Backscattering Characteristics Research Articles

Satellite-based InSAR Geodesy and Collocation with GNSS

Satellite radar interferometry (InSAR) enables large-scale monitoring of displacement with precision akin to ground-based methods. Using measurements from Sentinel-1 satellites, persistent scatterer networks cover areas up to hundreds of kilometers in size. One of the current challenges for the InSAR community is to tie these networks into a current and country-specific global terrestrial reference system through collocation with other geodetic techniques, especially Global Navigation Satellite Systems (GNSS). In this work, an optimal procedure for assessing a suitable collocation properties for InSAR and GNSS techniques is discussed, along with the proposal for the methodology to determine the spatial relationship between the reference points of both techniques. The study analyzes three collocation stations in Slovakia using their radar backscattering characteristics. InSAR achieves an intrinsic accuracy of up to 1 mm but has uncertainties several orders of magnitude larger in 3D space. A collocation of InSAR measurements with GNSS can tie relative InSAR displacement estimates to an absolute reference frame.

Using the AIEM and Radarsat-2 SAR to Retrieve Bare Surface Soil Moisture

Taking the Jiefangzha irrigation area of the Inner Mongolia Autonomous Region as the research area, the response relationships between the backscattering coefficient and radar frequency, radar incidence angle, root-mean-square height, correlation length, and soil water content under different conditions were simulated using advanced integral equations. The backscattering characteristics of exposed surfaces in cold and dry irrigation areas were discussed, and the reasons for the different effects were analyzed. Based on this, surface roughness models and statistical regression moisture inversion models were constructed through co-polarized backscatter coefficients and combined surface roughness. The correlation between the inverted surface roughness values and the measured values was R2 = 0.7569. The correlation between the soil moisture simulation values and the measured values was R2 = 0.8501, with an RMSE of 0.04. The findings showed a strong correlation between the values from the regression simulation and the measured data, indicating that the model can be applied to soil moisture inversion and has a good inversion accuracy. Compared with previous studies in the same area, the inversion model proposed in this paper has a higher accuracy and is more suitable for the inversion of soil moisture in the Jiefangzha irrigation area. These findings can support research on the water cycle and water environment assessment in the region.

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI.

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.

Experimental full waveform inversion for elastic material characterization with accurate transducer modeling

For quality assurance and in-service safety, sufficient characterization of the materials is essential during the development, manufacture, and processing of a material in any industrial setting. Evaluation of elastic coefficients, material microstructures, morphological features, and related mechanical properties are all part of the process of characterizing a material. Ultrasonic signals are sensitive to material properties such as wave speeds, attenuation, diffusion backscattering, microstructural variation, and elastic characteristics (e.g., elastic modulus, hardness, etc.). Ultrasonic computed tomography (USCT) is an emerging imaging method that can be implemented to obtain a quantitative estimation of material properties. In this study, a source estimation technique was initially proposed to obtain the source time function for accurate forward modeling by constructing a linear inverse problem for the unknown transducer modeling. Finally, a material characterization approach was proposed with accurate source estimation to extract wave speed distribution from an elastic material by employing a wave-based method, known as full waveform inversion (FWI). Systematic performance analysis of the proposed FWI model with accurate source estimation was assessed using experimental and synthetic data obtained from a 6061 aluminum sample. Overall, the proposed FWI technique has successfully reconstructed the wave speed distribution, exhibiting the potential of the proposed method of material characterization in various engineering applications.

MMF-CNN: a multimodal fusion CNN network for winter wheat extraction incorporating active and passive time series data

ABSTRACT Timely and accurate acquisition of winter wheat planting areas is crucial for food security. In this study, Sentinel-1 and Sentinel-2 time-series data are integrated at the feature level to enhance the accuracy of winter wheat extraction. However, existing feature-level fusion models suffer from insufficient feature extraction and lack of feature completeness, thereby overlooking the complementarity and correlation between these two modalities. A Multimodal Fusion Convolutional Neural Network (MMF-CNN) model is proposed to address the issues above. Firstly, the images of Sentinel-1 and Sentinel-2 are processed to obtain the NDVI and backscatter characteristics of the winter wheat time series life cycle. A single feature and a combination of two features are then imported into each end of the model. The model adds a feature fusion module, which can fully extract the feature information. At the same time, the original features are retained in the process of multiscale feature fusion, which avoids the loss of the original information. Finally, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Visual Geometry Group (VGG) are selected as the comparison models for comparative experiments, and the classification results of remote sensing images are obtained. These results demonstrate that the joint utilization of SAR and optical data yields the highest classification accuracy, with an F1 score of 97.42% for winter wheat. The overall accuracy (OA) of the proposed MMF-CNN method in this study is 96.87%, representing a 1.86% improvement compared to the Conv1D-CNN model. This improvement signifies adaptive feature learning at different hierarchical levels. Comparing the accuracy with other mainstream methods, the OA improves by 1.75%–4.37%, reveals finer ground details, and demonstrates faster performance. This study can provide methodological references for crop extraction studies based on multi-source data and time series analysis.

Echo-Level SAR Imaging Simulation of Wakes Excited by a Submerged Body.

The paper introduces a numerical simulation method for Synthetic Aperture Radar (SAR) imaging of submerged body wakes by integrating hydrodynamics, electromagnetic scattering, and SAR imaging simulation. This work is helpful for better understanding SAR images of submerged body wakes. Among these, the hydrodynamic model consists of two sets of ocean dynamics closely related to SAR imaging, namely the wake of the submerged body and wind waves. For the wake, we simulated it using computational fluid dynamics (CFD) numerical methods. Furthermore, we compared and computed the electromagnetic scattering characteristics of wakes under various navigation parameters and sea surface conditions. Following that, based on the operational principles and imaging theory of synthetic aperture radar (SAR), we established the SAR raw echo signal of the wake. Employing a Range-Doppler (RD) algorithm, we generated simulated SAR images of the wake. The results indicate that utilizing Computational Fluid Dynamics (CFD) numerical methods enables the simulation of wake characteristics generated by the motion of a submerged body with different velocities. The backscattering features of wakes are closely associated with the relative orientation between the wake and the radar line of sight. Under specific wind speeds, the wake gets masked within the sea surface background, resulting in less discernible characteristics of the wake in SAR images. This suggests that at lower speeds of submerged body or under specific wind conditions, the detectability of the wake in SAR images significantly diminishes.

Dense Neural Network for Classification of Seafloor Sediment using Backscatter Mosaic Feature

Water transportation plays a vital role in global economic activities, facilitating more than 85% of international trade and serving as a cost-effective and essential means to fulfill the demand for goods and services. Similarly, the Benoa Port, situated in the southern part of Denpasar City, operates in the same manner. By utilizing Multibeam Echo Sounder (MBES) backscatter data, backscatter mosaics can be generated to identify various seafloor sediment types, which consist of rock fragments, minerals, and organic materials. The characteristics of these sediments, such as grain size, density, composition, and others, can be observed. To improve the classification of sediments, the integration of backscatter data and backscatter features, such as ASM (Angular Second Moment), Energy, Contrast, and Correlation, can be employed. Supervised classification models like Dense Neural Network (DNN) can be utilized to accurately determine the types of seafloor sediments. The application of DNN modeling resulted in a training accuracy rate of 88% and a testing accuracy rate of 100%. The accuracy results delineated six distinct sediment types. Notably, sandy silt exhibited the highest distribution, accounting for 49.30%, whereas soft clayey silt registered the lowest distribution at 0.53%, as determined by their respective spatial prevalence.

Influence factors and mechanism of backscattering characteristics of electromagnetic waves in a single layer plasma tube array

A single-row plasma tube array (PTA) experimental system is established to improve the backscattering attenuation of a metal plate covered by a plasma tube array. The backscattering test system is utilized in a microwave anechoic chamber to examine the effects of gas composition, pressure, tube diameter, and discharge power on the backscattering attenuation of a metal plate using a plasma tube array. The electron density is obtained via microwave diagnosis. The backscattering attenuation mechanism in different frequency bands is revealed via numerical simulation. The results show that the reasonable selection of PTA parameters achieves strong attenuation in different frequency bands. The strong attenuation frequency bands of Ar–Hg PTA are in low frequency (1.5–3.5 GHz) and high frequency (13–17 GHz), while that of Ne–Hg discharge is in medium frequency (6.4–11.7 GHz). When the pressure is 0.5 and 1 Torr, the PTA shows a low, medium, and high multi-band distribution for the backscattering strong attenuation region. The backscattering strong attenuation region shows a low and high dual-band distribution, while the pressure is 2–4 Torr. As the tube diameter increases, the strong attenuation region maintains the dual-band, but it changes from low and high frequency bands to medium frequency (6-12 GHz), where the backscattering attenuation mechanism is collisional absorption when the frequency of plasma electron oscillation is close to that of electro-magnetic waves. The backscattering attenuation mechanism in the low frequency band involves the periodic structure of PTA generating local surface plasmon to absorb electromagnetic waves.

Broadband Scattering Characteristic Quantization Technique for Single Fish Based on a Split-Beam Echosounder

The utilization of broadband quantization data increases the possibility of practical applications for fish target recognition based on the acoustic scattering theory. However, the quantification of broadband data is more complex than that of narrowband systems, requiring consideration of the broadband characteristics of split-beam transducers, seawater absorption, and circuits. This paper elucidates the scatterer acoustic field equation, transducer power equation, and signal processing flow in split-beam broadband quantization technology for engineering applications. A broadband calculation model based on transducer parameters is proposed to enhance the generalization ability of broadband quantization technology to different types of sonar. The classical echo integration method is combined with Fourier transform to meet the requirements of target strength (TS) estimation under broadband signals. This paper includes a series of experiments to prove the rationality and effectiveness of the method. The results demonstrate that the provided calculation model can more accurately reflect the backscattering characteristics of the scatterer, but certain errors remain. This article analyzes the sources of errors and validates the effectiveness of the new TS calculation method.

Multipath artifacts enable angular contrast in multimodal endoscopic optical coherence tomography.

Multipath artifacts are inherent to double-clad fiber based optical coherence tomography (OCT), appearing as ghost images blurred in the A-line direction. They result from the excitation of higher-order inner-cladding modes in the OCT sample arm which cross-couple into the fundamental mode at discontinuities and thus are detected in single-mode fiber-based interferometers. Historically, multipath artifacts have been regarded as a drawback in single fiber endoscopic multimodal OCT systems as they degrade OCT quality. In this work, we reveal that multipath artifacts can be projected into high-quality two-dimensional en face images which encode high angle backscattering features. Using a combination of experiment and simulation, we characterize the coupling of Mie-range scatterers into the fundamental image (LP01 mode) and higher-order image (multipath artifact). This is validated experimentally through imaging of microspheres with an endoscopic multimodal OCT system. The angular dependence of the fundamental image and higher order image generated by the multipath artifact lays the basis for multipath contrast, a ratiometric measurement of differential coupling which provides information regarding the angular diversity of a sample. Multipath contrast images can be generated from OCT data where multipath artifacts are present, meaning that a wealth of clinical data can be retrospectively examined.

Crop mapping through a hybrid machine learning and deep learning method

A major function of satellite imagery is crop identification and separation, which is of key importance in acreage estimation, yield estimation, and agricultural management. This study aimed to examine the capability of the object-based method and machine learning and deep learning algorithms in combining optical and radar images to generate crop-type maps. For this purpose, Sentinel-1 and Sentinel-2 satellite images were used for the 2019 crop year in the northwest of Ardabil, Iran. Image segmentation was performed through the multiscale method and various spectral, textural, and radar backscattering characteristics. The main features were identified using Random Forest feature selection and fed to support vector machine, random forest, and convolutional neural network algorithms. The results of these algorithms were combined via the majority voting method. The results showed that the convolutional neural network algorithm performed better than other methods with an overall accuracy of 88.03% and a kappa coefficient of 85.99%. Meanwhile, the support vector machine method showed poor performance compared to the other two methods. According to the findings, combining the results of these algorithms through the majority voting method yielded the best crop identification result with an overall accuracy of 90.34% and a Kappa coefficient of 88.72%.

Seafloor Investigations of the Kemp Caldera, the Southernmost Arc Caldera Volcano From the South Sandwich Island Arc

AbstractKemp Caldera, situated in the south of the intra‐oceanic South Sandwich arc, is one of the least explored submarine calderas that hosts hydrothermally active vent sites. The caldera was discovered in 2009. Since then, the focus has been primarily on biological studies. During the R/V Polarstern cruise PS119 in 2019, we gained new insights into the morphology, petrology and the formation of the Kemp Caldera. The ship's multibeam data provide an overview of the caldera bathymetry and backscatter characteristics. The new data revealed that the caldera is nested with two or possibly three concentric calderas. TV‐sled and remotely operated vehicle (ROV) observations provide detailed visual data for the hydrothermally active sites of the vent field at the central resurgent cone and flare site at the NNW caldera rim. The central vent field is dominated by white smokers, where clams, sponges and other fauna thrive, while at the flare site inactive as well as actively venting chimneys have been found. The latter are characterized by metal‐enriched fluids of temperatures ≥200°C. During ROV dives, rock samples were collected from the cone, providing the first information about the Kemp Caldera rock composition. The caldera rocks are dacitic, in contrast to the basalts and andesites of the neighboring Kemp Seamount. This suggests that the dacitic cone was formed by one or more later eruptions of differentiated magma, probably stored in shallow intrusions which are driving hydrothermal activity.

94 GHz Radar Backscatter Characteristics of Alpine Glacier Ice

AbstractMeasuring the radar backscatter characteristics of glacier ice at different frequencies and incidence angles is fundamental to predicting the glacier mapping performance of a sensor. However, such measurements at 94 GHz do not exist. To address this knowledge gap, we collected 94 GHz radar backscatter data from the surface of Rhônegletscher in Switzerland using the All‐Weather Volcano Topography Imaging Sensor (AVTIS2) real‐aperture Frequency Modulated Continuous Wave radar. We determine the mean normalized radar cross section to be −9.9 dB. The distribution closely follows a log‐normal distribution with a high goodness of fit (R2 = 0.99) which suggests that radar backscatter is diffuse and driven by surface roughness. Further, we quantified the uncertainty of AVTIS2 3D point clouds to be 1.30–3.72 m, which is smaller than other ground‐based glacier surface mapping radars. These results demonstrate that glacier surfaces are an efficient scattering target at 94 GHz, hence demonstrating the suitability of millimeter‐wave radar for glacier monitoring.

Seasonal patterns in the mesopelagic fish community and associated deep scattering layers of an enclosed deep basin

Mesopelagic fish constitute the most abundant vertebrate group in the marine environment. The current work reports on results of three seasonal acoustic cruises carried out in the Gulf of Corinth, a relatively small, deep, isolated basin located in the Central Mediterranean (Greece) that presents some unique geomorphological and ecological features. The aim of this study was to describe seasonal echo-types and the vertical distribution of the Deep Scattering Layers (DSLs) as well as to relate them with specific species or species groups. Mesopelagic fish dominated the pelagic ecosystem as confirmed by biological sampling with different gears during daytime and nighttime. In total, at least 15 species were caught, belonging to the families Myctophidae, Paralepididae, Sternoptychidae and Stomiidae, while the—elsewhere very abundant—families Gonostomatidae and Phosichthyidae were completely absent. Common echo-types included: (a) shoals and schools formed by the silvery lightfish Maurolicus muelleri, usually located along the shelf break (80–225 m), (b) a non-migrant thin DSL found at 150–280 m throughout the deep parts of the Gulf, dominated by juvenile half-naked hatchetfish Argyropelecus hemigymnus, and (c) one thick, partially migratory DSL at 250–600 m, mainly consisting of myctophids. The echo backscatter characteristics and species composition of the DSLs as well as the length distribution of the populations were found to differ seasonally. Species-specific and size related patterns in the vertical distribution of fish were detected both during daytime and nighttime. Overall, the Gulf of Corinth seems to sustain high densities of mesopelagic fish that constitute the basic food resource for the abundant dolphin populations that inhabit the area.

Abandoned Land Mapping Based on Spatiotemporal Features from PolSAR Data via Deep Learning Methods

Monitoring agricultural abandonment is essential in understanding the effects on the environment and food security. Polarimetric synthetic aperture radar (PolSAR) is an efficient approach for the monitoring of large-scale agricultural land cover in cloudy and rainy areas. However, previous studies have not taken advantage of the valuable phase information and not fully utilized the spatiotemporal features of farmland parcels, which has seriously limited the abandoned land identification accuracy. In this study, we developed a new method for the mapping of abandoned land based on the spatiotemporal features from PolSAR Single Look Complex (SLC) images via deep learning methods. First, backscattering coefficients (σ0VV, σ0VH) were derived, and the polarimetric parameters (entropy, anisotropy and mean alpha angle) were obtained based on Cloude–Pottier polarimetric decomposition. Then, the VGG16 deep convolutional network was innovatively used to extract spatial features from both the backscattering coefficients and polarimetric parameters. Next, the separability index was calculated to select the most effective spatial features. Finally, LSTM classifications were conducted based on the time series of backscattering features, the polarimetric parameters, the extracted spatial features and their combinations. The results showed that the introduction of multitemporal polarimetric parameters and spatial features both led to an improvement in the abandoned land identification accuracy. The combination of backscattering features, polarimetric parameters and spatial features yielded the best performance in identifying abandoned land, with producer’s accuracy of 88.29% and user’s accuracy of 84.03%. This study demonstrated the potential of polarimetric parameters and validated the effectiveness of spatiotemporal features in abandoned land identification. It provided a practical method for the production of a highly reliable abandoned land mapping in cloudy and rainy areas.

Automated detection and tracking of medium-large icebergs from Sentinel-1 imagery using Google Earth Engine

Monitoring Antarctic icebergs helps us understand the interaction between ocean, atmosphere, and sea ice in the Southern Ocean. Although gigantic icebergs have been the objects of many remote sensing studies, medium icebergs in the Southern Ocean have been rarely monitored or traced. In this study, we develop an iceberg detection and tracking tool particularly for medium and large icebergs (0.4–10 km2), by using Python programming language and Sentinel-1 (S1) imagery, based on Google Earth Engine (GEE). To detect icebergs, we employ the simple non-iterative clustering (SNIC) and region adjacency graph (RAG) merging for object-based image segmentation and train/test the support vector machine (SVM) model with 6432 labeled segments of iceberg or non-icebergs from 40 images of S1 (2019–2021). Radar backscatter features and morphological features of those segments are used as the inputs of the SVM model. After icebergs are detected in two image scenes of different dates, we track the displacements of detected icebergs by comparing their 1-D shape signals. Our model shows ∼99% of accuracy in detecting icebergs and ∼ 93–98% of accuracy in tracking icebergs depending on the day difference between image scenes. When using our tool for the monitoring of icebergs in the Amundsen Sea, we find that the iceberg fraction varies from 2% to 8% in 2021 and most of icebergs move westward with a speed of <0.2 km/day.

Library of rough hailstone backscattering coefficients at 2.8 GHz

The rough hailstone scattering library is created to fill the need for results of non-spheroidal hailstone backscattering characteristics. The listed backscattering coefficients at the S-band (2.8 GHZ) are specifically tailored for computing the polarimetric variables. The National Weather Service’s radar operates in this band; hence the library is pertinent to those studying hail in the USA. The library contains several axis ratios and a range of hail sizes from 5 mm to 100 mm. Moreover, hailstone surface roughness is expressed as a percentage of the equatorial diameter. Our computational electromagnetic approach to modeling hailstones is compared with typically employed simpler methods. The polarimetric variables calculated using our models can be compared to observations. Furthermore, we illustrate the potency of the CEM approach to hydrometeors with extreme axis ratios for which simpler methods (i.e., the T-matrix) fail.

Crop Type Mapping Based on Polarization Information of Time Series Sentinel-1 Images Using Patch-Based Neural Network

Large-scale crop mapping is of fundamental importance to tackle food security problems. SAR remote sensing has lately received great attention for crop type mapping due to its stability in the revisit cycle and is not hindered by cloud cover. However, most SAR image-classification studies focused on the application of backscattering characteristics with machine learning models, while few investigated the potential of the polarization decomposition and deep-learning models. This study investigated whether the radar polarization information mined by polarization decomposition, the patch strategy and the approaches for combining recurrent and convolutional neural networks (Conv2d + LSTM and ConvLSTM2d) could effectively improve the accuracy of crop type mapping. Sentinel-1 SLC and GRD products in 2020 were collected as data sources to extract VH, VV, VH/VV, VV + VH, Entropy, Anisotropy, and Alpha 7-dimensional features for classification. The results showed that the three-dimensional Convolutional Neural Network (Conv3d) was the best classifier with an accuracy and kappa up to 88.9% and 0.875, respectively, and the ConvLSTM2d and Conv2d + LSTM achieved the second and third position. Compared to backscatter coefficients, the polarization decomposition features could provide additional phase information for classification in the time dimension. The optimal patch size was 17, and the patch-based Conv3d outperformed the pixel-based Conv1d by 11.3% in accuracy and 0.128 in kappa. This study demonstrated the value of applying polarization decomposition features to deep-learning models and provided a strong technical support to efficient large-scale crop mapping.

94 GHz Radar Backscatter Characteristics of Alpine Glacier Ice

94 GHz Radar Backscatter Characteristics of Alpine Glacier Ice

Application of a parametric array over a mid-frequency band (4–10 kHz) –measurements of bottom backscattering strength

Study on characteristics of bottom backscattering at midfrequency (usually referring to 1–10 kHz) is essential for underwater acoustic communication, subbottom structure detection, buried target detection, and antisubmarine sonar. Measurements of bottom backscattering strength in the grazing angle range of 70°–90° over a wide mid-frequency band of 4–10 kHz were taken using a parametric array transmitter system and an omnidirectional hydrophone. Details of the parametric array transmitter system are introduced. The experimental site locates in the South Yellow Sea of China, where the water depth is 37 m. Core data showed that the sediment was fine sand mixed with a small amount of shell fragments. The backscattering strength was extracted from monostatic backscattering data according to the sonar equation. A calibration experiment indicated that the distance from the parametric array to the seafloor should be larger than a conversion distance, where the difference-frequency source level and beamwidth of the parametric array tended to be stable, to efficiently generate low-frequency difference-frequency waves used to measure the bottom backscattering strength. The backscattering strength increases with the increase of the grazing angle, reaching a peak at the grazing angle of 90°. In the point of frequency, the frequency dependence of the backscattering strength is weak. The characteristics of bottom backscattering agree with a sandy site with similar sediment properties near the experimental site.