Search Results (6)

Synthetic Aperture Radar (SAR) is one of the few instruments capable of providing high-resolution global two-dimensional (2D) measurements of ocean waves. Since 2014 and then 2016, the Sentinel-1A/B satellites, whenever operating in a specific wave mode (WV), have been providing ocean swell spectrum data as Level-2 (L2) OCeaN products (OCN), derived through a quasi-linear inversion process. This WV acquires small SAR images of 20 × 20 km footprints alternating between two sub-beams, WV1 and WV2, with incidence angles of approximately 23° and 36°, respectively, to capture ocean surface dynamics. The SAR imaging process is influenced by various modulations, including hydrodynamic, tilt, and velocity bunching. While hydrodynamic and tilt modulations can be approximated as linear processes, velocity bunching introduces significant distortion due to the satellite’s relative motion with respect to the ocean surface and leads to constructive but also destructive effects on the wave imaging process. Due to the associated azimuth cut-off, the quasi-linear inversion primarily detects ocean swells with, on average, wavelengths longer than 200 m in the SAR azimuth direction, limiting the resolution of smaller-scale wave features in azimuth but reaching 10 m resolution along range. The 2D spectral partitioning technique used in the Sentinel-1 WV OCN product separates different swell systems, known as partitions, based on their frequency, directional, and spectral characteristics. The accuracy of these partitions can be affected by several factors, including non-linear effects, large-scale surface features, and the relative direction of the swell peak to the satellite’s flight path. To address these challenges, this study proposes a novel quality control framework using a machine learning (ML) approach to develop a quality flag (QF) parameter associated with each swell partition provided in the OCN products. By pairing collocated data from Sentinel-1 (S1) and WaveWatch III (WW3) partitions, the QF parameter assigns each SAR-derived swell partition one of five quality levels: “very good,” “good,” “medium,” “low,” or “poor”. This ML-based method enhances the accuracy of wave partitions, especially in cases where non-linear effects or large-scale oceanic features distort the data. The proposed algorithm provides a robust tool for filtering out problematic partitions, improving the overall quality of ocean wave measurements obtained from SAR. Moreover, the variability in the accuracy of swell partitions, depending on the swell direction relative to the satellite’s flight heading, is effectively addressed, enabling more reliable data for oceanographic studies. This work contributes to a better understanding of ocean swell dynamics derived from SAR observations and supports the numerical swell modeling community by aiding in the refinement of models and their integration into operational systems, thereby advancing both theoretical and practical aspects of ocean wave forecasting. Full article

This study introduces a new machine learning-based algorithm for the retrieving significant wave height (SWH) using synthetic aperture radar (SAR) images. This algorithm is based on the azimuthal cut-off wavelength and was developed in quad-polarized stripmap (QPS) mode in coastal waters. The collected images are collocated with a wave simulation from the numeric model, called WAVEWATCH-III (WW3), and the current speed from the HYbrid Coordinate Ocean Model (HYCOM). The sea surface wind is retrieved from the image at the vertical–vertical polarization channel, using the geophysical model function (GMF) CSARMOD-GF. The results of the algorithm were validated against the measurements obtained from the Haiyang-2B (HY-2B) scatterometer, yielding a root mean squared error (RMSE) of 1.99 m/s with a 0.82 correlation (COR) and 0.27 scatter index of wind speed. It was found that the SWH depends on the wind speed and azimuthal cut-off wavelength. However, the current speed has less of an influence on azimuthal cut-off wavelength. Following this rationale, four widely known machine learning methods were employed that take the SAR-derived azimuthal cut-off wavelength, wind speed, and radar incidence angle as inputs and then output the SWH. The validation result shows that the SAR-derived SWH by eXtreme Gradient Boosting (XGBoost) against the HY-2B altimeter products has a 0.34 m RMSE with a 0.97 COR and a 0.07 bias, which is better than the results obtained using an existing algorithm (i.e., a 1.10 m RMSE with a 0.77 COR and a 0.44 bias) and the other three machine learning methods (i.e., a >0.58 m RMSE with a <0.95 COR), i.e., convolutional neural networks (CNNs), Support Vector Regression (SVR) and the ridge regression model (RR). As a result, XGBoost is a highly efficient approach for GF-3 wave retrieval at the regular sea state. Full article

Based on the simulated signal, a numerical simulation method of synthetic aperture radar (SAR) imaging for time-varying sea surfaces is proposed, which is helpful to study the SAR imaging mechanism of time-varying sea surfaces so as to better extract ocean wave parameters from SAR images. Not only are the modulation of ocean waves, speckle noise, and temporal decorrelation of the small-scale waves considered, but the velocity bunching (VB) effect caused by the motion of large-scale waves is also effectively added to the simulation of the SAR echo signal. To verify the reliability of the simulation method, the simulated SAR images using the parameters of the RADARSAT-2 SAR, the corresponding wind wave information measured by an in-situ buoy, and the reanalysis wave spectra have been compared with the actual RADARSAT-2 SAR images. The comparisons demonstrate that the characteristics of simulated SAR images, such as the intensity distribution and the image spectra, are consistent with those of actual RADARSAT-2 SAR images. Based on the numerical simulation method proposed by us, SAR images of ocean waves for different marine environments and radar platform parameters are simulated. The imaging results indicate that the texture feature of the wind waves would be severely damaged due to the VB effect, while the texture of swells in the simulated SAR images may not be damaged or even becomes clearer. From the simulated SAR image spectrum, it can be found that the azimuth wavenumber is cut off when the VB effect is considered in the simulation process, and the azimuth cut-off wavelength increases with the range-to-velocity ratio. Full article

Most works that address 2-D array ultrasonic transducers for underwater applications are about the geometry aspects of the array and beamforming techniques to make 3-D images. They look for techniques to reduce the number of elements from wide apertures, maintaining the side lobes and the grating lobes at acceptable levels, but not many details about the materials and fabrication processes are described. To overcome these gaps, this paper presents in detail the development of a 2-D array ultrasonic transducer prototype that can individually emit and receive ultrasonic pulses to make 3-D images of immersed reflectors within a volume of interest (VOI). It consists of a 4 × 4 matrix ultrasonic transducer with a central frequency of 480 kHz. Each element is a 5 mm sided square cut into a 1–3 piezocomposite. The center-to-center distance of two contiguous elements (pitch) was chosen to be greater than half wavelength, to increase the amplitude of emission and reception of signals with larger elements. Artifacts generated by grating lobes were avoided by restricting the field of view in the azimuth and elevation directions within 40° × 40° and applying the sign coherence factor (SCF) filter. Two types of backing layer materials were tested, one with air and another made of epoxy resin, on the transducers called T1 and T2, respectively. The pulse echoes measured with T1 had 2.6 dB higher amplitude than those measured with T2, and the bandwidths were 54% and 50% @ −6 dB, respectively, exciting the element with a single rectangular negative pulse. The 3-D images obtained with full matrix capture (FMC) data sets acquired of objects from 0.2 to 1.15 m motivate the development of a 2-D array transducer with more elements, to increase the angular resolution and the range. Full article

Sentinel-1 synthetic aperture radar (SAR) is one of the most advanced open-access satellite systems available, benefitting from its capability for earth observation under all-weather conditions. In this study, more than 280 Sentinel-1 SAR images are used to derive significant wave heights (H_s) of the sea surface using a polarization-enhanced methodology. Two study areas are selected: one is located near Hawai’i in a deep water region, and the other is in transitional water off the U.S. west coast, where the U.S. National Oceanic and Atmospheric Administration (NOAA) buoy data are available for validations. The enhanced H_s retrieval methodology utilizes dual-polarization SAR image data with strong non-Bragg radar backscattering, resulting in a better estimate of the cut-off wavelength than from those using single-polarization SAR data. The new method to derive H_s is applied to SAR images from 2017 taken from both deep water (near Hawai’i) and coastal water locations (off the U.S. West coast). The assessments of the retrieved H_s from SAR images suggest that the dual-polarization methodology can reduce the estimated H_s RMSE by 24.6% as compared to a single-polarization approach. Long-term reliability of the SAR image-derived H_s products based on the new methodology is also consolidated by large amount of in-situ buoy observations for both the coastal and deep waters. Full article

InGaN quantum wells were grown using metalorganic chemical vapor phase epitaxy (vertical and horizontal types of reactors) on stripes made on GaN substrate. The stripe width was 5, 10, 20, 50, and 100 µm and their height was 4 and 1 µm. InGaN wells grown on stripes made in the direction perpendicular to the off-cut had a rough morphology and, therefore, this azimuth of stripes was not further explored. InGaN wells grown on the stripes made in the direction parallel to the GaN substrate off-cut had a step-flow-like morphology. For these samples (grown at low temperatures), we found out that the InGaN growth rate was higher for the narrower stripes. The higher growth rate induces a higher indium incorporation and a longer wavelength emission in photoluminescence measurements. This phenomenon is very clear for the 4 µm high stripes and less pronounced for the shallower 1 µm high stripes. The dependence of the emission wavelength on the stripe width paves a way to multicolor emitters. Full article