Backscattering Model Research Articles

Preliminary study of coda wave attenuation around Yogyakarta based on Meramex temporary stations data

Abstract The tail of a seismogram record, the coda, usually comes from the high-order scattered waves from some heterogeneities in the earth’s crust. Coda waves can be used to estimate the earthquake attenuation, site amplification and to determine the magnitudes. This preliminary study investigates the seismic attenuation property around the Yogyakarta region, Indonesia, from the Meramex temporary network. The Meramex network was installed from May to October 2004. We estimated the coda wave attenuation using the back-scattering model. We presented results from two station measurements (AJ1 and AI1) in this preliminary study. We analyzed shallow earthquakes (<40km) with distances less than 3 degrees. We bandpass filter the data with central frequencies of 1, 2, 4, 6, 8, 10, 12, and 14 Hz. The coda window begins from twice the S-wave arrival time with a window length between 20 and 80 seconds. We estimated the coda quality factor (Qc) for each central frequency. The Qc at reference frequency 1 Hz yields the estimation for Q0 and the frequency-dependent factor (η) of 64 - 134 and 1.14-0.95, respectively. The Q0 value increased significantly in the window length of 20 to 30 seconds and was relatively stable in the window length of 30 seconds, but the η value is the contrary. The rise of the Q0 and decrease of η is apparently due to the heterogeneity of the earth’s crust, and the flat may be associated with the uniformity of the upper mantle.

Snow depth retrieval method for PolSAR data using multi-parameters snow backscattering model

Snow depth (SD) is a crucial property of snow, its spatial and temporal variation is important for global change, snowmelt runoff simulation, disaster prediction, and freshwater storage estimation. Polarimetric Synthetic Aperture Radar (PolSAR) can precisely describe the backscattering of the target and emerge as an effective tool for SD retrieval. The backscattering component of dry snow is mainly composed of volume scattering from the snowpack and surface scattering from the snow-ground interface. However, the existing method for retrieving SD using PolSAR data has the problems of over-reliance on in-situ data and ignoring surface scattering from the snow-ground interface. We proposed a novel SD retrieval method for PolSAR data by fully considering the primary backscattering components of snow and through multi-parameter estimation to solve the snow backscattering model. Firstly, a snow backscattering model was formed by combining the small permittivity volume scattering model and the Michigan semi-empirical surface scattering model to simulate the different scattering components of snow, and the corresponding backscattering coefficients were extracted using the Yamaguchi decomposition. Then, the snow permittivity was calculated through generalized volume parameters and the extinction coefficient was further estimated through modeling. Finally, the snow backscattering model was solved by these parameters to retrieve SD. The proposed method was validated by Ku-band UAV SAR data acquired in Altay, Xinjiang, and the accuracy was evaluated by in-situ data. The correlation coefficient, root mean square error, and mean absolute error are 0.80, 4.49 cm, and 3.95 cm, respectively. Meanwhile, the uncertainties generated by different SD, model parameters estimation, solution method, and underlying surface are analyzed to enhance the generality of the proposed method.

Retrieval of snow and soil properties for forward radiative transfer modeling of airborne Ku-band SAR to estimate snow water equivalent: the Trail Valley Creek 2018/19 snow experiment

Abstract. Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth observation mission. The snow scientific community has shown that Ku-band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent developments of tools like the snow micropenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer (SMRT) model, it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada, was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-band radar backscatter at a 100 m scale. Airborne Ku-band data were acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using RADARSAT-2 and TerraSAR-X data, soil background roughness properties were retrieved (msssoil=0.010±0.002), and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, were retrieved. Using the geometrical optics surface backscatter model, the retrieved effective soil permittivity increased from C-band (εsoil=2.47) to X-band (εsoil=2.61) and to Ku-band (εsoil=2.77) for the TVC domain. Using the SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve snow water equivalent (SWE) at the 100 m scale.

The critical role of cross-polarized backscatter in understanding L-band PolSAR data in forested and urban environments

Ambiguity exists in interpreting polarimetric synthetic aperture radar (PolSAR) L-band backscatter data from forested and urban environments. Two types of ambiguity are studied. The first is between flooded forests and urban buildings with small azimuth angles, and the other is between upland forests and urban buildings with large azimuth angles. To resolve the ambiguity, we developed an algorithm based on the PolSAR decomposition and backscatter model construction. First, the PolSAR backscatter data, including four co−/cross-polarized (XP) backscatter elements (i.e., the elements were not assumed to be zero) in a 3 × 3 covariance matrix, were decomposed into three components. Next, the decomposed XP backscatter determined each component's scattering mechanism of the single, double, or volume scattering. Then, with backscatter models, we resolved the first ambiguity by considering the volume and single scattering mechanisms and the second ambiguity by studying the volume and double scattering mechanisms. Thus, the volume scattering, primarily from the XP backscatter, was used in both ambiguity removals. The algorithm was evaluated using multiple L-band PolSAR data. The average resolving rate of the two types of ambiguity was 74.3%. Also, the relative powers of the single and double scattering mechanisms of radar targets decreased slightly (≤ 30.6%) in absolute value, while the volume scattering increased significantly (≥ 91.9%). The small change in the single or double scattering but the significant increase in the volume scattering quantitatively revealed the XP backscatter's criticality. The volume scattering component is vital for understanding the PolSAR backscatter data in forested and urban environments.

Attenuation characteristics of coda wave in Northern Aceh, Sumatra, Indonesia

Abstract Sumatra Island is located in the subduction zone between Indo-Australian and Eurasian Plates. The tectonic activity has resulted in an active region. Hence, it may cause more heterogeneous crust in the region. To understand the relationship between the attenuation factor and the tectonic setting, we apply coda wave attenuation study Sumatra Island. This preliminary result focuses at the northern Aceh from observation at LHMI station. A total of about 123 earthquake waveforms of local distance (less than 3°) with depth less than 40 km is analyzed in this study. We employ a back-scattering model assumption to determine the attenuation of coda waves. Our coda wave quality factor (Qc ) estimation covers frequencies from 1 to 8 Hz. Onset of the coda window is determined by considering twice the arrival time of shear wave, with varying window lengths between 20 and 120 s. The Q0 (Qc at frequency 1 Hz) varies from 65.84 ± 5.50 to 183.09 ± 7.17. We observe a decrease in the factor of frequency dependence (η) between 1.14 ± 0.06 and 1.04 ± 0.03. Depth dependent of coda wave attenuation can be seen from the increase of Q0 (which is Qc at 1 Hz) with window length. We suggest that the Low Q0 (< 200) and high η (> 1) may be associated with high tectonic activity in this region.

Attenuation of Coda and Body Waves for Tbilisi and Surrounding Area, Georgia

The attenuation of high-frequency seismic waves was investigated in the crust beneath Tbilisi and the surrounding territory by analysing 225 local earthquakes that occurred from 2008 to 2020 and were recorded by eight seismic stations. The quality factors of coda waves QC and direct P and S waves, QP and QS, were estimated using the single backscattering model and the extended coda normalization method, respectively. The separation of intrinsic quality factors Qi from scattering quality factor QSC was fulfilled by Wennerberg’s method. Observed results show that all evaluated attenuation parameters are frequency-dependent in the frequency range of 1-32 Hz and increase with increasing frequency. Coda QC values increase also with increasing lapse time window from 20 s to 50 s and vary from 91±5 at 1.5 Hz to 1779±108 at 24 Hz, respectively. P waves attenuate slightly faster than S waves, and the ratio of QS/QP is more than unity and varies in a range of 1.5-1.8. The intrinsic and scattering quality factors are expressed by the following power laws: Qi=77±4f0.930±0.046 and QSC=219±6f0.924±0.050. The results show that Qi is close to QC, but QSC is larger than Qi, which means that intrinsic attenuation has a dominant role compared with the scattering effect. Our results were compared with those obtained in two other seismically active regions of Georgia, as well as with regions of the world. In general, the observed quality factors and their frequency-dependent relationships follow a similar trend, characterizing seismically active regions with complex tectonics. The calculated attenuation parameters characterize the entire earth’s crust under Tbilisi and the surrounding area. The results obtained will be useful in future seismological studies since the Q parameters are estimated for the first time for the given region.

Relation Models of Surface Parameters and Backscattering (or Radiation) Fields as a Tool for Solving Remote Sensing Problems

In this paper, an analysis of existing models for describing surfaces of various types is performed, and the possibilities of their application at the level of mathematical modeling are analyzed. Moreover, due to the large number of models and the complexity of selecting the appropriate model, e.g., when conducting a practical experiment, an algorithm for choosing a specific model depending on the initial data is proposed. According to the algorithm, a software prototype that implements this algorithm (written in Python) is proposed.

Ensembles of multiple models for soil moisture retrieval from remote sensing data over agricultural areas: A deep learning-based framework

In agricultural areas, most surface soil moisture (SM) retrieval models are unstable in terms of their accuracy and performance during crop growth. As a result, there is no consensus on which model performs optimally during the agricultural season. This is because of the uncertainties associated with model physics, initial conditions, model inversion processes, input data, vegetation attenuation and soil characteristics. To better deal with these practical concerns, we propose a simple, but robust SM retrieval method of using combination of multiple models based on deep learning and multi-model ensemble approach, called the DL-MME method, which makes use of the ‘collective intelligence’ and ‘wisdom of crowds’ concepts. The advantages of this method are: (1) robustness to model selection, and (2) robustness to model calibration during the growing season. In addition, this method is less dependent on one type of data across various agricultural areas compared to the single model approach. Firstly, the coupled water cloud model (WCM) and soil backscattering models (Oh model or advanced integral equation model (AIEM)) with different vegetation descriptors were calibrated and validated during the growing season in sugarcane and winter wheat fields for Sentinel-1 backscattering coefficients (VV and VH). SM was also retrieved by employing the trapezoid model (OPTRAM) with different parameters from Sentinel-2 images. To optimize SM retrieval computations, we used the outputs from optical and SAR models, auxiliary features, and reliable in situ SM measurements as inputs to a deep learning convolutional neural network (DL-CNN). For sugarcane and wheat fields in the early stages of crop growth, WCM models retrieved more accurate time-series SM than optical models. OPTRAM soil moisture retrievals showed greater accuracy in the late crop growing season. Time-series SM retrieval accuracy using DL-MME was higher than for the optical and semi-empirical SAR models. According to the results of the in situ validation for wheat (sugarcane) fields, the minimum MAE by an optimal combination of models was around 0.01 (0.02) m3m−3 (RMSE = 0.036 (0.074) m3m−3; R = 0.87 (0.71)). The findings demonstrate that our method is reliable and feasible for SM retrieval. Additionally, our method provides a way to select an optimal model for retrieving time-series SM during the crop growing season.

Crop Height Estimation Based on a Novel Semi-Empirical Model Considering Double-Bounce Scattering Using RADARSAT-2 PolSAR Data

Abstract. Obtaining precise and rapid crop height is essential to facilitate agricultural production services, field management, disaster monitoring, and yield assessment. With the capability to penetrate vegetation and record vertical structure information, Polarimetric Synthetic Aperture Radar (PolSAR) holds significant potential for application in vegetation height inversion. The Water Cloud Model (WCM) and its enhanced versions are extensively utilized for estimating crop heights from PolSAR data owing to their physical significance and simplicity. However, the method is not practical for stalk crops due to the neglect of double-bounce scattering considerations. Therefore, according to the growth characteristics of stalk crops, a three-component polarimetric coherent backscattering model considering crop target double-bounce scattering is established by simplifying the Random Volume over Ground (RVoG) coherent scattering model. The empirical coefficient is introduced to simplify the model into a semi-empirical for crop height inversion. The suitability of applying the RVoG-B three-component model for crop height inversion at the early stage in corn fields was assessed using Multi-temporal C-band PolSAR RADARSAT-2 data in three polarimetric channels. The results show that the HV channel exhibits superior potential in inverting the height of corn compared with the HH and the VV channels. The results of corn height inversion demonstrate that the RVoG-B three-component semi-empirical model performs effectively in estimating corn height, with its inversion accuracy having an RMSE ranging from 11.66cm to 24.51cm. This study demonstrates the potential application of the RVoG-B three-component semi-empirical model for inverting crop height at the early stage dominated by double-bounce scattering.

Bottom Multi-Parameter Bayesian Inversion Based on an Acoustic Backscattering Model

The geoacoustic and physical properties of the bottom, as well as spatial distribution, are crucial factors in analyzing the underwater acoustic field structure and establishing a geoacoustic model. Acoustic inversion has been widely used as an economical and effective method to obtain multi-parameters of the bottom. Compared with traditional inversion methods based on acoustic propagation models, acoustic backscattering models are more suitable for multi-parameter inversion, because they contain more bottom information. In this study, a Bayesian inversion method based on an acoustic backscattering model is proposed to obtain bottom multi-parameters, including geoacoustic parameters (the sound speed and loss parameter), partial physical parameters of the sediment, and statistical parameters of the seafloor roughness and sediment heterogeneity. The bottom was viewed as a kind of fluid medium. A high-frequency backscattering model based on fluid theory was adopted as the forward model to fit the scattering strength between the model prediction and the measured data. The Bayesian inversion method was used to obtain the posterior probability density (PPD) of the inversion parameters. Parameter estimation, uncertainty, and correlation were acquired by calculating the maximum a posterior (MAP), the mean values, the one-dimensional marginal distributions of the PPD, and the covariance matrix. Finally, the high-frequency bottom backscattering strength from the Quinault Range site was employed for inversion tests. The estimated values and uncertainties of various bottom parameters are presented and compared with the directly measured bottom parameters. The comparison results demonstrate that the method proposed herein can be used to estimate the sediment/water sound speed ratio, the sediment/water density ratio, and the spectral exponent of the roughness spectrum effectively and reliably.

Ultrasonic backscattering model for Rayleigh waves in polycrystals with Born and independent scattering approximations

This paper presents theoretical and numerical models for the backscattering of 2D Rayleigh waves in single-phase, untextured polycrystalline materials with statistically equiaxed grains. The theoretical model, based on our prior inclusion-induced Rayleigh wave scattering model and the independent scattering approximation, considers single scattering of Rayleigh-to-Rayleigh (R–R) waves. The numerical finite element model is established to accurately simulate the scattering problem and evaluate the theoretical model. Good quantitative agreement is observed between the theoretical model and the finite element results, especially for weakly scattering materials. The agreement decreases with the increase of the anisotropy index, owing to the reduced applicability of the Born approximation. However, the agreement remains generally good when weak multiple scattering is involved. In addition, the R–R backscattering behaviour of 2D Rayleigh waves is similar to the longitudinal-to-longitudinal and transverse-to-transverse backscattering of bulk waves, with the former exhibiting stronger scattering. These findings establish a foundation for using Rayleigh waves in the quantitative characterisation of polycrystalline materials.

Numerical Evaluation of Planetary Radar Backscatter Models for Self-Affine Fractal Surfaces

Numerous analytical radar-scattering laws have been published through the past decades to interpret planetary radar observations, such as Hagfors’ law, which has been commonly used for the Moon, and the cosine law, which is commonly used in the shape modeling of asteroids. Many of the laws have not been numerically validated in terms of their interpretation and limitations. This paper evaluates radar-scattering laws for self-affine fractal surfaces using a numerical approach. Traditionally, the autocorrelation function and, more recently, the Hurst exponent, which describes the self-affinity, have been used to quantify the height correlation. Here, hundreds of three-dimensional synthetic surfaces parameterized using a root-mean-square (rms) height and a Hurst exponent were generated, and their backscattering coefficient functions were computed to evaluate their consistency with selected analytical models. The numerical results were also compared to empirical models for roughness and radar-scattering measurements of Hawaii lava flows and found consistent. The Gaussian law performed best at predicting the rms slope regardless of the Hurst exponent. Consistent with the literature, it was found to be the most reliable radar-scattering law for the inverse modeling of the rms slopes and the Fresnel reflection coefficient from the quasi-specular backscattering peak, when homogeneous statistical properties and a ray-optics approach can be assumed. The contribution of multiple scattering in the backscattered power increases as a function of rms slope up to about 20% of the backscattered power at normal incidence when the rms slope angle is 46°.

A Backscatter Model Combing Wind and Rain Effect at Low Incidence Angles

As the Wind blows over the ocean surface, the generated waves are the primary factor affecting the microwave backscatter of the remote sensing radar. In addition, there are also some influencing factors, such as rainfall, which is the best-known atmospheric phenomenon. This paper proposes a wind and rain backscatter model whose angle of incidence is from 1 to 6 deg. The model is developed by considering the influence of ocean surface wind, atmospheric rain attenuation, rain volume backscatter and interactions between raindrops and ocean surface. The wind-induced component of the model adopted a quasi-specular theoretical model for the isotropic rough surface of Gaussian statistics. Rain attenuation is expressed as a coefficient of the model, and rain volume backscatter is expressed as a function of the rain volume backscattering coefficient, the thickness of precipitation layer and the incidence angle. Interactions between raindrops and ocean surface are complicated and deduced from the collocated dataset from the Precipitation Radar onboard the Tropical Rainfall Measuring Mission satellite and the European Centre for Medium-Range Weather Forecasts. Based on the collocated dataset, the result shows the proposed model can estimate reasonably accurate estimates surface backscatter within wind speeds range from 8 to 18 m s−1.

A new fractal relationship for measuring surface roughness to be used in physical models of radar backscattering

A new fractal relationship for measuring surface roughness to be used in physical models of radar backscattering

Analysis of wave breaking on synthetic aperture radar at C-band during tropical cyclones

ABSTRACT The purpose of our work is to analyze the effect of wave breaking on dual-polarized (vertical-vertical (VV) and vertical-horizontal (VH)) synthetic aperture radar (SAR) image in the C-band during tropical cyclones (TCs) based on the machine learning method. In this study, more than 1300 Sentinel-1 (S-1) interferometric-wide (IW) and extra wide (EW) mode SAR images are collocated with wave simulations from the WAVEWATCH-III (WW3) model during 400 TCs. The validation of the significant wave height (SWH) simulated using the WW3 model against Jason-2 altimeter data. The winds for S-1 SAR images are reconstructed using wind retrievals in VV and VH polarization. The non-polarized (NP) contribution σ wb caused by wave breaking is assumed to be the result of the SAR-measured normalized radar cross-section (NRCS) σ 0 minus the Bragg resonant roughness σ br without the distortion of rain cells during TCs. The σ br is simulated by imputing wave spectra from the WW3 model into the theoretical backscattering model. It is found that the ratio (σ wb /σ 0) in VV polarization is related to the wind speed, the wind direction relative with the flight orientation, and radar incidence angle. Following this rationale, the Adaptive Boosting (AdaBoost) model was used for the estimation of NP contribution σ wb during TCs and are implemented for more than 300 dual-polarized S-1 images to validate the model. It is found that for the comparison between the sum of simulation NRCS and SAR observations, the root mean squared error (RMSE) is 1.95 dB and the coefficient (COR) is 0.86, which is better than a 2.83 dB RMSE and a 0.67 COR by empirical model. It is concluded that the AdaBoost model has a good performance on NP component simulation during TCs.

A Two-Stream LSTM-Based Backscattering Model at L-Band and S- Band for Dry Soil Surfaces Under Large Roughness Conditions

A Two-Stream LSTM-Based Backscattering Model at L-Band and S- Band for Dry Soil Surfaces Under Large Roughness Conditions

An Integrated Time-Series Relative Soil Moisture Monitoring Method Based on a SAR Backscattering Model

An Integrated Time-Series Relative Soil Moisture Monitoring Method Based on a SAR Backscattering Model

A Backscattering Model of Rain-Affected Sea Surface With Two-Scale Method

A Backscattering Model of Rain-Affected Sea Surface With Two-Scale Method

The Impact of Ambient Temperature on Electrothermal Characteristics in Stacked Nanosheet Transistors with Multiple Lateral Stacks.

With characteristic size scaling down to the nanoscale range, the confined geometry exacerbates the self-heating effect (SHE) in nanoscale devices. In this paper, the impact of ambient temperature (Tamb) on the SHE in stacked nanosheet transistors is investigated. As the number of lateral stacks (Nstack) increases, the nanoscale devices show more severe thermal crosstalk issues, and the current performance between n- and p-type nanoscale transistors exhibits different degradation trends. To compare the effect of different Tamb ranges, the temperature coefficients of current per stack and threshold voltage are analyzed. As the Nstack increases from 4 to 32, it is verified that the zero-temperature coefficient bias point (VZTC) decreases significantly in p-type nanoscale devices when Tamb is above room temperature. This can be explained by the enhanced thermal crosstalk. Then, the gate length-dependent electrothermal characteristics with different Nstacks are investigated at various Tambs. To explore the origin of drain current variation, the temperature-dependent backscattering model is utilized to explain the variation. At last, the simulation results verify the impact of Tamb on the SHE. The study provides an effective design guide for stacked nanosheet transistors when considering multiple stacks in circuit applications.

Microstructure quality assessment for hybrid additive manufactured Ti6Al4V components via ultrasonics

Metal components with functionally organized microstructures for specific applications are emerging thanks to hybrid additive manufacturing (AM). The customization of these high value components accentuates the need for nondestructive methods to characterize their microstructural functional patterns. Nondestructive evaluation (NDE) methods that are economical, fast, energy efficient, and easy to integrate into routine component inspections are preferred. Most importantly, NDE methods must be sensitive to changes in the microstructure such that regions that do not satisfy the design requirements (i.e. out-of-spec regions) can be detected. In this work, ultrasonic NDE methods grounded in diffuse backscatter modeling were used to detect and quantify spatial property variations resulting from a hybrid AM process. The manufacturing process coupled directed energy deposition (DED) with milling in a cyclical manner. These methods were successfully implemented to evaluate the microstructural uniformity of Ti6Al4V samples as well as to make comparisons across an ensemble of samples manufactured with identical parameters. Out-of-spec regions were mapped with respect to the sample geometry on a layer-by-layer basis. The results of this work are expected to inform future NDE strategies for both research and practitioner contexts, and limitations are discussed.