Polarimetric Research Articles

Exploring Alkyl Ester Salts of L-Amino Acid Derivatives of Ibuprofen: Physicochemical Characterization and Transdermal Potential.

This research presents novel ibuprofen derivatives in the form of alkyl ester salts of L-amino acids with potential analgesic, anti-inflammatory, and antipyretic properties for potential use in transdermal therapeutic systems. New derivatives of (RS)-2-[4-(2-methylpropyl)phenyl]propionic acid were synthesized using hydrochlorides of alkyl esters (ethyl, propyl, isopropyl, butyl, sec-butyl, tert-butyl, and pentyl) of L-glutamine. These were further transformed into alkyl esters of L-amino acid ibuprofenates through neutralization and protonation reactions. Characterization involved spectroscopic methods, including nuclear magnetic resonance and Fourier-transform infrared spectroscopy. Various physicochemical properties were investigated, such as UV-Vis spectroscopy, polarimetric analysis, thermogravimetric analysis, differential scanning calorimetry, X-ray diffraction, water solubility, octanol/water partition coefficient, and permeability through pig skin using Franz diffusion cells. The research confirmed the ionic structure of the obtained hydrochlorides of alkyl esters of L-amino acids and ibuprofenates of alkyl esters of L-glutamic acid. It revealed significant correlations between ester chain length and thermal stability, crystallinity, phase transition temperatures, lipophilicity, water solubility, skin permeability, and skin accumulation of these compounds. Compared to the parent ibuprofen, the synthesized derivatives exhibited higher water solubility, lower lipophilicity, and enhanced skin permeability. This study introduces promising ibuprofen derivatives with improved physicochemical properties, highlighting their potential for transdermal therapeutic applications. The findings shed light on the structure-activity relationships of these derivatives, offering insights into their enhanced solubility and skin permeation, which could lead to more effective topical treatments for pain and inflammation.

An Optimal Polarization SAR Three-Component Target Decomposition Based on Semi-Definite Programming

The model-based polarimetric synthetic aperture radar (PolSAR) target decomposition decodes the scattering mechanism of the target by analyzing the essential scattering components. This paper presents a new general three-component scattering power decomposition method by establishing optimization problems. It is known that the existing three-component decomposition method prioritizes the contribution of volume scattering, which often leads to volume scattering energy overestimation and may make double-bounce scattering and odd-bounce scattering component power negative. In this paper, a full parameter optimization method based on the remainder matrix is proposed, where all the elements of the coherency matrix will be taken into account including the remaining T13 component. The optimization is achieved with no priority order by solving the problem using semi-definite programming (SDP) based on the Schur complement theory. By doing so, the problem of volume scattering energy overestimation and negative powers will be avoided. The performance of the proposed approach is demonstrated and evaluated with AIRSAR and GF-3 PolSAR data sets. The experimental results show that by using the proposed method, the power contributions of volume scattering in two sets of data were reduced by at least 2.6% and 3.7% respectively, compared to traditional methods. And the appearance of negative power of double-bounce scattering and odd-bounce scattering are also avoided compared with those of the existing three-component decomposition.

Searching for the spectral depolarization of ASKAP one-off FRB sources

ABSTRACT Fast Radio Bursts (FRBs) are extragalactic transients of (sub-)millisecond duration that show wide-ranging spectral, temporal, and polarimetric properties. The polarimetric analysis of FRBs can be used to probe intervening media, study the emission mechanism, and test possible progenitor models. In particular, low-frequency depolarization of FRBs can identify dense, turbulent, magnetized, ionized plasma thought to be near the FRB progenitor. An ensemble of repeating FRBs has shown low-frequency depolarization. The depolarization is quantified by the parameter $\sigma _{\rm RM}\,$, which correlates with proxies for both the turbulence and mean magnetic field strength of the putative plasma. However, while many non-repeating FRBs show comparable scattering (and hence inferred turbulence) to repeating FRBs, it is unclear whether their surrounding environments are comparable to those of repeating FRBs. To test this, we analyse the spectro-polarimetric properties of five one-off FRBs and one repeating FRB, detected and localized by the Australian Square Kilometer Array Pathfinder. We search for evidence of depolarization due to $\sigma _{\rm RM}\,$ and consider models where the depolarization is intrinsic to the source. We find no evidence (for or against) the sample showing spectral depolarization. Under the assumption that FRBs have multipath propagation-induced depolarization, the correlation between our constraint on $\sigma _{\rm RM}\,$ and RM is consistent with repeating FRBs only if the values of $\sigma _{\rm RM}\,$ are much smaller than our upper limits. Additionally, the correlation between the constraints on $\sigma _{\rm RM}\,$ and τs is inconsistent with repeating FRBs. The observations provide further evidence for differences in the typical environments and sources of one-off and repeating FRBs.

Polarimetric Synthetic Aperture Radar Speckle Filter Based on Joint Similarity Measurement Criterion

Polarimetric Synthetic Aperture Radar (PolSAR) data is inherently characterized by speckle noise, which significantly deteriorates certain aspects of the quality of the PolSAR data processing, including the polarimetric decomposition and target interpretation. With the rapid increase in PolSAR resolution, SAR images in complex natural and artificial scenes exhibit non-homogeneous characteristics, which creates an urgent demand for high-resolution PolSAR filters. To address these issues, a new adaptive PolSAR filter based on joint similarity measure criterion (JSMC) is proposed in this paper. Firstly, a scale-adaptive filtering window is established in order to preserve the texture structure based on a multi-directional ratio edge detector. Secondly, the JSMC is proposed in order to accurately select homogeneous pixels; it describes pixel similarity based on both space distance and polarimetric distance. Thirdly, the homogeneous pixels are filtered based on statistical averaging. Finally, the airborne and spaceborne real data experiment results validate the effectiveness of our proposed method. Compared with other filters, the filter proposed in this paper provides a better outcome for PolSAR data in speckle suppression, edge texture, and the preservation of polarimetric properties.

Improving Polarimetric Radar-Based Drop Size Distribution Retrieval and Rain Estimation Using a Deep Neural Network

Abstract Raindrop size distributions (DSD) and rain rate have been estimated from polarimetric radar data using different approaches with the accuracy depending on the errors both in the radar measurements and the estimation methods. Herein, a deep neural network (DNN) technique was utilized to improve the estimation of the DSD and rain rate by mitigating these errors. The performance of this approach was evaluated using measurements from a two-dimensional video disdrometer (2DVD) at the Kessler Atmospheric and Ecological Field Station in Oklahoma as ground truth with the results compared against conventional estimation methods for the period 2006–17. Physical parameters (mass-/volume-weighted diameter and liquid water content), rain rate, and polarimetric radar variables (including radar reflectivity and differential reflectivity) were obtained from the DSD data. Three methods—physics-based inversion, empirical formula, and DNN—were applied to two different temporal domains (instantaneous and rain-event average) with three diverse error assumptions (fitting, measurement, and model errors). The DSD retrievals and rain estimates from 18 cases were evaluated by calculating the bias and root-mean-squared error (RMSE). DNN produced the best performance for most cases, with up to a 5% reduction in RMSE when model errors existed. DSD and rain estimated from a nearby polarimetric radar using the empirical and DNN methods were well correlated with the disdrometer observations; the rain-rate estimate bias of the DNN was significantly reduced (3.3% in DNN vs 50.1% in empirical). These results suggest that DNN has advantages over the physics-based and empirical methods in retrieving rain microphysics from radar observations.

A Comparison of Co-temporal Vector Magnetograms Obtained with HMI/SDO and SP/Hinode

Accurate measurement of magnetic fields is very important for understanding the formation and evolution of solar magnetic fields. Currently, there are two types of solar magnetic field measurement instruments: filter-based magnetographs and Stokes polarimeters. The former gives high temporal resolution magnetograms and the latter provides more accurate measurements of magnetic fields. Calibrating the magnetograms obtained by filter-based magnetographs with those obtained by Stokes polarimeters is a good way to combine the advantages of the two types. Our previous studies have shown that, compared to the magnetograms obtained by the Spectro-Polarimeter (SP) on board Hinode, those magnetograms obtained by both the filter-based Solar Magnetic Field Telescope (SMFT) of the Huairou Solar Observing Station and by the filter-based Michelson Doppler Imager (MDI) aboard SOHO have underestimated the flux densities in their magnetograms and systematic center-to-limb variations present in the magnetograms of both instruments. Here, using a sample of 75 vector magnetograms of stable alpha sunspots, we compare the vector magnetograms obtained by the Helioseismic and Magnetic Imager (HMI) aboard Solar Dynamics Observatory (SDO) with co-temporal vector magnetograms acquired by SP/Hinode. Our analysis shows that both the longitudinal and transverse flux densities in the HMI/SDO magnetograms are very close to those in the SP/Hinode magnetograms and the systematic center-to-limb variations in the HMI/SDO magnetograms are very minor. Our study suggests that using a filter-based magnetograph to construct a low spectral resolution Stokes profile, as done by HMI/SDO, can largely remove the disadvantages of the filter-type measurements and yet still possess the advantage of high temporal resolution.

Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data land cover classification

This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerous large-scale benchmark datasets for training and evaluating machine learning models for the analysis of optical data, the availability of labeled SAR or, more specifically, Pol-InSAR data is very limited. The lack of labeled data for training, as well as for testing and comparing different approaches, hinders the rapid development of machine learning algorithms for Pol-InSAR image analysis. The Pol-InSAR-Island benchmark dataset presented in this paper aims to fill this gap. The dataset consists of Pol-InSAR data acquired in S- and L-band by DLR's airborne F-SAR system over the East Frisian island Baltrum. The interferometric image pairs are the result of a repeat-pass measurement with a time offset of several minutes. The image data are given as 6 × 6 coherency matrices in ground range on a 1 m × 1m grid. Pixel-accurate class labels, consisting of 12 different land cover classes, are generated in a semi-automatic process based on an existing biotope type map and visual interpretation of SAR and optical images. Fixed training and test subsets are defined to ensure the comparability of different approaches trained and tested prospectively on the Pol-InSAR-Island dataset. In addition to the dataset, results of supervised Wishart and Random Forest classifiers that achieve mean Intersection-over-Union scores between 24% and 67% are provided to serve as a baseline for future work. The dataset is provided via KITopenData: https://doi.org/10.35097/1700.

A novel self‐supervised ensemble learning framework for land use and land cover classification of polarimetric synthetic aperture radar images

AbstractClassification with a few samples of training set has been a longstanding issue in the field of polarimetric synthetic aperture radar (PolSAR) image analysis and processing. Aiming at the small number of training samples of the PolSAR image classification task, a novel Self‐Supervised Ensemble Learning Framework (SSELF) is designed. The designed SSELF can automatically extract PolSAR features conducive to PolSAR image classification with a small number of training samples. In addition, it can significantly decrease the dependence of neural network algorithms on large labelled samples of training set. First, utilise the spatial–polarimetric features of PolSAR data perfectly, the EfficientNet‐B0 is presented and utilised as the main section of the Deep Learning (DL) model to extract DL features of PolSAR data. Then, using an optimisation function that constrains the cross‐correlation matrix of various distortions of each sample to the identity matrix, the designed DL model can obtain the effective features of homogeneous samples gathering and heterogeneous samples separating from each other in a self‐supervised manner. Moreover, following the great success of curriculum learning in the area of machine learning, a novel deep curriculum‐learning model is proposed, entitled Deep Curriculum Learning (DCL), to train the DL network in our self‐supervised model. The proposed DCL utilises the entropy‐alpha target decomposition to estimate the degree of complexity of each PolSAR image patch before applying it to the EfficientNet‐B0. Also, an accumulative mini‐batch pacing function is used to introduce more difficult patches to EfficientNet‐B0 in the training process of the designed self‐supervised model. Furthermore, two EL models, feature‐level and view‐level ensemble, are proposed to increase the feature extraction capability and classification result of PolSAR data by jointly using spatial features at different scales and polarimetric information at different bands. In fact, in the proposed feature‐level ensemble strategy, to improve the classification result, the extracted features of the different scales are used as the input of the designed feature fusion algorithm. Therefore, the extracted features are mapped to the new space with lower dimension. In general, in the proposed view‐level ensemble strategy, first, the Mutual Information (MI) between each feature and the other features calculates, and then based‐on the calculated MI, different group of features are selected as various views. Next, the final classification result of the PolSAR image classification is obtained by using the designed majority vote of the result in each view. It should be noted that unlike existing traditional methods, both amplitude and phase information of SAR images to devolve the training process of the proposed model are used. In addition, all of the parameters of the proposed network are developed to a complex domain. In addition, a complex backpropagation algorithm by using the gradient‐based model is used for training the network. Finally, experimental results on three well‐known PolSAR data sets illustrate that the designed SSELF can extract more discriminant features using the designed DL model and can achieve better classification results than the other six novel DL models in terms of small training samples and adequate labelled samples.

Towards improved short-term forecasting for Lake Victoria Basin: Part I – A radar-based convective mode analysis

Abstract East African countries benefit economically from the largest freshwater lake in Africa: Lake Victoria (LV). Around 30 million people live along its coastline and 5.4 million people subsist on its fishing industry. However, more than 1,000 fishermen die annually by high-wave conditions often produced by severe convective wind phenomena, which marks this lake one of the deadliest places in the world for hazardous weather impacts. The World Meteorological Organization launched the 3-year “HIGH impact Weather lAke sYstem” (HIGHWAY) project, with the main objective to reduce loss of lives and economic goods in the lake basin and improve the resilience of the local communities. The project conducted a field campaign in 2019 aiming to provide forecasters with high-resolution observations and to study the storm life cycle over the lake basin. The research discussed here used the S-band polarimetric Tanzania radar from the field campaign to investigate the diurnal cycle of the convective mode over the lake. We classified the lake storms occurring during the two wet seasons into six different convective modes and present their diurnal evolution, organization, and main radar-based attributes, thereby extending the knowledge of convection on the lake. The result is the creation of a “convection catalog for Lake Victoria,” using the operational forecast lake sectors, and defining the exact times for the different timeslots resulting from the HIGHWAY project for the marine forecast. This will inform methods to improve the marine operational forecasts for Lake Victoria, and to provide the basis for new Standard Operation Procedures (SOP) for severe weather surveillance and warning.

Investigating the magnetic field of the quiet Sun internetwork

Abstract We analyze the magnetism of the quiet Sun internetwork (IN) using high-spatial-resolution data obtained by the spectropolarimeter (SP) of the Solar Optical Telescope aboard the Hinode satellite near the disk center of the Sun. The SP data were inverted using the Stokes Inversion based on Response functions (SIR) inversion code with a single-component atmosphere with depth dependent in the solar photosphere, assuming gradients in physical parameters along the line of sight (LOS). To avoid the effect of noise, only pixels with Stokes U and/or Q signals above 4.5 times the noise level are considered. The inversion results show that the magnetic field of the IN has mainly hG field strength and the inclination distribution is quasi-isotropic at the solar surface. The field strength decreases with height and becomes predominantly horizontal at the upper layers. At the mid photosphere, the distributions of field strength and inclination are consistent with those derived by Milne–Eddington inversion. The mean transverse and longitudinal flux densities are 66 Mx cm−2 and 13 Mx cm−2 at log τ = −1.0; we also study the ratio between the transverse and longitudinal components in the IN region as a function of depth in the photosphere, finding that the ratio is almost 2.7 in the deep layer, increasing to 5.1 in the upper layer. The mean field strength is greater than 100 G in the upper photosphere, which is consistent with the results based on the Hanle effect. We present the LOS velocity probability distribution function for IN at different optical depths, where its distribution is mainly associated with upflow velocities of VLOS = 1.2 and 0.6 km s−1 in the deeper and upper layers, respectively. In addition, there exists a reliable inversion analysis, which is obvious from the comparison between the observed and calculated area asymmetries in both Fe i lines of Hinode SP data.

The Force-freeness of the Solar Photosphere: Revisit with New Approach and Large Data Sets

Although it is generally believed that the solar photosphere is not magnetically force-free owing to its high plasma β, the estimations of force-freeness using observed magnetograms have produced disputable results. Some studies confirmed that the photosphere is largely not force-free whereas some authors argued that the photosphere is not far away from being force-free. In a previous paper of ours we demonstrated that, due to the fact that the noise levels of the transverse field in the magnetograms are much larger than those of the vertical field, wrong judgments on the force-freeness could be made: a truly force-free field could be judged as being not-force-free and a truly not-force-free field could be judged as being force-free. Here in this Letter, we propose an approach to overcome this serious problem. By reducing the spatial resolution to lower the noise level, the heavy influence of the measurement noise on the force-freeness judgment can be significantly suppressed. We first use two analytical solutions to show the success and effectiveness of this approach. Then, we apply this new approach to two large data sets of active region magnetograms, obtained with the Helioseismic and Magnetic Imager/Solar Dynamics Observatory and Spectro-Polarimeter (SP)/Hinode, respectively. Our analysis shows that the photospheric magnetic fields are actually far away from being force-free. Particularly, and most notably, the mean value of F z /F p (where F z is the net Lorentz force in the vertical direction and F p the total Lorentz force) is as low as −0.47, with more than 98% of the active regions having ∣F z /F p ∣ > 0.1 when using the SP/Hinode magnetograms of true field strength.

A New Architecture of a Complex-Valued Convolutional Neural Network for PolSAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) image classification has been an important area of research due to its wide range of applications. Traditional machine learning methods were insufficient in achieving satisfactory results before the advent of deep learning. Results have significantly improved with the widespread use of deep learning in PolSAR image classification. However, the challenge of reconciling the complex-valued inputs of PolSAR images with the real-valued models of deep learning remains unsolved. Current complex-valued deep learning models treat complex numbers as two distinct real numbers, providing limited assistance in PolSAR image classification results. This paper proposes a novel, complex-valued deep learning approach for PolSAR image classification to address this issue. The approach includes amplitude-based max pooling, complex-valued nonlinear activation, and a cross-entropy loss function based on complex-valued probability. Amplitude-based max pooling reduces computational effort while preserving the most valuable complex-valued features. Complex-valued nonlinear activation maps feature into a high-dimensional complex-domain space, producing the most discriminative features. The complex-valued cross-entropy loss function computes the classification loss using the complex-valued model output and dataset labels, resulting in more accurate and robust classification results. The proposed method was applied to a shallow CNN, deep CNN, FCN, and SegNet, and its effectiveness was verified on three public datasets. The results showed that the method achieved optimal classification results on any model and dataset.

Influence of hematoxylin and eosin staining on linear birefringence measurement of fibrous tissue structures in polarization microscopy.

For microscopic polarization imaging of tissue slices, two types of samples are often prepared: one unstained tissue section for polarization imaging to avoid possible influence from staining dyes quantitatively and one hematoxylin-eosin (H&E) stained adjacent tissue section for histological diagnosis and structural feature identification. However, this sample preparation strategy requires high-quality adjacent tissue sections, and labeling the structural features on unstained tissue sections is impossible. With the fast development of data driven-based polarimetric analysis, which requires a large amount of pixel labeled images, a possible method is to directly use H&E stained slices, which are standard samples archived in clinical hospitals for polarization measurement. We aim to study the influence of hematoxylin and eosin staining on the linear birefringence measurement of fibrous tissue structures. We examine the linear birefringence properties of four pieces of adjacent bone tissue slices with abundant collagen fibers that are unstained, H&E stained, hematoxylin (H) stained, and eosin (E) stained. After obtaining the spatial maps of linear retardance values for the four tissue samples, we carry out a comparative study using a frequency distribution histogram and similarity analysis based on the Bhattacharyya coefficient to investigate how H&E staining affects the linear birefringence measurement of bone tissues. Linear retardance increased after H&E, H, or E staining (41.7%, 40.8%, and 72.5% increase, respectively). However, there is no significant change in the imaging contrast of linear retardance in bone tissues. The linear retardance values induced by birefringent collagen fibers can be enhanced after H&E, H, or E staining. However, the structural imaging contrasts based on linear retardance did not change significantly or the staining did not generate linear birefringence on the sample area without collagen. Therefore, it can be acceptable to prepare H&E stained slices for clinical applications of polarimetry based on such a mapping relationship.

Observations and modelling of the winter thunderstorm on 4 February 2022 at the Milešovka meteorological observatory

AbstractThe study analyses a winter thunderstorm that passed over the Milešovka meteorological observatory on 4 February 2022, between 2300 and 2330 UTC. Lightning was recorded directly over the observatory by both the observer and the EUCLID lightning network at 2320 UTC. To analyse the state of the atmosphere at the time when the lightning occurred, we used data from the X‐band Doppler polarimetric radar and the Ka‐band Doppler polarimetric vertical profiler, both located at the observatory. We also applied data from the Meteosat Second Generation satellite, and data from standard meteorological instruments located at the observatory. In addition, we run our cloud electrification model to simulate cloud electrification of the winter thunderstorm to find out whether the model develops conditions suitable for the occurrence of lightning and if so, under what circumstances. Our results show that the lightning appeared at the very end of the storm passage defined by high radar reflectivity. At the same time, it is clear from the radar observations that before lightning occurred, the cloud contained hydrometeors (graupel, cloud or rain water, and ice or snow) which are commonly associated with charge separation by collisions. Our analysis of the radar data also suggests that in at least several parts of the cloud the electric field was strong. Although the cloud top height was very low compared to summer storms, the model results indicate conditions suitable for lightning occurrence. However, uncertainty remains on how to properly formulate the initial conditions for model simulations for this type of storm which was shallow and occurs rarely in winter.

Semi-empirical SAR vegetation index for crop discrimination based on biomass in semi-arid region: A case study in Perambalur district, India

Crop discrimination helps monitor crops and strengthen the agricultural industry to secure food supplies. From existing crop-related studies and observations, found that vegetative Empirical models were limited in crop classification for Kharif and Rabi season crops based on biomass. To improve biomass assessment, this study proposes a modified Vegetation Index using the deviation factor from the plot between the Dual Polarization SAR Vegetation Index (DPSVI) and the Dual Polarimetric Radar Vegetation Index (DpRVI). The deviation factor corrects the difficulties in the lower and higher ranges and is found to be 0.5 for Kharif and Rabi at the harvest stage. Performance of Integrated SAR Vegetation Index carried out to both seasons and shows Kharif (R2 = 0.83), Rabi (R2 = 0.87). In addition, cross-validation with Sentinel-2 derived Normalized Difference Vegetation Index, which has R2 values of 0.84 and 0.86 for Kharif and Rabi seasonal crops respectively. A correlation was performed to assess the capability of the proposed model for individual crops and found that good correlation with vertically grown Kharif and rabi crops except for Sesamum Indicum. A classification capability of the Kharif (76%) and Rabi (84%) crops and variation shows crops are healthy and have more greenery. From the results, a modified measure outperformed than DPSVI and DpRVI at the harvest stage for a selected crop in the above season and proved an alternate.

Dense forests of microshots in bursts from FRB 20220912A

ABSTRACT We report on exceptionally bright bursts (>400 Jy ms) detected from the repeating fast radio burst source FRB 20220912A using the Nançay radio telescope (NRT), as part of the ECLAT (Extragalactic Coherent Light from Astrophysical Transients) monitoring campaign. These bursts exhibit extremely luminous, broad-band, short-duration structures (∼16 μs), which we term ‘microshots’ and which can be especially well studied in the NRT data given the excellent signal-to-noise and dynamic range (32-bit samples). The estimated peak flux density of the brightest microshot is 450 Jy. We show that the microshots are clustered into dense ‘forests’ by modelling them as Weibull distributions and obtaining Weibull shape parameters of approximately 0.5. Our polarimetric analysis reveals that the bursts are nearly 100 per cent linearly polarized; have ≲10 per cent circular polarization fractions; a near-zero average rotation measure of 0.10(6) rad m−2; and varying polarization position angles over the burst duration. For one of the bursts, we analyse raw voltage data from simultaneous observations with the Westerbork RT-1 single 25-m dish. These data allow us to measure the scintillation bandwidth, 0.30(3) MHz, and to probe the bursts on (sub-)microsecond time-scales. Some important nuances related to dedispersion are also discussed. We propose that the emission mechanism for the broad-band microshots is potentially different from the emission mechanism of the broader burst components, which still show a residual drift of a few hundred MHz ms−1 after correcting for dispersion using the microshots. We discuss how the observed emission is phenomenologically analogous to different types of radio bursts from the Sun.

A Five-Component Decomposition Method with General Rotated Dihedral Scattering Model and Cross-Pol Power Assignment

The model-based polarimetric decomposition is extensively studied due to its simplicity and clear physical interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) data. Though there are many fine basic scattering models and well-designed decomposition methods, the overestimation of volume scattering (OVS) may still occur in highly oriented buildings, resulting in severe scattering mechanism ambiguity. It is well known that not only vegetation areas but also oriented buildings may cause intense cross-pol power. To improve the scattering mechanism ambiguity, an appropriate scattering model for oriented buildings and a feasible strategy to assign the cross-pol power between vegetation and oriented buildings are of equal importance. From this point of view, we propose a five-component decomposition method with a general rotated dihedral scattering model and an assignment strategy of cross-pol power. The general rotated dihedral scattering model is established to characterize the integral and internal cross-pol scattering from oriented buildings, while the assignment of cross-pol power between volume and rotated dihedral scattering is achieved by using an eigenvalue-based descriptor DOOB. In addition, a simple branch condition with explicit physical meaning is proposed for model parameters inversion. Experiments on spaceborne Radarsat−2 C band and airborne UAVSAR L band PolSAR datasets demonstrate the effectiveness and advantages of the proposed method in the quantitative characterization of scattering mechanisms, especially for highly oriented buildings.

PolSAR Image Classification by Introducing POA and HA Variances

A polarimetric synthetic aperture radar (PolSAR) has great potential in ground target classification. However, current methods experience difficulties in separating forests and buildings, especially oriented buildings. To address this issue, inspired by the three-component decomposition method, multiple new scattering models were proposed to describe the difference between forest scattering and building scattering. However, this problem cannot effectively be solved with scattering power alone since HV polarization records significant scattering powers from building areas that are similar to vegetation. Therefore, in this study, two new parameters, the polarization orientation angle (POA) variance and helix angle (HA) variance, were defined to describe the distributions of buildings and forests. By combining scattering power with POA and HA variances, the random forest algorithm was used to conduct the land cover classification, focusing on distinguishing between forests and oriented buildings. Finally, the C- and L-band polarimetric SAR data acquired by the GF-3, ALOS1 PALSAR, and SAOCOM systems were selected to test the proposed method. The results indicate that it is feasible to improve PolSAR classification accuracy by introducing polarimetric parameters. Quantitatively, the classification accuracies increased by 23.78%, 10.80%, and 12.97% for the ALOS1 PALSAR, GF-3, and SAOCOM data, respectively.

Polarimetric Radar Observations of a Long-Lived Supercell and Associated Tornadoes on 10–11 December 2021

Abstract We present environmental and polarimetric radar observations of a long-lived December supercell that tracked approximately 750 km from Arkansas to northern Kentucky. The storm was associated with two long-track EF4 tornadoes, one of which was among the longest-tracked tornadoes recorded in the United States. The supercell’s life cycle is documented from 2000 UTC 10 to 0700 UTC 11 December 2021, using data from five operational polarimetric weather radars. After convection initiation in central Arkansas, it took nearly 4 h for a supercell to develop. Afterward, the storm’s ZDR column and arc became anomalously large leading up to genesis of the first EF4 tornado. During this time, the storm’s environment had moderate convective available potential energy (CAPE) and strong deep-layer shear. A cell interaction at about 0200 UTC disrupted the supercell updraft, weakening the ZDR arc and column, and initiating the largest radar-implied hailfall event observed with this storm. The remnant circulation associated with the first EF4 tornado did not fully dissipate, and it appeared to merge with the low-level mesocyclone on the nose of a rear-flank downdraft surge likely initiated by the hailfall. It is hypothesized that this merger was important to the intensification of the storm’s second EF4 tornado, which lasted nearly 3 h and traveled approximately 267 km. During the second EF4 tornado the storm experienced decreasing CAPE and increasing storm relative helicity. Increasing interactions with other cells eventually weakened the storm, and its original updraft was obscured before the storm’s remnants dissipated in northern Kentucky. Significance Statement In December 2021, a long-lived supercell thunderstorm produced two long-track, violent tornadoes, including one that produced historic damage across western Kentucky. Radar observations indicate that, once the storm became a supercell, its updraft became anomalously large relative to similar storms studied prior. Simultaneously its storm-relative inflow strengthened markedly, supporting the first long-lived tornado. Interactions with a developing thunderstorm disrupted the supercell’s updraft, leading to hail fallout and updraft weakening. The remnant circulation associated with the first strong tornado merged with the supercell’s updraft and became a rotation source for the second long-track tornado, which persisted for nearly 3 h. Eventually interactions with other thunderstorms weakened the supercell.

Polarimetric Radar Observations of Biological Scatterers in the Eye of Typhoon Lekima (2019)

AbstractTropical cyclones not only cause strong winds and heavy rainfall, but they can also facilitate the transport of birds and insects from tropical regions to areas along their paths. Before super Typhoon Lekima made landfall in 2019, an operational polarimetric radar in Wenzhou City observed biological scatterers in the typhoon's eye. These scatterers were likely birds and insects that were trapped in the calm center of the typhoon by strong winds and heavy rain. The polarimetric variables of these biological scatterers had specific characteristics: low reflectivity factor (ZH) values with a median of 7.5 dBZ, low cross‐correlation coefficient (ρhv) values with a median of 0.65, large differential reflectivity (ZDR) values with a median of 2.8 dB and a maximum of ∼7.8 dB, and widely‐distributed differential phase (ΦDP) values with 25th and 75th percentiles ranging from 1.8° to 36.0° and a median of 20.1°. When the edge of the eye reached the coastline, the birds and insects landed, with the biological scatterer signature changing from a circular shape to a band shape. We further compared Lekima with Atlantic Hurricane Irene which happened in 2011, and both storms had similar polarimetric characteristics contributed by both birds and insects. However, the biological echoes in Lekima better exhibited the characteristics of birds with a larger proportion of samples with ZDR lower than 5 dB as well as large ΦDP and low ρhv values. This finding could help to understand the role of typhoons in driving biological migration between oceans and/or islands and continents.