The spaceborne wave scatterometer Surface Waves Investigation and Monitoring(SWIM) can provide global ocean Directional Wave Height Spectra(DWHS) data product. However, under specific sea conditions, the performance of SWIM DWHS data product decreases due to the presence of parasitic peaks at low wavenumbers, the non-linear surfboard effect in the radar imaging mechanism, and a slight underestimation of the speckle noise spectral density. In this study, by leveraging the DWHS measured by National Data Buoy Center(NDBC) buoys and employing an indirect colocation method, DWHS calibration models based on the AU-Net(Attention U-Net) were developed. These models were established for the pure wind wave sea with wind speeds ranging from 9m/s to 19m/s and the single swell sea with significant wave heights between 1m and 5m, respectively, for SWIM beams 6°, 8°, and 10°. The effectiveness of the established calibration model was verified from two aspects. Firstly, by comparing the SWIM DWHS before and after correction with buoy DWHS, the calibration results show that the wavelength measurement upper limit of SWIM DWHS has been extended from 500m to 1100m; under pure wind wave sea(the single swell sea), the correlation coefficients(CC) of DWHS by SWIM beams 6°, 8°, and 10° increased from 0.35, 0.47, and 0.43(0.72, 0.72, and 0.76) to 0.99, 0.99, and 0.99(1, 0.99, and 0.99), and the structural similarity index(SSIM) has increased from 0.30, 0.42, and 0.37(0.70, 0.57, and 0.64) to 0.99(0.99). Secondly, the performance of significant wave heightHsand peak wavelength λpcalculated from each SWIM DWHS sample before and after correction, has been verified using MFWAM reanalysis data. The validation results show that for SWIM beams 6°, 8°, and 10° under pure wind wave sea(the single swell sea), the RMSE ofHsis merely 0.2m, 0.19m, and 0.17m(0.12m, 0.09m, and 0.14m) after calibration, while the mean bias(MB) is only 0m, -0.04m, and -0.01m(-0.06m, -0.02m, and -0.09m). Similarly, the RMSE of λpis just 8.99m, 8.85m, and 8.44m(20.44m, 19.50m, and 21.77m), the MB of λpis merely 0.97m, 1.33m, and 0.15m(1.59m, 2.06m, and 1.90m).

The amplitude of internal solitary waves (ISWs) is a crucial parameter characterizing their properties. Leveraging machine learning and optical remote sensing images for ISW amplitude inversion has proven highly efficient. However, determining the best input features in the inversion model is often overlooked. This study addresses the feature selection problem in ISW amplitude inversion using a random forest (RF) method. The peak-to-peak distance and relative grayscale differences significantly influence ISW amplitude inversion. When solely using imaging features for ISW amplitude inversion, more significant errors are observed for low-amplitude ISWs due to their weak modulation. In amplitude inversion, selecting the dimensionless ISW amplitude for the output is necessary because it better represents the amplitude magnitude. We find that adding stratification parameters improves the inversion effect, especially the depth ratio. Thus, the impact of physical mechanisms on ISW amplitude inversion is pivotal, and incorporating more hydrological parameters as inputs would lead to further improvements in ISW amplitude inversion.

Based on SAR remote sensing observation and theoretical calculation, the internal solitary waves (ISWs) in the Arctic Kara Sea are studied. A total of 320 Sentinel-1 SAR remote sensing images between July and October 2022 were processed. Among these images, 834 ISWs were identified in 97 remote sensing images. The spatial distribution and propagation patterns of ISWs in the Kara Sea were interpreted. ISWs are widely present in the region east of Novaya Zemlya in the Kara Sea with water depth exceeding 100 m. Different months exhibit distinct spatial characteristics of ISWs in the Kara Sea. The primary reason for the generation of ISWs is the forcing because of the interaction of the tide currents with the intricate topography. The ocean currents also play a role in regulating ISWs in the Kara Sea. The propagation of ISWs in the Kara Sea is closely associated with the tidal transport process. The amplitudes of ISWs in the Kara Sea were analyzed using the KdV theory and the eKdV theory. The analysis indicates that amplitudes of ISWs between 10 and 19.9 m account for 67% and 29%, respectively. The maximum amplitudes obtained were 41.3 and 64.5 m. Comparing the theoretical results with in situ data, it was observed that the amplitudes of ISWs inverted by eKdV theory are closer agreement with the CTD measurement results.

SWIM is a wave spectrometer on board CFOSAT designed to measure the wave spectrum over a width of 180 km [1]. In this paper, the ability of SWIM to detect ISWs is explored for the first time using SWIM data to investigate the internal isolated waves (ISWs) in the Sulu Sea. As an example, two identical ISWs are detected by the 8 ° and 10 ° incidence angle beams, and the empirical mode decomposition (EMD) method is used to separate and extract the ISWs signal from the echo power of SWIM and match the extracted signal with the ISWs; the SWIM data are detrended and moving average processed to calculate the perturbation changes of the SWIM echo signal caused by ISWs. The qualitative analysis shows that ISWs cause a perturbation in the SWIM echo power and that SWIM has the ability to detect ISWs. A preliminary comparison shows that the magnitude of the SWIM echo power variation is related to the characteristics of the ISWs themselves and the magnitude of the energy contained in the beam when the ISWs are detected.

Internal solitary wave is a common mesoscale dynamic process in the global ocean. In this study, SAR and MODIS satellite remote sensing images are effectively combined to make up for each other’s shortcomings and carry out remote sensing detection of internal solitary wave distribution in the global ocean. Using 1231 ENVISAT ASAR, Sentinel-1A/B and MODIS satellite remote sensing images, the position distribution of internal solitary wave in the global ocean was mapped by extracting the internal solitary wave crest lines in each remote sensing image. The internal solitary waves in the global ocean are mainly distributed between 60 degrees south and north latitude, which is consistent with the research results of Jackson (2007) using MODIS. No internal solitary waves have been detected above 60 degrees south latitude. MODIS remote sensing image can only detect the internal solitary waves in the middle and low latitudes, while SAR can detect the internal solitary waves in the higher latitudes.

The modulation of internal solitary waves (ISWs) produces complex and intriguing phenomena on the sea surface, such as changes in sea surface slope. Measuring this slope change using in situ observation is challenging. This manuscript employs SAR image inversion methods to describe it. The slope changes in the range and azimuthal directions are calculated based on the average scattering angle and polarization orientation angle, respectively. The mean square slope change of surface roughness is calculated in combination with the propagation direction of ISWs. The calculation results from L-band SAR images show that the sea surface slope at the regions where ISWs act is significantly lower than the surrounding water, reaching -45%, indicating that ISWs have a suppressive effect on sea surface slope change. This can be explained by the occurrence of quasi-specular reflection after ISWs modulate the sea surface under the SAR side-look detection structure.

Oceanic internal waves (IWs) are an important oceanic phenomenon, and the realization of the fast and efficient extraction of IWs is of fundamental significance. The development of deep learning techniques provides new opportunities for the signature extraction of oceanic IWs. In this letter, we propose a two-stage oceanic IW signature segmentation algorithm for synthetic aperture radar (SAR) images. The algorithm includes a decision fusion-based oceanic IW images classification stage and a pixel attention U-Net (PAU-Net)-based stripe segmentation stage. First, we adopt an IW classification algorithm by fusing the weak decision results of two different classifiers to get the final strong decision result to screen the image blocks containing oceanic IWs. Then we develop a PAU-Net to segment the IWs stripe. Finally, we concatenate them to obtain the extract results of the whole image. Experiments are performed using 527 image scenes from the Sulu Sea that contain IWs. The results show that the proposed algorithm can achieve the performance of oceanic IW signature extraction from SAR images.

Internal solitary waves (ISWs) cause changes in the flow field of a water column, which in turn excite convergent and divergent phenomena on the water surface. Due to the variation in wave elements and the complex marine environment, the optical remote sensing imaging of ISWs shows significant differences, which need to be further investigated. In this study, we propose a method that combines physical simulations with software simulations to investigate the optical imaging signals generated by ISWs. We designed three experiments based on the physical simulation platform to capture the convergent and divergent phenomenon and free surface displacement caused by ISWs. The influence of surface changes on imaging characteristics under different conditions was analyzed. The grayscale of ISW patterns with a high-density ratio deviates more from the background than those with a low-density ratio. In windy conditions, the imaging of convergent and divergent areas is more pronounced than that of surface waves and free surface displacement (FSD). Moreover, the bright-dark ratio of ISW patterns is mostly asymmetric. Next, we used software simulation to further explore the mechanism of optical remote sensing imaging of the ISWs. Different types of surface models were established using Unigraphics NX (UG) at the experimental scale to investigate the relationship between surface and imaging further. These models were imported into LightTools (LTs) to simulate the optical imaging. According to the sensitivity analysis of irradiance changes to wavelength and amplitude, one reason for the asymmetric bright-dark ratios is given. Finally, we find that the FSD only plays a certain role in imaging when the surface is calm. When ISWs modulate capillary waves, the convergent and divergent phenomena primarily influence imaging.

Taking advantage of AIS data to mine the dynamic characteristics of fishery resource exploitation helps to carry out scientific management of fishery and realize the sustainable development of marine resources. The paper selected 210 million records of AIS data of approximately 115,000 fishing vessels in the six Chinese fishing grounds. After processing the AIS dataset for fishing activities and fishing vessel types identification, we conducted a thorough mining and analysis of the characteristics of fishing vessel activities in winter and spring of 2019. The results showed that the number of fishing vessels was gradually increasing as the latitude decreased in winter, and that were quite different between winter and spring in the northern fishing grounds. Gillnetters were the most numerous fishing vessel type operating in the inshore fishing grounds with increased in spring, while seiners had an absolute advantage in the Xisha-Zhongsha fishing ground.

Compared with traditional underwater topography measurement methods such as multi-beam or sonar, remote sensing satellites provide a new method for underwater topography detection, but it's difficult to retrieve high-resolution and high-precision underwater topography only using a single SAR image. This paper aims to perform the complementary and synergetic effect of Multi-source SAR data and proposes a shallow sea topography detection model based on Multi-source SAR. We also verified the model with four SAR images of GF-3, Sentinel-1, ALOS PALSAR and ENVISAT ASAR satellite. The mean relative error of the detected topography is 12.50%, and the correlation coefficient is 0.91. The results show that the model proposed in this paper can effectively retrieve high-precision and high-resolution underwater topography maps.

The Sentinel-3 synthetic aperture radar altimeter provides two kinds of rain flag by using the changes of the three backscatter coefficients, which provides a foundation for the comprehensive use of all the backscatter coefficients to determine rain conditions. This paper analyzes the statistical relationship between the two backscatter coefficients of Ku band SAR mode and PLRM mode and the C band backscatter coefficient when it is not rain, then the deviation generated by the backscatter coefficients of the two bands in the rainfall state is compared, provides a new method for determining rain flag. Finally, the measurement data of precipitation radar and altimeter data are matched and verified, and the result of the new method of updating the threshold judgment condition is more accurate.

Internal solitary waves play an important role in oceanic energy mixing, engineering construction, and will cause convergence and divergence phenomena, leading to tilt changes on the sea surface. The introduction of the X-Bragg model realizes the description of the rough disturbance effect, obtains the angle$\beta$that can characterize the sea surface tilt, and constructs the internal solitary wave-induced sea surface tilt change index$\Delta\beta$. The statistical values of sea surface tilt changes caused by internal solitary waves calculated using ALOS PALSAR images range from 0 to 30%.

The comparison and verification of ocean wave spectrum by remote sensing and by in-situ measurements at the spectral level is quite rare, because the use of the traditional comparison method lead to very limited spatio-temporal matching pairs. In this paper, a new comparison method is proposed. With this method, under different sea conditions (wind wave mainly/swell mainly) and sea surface conditions (wind speed smaller than 20m/s, significant wave height from 1m to 7m), mean directional wave height spectra from SWIM (Surface Waves Investigation and Monitoring) are compared at the spectral level to the buoy counterparts, in different classes of sea-state. This includes the comparison of the omni-directional wave height spectrum and the directional function at the peak wave number. The comparison results show that under medium and high sea conditions, wave directional spectra provided by the SWIM beams at 8 ° and 10 ° incidence have a high consistency with those from buoy data. Under low sea conditions, the measurement bias of SWIM wave directional spectra mainly comes from three phenomena which are, by order of importance, an abnormal lifting of spectral energy caused by non- wave components at low wave numbers (parasitic peak), from the non-linear surfboard effect in the radar imaging mechanism and from a slight underestimation of speckle noise spectral density.

Compared with transmit/receive (T/R) monostatic High-frequency surface wave radar (HFSWR), the T-R bistatic HFSWR has the advantages of flexibility, receiver concealment and large coverage because of the separation between the radar transmitter and receiver locations. In this paper, a target monitoring method with bistatic compact HFSWR was proposed. The results of a target detection experiment using T-R bistatic compact HFSWR conducted in 2015 were presented, and the validity of the method and the tracing results were verified by using synchronous automatic identification system (AIS) data.

As the improving of the synthetic aperture radar (SAR) resolution and the increase in the amount of data acquisition, the ship type recognition has become an important research topic. In order to meet the precise identification for ship types, 101 SAR data and the Automatic Identification System (AIS) were used to build a SAR ship database. The database contains 5288 ship samples with different polarizations, incidence angle and resolutions, including more than 20 kinds of ship type such as cargo, container, oil tankers, and fishing boats. Furthermore, the influence of different polarization, incidence angle and heading on ship geometry parameters was analyzed. Moreover, a random forest (RF) classifier was used to carry out the ship type recognition experiment, and the classification accuracy reached more than 60%.

Sea ice type is one of the most sensitive variables in Arctic ice monitoring and detailed information about it is essential for ice situation evaluation, vessel navigation, and climate prediction. Many machine-learning methods including deep learning can be employed for ice-type detection, and most classifiers tend to prefer different feature combinations. In order to find the optimal classifier-feature assembly (OCF) for sea ice classification, it is necessary to assess their performance differences. The objective of this letter is to make a recommendation for the OCF for sea ice classification using Cryosat-2 (CS-2) data. Six classifiers including convolutional neural network (CNN), Bayesian, K nearest-neighbor (KNN), support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were studied. CS-2 altimeter data of November 2015 and May 2016 in the whole Arctic were used. The overall accuracy was estimated using multivalidation to evaluate the performances of individual classifiers with different feature combinations. Overall, RF achieved a mean accuracy of 89.15%, followed by Bayesian, SVM, and BPNN (~86%), outperforming the worst (CNN and KNN) by 7%. Trailing-edge width (TeW) and leading-edge width (LeW) were the most important features, and feature combination of TeW, LeW, Sigma0, maximum of the returned power waveform (MAX), and pulse peakiness (PP) was the best choice. RF with feature combination of TeW, LeW, Sigma0, MAX, and PP was finally selected as the OCF for sea ice classification and the results that demonstrated this method achieved a mean accuracy of 91.45%, which outperformed the other state-of-art methods by 9%.

The TanDEM-X constellation is served by two X-band SAR satellites, which fly in close orbit formation acting as a large and flexible single-pass radar interferometer. This paper investigates the potentials for monitoring sea ice in the Bohai Sea with the unique constellation. Our results show that the coherence and interferometric phase of TanDEM-X data can be used to detect sea ice, and the radial velocity of sea ice can be measured with the along-track phase.

This letter presents a novel method for ship classification that uses synthetic-aperture-radar images to distinguish ships based on superstructure scattering features. The ratio of dimensions, which combines the 2-D and 3-D properties of scattering, is explored as an effective and credible means to describe the scattering features of ships. The proposed method consists of three main stages: 1) ship isolation from the sea; 2) parametric vector (F) estimation; and 3) categorization using a support vector machine (SVM) classifier. To depict ship features more accurately and reduce feature redundancy, we propose employing peak extraction to divide a ship into bow, middle, and stern instead of into three equal parts. The classification method is tested with RadarSat-2 images, and ground-truth information is supplied by an automatic identification system. The experimental results show that the proposed method can achieve satisfactory ship-classification performance compared with existing methods, with an overall accuracy exceeding 80%.

We address the problem of sea surface distribution modeling in a synthetic aperture radar (SAR) image by developing an innovative nonparametric method to tackle the main weakness of the traditional Parzen window kernel method, i.e., relatively low computation speed. We derive an explicit analytical solution of modeling sea surface distribution by a composite cubic Bézier curve and propose an adaptive segmentation strategy to improve the modeling precision. A comparative study validates that the average computation time of the proposed method is only 1/60 of the Parzen window kernel method and about 1/6 of the k-root and G0 methods. More importantly, in terms of modeling performance, the proposed method can achieve more adaptability and stability to different SAR sensors, resolutions, and sea scenes. The average goodness of fit tested on eight sea scenes of the proposed method, measured by |R̂̅2̅| (the smaller the better), is only 0.0006 and outperforms that of the Parzen window kernel method (0.0059), k-root (0.0390), and G0 (0.0678).

Selecting discriminate features and constructing an appropriate classifier are two essential factors for ship classification in a synthetic aperture radar (SAR) image. Unfortunately, these two factors are rarely considered together by existing studies. We propose a joint feature and classifier selection method by integrating the classifier selection strategy into a wrapper feature selection framework. The sequential forward floating searching algorithm is improved to conduct efficient searching for an optimal triplet of feature-scaling-classifier. Comprehensive experiments on two data sets demonstrate that the proposed method can select the optimal combination of a nonredundant complementary feature subset, appropriate scaling, and classifier to improve the performance of ship classification in a SAR image.

Probability density function (pdf) estimation of sea clutter in synthetic aperture radar (SAR) imagery has a fundamental role in constructing a constant false alarm rate (CFAR) based ship detector. This paper proposes a semi-parametric sea clutter modeling method for SAR amplitude imagery. The pdf of sea clutter is estimated point by point for each amplitude value, by selecting an optimal component from a given dictionary. For a specific point, the optimal component is selected by measuring the statistical consistency between pdfs of different components and the pdf of sample data within a local window in pdf domain. The statistical consistency is measured by Kullback-Leibler distance (KL-distance). The size of local window is determined based on smoothness criterion. Experimental results on several real SAR imageries demonstrate that the proposed method accurately models the sea clutter, and is flexible to combine with CFAR to construct a ship detector.