Global sea surface salinity (SSS) has been obtained from space since 2009 by the Soil Moisture and Ocean Salinity (SMOS) mission and has been further enhanced by Aquarius in 2011 and Soil Moisture Active‐Passive (SMAP) missions in 2015. Due to the differences between SMOS, Aquarius, and SMAP in the instruments used, retrieval algorithms, and error correction strategies, the quality of their gridded products are different. In this paper, we have assessed the accuracy of three satellite products using in situ gridded data and buoy data. Compared with gridded in situ salinity measurements, the monthly Aquarius data are of the best quality, reaching the mission target accuracy (0.2 PSU) in the open ocean. SMOS and SMAP agree well with in situ data in the open ocean between 40°S and 40°N (root‐mean‐square deviation [RMSD]: SMOS 0.211 PSU, SMAP 0.233 PSU). The RMSD of SMAP is lower than that of SMOS at high latitudes, which may due to the fact that the roughness correction of SMAP is based on the Aquarius geophysical model function. Meanwhile, time series comparison of salinity measured at 1 m by moored buoys indicates that satellite SSS captures variability of SSS at weekly time scales with reasonably good accuracy (RMSD: SMOS 0.25 PSU, SMAP 0.26 PSU), when excluding suspicious buoy data. Synergetic analysis of satellite SSS and Argo data indicates that satellite SSS can be applied as real‐time quality control of buoy 1‐m salinity data.

Oceanic mesoscale eddies greatly influence energy and matter transport and acoustic propagation. However, the traditional detection method for oceanic mesoscale eddies relies too much on the threshold value and has significant subjectivity. The existing machine learning methods are not mature or purposeful enough, as their train set lacks authority. In view of the above problems, this paper constructs a mesoscale eddy automatic identification and positioning network—OEDNet—based on an object detection network. Firstly, 2D image processing technology is used to enhance the data of a small number of accurate eddy samples annotated by marine experts to generate the train set. Then, the object detection model with a deep residual network, and a feature pyramid network as the main structure, is designed and optimized for small samples and complex regions in the mesoscale eddies of the ocean. Experimental results show that the model achieves better recognition compared to the traditional detection method and exhibits a good generalization ability in different sea areas.

A nonlinear empirical method, called the generalized regression neural network with the fruit fly optimization algorithm (FOAGRNN), is proposed to estimate subsurface salinity profiles from sea surface parameters in the Pacific Ocean. The purpose is to evaluate the ability of the FOAGRNN methodology and satellite salinity data to reconstruct salinity profiles. Compared with linear methodology, the estimated salinity profiles from the FOAGRNN method are in better agreement with the measured profiles at the halocline. Sensitivity studies of the FOAGRNN estimation model shows that, when applied to various types of sea surface parameters, latitude is the most significant variable in estimating salinity profiles in the tropical Pacific Ocean (correlation coefficient R greater than 0.9). In comparison, sea surface temperature (SST) and height (SSH) have minimal effects on the model. Based on FOAGRNN modeling, Pacific Ocean three-dimensional salinity fields are estimated for the year 2014 from remote sensing sea surface salinity (SSS) data. The performance of the satellite-based salinity field results and possible sources of error associated with the estimation methodology are briefly discussed. These results suggest a potential new approach for salinity profile estimation derived from sea surface data. In addition, the potential utilization of satellite SSS data is discussed.

Combining the dynamical surface‐trapped mode derived from the Surface Quasi‐Geostrophic (SQG) function with the statistical mode calculated from multivariate empirical orthogonal function (EOF) reconstruction (mEOF‐R) method, this paper proposes a new method, SQG‐mEOF‐R, to estimate the interior density from the sea surface density and sea surface height. This method is applied to the eddy‐resolving Ocean General Circulation Model For the Earth simulator simulation and compared with the conventional SQG, isQG (interior plus SQG), and mEOF‐R methods. Two different dynamical regimes were considered: the NorthWest Pacific, dominated by surface‐intensified eddies, and the Southeast Pacific, characterized by the occurrence of subsurface‐intensified eddies. In both regions, the proposed method proves to be robust in reconstructing mesoscale features, with reduced root‐mean square error and relatively high correlation coefficient. The superiority of SQG‐mEOF‐R is highlighted in those subsurface‐intensified eddies that are little or nothing reconstructed by SQG or isQG method.

Sea surface temperature (SST) has important practical value in ocean related fields. Numerical prediction is a common method for forecasting SST at present. However, the forecast results produced by the numerical forecast models often deviate from the actual observation data, so it is necessary to correct the bias of the numerical forecast products. In this paper, an SST correction approach based on the Convolutional Long Short-Term Memory (ConvLSTM) network with multiple attention mechanisms is proposed, which considers the spatio-temporal relations in SST data. The proposed model is appropriate for correcting SST numerical forecast products by using satellite remote sensing data. The approach is tested in the region of the South China Sea and reduces the root mean squared error (RMSE) to 0.35 °C. Experimental results reveal that the proposed approach is significantly better than existing models, including traditional statistical methods, machine learning based methods, and deep learning methods.

Satellite sea surface salinity (SSS) observations play a critical role in the study of ocean circulation and climate regulation. However, mesoscale salinity dynamics (e.g., eddies, fronts) remain poorly resolved by current salinity satellites, such as Soil Moisture and Ocean Salinity (SMOS), due to their low effective resolution (>100 km). To address this, we proposed the SMOS Sea Surface Salinity Super-Resolution Reconstruction (S5R2) network. This deep learning framework achieved superresolution (SR) reconstruction of the SMOS L3 SSS product from 1/4° to 1/12° by fusing multivariate satellite observations. Our approach integrated a land filtering mechanism into a hybrid Transformer-CNN architecture, enhancing both global and local attention to ocean dynamics while suppressing interference from land-based information. Meanwhile, we improved the search efficiency of the optimal subset of input variables by guiding the search direction and step size using prior knowledge. The results demonstrated that S5R2 outperformed existing L3 and L4 satellite SSS products and mainstream SR algorithms. Compared to the input SMOS L3 SSS product, S5R2 achieved a 20% and 60% reduction in root mean square error in the Kuroshio Extension and Gulf Stream regions, respectively. Additionally, it improved the effective resolution from 100 km to 20–30 km, enabling the dynamic tracking of mesoscale eddies. This advance provides a near-realtime solution for monitoring fine-scale ocean salinity processes, with practical implications for ocean dynamics research and the operational application of salinity satellite products.