Backscatter properties of two-layer phantoms using a high-frequency ultrasound annular array

In a previous study, an annular-array transducer was employed to characterize homogeneous scattering phantoms and excised rat livers using backscatter envelope statistics and frequency domain analysis. A sound field correction method was also applied to take into account the average attenuation of the entire scattering medium. Here, we further generalized the evaluation of backscatter coefficient (BSC) using the annular array in order to study skin tissues with a complicated structure. In layered phantoms composed of two types of media with different scattering characteristics, the BSC was evaluated by the usual attenuation correction method which revealed an expected large difference from the predicted BSC. In order to improve the BSC estimate, a correction method that applied the attenuation of each layer as a reference combined with a method that corrects based on the attenuation of the analysis position were applied. It was found that the method using the average attenuation of each layer is the most effective. This correction method is well adapted to the extended depth of field provided by an annular array.

Two months of measurements with an autonomous thermodynamic Raman lidar in Lindenberg

<p>Between 15 July 2020 and 19 September 2021, the Atmospheric Raman Temperature and Humidity Sounder (ARTHUS) collected data at the Lindenberg Observatory of the Deutscher Wetterdienst (DWD), including temperature and water vapor mixing ratio with a high temporal and range resolution.</p> <p>During the operation period, very stable 24/7 operation was achieved, and ARTHUS demonstrated that is capable to observe the atmospheric boundary layer and lower free troposphere during both daytime and nighttime up to the turbulence scale, with high accuracy and precision, and very short latency. During nighttime, the measurement range increases even up to the tropopause and lower stratosphere.</p> <p>ARTHUS measurements resolve the strength of the inversion layer at the planetary boundary layer top, elevated lids in the free troposphere, and turbulent fluctuations in water vapor and temperature, simultaneously (Lange et al., 2019, Wulfmeyer et al., 2015). In addition to thermodynamic variables, ARTHUS provides also independent profiles of the particle backscatter coefficient and the particle extinction coefficient from the rotational Raman signals at 355 nm with much better resolution than a conventional vibrational Raman lidar.</p> <p>At the conference, highlights of the measurements will be presented. Furthermore, the statistics of more than 150 comparisons with local radiosondes will be presented which confirm the high accuracy of the temperature and moisture measurements of ARTHUS.</p> <p><strong><em>Acknowledgements</em></strong></p> <p>The development of ARTHUS was supported by the Helmholtz Association of German Research Centers within the project Modular Observation Solutions for Earth Systems (MOSES). The measurements in Lindenberg were funded by DWD.</p> <p><strong><em>References </em></strong></p> <p>Lange, D., Behrendt, A., and Wulfmeyer, V. (2019). Compact operational tropospheric water vapor and temperature Raman lidar with turbulence resolution. <em>Geophysical Research Letters</em>, 46. https://doi.org/10.1029/2019GL085774</p> <p>Wulfmeyer, V., R. M. Hardesty, D. D. Turner, A. Behrendt, M. P. Cadeddu, P. Di Girolamo, P. Schlüssel, J. Van Baelen, and F. Zus (2015), A review of the remote sensing of lower tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles, <em>Rev. Geophys.</em>, 53,819–895, doi:10.1002/2014RG000476</p>

Advances in liver US, CT, and MRI: moving toward the future

Over the past two decades, the epidemiology of chronic liver disease has changed with an increase in the prevalence of nonalcoholic fatty liver disease in parallel to the advent of curative treatments for hepatitis C. Recent developments provided new tools for diagnosis and monitoring of liver diseases based on ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI), as applied for assessing steatosis, fibrosis, and focal lesions. This narrative review aims to discuss the emerging approaches for qualitative and quantitative liver imaging, focusing on those expected to become adopted in clinical practice in the next 5 to 10 years. While radiomics is an emerging tool for many of these applications, dedicated techniques have been investigated for US (controlled attenuation parameter, backscatter coefficient, elastography methods such as point shear wave elastography [pSWE] and transient elastography [TE], novel Doppler techniques, and three-dimensional contrast-enhanced ultrasound [3D-CEUS]), CT (dual-energy, spectral photon counting, extracellular volume fraction, perfusion, and surface nodularity), and MRI (proton density fat fraction [PDFF], elastography [MRE], contrast enhancement index, relative enhancement, T1 mapping on the hepatobiliary phase, perfusion). Concurrently, the advent of abbreviated MRI protocols will help fulfill an increasing number of examination requests in an era of healthcare resource constraints.

A Novel Method for Layover Detection in Mountainous Areas with SAR Images

It is well known that there are geometric distortions in synthetic aperture radar (SAR) images when the terrain undulates. Layover is the most common one, which brings challenges to the application of SAR remote sensing. This study proposes a novel detection method that is mainly aimed at the layover caused by mountains and can be performed with only medium-resolution SAR images and no other auxiliary data. The detection includes the following four stages: initial processing, difference image calculation and rough and fine layover detection. Initial processing mainly obtains the potential layover areas, which are mixed with the built-up areas after classification. Additionally, according to the analysis of the backscatter coefficient (BC) of various ground objects with different polarization images, the layover areas are detected step-by-step from the mixed areas, in which the region-based FCM segmentation algorithm and spatial relationship criteria are used. Taking the Danjiangkou Reservoir area as the study area, the relevant experiments with Sentinel-1A SAR images were conducted. The quantitative analysis of detection results adopted the figure of merit (FoM), and the highest accuracy was up to 87.6% of one selected validation region. Experiments in the South Taihang area also showed the satisfactory effect of layover detection, and the values of FoM were all above 85%. These results show that the proposed method can do well in the layover detection caused by mountains. Its simplicity and effectiveness are helpful in removing the influence of layover on SAR image applications to a certain extent and improving the development of SAR remote sensing technology.

Evaluation of the Performance of Time-Series Sentinel-1 Data for Discriminating Rock Units

The potential use of time-series Sentinel-1 synthetic aperture radar (SAR) data for rock unit discrimination has never been explored in previous studies. Here, we employed time-series Sentinel-1 data to discriminate Dananhu formation, Xinjiang group, Granite, Wusu group, Xishanyao formation, and Diorite in Xinjiang, China. Firstly, the temporal variation of the backscatter metrics (backscatter coefficient and coherence) from April to October derived from Sentinel-1, was analyzed. Then, the significant differences of the time-series SAR metrics among different rock units were checked using the Kruskal–Wallis rank sum test and Tukey’s honest significant difference test. Finally, random forest models were used to discriminate rock units. As for the input features, there were four groups: (1) time-series backscatter metrics, (2) single-date backscatter metrics, (3) time-series backscatter metrics at VV, and (4) VH channel. In each feature group, there were three sub-groups: backscatter coefficient, coherence, and combined use of backscatter coefficient and coherence. Our results showed that time-series Sentinel-1 data could improve the discrimination accuracy by roughly 9% (from 55.4% to 64.4%), compared to single-date Sentinel-1 data. Both VV and VH polarization provided comparable results. Coherence complements the backscatter coefficient when discriminating rock units. Among the six rock units, the Granite and Xinjiang group can be better differentiated than the other four rock units. Though the result still leaves space for improvement, this study further demonstrates the great potential of time-series Sentinel-1 data for rock unit discrimination.

The characterization of long-range transported North American biomass burning plumes: what can a multi-wavelength Mie-Raman-polarization-fluorescence lidar provide?

This article presents a study of long-range transported biomass burning aerosols (BBA) originated from the North American wildfires in September 2020. The BBA plumes presented in this study were in the troposphere and underwent 1–2 weeks aging before arriving at the observations site. A novel dataset 2α+3β+3δ+φ (α: extinction coefficient, β: backscatter coefficient, δ: particle linear depolarization ratio, PLDR, φ: fluorescence capacity) derived from lidar observations is provided for the characterization of long-range transported BBA. The fluorescence capacity describes the ability of aerosols in producing fluorescence when exposed to UV excitation. In the observations of BBA episode, plumes from different wildfire activities have been characterized. In the BBA plumes, we detected low PLDRs, i.e. lower than 0.03 at all wavelengths, as well as enhanced PLDRs (PLDR355,532,1064 ≈ 0.15–0.18, 0.12–0.14, 0.01–0.02) with a similar spectral dependence that had been observed in the aged BBA plumes in the upper troposphere and lower stratosphere (Canadian smoke in 2017 and Australian smoke in 2019–2020). Obvious variations in Angström exponent (−0.3–1.5), lidar ratios (20–50 sr at 355 nm, 42–90 sr at 532 nm) and fluorescence capacity (1.0 × 10−4– 4.0 × 10−4) are also observed during the BBA episode. These variations are coupled with the variation of altitudes, water vapor content and wildfire events. It reflects that the properties of aged BBA particles are highly varied and depend on complex mechanisms, such as burning process and the aging process. The results also pointed out the inhomogeneity of the aging process in the BBA plumes, which means that particles in the core of the plume aged differently with those at the plume edge due to the impact of water vapor, temperatures, particle concentration and so on. These chemical and physical processes involved in BBA aging and how they could impact the particle properties are not yet well understood. In addition, our observations identified the ice crystals mixing with BBA particles, which indicates that BBA could act as ice nucleating particles (INP) at tropospheric conditions. The lidar fluorescence proves to be an efficient tool in studying the interaction of clouds and BBAs due to its high sensitivity. Recent studies claimed that aged BBA particles are more effective INPs than they were thought. As BBAs are becoming an important atmospheric aerosols with growing global wildfires, our observations could improve our characterization about aged BBA particles and the understanding of their importance in ice cloud formation.

Comparison of Field and SAR-Derived Descriptors in the Retrieval of Soil Moisture from Oil Palm Crops Using PALSAR-2

Synthetic-aperture radar’s (SAR’s) capacity to resolve the cloud cover concerns encountered while gathering optical data has tremendous potential for soil moisture data retrieval using SAR data. It is possible to use SAR data to recover soil moisture because the backscatter coefficient is sensitive to both soil and vegetation by penetrating through the vegetation layer. This study investigated the feasibility of employing a SAR-derived radar vegetation index (RVI), the ratios of the backscatter coefficients using polarizations of HH/HV (RHH/HV) and HV/HH (RHH/HV) to an oil palm crops as vegetation indicators in the water cloud model (WCM) using phased-array L-band SAR-2 (PALSAR-2). These data were compared to the manual leaf area index (LAI) and a physical soil sampling method for computing soil moisture. The field data included the LAI input parameters and, more importantly, physical soil samples from which to calculate the soil moisture. The fieldwork was carried out in Chuping District, Perlis State, Malaysia. Corresponding PALSAR-2 data were collected on three observation dates in 2019: 17 January, 16 April, and 9 July. The results showed that the WCM modeled using the LAI under HV polarization demonstrated promising accuracy, with the root mean square error recorded as 0.033 m3/m3. This was comparable to the RVI and RHH/HV under HV polarization, which had accuracies of 0.031 and 0.049 m3/m3, respectively. The findings of this study suggest that SAR-based indicators, RHH/HV and RVI using PALSAR-2, can be used to reduce field-related input in the retrieval of soil moisture data using the WCM for oil palm crop.

Soil Moisture Retrieval Using Microwave Remote Sensing Data and a Deep Belief Network in the Naqu Region of the Tibetan Plateau

Soil moisture plays an important role in the land surface model. In this paper, a method of using VV polarization Sentinel-1 SAR and Landsat optical data to retrieve soil moisture data was proposed by combining the water cloud model (WCM) and the deep belief network (DBN). Since the simple combination of training data in the neural network cannot effectively improve the accuracy of the soil moisture inversion results, a WCM physical model was used to eliminate the effect of vegetation cover on the ground backscatter, in order to obtain the bare soil backscatter coefficient. This improved the correlation of ground soil backscatter characteristics with soil moisture. A DBN soil moisture inversion model based on the bare soil backscatter coefficients as the foundation training data combined with radar incidence angle and terrain factors obtained good inversion results. Studies in the Naqu area of the Tibetan Plateau showed that vegetation cover had a significant effect on the soil moisture, and the goodness of fit (R2) between the backscatter coefficient and soil moisture before and after the elimination of vegetation cover was 0.38 and 0.50, respectively. The correlation between the backscatter coefficient and the soil moisture was improved after eliminating the vegetation cover. The inversion results of the DBN soil moisture model were further improved through iterative parameters. The model prediction reached its highest level of accuracy when the restricted Boltzmann machine (RBM) was set to seven layers, the bias and R were 0.007 and 0.88, respectively. Ten-fold cross-validation showed that the DBN soil moisture model performed stably with different data. The prediction was further improved when the bare soil backscatter coefficient was used as the training data. The mean values of the root mean square error (RMSE), the inequality coefficient (TIC), and the mean absolute percent error (MAPE) were 0.023, 0.09, and 11.13, respectively.

Investigation of the Anisotropic Patterns in the Altimeter Backscatter Measurements Over Ocean Wave Surfaces

This article attempts to analyze the influence of the anisotropic effects of the ocean wave surface on SAR altimetry backscatter coefficient (Sigma-0) measurements, which has not been intensively addressed in publications. Data of Sentinel-3A, Cryosat-2, and Jason-3 altimeters allocated by the WW3 numeric wave model were analyzed, and the patterns of Sigma-0 with respect to the wave direction were acquired under ∼2 m significant wave height. The ocean waves were classified into six categories, among which the moderate swell and short win-wave cases were analyzed intensively. Swell-dominated ocean surface shows less randomness than the wind-wave-dominated ocean surface. Clear and significant sinusoid trends are found in the Sigma-0 and SSB patterns of both operational modes (SAR mode and PLRM mode) of the Sentinel-3A altimeter for the moderate swell case, indicating the sensitivity of Sigma-0 and SSB measurements to the anisotropic features of the altimeter measurements. The anisotropic pattern in the Sentinel-3A PLRM Sigma-0 is somewhat counterintuitive, but the analysis of Jason-3 altimeter data would show similar results. Additionally, by comparing the anisotropic patterns of two orthogonally polarized SAR altimeters (Sentinel-3A and Cryosat-2), we could draw the conclusion that the Sigma-0 measurements are not sensitive to the polarization mode. As for the SSHA patterns, no clear sinusoid could be identified for the moderate swell. A possible explanation is that the SSB pattern may be overwhelmed in the complicated factors that can influence the SSHA pattern.

Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees

In oil palm crop, soil fertility is less important than the physical soil characteristics. It is important to have a balance and sufficient soil moisture to sustain high yields in oil palm plantations. However, conventional methods of soil moisture determination are laborious and time-consuming with limited coverage and accuracy. In this research, we evaluated synthetic aperture radar (SAR) and in-situ observations at an oil palm plantation to determine SAR signal sensitivity to oil palm crop by means of water cloud model (WCM) inversion for retrieving soil moisture from L-band HH and HV polarized data. The effects of vegetation on backscattering coefficients were evaluated by comparing Leaf Area Index (LAI), Leaf Water Area Index (LWAI) and Normalized Plant Water Content (NPWC). The results showed that HV polarization effectively simulated backscatter coefficient as compared to HH polarization where the best fit was obtained by taking the LAI as a vegetation descriptor. The HV polarization with the LAI indicator was able to retrieve soil moisture content with an accuracy of at least 80%.