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Introduction: Remotely-sensed acoustic backscatter is an indispensable tool for seabed mapping, among other disciplines. Almost a decade after the GeoHab Backscatter Working Group published its guidelines and recommendations report, new technologies, new challenges and new questions have emerged. Given the range of potential backscatter research avenues, it can be difficult to align research programs with the priorities of the community of practice. Prioritization of backscatter research topics is thus necessary to establish a roadmap for acoustic backscatter research efforts. Methods: We asked the international community working with acoustic backscatter to submit their priority research questions over a 5- to 10-year horizon. We analyzed and curated a total of 177 research questions from 73 contributors, and the resulting 104 questions were grouped into eight broad recurring themes: "Technologies", "Calibration", "Data acquisition and ground-truthing", "Data processing", "Post-processing, quality control, data handling, and curation", "Data analysis", "Data interpretation", and "Applications and end uses". A follow-up survey based on the final list of questions was distributed to characterize the community working with backscatter and to identify key research priorities. Results: A total of 120 responses originating from 23 countries were used for the analyses. Most respondents were researchers (68%), while others were technicians (25%) or department or program managers (11%), among other roles. Affiliations of respondents included academia (43%), governmental agencies (37%), and industry/private sector (18%). After scaling the responses, the most commonly selected theme was "Post-processing, quality control, data handling, and curation", followed by "Calibration" and "Data analysis". Respondents consistently ranked several research questions as priorities. The two questions that were identified as priorities by over 25% of respondents were "How can we move towards absolute calibration of different systems to allow interregional comparisons?", and "How can we quantify seafloor backscatter quality and develop standards similar to what exists with bathymetry?". Discussion: All eight themes are represented in the top 10 priority questions, underscoring the need for contributions to backscatter research from multiple perspectives to advance the field. The ranking of priority questions encourages collaboration within the community and will serve as a roadmap for backscatter research programs over the next decade. [ABSTRACT FROM AUTHOR]

Purpose: This retrospective study was conducted to investigate the diagnostic accuracy of ultrasound-derived fat fraction (UDFF) for grading hepatic steatosis using liver histology as the reference standard. Methods: Seventy-three patients with liver disease were assessed using UDFF and liver biopsy. Pearson's test and the Bland–Altman plot were used to assess the correlation between UDFF and histological fat content in liver sections. The UDFF cutoff values for histologically proven steatosis grades were determined using the area under the receiver operating characteristic curve (AUROC). Results: The median age of the patients was 66 (interquartile range 54–74) years, and 33 (45%) were females. The UDFF values showed a stepwise increase with increasing steatosis grade (p <.001) and were strongly correlated with the histological fat content (r =.7736, p <.001). The Bland–Altman plot revealed a mean bias of 2.384% (95% limit of agreement, − 6.582 to 11.351%) between them. Univariate regression analysis revealed no significant predictors of divergence. The AUROCs for distinguishing steatosis grades of ≥ 1, ≥2, and 3 were 0.956 (95% confidence interval [CI], 0.910–1.00), 0.926 (95% CI, 0.860–0.993), and 0.971 (95% CI, 0.929–1.000), respectively. The UDFF cutoff value of > 6% had a sensitivity and specificity of 94.8% and 82.3%, respectively, for diagnosing steatosis grade ≥ 1. There was no association between UDFF and the fibrosis stage. Conclusion: UDFF shows strong agreement with the histological fat content and excellent diagnostic accuracy for grading steatosis. UDFF is a promising tool for detecting and quantifying hepatic steatosis in clinical practice. [ABSTRACT FROM AUTHOR]

Grasslands, being vital ecosystems with significant ecological and socio-economic importance, have been the subject of increasing attention due to their role in biodiversity conservation, carbon sequestration, and agricultural productivity. However, accurately classifying grassland management intensity, namely extensive and intensive practices, remains challenging, especially across large spatial extents. This research article presents a comprehensive investigation into the classification of grassland management intensity in two distinct regions of Poland, NUTS2 - namely Podlaskie (PL84) and Wielkopolskie (PL41), by integrating data from Sentinel-1 and Sentinel-2 satellite imagery. The study leverages the unique capabilities of Sentinel-1, a radar satellite, and Sentinel-2, an optical multispectral satellite, to overcome the limitations of using a single data source. Preprocessed Sentinel-1 and Sentinel-2 data were combined to extract spectral and textural features, providing valuable insights into grassland characteristics and patterns. Supervised classification using the Random Forest algorithm was used, and ground truth data from field surveys facilitated the creation of training samples. In Podlaskie, extensive grasslands achieved an overall accuracy (OA) of 84%, while intensive grasslands attained an OA of 83%. In Wielkopolskie, extensive grasslands exhibited an OA of 84%, while intensive grasslands achieved an OA of 83%. Additionally, the classification metrics, including user's accuracy (UA), F1 score, and producer's accuracy (PA), further highlighted the variations in classification accuracy. This comprehensive mapping of grassland management intensity using combined Sentinel-1 and Sentinel-2 data provides valuable insights for conservation agencies, agricultural stakeholders, and land managers. The study's findings contribute to sustainable land management and decision-making processes, facilitating the identification of ecologically valuable areas, optimizing agricultural productivity, and assessing the impacts of different management strategies. Furthermore, the research highlights the potential of Sentinel missions for grassland monitoring and emphasizes the importance of advanced remote sensing techniques for understanding and preserving these crucial ecosystems. [ABSTRACT FROM AUTHOR]

In the 6G mobile networks, ensuring low latency and low energy consumption is paramount. This study explores a novel approach for addressing these issues in a backscatter communication (BC) - based multiple user unmanned aerial vehicle (UAV) - enabled mobile edge computing (MEC) Internet of Things (IoT) network. Our proposed framework incorporates a partial offloading strategy, a time division multiple access (TDMA) scheme, and a radio frequency energy harvesting mechanism. We use the channel gains statistical characteristics to derive approximate closed-form expressions for the successful computation and energy outage probabilities. Using these benchmarks, we investigate the impact of critical parameters such as transmit power, number of sensor nodes, task division ratio, the altitude of the UAV, and threshold tolerance. We validate our analysis through computer simulations and provide results to support our findings. The study reveals that selecting an optimal UAV altitude can significantly improve latency and energy consumption performance. [ABSTRACT FROM AUTHOR]

High-resolution mapping of deep-sea polymetallic nodules is needed (a) to understand the reasons behind their patchy distribution, (b) to associate nodule coverage with benthic fauna occurrences, and (c) to enable an accurate resource estimation and mining path planning. This study used an autonomous underwater vehicle to map 37 km2 of a geomorphologically complex site in the Eastern Clarion–Clipperton Fracture Zone. A multibeam echosounder system (MBES) at 400 kHz and a side scan sonar at 230 kHz were used to investigate the nodule backscatter response. More than 30,000 seafloor images were analyzed to obtain the nodule coverage and train five machine learning (ML) algorithms: generalized linear models, generalized additive models, support vector machines, random forests (RFs) and neural networks (NNs). All models ML yielded similar maps of nodule coverage with differences occurring in the range of predicted values, particularly at parts with irregular topography. RFs had the best fit and NNs had the worst spatial transferability. Attention was given to the interpretability of model outputs using variable importance ranking across all models, partial dependence plots and domain knowledge. The nodule coverage is higher on relatively flat seafloor (< 3°) with eastward-facing slopes. The most important predictor was the MBES backscatter, particularly from incident angles between 25 and 55°. Bathymetry, slope, and slope orientation were important geomorphological predictors. For the first time, at a water depth of 4500 m, orthophoto-mosaics and image-derived digital elevation models with 2-mm and 5-mm spatial resolutions supported the geomorphological analysis, interpretation of polymetallic nodules occurrences, and backscatter response. [ABSTRACT FROM AUTHOR]

We show that analyzing archived and future multibeam backscatter and bathymetry data, in tandem with regional environmental parameters, can help to identify polymetallic nodule fields in the world's oceans. Extensive archived multibeam transit data through remote areas of the world's oceans are available for data mining. New multibeam data will be made available through the Seabed 2030 Project. Uniformity of along- and across-track backscatter, backscatter intensity, angular response, water depth, nearby ground-truth data, local slope, sedimentation rate, and seafloor age provide thresholds for discriminating areas that are permissive to nodule presence. A case study of this methodology is presented, using archived multibeam data from a remote section of the South Pacific along the Foundation Seamounts between the Selkirk paleomicroplate and East Pacific Rise, that were collected during the 1997 Foundation–Hotline expedition on R/V Atalante. The 12 kHz Simrad EM12D multibeam data and the other forementioned data strongly suggest that a previously unknown nodule occurrence exists along the expedition transit. We also compare the utility of three different backscatter products to demonstrate that scans of printed backscatter maps can be a useful substitute for digital backscatter mosaics calculated using primary multibeam data files. We show that this expeditious analysis of legacy multibeam data could characterize benthic habitat types efficiently in remote deep-ocean areas, prior to more time-consuming and expensive video and sample acquisition surveys. Additionally, utilizing software other than specialty sonar processing programs during this research allows an exploration of how multibeam data products could be interrogated by a broader range of scientists and data users. Future mapping, video, and sampling cruises in this area would test our prediction and investigate how far it might extend to the north and south. [ABSTRACT FROM AUTHOR]

The validation of seafloor scattering models used for seabed characterisation requires quantifying the contributions from the sediment interface and volume to the total acoustic returns. At low‐frequencies, direct measurements of sediment volume scattering have rarely been made, due to the bias in interface roughness scattering caused by large beamwidths of low‐frequency sonars. Endfire Synthetic Aperture Sonar (EF‐SAS) can achieve narrower beamwidths by forming a vertically oriented synthetic array as a transmitter and/or receiver and moving it through the water column. The narrower beamwidths achieved by EF‐SAS allow for more accurate measurements of volume scattering by reducing interface scattering bias in acoustic returns. The application of EF‐SAS for sediment characterisation is explored for the first time. The authors demonstrate that EF‐SAS can be used to construct the angular response curve for both interface and volume scattering as well as to estimate the attenuation and reflection coefficients, which can be inverted for grain size. [ABSTRACT FROM AUTHOR]

In quantitative analysis of seafloor scattering measurements, it is common to model the single‐point probability density function of the scattered intensity or amplitude. For more complex seafloors, the pixel amplitude distribution has previously been modelled with a mixture model consisting of two K distributions, but the environment may have more identifiable scattering mechanisms. Choosing the number of components of a mixture model is a decision that must be made, using a priori information, or using a data driven approach. Several common model selection techniques from the statistics literature are explored (the Akaike, Bayesian, deviance, and Watanabe‐Akaike information criteria) and compared to the authors' choice. Examples are given for synthetic aperture sonar data collected by an autonomous underwater vehicle in a rocky environment off the coast of Bergen, Norway, using the HISAS‐1032 synthetic aperture sonar system. The Bayesian information criterion aligned most closely with the interpretation of both the acoustic images and the plots of the probability of false alarm. [ABSTRACT FROM AUTHOR]

Ocean mesoscale eddies are often poorly represented in climate models, and therefore, their effects on the large scale circulation must be parameterized. Traditional parameterizations, which represent the bulk effect of the unresolved eddies, can be improved with new subgrid models learned directly from data. Zanna and Bolton (2020), https://doi.org/10.1029/2020gl088376 (ZB20) applied an equation‐discovery algorithm to reveal an interpretable expression parameterizing the subgrid momentum fluxes by mesoscale eddies through the components of the velocity‐gradient tensor. In this work, we implement the ZB20 parameterization into the primitive‐equation GFDL MOM6 ocean model and test it in two idealized configurations with significantly different dynamical regimes and topography. The original parameterization was found to generate excessive numerical noise near the grid scale. We propose two filtering approaches to avoid the numerical issues and additionally enhance the strength of large‐scale energy backscatter. The filtered ZB20 parameterizations led to improved climatological mean state and energy distributions, compared to the current state‐of‐the‐art energy backscatter parameterizations. The filtered ZB20 parameterizations are scale‐aware and, consequently, can be used with a single value of the non‐dimensional scaling coefficient for a range of resolutions. The successful application of the filtered ZB20 parameterizations to parameterize mesoscale eddies in two idealized configurations offers a promising opportunity to reduce long‐standing biases in global ocean simulations in future studies. Plain Language Summary: This research focuses on improving the accuracy of ocean models by addressing the challenges of representing the mesoscale eddies on coarse grids. These eddies play a crucial role in the Earth's climate system, but traditional climate models struggle to capture their effects. Here, we implemented a new data‐driven parameterization simulating the physics of the mesoscale eddies into the state‐of‐the‐art ocean model. The parameterization is interpretable and captures key physical processes related to the mesoscale eddies known as energy backscatter. We tested this parameterization in two idealized ocean scenarios and found that it significantly improves the biases in the representation of the mean state and energetics. We propose new filtering schemes which improve the physical and numerical properties of the parameterization. Accurate representation of the mesoscale eddies by the present scheme has the potential to resolve long‐standing biases present in global ocean models and thus allow for more reliable climate simulations. Key Points: A data‐driven mesoscale eddy parameterization is implemented and evaluated in different configurations of the GFDL MOM6 ocean modelWe introduce filtering schemes to reduce the generation of grid‐scale noise and enhance the large‐scale backscatterThe subgrid parameterization improves the representation of the energy distributions and the climatological mean state [ABSTRACT FROM AUTHOR]

Accurate and high-speed object movement tracking systems often face significant challenges due to signal instability caused by the object's movement and rotation. To address these issues, we present STABack, a novel object tracking system using backscatter tags and accelerometer sensors, designed for high-accuracy, high-speed movement tracking. We developed an amplitude stabilization algorithm which uses envelope detection to reduce the impact of high-frequency movement on the signal, and uses dynamic threshold output to lower the BER in backscatter demodulation. Our method reduces the BER by 0.3757 compared to the regular demodulation method, resulting in a final BER of 0.07. Our evaluation of the STABack prototype shows that it achieves a median distance measurement accuracy of 6.45 cm with a standard deviation of 6.95 cm, under the condition of a speed of 120 cm. The attitude angle estimation's mean error is under 7 degrees. The system's accuracy in detecting the target object's trajectory is as high as 99%, and it can still decode with a bit error rate of no more than 0.034 at a speed of 166 cm/s. The power consumption of our system prototype is only 38.54 μW based on our experimental results. Overall, our results demonstrate that STABack can accurately estimate the movement and rotation of target objects in unstable backscatter channels. [ABSTRACT FROM AUTHOR]

Prior to use in operational systems, it is essential to validate ionospheric models in a manner relevant to their intended application to ensure satisfactory performance. For Over‐the‐Horizon radars (OTHR) operating in the high‐frequency (HF) band (3–30 MHz), the problem of model validation is severe when used in Coordinate Registration (CR) and Frequency Management Systems (FMS). It is imperative that the full error characteristics of models is well understood in these applications due to the critical relationship they impose on system performance. To better understand model performance in the context of OTHR, we introduce an ionospheric model validation technique using the oblique ground backscatter measurements in soundings from the Super Dual Auroral Radar Network (SuperDARN). Analysis is performed in terms of the F‐region leading edge (LE) errors and assessment of range‐elevation distributions using calibrated interferometer data. This technique is demonstrated by validating the International Reference Ionosphere (IRI) 2016 for January and June in both 2014 and 2018. LE RMS errors of 100–400 km and 400–800 km are observed for winter and summer months, respectively. Evening errors regularly exceeding 1,000 km across all months are identified. Ionosonde driven corrections to the IRI‐2016 peak parameters provide improvements of 200–800 km to the LE, with the greatest improvements observed during the nighttime. Diagnostics of echo distributions indicate consistent underestimates in model NmF2 during the daytime hours of June 2014 due to offsets of −8° being observed in modeled elevation angles at 18:00 and 21:00 UT. Plain Language Summary: Models of the ionized upper atmosphere, a region known as the ionosphere, must be validated using appropriate techniques prior to their use in operational systems. This is of greatest importance for Over‐the‐Horizon radars (OTHR) that rely on the reflection of radio waves in the 3–30 MHz band from the ionosphere for their operation. The accuracy of OTHR is largely related to the performance of the model ionosphere used to establish target positions, and so it is essential to understand how models behave under different circumstances. We introduce a new technique for validating models using measurements from the Super Dual Auroral Radar Network (SuperDARN) of research radars. Using a dominant feature present within these radar echoes, we perform an example validation of the International Reference Ionosphere (IRI) 2016 by modeling the expected path of radio waves. The performance is seen to be best during winter and typically worse in the evening. Using further information present within the measurements, we diagnose the likely cause of errors to be due to underestimates in a key model parameter. This is confirmed when we offset model parameters using direct measurements of the ionosphere and observe a significant improvement in model performance. Key Points: We introduce an ionospheric model validation technique using SuperDARN ground backscatterPerformance of the IRI‐2016 is best during the daytime of January 2014 and 2018, whilst sporadic‐E in June causes significant degradationsIRI‐2016 range errors are seen to be most significant near the terminator and during the nighttime [ABSTRACT FROM AUTHOR]

Wireless sensor networks and nodes are faced with severe constraints in power capacity and lifespan, especially in harsh and cold environments. Electromagnetic radiation energy harvesters serve as a promising alternative power source for sensor nodes. However, the power output from reported energy harvesters remains limited, emphasizing the critical need to reduce power consumption in sensor nodes. Here, a miniaturized, low‐voltage, low‐power active frequency selective surface solution is proposed for backscatter communication under ice. It is designed for the 2.4 GHz wireless band and can operate under extreme conditions, such as sub‐ice environments, requiring only a 3.3 V control voltage and consuming approximately 0.3 μW$\upmu\text{W}$ of energy. Experimental results in sub‐ice scenarios demonstrate its reliable reflection control characteristics, making it particularly suitable for backscatter communication nodes in wireless sensor networks deployed in harsh environments. [ABSTRACT FROM AUTHOR]

Measuring the size of suspended particles in water is crucial in related fields such as environmental engineering, marine engineering, and hydraulic engineering. Considering the size distribution of suspended particles in real rivers, the amplitude (Amp) of the backscattering of particles with sizes ranging from 0.006 mm to 0.030 mm was analysed in this research using a lightweight ultrasonic meter developed by the authors and four probes with bandwidths ranging from 3.0 MHz to 30.0 MHz. In the analysis of Amp measurements for different particle sizes, using 0.008 mm as the reference particle size and converting to the rest of the particle sizes, if the Rayleigh scattering condition is satisfied between the particle sizes and the probe frequency, the conversion error value will be between −10% and 10%. This verifies the correctness of the theoretically derived particle size-Amp theory (the positive power relationship between the particle sizes and the Amp) and reflects the validity constraints of designing this experimental setup. The measurement method based on particle backscattering Amp analysis utilised in this study will help to achieve real-time measurements of suspended particles at river sites. [ABSTRACT FROM AUTHOR]

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Backscatter communications, which originated from World War II, have been widely applied in the logistics domain, and recently attract emerging interest from both academic and industrial circles. Here, the backscatter communication systems equipped with retrodirective arrays that can re‐transmit the impinging signals back toward the direction of incidence are studied so as to reduce the power loss of the signals. Specifically, the authors consider the tag is equipped with retrodirective arrays to improve reliability and enhance communication range. The probability density function of channel coefficients is then derived. Next, a channel estimator based on Bayesian theory is proposed to acquire the modulus values of channel parameters and calculate its Bayesian Cramer–Rao Lower Bound. Finally, simulation results are provided to corroborate these theoretical studies. [ABSTRACT FROM AUTHOR]

Mesoscale eddies modulate the stratification, mixing, tracer transport, and dissipation pathways of oceanic flows over a wide range of spatiotemporal scales. The parameterization of buoyancy and momentum fluxes associated with mesoscale eddies thus presents an evolving challenge for ocean modelers, particularly as modern climate models approach eddy‐permitting resolutions. Here we present a parameterization targeting such resolutions through the use of a subgrid mesoscale eddy kinetic energy budget (MEKE) framework. Our study presents two novel insights: (a) both the potential and kinetic energy effects of eddies may be parameterized via a kinetic energy backscatter, with no Gent‐McWilliams along‐isopycnal transport; (b) a dominant factor in ensuring a physically‐accurate backscatter is the vertical structure of the parameterized momentum fluxes. We present simulations of 1/2° and 1/4° resolution idealized models with backscatter applied to the equivalent barotropic mode. Remarkably, the global kinetic and potential energies, isopycnal structure, and vertical energy partitioning show significantly improved agreement with a 1/32° reference solution. Our work provides guidance on how to parameterize mesoscale eddy effects in the challenging eddy‐permitting regime. Plain Language Summary: Ocean eddies evolving on horizontal lengthscales of order 10–100 km are not sufficiently resolved in modern global ocean models that have horizontal resolutions of about 25–100 km. The under‐representation of such eddies leads to inaccuracies in the modeled ocean state, including weakened current systems, incorrect stratification, and erroneous distributions of physical and biological ocean tracers. Here we develop a novel approach to mimicking the unresolved eddy effects by artificially energizing the flow in a way that is consistent with eddy dynamics, specifically their vertical structure. We find that our approach is able to correct for a variety of unresolved eddy effects when employed in a coarse‐resolution ocean model and compared against a high‐resolution reference case. Our work provides new insights on how to account for unresolved eddies in the next generation of climate models. Key Points: We propose a parameterization for mesoscale eddies targeting ocean models at eddy‐permitting resolutionsWe find that both the potential and kinetic energy effects of eddies on the flow may be parameterized via a kinetic energy backscatterThe novel approach of our backscatter scheme is its use of vertical structure, that is, backscattering onto the equivalent barotropic mode [ABSTRACT FROM AUTHOR]

Snow cover and runoff play an important role in the Arctic environment, which is increasingly affected by climate change. Over the past 30 years, winter temperatures in northern Sweden have risen by 2 °C, accompanied by an increase in precipitation. This has led to a higher incidence of thaw–freeze and rain-on-snow events. Snow properties, such as the snow depth and longevity, and the timing of snowmelt in spring significantly impact the alpine tundra vegetation. The emergent vegetation at the edge of the snow patches during spring and summer constitutes an essential nutrient supply for reindeer. We have used Sentinel-1 synthetic aperture radar (SAR) to determine the onset of the surface melt and the end of the snow cover in the core reindeer grazing area of the Laevás Sámi reindeer-herding community in northern Sweden. Using SAR data from March to August during the period 2017 to 2021, the start of the surface melt is identified by detecting the season's backscatter minimum. The end of the snow cover is determined using a threshold approach. A comparison between the results of the analysis of the end of the snow cover from Sentinel-1 and in situ measurements, for the years 2017 to 2020, derived from an automatic weather station located in Laevásvággi reveals a 2- to 10-day difference in the snow-free ground conditions, which indicates that the method can be used to investigate when the ground is free of snow. VH data are preferred to VV data due to the former's lower sensitivity to temporary wetting events. The outcomes from the season backscatter minimum demonstrate a distinct 25-day difference in the start of the runoff between the 5 investigated years. The backscatter minimum and threshold-based method used here serves as a valuable complement to global snowmelt monitoring. [ABSTRACT FROM AUTHOR]

With the proliferation of smart devices, the Internet of Things (IoT) is rapidly expanding. This study proposes a miniaturized controllable metamaterial with low control voltage for achieving low-power and compact designs in IoT node devices. Operating at a target frequency of 2.4 GHz, the proposed metamaterial requires only a 3.3 V control voltage and occupies approximately one-third of the wavelength in size. Experimental validation demonstrates its excellent reflective control performance, positioning it as an ideal choice for low-power IoT systems, particularly in the context of miniaturized and low-power IoT node applications. [ABSTRACT FROM AUTHOR]

The accurate mapping of seafloor substrate types plays a major role in understanding the distribution of benthic marine communities and planning a sustainable exploitation of marine resources. Traditionally, this activity has relied on the efforts of marine geology experts, who accomplish it manually by examining information from acoustic data along with the available ground- truth samples. However, this approach is challenging and time-consuming. Hence, it is important to explore automatic methods to replace this manual process. In this study, we investigated the potential of deep learning (U-Net) for classifying the seabed as either "bedrock" or "non-bedrock" using bathymetry and/or backscatter data, acquired with multibeam echosounders (MBES). Slope and hillshade data, derived from the bathymetry, were also included in the experiment. Several U-Net models, taking as input either one of these datasets or a combination of them, were trained using an expert delineated map as reference. The analysis revealed that U-Net has the ability to map bedrock and non-bedrock areas reliably. On our test set, the models using either bathymetry or slope data showed the highest performance metrics and the best visual match with the reference map. We also observed that they often identified topographically rough features as bedrock, which were not interpreted as such by the human expert. While such discrepancy would typically be considered an error of the model, the scale of the expert annotations as well as the different methods used by the experts to manually generate maps must be considered when evaluating the predictions quality. While encouraging results were obtained here, further research is necessary to explore the potential of deep learning in mapping other seabed types and evaluating the models' generalization capabilities on similar datasets but different geographical locations. [ABSTRACT FROM AUTHOR]

The use of multiple synthetic aperture radar polarizations can improve biomass estimations compared to using a single polarization. In this study, we compared predictions of aboveground biomass change from ALOS-2 PALSAR-2 backscatter using linear regression based on (1) the cross-polarization channels, (2) the co- and cross-polarizations from fully polarimetric SAR, (3) Freeman–Durden polarimetric decomposition, and (4) the polarimetric radar vegetation index (RVI). Additionally, the impact of forest structure on the sensitivity of the polarimetric backscatter to AGB and AGB change was assessed. The biomass consisted of mainly coniferous trees at the hemi-boreal test site Remningstorp, located in southern Sweden. We found some improvements in the predictions when quad-polarized data (RMSE = 79.4 tons/ha) were used instead of solely cross-polarized data (RMSE = 84.9 tons/ha). However, when using Freeman–Durden decomposition, the prediction accuracy improved further (RMSE = 69.7 tons/ha), and the highest accuracy was obtained with the radar vegetation index (RMSE = 50.4 tons/ha). The corresponding R2 values ranged from 0.45 to 0.82. The bias was less than 1 t/ha for all models. An analysis of forest variables showed that the sensitivity to AGB was reduced for high values of basal-area-weighted mean height, basal area, and stem density when predicting absolute AGB, but the best change prediction model was sensitive to changes larger than the apparent saturation point for AGB state estimates. We conclude that by using fully polarimetric SAR images, forest biomass changes can be estimated more accurately compared to using single- or dual-polarization images. The results were improved the most (in terms of RMSE and R2) by using the Freeman–Durden decomposition model or the RVI, which captured especially the large changes better. [ABSTRACT FROM AUTHOR]

BLE-based (Bluetooth Low Energy-based) backscatter has received considerable attention, as it aims to communicate with everyday smart devices such as smartphones, smartwatches, and tablets in passive IoT. The state-of-the-art BLE backscatter systems enable communication using a specialized continuous wave (CW) generator or entirely using commodity BLE 4.0 radios as an RF source. However, the existing BLE communication systems suffer from several key issues, including a short carrier length and a large frequency shift. This paper presents a passive BLE (PBLE) backscatter communication system that utilizes commodity BLE 5.0 radios. The system uses a BLE 5.0 extended advertising packet with partial single tones as excitations transmitting on the secondary advertising channel of BLE 5.0, and the BLE backscatter tag produces bandpass frequency-shift keying modulation at 1 Mb/s, which enables compatibility with BLE advertising channels. The prototype is implemented using an NRF52832 BLE 5.0 commodity chip, smart devices, and tags with FPGAS and chips. In FPGA board-level verification, when the downlink distance is 0.5 m, the uplink distance can reach 10 m. In chip testing, the uplink distance can reach 7 m when the downlink distance is 1 m. The baseband power consumption is 2 μW, with a total power consumption of 10 μW. This system eliminates the need for expensive and costly specialized RF sources, unlike the BLE backscatter communication system that uses a specialized CW generator. Compared to the BLE backscatter communication system that uses commodity BLE 4.0 radios, this system reduces the minimum frequency shift from 24 MHz to 2 MHz and increases the length of the single tones as a CW by a factor of about seven, from 31 bytes to 254 bytes. [ABSTRACT FROM AUTHOR]

Recent works have achieved considerable success in improving the concurrency of backscatter network. However, they do not optimize the balance between throughput and spectrum occupancy, both of which serve as pivotal parameters in concurrent transmissions. Moreover, these works also introduce complex components on tag thereby increasing both power consumption and deployment costs. In this article, we propose Spray, a tag-lightweight system to achieve high throughput and narrow-band occupancy with low power. The key idea is to incorporate an agile channel allocating and scheduling mechanism into the backscatter network. This approach allows for efficient spectrum utilization and concurrency without the need for energy-intensive components. To optimize throughput in the presence of the challenge of harmonic interference, we introduce a novel algorithm that determines the channels with an optimal combination of central frequencies and bandwidths. Additionally, we propose a fair scheduling strategy to ensure equitable transmission opportunities for all tags. We prototype the Spray tag using commercial off-the-shelf components and implement the excitation and receiver with software-defined radio platform. Our evaluation shows that the system supports 30 parallel tags transmitting in the bandwidth of 600 kHz and the throughput can reach more than 280 kbps. [ABSTRACT FROM AUTHOR]

Electron beams are often used to treat superficial lesions of the lip, cheek,nose, and ear.Lead is frequently used to block distal structures.It is customary to place an internal bolus of low atomic number in between the tissue and the lead to reduce electron backscatter from the lead. Space for the lead and the internal bolus is quite limited.A previous method for estimating the thickness of the lead plus internal bolus is not self-consistent and leads to a larger than necessary thickness. A new method is described here to provide a quick, accurate, and self-consistent estimate of the minimum necessary thickness of the internal bolus and the lead for incident electron beam energies of 4, 6, 8, 9, and 10 MeV as a function of the thickness of the overlying tissue. This method limits the dose enhancement at the tissue/bolus interface due to the underlying lead to 10%.Measurements made with gafchromic film validate this methodology. [ABSTRACT FROM AUTHOR]

The triterpene squalene is widely used in the food, cosmetics and pharmaceutical industries due to its antioxidant, antistatic and anti-carcinogenic properties. It is usually obtained from the liver of deep sea sharks, which are facing extinction. Alternative production organisms are marine protists from the family Thraustochytriaceae, which produce and store large quantities of various lipids. Squalene accumulation in thraustochytrids is complex, as it is an intermediate in sterol biosynthesis. Its conversion to squalene 2,3-epoxide is the first step in sterol synthesis and is heavily oxygen dependent. Hence, the oxygen supply during cultivation was investigated in our study. In shake flask cultivations, a reduced oxygen supply led to increased squalene and decreased sterol contents and yields. Oxygen-limited conditions were applied to bioreactor scale, where squalene accumulation and growth of Schizochytrium sp. S31 was determined in batch, fed-batch and continuous cultivation. The highest dry matter (32.03 g/L) was obtained during fed-batch cultivation, whereas batch cultivation yielded the highest biomass productivity (0.2 g/L*h−1). Squalene accumulation benefited from keeping the microorganisms in the growth phase. Therefore, the highest squalene content of 39.67 ± 1.34 mg/g was achieved by continuous cultivation (D = 0.025 h−1) and the highest squalene yield of 1131 mg/L during fed-batch cultivation. Volumetric and specific squalene productivity both reached maxima in the continuous cultivation at D = 0.025 h−1 (6.94 ± 0.27 mg/L*h−1 and 1.00 ± 0.03 mg/g*h−1, respectively). Thus, the choice of a suitable cultivation method under oxygen-limiting conditions depends heavily on the process requirements. Key points: • Measurements of respiratory activity and backscatter light of thraustochytrids • Oxygen limitation increased squalene accumulation in Schizochytrium sp. S31 • Comparison of different cultivation methods under oxygen-limiting conditions [ABSTRACT FROM AUTHOR]

This paper proposes to combat active eavesdropping using intelligent reflecting surface (IRS) backscatter techniques. To be specific, the source (Alice) sends the confidential information to the intended user (Bob), while the eavesdropper (Willie) transmits a jamming signal to interrupt the transmission for more data interception. To enhance the secrecy, an IRS is deployed and connected with Alice through fiber to transform the jamming signal into the confidential signal by employing backscatter techniques. Based on the considered model, an optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio (SINR) at Bob under the constraints of the transmit power at Alice, the reflection vector at the IRS, and the allowable maximum the SINR at Willie. To address the optimization problem, an alternate optimization algorithm is developed. The simulation results verify the achievable secrecy gain of the proposed scheme. The proposed scheme is effective in combating active eavesdropping. Furthermore, the deployment of large-scale IRS significantly enhances the secrecy rate. [ABSTRACT FROM AUTHOR]

The triterpene squalene is widely used in the food, cosmetics and pharmaceutical industries due to its antioxidant, antistatic and anti-carcinogenic properties. It is usually obtained from the liver of deep sea sharks, which are facing extinction. Alternative production organisms are marine protists from the family Thraustochytriaceae, which produce and store large quantities of various lipids. Squalene accumulation in thraustochytrids is complex, as it is an intermediate in sterol biosynthesis. Its conversion to squalene 2,3-epoxide is the first step in sterol synthesis and is heavily oxygen dependent. Hence, the oxygen supply during cultivation was investigated in our study. In shake flask cultivations, a reduced oxygen supply led to increased squalene and decreased sterol contents and yields. Oxygen-limited conditions were applied to bioreactor scale, where squalene accumulation and growth of Schizochytrium sp. S31 was determined in batch, fed-batch and continuous cultivation. The highest dry matter (32.03 g/L) was obtained during fed-batch cultivation, whereas batch cultivation yielded the highest biomass productivity (0.2 g/L*h−1). Squalene accumulation benefited from keeping the microorganisms in the growth phase. Therefore, the highest squalene content of 39.67 ± 1.34 mg/g was achieved by continuous cultivation (D = 0.025 h−1) and the highest squalene yield of 1131 mg/L during fed-batch cultivation. Volumetric and specific squalene productivity both reached maxima in the continuous cultivation at D = 0.025 h−1 (6.94 ± 0.27 mg/L*h−1 and 1.00 ± 0.03 mg/g*h−1, respectively). Thus, the choice of a suitable cultivation method under oxygen-limiting conditions depends heavily on the process requirements. Key points: • Measurements of respiratory activity and backscatter light of thraustochytrids • Oxygen limitation increased squalene accumulation in Schizochytrium sp. S31 • Comparison of different cultivation methods under oxygen-limiting conditions [ABSTRACT FROM AUTHOR]

Radio Frequency Identification (RFID) is a burgeoning technology pivotal for optimizing intricate supply chains. Although its potential benefits in healthcare and pharmaceuticals are substantial, RFID implementation in these sectors has been slow-moving. Surprisingly, limited research has probed RFID performance on pharmaceutical products and their packaging. This study aims to explore the influence of diverse liquid drug formulations on RFID performance, utilizing a pre-optimized Ultra High Frequency (UHF) RFID tag. Five distinct drug formulations, encompassing water, saline, and lipids, were studied, alongside deionized (DI) water and empty vials as control subjects. The products were transferred to separate vials, each affixed with a pre-optimized UHF RFID tag. Performance evaluations, including Threshold Sweep, Orientation Sweep, and Population Analysis, were conducted to assess tag readability, focusing on sensitivity, backscatter, and theoretical read range. The results underscore how the composition of different pharmaceutical drug formulations significantly impact RFID tag readability in a controlled, reflection-free environment. These findings provide valuable insights for designing intelligent packaging to enhance the readability of RFID-tagged pharmaceutical products. [ABSTRACT FROM AUTHOR]

Parkinson, F.; Douglas, K.; Li, Z.; Meijer, A.; Stacey, C.D.; Kung, R., and Podhorodeski, A., 2024. A generalized semiautomated method for seabed geology classification using multibeam data and maximum likelihood classification. Journal of Coastal Research, 40(1), 1–16. Charlotte (North Carolina), ISSN 0749-0208. This paper presents a GIS-based model to perform semiautomated seabed classification that can act as a first-pass, pseudoclassified surficial geological map. The user can then edit the output into a finalized map in less time than by manual classification. The model uses maximum likelihood classification with unsupervised classification through iterative self-organizing clusters. This model is fully contained within the ArcGIS software suite as a ModelBuilder workflow composed of geoprocessing tools and Python script tools. Model inputs tested include different combinations of multibeam echosounder–derived data: slope, backscatter, and terrain ruggedness. Furthermore, to test the assumption of Gaussian distribution of input data required for maximum likelihood classification, Box–Cox power transformations were applied to slope and backscatter data and were used as model inputs. To illustrate the performance of the model, two locations are highlighted as case studies: Milbanke Sound and Spiller Channel, located on the central coast of British Columbia, Canada. Association between model outputs and ground-truth classes was generally weak to moderate when measured using Cramér's V association scores. Overall, the slope and backscatter parameter model had the highest scores of association. Results from an overlay analysis comparing model outputs with user-confirmed polygons show that the slope and backscatter model performs best in regions with distinct changes in the hardness of sediments but that in fjord regions dominated geologically by steeper bathymetric change, the slope parameter model may perform better. However, all model outputs had difficulty delineating bedrock units. The model has the flexibility to identify certain seabed habitat features as well, including glass sponge reefs—biologically active bioherms that have led to marine protected area designations in other areas of British Columbia. [ABSTRACT FROM AUTHOR]

Ongoing backscatter communications and localisation research have been able to obtain incredibly accurate results in controlled environments. The main issue with these systems is faced in complex RF environments. This paper investigates concurrent localization and ambient radio frequency (RF) energy harvesting using backscatter communication systems for Internet of Things networks. Dynamic real-world environments introduce complexity from multipath reflection and shadowing, as well as interference from movements. A machine learning framework leveraging K-Nearest Neighbors and Random Forest classifiers creates robustness against such variability. Historically, received signal measurements construct a location fingerprint database resilient to perturbations. The Random Forest model demonstrates precise localization across customized benches with programmable shuffling of chairs outfitted with RF identification tags. Average precision accuracy exceeds 99% despite deliberate placement modifications, inducing signal fluctuations emulating mobility and clutter. Significantly, directional antennas can harvest over −3 dBm, while even omnidirectional antennas provide −10 dBm—both suitable for perpetually replenishing low-energy electronics. Consequently, the intelligent backscatter platform localizes unmodified objects to customizable precision while promoting self-sustainability. [ABSTRACT FROM AUTHOR]

Purpose: To compare corneal densitometry in a consecutive series of 52 endothelial keratoplasties (DMEK/DSAEK) using a Scheimpflug-based device after six months of follow-up. Methods: Corneal densitometry (CD) values of 102 eyes were divided into three main groups: 33 DMEKs, 19 DSAEKs, and 50 healthy eyes without previous ocular surgery. The CD values were then analyzed and compared between the groups. We measured three main layers in depth and four different concentric zones at 1, 3, and 6 months postoperatively. Results: In the DMEK group, total CD significantly decreased from 38.02 ± 10.21 grayscale units (GSU) to 31.13 ± 9.25 GSU (P < 0.01) between the first and the sixth month postoperative. In the DSAEK group, we found significant changes only between the first and three months after surgery (from 42.62 ± 9.31 GSU to 38.71 ± 10.53 GSU (P < 0.01). Regarding the concentric zones, CD in the DMEK group significantly decreased in the central zone from 33.55 ± 12.07 GSU to 30.63 ± 10.15 GSU (P < 0.01) and significantly increased in the periphery from 30.63 ± 10.15 GSU to 36.72 ± 9.37 GSU, (P < 0.01). The DSAEK group showed no changes in the central zone (from 36.91 ± 13.80 GSU to 36.14 ± 11.47 GSU, P = 0.52) and CD significantly increased in the periphery (41.91 ± 9.28 GSU, P < 0.01). Conclusion: When comparing CD values in DMEK versus DSAEK, we found no differences by layers or at central-paracentral concentric zones, although CD differences in the peripheral zones were statistically significant. This finding may be attributed to the thicker graft at periphery with a delayed clearance and less anatomical interphase in DSAEK. [ABSTRACT FROM AUTHOR]

This document is a corrigendum for an article titled "Seabed classification of multibeam echosounder data into bedrock/non-bedrock using deep learning." The corrigendum addresses minor inaccuracies in the article, specifically regarding metrics values for multiple-input models and misclassification values in tables. The corrections have been made to the tables and corresponding confusion matrices. The authors apologize for the errors and state that they do not change the scientific conclusions of the article. [Extracted from the article]

Backscatter communication (BackCom), the underlying technology for modern‐day Radio Frequency Identification, has been studied as a promising solution for future ultra‐low power Internet‐of‐Things (IoT) applications. Its development has pushed the performance boundaries significantly, in terms of communication distance, data transmission rate, and power consumption. An up‐to‐date review of one branch of BackCom systems, namely Ambient BackCom, which utilises the already available ambient signals, instead of a dedicated radio frequency carrier in most of the BackCom works, to establish BackCom links is conducted. This further reduces the cost and complexity of the system and opens an opportunity for mass deployment. [ABSTRACT FROM AUTHOR]

With the advent of the 5G era, radio frequency identification (RFID) has been widely applied in various fields as one of the key technologies for the Internet of Things (IoT) to realize the Internet of Everything (IoE). In recent years, RFID-based motion sensing has emerged as an important research area with great potential for development. In this paper, an RFID backscatter sport motion sensing scheme is proposed, which effectively solves the multi-classification problem by using the received signal strength (RSS) of the backscattered RFID and the error correcting output coding (ECOC)-based support vector machine (SVM). We conduct extensive experiments to validate the effectiveness of the proposed scheme, in which the signal intensities of different types of action poses are collected and the SVM is used as the classification algorithm to achieve high classification accuracies. [ABSTRACT FROM AUTHOR]

The retrieval of the biophysical parameters and subsequent estimation of the above-ground biomass (AGB) of vegetation stands are made possible by the simulation of the extinction and scattering components from the canopy layer using vector radiative transfer (VRT) theory-based scattering models. With the use of such a model, this study aims to evaluate and compare the potential of dual-pol, multi-frequency SAR data for estimating above-ground biomass. The data selected for this work are L-band dual polarized (HH/HV) ALOS-2 data, S-band dual polarized (HH/HV) NovaSAR data, and C-band dual polarized (VV/VH) Sentinel-1 data. The two key biophysical parameters, tree height, and trunk radius are retrieved using the proposed methodology, applying the frequencies independently. A general allometric equation with vegetation-specific coefficients is used to estimate the AGB from the retrieved biophysical parameters. The retrieval results are validated using ground truth measurements collected from the study area. The L-band, with the coefficient of determination ( R 2 ) of 0.73 and the root mean square error (RMSE) of 35.90 t/ha, has the best correlation between the modeled and field AGBs, followed by the S-band with an R 2 of 0.37 and an RMSE of 63.37 t/ha, and the C-band with an R 2 of 0.25 and an RMSE of 72.32 t/ha. The L-band has yielded improved estimates of AGB in regression analysis as well, with an R 2 of 0.48 and an RMSE of 50.02 t/ha, compared to the S- and C-bands, which have the R 2 of 0.12 and 0.03 and the RMSE of 70.98 t/ha and 80.84 t/ha, respectively. [ABSTRACT FROM AUTHOR]

Concepts for two cooperative active reflector tags (ARTs) for joint ranging and communication in a linear frequency modulated continuous wave secondary radar system are presented along with corresponding theoretical derivations. To prove the concepts, the ARTs operating from 24 to 24.5 GHz are implemented using custom integrated superregenerative amplifiers (SRAs) designed in a 130 nm SiGe BiCMOS technology. The SRAs have a measured minimum detected input power greater than −80 dBm, and an output power greater than 3.2 dBm after wire‐bonding. The ARTs also feature roll invariance using bandwidth enhanced microstrip patch antenna diversity and a novel quench frequency shift keying based low data rate simplex communication. Indoor ranging experiments done to prove the concepts and the theoretical considerations show a maximum distance of 77 m in the ranging mode and a data rate of 0.5 symbols per second in the data transfer mode. For distances less than 5 m, the characterised accuracy is better than 12 cm and the precision is better than 2 cm. To the best of authors' knowledge this is one of the first reported scientific works leading to a proof of concept for integrated superregenerative amplifier based joint ranging and quench frequency shift keying. The combination of maximum range and precision are also one of the best reported among similar works in the literature. [ABSTRACT FROM AUTHOR]

Satellite image analysis is a potentially powerful tool for monitoring coastal shoreline positions. This study explores the use of multi-temporal, dual-polarised Sentinel-1 GRD synthetic aperture radar (SAR) imagery with a spatial resolution of 10 m for delineating shorelines. It was conducted in a data-deficient and complex environment (the Niger delta of Nigeria), in a developing country with a cloud-heavy climate. The study focuses on exploring and testing the capability of using multitemporal waterlines from SAR images to derive shoreline positions at high and low tidal states. From 54 Sentinel-1 images recorded in 2017, the study selected 12 images to represent both high and low tidal states. These were spread across the wet and dry seasons in order to account for seasonal differences. Shoreline positions were obtained by identifying the land–water boundary via segmentation using histogram-minimum thresholding, vectorizing and smoothing that boundary, and averaging its position over multiple waterlines. The land–water segmentation had an overall accuracy of 95–99%. It showed differences between wet and dry season shoreline positions in areas dominated by complex creek networks, but similarities along open coasts. The SAR-derived shorelines deviated from the reference lines by a maximum of 43 m (approximately four pixels), and often less than 10 m (one pixel) in most locations (open coast, estuarine, complex creek networks) at high and low tides, except low tide lines in areas with extensive inter-tidal flats at shorelines 70 m to 370 m from the reference lines. However, for applications such as coastal vulnerability assessment, the high tide shoreline is of greater importance. Thus, depending on the application of interest, problems with low tide shoreline delineation may be irrelevant. Despite limitations, notably the relatively small number of images available that were recorded at high or low tide, the method provides a simple, objective, and cost-effective approach to monitoring shorelines at high and low tide. [ABSTRACT FROM AUTHOR]

The traditional convolutional neural network (CNN)-based methods usually use the spatial information in the amplitude of complex synthetic aperture radar (SAR) images. Several studies have started to concentrate on merging the unique physical properties of SAR images, such as DSN-v1, extracting the backscattering characteristic from the frequency domain. Although DSN-v1 has obtained impressive classification ability, there is some room for improvement. In this letter, DSN-v2 is proposed to boost the classification ability of man-made and natural objects in SAR images. The improvement is reflected in two aspects. First, a multiscale subband feature extraction (MSFE) component is designed for natural objects. Since we observe their multiscale subband spectrum is significantly different, multiple encoders are used to extract effective features. Second, the additive angular margin (AAM) loss is introduced to distinguish man-made objects more clearly by manually adding a margin to the decision boundary. The experimental results on the Sentinel-1 (S1) dataset show that DSN-v2 achieves superior classification performance and model training speed compared with DSN-v1. [ABSTRACT FROM AUTHOR]

In recent years, the application of deep learning for water-body detection in synthetic aperture radar (SAR) images has seen extensive development. However, a significant proportion of these works primarily concentrate on enhancing and optimizing the model structure, with inadequate exploration of the potential impact of hyperparameter settings, a critical determinant of model performance. Thus, to fully exploit the power of deep learning in water-body detection from SAR images, this letter presents a diversified optimization strategy that revolves around the dung beetle optimizer-convolutional neural network (DBO-CNN) model, complemented by characteristic fusion and decision-level fusion. The DBO-CNN model employs the DBO algorithm to search for the optimal hyperparameter of the CNN model for bolstering the performance of water-body detection in SAR images. To further enhance the performance, the DBO-CNN model uses unique input data which is constructed by integrating the polarimetric characteristic obtained from $H/\alpha $ and model-based (MB) polarization decomposition methods with backscatter characteristics. Finally, two decision-level fusion methods are proposed to optimize detection results, enhancing the recall and intersection over union (IoU) to 96.5% and 91.5%, respectively. In summary, Spaceborne SAR images, with the application of polarization decomposition and neural network, provide new insights and in-depth understanding for detecting water-body. [ABSTRACT FROM AUTHOR]

Reliable and accurate rice mapping using synthetic aperture radar (SAR) in cloudy and rainy areas is essential for achieving the United Nations Sustainable Development Goal 2 of 2030. An interpretable deep learning SAR rice area mapping method is proposed in this letter to suppress the interference of wetlands and other land covers to multitemporal SAR rice area mapping and improve the accuracy and confidence of the “black box” deep learning model results. Combining the temporal backscattering coefficients and interferometric coherence, three interpretable temporal features are extracted to effectively distinguish rice. Then, the explainable feature-aware network (XFANet), which can provide the learned importance weights of the normalization methods as self-interpretation, is constructed, and the pixel-wise gradient-weighted class activation mapping (PGCAM) post-hoc interpretation method is introduced to interpret the feature variation within the model. The experimental results in the Kampong Chhang and Kampong Chham provinces of Cambodia show that the proposed three interpretable features well suppressed the wetland disturbance to rice. With high interpretability, the overall accuracy (OA) of XFANet reaches 93.43%. [ABSTRACT FROM AUTHOR]

Heavy rain is associated with strong winds and extreme waves in a tropical cyclone (TC). In this letter, a practical algorithm for rain rate retrieval in TCs is proposed through 24 dual-polarized [vertical-vertical (VV) and vertical-horizontal (VH)] Sentinel-1 (S-1) synthetic aperture radar (SAR) images acquired in interferometric-wide (IW) swath mode, in which 13 images are collocated with the observations from stepped-frequency microwave radiometers (SFMRs). TC winds are directly obtained from VH-polarized images utilizing the geophysical model function (GMF) S-1 IW mode wind speed retrieval model after noise removal (S1IW.NR). The normalized radar cross section (NRCS) at VV-polarization channel is simulated using GMF CMOD5N and VH-polarized SAR wind. It is found that the difference between the simulated NRCSs and measurements from SAR is linearly related to the rain rate and oscillates with the incidence angle. Following this finding, an empirical algorithm for SAR rain rate retrieval is developed, denoted as CRAIN2_S1, which considers the influence of the radius of the maximum wind speed. The proposed algorithm is applied to 11 images in the dataset, and the validation of the rain rate (up to 35 mm/hr) against the products from global precipitation measurements (GPMs) has a root mean square error (RMSE) of 1.74 mm/hr, a correlation coefficient of 0.92 and a scatter index (SI) of 0.29. Collectively, it is concluded that the algorithm CRAIN2_S1 can be practically applied for dual-polarized SAR rain rate retrieval without any external information. [ABSTRACT FROM AUTHOR]

The accuracy of soil moisture estimated from synthetic aperture radar (SAR) backscatter data at high resolutions is limited by speckle. A common practice to mitigate speckle is to multilook the data prior to retrieving soil moisture. While multilooking indeed reduces speckle, it also decreases the spatial resolution and removes possibly useful high resolution information from the data. We, therefore, hypothesized that using higher resolution backscatter data for soil moisture retrieval would lead to higher retrieval accuracies. A high resolution field study combined with a synthetic experiment showed that calculating soil moisture prior to multilooking to the final target resolution calculate-then-average (CtA) has substantial advantages over the average-then-calculate (AtC) approach. Currently, the AtC strategy is most often applied in soil moisture studies, mainly due to its computational advantage compared to the CtA approach. We show that by making use of a higher source resolution backscatter data than the target resolution, we could improve soil moisture retrieval over an agricultural field. [ABSTRACT FROM AUTHOR]

Forest fire not only seriously affects the stability of the forest ecosystem but also threatens the safety of human life and property. The previously burned areas extracting method mainly focuses on optical images, susceptible to cloud and fog objective environmental factors. Although there are related studies on the threshold segmentation of single SAR feature types, further information mining for SAR image data, e.g., backscattering intensity, polarization decomposition, texture, and other features, is still insufficient. Therefore, this letter proposes a burned area extracting method using polarization and texture features of Sentinel-1A images to combine and comprehensively mine various SAR feature change information caused by forest fires with a random forest (RF) model. For validation purposes, we compared the burned areas’ extracted results with the reference data acquired based on Sentinel-2A optical imagery. The comparative results show that the SAR extraction results highly agree with the reference data, with an accuracy of 87.12%, and the commission and omission errors were 20.44% and 12.88%, respectively. The proposed machine learning method helps extract fire areas covered by thick smoke or persistent clouds and provides a reference to related research. [ABSTRACT FROM AUTHOR]

This letter describes a model-based algorithm for estimating tree height and other bio-physical land parameters from time series of synthetic aperture radar (SAR) interferometric coherence and backscatter supported by sparse lidar data. The random-motion-over-ground model (RMoG) is extended to time series and revisited to capture the short- and long-term temporal coherence variability caused by motion of the scatterers and changes in the soil and canopy backscatter. The proposed retrieval algorithm estimates first the spatially slow-varying RMoG model parameters using sparse lidar data, and subsequently the spatially fast-varying model parameters such as tree height. The recently published global Sentinel-1 (S-1) interferometric coherence and backscatter data set and sparse spaceborne GEDI lidar data are used to illustrate the algorithm. Results obtained for a small region over Spain show that the temporal coherence and backscatter time series have the potential to be used for global, model-based land parameter estimation. [ABSTRACT FROM AUTHOR]

Methods to assess ultrasound backscatter anisotropy from clinical array transducers have recently been developed. However, they do not provide information about the anisotropy of microstructural features of the specimens. This work develops a simple geometric model, referred to as the secant model, of backscatter coefficient anisotropy. Specifically, we evaluate anisotropy of the frequency dependence of the backscatter coefficient parameterized in terms of effective scatterer size. We assess the model in phantoms with known scattering sources and in a skeletal muscle, a well-known anisotropic tissue. We demonstrate that the secant model can determine the orientation of the anisotropic scatterers, as well as accurately determining effective scatterer sizes, and it may classify isotropic versus anisotropic scatterers. The secant model may find utility in monitoring disease progression as well as characterizing normal tissue architectures. [ABSTRACT FROM AUTHOR]

Currently, climate change requires the quantification of carbon stored in forest biomass. Synthetic aperture radar (SAR) data offers a significant advantage over other remote detection measurement methods in providing structural and biomass-related information about ecosystems. This study aimed to develop non-parametric Random Forest regression models to assess the changes in the aboveground forest biomass (AGB), basal area (G), and tree density (N) of Mediterranean pine forests by integrating ALOS-PALSAR, Sentinel 1, and Landsat 8 data. Variables selected from the Random Forest models were related to NDVI and optical textural variables. For 2015, the biomass models with the highest performance integrated ALS-ALOS2-Sentinel 1-Landsat 8 data (R2 = 0.59) by following the model using ALS data (R2 = 0.56), and ALOS2-Sentinel 1-Landsat 8 (R2 = 0.50). The validation set showed that R2 values vary from 0.55 (ALOS2-Sentinel 1-Landsat 8) to 0.60 (ALS-ALOS2-Sentinel 1-Landsat 8 model) with RMSE below 20 Mg ha−1. It is noteworthy that the individual Sentinel 1 (R2 = 0.49). and Landsat 8 (R2 = 0.47) models yielded equivalent results. For 2020, the AGB model ALOS2-Sentinel 1-Landsat 8 had a performance of R2 = 0.55 (validation R2 = 0.70) and a RMSE of 9.93 Mg ha−1. For the 2015 forest structural variables, Random Forest models, including ALOS PAL-SAR 2-Sentinel 1 Landsat 8 explained between 30% and 55% of the total variance, and for the 2020 models, they explained between 25% and 55%. Maps of the forests' structural variables were generated for 2015 and 2020 to assess the changes during this period using the ALOS PALSAR 2-Sentinel 1-Landsat 8 model. Aboveground biomass (AGB), diameter at breast height (dbh), and dominant height (Ho) maps were consistent throughout the entire study area. However, the Random Forest models underestimated higher biomass levels (>100 Mg ha−1) and overestimated moderate biomass levels (30–45 Mg ha−1). The AGB change map showed values ranging from gains of 43.3 Mg ha−1 to losses of −68.8 Mg ha−1 during the study period. The integration of open-access satellite optical and SAR data can significantly enhance AGB estimates to achieve consistent and long-term monitoring of forest carbon dynamics. [ABSTRACT FROM AUTHOR]

The measurement of suspended sediments from acoustic backscatter was originally developed in marine science for monitoring near‐bottom suspensions usually composed of sand particles. In the last decade, there has been a growing interest in adapting these techniques to rivers. The so‐called "solid particle theory" developed in oceanography mainly applies to suspensions of non‐cohesive solid particles producing incoherent backscatter signal. So far, this theory has been used for interpreting river backscatter even if it relies on assumptions that are not obviously met in rivers. This study uses a set of measurements made on the Rhône River in France to discuss the typical issues which challenge the interpretation of sound backscattering for monitoring suspended sediments in rivers. Large discrepancies between model outputs and measurements for frequencies lower than 2.5 MHz suggest that other scattering processes including flocculation and air micro‐bubbles may have a large impact on acoustic backscatter and attenuation. Deviations of the backscatter echo distribution from Rayleigh statistics were observed, suggesting that the assumption of incoherent backscattering is not always met. This work calls for the development of a more complete theory for interpreting river backscatter. Key Points: Modeling the acoustic backscatter of suspended sediments in rivers often results in large errorsVarious sources of error may arise from the diversity and complexity of scatterers in riversSpecific experiments and theoretical developments are required to identify and reduce modeling errors [ABSTRACT FROM AUTHOR]