Backscattering Model Research Articles

Mesopelagic flesh shear viscosity estimation from in situ broadband backscattering measurements by a viscous–elastic model inversion

Abstract In fisheries acoustics, target strength (TS) is a key parameter in converting acoustic measurements to biological information such as biomass. Modelling is a versatile tool to estimate TS of marine organisms. For swimbladdered fish, flesh shear viscosity is one of the required parameters to correctly calculate TS around the resonance frequency, where the target scatters most strongly. Resonance of mesopelagic swimbladdered fish can occur over a range of frequencies and can be within commonly used frequencies (e.g. 18, 38, or 70 kHz). Since there is little information on flesh shear viscosity of fish, especially for mesopelagic species, their resonance can bias the biological information extracted from acoustic measurements. Here, first, the applicability of using a spherical model to estimate resonant backscattering of a generic swimbladder is investigated. Subsequently, a viscous–elastic spherical gas backscattering model is used to estimate the flesh shear viscosity of swimbladdered mesopelagic fish (most likely Cyclothone spp., Family: Gonostomatidae) from in situ broadband backscattering measurements. Finally, the effects of flesh shear viscosity on the TS of swimbladdered mesopelagic fish at 18, 38 (a widely used channel to study mesopelagic layers), and 70 kHz are examined.

Radar backscattering modelling and micro‐motion parameter estimation method for quadcopter

Due to the high-speed rotors and overlapped propellers, it is still a challenge to extract the micro-motion parameters of the quadcopter. In this work, the authors establish a radar backscattering model of the quadcopter based on the flight control theory and radar backscattering characteristics. Then, the improved template matching method and peak grouping method are proposed to capture the periodic information for overlapped propellers on a range-time profile and extract the rotation speeds of rotors. By simulating the nine types of quadcopter movements, the relative error of the rotation speeds is estimated to be lower than 1.4% for each rotor with a signal-to-noise ratio of 15 d B.

Assessment of aerosol burden over Ghana

Although air pollution in Ghana is ranked number one in environmental health threats to public health and sixth to cause of deaths, routine monitoring is rare. This paper presents fourteen years (2005-2018) assessment of aerosol optical depth (AOD) at 3 km resolution from MODIS Aqua and Terra satellites to ascertain the Spatio-temporal and seasonal distribution of aerosols over Ghana and its major cities. The MODIS AOD at 3 km were validated against ground-based Aerosol Robotic Network (AERONET) AODs to ascertain the suitability of the MODIS 3 km data for air quality application in the region. The contribution of distant aerosols to city aerosol loadings was also assessed with Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backscatter model. A moderate-high aerosol burden (AODs ∼ 0.50) was observed over Ghana with a significant contribution from the pre-monsoon season. City centres of Takoradi and Kumasi showed higher aerosol loads (AODs ∼ 0.80) than Accra and Tamale. The HYSPLIT model showed that distant or transported aerosol sources to the city centres were of both marine and land generated origins. Linear regression analysis between MODIS AOD and AERONET AOD showed a reasonably good correlation of ∼ 0.60 for Aqua and Terra. From the validation analysis, both Aqua and Terra satellites can be used for air quality monitoring over Ghana; however, more ground research must be conducted to ascertain better aerosol model assumptions for the region.

Machine Learning to Estimate Surface Roughness from Satellite Images

We apply the Support Vector Regression (SVR) machine learning model to estimate surface roughness on a large alluvial fan of the Kosi River in the Himalayan Foreland from satellite images. To train the model, we used input features such as radar backscatter values in Vertical–Vertical (VV) and Vertical–Horizontal (VH) polarisation, incidence angle from Sentinel-1, Normalised Difference Vegetation Index (NDVI) from Sentinel-2, and surface elevation from Shuttle Radar Topographic Mission (SRTM). We generated additional features (VH/VV and VH–VV) through a linear data fusion of the existing features. For the training and validation of our model, we conducted a field campaign during 11–20 December 2019. We measured surface roughness at 78 different locations over the entire fan surface using an in-house-developed mechanical pin-profiler. We used the regression tree ensemble approach to assess the relative importance of individual input feature to predict the surface soil roughness from SVR model. We eliminated the irrelevant input features using an iterative backward elimination approach. We then performed feature sensitivity to evaluate the riskiness of the selected features. Finally, we applied the dimension reduction and scaling to minimise the data redundancy and bring them to a similar level. Based on these, we proposed five SVR methods (PCA-NS-SVR, PCA-CM-SVR, PCA-ZM-SVR, PCA-MM-SVR, and PCA-S-SVR). We trained and evaluated the performance of all variants of SVR with a 60:40 ratio using the input features and the in-situ surface roughness. We compared the performance of SVR models with six different benchmark machine learning models (i.e., Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Binary Decision Tree (BDT), Bragging Ensemble Learning, Boosting Ensemble Learning, and Automated Machine Learning (AutoML)). We observed that the PCA-MM-SVR perform better with a coefficient of correlation (R = 0.74), Root Mean Square Error (RMSE = 0.16 cm), and Mean Square Error (MSE = 0.025 cm2). To ensure a fair selection of the machine learning model, we evaluated the Akaike’s Information Criterion (AIC), corrected AIC (AICc), and Bayesian Information Criterion (BIC). We observed that SVR exhibits the lowest values of AIC, corrected AIC, and BIC of all the other methods; this indicates the best goodness-of-fit. Eventually, we also compared the result of PCA-MM-SVR with the surface roughness estimated from different empirical and semi-empirical radar backscatter models. The accuracy of the PCA-MM-SVR model is better than the backscatter models. This study provides a robust approach to measure surface roughness at high spatial and temporal resolutions solely from the satellite data.

Characteristics of suspended sediment in Sentinel-1 synthetic aperture radar observations

ABSTRACT The main purpose of this study is to quantitatively examine the characteristics of suspended sediment on synthetic aperture radar (SAR) observations of sea roughness. In this work, seven Sentinel-1 (S-1) SAR images in 2019 acquired in the Interferometric Wide-swath (IW) mode at vertical-vertical (VV) polarization channel were collected, where Yangtze River freshwater dominants and suspended sediment concentration (SSC) are higher in continental shelf of East China Sea (ECS). These images were collocated with waves simulated from WAVEWATCH-III (WW3) and SSC inverted from the Haiyang-1C (HY-1C) Coastal Zone Imager (CZI) image. The comparison of WW3-simulated SWH yielded a 0.45-m root-mean-square error (RMSE) with a 0.78 correlation (COR) when comparing the simulations with the measurements from Haiyang-2B (HY-2B). The SAR-measured normalized radar cross-section (NRCS) is linearly related with the HY-1 C SSC at SSC > 1200 mg l–1, which is caused by the change of dielectric constant of sea water and viscosity. Moreover, the NRCS is simulated by the three-scale radar backscattering model. It is generally found that HY-1 C SSC enhances the NRCS in difference between simulations and observations up to 8 dB at SSC of 2000 mg l–1, which could distort the accuracy of SAR wind retrieval.

Acoustic Backscatter and Attenuation Due to River Fine Sediments: Experimental Evaluation of Models and Inversion Methods

AbstractThe hydroacoustic monitoring of suspended sediment concentration (SSC) in rivers is based on the inversion of backscatter and attenuation models. To evaluate such models, acoustic backscatter and attenuation were measured from a homogeneous suspension of fine river sediments (clay) in a laboratory tank at various concentrations in the range 1–18 g/l. Agreement between the modeled and measured acoustic backscatter and attenuation values was found to be relatively poor. The results are highly sensitive to particle size and shape which come with large measurement uncertainties and they can be significantly improved by adjusting plausible particle parameters. Various inversion methods combining single or multiple frequencies, analysis of backscatter and/or attenuation, spherical or oblate shape hypothesis for particles and fixed or estimated lognormal grain size distribution are tested. The most promising inversion methods using both backscatter and attenuation information led to accurate SSC estimates.

Subwavelength ultrasonic imaging using a deep convolutional neural network trained on structural noise

Subwavelength ultrasonic imaging (SUI) can detect subwavelength flaws beyond the diffraction limit, however, SUI sometimes fails to clearly reveal flaws in C-scans when the signal-to-noise ratio (SNR) is low. In this work, a convolutional neural network (CNN) that takes structural noise into account is developed for SUI to distinguish flaw echoes from structural noise. The network contains a regression CNN for learning features from the structural noise and a learnable soft thresholding layer for classification. Experiments show that the proposed method performs well for imaging subwavelength flaws at different depths and of different sizes. It achieved an F1 score of 97.69 ± 1.56% in detecting flaws as compared to the enhanced ultrasonic flaw detection method with time-dependent threshold. As an example of general application of the method, we also performed SUI on natural flaws in a spheroidal graphite cast iron specimen. The results show that the method can achieve SUI without a theoretical backscattering model and is not limited by noise distribution, multiple scattering, or complex microstructures. Furthermore, the network does not need to prepare flaw echoes for training.

ASCAT IB: A radar-based vegetation optical depth retrieved from the ASCAT scatterometer satellite

Vegetation optical depth (VOD), as a microwave-based vegetation index for vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. Currently, VOD is mainly retrieved from passive microwave data and few studies focused on VOD retrievals from active microwave data. The Advanced SCATterometer (ASCAT) provides long-term C-band backscatter data at Vertical-Vertical (VV) polarization. In this study, a new ASCAT INRAE Bordeaux (IB) VOD (hereafter, IB VOD), was developed based on the Water Cloud Model (WCM) coupled with the Ulaby linear model for soil backscattering. The main features of IB VOD are that (i) the ERA5-Land soil moisture (SM) dataset was used as an auxiliary SM dataset in the retrievals, (ii) pixel-based soil model parameters were mapped using Random Forest (RF), and (iii) the vegetation model parameter was calibrated for each day. The IB VOD product was retrieved over Africa during 2015–2019, and its performances were evaluated in space and time by comparing with aboveground biomass (AGB), lidar tree height (TH), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and leaf area index (LAI). Results were inter-compared with three other VOD products at the same frequency. In terms of spatial correlation with AGB (R = 0.92) and TH (R = 0.89), IB VOD outperforms the other VOD products, suggesting IB VOD has a strong ability to capture spatial patterns of AGB and TH. By comparing all VOD products against NDVI, EVI and LAI, we found that the highest temporal correlation with NDVI (EVI, LAI) was obtained with IB VOD over 29.94% (36.65%, 30.19%) of the study region. Considering all three vegetations indices, highest temporal correlation values with IB VOD could be particularly noted for deciduous broadleaf forests, woody savannas and savannas.

Modified Backscatter Communication Model for Wireless Communication Network Applications

The green communication and large-scale connection issues will be faced by the wireless communication networks with futuristic sixth generation (6G) technology. The radio-frequency (RF) and spectrum sources may be shared simultaneously to achieve optimal communication in these networks by means of backscatter devices (BD) that may function in constrained spectrums as well as the stringent energy scenarios of green Internet-of-things (IoT) by means of the proposed novel modified backscatter communication model (BCM). Unlicensed eavesdroppers may interfere with the BD due to its vulnerability caused by the wireless communication channels and their broadcasting nature. The intrusion of an unlicensed eavesdropper is detected in an efficient manner by means of the proposed BCM. The analytical derivations of intercept probability (IP) and outage probability (OP) are invoked to analyze the security and reliability of the proposed architecture. Under high main-to-eavesdropper ratio (MER) regime, the IP and under high signal-to-noise ratio (SNR) regime, the OP asymptotic behaviors are estimated additionally. Based on the results of performance evaluation, it is evident that there is a decrease in the security of BD with the increase in MER while there is a simultaneous increase in the legitimate user security. Various system parameters may be adjusted for optimizing the security and reliability performance trade-off. For diverse orders, the existence of error floors are indicated by the non-zero fixed constant of BD and the legitimate user’s OP when high SNR value is observed at the system.

Inversion of bottom parameters using a backscattering model based on the effective density fluid approximation

An inversion method using backscattering strength is used to obtain bottom parameters. A backscattering model based on the effective density fluid approximation was established as the forward model. The objective function used to scale the mismatch between the model predictions and the measured data (obtained by employing two monostatic transducers with narrow directivity) was established on the assumption that the data errors follow a Gaussian distribution. The maximum a posteriori estimations of inversion parameters were obtained by using a two-step hybrid optimization algorithm combining the differential evolution algorithm and the particle swarm optimization algorithm. The uncertainty and the correlation of inversion parameters were presented using marginal probability distributions and a covariance matrix through Bayesian inversion. Using this method, we can acquire not only geoacoustic parameters (including sound speed and attenuation) but also partial physical parameters of marine sediments and statistical character parameters of seafloor roughness and sediment heterogeneity. Finally, the efficiency of this inversion method was verified through comparison between inversion results and sampling data from sand sediment at the bottom of a water tank.

Ka-Band Radar Cross-Section of Breaking Wind Waves

The effective normalized radar cross section (NRCS) of breaking waves, σwb, is empirically derived based on joint synchronized Ka-band radar and video records of the sea surface from a research tower. The σwb is a key parameter that, along with the breaker footprint fraction, Q, defines the contribution of non-polarized backscattering, NP =σwbQ, to the total sea surface NRCS. Combined with the right representation of the regular Bragg and specular backscattering components, the NP component is fundamental to model and interpret sea surface radar measurements. As the first step, the difference between NRCS values for breaking and non-breaking conditions is scaled with the optically-observed Q and compared with the geometric optics model of breaker backscattering. Optically-derived Q might not be optimal to represent the effect of breaking waves on the radar measurements. Alternatively, we rely on the breaking crest length that is firmly detected by the video technique and the empirically estimated breaker decay (inverse wavelength) scale in the direction of breaking wave propagation. A simplified model of breaker NRCS is then proposed using the geometric optics approach. This semi-analytical model parameterizes the along-wave breaker decay from reported breaker roughness spectra, obtained in laboratory experiments with mechanically-generated breakers. These proposed empirical breaker NRCS estimates agree satisfactorily with observations.

Inversion methodology for ultrasonic characterization of polycrystals with clusters of preferentially oriented grains

Titanium alloys are widely used in the aerospace industry. However, due to presence of microtexture, which is characterized by preferred crystallographic orientation clustering of thousands of alpha crystallites, cold dwell fatigue may significantly reduce the part life. To satisfy the practical need for nondestructive microtexture characterization, an inverse ultrasonic methodology is proposed to quantify mean parameters of microtexture regions (MTRs) having ellipsoidal shapes. One limitation of previous model-based ultrasonic inversion methods is required knowledge of elastic constants of the crystallites, which are rarely available for engineering alloys. This study overcomes this constraint by adopting the far field attenuation model, JASA, 137 (5), 2655–2669 (2015), and the backscattering model for ultrasonic wave interaction with microtexture. In the methodology developed, all necessary averaged MTR characteristics are obtained solely from directional ultrasonic measurements (backscattering, attenuation, and velocity) without a prior knowledge of material microstructures or elastic properties of different material phases. The inversion method is illustrated by simulations. To support the inversion methodology, the mean MTR sizes, morphology, and elastic scattering factors are determined from the ultrasonic experiment on a Ti-6242 alloy sample. The inversion results are compared with destructive electron backscatter diffraction (EBSD) analysis from which the MTRs are segmented using a non-contiguous grouping criteria. Good agreement is found.

An Improved Radiative Transfer Model for Polarimetric Backscattering from Agricultural Fields at C- and X-Bands

The first-order vector radiative transfer model (FVRTM) is modified mainly by examining the effects of leaf curvature of vegetation canopies, the higher-order multiple scattering among vegetation scattering particles, and the underlying-surface roughness for forward reflection on radar backscattering from farming fields at C- and X-bands. At first, we collected the backscattering coefficients measured by scatterometers and space-borne synthetic aperture radar (SAR), field-measured ground-truth data sets, and theoretical scattering models for radar backscattering from vegetation fields at microwaves. Then, these effects on the RTM were examined using the database at the C- and X-bands. Finally, an improved RTM was obtained by adjusting its parameters, mainly related with the leaf curvature, the higher-order multiple scattering, and the underlying-surface small-roughness characteristics, and its accuracy was verified by comparisons between the improved RTM and measurement data sets.

Ku- and Ka-Band Ocean Surface Radar Backscatter Model Functions at Low-Incidence Angles Using Full-Swath GPM DPR Data

This paper presents the results of the first characterization of coincident Ku- and Ka-band ocean surface normalized radar cross section measurements at earth incidence angles 0°–18° using one year of wide swath Global Precipitation Measurement (GPM) mission dual frequency precipitation radar (DPR) data. Empirical geophysical model functions were derived for both bands, isotropic and directorial sensitivity were assessed, and finally, sea surface temperature (SST) dependence of radar backscatter, at both bands, were investigated. The Ka-band exhibited higher vector wind sensitivity for a low-to-moderate wind speeds regime, and the SST effects were also observed to be substantially larger at Ka-band than at Ku-band.

Cereal Crops Soil Parameters Retrieval Using L-Band ALOS-2 and C-Band Sentinel-1 Sensors

This paper discusses the potential of L-band Advanced Land Observing Satellite-2 (ALOS-2) and C-band Sentinel-1 radar data for retrieving soil parameters over cereal fields. For this purpose, multi-incidence, multi-polarization and dual-frequency satellite data were acquired simultaneously with in situ measurements collected over a semiarid area, the Merguellil Plain (central Tunisia). The L- and C-band signal sensitivity to soil roughness, moisture and vegetation was investigated. High correlation coefficients were observed between the radar signals and soil roughness values for all processed multi-configurations of ALOS-2 and Sentinel-1 data. The sensitivity of SAR (Synthetic Aperture Radar) data to soil moisture was investigated for three classes of the normalized difference vegetation index (NDVI) (low vegetation cover, medium cover and dense cover), illustrating a decreasing sensitivity with increasing NDVI values. The highest sensitivity to soil moisture under the dense cover class is observed in L-band data. For various vegetation properties (leaf area index (LAI), height of vegetation cover (H) and vegetation water content (VWC)), a strong correlation is observed with the ALOS-2 radar signals (in HH(Horizontal-Horizontal) and HV(Horizontal-Vertical) polarizations). Different empirical models that link radar signals (in the L- and C-bands) to soil moisture and roughness parameters, as well as the semi-empirical Dubois modified model (Dubois-B) and the modified integral equation model (IEM-B), over bare soils are proposed for all polarizations. The results reveal that IEM-B performed a better accuracy comparing to Dubois-B. This analysis is also proposed for covered surfaces using different options provided by the water cloud model (WCM) (with and without the soil–vegetation interaction scattering term) coupled with the best accuracy bare soil backscattering models: IEM-B for co-polarization and empirical models for the entire dataset. Based on the validated backscattering models, different options of coupled models are tested for soil moisture inversion. The integration of a soil–vegetation interaction component in the WCM illustrates a considerable contribution to soil moisture precision in the HV polarization mode in the L-band frequency and a neglected effect on C-band data inversion.

Rain-induced characteristics in C- and X-band synthetic aperture radar observations of tropical cyclones

ABSTRACT This work focused on the rain-induced characteristics of synthetic aperture radar (SAR) at the C- and X-bands. Six SAR images were collected in tropical cyclones: four C-band dual-polarized Sentinel-1 (S-1) images during the 2016 hurricane season and two vertical–vertical) polarized TerraSAR-X (TS-X) images of Hurricane Sandy (2012). Wind speeds were retrieved from the S-1 images at the vertical–horizontal polarization channel, and winds from the TS-X images were obtained from the Hurricane Research Division of the National Oceanic and Atmospheric Administration . Simultaneously, Tropical Rainfall Measuring Mission (TRMM) rain rates, current data from the HYbrid Coordinate Ocean Model and wave spectra simulated by the WAVEWATCH-III (WW3) model were collocated. The three-scale backscattering model was employed to simulate the normalized radar cross-section (NRCS)without considering the rain effect, yielding a 2.21-dB root mean square errorand 2.23-dB for S-1 and TS-X, respectively. The difference between model-simulated and observed NRCS was analysed using the TRMM rain rate. The results indicate a linear relationship of the difference at the X-band with the TRMM rain rate, while the difference at the C-band exhibited a ‘V’ relationship with the TRMM rain rate. It was also discovered that the difference at the X-band decreased with increasing wind speed, while the difference at the C-band increased with increasing wind speed.

Deep Learning for Transient Image Reconstruction from ToF Data

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

Acoustic and In‐Situ Observations of Deep Seafloor Hydrothermal Discharge: An OOI Cabled Array ASHES Vent Field Case Study

AbstractThe Cabled Observatory Vent Imaging Sonar (COVIS) was installed on the Ocean Observatories Initiative's Regional Cabled Array observatory at ASHES hydrothermal vent field on Axial Seamount in July 2018. The acoustic backscatter data recorded by COVIS in August–September 2018, in conjunction with in situ temperature measurements, are used to showcase and verify the use of COVIS for long‐term, quantitative monitoring of hydrothermal discharge. Specifically, sonar data processing generates three‐dimensional backscatter images of the buoyant plumes above major sulfide structures and two‐dimensional maps of diffuse flows within COVIS's field‐of‐view. The backscatter images show substantial changes of plume appearance and orientation that mostly reflect plume bending in the presence of ambient currents and potentially the variations of outflow fluxes. The intensity of acoustic backscatter decreases significantly for highly bent plumes as compared to nearly vertical plumes, reflecting enhanced mixing of plume fluids with seawater driven by ambient currents. A forward model of acoustic backscatter from a buoyancy‐driven plume developed in this study yields a reasonable match with the observation, which paves the way for inversely estimating the source heat flux of a hydrothermal plume from acoustic backscatter measurements. The acoustic observations of diffuse flows show large temporal variations on time scales of hours to days, especially at tidal frequencies, but no apparent long‐term trend. These findings demonstrate COVIS's ability to quantitatively monitor hydrothermal discharge from both focused and diffuse sources to provide the research community with key observational data for studying the linkage of hydrothermal activity with oceanic and geological processes.

Characteristics of Saline Soil in Extremely Arid Regions: A Case Study Using GF-3 and ALOS-2 Quad-Pol SAR Data in Qinghai, China

Soil salinization is a global problem, which seriously damages the ecological environment and considerably reduces agricultural productivity, especially in arid regions. Synthetic aperture radar (SAR) has been widely used in remote sensing due to its weather and sunlight independence. Polarimetric SAR has great potential for large-scale mapping and monitoring salt-affected soils. In this study, we investigate the characteristics of saline soil in extremely arid regions using dual-band quadrature-polarimetric (quad-pol) SAR images acquired by GF-3 (C-band) and ALOS-2 (L-band). Firstly, the effectiveness of the modified dielectric mixing model and integral equation model (IEM) in describing saline soil is evaluated. Secondly, the potential relationships between polarimetric parameters and salinity are discussed in both the C- and L-band, respectively, such as co-polarization ratio, scattering entropy H, and scattering angle α. Finally, a linear regression model for monitoring salt content is established. The main contributions of this article are as follows: (1) Simulation results suggest that the radar backscattering coefficient is a weak function of salinity at low water content, but our experimental data show that soil salinity significantly contributes to the radar backscattering coefficient, which indicates the modified dielectric mixing model and IEM model is not applicable in extremely arid areas. (2) A negative correlation between the co-polarization ratio and salinity is observed, and the correlation coefficients are 0.64 (C-band) and 0.71 (L-band). Besides, scattering entropy and scattering angle exhibit a positive correlation with salinity in the C-band with correlation coefficients 0.686 and 0.669, respectively, whereas a negative correlation is found in the L-band with correlation coefficients 0.682 and 0.680, respectively. This can be attributed to the different penetration depths and sensitivity to the surface roughness of the electromagnetic waves at two frequencies. (3) A regression model for salinity estimating based on radar backscattering coefficient, co-polarization ratio, and scattering entropy is established, with a determination coefficient (R2) of 0.79 and a root mean square error (RMSE) of 6.56%, allowing us to determine soil salinity from quad-pol SAR images without using backscattering models. Therefore, our results can be a reference for future soil salinity monitoring and inversion.

Ex situandin situtarget strength measurements of European anchovy in the Bay of Biscay

AbstractThis study measures the dorsal aspect target strength (TS; dB re 1 m2) and TS–length (standard length, SL; cm) relationships for European anchovy, attained both ex situ and in situ in two different seasons across 7 years in the Bay of Biscay. The measurements were made at three frequently used acoustic frequencies (38, 120, and 200 kHz). A backscattering model for physostome fish was utilized to help interpret the results. The obtained experimental mean TS for anchovies with an SL of 3.5–19.5 cm was −44.6 (±2.3), −46.9 (±3), and −48.4 (±2.7) dB at 38, 120, and 200 kHz, respectively, yielding b20 values of −66.4, −68.7, and −70.4 dB, respectively. The results were consistent across seasons and between in situ and ex situ conditions, presenting TS–length relationships with statistically significant slopes (p-values <0.05) for all frequencies. This research represents part of a series of efforts planned to obtain a comprehensive TS vs. length and depth relationship to update the acoustic assessment methodology of European anchovy in the Bay of Biscay.