Remote sensing of ocean currents utilizes satellite altimetry and radar backscatter from the sea surface to measure velocity.
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Peer-reviewed literature and reference materials demonstrate that satellite altimetry and radar backscatter or surface wave radar are used to observe ocean circulation, sea surface topography, and current velocities.
Abstract. This paper addresses physics-informed deep learning schemes for satellite ocean remote sensing data. Such observation datasets are characterized by the irregular space-time sampling of the ocean surface due to sensors’ characteristics and satellite orbits. With a focus on satellite altimetry, we show that end-to-end learning schemes based on variational formulations provide new means to explore and exploit such observation datasets. Through Observing System Simulation Experiments (OSSE) using numerical ocean simulations and real nadir and wide-swath altimeter sampling patterns, we demonstrate their relevance w.r.t. state-of-the-art and operational methods for space-time interpolation and short-term forecasting issues. We also stress and discuss how they could contribute to the design and calibration of ocean observing systems.
Abstract. The launch of the US/French mission Topex/Poseidon (T/P) (CNES/NASA) in August 1992 was the start of a revolution in oceanography. For the first time, a very precise altimeter system optimized for large scale sea level and ocean circulation observations was flying. T/P alone could not observe the mesoscale circulation. In the 1990s, the ESA satellites ERS-1/2 were flying simultaneously with T/P. Together with my CLS colleagues, we demonstrated that we could use T/P as a reference mission for ERS-1/2 and bring the ERS-1/2 data to an accuracy level comparable to T/P. Near real time high resolution global sea level anomaly maps were then derived. These maps have been operationally produced as part of the SSALTO/DUACS system for the last 15 yr. They are now widely used by the oceanographic community and have contributed to a much better understanding and recognition of the role and importance of mesoscale dynamics. Altimetry needs to be complemented with global in situ observations. In the end of the 90s, a major international initiative was launched to develop Argo, the global array of profiling floats. This has been an outstanding success. Argo floats now provide the most important in situ observations to monitor and understand the role of the ocean on the earth climate and for operational oceanography. This is a second revolution in oceanography. The unique capability of satellite altimetry to observe the global ocean in near real time at high resolution and the development of Argo were essential to the development of global operational oceanography, the third revolution in oceanography. The Global Ocean Data Assimilation Experiment (GODAE) was instrumental in the development of the required capabilities. This paper provides an historical perspective on the development of these three revolutions in oceanography which are very much interlinked. This is not an exhaustive review and I will mainly focus on the contributions we made together with many colleagues and friends.
Imaging Radar Altimeter (IRA) is the current development tendency for ocean surface topography (OST) altimetry, which utilizes Synthetic Aperture Radar (SAR) and interferometry to improve the spatial resolution of OST to several kilometers or even better. Meanwhile, centimetric altimetry accuracy should be guaranteed for applications such as geostrophic currents or marine gravity anomaly inversion. However, the baseline length of IRA which determines the altimetric sensitivity is confined by the satellite platform, in consideration of baseline vibration and payload capability. Therefore, the baseline length from a single satellite can extend to only tens of meters, making it difficult to achieve centimetric accuracy. Referring to the successful experience from TerraSAR-X/TanDEM-X, satellite formation can easily extend the baseline length to hundreds or thousands of meters, depending on the helix orbit. Therefore, we propose the large baseline IRA (LB-IRA) from satellite formation for OST altimetry: the carrier frequency shift (CFS) is brought in to compensate for the severe baseline decorrelation, and the helix orbit is carefully selected to prevent severe time decorrelation from along-track baseline. The numerical results indicate that the LB-IRA, whose cross-track baseline ranges between 629~1000 m and along-tack baseline ranges between 0~40 m, can achieve ~1 cm relative accuracy at 1 km resolution.
In order to address the issue of limited common coverage and high cost in mapping ocean surface vector current by two (or more) high-frequency surface wave radars, this paper proposes a single-station surface wave radar vector current inversion algorithm. The feasibility of this algorithm has been validated in the Taiwan Strait. Based on the ocean dynamic characteristics of the Taiwan Strait, the algorithm utilizes the radial current obtained from a high-frequency surface wave radar (HFSWR) in Fujian Province to invert the ocean surface vector current. The surface vector current can be decomposed into three primary dynamic components: tidal currents, wind-driven currents, and geostrophic currents. Firstly, tidal current forecasting models and Ekman and Stokes theories are used to calculate the tidal and wind-driven currents in the Taiwan Strait, respectively. Subsequently, the directions of geostrophic currents in the Taiwan Strait are determined with sea surface height data, and the magnitudes of the geostrophic currents are constrained using the radial current from the single HFSWR. Finally, the three components are added together to obtain the vector current. Comparative results demonstrate that the efficacy of the algorithm has been validated through field experiments (with two HFSWRs and two drifting buoys) conducted in the southwestern of the Taiwan Strait. Further research is needed on the applicability of this algorithm to other sea areas and monitoring systems.
physically measuring at that location. Remote sensing in oceanography mostly refers to measuring properties of the ocean surface with sensors on satellites or
Remote sensing in oceanography is a widely used observational technique which enables researchers to acquire data of a location without physically measuring at that location. Remote sensing in oceanography mostly refers to measuring properties of the ocean surface with sensors on satellites or planes, which compose an image of captured electromagnetic radiation. A remote sensing instrument can eit
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is the local height difference due to atmospheric pressure difference above the surface.
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{\displaystyle h_{tide}}
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for a certain moment and location. As a solution remote sensing analysts use the Sea Surface Height Anomaly (SSHA) which only requires information on the tidal height and atmospheric pressure, which can be deduced from drifters, weather programs and tidal models. The geoid height for SSHA retrieval…
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{\displaystyle h_{ADT}=h_{SSHA}+h_{MSS}-h_{geoid}}
with the geoid height as a measurement from instruments like the Gravity and Ocean Circulations Explorer (GOCE) or Gravity Recovery and Climate Experiment (GRACE).
With the launch of TOPEX/POSEIDON in 1992 started a continuous time series of global SSH data which, has been extremely valuable in assessing sea level rise in the past decades by combining data with local tide gauges. The dynamical sea surface height from radar altimetry provides useful insight into ocean currents. If assuming geostrophic balance, the velocity anomaly and direction of surface currents perpendicular to the satellite overpass can be computed using the formula:
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