometry are first introduced to describe the properties of the 3-D surface. The performance of these new polarimetric features is investigated with spaceborne polarimetric synthetic aperture radar (PolSAR) data. Experimental results demonstrate the advantage of the proposed 3-D PCP features in enhancing the target clutter ratio (TCR), especially for the weak ship area. Based on this, a non-local superpixel-level contrast measure (NSLCM) method for ship detection is proposed. Pure sea samples can be determined adaptively for salient map construction. Comparison results demonstrate better detection performance for both the inshore dense ship area and weak ship area.

Application of Microwave Polarimetry to the Characterization of Fiber Misalignment in Composites

In this paper synthetic aperture radar (SAR) polarimetry techniques are applied to detect and characterize fiber misalignment in both carbon fiber sheets and glass fiber reinforced polymer (GFRP) composites. The principle behind SAR polarimetry technique to characterize fiber orientation is described, making use of the fact that carbon and glass fibers are polarizing when irradiated with a microwave signal. The difficulties in using 2D polarimetry techniques to make the 3D orientation measurements, required to characterize out-of-plane fiber misalignment, are discussed as well. Subsequently, the feasibility of a recently-developed 3D SAR polarimetry method for this purpose is demonstrated. Several carbon fiber sheet and glass fiber reinforced polymer (GFRP) samples were manufactured with both in-and out-of-plane fiber misalignment. Polarimetric SAR images of the samples were then produced to show the spatially-varying relative orientation (both in-plane and out-of-plane) of the fibers for each sample. These images can be used to both detect and characterize any fiber misalignment, successfully demonstrating the potential for SAR polarimetry as a tool for the inspection of carbon and glass fiber reinforced composites.

Signal Models for Changes in Polarimetric SAR Data

Synthetic aperture radar (SAR) polarimetry can improve change detection in terms of detection capabilities. In this work, we are proposing to extend the idea of target decomposition to changes affecting partial targets. This will allow the separation of polarimetric-dependent changes, providing extra information that can be used to better understand the processes affecting the targets. Three models for changes are proposed and compared. The methodologies are based on Lagrangian optimizations of distinct operators built using quadratic forms for a power ratio and a power difference. The optimizations can be accomplished by diagonalizations of specific matrices derived from polarimetric covariance matrices. These are, therefore, spectral decompositions of an appropriate matrix which we define as change matrix. The theoretical validity of the models is assessed using Monte Carlo simulations. Additionally, we perform real data validation exploiting L-band quad-polarimetric data from the E-SAR (DLR) SARTOM 2006 campaign and ALOS PALSAR (JAXA) acquisitions in Morecombe Bay (U.K.). We observed that the two algorithms based on power difference allow to decompose the change into the minimal set of scattering mechanisms (SMs) that have been added or removed from the scene. The two algorithms differ on the initial assumption on the change. On the other hand, the ratio operator provides a better detection performance although the eigenvalues do not correspond to meaningful SMs. A combination of the three methodologies can, therefore, improve detection and classification of changes.

Overview: Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes

Abstract. Cloud and precipitation processes are still a main source of uncertainties in numerical weather prediction and climate change projections. The Priority Programme “Polarimetric Radar Observations meet Atmospheric Modelling (PROM)”, funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), is guided by the hypothesis that many uncertainties relate to the lack of observations suitable to challenge the representation of cloud and precipitation processes in atmospheric models. Such observations can, however, at present be provided by the recently installed dual-polarization C-band weather radar network of the German national meteorological service in synergy with cloud radars and other instruments at German supersites and similar national networks increasingly available worldwide. While polarimetric radars potentially provide valuable in-cloud information on hydrometeor type, quantity, and microphysical cloud and precipitation processes, and atmospheric models employ increasingly complex microphysical modules, considerable knowledge gaps still exist in the interpretation of the observations and in the optimal microphysics model process formulations. PROM is a coordinated interdisciplinary effort to increase the use of polarimetric radar observations in data assimilation, which requires a thorough evaluation and improvement of parameterizations of moist processes in atmospheric models. As an overview article of the inter-journal special issue “Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes”, this article outlines the knowledge achieved in PROM during the past 2 years and gives perspectives for the next 4 years.

Survey of Transverse Range Fire Scars in 10Years of UAVSAR Polarimetry.

Because cross‐polarized radar returns are highly associated with volume scatter, radar polarimetry returns tend to show strong evidence of wildfire scars and recovery in forest and chaparral. We focus on the polarimetry images from UAVSAR (PolSAR) line SanAnd_08525, which covers a roughly 20 km wide swath over the Transverse Range including parts of the Santa Monica, San Gabriel and San Bernardino Mountains. We select images from four acquisition dates from October 2009 to September 2020, very roughly 4 years apart. These are compared to fire perimeters from the national Geospatial Multi‐Agency Coordination and NIFC databases for years 2003–2020, which shows the areas affected by the major fires (west to east) Springs2013, Woolsey2018, Topanga2005, LaTuna2017, Station2009, BlueCut2016, Pilot2016, Slide2007, Butler2007, and many smaller fires. PolSAR images are shown to be helpful in identifying types and boundaries of fire, 50‐meter scale details of vegetation loss, and variability of vegetation recovery in post‐fire years.