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This review paper reports on the state-of-the-art concerning observations of surface winds, waves, and currents from space and their use for scientific research and subsequent applications. The development of observations of sea state parameters from space dates back to the 1970s, with a significant increase in the number and diversity of space missions since the 1990s. Sensors used to monitor the sea-state parameters from space are mainly based on microwave techniques. They are either specifically designed to monitor surface parameters or are used for their abilities to provide opportunistic measurements complementary to their primary purpose. The principles on which is based on the estimation of the sea surface parameters are first described, including the performance and limitations of each method. Numerous examples and references on the use of these observations for scientific and operational applications are then given. The richness and diversity of these applications are linked to the importance of knowledge of the sea state in many fields. Firstly, surface wind, waves, and currents are significant factors influencing exchanges at the air/sea interface, impacting oceanic and atmospheric boundary layers, contributing to sea level rise at the coasts, and interacting with the sea-ice formation or destruction in the polar zones. Secondly, ocean surface currents combined with wind- and wave- induced drift contribute to the transport of heat, salt, and pollutants. Waves and surface currents also impact sediment transport and erosion in coastal areas. For operational applications, observations of surface parameters are necessary on the one hand to constrain the numerical solutions of predictive models (numerical wave, oceanic, or atmospheric models), and on the other hand to validate their results. In turn, these predictive models are used to guarantee safe, efficient, and successful offshore operations, including the commercial shipping and energy sector, as well as tourism and coastal activities. Long-time series of global sea-state observations are also becoming increasingly important to analyze the impact of climate change on our environment. All these aspects are recalled in the article, relating to both historical and contemporary activities in these fields.

Satellite altimetry offers a unique approach for direct sea surface current observation, but it is limited to measuring the surface‐constrained geostrophic component. Ageostrophic dynamics, prevalent at horizontal scales below 100 km and time scales below 10 days, are often underestimated by ocean reanalyzes employing data assimilation schemes. To address this limitation, we introduce a novel deep learning scheme, rooted in a variational data assimilation formulation with trainable observations and a priori terms, that harnesses the synergies between satellite‐derived sea surface observations, namely sea surface height (SSH) and sea surface temperature (SST), to enhance sea surface current reconstruction. Numerical experiments, conducted using realistic simulations, in a case study area of the Gulf Stream, demonstrate the potential of the proposed scheme to capture ageostrophic dynamics at time scales of 2.5–3.0 days and horizontal scales of 0.5°–0.7°. The analysis of diverse observation configurations, encompassing nadir along‐track altimetry, wide‐swath SWOT (Surface Water and Ocean Topography) altimetry, and SST data, highlights the pivotal role of SST features in retrieving a significant portion of the ageostrophic dynamics (approximately 47%). These findings underscore the potential of deep learning and 4DVarNet schemes in improving ocean reanalyzes and enhancing our understanding of ocean dynamics. Plain Language Summary Satellite altimetry provides a unique means for direct observation of sea surface currents, but it is confined to the geostrophic component, limiting the recovery of a substantial portion of mesoscale sea surface currents in operational products. To address this limitation, we present a novel deep learning framework, rooted in a variational data assimilation paradigm, that unlocks new avenues for leveraging the synergistic relationships between satellite‐derived sea surface observations, namely sea surface height and sea surface temperature. This innovative scheme demonstrates its remarkable potential to enhance sea surface current reconstruction and recover a substantial portion of the elusive ageostrophic dynamics. Numerical experiments, employing realistic simulations, in a case study area along the Gulf Stream, underscore the efficacy of our proposed approach. These findings support the pivotal role of physics‐informed deep learning in maximizing the utilization of available multimodal observation data sets and numerical simulations to elucidate partially observed sea surface dynamics. Key Points We present end‐to‐end deep learning schemes to improve the reconstruction of total sea surface currents from satellite‐derived observations Experiments in a region of the Gulf Stream support the synergistic analysis of sea surface temperature and sea surface height data The strain of sea surface dynamics is a proxy of the uncertainty of the retrieved estimation

Global surface currents are usually inferred from directly observed quantities like sea-surface height, wind stress by applying diagnostic balance relations (like geostrophy and Ekman flow), which provide a good approximation of the dynamics of slow, large-scale currents at large scales and low Rossby numbers. However, newer generation satellite altimeters (like the upcoming SWOT mission) will capture more of the high wavenumber variability associated with the unbalanced components, but the low temporal sampling can potentially lead to aliasing. Applying these balances directly may lead to an incorrect un-physical estimate of the surface flow. In this study we explore Machine Learning (ML) algorithms as an alternate route to infer surface currents from satellite observable quantities. We train our ML models with SSH, SST, and wind stress from available primitive equation ocean GCM simulation outputs as the inputs and make predictions of surface currents (u,v), which are then compared against the true GCM output. As a baseline example, we demonstrate that a linear regression model is ineffective at predicting velocities accurately beyond localized regions. In comparison, a relatively simple neural network (NN) can predict surface currents accurately over most of the global ocean, with lower mean squared errors than geostrophy + Ekman. Using a local stencil of neighboring grid points as additional input features, we can train the deep learning models to effectively “learn” spatial gradients and the physics of surface currents. By passing the stenciled variables through convolutional filters we can help the model learn spatial gradients much faster. Various training strategies are explored using systematic feature hold out and multiple combinations of point and stenciled input data fed through convolutional filters (2D/3D), to understand the effect of each input feature on the NN's ability to accurately represent surface flow. A model sensitivity analysis reveals that besides SSH, geographic information in some form is an essential ingredient required for making accurate predictions of surface currents with deep learning models.

The velocity of ocean currents significantly affects the trajectory prediction of ocean drifters and the safe navigation of intelligent vessels. Currently, most ocean current predictions focus on time-based forecasts at specific fixed points. In this study, deep learning based on the flow field prediction model (CNNs–MHA–BiLSTMs) is proposed, which predicts the changes in ocean currents by learning from historical flow fields. Unlike conventional models that focus on single-point current velocity data, the CNNs–MHA–BiLSTMs model focuses on the ocean surface current information within a specific area. The CNNs–MHA–BiLSTMs model integrates multiple convolutional neural networks (CNNs) in parallel, multi-head attention (MHA), and bidirectional long short-term memory networks (BiLSTMs). The model demonstrated exceptional modelling capabilities in handling spatiotemporal features. The proposed model was validated by comparing its predictions with those predicted by the MIKE21 flow model of the ocean area within proximity to Dalian Port (which used a commercial numerical model), as well as those predicted by other deep learning algorithms. The results showed that the model offers significant advantages and efficiency in simulating and predicting ocean surface currents. Moreover, the accuracy of regional flow field prediction improved with an increase in the number of sampling points used for training. The proposed CNNs–MHA–BiLSTMs model can provide theoretical support for maritime search and rescue, the control or path planning of Unmanned Surface Vehicles (USVs), as well as protecting offshore structures in the future.

Surface currents in oceanic environment are of vital importance from economical, biological and environmental aspects. Modelling ocean currents has generally been performed using numerical ocean circulation models as a solution to initial-boundary value problems in oceanic domain. Due to lack of knowledge about model parameters as well as initial and boundary values, they need to be externally calibrated for accurate local and regional applications. In this study, an alternative approach is proposed to incorporate spaceborne geodetic observations as well as hydrographic data to estimate the total surface current in the Persian Gulf and the Oman Sea. Being the data-driven approach, the method is comparable to numerical ocean models and regionally it is more accurate and simpler in application. The proposed method focuses on the computation of dynamic topography (DT) by least squares variance component estimation combining two different schemes. They are (1) DT estimation via direct observations of sea surface height from satellite altimetry and (2) steric and non-steric modeling of sea level anomaly using temperature and salinity data for the steric component; and Gravity Recovery and Climate Experiment observations for the non-steric component. Ultimately, the total surface current is obtained by computing the horizontal gradient of DT using geostrophic equation and adding the components of the Ekman current. Moreover, the estimated total surface current is further improved by assimilating with in situ current meter data using 3D-Variational data assimilation method and it is validated against two control stations. This assimilation leads to improvement of about 3 to 15 cm/s in total surface current computed using geostrophic equation and Ekman current. Besides, to illustrate the significance of the proposed approach, the estimated total surface current is externally validated and compared with the output of Copernicus Marine Environment Monitoring Service (CMEMS), as a numerical ocean model developed for oceanographic applications. Our comparison reveals that the proposed method is more accurate and reliable than CMEMS products. As for the circulation and current pattern, the estimated surface velocities reveal the existence of eddies in the region of the Persian Gulf and the Oman Sea, indicating the occurrence of cyclonic and anti-cyclonic circulations. Moreover, they elucidate that the velocities are lower in spring and summer and higher in autumn and winter.

Accurate forecasting of ocean surface currents is crucial for the planning of marine activities, including fisheries, shipping, and pollution control. Previous studies have often neglected the consideration of spatiotemporal correlations and interdependencies among ocean elements, leading to suboptimal accuracy in medium to long-term forecasts, especially in regions characterized by intricate ocean currents. This paper proposes an adaptive spatiotemporal and multi-element fusion network for ocean surface currents forecasting (ASTMEN). Specifically, we use an improved Swin Transformer (Swin-T) to perform self-attention computation at any given moment, enabling the adaptive generation of multi-element time series with spatial dependencies. Then, we utilize a Long Short-Term Memory network (LSTM) to encode and decode these series in the dimensions of temporal and multi-element features, resulting in accurate forecasts of ocean surface currents. This study takes the Kuroshio region in the northwest Pacific Ocean as the study area with data from the ocean reanalysis dataset. The experimental results show that ASTMEN significantly outperforms the baseline model and the climate state method, and is the only model whose correlation coefficient is still higher than 0.8 at day 12. In the experiments during the summer, when the currents are most variable, ASTMEN provides better forecasts at the sea-land interface and at the junction of different currents, which has the potential to fill the gap of poor forecast performance of previous methods for complex current fields.

Values for Doppler center frequency are calculated from the echo signal at the satellite using the Doppler centroid method and so include the predicted Doppler frequency caused by the relative motion of the satellite and the Earth, which is the main component of Doppler center frequency and must be removed to obtain the Doppler frequency anomaly for ocean current measurement. In this paper, a new Doppler frequency anomaly algorithm was proposed when measuring surface currents with synthetic aperture radar (SAR). The key of the proposed algorithm involved mean filtering method in the range direction and linear fitting in the azimuth direction to remove the radial and the azimuthal component of predicted Doppler frequency from the Doppler center frequency, respectively. The basis is that the theoretical Doppler center frequency model of SAR exhibits an approximately linear characteristic in both the range direction and in the azimuth direction. With the help of the new algorithm for predicted Doppler frequency removal, the estimation error of Doppler frequency anomaly can be reduced by avoiding employing the theoretical antenna pattern and imperfect satellite attitude parameters in the conventional Doppler frequency method. SAR measurement results demonstrated that, compared to the conventional Doppler frequency with/without error correction method, the proposed algorithm allows for a pronounced improvement in the current measuring accuracy in comparison with the global ocean multi-observation (MOB) products. In addition, the effectiveness and robustness of the proposed Doppler algorithm has been demonstrated by its application in the high velocity current in the Kuroshio region.

GPS-based surface drifters were used to investigate the surface currents in Daya Bay and along the eastern Guangdong coast in China. Surface current vectors were measured based on the GPS location and corresponding time information sent by drifters through the mobile phone network. The analysis of data from 120 drifters, deployed in late spring 2018 in the case-study region, shows that the drifters are generally capable of capturing the surface (tidal and residual) currents. The drifter trajectories suggest an anticlockwise surface current inside Daya Bay and a north-eastward current along the eastern Guangdong coast, where the coastal current along the eastern Guangdong coast is faster than that inside Daya Bay. The surface currents in the investigated region follow an irregular semidiurnal cycle due to the influence of the tidal current, while the currents inside Daya Bay are strongly affected by the topography. According to the harmonic analysis, an irregular semidiurnal type of tidal current is evident at a study grid inside Daya Bay, with an Eulerian residual current speed of 9.0 cm/s and a direction of 276°. The Lagrangian residual current outside Daya Bay moves north-eastward with a mean speed of 22 cm/s along the eastern Guangdong coast, while the current inside Daya Bay moves northward to the bay head with a mean speed of about 8.0 cm/s, which agrees well with the one reported in other literatures.

Drifting buoys (DBs) are widely deployed to observe near-surface ocean currents and sea surface temperature. The National Institute of Ocean Technology (NIOT), Chennai, India, had indigenised the DB with the Indian satellite (INSAT) in 2012. This paper describes the results of various studies conducted by NIOT using the indigenised DBs and also describes unique features attempted in indigenised DBs to measure the near-surface ocean current with 24 position acquisitions per day to capture small-scale surface eddies and the use of real-time geostationary satellite communication every hour. Additionally, the surface currents observed with indigenous DB are compared with DBs available in the market, Marlin-Yug (coefficient of determination R 2 > 0.88) and forecast using the ocean surface current analyses real-time (coefficient of determination R 2 > 0.90). Our results show that the DBs in the Bay of Bengal are carried with the East India Coastal Current in the March–May periods and the in-situ observations by the DB provide accurate surface current observations than satellite-based data. Furthermore, the new observations near the world’s largest tidal mangrove and delta system, the Sundarbans and Bengal Delta, will help in further enhancing our understanding of the spatiotemporal variability in the region in terms of coastal currents and its influence on marine environments.

Ocean surface currents play a key role in the earth’s climate. They affect virtually all processes occurring in the ocean and can also directly affect many important socio-economic activities. Himawari-8 meteorological satellite has an international advanced geostationary orbit imager sensor, AHI, with high time resolution and spatial coverage, Himawari-8 can be used to observe the subtle changes in marine environments. In this study, we used Himawari-8 data received from the Joint Receiving Station for Satellite Remote Sensing of Xiamen University to retrieve coastal currents in Hangzhou Bay. Particularly, the Maximum Correlation Coefficient (MCC) and the Generalized Hough Transform (GHT) methods were used to retrieve them respectively The retrieved sea surface currents are analyzed and verified by the numerical model data of the Taiwan Strait current forecasting system (TFOR). The results show that (1) the Himawari-8 satellite data can be used to effectively estimate the ocean current; (2) The results of the two methods are in agreement with each other, and the error in the current measured using the GHT method is smaller in the Yangtze estuary and offshore areas, where the turbidity characteristic front is stronger.