At about 21:00 on April 20, 2021 (UTC), the Indonesian Navy submarine “KRI Nanggala-402” lost contact in the north of Bali Island and eventually sank. To explore the impact of internal waves (IWs) on the sinking of the “Nanggala” submarine, an IW parameter inversion method from the synthetic aperture radar (SAR) image based on an Euler numerical model is proposed. The IW amplitude and propagation speed, 25.2 m and 2.36 m/s, respectively, at the wreck site are retrieved from a Sentinel-1A/SAR image acquired two days before the losing contact using the Euler numerical model with the assistance of geostationary satellite Himawari-8 data. Further considering the barotropic tide current variation trends in the Lombok Strait (LS), the amplitude there was no more than 25 m when the submarine sank. The IWs with such small amplitude could contribute little to the submarine sinking. Therefore, we do not consider IWs as the primary cause of this submarine sinking accident.

The widespread presence of oceanic internal waves (IWs) across continental shelves, straits, and islands has been confirmed using multiple satellite payloads, including optical and synthetic aperture radar (SAR) sensors. However, the efficiency and accuracy of IWs detection are severely limited by the cloud contamination of optical images and the availability of SAR data. In other words, although IWs can be observed by multiple sensors, achieving full-time coverage remains challenging. The SAR altimeter (SRAL), characterized by high spatial resolution and continuous observation capability, holds substantial potential for IWs detection. Consequently, this study proposes a deep-learning-based method, named the IWs detection network (IWD-Net), to detect IWs from SRAL data. The IWD-Net is trained and tested in the Andaman Sea, achieving a detection precision of 96.2%. In addition, the model is subsequently applied to the South China Sea (SCS), where the detection precision of 94.9% reconfirms its robustness and reliability in detecting IWs. Statistical results indicate that the IWs detection efficiency using altimeter data improves by 227% compared to SAR and optical sensors combined. Finally, spatiotemporal analysis reveals that IWs are primarily concentrated in the western Luzon Strait and the Sulu Sea, but the seasonal variations of IWs in these two regions exhibit opposite trends: IWs are more active in summer/autumn within the western Luzon Strait, whereas IWs are more active in late winter/early spring within the Sulu Sea. These findings highlight the potential of altimeter data to fill gaps in IWs observations and enhance our understanding of ocean dynamics.

The echo signal received by ionosonde contains both ordinary wave (O-wave) and extraordinary wave (X-wave), and its separation result directly determines the accuracy of mode discrimination and ionospheric parameter inversion, which is of great significance to ionospheric research. The separation of O-wave and X-wave is exceptionally complex due to environmental noise, instrumental thermal noise, external interference, and the time-varying dispersion properties of the ionosphere itself. In this article, a method for separating O-wave and X-wave of ionosonde based on dual-channel phase difference statistics is proposed, which uses constant false alarm rate (CFAR) detection to extract the vertical ionospheric measurement echo signals and dynamically calculates the compensated phases of O-wave and X-wave separations at each frequency, achieving the robust and effective separation of O-wave and X-wave. The results of the measured data show that: 1) this method dynamically calculates the phase compensated for O-wave and X-wave separation by considering the variation of the compensation phase with frequency and time, through real-time statistics of the phase difference in the echo signal; 2) it compensates for the amplitude difference between the two channels and the signal-to-noise ratio of the separated O-wave and X-wave is improved; 3) it exhibits strong robustness and is suitable for vertical ionospheric signals in various modes; and 4) it demonstrates good performance, with the accuracy of O-wave and X-wave separation reaching 98.64%, which is 21.37% higher than the traditional dual-channel phase compensation methods.

Tropospheric path delay (TPD) is one of the primary factors affecting the accuracy of sea level anomaly (SLA) measurements by satellite altimetry. This article focuses on the effects of differential tropospheric error (DTE), resulting from the different TPD between two antennas with different incident angles in the cross-track direction of the wide-swath interferometric altimetry (WSIA). Based on the principle of interferometry, this article presents the mathematical model of the DTE and conducts a simulation experiment for validation, demonstrating a tangent-squared of incidence angles relationship between the DTE and TPD. Combining observational parameters of the SWOT and Guanlan satellites, the DTE increases nonlinearly from the near nadir, reaching approximately 1 and 3 cm at the far end of the swath for SWOT and Guanlan satellites, respectively. In addition, a comparison of relative magnitude between the SLA and DTE is carried out by computing the root mean square (rms) values using the wavenumber spectrum. The ratio of rms values is larger in the typical strong current regions [Kuroshio, Gulf Stream, and Antarctic Circumpolar Current (ACC)], while smaller in the low-latitude tropics regions. Furthermore, the global distribution of the power ratio is analyzed by calculating the ratio of spectral densities between the SLA and DTE at different wavelengths. The ratio of power is largest at mesoscale (250 km) and gradually homogeneous over the global ocean at submesoscale (50 km).

Wide swath near-nadir interferometric altimetry is a newly developed technology for sea surface height (SSH) measurement. However, the absence of actual measurement data makes this novel SSH mapping technique difficult to verify and apply for wide swath interferometric altimeters. To verify the designed performance of the scheduled wide swath single-pass interferometric altimeter in the "Guanlan Mission", an airborne campaign was carried out off the coast of Rizhao, China on November 16, 2020. An airborne dual-frequency interferometric radar altimeter system (ADIRAS) with a single-pass mode was utilized for SSH measurement as the first flight. Two pioneering and fundamental works have been conducted: an intensive altimetry error analysis according to the ADIRAS parameter settings along the incident direction, an effective SSH reconstruction approach based on a multichannel likelihood (ML) function, and detailed validation procedures through airborne campaigns illustrated in this study. The results indicated that the difference between the wave-induced sea surface elevation (WSSE) variances derived by the ML approach and GNSS buoy was 2 cm2, which was smaller than the results of the single band on Ku (11 cm2) and Ka (6 cm2). Moreover, the estimated Significant Waves Height (SWH) bias of joint bands was 10 cm, which was also superior to that of Ku (39 cm) and Ka (24 cm). Both simulated data and real airborne dual-frequency InSAR data were employed in this study for cross-validation of the proposed method. This approach represents an effective technique for SSH reconstruction of future spaceborne/airborne interferometric altimeters.

Ku-band scatterometers are sensitive to rain effects due to their cm-scale radar wavelength. The NSCAT-4DS geophysical model function (GMF) corrects for sea surface temperature (SST), whereas it doesn’t consider rain. Rain causes biases in the retrieved wind fields and to prevent these, quality control (QC) flags play an important role in rain identification. Since horizontal polarization and vertical polarization radar beams have a particular sensitivity to rain clouds, a noticeable difference between the rain-dominated backscatter distribution and the wind-dominated backscatter distribution is observed. Employing a Bayesian approach and exploiting these particular wind and rain backscatter characteristics, the authors propose an algorithm to provide the posterior rain probability for each measurement in a Wind Vector Cell and test the method for the Haiyang-2C scatterometer. In a comprehensive comparison between posterior rain probability, KNMI QC flag and Joss flag, for posterior rain probabilities higher than 0.5, the rejection rate is approximately a quarter of that of the KNMI QC flag with better rain detection behavior. While the Joss flag, the difference between the retrieved wind speed and the two-dimensional variational ambiguity removal analysis wind speed, has the best performance in identifying rain in the sweet swath, it comes at the cost of a higher missing rate. The comparison with ASCAT winds also proves the method’s effectiveness. Posterior rain probability has the best rain identification ability in the nadir swath. A combination of different QC flags should be beneficial and applied in wind retrieval.

Satellite scatterometer wind retrieval is affected by rain. Both the precipitating clouds in the atmosphere and the sea surface rain effects can enhance or reduce the backscatter signal. Ku-band scatterometer retrievals suffer more rain effects than C-band scatterometer due to the shorter wavelength. Because of the lack of understanding of the potential physical mechanism, the current Geophysical Model Functions (GMF) don’t include rain effects, which leads to wind field retrieval biases in rainy areas. The usual method to avoid rain effects is flagging the possible rain-contaminated data in the quality control procedure and removing these flagged data in the processing. However, rain is often associated with extreme weather events, where accurate wind (and rain) retrieval is particularly relevant. Therefore, the authors propose a conceptual model which describes the relationship between Ku-band scatterometer measured normalized radar cross-section (NRCS) biases and the sea surface wind-induced NRCS and rain rates. The model assumes that the area-weighted rain rate in each wind vector cell (WVC) is a function of the rain coverage area fraction. The received NRCS is constituted by a wind and rain contribution. Model parameters are fitted based on Haiyang-2C scatterometer measurements, collocated ASCAT measurements, and the Level 3 Integrated Multi-satellitE Retrievals average area-weighted rain rates. Scatterometer measured NRCS biases are much reduced by comparing the original measured NRCS biases and the residual NRCS biases after correction. The model can help to better understand rain effects on scatterometers and paves the way towards a Ku-band scatterometer wind retrieval method considering rain effects.

This paper uses the level-2 Ocean data (OCN) of Sentinel-1 Interferometric Wide swath (IW) model to retrieve the sea surface radial current velocity. The level-2 OCN data of IW mode includes two modules: radial surface velocity (RVL) and ocean wind field (OWI). The RVL module provides parameters such as radial Doppler frequency shift, which can be used to retrieve the sea surface current velocity. The OWI module provides wind field retrieved from the SAR image and is used as the input of the empirical model CDOP to remove the Doppler frequency shift caused by the sea surface wind-wave field. The retrieval results are verified by using HYCOM data, and the bias was less than 0.1m/s and the root mean square error was about 0.2m/s in 4 test areas, which showed good agreement and utility of Sentinel-1 Ocean data in retrieving ocean current data.

Interferometric radar altimeter (IRA) is a new ocean remote sensing sensor. It can be used to retrieve sea surface height (SSH) by means of cross-track interferometry. Compared with the traditional cross-track interferometric synthetic aperture radar (XT-InSAR), IRA works at very small incidence angles for higher altimetry sensitivity. In this case, multiple discontinuous surface scatterers at sea surface would be cut into a same range pixel which leads to severe layover. This layover induced by ocean waves will reduce the correlation between the master-slave images acquired by IRA and increase random interferometric phase noise. At present, how to quantitatively analyze the impact of the ocean wave layover on the decorrelation of IRA images is still a problem that needs in-depth discussion. In this letter, theoretical analysis of the effect of ocean waves on the decorrelation of IRA images has been carried out when the ocean waves layover is considered. And the theoretical results are also compared with the airborne IRA data. It is found that the layover of ocean waves has significant influence on the decorrelation between the master-slave IRA images, especially at very low incidence angles.

The interferometric radar altimeter (IRA) is one of the main payloads of the “Guanlan” ocean science satellite proposed by the National Laboratory for Marine Science and Technology of China. To evaluate the effectiveness and accuracy of the IRA in retrieving the ocean dynamic parameters, such as sea surface height (SSH), ocean wave spectrum, and wind speed, two airborne IRA experiments were carried out at Qingdao Xiaomaidao (XMD) sea area on March 31, 2019, and Rizhao sea area on November 16, 2020. In the present work, wave-induced sea surface elevation (SSE) and its spectrum have been retrieved based on the interferograms acquired by the airborne IRA. To suppress the random phase noise, a mean filtering algorithm has been used in the multilook process of calculating the complex IRA images. The results show that the size of the filter window has a significant effect on the retrieved SSE. If the size of the filter window along THE range direction is too large, the flat earth would cause the spectral density of the retrieved ocean wave to be higher. In addition, the comparisons of the retrieved spectra with the buoy measurements demonstrate that the swell can be well-retrieved by IRA images at low sea-state conditions with significant wave height (SWH) less than 0.7 m. However, for wind wave, because of the effect of the velocity bunching along the azimuth direction, the wind wave spectrum can be extracted only when it propagates approximately along the ground-range direction of the IRA images.

Aiming at the observation of sub-mesoscale ocean dynamics, Pilot National Laboratory for Marine Science and Technology (Qingdao) proposed the GuanLan marine satellite program. The new interferometric imaging radar altimeter (InIRA) sensor is used to realize high accuracy observation of marine dynamics through Ku band and Ka band, which makes up for the deficiency of traditional altimeter in observation of mesoscale and sub-mesoscale marine dynamics. This paper mainly discusses the influence of random errors on the measurement results of InIRA. In the discussion, the random error caused by significant wave height (SWH) of global sea surface at different times, radar frequency, incident angle and correlation coefficient are considered and discussed respectively. The random errors caused by different factors are analyzed quantitatively.

C band microwave radars have been widely used in ocean observations, e.g. oil spill monitoring, ship detection, wind speed retrieval, etc. All these applications rely on the measured normalized radar cross section (NRCS) of sea surface. Therefore, it is necessary to develop accurate models to predict the radar cross sections of sea surface. In this paper, based on the six commonly used sea spectra models, i.e. Elfouhaily, Hwang, Romeiser, Apel, Fung and Pierson spectra, the normalized radar cross sections are calculated by Small Perturbation Method (SPM), Two Scale Model (TSM) and the first order Small Slope Approximation (SSA-1), respectively. Meanwhile, to better evaluate the accuracy of different sea spectra, comparisons between numerical calculations and the empirical CMOD5 model are made for various incident angles, wind speeds, and wind directions. The comparisons show that the normalized radar cross sections calculated based on Romeiser spectrum agree better with CMOD5 in some specific cases. The works presented in this paper will be helpful for applications of C band microwave radars in ocean observations.

The general process of oil spill detection from SAR image with artificial neural network (ANN) classifier briefly includes five steps, target extraction, feature extraction, feature selection, ANN training and ANN classification. Feature extraction and feature selection are concerned in this paper. Firstly, 68 features are calculated for each target. By cross-correlation analysis, 24 features are selected to build a neural network to classify oil spills and look-alikes. The impact of imbalance sample data set on the performance of classification is also considered. In the end, principal component analysis (PCA) is applied on 24 features to reduce the dimension of feature space. The best number of principal components is found out.

Results of microwave radar Doppler spectra from 1-D nonlinear ocean surface at moderate- and low-grazing angles are calculated by the composite surface scattering model. For the large-scale undulating surface description, the narrow-band Lagrange model is used, which takes into account the vertical and horizontal skewnesses. Moreover, the shadow and the curvature effects of large-scale waves on the Doppler spectra are also considered in our calculations. Comparisons of computed curves with experimentally measured Doppler spectra at different incidence angles and at various wind speeds show that the simulated results can fit the measured data well at moderate incident angles. From the simulations, we also find that the hydrodynamic modulation and the horizontal skewness of the large-scale waves can induce remarkable influence on Doppler shift. In addition, when the shadow and the curvature effects of large-scale waves are considered in the calculations, the Doppler shifts grow more quickly and the spectral widths become narrower at low-grazing angles, and this is consistent with the numerical results given by Toporkov in the nonlinear surface case. The conclusions obtained in this work seem promising for better understanding the properties of time-dependent radar echoes from oceanic surfaces.

We analyze the refraction of long oceanic waves at the Gulf Stream's north wall off the Florida coast as observed in imagery obtained from the RADARSAT-1 synthetic aperture radar (SAR) during the passage of Hurricane Bonnie on August 25, 1998. The wave spectra are derived from RADARSAT-1 SAR images from both inside and outside the Gulf Stream. From the image spectra, we can determine both the long wave's dominant wavelength and its propagation direction with 180/spl deg/ ambiguity. We find that the wavelength of hurricane-generated ocean waves can exceed 200 m. The calculated dominant wavelength from the SAR image spectra agree very well with in situ measurements made by National Oceanic and Atmospheric Administration National Data Buoy Center buoys. Since the waves mainly propagate toward the continental shelf from the open ocean, we can eliminate the wave propagation ambiguity. We also discuss the velocity-bunching mechanism. We find that in this very long wave case, the RADARSAT-1 SAR wave spectra should not be appreciably affected by the azimuth falloff, and we find that the ocean swell measurements can be considered reliable. We observe that the oceanic long waves change their propagation directions as they leave the Gulf Stream current. A wave-current interaction model is used to simulate the wave refraction at the Gulf Stream boundary. In addition, the wave shoaling effect is discussed. We find that wave refraction is the dominant mechanism at the Gulf Stream boundary for these very long ocean swells, while wave reflection is not a dominant factor. We extract 256-by-256 pixel full-resolution subimages from the SAR image on both sides of the Gulf Stream boundary, and then derive the wave spectra. The SAR-observed swell refraction angles at the Gulf Stream north wall agree reasonably well with those calculated by the wave-current interaction model.