Synthetic Aperture Radar (SAR) utilizes Doppler frequency shift to measure the velocity of ocean surface, but due to imaging mechanism limitations, SAR cannot provide complete information on the direction of ocean surface current. In contrast, using time-series optical remote sensing images and Maximum Cross Correlation (MCC) to track image features, two-dimensional surface current information can be obtained. However, this method is subject to significant errors in current velocity estimation.In this paper, a two-dimensional current field retrieval method with multi-source data fusion is proposed. The method utilizes Sentinel-1B synthetic aperture radar to retrieve radial current velocities and sea surface temperature (SST) data from Himawari-8 to retrieve current directions. By fusing the retrieval results from these two types of data, a more accurate retrieval of the two-dimensional current field at the sea surface can be realized.

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.

The optical imaging mechanism of convex mode-2 Internal Solitary Waves (ISWs) in calm water without wind is investigated based on experimental and simulation calculations. By constructing an optical imaging system in the laboratory, the optical images of ISWs, the matching in-situ data, and the free surface displacement caused by ISWs are acquired simultaneously. The experimental outcomes show that the convex mode-2 ISWs exhibit bright-dark stripes in the optical image and produce a depressed free surface displacement on the water surface. The physical model of free surface displacement is constructed in Unigraphics NX (UG) and imported into Light Tools (LT) for ray tracing. The consistency between simulation calculations and experimental results shows that the free surface displacement generated by the mode-2 ISWs will change the reflection of light, leading to bright-dark stripes in the optical image. A series of experiments were designed to explore the influence of amplitude on the optical imaging of mode-2 ISWs when the thickness of the intermediate layer is different or the same. The results show that the amplitude of the wave will affect the imaging effect of ISWs in optical image, and the large amplitude will make the stripes clearer in the image.

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.

Wet tropospheric delay (WPD) is a critical error source of altimeters. The current radiometers have been able to obtain ideal along-track correction values. However, the small-scale spatial variability of the water vapor is a huge challenge for correcting the wide-swath altimeter. Even though the total residual error can meet the error budget by a two-beam radiometer, the correction effect at the edge of the swath is still difficult to control. This paper proposes a WPD correction method based on mixing nadir radiometer and gridded data product by spatial correlation function. The corrected residual root mean square error (RMSE) and cross-track distribution characteristics of different methods are compared through the simulation data. The results show that the total RMSE of the mixed method is similar to the two-beam method but has better cross-track stability.

The Interferometric Imaging Radar Altimeter (InIRA) carried on the Tiangong-2 Space Laboratory can observe three-dimensional sea surface topography over a swath tens of kilometers wide. The InIRA represents a test mission that will provide valuable experience for the design and data processing technology of future wide-swath altimeters. In addition to the tropospheric delay, ionospheric delay, and sea state bias that affect traditional altimeters, InIRA was also affected by the cross-track error derived from roll error, baseline length error, and interference phase error, which had to be properly corrected. In this paper, range error correction based on correction models and cross-track error correction based on the reference topography data method were applied to a 15-day InIRA dataset and evaluated using the Jason-2, Jason-3, Saral/AltiKa, and Sentinel-3A. The results showed that the standard deviation between corrected InIRA and the combined dataset was 7.96 cm after eliminating the influence of tides and dynamic atmospheric correction, which was comparable to nadir altimeter measurement accuracy.

Wet troposphere path delay (WPD) is a crucial error source for wide-swath altimeters with high-resolution observations. This study generated a simulated wide-swath through the nadir microwave radiometer (MWR) track and evaluated four wide-swath wet tropospheric correction (WTC) methods based on multisource data from 2017 with strict spacetime windows. The Numerical Weather Model (NWM) method can provide a basic correction for the wide-swath altimeter. The MWR method, based on the extension of nadir 1-beam MWR, could not provide a reliable correction. The residual error increased rapidly after deviating from the nadir profile. The overall root-mean-square error (RMSE) of the combination method was approximately 0.58 cm and had high uniformity within the swath. Even at the far end of the swath, the correction was better than that on the NWM and MWR methods. The objective analysis (OA) method using linear spatiotemporal objective analysis had the best result among the four correction methods. The overall RMSE was only 0.49 cm, which was 34% lower than the NWM method. High consistency was maintained at each cross-track position of the swath. The last two wide-swath WTC methods provided in this study had high performance. For the wide-swath altimeter, the combination method is a scheme worthy of consideration, as it can provide WTC results conveniently, quickly, and accurately. Although the operation of the OA method is complex and depends on multisource observation data, it promises to be a high-quality scheme for the wide-swath altimeter WTC when a more accurate WTC value is required.

Optical remote sensing has been an important method to investigate the internal solitary wave (ISW). However, acquiring the ISW amplitude from optical remote sensing images has been the most difficult problem. In this letter, a method is proposed for inverting the amplitude of the ISW. A simulation platform of optical remote sensing is established to detect ISWs in the laboratory, which offers corresponding ISWs and remote sensing images. The experimental results show that the characteristic parameters of optical remote sensing images are changed with ISW amplitude. The relationship between them is nonlinear and related to the hierarchical structure. Hence, based on the support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and multilayer perceptron (MLP), four inversion models of ISW amplitude are built and tested by the in situ data of the Wenchang area. The inversion results indicate that the average relative error of the SVM model is the smallest at 12.4%. It can be applied to the area with a ratio of stratification limited to 0.1-0.6.